From 2e99d35f9e2931260552f27549f01169421ab8fd Mon Sep 17 00:00:00 2001 From: Gabriele Sarti Date: Thu, 13 Apr 2023 17:07:34 +0200 Subject: [PATCH 01/23] Added Granular Tagger template --- divemt/qe_taggers.py | 84 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 84 insertions(+) diff --git a/divemt/qe_taggers.py b/divemt/qe_taggers.py index 8576663..9f91f6d 100644 --- a/divemt/qe_taggers.py +++ b/divemt/qe_taggers.py @@ -400,3 +400,87 @@ def generate_tags( ) clear_nlp_cache() return src_tags, mt_tags + + +class NameTBDTagger(QETagger): + + def __init__( + self, + aligner: Optional[SentenceAligner] = None, + ): + self.aligner = aligner if aligner else SentenceAligner(model="bert", token_type="bpe", matching_methods="mai") + + def align_source_mt( + self, + src_tokens: List[List[str]], + mt_tokens: List[List[str]], + src_langs: List[str], + mt_langs: List[str], + ) -> List[List[Tuple[int, int]]]: + return [ + self.aligner.get_word_aligns(src_tok, mt_tok)["inter"] + for src_tok, mt_tok in tqdm( + zip(src_tokens, mt_tokens), total=len(src_tokens), desc="Aligning src-mt" + ) + ] + + def align_mt_pe( + self, + mt_tokens: List[List[str]], + pe_tokens: List[List[str]], + langs: List[str], + ) -> List[Tuple[int, int]]: + return [ + self.aligner.get_word_aligns(mt_tok, pe_tok)["inter"] + for mt_tok, pe_tok in tqdm( + zip(mt_tokens, pe_tokens), total=len(mt_tokens), desc="Aligning mt-pe" + ) + ] + + @staticmethod + def tags_from_edits( + mt_tokens: List[List[str]], + pe_tokens: List[List[str]], + alignments: List[List[Tuple[int, int]]], + ) -> List[List[str]]: + """ Produce tags on MT tokens from edits found in the PE tokens. """ + # 1:1 match: OK if same, SUB if different + # 1:n match: + # - Find highest match for 1 in n (lexical, LaBSE if not found) + # - If all matches are < threshold, tag as EXP (expansion) + # - Else, assign OK if same, SUB if different + # - If match preceded by some of the n, assign also INS to match + # - If match followed by some of the n, push an INS tag to the next token + # n:1 match: + # - Find highest match for 1 in n (lexical, LaBSE if not found) + # - If all matches are < threshold, tag as CON (contraction) + # - Else, assign OK if same, SUB if different + # - All n different than match are assigned DEL + # n:m match: + # - For each 1 in n, find highest match for 1 in m (lexical, LaBSE if not found, from highest score to lowest) + # - If all matches are < threshold, skip and continue + # - Else assign OK if same, SUB if different, remove from available m matches + # If in a block with multiple crossing alignments (with blocks named A, B, ...): + # - Swapped pair A, B -> B, A: Both blocks recive SHF + # - For n > 2, all blocks changing relative position recive SHF, others don't + raise NotImplementedError() + + @staticmethod + def tags_to_source( + src_tokens: List[List[str]], + mt_tokens: List[List[str]], + alignments: List[List[Tuple[int, int]]], + mt_tags: List[List[str]], + ) -> List[List[str]]: + """ Propagate tags from MT to source. """ + # 1:1 match: copy tags from MT + # 1:n match: + # - Find highest match for 1 in n (lexical, LaBSE if not found) + # - If all matches are < threshold, TBD + # - Else, copy tags from top match in MT and ignore other matches + # n:1 match: copy tags from 1 to all n + # n:m match: + # - For each 1 in n, find highest match for 1 in m (lexical, LaBSE if not found) + # - If all matches are < threshold, ignore and continue + # - Copy tags from top match in MT and ignore other matches + raise NotImplementedError() From 6a9a04aa815e47a099cda1ec072009a632df21fd Mon Sep 17 00:00:00 2001 From: Konstantin Chernyshev Date: Thu, 27 Apr 2023 21:35:07 +0200 Subject: [PATCH 02/23] feat: tags from edits /w tests --- divemt/qe_taggers.py | 209 ++++++++++++++++++++++- pyproject.toml | 1 + tests/test_qe_taggers_name_tbd_tagger.py | 190 +++++++++++++++++++++ 3 files changed, 391 insertions(+), 9 deletions(-) create mode 100644 tests/test_qe_taggers_name_tbd_tagger.py diff --git a/divemt/qe_taggers.py b/divemt/qe_taggers.py index 9f91f6d..a3b8d0f 100644 --- a/divemt/qe_taggers.py +++ b/divemt/qe_taggers.py @@ -3,13 +3,16 @@ import subprocess from abc import ABC, abstractmethod from collections import defaultdict +from itertools import groupby from pathlib import Path -from typing import List, Optional, Tuple, Union +from typing import List, Optional, Tuple, Union, Set, Generator from xml.sax.saxutils import escape +import numpy as np from simalign import SentenceAligner from strenum import StrEnum from tqdm import tqdm +import Levenshtein as lev from .parse_utils import clear_nlp_cache, tokenize from .wmt22qe_utils import align_sentence_tercom, parse_tercom_xml_file @@ -402,8 +405,23 @@ def generate_tags( return src_tags, mt_tags +class NameTBDGeneralTags(StrEnum): + OK = 'OK' + + BAD_SUBSTITUTION = 'BAD-SUB' + BAD_DELETION_RIGHT = 'BAD-DEL-R' # smth deleted on the right side of this token + BAD_DELETION_LEFT = 'BAD-DEL-L' # smth deleted on the left side of this token + BAD_INSERTION = 'BAD-INS' # 1:n + BAD_SHIFTING = 'BAD-SHF' # change words order n:m with hight threshold + + BAD_CONTRACTION = 'BAD-CON' # 1:n + BAD_EXPANSION = 'BAD-EXP' + + class NameTBDTagger(QETagger): + ID = "tbd_qe" + def __init__( self, aligner: Optional[SentenceAligner] = None, @@ -429,7 +447,7 @@ def align_mt_pe( mt_tokens: List[List[str]], pe_tokens: List[List[str]], langs: List[str], - ) -> List[Tuple[int, int]]: + ) -> List[List[Tuple[int, int]]]: return [ self.aligner.get_word_aligns(mt_tok, pe_tok)["inter"] for mt_tok, pe_tok in tqdm( @@ -437,13 +455,64 @@ def align_mt_pe( ) ] + @staticmethod + def _group_by_node(alignments: List[Tuple[Optional[int], Optional[int]]], by_start_node: bool = True, sort: bool = False) -> Generator[Tuple[int, List[int]], None, None]: + """Yield a node id and a list of connected nodes.""" + _by_index = 0 if by_start_node else 1 + if sort: + alignments = sorted(alignments, key=lambda x: x[_by_index] if x[_by_index] is not None else -1) + for start_node, connected_alignments in groupby(alignments, lambda x: x[_by_index]): + yield start_node, [end_id if by_start_node else start_id for start_id, end_id in connected_alignments] + + @staticmethod + def _detect_crossing_edges(mt_tokens: List[str], pe_tokens: List[str], alignments: List[Tuple[Optional[int], Optional[int]]]) -> List[bool]: + """Detect crossing edges in the alignments. Return List of clusters of nodes that are connected.""" + # TODO: optimize from n^2 to n as 2 pointers + shifted_mt_mask = [False] * len(mt_tokens) + + for i in range(len(alignments)): + for j in range(i + 1, len(alignments)): + edge_1, edge_2 = alignments[i], alignments[j] + + # skip if one of the edges is None + if edge_1[0] is None or edge_1[1] is None or edge_2[0] is None or edge_2[1] is None: + continue + + # skip if starting same node + if edge_1[0] == edge_2[0]: + continue + + assert edge_1[0] < edge_2[0], "Alignments have to be are sorted by mt" + + # Check if crossing edges + if edge_1[0] < edge_2[0] and edge_1[1] > edge_2[1]: + # mark the mt token as shifted + shifted_mt_mask[edge_1[0]] = True + shifted_mt_mask[edge_2[0]] = True + + return shifted_mt_mask + + @staticmethod + def _lev_similarity(mt_tok: str, pe_tok: str) -> float: + """Calculate Lev similarity between two tokens in [0, 1] range.""" + if mt_tok == pe_tok: + return 1.0 + + # calculate similarity using Lev distance + return lev.ratio(mt_tok, pe_tok) + @staticmethod def tags_from_edits( mt_tokens: List[List[str]], pe_tokens: List[List[str]], - alignments: List[List[Tuple[int, int]]], - ) -> List[List[str]]: + mt_pe_alignments: List[List[Tuple[int, int]]], + mt_tokens_embeddings: Optional[List[List[np.ndarray]]] = None, + pe_tokens_embeddings: Optional[List[List[np.ndarray]]] = None, + threshold: float = 0.5, + ) -> List[List[Set[str]]]: """ Produce tags on MT tokens from edits found in the PE tokens. """ + # TODO: check. now - if embeddings are not provided, use Lev distance + # TODO: update docs with ERRORS approach rather than EDITS # 1:1 match: OK if same, SUB if different # 1:n match: # - Find highest match for 1 in n (lexical, LaBSE if not found) @@ -461,16 +530,118 @@ def tags_from_edits( # - If all matches are < threshold, skip and continue # - Else assign OK if same, SUB if different, remove from available m matches # If in a block with multiple crossing alignments (with blocks named A, B, ...): - # - Swapped pair A, B -> B, A: Both blocks recive SHF - # - For n > 2, all blocks changing relative position recive SHF, others don't - raise NotImplementedError() + # - Swapped pair A, B -> B, A: Both blocks receive SHF + # - For n > 2, all blocks changing relative position receive SHF, others don't + + mt_tags = [] + for mt_tok, pe_tok, mt_pe_align in tqdm(zip(mt_tokens, pe_tokens, mt_pe_alignments), desc="Tagging MT", total=len(mt_tokens)): + + mt_sent_tags: List[Set[str]] = [set() for _ in range(len(mt_tok))] + + # clear 1-n and n-1 nodes with low threshold + # e.g. if 1-n or n-1 have same token or high similarity, remove low similarity as deletions/insertions + aligns_remove_1_to_n, aligns_remove_n_to_1 = set(), set() + # 1-n match + for mt_node_id, connected_pe_nodes_ids in NameTBDTagger._group_by_node(mt_pe_align, by_start_node=True, sort=False): + if mt_node_id is not None and len(connected_pe_nodes_ids) > 1: + pe_similarity = [ + (pe_node_id, NameTBDTagger._lev_similarity(mt_tok[mt_node_id], pe_tok[pe_node_id])) + for pe_node_id in connected_pe_nodes_ids + if pe_node_id is not None + ] + if all(sim < threshold for _, sim in pe_similarity): + continue + if all(sim > threshold for _, sim in pe_similarity): + continue + aligns_remove_1_to_n.update([ + (mt_node_id, pe_node_id) + for pe_node_id, sim in pe_similarity + if sim < threshold + ]) + # remove selected aligns and add None connected nodes instead + mt_pe_align = [(None, align[1]) if align in aligns_remove_1_to_n else align for align in mt_pe_align] + # n-1 match + for pe_node_id, connected_mt_nodes_ids in NameTBDTagger._group_by_node(mt_pe_align, by_start_node=False, sort=True): + if pe_node_id is not None and len(connected_mt_nodes_ids) > 1: + mt_similarity = [ + (mt_node_id, NameTBDTagger._lev_similarity(mt_tok[mt_node_id], pe_tok[pe_node_id])) + for mt_node_id in connected_mt_nodes_ids + if mt_node_id is not None + ] + if all(sim < threshold for _, sim in mt_similarity): + continue + if all(sim > threshold for _, sim in mt_similarity): + continue + aligns_remove_n_to_1.update([ + (mt_node_id, pe_node_id) + for mt_node_id, sim in mt_similarity + if sim < threshold + ]) + # remove selected aligns and add None connected nodes instead + mt_pe_align = [(align[0], None) if align in aligns_remove_n_to_1 else align for align in mt_pe_align] + + # Solve all n-1: setup expansions tags and solve n-1 matches < threshold as smth+insertion + # TODO: check with threshold, now doing without threshold + for pe_node_id, connected_mt_nodes_ids in NameTBDTagger._group_by_node(mt_pe_align, by_start_node=False, sort=True): + if pe_node_id is not None and len(connected_mt_nodes_ids) > 1: + # expansion, mark related mt nodes + for mt_node_id in connected_mt_nodes_ids: + if mt_node_id is not None: + mt_sent_tags[mt_node_id].add(NameTBDGeneralTags.BAD_EXPANSION.value) + + # Solve al deletions, add deletion tags on left and right sides + mt_position = 0 + for mt_node_id, connected_pe_nodes_ids in NameTBDTagger._group_by_node(mt_pe_align, by_start_node=True, sort=False): + if mt_node_id is None: + # deleted word error, mark left and right modes + if 0 <= mt_position - 1 < len(mt_sent_tags): + mt_sent_tags[mt_position - 1].add(NameTBDGeneralTags.BAD_DELETION_RIGHT.value) + if mt_position < len(mt_sent_tags): + mt_sent_tags[mt_position].add(NameTBDGeneralTags.BAD_DELETION_LEFT.value) + else: + mt_position += 1 + # clear all (None, i) to not mess grouping + mt_pe_align = [align for align in mt_pe_align if align[0] is not None] + + # Solve all 1-n matches + for mt_node_id, connected_pe_nodes_ids in NameTBDTagger._group_by_node(mt_pe_align, by_start_node=True, sort=True): + print(mt_node_id, ' -> ', connected_pe_nodes_ids, '\t\tmt_position=', mt_position) + assert mt_node_id is not None, "Already should be filtered all (None, smth) cases" + if NameTBDGeneralTags.BAD_EXPANSION.value in mt_sent_tags[mt_node_id]: + continue # TODO: check with gabrielle the priority for EXPANSION and CONTRACTION + if len(connected_pe_nodes_ids) > 1: + # contraction, mark the node + mt_sent_tags[mt_node_id].add(NameTBDGeneralTags.BAD_CONTRACTION.value) + elif connected_pe_nodes_ids[0] is None: + # insertion, mark the node + mt_sent_tags[mt_node_id].add(NameTBDGeneralTags.BAD_INSERTION.value) + elif mt_tok[mt_node_id] != pe_tok[connected_pe_nodes_ids[0]]: + # substitution, mark the node + mt_sent_tags[mt_node_id].add(NameTBDGeneralTags.BAD_SUBSTITUTION.value) + else: + # OK, mark the node + mt_sent_tags[mt_node_id].add(NameTBDGeneralTags.OK.value) + + # Add shifted tags if so + for mt_node_id, mask in enumerate(NameTBDTagger._detect_crossing_edges(mt_tok, pe_tok, mt_pe_align)): + if mask: + mt_sent_tags[mt_node_id].add(NameTBDGeneralTags.BAD_SHIFTING.value) + + # Save tags for this sentence + mt_tags.append(mt_sent_tags) + + # Basic sanity check + assert all( + [len(mt_sent_tokens) == len(mt_sent_tags) for mt_sent_tokens, mt_sent_tags in zip(mt_tokens, mt_tags)] + ), "MT tags creation failed, number of tokens and tags do not match" + return mt_tags @staticmethod def tags_to_source( src_tokens: List[List[str]], mt_tokens: List[List[str]], - alignments: List[List[Tuple[int, int]]], - mt_tags: List[List[str]], + src_mt_alignments: List[List[Tuple[int, int]]], + mt_tags: List[List[Set[str]]], ) -> List[List[str]]: """ Propagate tags from MT to source. """ # 1:1 match: copy tags from MT @@ -484,3 +655,23 @@ def tags_to_source( # - If all matches are < threshold, ignore and continue # - Copy tags from top match in MT and ignore other matches raise NotImplementedError() + + def generate_tags( + self, + srcs: List[str], + mts: List[str], + pes: List[str], + src_langs: Union[str, List[Set[str]]], + tgt_langs: Union[str, List[Set[str]]], + ) -> Tuple[List[str], List[str]]: + src_tokens, src_langs = self.get_tokenized(srcs, src_langs) + mt_tokens, tgt_langs = self.get_tokenized(mts, tgt_langs) + pe_tokens, _ = self.get_tokenized(pes, tgt_langs) + src_mt_alignments = self.align_source_mt(src_tokens, mt_tokens, src_langs, tgt_langs) + mt_pe_alignments = self.align_mt_pe(mt_tokens, pe_tokens, tgt_langs) + mt_tags = self.tags_from_edits(mt_tokens, pe_tokens, mt_pe_alignments) + src_tags = self.tags_to_source( + src_tokens, pe_tokens, src_mt_alignments, mt_tags + ) + clear_nlp_cache() + return src_tags, mt_tags diff --git a/pyproject.toml b/pyproject.toml index 1b8a76c..e8b0f4a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -22,6 +22,7 @@ dependencies = [ "simalign", "strenum", "sentencepiece", + "sentence-transformers", # for LaBSE "tqdm", "black", "flake8", diff --git a/tests/test_qe_taggers_name_tbd_tagger.py b/tests/test_qe_taggers_name_tbd_tagger.py new file mode 100644 index 0000000..ea13df6 --- /dev/null +++ b/tests/test_qe_taggers_name_tbd_tagger.py @@ -0,0 +1,190 @@ +from typing import List, Tuple, Set + +import pytest +from strenum import StrEnum + +from divemt.qe_taggers import NameTBDTagger +from divemt.qe_taggers import NameTBDGeneralTags as Tags + + +class TestUtils: + @pytest.mark.parametrize("mt_len, mt_pe_alignments, true_mt_shifts_mask", [ + (1, [(0, 0)], [False]), + (2, [(0, 0), (1, 1)], [False, False]), + (3, [(0, 0), (1, 1), (2, 2)], [False, False, False]), + (3, [(0, 0), (1, None), (2, 1)], [False, False, False]), + # easiest case + (2, [(0, 1), (1, 0)], [True, True]), + # central one is not moved, but have crossing edges + (3, [(0, 2), (1, 1), (2, 0)], [True, True, True]), + # the central one deleted, so not shifted, no crossing edges + (3, [(0, 1), (1, None), (2, 0)], [True, False, True]), + # TODO: check with gabrielle + (4, [(0, 0), (1, 3), (1, 4), (1, 5), (2, 2), (2, 0), (3, None)], [False, True, True, False]), + ]) + def test_detect_crossing_edges(self, mt_len: int, mt_pe_alignments: List[Tuple[int, int]], true_mt_shifts_mask: List[bool]) -> None: + tagger = NameTBDTagger() + mt_shifts_mask = tagger._detect_crossing_edges([str(i) for i in range(mt_len)], [str(i) for i in range(mt_len)], mt_pe_alignments) + assert mt_shifts_mask == true_mt_shifts_mask + + +class TestTagsFromEdits: + @pytest.mark.parametrize("mt_tokens, pe_tokens, mt_pe_alignments, true_mt_tags", [ + (["A", "B"], ["A", "B"], [(0, 0), (1, 1)], [{Tags.OK}, {Tags.OK}]), + (["A", "B", "C", "D"], ["A", "B", "C", "D"], [(0, 0), (1, 1), (2, 2), (3, 3)], [{Tags.OK}, {Tags.OK}, {Tags.OK}, {Tags.OK}]), + ([], [], [], []), + ]) + def test_single_error_ok( + self, + mt_tokens: List[str], + pe_tokens: List[str], + mt_pe_alignments: List[Tuple[int, int]], + true_mt_tags: List[Set[StrEnum]], + ) -> None: + tagger = NameTBDTagger() + predicted_tags = tagger.tags_from_edits([mt_tokens], [pe_tokens], [mt_pe_alignments])[0] + assert len(predicted_tags) == len(true_mt_tags) + for predicted_tags, true_tags in zip(predicted_tags, true_mt_tags): + assert predicted_tags == {t.value for t in true_tags} + + @pytest.mark.parametrize("mt_tokens, pe_tokens, mt_pe_alignments, true_mt_tags", [ + (["A", "B", "C"], ["A", "X", "Z"], [(0, 0), (1, 1), (2, 2)], [{Tags.OK}, {Tags.BAD_SUBSTITUTION}, {Tags.BAD_SUBSTITUTION}]), + (["A", "B"], ["Z", "X"], [(0, 0), (1, 1)], [{Tags.BAD_SUBSTITUTION}, {Tags.BAD_SUBSTITUTION}]), + # For 1-n and n-1 cases see contraction and expansion tests + ]) + def test_single_error_substitution( + self, + mt_tokens: List[str], + pe_tokens: List[str], + mt_pe_alignments: List[Tuple[int, int]], + true_mt_tags: List[Set[StrEnum]], + ) -> None: + tagger = NameTBDTagger() + predicted_tags = tagger.tags_from_edits([mt_tokens], [pe_tokens], [mt_pe_alignments])[0] + assert len(predicted_tags) == len(true_mt_tags) + for predicted_tags, true_tags in zip(predicted_tags, true_mt_tags): + assert predicted_tags == {t.value for t in true_tags} + + @pytest.mark.parametrize("mt_tokens, pe_tokens, mt_pe_alignments, true_mt_tags", [ + (["A", "B"], ["A"], [(0, 0), (1, None)], [{Tags.OK}, {Tags.BAD_INSERTION}]), + (["A", "B"], ["B"], [(0, None), (1, 0)], [{Tags.BAD_INSERTION}, {Tags.OK}]), + (["A", "B"], [], [(0, None), (1, None)], [{Tags.BAD_INSERTION}, {Tags.BAD_INSERTION}]), + # For 1-n and n-1 cases see contraction and expansion tests + ]) + def test_single_error_insertion( + self, + mt_tokens: List[str], + pe_tokens: List[str], + mt_pe_alignments: List[Tuple[int, int]], + true_mt_tags: List[Set[StrEnum]], + ) -> None: + tagger = NameTBDTagger() + predicted_tags = tagger.tags_from_edits([mt_tokens], [pe_tokens], [mt_pe_alignments])[0] + assert len(predicted_tags) == len(true_mt_tags) + for predicted_tags, true_tags in zip(predicted_tags, true_mt_tags): + assert predicted_tags == {t.value for t in true_tags} + + @pytest.mark.parametrize("mt_tokens, pe_tokens, mt_pe_alignments, true_mt_tags", [ + (["A"], ["A", "X"], [(0, 0), (None, 1)], [{Tags.OK, Tags.BAD_DELETION_RIGHT}]), + (["A"], ["X", "A"], [(None, 0), (0, 1)], [{Tags.OK, Tags.BAD_DELETION_LEFT}]), + (["A", "B"], ["A", "X", "B"], [(0, 0), (None, 1), (1, 2)], [{Tags.OK, Tags.BAD_DELETION_RIGHT}, {Tags.OK, Tags.BAD_DELETION_LEFT}]), + # Delete multiple tokens, but tag error as deleted one + (["A"], ["A", "X", "Y", "Z"], [(0, 0), (None, 1), (None, 2), (None, 3)], [{Tags.OK, Tags.BAD_DELETION_RIGHT}]), + (["A"], ["X", "Y", "Z", "A"], [(None, 0), (None, 1), (None, 2), (0, 3)], [{Tags.OK, Tags.BAD_DELETION_LEFT}]), + (["A", "B"], ["A", "X", "Y", "Z", "B"], [(0, 0), (None, 1), (None, 2), (None, 3), (1, 4)], [{Tags.OK, Tags.BAD_DELETION_RIGHT}, {Tags.OK, Tags.BAD_DELETION_LEFT}]), + # deleted both left and right sides + (["A"], ["X", "A", "Y"], [(None, 0), (0, 1), (None, 2)], [{Tags.OK, Tags.BAD_DELETION_LEFT, Tags.BAD_DELETION_RIGHT}]), + # deleted for empty target + ([], ["X"], [(None, 0)], []), + ]) + def test_single_error_deletion( + self, + mt_tokens: List[str], + pe_tokens: List[str], + mt_pe_alignments: List[Tuple[int, int]], + true_mt_tags: List[Set[StrEnum]], + ) -> None: + tagger = NameTBDTagger() + predicted_tags = tagger.tags_from_edits([mt_tokens], [pe_tokens], [mt_pe_alignments])[0] + assert len(predicted_tags) == len(true_mt_tags) + for predicted_tags, true_tags in zip(predicted_tags, true_mt_tags): + assert predicted_tags == {t.value for t in true_tags} + + @pytest.mark.parametrize("mt_tokens, pe_tokens, mt_pe_alignments, true_mt_tags", [ + # Have same BBB token, so should filter CCC and TTT out as Deletion error and BBB as Ok + (["AAA", "BBB"], ["AAA", "BBB", "CCC", "TTT"], [(0, 0), (1, 1), (1, 2), (1, 3)], [{Tags.OK}, {Tags.OK, Tags.BAD_DELETION_RIGHT}]), + (["AAA", "BBB"], ["AAA", "TTT", "BBB", "CCC"], [(0, 0), (1, 1), (1, 2), (1, 3)], [{Tags.OK, Tags.BAD_DELETION_RIGHT}, {Tags.OK, Tags.BAD_DELETION_RIGHT, Tags.BAD_DELETION_LEFT}]), + # XXX, TTT and CCC >threshold are same BBB token, so its bad Contradiction + (["AAA", "BBB"], ["AAA", "XXX", "CCC", "TTT"], [(0, 0), (1, 1), (1, 2), (1, 3)], [{Tags.OK}, {Tags.BAD_CONTRACTION}]), + # BBX is >threshold, CCC/TTT threshold, so all are Contractions + (["AAA", "BBB"], ["AAA", "BBX", "XBB"], [(0, 0), (1, 1), (1, 2)], [{Tags.OK}, {Tags.BAD_CONTRACTION}]), + # BBX and XBB >threshold while TTT is None: + tagger = NameTBDTagger() + predicted_tags = tagger.tags_from_edits([mt_tokens], [pe_tokens], [mt_pe_alignments])[0] + assert len(predicted_tags) == len(true_mt_tags) + for predicted_tags, true_tags in zip(predicted_tags, true_mt_tags): + assert predicted_tags == {t.value for t in true_tags} + + @pytest.mark.parametrize("mt_tokens, pe_tokens, mt_pe_alignments, true_mt_tags", [ + # BB token is same, so CCC and TTT are insertions + (["AAA", "BBB", "CCC", "TTT"], ["AAA", "BBB"], [(0, 0), (1, 1), (2, 1), (3, 1)], [{Tags.OK}, {Tags.OK}, {Tags.BAD_INSERTION}, {Tags.BAD_INSERTION}]), + (["AAA", "TTT", "BBB", "CCC"], ["AAA", "BBB"], [(0, 0), (1, 1), (2, 1), (3, 1)], [{Tags.OK}, {Tags.BAD_INSERTION}, {Tags.OK}, {Tags.BAD_INSERTION}]), + # XXX, TTT and CCC >threshold are same BBB token, so its bad Expansion + (["AAA", "XXX", "CCC", "TTT"], ["AAA", "BBB"], [(0, 0), (1, 1), (2, 1), (3, 1)], [{Tags.OK}, {Tags.BAD_EXPANSION}, {Tags.BAD_EXPANSION}, {Tags.BAD_EXPANSION}]), + # BBX is >threshold, CCC/TTT threshold, so all are Expansion + (["AAA", "BBX", "XBB"], ["AAA", "BBB"], [(0, 0), (1, 1), (2, 1)], [{Tags.OK}, {Tags.BAD_EXPANSION}, {Tags.BAD_EXPANSION}]), + # BBX and XBB >threshold while TTT is None: + tagger = NameTBDTagger() + predicted_tags = tagger.tags_from_edits([mt_tokens], [pe_tokens], [mt_pe_alignments])[0] + assert len(predicted_tags) == len(true_mt_tags) + for predicted_tags, true_tags in zip(predicted_tags, true_mt_tags): + assert predicted_tags == {t.value for t in true_tags} + + @pytest.mark.parametrize("mt_tokens, pe_tokens, mt_pe_alignments, true_mt_tags", [ + # simple case + (["A", "B"], ["B", "A"], [(0, 1), (1, 0)], [{Tags.OK, Tags.BAD_SHIFTING}, {Tags.OK, Tags.BAD_SHIFTING}]), + # middle intact, but crossing edges, so shifted + (["A", "X", "Y", "B"], ["B", "X", "Y", "A"], [(0, 3), (1, 1), (2, 2), (3, 0)], [{Tags.OK, Tags.BAD_SHIFTING}, {Tags.OK, Tags.BAD_SHIFTING}, {Tags.OK, Tags.BAD_SHIFTING}, {Tags.OK, Tags.BAD_SHIFTING}]), + # node inserted, so should not be marked as shifted TODO: check with gabrielle + (["A", "X", "B"], ["B", "A"], [(0, 1), (1, None), (2, 0)], [{Tags.OK, Tags.BAD_SHIFTING}, {Tags.BAD_INSERTION}, {Tags.OK, Tags.BAD_SHIFTING}]), + # node deleted, nothing to mark as shifted + (["A", "B"], ["B", "X", "A"], [(0, 2), (None, 1), (1, 0)], [{Tags.OK, Tags.BAD_SHIFTING, Tags.BAD_DELETION_RIGHT}, {Tags.OK, Tags.BAD_SHIFTING, Tags.BAD_DELETION_LEFT}]), + ]) + def test_single_error_shifted( + self, + mt_tokens: List[str], + pe_tokens: List[str], + mt_pe_alignments: List[Tuple[int, int]], + true_mt_tags: List[Set[StrEnum]], + ) -> None: + tagger = NameTBDTagger() + predicted_tags = tagger.tags_from_edits([mt_tokens], [pe_tokens], [mt_pe_alignments])[0] + assert len(predicted_tags) == len(true_mt_tags) + for predicted_tags, true_tags in zip(predicted_tags, true_mt_tags): + assert predicted_tags == {t.value for t in true_tags} From 9b49b467aa31f63b38ce7e9ddb0826a945fe922a Mon Sep 17 00:00:00 2001 From: Konstantin Chernyshev Date: Mon, 1 May 2023 13:14:38 +0200 Subject: [PATCH 03/23] style: update tags_from_edits docs and some style fixes for typings --- divemt/qe_taggers.py | 142 ++++++++++++++++++++++++------------------- 1 file changed, 79 insertions(+), 63 deletions(-) diff --git a/divemt/qe_taggers.py b/divemt/qe_taggers.py index a3b8d0f..4ea2c94 100644 --- a/divemt/qe_taggers.py +++ b/divemt/qe_taggers.py @@ -5,7 +5,7 @@ from collections import defaultdict from itertools import groupby from pathlib import Path -from typing import List, Optional, Tuple, Union, Set, Generator +from typing import List, Optional, Tuple, Union, Set, Generator, Any from xml.sax.saxutils import escape import numpy as np @@ -20,6 +20,9 @@ logger = logging.getLogger(__name__) +TTag = Union[str, Set[str]] + + class QETagger(ABC): """An abstract class to produce quality estimation tags from src-mt-pe triplets.""" @@ -29,7 +32,7 @@ def align_source_mt( self, src_tokens: List[List[str]], mt_tokens: List[List[str]], - **align_source_mt_kwargs, + **align_source_mt_kwargs: Any, ) -> List[List[Tuple[int, int]]]: """Align source and machine translation tokens.""" raise NotImplementedError(f"{self.__class__.__name__} does not implement align_source_mt()") @@ -38,7 +41,7 @@ def align_source_pe( self, src_tokens: List[List[str]], pe_tokens: List[List[str]], - **align_source_pe_kwargs, + **align_source_pe_kwargs: Any, ) -> List[List[Tuple[int, int]]]: """Align source and post-edited tokens.""" raise NotImplementedError(f"{self.__class__.__name__} does not implement align_source_pe()") @@ -48,7 +51,7 @@ def align_mt_pe( self, mt_tokens: List[List[str]], pe_tokens: List[List[str]], - **align_mt_pe_kwargs, + **align_mt_pe_kwargs: Any, ) -> List[List[Tuple[int, int]]]: """Align machine translation and post-editing tokens.""" pass @@ -59,8 +62,8 @@ def tags_from_edits( mt_tokens: List[List[str]], pe_tokens: List[List[str]], alignments: List[List[Tuple[int, int]]], - **mt_tagging_kwargs, - ) -> List[List[str]]: + **mt_tagging_kwargs: Any, + ) -> List[List[TTag]]: """Produce tags on MT tokens from edits found in the PE tokens.""" pass @@ -69,8 +72,8 @@ def tags_from_edits( def tags_to_source( src_tokens: List[List[str]], tgt_tokens: List[List[str]], - **src_tagging_kwargs, - ) -> List[List[str]]: + **src_tagging_kwargs: Any, + ) -> List[List[TTag]]: """Propagate tags from MT to source.""" pass @@ -93,7 +96,7 @@ def generate_tags( pes: List[str], src_langs: Union[str, List[str]], tgt_langs: Union[str, List[str]], - ) -> Tuple[List[str], List[str]]: + ) -> Tuple[List[TTag], List[TTag]]: """Generate word-level quality estimation tags from source-mt-pe triplets. Args: @@ -111,7 +114,7 @@ def generate_tags( (one per machine translation). Returns: - `Tuple[List[str], List[str]]`: A tuple containing the lists of quality tags for all source and the machine + `Tuple[List[TTag], List[TTag]]`: A tuple containing the lists of quality tags for all source and the machine translation sentence, respectively. """ pass @@ -230,7 +233,7 @@ def tags_from_edits( alignments: List[List[Tuple[int, int]]], use_gaps: bool = False, omissions: str = OmissionRule.RIGHT.value, - ) -> List[List[str]]: + ) -> List[List[TTag]]: """Produce tags on MT tokens from edits found in the PE tokens.""" if use_gaps: omissions = OmissionRule.NONE.value @@ -324,7 +327,7 @@ def tags_to_source( src_pe_alignments: List[List[Tuple[int, int]]], mt_pe_alignments: List[List[Tuple[int, int]]], fluency_rule: str = FluencyRule.NORMAL.value, - ) -> List[List[str]]: + ) -> List[List[TTag]]: """Propagate tags from MT to source.""" # Reorganize source-target alignments as a dict pe2source = [] @@ -386,7 +389,7 @@ def generate_tags( use_gaps: bool = False, omissions: str = OmissionRule.RIGHT.value, fluency_rule: str = FluencyRule.NORMAL.value, - ) -> Tuple[List[List[str]], List[List[str]]]: + ) -> Tuple[List[List[TTag]], List[List[TTag]]]: src_tokens, src_langs = self.get_tokenized(srcs, src_langs) mt_tokens, tgt_langs = self.get_tokenized(mts, tgt_langs) pe_tokens, _ = self.get_tokenized(pes, tgt_langs) @@ -406,20 +409,21 @@ def generate_tags( class NameTBDGeneralTags(StrEnum): - OK = 'OK' + """Error types tags for NameTBD.""" + OK = 'OK' # 1:1 - the MT uses the same single word as the PE + BAD_SUBSTITUTION = 'BAD-SUB' # 1:1 - the MT uses a different single word than the PE - BAD_SUBSTITUTION = 'BAD-SUB' - BAD_DELETION_RIGHT = 'BAD-DEL-R' # smth deleted on the right side of this token - BAD_DELETION_LEFT = 'BAD-DEL-L' # smth deleted on the left side of this token - BAD_INSERTION = 'BAD-INS' # 1:n - BAD_SHIFTING = 'BAD-SHF' # change words order n:m with hight threshold + BAD_DELETION_RIGHT = 'BAD-DEL-R' # None:1 - the MT does not have a word existed in PE, deletion on the right + BAD_DELETION_LEFT = 'BAD-DEL-L' # None:1 - the MT does not have a word existed in PE, deletion on the left + BAD_INSERTION = 'BAD-INS' # 1:None - the MT wrongly inserted a words that is not in the PE - BAD_CONTRACTION = 'BAD-CON' # 1:n - BAD_EXPANSION = 'BAD-EXP' + BAD_SHIFTING = 'BAD-SHF' # for any number of tokens - detect crossing edges + BAD_CONTRACTION = 'BAD-CON' # 1:n - the MT uses a single word instead of multiple words in the PE + BAD_EXPANSION = 'BAD-EXP' # n:1 - the MT uses a multiple words instead of one in the PE -class NameTBDTagger(QETagger): +class NameTBDTagger(QETagger): ID = "tbd_qe" def __init__( @@ -466,7 +470,7 @@ def _group_by_node(alignments: List[Tuple[Optional[int], Optional[int]]], by_sta @staticmethod def _detect_crossing_edges(mt_tokens: List[str], pe_tokens: List[str], alignments: List[Tuple[Optional[int], Optional[int]]]) -> List[bool]: - """Detect crossing edges in the alignments. Return List of clusters of nodes that are connected.""" + """Detect crossing edges in the alignments. Return mask list of nodes that cross some other node.""" # TODO: optimize from n^2 to n as 2 pointers shifted_mt_mask = [False] * len(mt_tokens) @@ -509,41 +513,49 @@ def tags_from_edits( mt_tokens_embeddings: Optional[List[List[np.ndarray]]] = None, pe_tokens_embeddings: Optional[List[List[np.ndarray]]] = None, threshold: float = 0.5, - ) -> List[List[Set[str]]]: - """ Produce tags on MT tokens from edits found in the PE tokens. """ + ) -> List[List[TTag]]: + """ Produce tags on MT tokens from edits found in the PE tokens. + + Note: The tags indicate the type of error particular MT token is affected by. + + The following situations are considered: + 1:1 match: OK if same, SUB if different + 1:n match: + - Obtain similarity between 1 and n (lexical, LaBSE if not found) + - If all matches are threshold, tag as CON (contraction) + - Else, tackle the highest match as 1:1 (OK/SUB) and the rest as None:1 (deletions) + n:1 match: + - Obtain similarity between n and 1 (lexical, LaBSE if not found) + - If all matches are threshold, tag as EXP (expansion) + - Else, tackle the highest match as 1:1 (OK/SUB) and the rest as 1:None (insertions) + n:m match: + - Prioritize n:1 matches with the EXP (expansion) tag + - Clear all None:1 cases + - Consider all n:m as 1:m cases, if current MT token is not tagged as EXP + shifting: + - First, clear all None:1 and 1:None cases - deleted and inserted words can't be shifted + - Then for all edges check if they cross with any other edge + - If they do, mark both nodes (2 edges starting node) in MT as SHF (shifted) + - TODO: + If in a block with multiple crossing alignments (with blocks named A, B, ...): + - Swapped pair A, B -> B, A: Both blocks receive SHF + - For n > 2, all blocks changing relative position receive SHF, others don't + """ # TODO: check. now - if embeddings are not provided, use Lev distance - # TODO: update docs with ERRORS approach rather than EDITS - # 1:1 match: OK if same, SUB if different - # 1:n match: - # - Find highest match for 1 in n (lexical, LaBSE if not found) - # - If all matches are < threshold, tag as EXP (expansion) - # - Else, assign OK if same, SUB if different - # - If match preceded by some of the n, assign also INS to match - # - If match followed by some of the n, push an INS tag to the next token - # n:1 match: - # - Find highest match for 1 in n (lexical, LaBSE if not found) - # - If all matches are < threshold, tag as CON (contraction) - # - Else, assign OK if same, SUB if different - # - All n different than match are assigned DEL - # n:m match: - # - For each 1 in n, find highest match for 1 in m (lexical, LaBSE if not found, from highest score to lowest) - # - If all matches are < threshold, skip and continue - # - Else assign OK if same, SUB if different, remove from available m matches - # If in a block with multiple crossing alignments (with blocks named A, B, ...): - # - Swapped pair A, B -> B, A: Both blocks receive SHF - # - For n > 2, all blocks changing relative position receive SHF, others don't - mt_tags = [] + mt_tags: List[List[Set[str]]] = [] + for mt_tok, pe_tok, mt_pe_align in tqdm(zip(mt_tokens, pe_tokens, mt_pe_alignments), desc="Tagging MT", total=len(mt_tokens)): mt_sent_tags: List[Set[str]] = [set() for _ in range(len(mt_tok))] # clear 1-n and n-1 nodes with low threshold - # e.g. if 1-n or n-1 have same token or high similarity, remove low similarity as deletions/insertions + # e.g. if 1-n or n-1 have same token or high similarity, remove low similarity as deletions/insertions (None:1 and 1:None) aligns_remove_1_to_n, aligns_remove_n_to_1 = set(), set() # 1-n match for mt_node_id, connected_pe_nodes_ids in NameTBDTagger._group_by_node(mt_pe_align, by_start_node=True, sort=False): if mt_node_id is not None and len(connected_pe_nodes_ids) > 1: + # TODO: check alignments lib to have sim pe_similarity = [ (pe_node_id, NameTBDTagger._lev_similarity(mt_tok[mt_node_id], pe_tok[pe_node_id])) for pe_node_id in connected_pe_nodes_ids @@ -581,7 +593,6 @@ def tags_from_edits( mt_pe_align = [(align[0], None) if align in aligns_remove_n_to_1 else align for align in mt_pe_align] # Solve all n-1: setup expansions tags and solve n-1 matches < threshold as smth+insertion - # TODO: check with threshold, now doing without threshold for pe_node_id, connected_mt_nodes_ids in NameTBDTagger._group_by_node(mt_pe_align, by_start_node=False, sort=True): if pe_node_id is not None and len(connected_mt_nodes_ids) > 1: # expansion, mark related mt nodes @@ -589,7 +600,7 @@ def tags_from_edits( if mt_node_id is not None: mt_sent_tags[mt_node_id].add(NameTBDGeneralTags.BAD_EXPANSION.value) - # Solve al deletions, add deletion tags on left and right sides + # Solve all deletions, add deletion tags on left and right sides mt_position = 0 for mt_node_id, connected_pe_nodes_ids in NameTBDTagger._group_by_node(mt_pe_align, by_start_node=True, sort=False): if mt_node_id is None: @@ -608,7 +619,7 @@ def tags_from_edits( print(mt_node_id, ' -> ', connected_pe_nodes_ids, '\t\tmt_position=', mt_position) assert mt_node_id is not None, "Already should be filtered all (None, smth) cases" if NameTBDGeneralTags.BAD_EXPANSION.value in mt_sent_tags[mt_node_id]: - continue # TODO: check with gabrielle the priority for EXPANSION and CONTRACTION + continue if len(connected_pe_nodes_ids) > 1: # contraction, mark the node mt_sent_tags[mt_node_id].add(NameTBDGeneralTags.BAD_CONTRACTION.value) @@ -642,18 +653,23 @@ def tags_to_source( mt_tokens: List[List[str]], src_mt_alignments: List[List[Tuple[int, int]]], mt_tags: List[List[Set[str]]], - ) -> List[List[str]]: - """ Propagate tags from MT to source. """ - # 1:1 match: copy tags from MT - # 1:n match: - # - Find highest match for 1 in n (lexical, LaBSE if not found) - # - If all matches are < threshold, TBD - # - Else, copy tags from top match in MT and ignore other matches - # n:1 match: copy tags from 1 to all n - # n:m match: - # - For each 1 in n, find highest match for 1 in m (lexical, LaBSE if not found) - # - If all matches are < threshold, ignore and continue - # - Copy tags from top match in MT and ignore other matches + ) -> List[List[TTag]]: + """ Propagate tags from MT to source. + + # TODO: update docstring with the final logic + The following cases are considered: + 1:1 match: copy tags from MT + 1:n match: + - Find highest match for 1 in n (lexical, LaBSE if not found) + - If all matches are threshold, TBD + - Else, copy tags from top match in MT and ignore other matches + n:1 match: copy tags from 1 to all n + n:m match: + - For each 1 in n, find highest match for 1 in m (lexical, LaBSE if not found) + - If all matches are threshold, ignore and continue + - Copy tags from top match in MT and ignore other matches + """ + raise NotImplementedError() def generate_tags( @@ -663,7 +679,7 @@ def generate_tags( pes: List[str], src_langs: Union[str, List[Set[str]]], tgt_langs: Union[str, List[Set[str]]], - ) -> Tuple[List[str], List[str]]: + ) -> Tuple[List[TTag], List[TTag]]: src_tokens, src_langs = self.get_tokenized(srcs, src_langs) mt_tokens, tgt_langs = self.get_tokenized(mts, tgt_langs) pe_tokens, _ = self.get_tokenized(pes, tgt_langs) From 9732403a278b37e2a8a5d0e260bdbbf4cc577de1 Mon Sep 17 00:00:00 2001 From: Konstantin Chernyshev Date: Thu, 4 May 2023 14:06:15 +0200 Subject: [PATCH 04/23] feat: tags to source and attention sim scores --- divemt/custom_simalign.py | 282 +++++++++++++++++++++++ divemt/qe_taggers.py | 150 ++++++------ divemt/wmt22qe_utils.py | 6 +- tests/test_qe_taggers_name_tbd_tagger.py | 162 ++++++++----- 4 files changed, 481 insertions(+), 119 deletions(-) create mode 100644 divemt/custom_simalign.py diff --git a/divemt/custom_simalign.py b/divemt/custom_simalign.py new file mode 100644 index 0000000..77a9559 --- /dev/null +++ b/divemt/custom_simalign.py @@ -0,0 +1,282 @@ +""" +Copy of the https://github.com/cisnlp/simalign version 0.3 +Custom changes: +- added embedding output +- black and ruff are applied +- deleted some unused imports +- fix typings +- logger deleted +""" +from typing import Dict, List, Tuple, Union, Optional + +import numpy as np +from scipy.sparse import csr_matrix +from sklearn.metrics.pairwise import cosine_similarity + +try: + import networkx as nx + from networkx.algorithms.bipartite.matrix import from_biadjacency_matrix +except ImportError: + nx = None +import torch +from transformers import ( + BertModel, + BertTokenizer, + XLMModel, + XLMTokenizer, + RobertaModel, + RobertaTokenizer, + XLMRobertaModel, + XLMRobertaTokenizer, + AutoConfig, + AutoModel, + AutoTokenizer, +) + + +class EmbeddingLoader(object): + def __init__(self, model: str = "bert-base-multilingual-cased", device=torch.device("cpu"), layer: int = 8): + TR_Models = { + "bert-base-uncased": (BertModel, BertTokenizer), + "bert-base-multilingual-cased": (BertModel, BertTokenizer), + "bert-base-multilingual-uncased": (BertModel, BertTokenizer), + "xlm-mlm-100-1280": (XLMModel, XLMTokenizer), + "roberta-base": (RobertaModel, RobertaTokenizer), + "xlm-roberta-base": (XLMRobertaModel, XLMRobertaTokenizer), + "xlm-roberta-large": (XLMRobertaModel, XLMRobertaTokenizer), + } + + self.model = model + self.device = device + self.layer = layer + self.emb_model = None + self.tokenizer = None + + if model in TR_Models: + model_class, tokenizer_class = TR_Models[model] + self.emb_model = model_class.from_pretrained(model, output_hidden_states=True) + self.emb_model.eval() + self.emb_model.to(self.device) + self.tokenizer = tokenizer_class.from_pretrained(model) + else: + # try to load model with auto-classes + config = AutoConfig.from_pretrained(model, output_hidden_states=True) + self.emb_model = AutoModel.from_pretrained(model, config=config) + self.emb_model.eval() + self.emb_model.to(self.device) + self.tokenizer = AutoTokenizer.from_pretrained(model) + + def get_embed_list(self, sent_batch: List[List[str]]) -> torch.Tensor: + if self.emb_model is not None: + with torch.no_grad(): + if not isinstance(sent_batch[0], str): + inputs = self.tokenizer( + sent_batch, is_split_into_words=True, padding=True, truncation=True, return_tensors="pt" + ) + else: + inputs = self.tokenizer( + sent_batch, is_split_into_words=False, padding=True, truncation=True, return_tensors="pt" + ) + hidden = self.emb_model(**inputs.to(self.device))["hidden_states"] + if self.layer >= len(hidden): + raise ValueError( + f"Specified to take embeddings from layer {self.layer}, but model has only" + f" {len(hidden)} layers." + ) + outputs = hidden[self.layer] + return outputs[:, 1:-1, :] + else: + return None + + +class SentenceAligner(object): + def __init__( + self, + model: str = "bert", + token_type: str = "bpe", + distortion: float = 0.0, + matching_methods: str = "mai", + return_similarity: Optional[str] = None, # new: ["max", "avg"] type of average similarity for words from tokens + device: str = "cpu", + layer: int = 8, + ): + model_names = {"bert": "bert-base-multilingual-cased", "xlmr": "xlm-roberta-base"} + all_matching_methods = {"a": "inter", "m": "mwmf", "i": "itermax", "f": "fwd", "r": "rev"} + + self.model = model + if model in model_names: + self.model = model_names[model] + self.token_type = token_type + self.distortion = distortion + self.matching_methods = [all_matching_methods[m] for m in matching_methods] + self.return_similarity = return_similarity + self.device = torch.device(device) + + self.embed_loader = EmbeddingLoader(model=self.model, device=self.device, layer=layer) + + @staticmethod + def get_max_weight_match(sim: np.ndarray) -> np.ndarray: + if nx is None: + raise ValueError("networkx must be installed to use match algorithm.") + + def permute(edge): + if edge[0] < sim.shape[0]: + return edge[0], edge[1] - sim.shape[0] + else: + return edge[1], edge[0] - sim.shape[0] + + G = from_biadjacency_matrix(csr_matrix(sim)) + matching = nx.max_weight_matching(G, maxcardinality=True) + matching = [permute(x) for x in matching] + matching = sorted(matching, key=lambda x: x[0]) + res_matrix = np.zeros_like(sim) + for edge in matching: + res_matrix[edge[0], edge[1]] = 1 + return res_matrix + + @staticmethod + def get_similarity(X: np.ndarray, Y: np.ndarray) -> np.ndarray: + return (cosine_similarity(X, Y) + 1.0) / 2.0 + + @staticmethod + def average_embeds_over_words(bpe_vectors: np.ndarray, word_tokens_pair: List[List[str]]) -> List[np.array]: + w2b_map = [] + cnt = 0 + w2b_map.append([]) + for wlist in word_tokens_pair[0]: + w2b_map[0].append([]) + for x in wlist: + w2b_map[0][-1].append(cnt) + cnt += 1 + cnt = 0 + w2b_map.append([]) + for wlist in word_tokens_pair[1]: + w2b_map[1].append([]) + for x in wlist: + w2b_map[1][-1].append(cnt) + cnt += 1 + + new_vectors = [] + for l_id in range(2): + w_vector = [] + for word_set in w2b_map[l_id]: + w_vector.append(bpe_vectors[l_id][word_set].mean(0)) + new_vectors.append(np.array(w_vector)) + return new_vectors + + @staticmethod + def get_alignment_matrix(sim_matrix: np.ndarray) -> Tuple[np.ndarray, np.ndarray]: + m, n = sim_matrix.shape + forward = np.eye(n)[sim_matrix.argmax(axis=1)] # m x n + backward = np.eye(m)[sim_matrix.argmax(axis=0)] # n x m + return forward, backward.transpose() + + @staticmethod + def apply_distortion(sim_matrix: np.ndarray, ratio: float = 0.5) -> np.ndarray: + shape = sim_matrix.shape + if (shape[0] < 2 or shape[1] < 2) or ratio == 0.0: + return sim_matrix + + pos_x = np.array([[y / float(shape[1] - 1) for y in range(shape[1])] for x in range(shape[0])]) + pos_y = np.array([[x / float(shape[0] - 1) for x in range(shape[0])] for y in range(shape[1])]) + distortion_mask = 1.0 - ((pos_x - np.transpose(pos_y)) ** 2) * ratio + + return np.multiply(sim_matrix, distortion_mask) + + @staticmethod + def iter_max(sim_matrix: np.ndarray, max_count: int = 2) -> np.ndarray: + alpha_ratio = 0.9 + m, n = sim_matrix.shape + forward = np.eye(n)[sim_matrix.argmax(axis=1)] # m x n + backward = np.eye(m)[sim_matrix.argmax(axis=0)] # n x m + inter = forward * backward.transpose() + + if min(m, n) <= 2: + return inter + + new_inter = np.zeros((m, n)) + count = 1 + while count < max_count: + mask_x = 1.0 - np.tile(inter.sum(1)[:, np.newaxis], (1, n)).clip(0.0, 1.0) + mask_y = 1.0 - np.tile(inter.sum(0)[np.newaxis, :], (m, 1)).clip(0.0, 1.0) + mask = ((alpha_ratio * mask_x) + (alpha_ratio * mask_y)).clip(0.0, 1.0) + mask_zeros = 1.0 - ((1.0 - mask_x) * (1.0 - mask_y)) + if mask_x.sum() < 1.0 or mask_y.sum() < 1.0: + mask *= 0.0 + mask_zeros *= 0.0 + + new_sim = sim_matrix * mask + fwd = np.eye(n)[new_sim.argmax(axis=1)] * mask_zeros + bac = np.eye(m)[new_sim.argmax(axis=0)].transpose() * mask_zeros + new_inter = fwd * bac + + if np.array_equal(inter + new_inter, inter): + break + inter = inter + new_inter + count += 1 + return inter + + def get_word_aligns(self, src_sent: Union[str, List[str]], trg_sent: Union[str, List[str]]) -> Dict[str, List]: + if isinstance(src_sent, str): + src_sent = src_sent.split() + if isinstance(trg_sent, str): + trg_sent = trg_sent.split() + l1_tokens = [self.embed_loader.tokenizer.tokenize(word) for word in src_sent] + l2_tokens = [self.embed_loader.tokenizer.tokenize(word) for word in trg_sent] + bpe_lists = [[bpe for w in sent for bpe in w] for sent in [l1_tokens, l2_tokens]] + + if self.token_type == "bpe": + l1_b2w_map = [] + for i, wlist in enumerate(l1_tokens): + l1_b2w_map += [i for x in wlist] + l2_b2w_map = [] + for i, wlist in enumerate(l2_tokens): + l2_b2w_map += [i for x in wlist] + + vectors = self.embed_loader.get_embed_list([src_sent, trg_sent]).cpu().detach().numpy() + vectors = [vectors[i, : len(bpe_lists[i])] for i in [0, 1]] + + if self.token_type == "word": + vectors = self.average_embeds_over_words(vectors, [l1_tokens, l2_tokens]) + + all_mats = {} + sim = self.get_similarity(vectors[0], vectors[1]) + sim = self.apply_distortion(sim, self.distortion) + + all_mats["fwd"], all_mats["rev"] = self.get_alignment_matrix(sim) + all_mats["inter"] = all_mats["fwd"] * all_mats["rev"] + if "mwmf" in self.matching_methods: + all_mats["mwmf"] = self.get_max_weight_match(sim) + if "itermax" in self.matching_methods: + all_mats["itermax"] = self.iter_max(sim) + + # new: get word-level similarity matrix + if self.return_similarity and self.token_type == "bpe": + words_similarity = np.zeros((len(l1_tokens), len(l2_tokens)), dtype=np.float32) + for i in l1_b2w_map: + for j in l2_b2w_map: + if self.return_similarity == "max": + words_similarity[i, j] = max(words_similarity[i, j], sim[i, j]) + elif self.return_similarity == "avg": + words_similarity[i, j] += sim[i, j] / len(l1_tokens[i]) / len(l2_tokens[j]) + else: + raise ValueError(f"return_similarity={self.return_similarity} is not implemented.") + + aligns = {x: set() for x in self.matching_methods} + for i in range(len(vectors[0])): + for j in range(len(vectors[1])): + for ext in self.matching_methods: + if all_mats[ext][i, j] > 0: + if self.token_type == "bpe": + if self.return_similarity: + aligns[ext].add((l1_b2w_map[i], l2_b2w_map[j], words_similarity[l1_b2w_map[i], l2_b2w_map[j]])) + else: + aligns[ext].add((l1_b2w_map[i], l2_b2w_map[j])) + else: + if self.return_similarity: + aligns[ext].add((i, j, sim[i, j])) + else: + aligns[ext].add((i, j)) + for ext in aligns: + aligns[ext] = sorted(aligns[ext]) + return aligns diff --git a/divemt/qe_taggers.py b/divemt/qe_taggers.py index 4ea2c94..094439b 100644 --- a/divemt/qe_taggers.py +++ b/divemt/qe_taggers.py @@ -1,6 +1,7 @@ import codecs import logging import subprocess +import sys from abc import ABC, abstractmethod from collections import defaultdict from itertools import groupby @@ -8,12 +9,14 @@ from typing import List, Optional, Tuple, Union, Set, Generator, Any from xml.sax.saxutils import escape -import numpy as np from simalign import SentenceAligner -from strenum import StrEnum +if sys.version_info < (3, 11): + from strenum import StrEnum +else: + from enum import StrEnum from tqdm import tqdm -import Levenshtein as lev +from .custom_simalign import SentenceAligner as CustomSentenceAligner from .parse_utils import clear_nlp_cache, tokenize from .wmt22qe_utils import align_sentence_tercom, parse_tercom_xml_file @@ -21,6 +24,7 @@ TTag = Union[str, Set[str]] +TAlignment = Union[Tuple[Optional[int], Optional[int]], Tuple[Optional[int], Optional[int], Optional[float]]] class QETagger(ABC): @@ -33,7 +37,7 @@ def align_source_mt( src_tokens: List[List[str]], mt_tokens: List[List[str]], **align_source_mt_kwargs: Any, - ) -> List[List[Tuple[int, int]]]: + ) -> List[List[TAlignment]]: """Align source and machine translation tokens.""" raise NotImplementedError(f"{self.__class__.__name__} does not implement align_source_mt()") @@ -42,7 +46,7 @@ def align_source_pe( src_tokens: List[List[str]], pe_tokens: List[List[str]], **align_source_pe_kwargs: Any, - ) -> List[List[Tuple[int, int]]]: + ) -> List[List[TAlignment]]: """Align source and post-edited tokens.""" raise NotImplementedError(f"{self.__class__.__name__} does not implement align_source_pe()") @@ -52,7 +56,7 @@ def align_mt_pe( mt_tokens: List[List[str]], pe_tokens: List[List[str]], **align_mt_pe_kwargs: Any, - ) -> List[List[Tuple[int, int]]]: + ) -> List[List[TAlignment]]: """Align machine translation and post-editing tokens.""" pass @@ -61,7 +65,7 @@ def align_mt_pe( def tags_from_edits( mt_tokens: List[List[str]], pe_tokens: List[List[str]], - alignments: List[List[Tuple[int, int]]], + alignments: List[List[TAlignment]], **mt_tagging_kwargs: Any, ) -> List[List[TTag]]: """Produce tags on MT tokens from edits found in the PE tokens.""" @@ -167,7 +171,7 @@ def align_source_pe( src_tokens: List[List[str]], pe_tokens: List[List[str]], pe_langs: List[str], - ) -> List[List[Tuple[int, int]]]: + ) -> List[List[TAlignment]]: return [ self.aligner.get_word_aligns(src_tok, mt_tok)["itermax" if mt_lang not in ["de", "cs"] else "inter"] for src_tok, mt_tok, mt_lang in tqdm( @@ -181,7 +185,7 @@ def align_mt_pe( self, mt_tokens: List[List[str]], pe_tokens: List[List[str]], - ) -> List[List[Tuple[int, int]]]: + ) -> List[List[TAlignment]]: ref_fname = self.tmp_dir / "ref.txt" hyp_fname = self.tmp_dir / "hyp.txt" # Adapted from https://github.com/deep-spin/qe-corpus-builder/corpus_generation/tools/format_tercom.py @@ -230,7 +234,7 @@ def align_mt_pe( def tags_from_edits( mt_tokens: List[List[str]], pe_tokens: List[List[str]], - alignments: List[List[Tuple[int, int]]], + alignments: List[List[TAlignment]], use_gaps: bool = False, omissions: str = OmissionRule.RIGHT.value, ) -> List[List[TTag]]: @@ -324,8 +328,8 @@ def tags_to_source( src_tokens: List[List[str]], pe_tokens: List[List[str]], mt_tokens: List[List[str]], - src_pe_alignments: List[List[Tuple[int, int]]], - mt_pe_alignments: List[List[Tuple[int, int]]], + src_pe_alignments: List[List[TAlignment]], + mt_pe_alignments: List[List[TAlignment]], fluency_rule: str = FluencyRule.NORMAL.value, ) -> List[List[TTag]]: """Propagate tags from MT to source.""" @@ -428,9 +432,9 @@ class NameTBDTagger(QETagger): def __init__( self, - aligner: Optional[SentenceAligner] = None, + aligner: Optional[CustomSentenceAligner] = None, ): - self.aligner = aligner if aligner else SentenceAligner(model="bert", token_type="bpe", matching_methods="mai") + self.aligner = aligner if aligner else CustomSentenceAligner(model="bert", token_type="bpe", matching_methods="mai", return_similarity="avg") def align_source_mt( self, @@ -438,7 +442,7 @@ def align_source_mt( mt_tokens: List[List[str]], src_langs: List[str], mt_langs: List[str], - ) -> List[List[Tuple[int, int]]]: + ) -> List[List[TAlignment]]: return [ self.aligner.get_word_aligns(src_tok, mt_tok)["inter"] for src_tok, mt_tok in tqdm( @@ -451,7 +455,7 @@ def align_mt_pe( mt_tokens: List[List[str]], pe_tokens: List[List[str]], langs: List[str], - ) -> List[List[Tuple[int, int]]]: + ) -> List[List[TAlignment]]: return [ self.aligner.get_word_aligns(mt_tok, pe_tok)["inter"] for mt_tok, pe_tok in tqdm( @@ -460,16 +464,17 @@ def align_mt_pe( ] @staticmethod - def _group_by_node(alignments: List[Tuple[Optional[int], Optional[int]]], by_start_node: bool = True, sort: bool = False) -> Generator[Tuple[int, List[int]], None, None]: + def _group_by_node(alignments: List[Tuple[Optional[int], Optional[int]]], by_start_node: bool = True, sort: bool = False) -> Generator[Tuple[int, List[int], List[float]], None, None]: """Yield a node id and a list of connected nodes.""" _by_index = 0 if by_start_node else 1 if sort: alignments = sorted(alignments, key=lambda x: x[_by_index] if x[_by_index] is not None else -1) for start_node, connected_alignments in groupby(alignments, lambda x: x[_by_index]): - yield start_node, [end_id if by_start_node else start_id for start_id, end_id in connected_alignments] + connected_alignments = list(connected_alignments) + yield start_node, [end_id if by_start_node else start_id for start_id, end_id, _ in connected_alignments], [similarity for _, _, similarity in connected_alignments] @staticmethod - def _detect_crossing_edges(mt_tokens: List[str], pe_tokens: List[str], alignments: List[Tuple[Optional[int], Optional[int]]]) -> List[bool]: + def _detect_crossing_edges(mt_tokens: List[str], pe_tokens: List[str], alignments: List[Tuple[Optional[int], Optional[int], float]]) -> List[bool]: """Detect crossing edges in the alignments. Return mask list of nodes that cross some other node.""" # TODO: optimize from n^2 to n as 2 pointers shifted_mt_mask = [False] * len(mt_tokens) @@ -496,23 +501,12 @@ def _detect_crossing_edges(mt_tokens: List[str], pe_tokens: List[str], alignment return shifted_mt_mask - @staticmethod - def _lev_similarity(mt_tok: str, pe_tok: str) -> float: - """Calculate Lev similarity between two tokens in [0, 1] range.""" - if mt_tok == pe_tok: - return 1.0 - - # calculate similarity using Lev distance - return lev.ratio(mt_tok, pe_tok) - @staticmethod def tags_from_edits( mt_tokens: List[List[str]], pe_tokens: List[List[str]], - mt_pe_alignments: List[List[Tuple[int, int]]], - mt_tokens_embeddings: Optional[List[List[np.ndarray]]] = None, - pe_tokens_embeddings: Optional[List[List[np.ndarray]]] = None, - threshold: float = 0.5, + mt_pe_alignments: List[List[TAlignment]], + threshold: float = 0.8, ) -> List[List[TTag]]: """ Produce tags on MT tokens from edits found in the PE tokens. @@ -545,55 +539,44 @@ def tags_from_edits( mt_tags: List[List[Set[str]]] = [] - for mt_tok, pe_tok, mt_pe_align in tqdm(zip(mt_tokens, pe_tokens, mt_pe_alignments), desc="Tagging MT", total=len(mt_tokens)): + for mt_sent_tok, pe_sent_tok, mt_pe_sent_align in tqdm(zip(mt_tokens, pe_tokens, mt_pe_alignments), desc="Tagging MT", total=len(mt_tokens)): - mt_sent_tags: List[Set[str]] = [set() for _ in range(len(mt_tok))] + mt_sent_tags: List[Set[str]] = [set() for _ in range(len(mt_sent_tok))] # clear 1-n and n-1 nodes with low threshold # e.g. if 1-n or n-1 have same token or high similarity, remove low similarity as deletions/insertions (None:1 and 1:None) aligns_remove_1_to_n, aligns_remove_n_to_1 = set(), set() # 1-n match - for mt_node_id, connected_pe_nodes_ids in NameTBDTagger._group_by_node(mt_pe_align, by_start_node=True, sort=False): + for mt_node_id, connected_pe_nodes_ids, connected_pe_similarity in NameTBDTagger._group_by_node(mt_pe_sent_align, by_start_node=True, sort=True): if mt_node_id is not None and len(connected_pe_nodes_ids) > 1: - # TODO: check alignments lib to have sim - pe_similarity = [ - (pe_node_id, NameTBDTagger._lev_similarity(mt_tok[mt_node_id], pe_tok[pe_node_id])) - for pe_node_id in connected_pe_nodes_ids - if pe_node_id is not None - ] - if all(sim < threshold for _, sim in pe_similarity): + if all(sim < threshold for sim in connected_pe_similarity): continue - if all(sim > threshold for _, sim in pe_similarity): + if all(sim > threshold for sim in connected_pe_similarity): continue aligns_remove_1_to_n.update([ - (mt_node_id, pe_node_id) - for pe_node_id, sim in pe_similarity + (mt_node_id, pe_node_id, sim) + for pe_node_id, sim in zip(connected_pe_nodes_ids, connected_pe_similarity) if sim < threshold ]) # remove selected aligns and add None connected nodes instead - mt_pe_align = [(None, align[1]) if align in aligns_remove_1_to_n else align for align in mt_pe_align] + mt_pe_sent_align = [(None, align[1], None) if align in aligns_remove_1_to_n else align for align in mt_pe_sent_align] # n-1 match - for pe_node_id, connected_mt_nodes_ids in NameTBDTagger._group_by_node(mt_pe_align, by_start_node=False, sort=True): + for pe_node_id, connected_mt_nodes_ids, connected_mt_similarity in NameTBDTagger._group_by_node(mt_pe_sent_align, by_start_node=False, sort=True): if pe_node_id is not None and len(connected_mt_nodes_ids) > 1: - mt_similarity = [ - (mt_node_id, NameTBDTagger._lev_similarity(mt_tok[mt_node_id], pe_tok[pe_node_id])) - for mt_node_id in connected_mt_nodes_ids - if mt_node_id is not None - ] - if all(sim < threshold for _, sim in mt_similarity): + if all(sim < threshold for sim in connected_mt_similarity): continue - if all(sim > threshold for _, sim in mt_similarity): + if all(sim > threshold for sim in connected_mt_similarity): continue aligns_remove_n_to_1.update([ - (mt_node_id, pe_node_id) - for mt_node_id, sim in mt_similarity + (mt_node_id, pe_node_id, sim) + for mt_node_id, sim in zip(connected_mt_nodes_ids, connected_mt_similarity) if sim < threshold ]) # remove selected aligns and add None connected nodes instead - mt_pe_align = [(align[0], None) if align in aligns_remove_n_to_1 else align for align in mt_pe_align] + mt_pe_sent_align = [(align[0], None, None) if align in aligns_remove_n_to_1 else align for align in mt_pe_sent_align] # Solve all n-1: setup expansions tags and solve n-1 matches < threshold as smth+insertion - for pe_node_id, connected_mt_nodes_ids in NameTBDTagger._group_by_node(mt_pe_align, by_start_node=False, sort=True): + for pe_node_id, connected_mt_nodes_ids, _ in NameTBDTagger._group_by_node(mt_pe_sent_align, by_start_node=False, sort=True): if pe_node_id is not None and len(connected_mt_nodes_ids) > 1: # expansion, mark related mt nodes for mt_node_id in connected_mt_nodes_ids: @@ -602,7 +585,7 @@ def tags_from_edits( # Solve all deletions, add deletion tags on left and right sides mt_position = 0 - for mt_node_id, connected_pe_nodes_ids in NameTBDTagger._group_by_node(mt_pe_align, by_start_node=True, sort=False): + for mt_node_id, connected_pe_nodes_ids, _ in NameTBDTagger._group_by_node(mt_pe_sent_align, by_start_node=True, sort=False): if mt_node_id is None: # deleted word error, mark left and right modes if 0 <= mt_position - 1 < len(mt_sent_tags): @@ -612,11 +595,10 @@ def tags_from_edits( else: mt_position += 1 # clear all (None, i) to not mess grouping - mt_pe_align = [align for align in mt_pe_align if align[0] is not None] + mt_pe_sent_align = [align for align in mt_pe_sent_align if align[0] is not None] # Solve all 1-n matches - for mt_node_id, connected_pe_nodes_ids in NameTBDTagger._group_by_node(mt_pe_align, by_start_node=True, sort=True): - print(mt_node_id, ' -> ', connected_pe_nodes_ids, '\t\tmt_position=', mt_position) + for mt_node_id, connected_pe_nodes_ids, _ in NameTBDTagger._group_by_node(mt_pe_sent_align, by_start_node=True, sort=True): assert mt_node_id is not None, "Already should be filtered all (None, smth) cases" if NameTBDGeneralTags.BAD_EXPANSION.value in mt_sent_tags[mt_node_id]: continue @@ -626,7 +608,7 @@ def tags_from_edits( elif connected_pe_nodes_ids[0] is None: # insertion, mark the node mt_sent_tags[mt_node_id].add(NameTBDGeneralTags.BAD_INSERTION.value) - elif mt_tok[mt_node_id] != pe_tok[connected_pe_nodes_ids[0]]: + elif mt_sent_tok[mt_node_id] != pe_sent_tok[connected_pe_nodes_ids[0]]: # substitution, mark the node mt_sent_tags[mt_node_id].add(NameTBDGeneralTags.BAD_SUBSTITUTION.value) else: @@ -634,7 +616,7 @@ def tags_from_edits( mt_sent_tags[mt_node_id].add(NameTBDGeneralTags.OK.value) # Add shifted tags if so - for mt_node_id, mask in enumerate(NameTBDTagger._detect_crossing_edges(mt_tok, pe_tok, mt_pe_align)): + for mt_node_id, mask in enumerate(NameTBDTagger._detect_crossing_edges(mt_sent_tok, pe_sent_tok, mt_pe_sent_align)): if mask: mt_sent_tags[mt_node_id].add(NameTBDGeneralTags.BAD_SHIFTING.value) @@ -651,7 +633,7 @@ def tags_from_edits( def tags_to_source( src_tokens: List[List[str]], mt_tokens: List[List[str]], - src_mt_alignments: List[List[Tuple[int, int]]], + src_mt_alignments: List[List[TAlignment]], mt_tags: List[List[Set[str]]], ) -> List[List[TTag]]: """ Propagate tags from MT to source. @@ -670,7 +652,43 @@ def tags_to_source( - Copy tags from top match in MT and ignore other matches """ - raise NotImplementedError() + src_tags: List[List[Set[str]]] = [] + + for src_sent_tok, mt_sent_tok, mt_sent_tags, mt_pe_sent_align in tqdm(zip(src_tokens, mt_tokens, mt_tags, src_mt_alignments), desc="Transfer to source", total=len(src_tokens)): + + src_sent_tags: List[Set[str]] = [set() for _ in range(len(src_sent_tok))] + + # Solve all as 1-n matches + for src_node_id, connected_mt_nodes_ids, connected_mt_similarity in NameTBDTagger._group_by_node(mt_pe_sent_align, by_start_node=True, sort=True): + if src_node_id is None: + continue + elif len(connected_mt_nodes_ids) == 0: + continue + elif len(connected_mt_nodes_ids) > 1: + # n-1 match, find best match + best_mt_node_id, best_mt_similarity = None, 0.0 + for mt_node_id, mt_similarity in zip(connected_mt_nodes_ids, connected_mt_similarity): + if mt_similarity is not None and mt_similarity > best_mt_similarity: + best_mt_node_id, best_mt_similarity = mt_node_id, mt_similarity + if best_mt_node_id is None: + # no good match, ignore + continue + else: + # copy tags from best match + src_sent_tags[src_node_id].update(mt_sent_tags[best_mt_node_id]) + elif connected_mt_nodes_ids[0] is None: + # nothing to copy from MT + continue + else: + # 1-1 match, copy tags + src_sent_tags[src_node_id].update(mt_sent_tags[connected_mt_nodes_ids[0]]) + + # Save tags for this sentence + src_tags.append(src_sent_tags) + + # Basic sanity checks + assert all(len(aa) == len(bb) for aa, bb in zip(src_tokens, src_tags)), "Source tags creation failed, number of tokens and tags do not match" + return src_tags def generate_tags( self, diff --git a/divemt/wmt22qe_utils.py b/divemt/wmt22qe_utils.py index 6ff2731..7322bb5 100644 --- a/divemt/wmt22qe_utils.py +++ b/divemt/wmt22qe_utils.py @@ -1,8 +1,12 @@ import re +import sys from typing import List, Tuple from xml.dom.minidom import parse -from strenum import StrEnum +if sys.version_info < (3, 11): + from strenum import StrEnum +else: + from enum import StrEnum class TercomEdit(StrEnum): diff --git a/tests/test_qe_taggers_name_tbd_tagger.py b/tests/test_qe_taggers_name_tbd_tagger.py index ea13df6..a087eba 100644 --- a/tests/test_qe_taggers_name_tbd_tagger.py +++ b/tests/test_qe_taggers_name_tbd_tagger.py @@ -1,37 +1,42 @@ +import sys from typing import List, Tuple, Set import pytest -from strenum import StrEnum +if sys.version_info < (3, 11): + from strenum import StrEnum +else: + from enum import StrEnum from divemt.qe_taggers import NameTBDTagger from divemt.qe_taggers import NameTBDGeneralTags as Tags +tagger = NameTBDTagger() + + class TestUtils: @pytest.mark.parametrize("mt_len, mt_pe_alignments, true_mt_shifts_mask", [ - (1, [(0, 0)], [False]), - (2, [(0, 0), (1, 1)], [False, False]), - (3, [(0, 0), (1, 1), (2, 2)], [False, False, False]), - (3, [(0, 0), (1, None), (2, 1)], [False, False, False]), + (1, [(0, 0, 0.9)], [False]), + (2, [(0, 0, 0.9), (1, 1, 0.9)], [False, False]), + (3, [(0, 0, 0.9), (1, 1, 0.9), (2, 2, 0.9)], [False, False, False]), + (3, [(0, 0, 0.9), (1, None, None), (2, 1, 0.9)], [False, False, False]), # easiest case - (2, [(0, 1), (1, 0)], [True, True]), + (2, [(0, 1, 0.9), (1, 0, 0.9)], [True, True]), # central one is not moved, but have crossing edges - (3, [(0, 2), (1, 1), (2, 0)], [True, True, True]), + (3, [(0, 2, 0.9), (1, 1, 0.9), (2, 0, 0.9)], [True, True, True]), # the central one deleted, so not shifted, no crossing edges - (3, [(0, 1), (1, None), (2, 0)], [True, False, True]), - # TODO: check with gabrielle - (4, [(0, 0), (1, 3), (1, 4), (1, 5), (2, 2), (2, 0), (3, None)], [False, True, True, False]), + (3, [(0, 1, 0.9), (1, None, None), (2, 0, 0.9)], [True, False, True]), + (4, [(0, 0, 0.9), (1, 3, 0.9), (1, 4, 0.9), (1, 5, 0.9), (2, 2, 0.9), (2, 0, 0.9), (3, None, None)], [False, True, True, False]), ]) def test_detect_crossing_edges(self, mt_len: int, mt_pe_alignments: List[Tuple[int, int]], true_mt_shifts_mask: List[bool]) -> None: - tagger = NameTBDTagger() mt_shifts_mask = tagger._detect_crossing_edges([str(i) for i in range(mt_len)], [str(i) for i in range(mt_len)], mt_pe_alignments) assert mt_shifts_mask == true_mt_shifts_mask class TestTagsFromEdits: @pytest.mark.parametrize("mt_tokens, pe_tokens, mt_pe_alignments, true_mt_tags", [ - (["A", "B"], ["A", "B"], [(0, 0), (1, 1)], [{Tags.OK}, {Tags.OK}]), - (["A", "B", "C", "D"], ["A", "B", "C", "D"], [(0, 0), (1, 1), (2, 2), (3, 3)], [{Tags.OK}, {Tags.OK}, {Tags.OK}, {Tags.OK}]), + (["A", "B"], ["A", "B"], [(0, 0, 0.9), (1, 1, 0.9)], [{Tags.OK}, {Tags.OK}]), + (["A", "B", "C", "D"], ["A", "B", "C", "D"], [(0, 0, 0.9), (1, 1, 0.9), (2, 2, 0.9), (3, 3, 0.9)], [{Tags.OK}, {Tags.OK}, {Tags.OK}, {Tags.OK}]), ([], [], [], []), ]) def test_single_error_ok( @@ -41,15 +46,14 @@ def test_single_error_ok( mt_pe_alignments: List[Tuple[int, int]], true_mt_tags: List[Set[StrEnum]], ) -> None: - tagger = NameTBDTagger() predicted_tags = tagger.tags_from_edits([mt_tokens], [pe_tokens], [mt_pe_alignments])[0] assert len(predicted_tags) == len(true_mt_tags) for predicted_tags, true_tags in zip(predicted_tags, true_mt_tags): assert predicted_tags == {t.value for t in true_tags} @pytest.mark.parametrize("mt_tokens, pe_tokens, mt_pe_alignments, true_mt_tags", [ - (["A", "B", "C"], ["A", "X", "Z"], [(0, 0), (1, 1), (2, 2)], [{Tags.OK}, {Tags.BAD_SUBSTITUTION}, {Tags.BAD_SUBSTITUTION}]), - (["A", "B"], ["Z", "X"], [(0, 0), (1, 1)], [{Tags.BAD_SUBSTITUTION}, {Tags.BAD_SUBSTITUTION}]), + (["A", "B", "C"], ["A", "X", "Z"], [(0, 0, 0.9), (1, 1, 0.9), (2, 2, 0.9)], [{Tags.OK}, {Tags.BAD_SUBSTITUTION}, {Tags.BAD_SUBSTITUTION}]), + (["A", "B"], ["Z", "X"], [(0, 0, 0.9), (1, 1, 0.9)], [{Tags.BAD_SUBSTITUTION}, {Tags.BAD_SUBSTITUTION}]), # For 1-n and n-1 cases see contraction and expansion tests ]) def test_single_error_substitution( @@ -59,16 +63,15 @@ def test_single_error_substitution( mt_pe_alignments: List[Tuple[int, int]], true_mt_tags: List[Set[StrEnum]], ) -> None: - tagger = NameTBDTagger() predicted_tags = tagger.tags_from_edits([mt_tokens], [pe_tokens], [mt_pe_alignments])[0] assert len(predicted_tags) == len(true_mt_tags) for predicted_tags, true_tags in zip(predicted_tags, true_mt_tags): assert predicted_tags == {t.value for t in true_tags} @pytest.mark.parametrize("mt_tokens, pe_tokens, mt_pe_alignments, true_mt_tags", [ - (["A", "B"], ["A"], [(0, 0), (1, None)], [{Tags.OK}, {Tags.BAD_INSERTION}]), - (["A", "B"], ["B"], [(0, None), (1, 0)], [{Tags.BAD_INSERTION}, {Tags.OK}]), - (["A", "B"], [], [(0, None), (1, None)], [{Tags.BAD_INSERTION}, {Tags.BAD_INSERTION}]), + (["A", "B"], ["A"], [(0, 0, 0.9), (1, None, None)], [{Tags.OK}, {Tags.BAD_INSERTION}]), + (["A", "B"], ["B"], [(0, None, None), (1, 0, 0.9)], [{Tags.BAD_INSERTION}, {Tags.OK}]), + (["A", "B"], [], [(0, None, None), (1, None, None)], [{Tags.BAD_INSERTION}, {Tags.BAD_INSERTION}]), # For 1-n and n-1 cases see contraction and expansion tests ]) def test_single_error_insertion( @@ -78,24 +81,23 @@ def test_single_error_insertion( mt_pe_alignments: List[Tuple[int, int]], true_mt_tags: List[Set[StrEnum]], ) -> None: - tagger = NameTBDTagger() predicted_tags = tagger.tags_from_edits([mt_tokens], [pe_tokens], [mt_pe_alignments])[0] assert len(predicted_tags) == len(true_mt_tags) for predicted_tags, true_tags in zip(predicted_tags, true_mt_tags): assert predicted_tags == {t.value for t in true_tags} @pytest.mark.parametrize("mt_tokens, pe_tokens, mt_pe_alignments, true_mt_tags", [ - (["A"], ["A", "X"], [(0, 0), (None, 1)], [{Tags.OK, Tags.BAD_DELETION_RIGHT}]), - (["A"], ["X", "A"], [(None, 0), (0, 1)], [{Tags.OK, Tags.BAD_DELETION_LEFT}]), - (["A", "B"], ["A", "X", "B"], [(0, 0), (None, 1), (1, 2)], [{Tags.OK, Tags.BAD_DELETION_RIGHT}, {Tags.OK, Tags.BAD_DELETION_LEFT}]), + (["A"], ["A", "X"], [(0, 0, 0.9), (None, 1, None)], [{Tags.OK, Tags.BAD_DELETION_RIGHT}]), + (["A"], ["X", "A"], [(None, 0, None), (0, 1, 0.9)], [{Tags.OK, Tags.BAD_DELETION_LEFT}]), + (["A", "B"], ["A", "X", "B"], [(0, 0, 0.9), (None, 1, None), (1, 2, 0.9)], [{Tags.OK, Tags.BAD_DELETION_RIGHT}, {Tags.OK, Tags.BAD_DELETION_LEFT}]), # Delete multiple tokens, but tag error as deleted one - (["A"], ["A", "X", "Y", "Z"], [(0, 0), (None, 1), (None, 2), (None, 3)], [{Tags.OK, Tags.BAD_DELETION_RIGHT}]), - (["A"], ["X", "Y", "Z", "A"], [(None, 0), (None, 1), (None, 2), (0, 3)], [{Tags.OK, Tags.BAD_DELETION_LEFT}]), - (["A", "B"], ["A", "X", "Y", "Z", "B"], [(0, 0), (None, 1), (None, 2), (None, 3), (1, 4)], [{Tags.OK, Tags.BAD_DELETION_RIGHT}, {Tags.OK, Tags.BAD_DELETION_LEFT}]), + (["A"], ["A", "X", "Y", "Z"], [(0, 0, 0.9), (None, 1, None), (None, 2, None), (None, 3, None)], [{Tags.OK, Tags.BAD_DELETION_RIGHT}]), + (["A"], ["X", "Y", "Z", "A"], [(None, 0, None), (None, 1, None), (None, 2, None), (0, 3, 0.9)], [{Tags.OK, Tags.BAD_DELETION_LEFT}]), + (["A", "B"], ["A", "X", "Y", "Z", "B"], [(0, 0, 0.9), (None, 1, None), (None, 2, None), (None, 3, None), (1, 4, 0.9)], [{Tags.OK, Tags.BAD_DELETION_RIGHT}, {Tags.OK, Tags.BAD_DELETION_LEFT}]), # deleted both left and right sides - (["A"], ["X", "A", "Y"], [(None, 0), (0, 1), (None, 2)], [{Tags.OK, Tags.BAD_DELETION_LEFT, Tags.BAD_DELETION_RIGHT}]), + (["A"], ["X", "A", "Y"], [(None, 0, None), (0, 1, 0.9), (None, 2, None)], [{Tags.OK, Tags.BAD_DELETION_LEFT, Tags.BAD_DELETION_RIGHT}]), # deleted for empty target - ([], ["X"], [(None, 0)], []), + ([], ["X"], [(None, 0, None)], []), ]) def test_single_error_deletion( self, @@ -104,7 +106,6 @@ def test_single_error_deletion( mt_pe_alignments: List[Tuple[int, int]], true_mt_tags: List[Set[StrEnum]], ) -> None: - tagger = NameTBDTagger() predicted_tags = tagger.tags_from_edits([mt_tokens], [pe_tokens], [mt_pe_alignments])[0] assert len(predicted_tags) == len(true_mt_tags) for predicted_tags, true_tags in zip(predicted_tags, true_mt_tags): @@ -112,17 +113,17 @@ def test_single_error_deletion( @pytest.mark.parametrize("mt_tokens, pe_tokens, mt_pe_alignments, true_mt_tags", [ # Have same BBB token, so should filter CCC and TTT out as Deletion error and BBB as Ok - (["AAA", "BBB"], ["AAA", "BBB", "CCC", "TTT"], [(0, 0), (1, 1), (1, 2), (1, 3)], [{Tags.OK}, {Tags.OK, Tags.BAD_DELETION_RIGHT}]), - (["AAA", "BBB"], ["AAA", "TTT", "BBB", "CCC"], [(0, 0), (1, 1), (1, 2), (1, 3)], [{Tags.OK, Tags.BAD_DELETION_RIGHT}, {Tags.OK, Tags.BAD_DELETION_RIGHT, Tags.BAD_DELETION_LEFT}]), + (["AAA", "BBB"], ["AAA", "BBB", "CCC", "TTT"], [(0, 0, 0.9), (1, 1, 0.9), (1, 2, 0.1), (1, 3, 0.1)], [{Tags.OK}, {Tags.OK, Tags.BAD_DELETION_RIGHT}]), + (["AAA", "BBB"], ["AAA", "TTT", "BBB", "CCC"], [(0, 0, 0.9), (1, 1, 0.1), (1, 2, 0.9), (1, 3, 0.1)], [{Tags.OK, Tags.BAD_DELETION_RIGHT}, {Tags.OK, Tags.BAD_DELETION_RIGHT, Tags.BAD_DELETION_LEFT}]), # XXX, TTT and CCC >threshold are same BBB token, so its bad Contradiction - (["AAA", "BBB"], ["AAA", "XXX", "CCC", "TTT"], [(0, 0), (1, 1), (1, 2), (1, 3)], [{Tags.OK}, {Tags.BAD_CONTRACTION}]), + (["AAA", "BBB"], ["AAA", "XXX", "CCC", "TTT"], [(0, 0, 0.9), (1, 1, 0.9), (1, 2, 0.9), (1, 3, 0.9)], [{Tags.OK}, {Tags.BAD_CONTRACTION}]), # BBX is >threshold, CCC/TTT threshold, so all are Contractions - (["AAA", "BBB"], ["AAA", "BBX", "XBB"], [(0, 0), (1, 1), (1, 2)], [{Tags.OK}, {Tags.BAD_CONTRACTION}]), + (["AAA", "BBB"], ["AAA", "BBX", "XBB"], [(0, 0, 0.9), (1, 1, 0.9), (1, 2, 0.9)], [{Tags.OK}, {Tags.BAD_CONTRACTION}]), # BBX and XBB >threshold while TTT is None: - tagger = NameTBDTagger() predicted_tags = tagger.tags_from_edits([mt_tokens], [pe_tokens], [mt_pe_alignments])[0] assert len(predicted_tags) == len(true_mt_tags) for predicted_tags, true_tags in zip(predicted_tags, true_mt_tags): @@ -140,17 +140,17 @@ def test_single_error_contraction( @pytest.mark.parametrize("mt_tokens, pe_tokens, mt_pe_alignments, true_mt_tags", [ # BB token is same, so CCC and TTT are insertions - (["AAA", "BBB", "CCC", "TTT"], ["AAA", "BBB"], [(0, 0), (1, 1), (2, 1), (3, 1)], [{Tags.OK}, {Tags.OK}, {Tags.BAD_INSERTION}, {Tags.BAD_INSERTION}]), - (["AAA", "TTT", "BBB", "CCC"], ["AAA", "BBB"], [(0, 0), (1, 1), (2, 1), (3, 1)], [{Tags.OK}, {Tags.BAD_INSERTION}, {Tags.OK}, {Tags.BAD_INSERTION}]), + (["AAA", "BBB", "CCC", "TTT"], ["AAA", "BBB"], [(0, 0, 0.9), (1, 1, 0.9), (2, 1, 0.1), (3, 1, 0.1)], [{Tags.OK}, {Tags.OK}, {Tags.BAD_INSERTION}, {Tags.BAD_INSERTION}]), + (["AAA", "TTT", "BBB", "CCC"], ["AAA", "BBB"], [(0, 0, 0.9), (1, 1, 0.1), (2, 1, 0.9), (3, 1, 0.1)], [{Tags.OK}, {Tags.BAD_INSERTION}, {Tags.OK}, {Tags.BAD_INSERTION}]), # XXX, TTT and CCC >threshold are same BBB token, so its bad Expansion - (["AAA", "XXX", "CCC", "TTT"], ["AAA", "BBB"], [(0, 0), (1, 1), (2, 1), (3, 1)], [{Tags.OK}, {Tags.BAD_EXPANSION}, {Tags.BAD_EXPANSION}, {Tags.BAD_EXPANSION}]), + (["AAA", "XXX", "CCC", "TTT"], ["AAA", "BBB"], [(0, 0, 0.9), (1, 1, 0.9), (2, 1, 0.9), (3, 1, 0.9)], [{Tags.OK}, {Tags.BAD_EXPANSION}, {Tags.BAD_EXPANSION}, {Tags.BAD_EXPANSION}]), # BBX is >threshold, CCC/TTT threshold, so all are Expansion - (["AAA", "BBX", "XBB"], ["AAA", "BBB"], [(0, 0), (1, 1), (2, 1)], [{Tags.OK}, {Tags.BAD_EXPANSION}, {Tags.BAD_EXPANSION}]), + (["AAA", "BBX", "XBB"], ["AAA", "BBB"], [(0, 0, 0.9), (1, 1, 0.9), (2, 1, 0.9)], [{Tags.OK}, {Tags.BAD_EXPANSION}, {Tags.BAD_EXPANSION}]), # BBX and XBB >threshold while TTT is None: - tagger = NameTBDTagger() predicted_tags = tagger.tags_from_edits([mt_tokens], [pe_tokens], [mt_pe_alignments])[0] assert len(predicted_tags) == len(true_mt_tags) for predicted_tags, true_tags in zip(predicted_tags, true_mt_tags): @@ -168,13 +167,13 @@ def test_single_error_expansion( @pytest.mark.parametrize("mt_tokens, pe_tokens, mt_pe_alignments, true_mt_tags", [ # simple case - (["A", "B"], ["B", "A"], [(0, 1), (1, 0)], [{Tags.OK, Tags.BAD_SHIFTING}, {Tags.OK, Tags.BAD_SHIFTING}]), + (["A", "B"], ["B", "A"], [(0, 1, 0.9), (1, 0, 0.9)], [{Tags.OK, Tags.BAD_SHIFTING}, {Tags.OK, Tags.BAD_SHIFTING}]), # middle intact, but crossing edges, so shifted - (["A", "X", "Y", "B"], ["B", "X", "Y", "A"], [(0, 3), (1, 1), (2, 2), (3, 0)], [{Tags.OK, Tags.BAD_SHIFTING}, {Tags.OK, Tags.BAD_SHIFTING}, {Tags.OK, Tags.BAD_SHIFTING}, {Tags.OK, Tags.BAD_SHIFTING}]), - # node inserted, so should not be marked as shifted TODO: check with gabrielle - (["A", "X", "B"], ["B", "A"], [(0, 1), (1, None), (2, 0)], [{Tags.OK, Tags.BAD_SHIFTING}, {Tags.BAD_INSERTION}, {Tags.OK, Tags.BAD_SHIFTING}]), + (["A", "X", "Y", "B"], ["B", "X", "Y", "A"], [(0, 3, 0.9), (1, 1, 0.9), (2, 2, 0.9), (3, 0, 0.9)], [{Tags.OK, Tags.BAD_SHIFTING}, {Tags.OK, Tags.BAD_SHIFTING}, {Tags.OK, Tags.BAD_SHIFTING}, {Tags.OK, Tags.BAD_SHIFTING}]), + # node inserted, so should not be marked as shifted + (["A", "X", "B"], ["B", "A"], [(0, 1, 0.9), (1, None, None), (2, 0, 0.9)], [{Tags.OK, Tags.BAD_SHIFTING}, {Tags.BAD_INSERTION}, {Tags.OK, Tags.BAD_SHIFTING}]), # node deleted, nothing to mark as shifted - (["A", "B"], ["B", "X", "A"], [(0, 2), (None, 1), (1, 0)], [{Tags.OK, Tags.BAD_SHIFTING, Tags.BAD_DELETION_RIGHT}, {Tags.OK, Tags.BAD_SHIFTING, Tags.BAD_DELETION_LEFT}]), + (["A", "B"], ["B", "X", "A"], [(0, 2, 0.9), (None, 1, None), (1, 0, 0.9)], [{Tags.OK, Tags.BAD_SHIFTING, Tags.BAD_DELETION_RIGHT}, {Tags.OK, Tags.BAD_SHIFTING, Tags.BAD_DELETION_LEFT}]), ]) def test_single_error_shifted( self, @@ -183,8 +182,67 @@ def test_single_error_shifted( mt_pe_alignments: List[Tuple[int, int]], true_mt_tags: List[Set[StrEnum]], ) -> None: - tagger = NameTBDTagger() predicted_tags = tagger.tags_from_edits([mt_tokens], [pe_tokens], [mt_pe_alignments])[0] assert len(predicted_tags) == len(true_mt_tags) for predicted_tags, true_tags in zip(predicted_tags, true_mt_tags): assert predicted_tags == {t.value for t in true_tags} + + +class TestTagsToSource: + @pytest.mark.parametrize("src_tokens, mt_tokens, mt_pe_alignments, mt_tags, true_src_tags", [ + # ok cases + (["A", "B"], ["A", "B"], [(0, 0, 0.9), (1, 1, 0.9)], [{Tags.OK}, {Tags.OK}], [{Tags.OK}, {Tags.OK}]), + (["A", "B", "C", "D"], ["A", "B", "C", "D"], [(0, 0, 0.9), (1, 1, 0.9), (2, 2, 0.9), (3, 3, 0.9)], [{Tags.OK}, {Tags.OK}, {Tags.OK}, {Tags.OK}], [{Tags.OK}, {Tags.OK}, {Tags.OK}, {Tags.OK}]), + ([], [], [], [], []), + # substitution cases + (["A", "B"], ["A", "C"], [(0, 0, 0.9), (1, 1, 0.9)], [{Tags.OK}, {Tags.BAD_SUBSTITUTION}], [{Tags.OK}, {Tags.BAD_SUBSTITUTION}]), + (["A", "B", "C", "D"], ["A", "B", "X", "D"], [(0, 0, 0.9), (1, 1, 0.9), (2, 2, 0.1), (3, 3, 0.9)], [{Tags.OK}, {Tags.OK}, {Tags.BAD_SUBSTITUTION}, {Tags.OK}], [{Tags.OK}, {Tags.OK}, {Tags.BAD_SUBSTITUTION}, {Tags.OK}]), + # multiple tags + (["A", "B"], ["A", "C"], [(0, 0, 0.9), (1, 1, 0.9)], [{Tags.OK, Tags.BAD_SHIFTING}, {Tags.BAD_SUBSTITUTION, Tags.BAD_DELETION_RIGHT}], [{Tags.OK, Tags.BAD_SHIFTING}, {Tags.BAD_SUBSTITUTION, Tags.BAD_DELETION_RIGHT}]), + ]) + def test_one_to_one( + self, + src_tokens: List[str], + mt_tokens: List[str], + mt_pe_alignments: List[Tuple[int, int]], + mt_tags: List[Set[StrEnum]], + true_src_tags: List[Set[StrEnum]], + ) -> None: + predicted_tags = tagger.tags_to_source([src_tokens], [mt_tokens], [mt_pe_alignments], [[{i.value for i in t} for t in mt_tags]])[0] + assert len(predicted_tags) == len(true_src_tags) + for predicted_tags, true_tags in zip(predicted_tags, true_src_tags): + assert predicted_tags == {t.value for t in true_tags} + + @pytest.mark.parametrize("src_tokens, mt_tokens, mt_pe_alignments, mt_tags, true_src_tags", [ + (["A"], ["A", "B"], [(0, 0, 0.9), (None, 1, None)], [{Tags.OK}, {Tags.BAD_SUBSTITUTION}], [{Tags.OK}]), + (["A", "B"], ["A", "B", "C"], [(0, 0, 0.9), (1, 1, 0.9), (None, 2, None)], [{Tags.BAD_SUBSTITUTION}, {Tags.OK}, {Tags.OK}], [{Tags.BAD_SUBSTITUTION}, {Tags.OK}]), + ]) + def test_src_deleted( + self, + src_tokens: List[str], + mt_tokens: List[str], + mt_pe_alignments: List[Tuple[int, int]], + mt_tags: List[Set[StrEnum]], + true_src_tags: List[Set[StrEnum]], + ) -> None: + predicted_tags = tagger.tags_to_source([src_tokens], [mt_tokens], [mt_pe_alignments], [[{i.value for i in t} for t in mt_tags]])[0] + assert len(predicted_tags) == len(true_src_tags) + for predicted_tags, true_tags in zip(predicted_tags, true_src_tags): + assert predicted_tags == {t.value for t in true_tags} + + @pytest.mark.parametrize("src_tokens, mt_tokens, mt_pe_alignments, mt_tags, true_src_tags", [ + (["A", "B", "C"], ["A", "B"], [(0, 0, 0.9), (1, 1, 0.9), (2, None, None)], [{Tags.BAD_SUBSTITUTION}, {Tags.OK}], [{Tags.BAD_SUBSTITUTION}, {Tags.OK}, set()]), + (["A", "B", "C", "D"], ["B"], [(0, None, None), (1, 0, 0.9), (2, None, None), (3, None, None)], [{Tags.BAD_SUBSTITUTION}], [set(), {Tags.BAD_SUBSTITUTION}, set(), set()]), + ]) + def test_mt_deleted( + self, + src_tokens: List[str], + mt_tokens: List[str], + mt_pe_alignments: List[Tuple[int, int]], + mt_tags: List[Set[StrEnum]], + true_src_tags: List[Set[StrEnum]], + ) -> None: + predicted_tags = tagger.tags_to_source([src_tokens], [mt_tokens], [mt_pe_alignments], [[{i.value for i in t} for t in mt_tags]])[0] + assert len(predicted_tags) == len(true_src_tags) + for predicted_tags, true_tags in zip(predicted_tags, true_src_tags): + assert predicted_tags == {t.value for t in true_tags} From f9e375c4d79cbf8275d27f85eda53e26a729b0a9 Mon Sep 17 00:00:00 2001 From: Konstantin Chernyshev Date: Thu, 4 May 2023 14:44:51 +0200 Subject: [PATCH 05/23] fix: filter None similarities when check deletions --- divemt/qe_taggers.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/divemt/qe_taggers.py b/divemt/qe_taggers.py index 094439b..4a8bc29 100644 --- a/divemt/qe_taggers.py +++ b/divemt/qe_taggers.py @@ -549,28 +549,28 @@ def tags_from_edits( # 1-n match for mt_node_id, connected_pe_nodes_ids, connected_pe_similarity in NameTBDTagger._group_by_node(mt_pe_sent_align, by_start_node=True, sort=True): if mt_node_id is not None and len(connected_pe_nodes_ids) > 1: - if all(sim < threshold for sim in connected_pe_similarity): + if all(sim < threshold for sim in connected_pe_similarity if sim is not None): continue - if all(sim > threshold for sim in connected_pe_similarity): + if all(sim > threshold for sim in connected_pe_similarity if sim is not None): continue aligns_remove_1_to_n.update([ (mt_node_id, pe_node_id, sim) for pe_node_id, sim in zip(connected_pe_nodes_ids, connected_pe_similarity) - if sim < threshold + if pe_node_id is not None and sim is not None and sim < threshold ]) # remove selected aligns and add None connected nodes instead mt_pe_sent_align = [(None, align[1], None) if align in aligns_remove_1_to_n else align for align in mt_pe_sent_align] # n-1 match for pe_node_id, connected_mt_nodes_ids, connected_mt_similarity in NameTBDTagger._group_by_node(mt_pe_sent_align, by_start_node=False, sort=True): if pe_node_id is not None and len(connected_mt_nodes_ids) > 1: - if all(sim < threshold for sim in connected_mt_similarity): + if all(sim < threshold for sim in connected_mt_similarity if sim is not None): continue - if all(sim > threshold for sim in connected_mt_similarity): + if all(sim > threshold for sim in connected_mt_similarity if sim is not None): continue aligns_remove_n_to_1.update([ (mt_node_id, pe_node_id, sim) for mt_node_id, sim in zip(connected_mt_nodes_ids, connected_mt_similarity) - if sim < threshold + if mt_node_id is not None and sim is not None and sim < threshold ]) # remove selected aligns and add None connected nodes instead mt_pe_sent_align = [(align[0], None, None) if align in aligns_remove_n_to_1 else align for align in mt_pe_sent_align] From 340bb8c873ea85f9188384124e566c564f5ca97f Mon Sep 17 00:00:00 2001 From: Konstantin Chernyshev Date: Fri, 5 May 2023 20:42:21 +0200 Subject: [PATCH 06/23] feat: function cache --- divemt/cache_utils.py | 140 ++++++++++++++++++++++++++++++++++++++++++ divemt/tag_utils.py | 15 +++-- 2 files changed, 149 insertions(+), 6 deletions(-) create mode 100644 divemt/cache_utils.py diff --git a/divemt/cache_utils.py b/divemt/cache_utils.py new file mode 100644 index 0000000..1428ed1 --- /dev/null +++ b/divemt/cache_utils.py @@ -0,0 +1,140 @@ +""" +The hashing idea adapted from https://death.andgravity.com/stable-hashing +https://github.com/lemon24/reader/blob/1efcd38c78f70dcc4e0d279e0fa2a0276749111e/src/reader/_hash_utils.py +""" +import dataclasses +import datetime +import functools +import hashlib +import inspect +import json +import pickle +from collections.abc import Collection +from pathlib import Path +from typing import Optional, Any, Dict, Callable + +import pandas as pd + + +_VERSION = 0 +_EXCLUDE = "_hash_exclude_" + + +def _json_dumps(thing: object) -> str: + return json.dumps( + thing, + default=_json_default, # force formatting-related options to known values + ensure_ascii=False, + sort_keys=True, + indent=None, + separators=(",", ":"), + ) + + +def _json_default(thing: object) -> Any: + try: + return _dataclass_dict(thing) + except TypeError: + pass + if isinstance(thing, datetime.datetime): + return thing.isoformat(timespec="microseconds") + raise TypeError(f"Object of type {type(thing).__name__} is not JSON serializable") + + +def _dataclass_dict(thing: object) -> Dict[str, Any]: + # we could have used dataclasses.asdict() + # with a dict_factory that drops empty values, + # but asdict() is recursive and we need to intercept and check + # the _hash_exclude_ of nested dataclasses; + # this way, json.dumps() does the recursion instead of asdict() + + # raises TypeError for non-dataclasses + fields = dataclasses.fields(thing) + # ... but doesn't for dataclass *types* + if isinstance(thing, type): + raise TypeError("got type, expected instance") + + exclude = getattr(thing, _EXCLUDE, ()) + + rv = {} + for field in fields: + if field.name in exclude: + continue + + value = getattr(thing, field.name) + if value is None or not value and isinstance(value, Collection): + continue + + rv[field.name] = value + + return rv + + +def calc_obj_hash(obj: object) -> bytes: + """Calculate hash of a single object""" + prefix = _VERSION.to_bytes(1, 'big') + hash_object = hashlib.sha256() + hash_object.update(_json_dumps(obj).encode("utf-8")) + return prefix + hash_object.digest() + + +def calc_args_hash(*args: Any, **kwargs: any) -> bytes: + """Calculate hash of arguments to function""" + prefix = _VERSION.to_bytes(1, 'big') + hash_object = hashlib.sha256() + for arg in args: + if isinstance(arg, pd.DataFrame) or isinstance(arg, pd.Series): + hash_object.update(str(pd.util.hash_pandas_object(arg).sum()).encode("utf-8")) + else: + hash_object.update(_json_dumps(arg).encode("utf-8")) + for key, value in kwargs.items(): + if isinstance(value, pd.DataFrame) or isinstance(value, pd.Series): + hash_object.update(key.encode("utf-8") + str(pd.util.hash_pandas_object(value).sum()).encode("utf-8")) + else: + hash_object.update(_json_dumps([key, value]).encode("utf-8")) + return prefix + hash_object.digest() + + +class CacheDecorator: + def __init__(self, cache_dir: Optional[Path] = None, version: int = 0): + self.version = version + self.cache_dir = cache_dir or Path(".cache") + + @staticmethod + def _is_bound_method(function: Callable, arg: Any): + return inspect.ismethod(function) or (hasattr(arg, "__class__") and function.__name__ in dir(arg.__class__)) + + def __call__(self, function: Callable) -> Any: + @functools.wraps(function) + def wrapper(*args: Any, **kwargs: Any) -> Any: + cache_key_args = args[1:] if self._is_bound_method(function, args[0]) else args + hash_val = calc_args_hash(*cache_key_args, **kwargs) + cache_file = self.cache_dir / f"{function.__name__}_v{self.version}_{hash_val.hex()}.pkl" + + # TODO: add logging, not printing + + if cache_file.exists(): + print(f"LOADING CACHE: {cache_file}") + with open(cache_file, "rb") as f: + return pickle.load(f) + else: + print(len(args), len(kwargs.items())) + result = function(*args, **kwargs) + print(f"CREATE CACHE: {cache_file}") + cache_file.parent.mkdir(parents=True, exist_ok=True) + with open(cache_file, "wb") as f: + pickle.dump(result, f) + return result + + return wrapper + + def __get__(self, instance, owner): + """note: adapted from chat-gpt-4 =)""" + # Support method decorators for class instances + if instance is None: + return self + + # Bind the decorated method to the instance + bound_method = functools.partial(self, instance) + + return bound_method diff --git a/divemt/tag_utils.py b/divemt/tag_utils.py index 71d4bdc..41189df 100644 --- a/divemt/tag_utils.py +++ b/divemt/tag_utils.py @@ -7,6 +7,8 @@ import stanza from tqdm import tqdm +from divemt.cache_utils import CacheDecorator + _STANZA_NLP_MAP = { "eng": {"lang": "en", "processors": "tokenize,pos,depparse,ner,lemma"}, "ara": {"lang": "ar", "processors": "tokenize,pos,depparse,ner,lemma,mwt"}, @@ -108,15 +110,14 @@ def get_tokens_annotations(text: Optional[str], lang: str) -> Tuple[Optional[Lis return tokens, annotations +@CacheDecorator() def texts2annotations(data: pd.DataFrame, unit_id_contains_lang: bool = True) -> pd.DataFrame: if "lang_id" not in data.columns and unit_id_contains_lang: data["lang_id"] = data.unit_id.str.split("-").map(lambda x: x[2]) - src_tokens = [] - src_annotations = [] - mt_tokens = [] - mt_annotations = [] - tgt_tokens = [] - tgt_annotations = [] + + src_tokens, mt_tokens, tgt_tokens = [], [], [] + src_annotations, mt_annotations, tgt_annotations = [], [], [] + for _i, row in tqdm(data.iterrows(), desc="Adding Stanza annotations...", total=len(data)): src_tok, src_ann = get_tokens_annotations(row.src_text, "eng") mt_tok, mt_ann = get_tokens_annotations(row.mt_text, row.lang_id) @@ -133,5 +134,7 @@ def texts2annotations(data: pd.DataFrame, unit_id_contains_lang: bool = True) -> data["mt_annotations"] = mt_annotations data["tgt_tokens"] = tgt_tokens data["tgt_annotations"] = tgt_annotations + clear_nlp_cache() + return data From 3926a2c2167b7d5947d6640bfe45b5f8d67ac308 Mon Sep 17 00:00:00 2001 From: Konstantin Chernyshev Date: Fri, 5 May 2023 20:43:20 +0200 Subject: [PATCH 07/23] feat: function cache --- .gitignore | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.gitignore b/.gitignore index eab2b88..6bfc3dd 100644 --- a/.gitignore +++ b/.gitignore @@ -9,6 +9,8 @@ data/raw/vie/*/* tmp/* outputs/* +cache/ +.cache/ .idea/ From 4e90560e57d5fdadba17e61367a77efd8fa4ce72 Mon Sep 17 00:00:00 2001 From: Konstantin Chernyshev Date: Fri, 5 May 2023 20:44:57 +0200 Subject: [PATCH 08/23] chore: add new comment for added material --- divemt/custom_simalign.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/divemt/custom_simalign.py b/divemt/custom_simalign.py index 77a9559..a7a4a7a 100644 --- a/divemt/custom_simalign.py +++ b/divemt/custom_simalign.py @@ -268,11 +268,13 @@ def get_word_aligns(self, src_sent: Union[str, List[str]], trg_sent: Union[str, for ext in self.matching_methods: if all_mats[ext][i, j] > 0: if self.token_type == "bpe": + # new: add similarity as third item if self.return_similarity: aligns[ext].add((l1_b2w_map[i], l2_b2w_map[j], words_similarity[l1_b2w_map[i], l2_b2w_map[j]])) else: aligns[ext].add((l1_b2w_map[i], l2_b2w_map[j])) else: + # new: add similarity as third item if self.return_similarity: aligns[ext].add((i, j, sim[i, j])) else: From bce4ac19c4d7285af5369b56354d94e289bb3100 Mon Sep 17 00:00:00 2001 From: Konstantin Chernyshev Date: Tue, 9 May 2023 13:12:23 +0200 Subject: [PATCH 09/23] chore: cmd line argument to run augmentation --- README.md | 1 + divemt/parse_utils.py | 11 +++++++---- divemt/qe_taggers.py | 33 +++++++++++++++++++++++++++++++++ scripts/preprocess.py | 6 ++++++ 4 files changed, 47 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 9f51916..a44050d 100644 --- a/README.md +++ b/README.md @@ -73,6 +73,7 @@ python scripts/preprocess.py \ --add_extra \ --add_annotations \ --add_wmt22_quality_tags \ +--add_name_tbd_quality_tags \ --output_single \ --output_merged_subjects \ --output_merged_languages diff --git a/divemt/parse_utils.py b/divemt/parse_utils.py index 0a1d291..8d15935 100644 --- a/divemt/parse_utils.py +++ b/divemt/parse_utils.py @@ -20,7 +20,7 @@ from .cer import cer from .tag_utils import clear_nlp_cache, texts2annotations, tokenize -from .qe_taggers import QETagger, WMT22QETagger # isort: skip <- due to circular import with tag_utils +from .qe_taggers import QETagger, WMT22QETagger, NameTBDTagger # isort: skip <- due to circular import with tag_utils logger = logging.getLogger(__name__) @@ -356,7 +356,7 @@ def texts2qe( pe_texts["mt_text"].tolist(), pe_texts["tgt_text"].tolist(), "eng", - pe_texts.unit_id.str.split("-").map(lambda x: x[2]), + pe_texts.unit_id.str.split("-").map(lambda x: x[2]).tolist(), ) pe_texts[f"src_{tagger.ID}"] = src_tags pe_texts[f"mt_{tagger.ID}"] = mt_tags @@ -377,12 +377,12 @@ def parse_from_folder( add_extra_information: bool = False, add_annotations_information: bool = False, add_wmt22_quality_tags: bool = False, + add_name_tbd_quality_tags: bool = False, rounding: Optional[int] = None, ) -> Union[pd.DataFrame, Tuple[pd.DataFrame, pd.DataFrame]]: """Parse all .per XML files in a folder and return a single dataframe containing all units.""" metrics_list_dfs = [per2metrics(os.path.join(path, f)) for f in os.listdir(path) if f.endswith(".per")] metrics_df = pd.concat([df for df in metrics_list_dfs if df is not None], ignore_index=True) - if ( output_texts or add_edit_information @@ -407,10 +407,13 @@ def parse_from_folder( if add_extra_information: metrics_df = metrics2extra(metrics_df) if add_annotations_information: - texts_df = texts2annotations(texts_df) + texts_df = texts2annotations(texts_df) # TODO: make cache optional if add_wmt22_quality_tags: tagger = WMT22QETagger() texts_df = texts2qe(texts_df, tagger) + if add_name_tbd_quality_tags: + tagger = NameTBDTagger() # TODO: make cache optional + texts_df = texts2qe(texts_df, tagger) if time_ordered: if output_texts: diff --git a/divemt/qe_taggers.py b/divemt/qe_taggers.py index 4a8bc29..557f56a 100644 --- a/divemt/qe_taggers.py +++ b/divemt/qe_taggers.py @@ -10,6 +10,9 @@ from xml.sax.saxutils import escape from simalign import SentenceAligner + +from .cache_utils import CacheDecorator + if sys.version_info < (3, 11): from strenum import StrEnum else: @@ -397,8 +400,10 @@ def generate_tags( src_tokens, src_langs = self.get_tokenized(srcs, src_langs) mt_tokens, tgt_langs = self.get_tokenized(mts, tgt_langs) pe_tokens, _ = self.get_tokenized(pes, tgt_langs) + src_pe_alignments = self.align_source_pe(src_tokens, pe_tokens, tgt_langs) mt_pe_alignments = self.align_mt_pe(mt_tokens, pe_tokens) + mt_tags = self.tags_from_edits(mt_tokens, pe_tokens, mt_pe_alignments, use_gaps, omissions) src_tags = self.tags_to_source( src_tokens, @@ -408,7 +413,9 @@ def generate_tags( mt_pe_alignments, fluency_rule, ) + clear_nlp_cache() + return src_tags, mt_tags @@ -436,6 +443,27 @@ def __init__( ): self.aligner = aligner if aligner else CustomSentenceAligner(model="bert", token_type="bpe", matching_methods="mai", return_similarity="avg") + def _fill_deleted_inserted_tokens(self, len_from: int, len_to: int, alignments: List[TAlignment]) -> List[TAlignment]: + """As aligner provides only actual alignments, add required (None, i), (i, None) tokens""" + new_alignments: List[TAlignment] = [] + + # Add (i, None) in correct place (ordered by i) + current_alignment_index = 0 + for align in alignments: + # Add missing index pairs with None + while current_alignment_index < align[0]: + new_alignments.append((current_alignment_index, None)) + current_alignment_index += 1 + + # Add the current alignment pair + new_alignments.append(align) + current_alignment_index += 1 + + raise NotImplementedError() + + return new_alignments + + # @CacheDecorator() def align_source_mt( self, src_tokens: List[List[str]], @@ -450,6 +478,7 @@ def align_source_mt( ) ] + # @CacheDecorator() def align_mt_pe( self, mt_tokens: List[List[str]], @@ -701,11 +730,15 @@ def generate_tags( src_tokens, src_langs = self.get_tokenized(srcs, src_langs) mt_tokens, tgt_langs = self.get_tokenized(mts, tgt_langs) pe_tokens, _ = self.get_tokenized(pes, tgt_langs) + src_mt_alignments = self.align_source_mt(src_tokens, mt_tokens, src_langs, tgt_langs) mt_pe_alignments = self.align_mt_pe(mt_tokens, pe_tokens, tgt_langs) + mt_tags = self.tags_from_edits(mt_tokens, pe_tokens, mt_pe_alignments) src_tags = self.tags_to_source( src_tokens, pe_tokens, src_mt_alignments, mt_tags ) + clear_nlp_cache() + return src_tags, mt_tags diff --git a/scripts/preprocess.py b/scripts/preprocess.py index ceab942..656feec 100644 --- a/scripts/preprocess.py +++ b/scripts/preprocess.py @@ -54,6 +54,7 @@ def preprocess(args: argparse.Namespace): add_extra_information=args.add_extra, add_annotations_information=args.add_annotations, add_wmt22_quality_tags=args.add_wmt22_quality_tags, + add_name_tbd_quality_tags=args.add_name_tbd_quality_tags, rounding=args.rounding, ) if args.output_texts: @@ -183,6 +184,11 @@ def preprocess(args: argparse.Namespace): action="store_true", help="Whether to add WMT22 quality tags to the text dataframe", ) + parser.add_argument( + "--add_name_tbd_quality_tags", + action="store_true", + help="Whether to add NameTBD quality tags to the text dataframe", + ) parser.add_argument( "--output_single", action="store_true", From 1d69f9afa4f374e8b98e444f71288c43f535df15 Mon Sep 17 00:00:00 2001 From: Konstantin Chernyshev Date: Tue, 9 May 2023 13:22:44 +0200 Subject: [PATCH 10/23] style: apply black and ruff --- divemt/cache_utils.py | 11 +- divemt/custom_simalign.py | 28 +- divemt/qe_taggers.py | 142 +++--- scripts/preprocess.py | 4 +- tests/test_qe_taggers_name_tbd_tagger.py | 601 ++++++++++++++++------- 5 files changed, 522 insertions(+), 264 deletions(-) diff --git a/divemt/cache_utils.py b/divemt/cache_utils.py index 1428ed1..c9ed2b4 100644 --- a/divemt/cache_utils.py +++ b/divemt/cache_utils.py @@ -11,11 +11,10 @@ import pickle from collections.abc import Collection from pathlib import Path -from typing import Optional, Any, Dict, Callable +from typing import Any, Callable, Dict, Optional import pandas as pd - _VERSION = 0 _EXCLUDE = "_hash_exclude_" @@ -72,7 +71,7 @@ def _dataclass_dict(thing: object) -> Dict[str, Any]: def calc_obj_hash(obj: object) -> bytes: """Calculate hash of a single object""" - prefix = _VERSION.to_bytes(1, 'big') + prefix = _VERSION.to_bytes(1, "big") hash_object = hashlib.sha256() hash_object.update(_json_dumps(obj).encode("utf-8")) return prefix + hash_object.digest() @@ -80,15 +79,15 @@ def calc_obj_hash(obj: object) -> bytes: def calc_args_hash(*args: Any, **kwargs: any) -> bytes: """Calculate hash of arguments to function""" - prefix = _VERSION.to_bytes(1, 'big') + prefix = _VERSION.to_bytes(1, "big") hash_object = hashlib.sha256() for arg in args: - if isinstance(arg, pd.DataFrame) or isinstance(arg, pd.Series): + if isinstance(arg, (pd.DataFrame, pd.Series)): hash_object.update(str(pd.util.hash_pandas_object(arg).sum()).encode("utf-8")) else: hash_object.update(_json_dumps(arg).encode("utf-8")) for key, value in kwargs.items(): - if isinstance(value, pd.DataFrame) or isinstance(value, pd.Series): + if isinstance(value, (pd.DataFrame, pd.Series)): hash_object.update(key.encode("utf-8") + str(pd.util.hash_pandas_object(value).sum()).encode("utf-8")) else: hash_object.update(_json_dumps([key, value]).encode("utf-8")) diff --git a/divemt/custom_simalign.py b/divemt/custom_simalign.py index a7a4a7a..579c402 100644 --- a/divemt/custom_simalign.py +++ b/divemt/custom_simalign.py @@ -7,7 +7,7 @@ - fix typings - logger deleted """ -from typing import Dict, List, Tuple, Union, Optional +from typing import Dict, List, Optional, Tuple, Union import numpy as np from scipy.sparse import csr_matrix @@ -20,21 +20,21 @@ nx = None import torch from transformers import ( + AutoConfig, + AutoModel, + AutoTokenizer, BertModel, BertTokenizer, - XLMModel, - XLMTokenizer, RobertaModel, RobertaTokenizer, + XLMModel, XLMRobertaModel, XLMRobertaTokenizer, - AutoConfig, - AutoModel, - AutoTokenizer, + XLMTokenizer, ) -class EmbeddingLoader(object): +class EmbeddingLoader: def __init__(self, model: str = "bert-base-multilingual-cased", device=torch.device("cpu"), layer: int = 8): TR_Models = { "bert-base-uncased": (BertModel, BertTokenizer), @@ -89,14 +89,16 @@ def get_embed_list(self, sent_batch: List[List[str]]) -> torch.Tensor: return None -class SentenceAligner(object): +class SentenceAligner: def __init__( self, model: str = "bert", token_type: str = "bpe", distortion: float = 0.0, matching_methods: str = "mai", - return_similarity: Optional[str] = None, # new: ["max", "avg"] type of average similarity for words from tokens + return_similarity: Optional[ + str + ] = None, # new: ["max", "avg"] type of average similarity for words from tokens device: str = "cpu", layer: int = 8, ): @@ -145,14 +147,14 @@ def average_embeds_over_words(bpe_vectors: np.ndarray, word_tokens_pair: List[Li w2b_map.append([]) for wlist in word_tokens_pair[0]: w2b_map[0].append([]) - for x in wlist: + for _x in wlist: w2b_map[0][-1].append(cnt) cnt += 1 cnt = 0 w2b_map.append([]) for wlist in word_tokens_pair[1]: w2b_map[1].append([]) - for x in wlist: + for _x in wlist: w2b_map[1][-1].append(cnt) cnt += 1 @@ -270,7 +272,9 @@ def get_word_aligns(self, src_sent: Union[str, List[str]], trg_sent: Union[str, if self.token_type == "bpe": # new: add similarity as third item if self.return_similarity: - aligns[ext].add((l1_b2w_map[i], l2_b2w_map[j], words_similarity[l1_b2w_map[i], l2_b2w_map[j]])) + aligns[ext].add( + (l1_b2w_map[i], l2_b2w_map[j], words_similarity[l1_b2w_map[i], l2_b2w_map[j]]) + ) else: aligns[ext].add((l1_b2w_map[i], l2_b2w_map[j])) else: diff --git a/divemt/qe_taggers.py b/divemt/qe_taggers.py index 557f56a..924a9cb 100644 --- a/divemt/qe_taggers.py +++ b/divemt/qe_taggers.py @@ -6,13 +6,11 @@ from collections import defaultdict from itertools import groupby from pathlib import Path -from typing import List, Optional, Tuple, Union, Set, Generator, Any +from typing import Any, Generator, List, Optional, Set, Tuple, Union from xml.sax.saxutils import escape from simalign import SentenceAligner -from .cache_utils import CacheDecorator - if sys.version_info < (3, 11): from strenum import StrEnum else: @@ -121,8 +119,8 @@ def generate_tags( (one per machine translation). Returns: - `Tuple[List[TTag], List[TTag]]`: A tuple containing the lists of quality tags for all source and the machine - translation sentence, respectively. + `Tuple[List[TTag], List[TTag]]`: A tuple containing the lists of quality tags for all source and the + machine translation sentence, respectively. """ pass @@ -421,17 +419,18 @@ def generate_tags( class NameTBDGeneralTags(StrEnum): """Error types tags for NameTBD.""" - OK = 'OK' # 1:1 - the MT uses the same single word as the PE - BAD_SUBSTITUTION = 'BAD-SUB' # 1:1 - the MT uses a different single word than the PE - BAD_DELETION_RIGHT = 'BAD-DEL-R' # None:1 - the MT does not have a word existed in PE, deletion on the right - BAD_DELETION_LEFT = 'BAD-DEL-L' # None:1 - the MT does not have a word existed in PE, deletion on the left - BAD_INSERTION = 'BAD-INS' # 1:None - the MT wrongly inserted a words that is not in the PE + OK = "OK" # 1:1 - the MT uses the same single word as the PE + BAD_SUBSTITUTION = "BAD-SUB" # 1:1 - the MT uses a different single word than the PE + + BAD_DELETION_RIGHT = "BAD-DEL-R" # None:1 - the MT does not have a word existed in PE, deletion on the right + BAD_DELETION_LEFT = "BAD-DEL-L" # None:1 - the MT does not have a word existed in PE, deletion on the left + BAD_INSERTION = "BAD-INS" # 1:None - the MT wrongly inserted a words that is not in the PE - BAD_SHIFTING = 'BAD-SHF' # for any number of tokens - detect crossing edges + BAD_SHIFTING = "BAD-SHF" # for any number of tokens - detect crossing edges - BAD_CONTRACTION = 'BAD-CON' # 1:n - the MT uses a single word instead of multiple words in the PE - BAD_EXPANSION = 'BAD-EXP' # n:1 - the MT uses a multiple words instead of one in the PE + BAD_CONTRACTION = "BAD-CON" # 1:n - the MT uses a single word instead of multiple words in the PE + BAD_EXPANSION = "BAD-EXP" # n:1 - the MT uses a multiple words instead of one in the PE class NameTBDTagger(QETagger): @@ -441,9 +440,15 @@ def __init__( self, aligner: Optional[CustomSentenceAligner] = None, ): - self.aligner = aligner if aligner else CustomSentenceAligner(model="bert", token_type="bpe", matching_methods="mai", return_similarity="avg") + self.aligner = ( + aligner + if aligner + else CustomSentenceAligner(model="bert", token_type="bpe", matching_methods="mai", return_similarity="avg") + ) - def _fill_deleted_inserted_tokens(self, len_from: int, len_to: int, alignments: List[TAlignment]) -> List[TAlignment]: + def _fill_deleted_inserted_tokens( + self, len_from: int, len_to: int, alignments: List[TAlignment] + ) -> List[TAlignment]: """As aligner provides only actual alignments, add required (None, i), (i, None) tokens""" new_alignments: List[TAlignment] = [] @@ -473,9 +478,7 @@ def align_source_mt( ) -> List[List[TAlignment]]: return [ self.aligner.get_word_aligns(src_tok, mt_tok)["inter"] - for src_tok, mt_tok in tqdm( - zip(src_tokens, mt_tokens), total=len(src_tokens), desc="Aligning src-mt" - ) + for src_tok, mt_tok in tqdm(zip(src_tokens, mt_tokens), total=len(src_tokens), desc="Aligning src-mt") ] # @CacheDecorator() @@ -487,23 +490,27 @@ def align_mt_pe( ) -> List[List[TAlignment]]: return [ self.aligner.get_word_aligns(mt_tok, pe_tok)["inter"] - for mt_tok, pe_tok in tqdm( - zip(mt_tokens, pe_tokens), total=len(mt_tokens), desc="Aligning mt-pe" - ) + for mt_tok, pe_tok in tqdm(zip(mt_tokens, pe_tokens), total=len(mt_tokens), desc="Aligning mt-pe") ] @staticmethod - def _group_by_node(alignments: List[Tuple[Optional[int], Optional[int]]], by_start_node: bool = True, sort: bool = False) -> Generator[Tuple[int, List[int], List[float]], None, None]: + def _group_by_node( + alignments: List[Tuple[Optional[int], Optional[int]]], by_start_node: bool = True, sort: bool = False + ) -> Generator[Tuple[int, List[int], List[float]], None, None]: """Yield a node id and a list of connected nodes.""" _by_index = 0 if by_start_node else 1 if sort: alignments = sorted(alignments, key=lambda x: x[_by_index] if x[_by_index] is not None else -1) for start_node, connected_alignments in groupby(alignments, lambda x: x[_by_index]): connected_alignments = list(connected_alignments) - yield start_node, [end_id if by_start_node else start_id for start_id, end_id, _ in connected_alignments], [similarity for _, _, similarity in connected_alignments] + yield start_node, [ + end_id if by_start_node else start_id for start_id, end_id, _ in connected_alignments + ], [similarity for _, _, similarity in connected_alignments] @staticmethod - def _detect_crossing_edges(mt_tokens: List[str], pe_tokens: List[str], alignments: List[Tuple[Optional[int], Optional[int], float]]) -> List[bool]: + def _detect_crossing_edges( + mt_tokens: List[str], pe_tokens: List[str], alignments: List[Tuple[Optional[int], Optional[int], float]] + ) -> List[bool]: """Detect crossing edges in the alignments. Return mask list of nodes that cross some other node.""" # TODO: optimize from n^2 to n as 2 pointers shifted_mt_mask = [False] * len(mt_tokens) @@ -537,7 +544,7 @@ def tags_from_edits( mt_pe_alignments: List[List[TAlignment]], threshold: float = 0.8, ) -> List[List[TTag]]: - """ Produce tags on MT tokens from edits found in the PE tokens. + """Produce tags on MT tokens from edits found in the PE tokens. Note: The tags indicate the type of error particular MT token is affected by. @@ -568,44 +575,60 @@ def tags_from_edits( mt_tags: List[List[Set[str]]] = [] - for mt_sent_tok, pe_sent_tok, mt_pe_sent_align in tqdm(zip(mt_tokens, pe_tokens, mt_pe_alignments), desc="Tagging MT", total=len(mt_tokens)): - + for mt_sent_tok, pe_sent_tok, mt_pe_sent_align in tqdm( + zip(mt_tokens, pe_tokens, mt_pe_alignments), desc="Tagging MT", total=len(mt_tokens) + ): mt_sent_tags: List[Set[str]] = [set() for _ in range(len(mt_sent_tok))] # clear 1-n and n-1 nodes with low threshold - # e.g. if 1-n or n-1 have same token or high similarity, remove low similarity as deletions/insertions (None:1 and 1:None) + # e.g. if 1-n or n-1 have same token or high similarity, remove low similarity as deletions/insertions + # (None:1 and 1:None) aligns_remove_1_to_n, aligns_remove_n_to_1 = set(), set() # 1-n match - for mt_node_id, connected_pe_nodes_ids, connected_pe_similarity in NameTBDTagger._group_by_node(mt_pe_sent_align, by_start_node=True, sort=True): + for mt_node_id, connected_pe_nodes_ids, connected_pe_similarity in NameTBDTagger._group_by_node( + mt_pe_sent_align, by_start_node=True, sort=True + ): if mt_node_id is not None and len(connected_pe_nodes_ids) > 1: if all(sim < threshold for sim in connected_pe_similarity if sim is not None): continue if all(sim > threshold for sim in connected_pe_similarity if sim is not None): continue - aligns_remove_1_to_n.update([ - (mt_node_id, pe_node_id, sim) - for pe_node_id, sim in zip(connected_pe_nodes_ids, connected_pe_similarity) - if pe_node_id is not None and sim is not None and sim < threshold - ]) + aligns_remove_1_to_n.update( + [ + (mt_node_id, pe_node_id, sim) + for pe_node_id, sim in zip(connected_pe_nodes_ids, connected_pe_similarity) + if pe_node_id is not None and sim is not None and sim < threshold + ] + ) # remove selected aligns and add None connected nodes instead - mt_pe_sent_align = [(None, align[1], None) if align in aligns_remove_1_to_n else align for align in mt_pe_sent_align] + mt_pe_sent_align = [ + (None, align[1], None) if align in aligns_remove_1_to_n else align for align in mt_pe_sent_align + ] # n-1 match - for pe_node_id, connected_mt_nodes_ids, connected_mt_similarity in NameTBDTagger._group_by_node(mt_pe_sent_align, by_start_node=False, sort=True): + for pe_node_id, connected_mt_nodes_ids, connected_mt_similarity in NameTBDTagger._group_by_node( + mt_pe_sent_align, by_start_node=False, sort=True + ): if pe_node_id is not None and len(connected_mt_nodes_ids) > 1: if all(sim < threshold for sim in connected_mt_similarity if sim is not None): continue if all(sim > threshold for sim in connected_mt_similarity if sim is not None): continue - aligns_remove_n_to_1.update([ - (mt_node_id, pe_node_id, sim) - for mt_node_id, sim in zip(connected_mt_nodes_ids, connected_mt_similarity) - if mt_node_id is not None and sim is not None and sim < threshold - ]) + aligns_remove_n_to_1.update( + [ + (mt_node_id, pe_node_id, sim) + for mt_node_id, sim in zip(connected_mt_nodes_ids, connected_mt_similarity) + if mt_node_id is not None and sim is not None and sim < threshold + ] + ) # remove selected aligns and add None connected nodes instead - mt_pe_sent_align = [(align[0], None, None) if align in aligns_remove_n_to_1 else align for align in mt_pe_sent_align] + mt_pe_sent_align = [ + (align[0], None, None) if align in aligns_remove_n_to_1 else align for align in mt_pe_sent_align + ] # Solve all n-1: setup expansions tags and solve n-1 matches < threshold as smth+insertion - for pe_node_id, connected_mt_nodes_ids, _ in NameTBDTagger._group_by_node(mt_pe_sent_align, by_start_node=False, sort=True): + for pe_node_id, connected_mt_nodes_ids, _ in NameTBDTagger._group_by_node( + mt_pe_sent_align, by_start_node=False, sort=True + ): if pe_node_id is not None and len(connected_mt_nodes_ids) > 1: # expansion, mark related mt nodes for mt_node_id in connected_mt_nodes_ids: @@ -614,7 +637,9 @@ def tags_from_edits( # Solve all deletions, add deletion tags on left and right sides mt_position = 0 - for mt_node_id, connected_pe_nodes_ids, _ in NameTBDTagger._group_by_node(mt_pe_sent_align, by_start_node=True, sort=False): + for mt_node_id, connected_pe_nodes_ids, _ in NameTBDTagger._group_by_node( + mt_pe_sent_align, by_start_node=True, sort=False + ): if mt_node_id is None: # deleted word error, mark left and right modes if 0 <= mt_position - 1 < len(mt_sent_tags): @@ -627,7 +652,9 @@ def tags_from_edits( mt_pe_sent_align = [align for align in mt_pe_sent_align if align[0] is not None] # Solve all 1-n matches - for mt_node_id, connected_pe_nodes_ids, _ in NameTBDTagger._group_by_node(mt_pe_sent_align, by_start_node=True, sort=True): + for mt_node_id, connected_pe_nodes_ids, _ in NameTBDTagger._group_by_node( + mt_pe_sent_align, by_start_node=True, sort=True + ): assert mt_node_id is not None, "Already should be filtered all (None, smth) cases" if NameTBDGeneralTags.BAD_EXPANSION.value in mt_sent_tags[mt_node_id]: continue @@ -645,7 +672,9 @@ def tags_from_edits( mt_sent_tags[mt_node_id].add(NameTBDGeneralTags.OK.value) # Add shifted tags if so - for mt_node_id, mask in enumerate(NameTBDTagger._detect_crossing_edges(mt_sent_tok, pe_sent_tok, mt_pe_sent_align)): + for mt_node_id, mask in enumerate( + NameTBDTagger._detect_crossing_edges(mt_sent_tok, pe_sent_tok, mt_pe_sent_align) + ): if mask: mt_sent_tags[mt_node_id].add(NameTBDGeneralTags.BAD_SHIFTING.value) @@ -654,7 +683,7 @@ def tags_from_edits( # Basic sanity check assert all( - [len(mt_sent_tokens) == len(mt_sent_tags) for mt_sent_tokens, mt_sent_tags in zip(mt_tokens, mt_tags)] + len(mt_sent_tokens) == len(mt_sent_tags) for mt_sent_tokens, mt_sent_tags in zip(mt_tokens, mt_tags) ), "MT tags creation failed, number of tokens and tags do not match" return mt_tags @@ -665,7 +694,7 @@ def tags_to_source( src_mt_alignments: List[List[TAlignment]], mt_tags: List[List[Set[str]]], ) -> List[List[TTag]]: - """ Propagate tags from MT to source. + """Propagate tags from MT to source. # TODO: update docstring with the final logic The following cases are considered: @@ -683,12 +712,15 @@ def tags_to_source( src_tags: List[List[Set[str]]] = [] - for src_sent_tok, mt_sent_tok, mt_sent_tags, mt_pe_sent_align in tqdm(zip(src_tokens, mt_tokens, mt_tags, src_mt_alignments), desc="Transfer to source", total=len(src_tokens)): - + for src_sent_tok, _mt_sent_tok, mt_sent_tags, mt_pe_sent_align in tqdm( + zip(src_tokens, mt_tokens, mt_tags, src_mt_alignments), desc="Transfer to source", total=len(src_tokens) + ): src_sent_tags: List[Set[str]] = [set() for _ in range(len(src_sent_tok))] # Solve all as 1-n matches - for src_node_id, connected_mt_nodes_ids, connected_mt_similarity in NameTBDTagger._group_by_node(mt_pe_sent_align, by_start_node=True, sort=True): + for src_node_id, connected_mt_nodes_ids, connected_mt_similarity in NameTBDTagger._group_by_node( + mt_pe_sent_align, by_start_node=True, sort=True + ): if src_node_id is None: continue elif len(connected_mt_nodes_ids) == 0: @@ -716,7 +748,9 @@ def tags_to_source( src_tags.append(src_sent_tags) # Basic sanity checks - assert all(len(aa) == len(bb) for aa, bb in zip(src_tokens, src_tags)), "Source tags creation failed, number of tokens and tags do not match" + assert all( + len(aa) == len(bb) for aa, bb in zip(src_tokens, src_tags) + ), "Source tags creation failed, number of tokens and tags do not match" return src_tags def generate_tags( @@ -735,9 +769,7 @@ def generate_tags( mt_pe_alignments = self.align_mt_pe(mt_tokens, pe_tokens, tgt_langs) mt_tags = self.tags_from_edits(mt_tokens, pe_tokens, mt_pe_alignments) - src_tags = self.tags_to_source( - src_tokens, pe_tokens, src_mt_alignments, mt_tags - ) + src_tags = self.tags_to_source(src_tokens, pe_tokens, src_mt_alignments, mt_tags) clear_nlp_cache() diff --git a/scripts/preprocess.py b/scripts/preprocess.py index 656feec..0154afa 100644 --- a/scripts/preprocess.py +++ b/scripts/preprocess.py @@ -27,9 +27,7 @@ def preprocess(args: argparse.Namespace): for path in lang_output_paths.values(): os.makedirs(path, exist_ok=True) if args.tasks is None: - tasks = { - lang: list({f.split("_")[1] for f in os.listdir(lang_source_paths[lang])}) for lang in args.languages - } + tasks = {lang: list({f.split("_")[1] for f in os.listdir(lang_source_paths[lang])}) for lang in args.languages} else: tasks = {lang: args.tasks for lang in args.languages} results_dict = {lang: {task: [] for task in tasks[lang]} for lang in args.languages} diff --git a/tests/test_qe_taggers_name_tbd_tagger.py b/tests/test_qe_taggers_name_tbd_tagger.py index a087eba..0350f5f 100644 --- a/tests/test_qe_taggers_name_tbd_tagger.py +++ b/tests/test_qe_taggers_name_tbd_tagger.py @@ -1,248 +1,473 @@ import sys -from typing import List, Tuple, Set +from typing import List, Set, Tuple import pytest + if sys.version_info < (3, 11): from strenum import StrEnum else: from enum import StrEnum -from divemt.qe_taggers import NameTBDTagger from divemt.qe_taggers import NameTBDGeneralTags as Tags - +from divemt.qe_taggers import NameTBDTagger tagger = NameTBDTagger() class TestUtils: - @pytest.mark.parametrize("mt_len, mt_pe_alignments, true_mt_shifts_mask", [ - (1, [(0, 0, 0.9)], [False]), - (2, [(0, 0, 0.9), (1, 1, 0.9)], [False, False]), - (3, [(0, 0, 0.9), (1, 1, 0.9), (2, 2, 0.9)], [False, False, False]), - (3, [(0, 0, 0.9), (1, None, None), (2, 1, 0.9)], [False, False, False]), - # easiest case - (2, [(0, 1, 0.9), (1, 0, 0.9)], [True, True]), - # central one is not moved, but have crossing edges - (3, [(0, 2, 0.9), (1, 1, 0.9), (2, 0, 0.9)], [True, True, True]), - # the central one deleted, so not shifted, no crossing edges - (3, [(0, 1, 0.9), (1, None, None), (2, 0, 0.9)], [True, False, True]), - (4, [(0, 0, 0.9), (1, 3, 0.9), (1, 4, 0.9), (1, 5, 0.9), (2, 2, 0.9), (2, 0, 0.9), (3, None, None)], [False, True, True, False]), - ]) - def test_detect_crossing_edges(self, mt_len: int, mt_pe_alignments: List[Tuple[int, int]], true_mt_shifts_mask: List[bool]) -> None: - mt_shifts_mask = tagger._detect_crossing_edges([str(i) for i in range(mt_len)], [str(i) for i in range(mt_len)], mt_pe_alignments) + @pytest.mark.parametrize( + "mt_len, mt_pe_alignments, true_mt_shifts_mask", + [ + (1, [(0, 0, 0.9)], [False]), + (2, [(0, 0, 0.9), (1, 1, 0.9)], [False, False]), + (3, [(0, 0, 0.9), (1, 1, 0.9), (2, 2, 0.9)], [False, False, False]), + (3, [(0, 0, 0.9), (1, None, None), (2, 1, 0.9)], [False, False, False]), + # easiest case + (2, [(0, 1, 0.9), (1, 0, 0.9)], [True, True]), + # central one is not moved, but have crossing edges + (3, [(0, 2, 0.9), (1, 1, 0.9), (2, 0, 0.9)], [True, True, True]), + # the central one deleted, so not shifted, no crossing edges + (3, [(0, 1, 0.9), (1, None, None), (2, 0, 0.9)], [True, False, True]), + ( + 4, + [(0, 0, 0.9), (1, 3, 0.9), (1, 4, 0.9), (1, 5, 0.9), (2, 2, 0.9), (2, 0, 0.9), (3, None, None)], + [False, True, True, False], + ), + ], + ) + def test_detect_crossing_edges( + self, mt_len: int, mt_pe_alignments: List[Tuple[int, int]], true_mt_shifts_mask: List[bool] + ) -> None: + mt_shifts_mask = tagger._detect_crossing_edges( + [str(i) for i in range(mt_len)], [str(i) for i in range(mt_len)], mt_pe_alignments + ) assert mt_shifts_mask == true_mt_shifts_mask class TestTagsFromEdits: - @pytest.mark.parametrize("mt_tokens, pe_tokens, mt_pe_alignments, true_mt_tags", [ - (["A", "B"], ["A", "B"], [(0, 0, 0.9), (1, 1, 0.9)], [{Tags.OK}, {Tags.OK}]), - (["A", "B", "C", "D"], ["A", "B", "C", "D"], [(0, 0, 0.9), (1, 1, 0.9), (2, 2, 0.9), (3, 3, 0.9)], [{Tags.OK}, {Tags.OK}, {Tags.OK}, {Tags.OK}]), - ([], [], [], []), - ]) + @pytest.mark.parametrize( + "mt_tokens, pe_tokens, mt_pe_alignments, true_mt_tags", + [ + (["A", "B"], ["A", "B"], [(0, 0, 0.9), (1, 1, 0.9)], [{Tags.OK}, {Tags.OK}]), + ( + ["A", "B", "C", "D"], + ["A", "B", "C", "D"], + [(0, 0, 0.9), (1, 1, 0.9), (2, 2, 0.9), (3, 3, 0.9)], + [{Tags.OK}, {Tags.OK}, {Tags.OK}, {Tags.OK}], + ), + ([], [], [], []), + ], + ) def test_single_error_ok( - self, - mt_tokens: List[str], - pe_tokens: List[str], - mt_pe_alignments: List[Tuple[int, int]], - true_mt_tags: List[Set[StrEnum]], + self, + mt_tokens: List[str], + pe_tokens: List[str], + mt_pe_alignments: List[Tuple[int, int]], + true_mt_tags: List[Set[StrEnum]], ) -> None: predicted_tags = tagger.tags_from_edits([mt_tokens], [pe_tokens], [mt_pe_alignments])[0] assert len(predicted_tags) == len(true_mt_tags) - for predicted_tags, true_tags in zip(predicted_tags, true_mt_tags): - assert predicted_tags == {t.value for t in true_tags} - - @pytest.mark.parametrize("mt_tokens, pe_tokens, mt_pe_alignments, true_mt_tags", [ - (["A", "B", "C"], ["A", "X", "Z"], [(0, 0, 0.9), (1, 1, 0.9), (2, 2, 0.9)], [{Tags.OK}, {Tags.BAD_SUBSTITUTION}, {Tags.BAD_SUBSTITUTION}]), - (["A", "B"], ["Z", "X"], [(0, 0, 0.9), (1, 1, 0.9)], [{Tags.BAD_SUBSTITUTION}, {Tags.BAD_SUBSTITUTION}]), - # For 1-n and n-1 cases see contraction and expansion tests - ]) + for pred_tags, true_tags in zip(predicted_tags, true_mt_tags): + assert pred_tags == {t.value for t in true_tags} + + @pytest.mark.parametrize( + "mt_tokens, pe_tokens, mt_pe_alignments, true_mt_tags", + [ + ( + ["A", "B", "C"], + ["A", "X", "Z"], + [(0, 0, 0.9), (1, 1, 0.9), (2, 2, 0.9)], + [{Tags.OK}, {Tags.BAD_SUBSTITUTION}, {Tags.BAD_SUBSTITUTION}], + ), + (["A", "B"], ["Z", "X"], [(0, 0, 0.9), (1, 1, 0.9)], [{Tags.BAD_SUBSTITUTION}, {Tags.BAD_SUBSTITUTION}]), + # For 1-n and n-1 cases see contraction and expansion tests + ], + ) def test_single_error_substitution( - self, - mt_tokens: List[str], - pe_tokens: List[str], - mt_pe_alignments: List[Tuple[int, int]], - true_mt_tags: List[Set[StrEnum]], + self, + mt_tokens: List[str], + pe_tokens: List[str], + mt_pe_alignments: List[Tuple[int, int]], + true_mt_tags: List[Set[StrEnum]], ) -> None: predicted_tags = tagger.tags_from_edits([mt_tokens], [pe_tokens], [mt_pe_alignments])[0] assert len(predicted_tags) == len(true_mt_tags) - for predicted_tags, true_tags in zip(predicted_tags, true_mt_tags): - assert predicted_tags == {t.value for t in true_tags} - - @pytest.mark.parametrize("mt_tokens, pe_tokens, mt_pe_alignments, true_mt_tags", [ - (["A", "B"], ["A"], [(0, 0, 0.9), (1, None, None)], [{Tags.OK}, {Tags.BAD_INSERTION}]), - (["A", "B"], ["B"], [(0, None, None), (1, 0, 0.9)], [{Tags.BAD_INSERTION}, {Tags.OK}]), - (["A", "B"], [], [(0, None, None), (1, None, None)], [{Tags.BAD_INSERTION}, {Tags.BAD_INSERTION}]), - # For 1-n and n-1 cases see contraction and expansion tests - ]) + for pred_tags, true_tags in zip(predicted_tags, true_mt_tags): + assert pred_tags == {t.value for t in true_tags} + + @pytest.mark.parametrize( + "mt_tokens, pe_tokens, mt_pe_alignments, true_mt_tags", + [ + (["A", "B"], ["A"], [(0, 0, 0.9), (1, None, None)], [{Tags.OK}, {Tags.BAD_INSERTION}]), + (["A", "B"], ["B"], [(0, None, None), (1, 0, 0.9)], [{Tags.BAD_INSERTION}, {Tags.OK}]), + (["A", "B"], [], [(0, None, None), (1, None, None)], [{Tags.BAD_INSERTION}, {Tags.BAD_INSERTION}]), + # For 1-n and n-1 cases see contraction and expansion tests + ], + ) def test_single_error_insertion( - self, - mt_tokens: List[str], - pe_tokens: List[str], - mt_pe_alignments: List[Tuple[int, int]], - true_mt_tags: List[Set[StrEnum]], + self, + mt_tokens: List[str], + pe_tokens: List[str], + mt_pe_alignments: List[Tuple[int, int]], + true_mt_tags: List[Set[StrEnum]], ) -> None: predicted_tags = tagger.tags_from_edits([mt_tokens], [pe_tokens], [mt_pe_alignments])[0] assert len(predicted_tags) == len(true_mt_tags) - for predicted_tags, true_tags in zip(predicted_tags, true_mt_tags): - assert predicted_tags == {t.value for t in true_tags} - - @pytest.mark.parametrize("mt_tokens, pe_tokens, mt_pe_alignments, true_mt_tags", [ - (["A"], ["A", "X"], [(0, 0, 0.9), (None, 1, None)], [{Tags.OK, Tags.BAD_DELETION_RIGHT}]), - (["A"], ["X", "A"], [(None, 0, None), (0, 1, 0.9)], [{Tags.OK, Tags.BAD_DELETION_LEFT}]), - (["A", "B"], ["A", "X", "B"], [(0, 0, 0.9), (None, 1, None), (1, 2, 0.9)], [{Tags.OK, Tags.BAD_DELETION_RIGHT}, {Tags.OK, Tags.BAD_DELETION_LEFT}]), - # Delete multiple tokens, but tag error as deleted one - (["A"], ["A", "X", "Y", "Z"], [(0, 0, 0.9), (None, 1, None), (None, 2, None), (None, 3, None)], [{Tags.OK, Tags.BAD_DELETION_RIGHT}]), - (["A"], ["X", "Y", "Z", "A"], [(None, 0, None), (None, 1, None), (None, 2, None), (0, 3, 0.9)], [{Tags.OK, Tags.BAD_DELETION_LEFT}]), - (["A", "B"], ["A", "X", "Y", "Z", "B"], [(0, 0, 0.9), (None, 1, None), (None, 2, None), (None, 3, None), (1, 4, 0.9)], [{Tags.OK, Tags.BAD_DELETION_RIGHT}, {Tags.OK, Tags.BAD_DELETION_LEFT}]), - # deleted both left and right sides - (["A"], ["X", "A", "Y"], [(None, 0, None), (0, 1, 0.9), (None, 2, None)], [{Tags.OK, Tags.BAD_DELETION_LEFT, Tags.BAD_DELETION_RIGHT}]), - # deleted for empty target - ([], ["X"], [(None, 0, None)], []), - ]) + for pred_tags, true_tags in zip(predicted_tags, true_mt_tags): + assert pred_tags == {t.value for t in true_tags} + + @pytest.mark.parametrize( + "mt_tokens, pe_tokens, mt_pe_alignments, true_mt_tags", + [ + (["A"], ["A", "X"], [(0, 0, 0.9), (None, 1, None)], [{Tags.OK, Tags.BAD_DELETION_RIGHT}]), + (["A"], ["X", "A"], [(None, 0, None), (0, 1, 0.9)], [{Tags.OK, Tags.BAD_DELETION_LEFT}]), + ( + ["A", "B"], + ["A", "X", "B"], + [(0, 0, 0.9), (None, 1, None), (1, 2, 0.9)], + [{Tags.OK, Tags.BAD_DELETION_RIGHT}, {Tags.OK, Tags.BAD_DELETION_LEFT}], + ), + # Delete multiple tokens, but tag error as deleted one + ( + ["A"], + ["A", "X", "Y", "Z"], + [(0, 0, 0.9), (None, 1, None), (None, 2, None), (None, 3, None)], + [{Tags.OK, Tags.BAD_DELETION_RIGHT}], + ), + ( + ["A"], + ["X", "Y", "Z", "A"], + [(None, 0, None), (None, 1, None), (None, 2, None), (0, 3, 0.9)], + [{Tags.OK, Tags.BAD_DELETION_LEFT}], + ), + ( + ["A", "B"], + ["A", "X", "Y", "Z", "B"], + [(0, 0, 0.9), (None, 1, None), (None, 2, None), (None, 3, None), (1, 4, 0.9)], + [{Tags.OK, Tags.BAD_DELETION_RIGHT}, {Tags.OK, Tags.BAD_DELETION_LEFT}], + ), + # deleted both left and right sides + ( + ["A"], + ["X", "A", "Y"], + [(None, 0, None), (0, 1, 0.9), (None, 2, None)], + [{Tags.OK, Tags.BAD_DELETION_LEFT, Tags.BAD_DELETION_RIGHT}], + ), + # deleted for empty target + ([], ["X"], [(None, 0, None)], []), + ], + ) def test_single_error_deletion( - self, - mt_tokens: List[str], - pe_tokens: List[str], - mt_pe_alignments: List[Tuple[int, int]], - true_mt_tags: List[Set[StrEnum]], + self, + mt_tokens: List[str], + pe_tokens: List[str], + mt_pe_alignments: List[Tuple[int, int]], + true_mt_tags: List[Set[StrEnum]], ) -> None: predicted_tags = tagger.tags_from_edits([mt_tokens], [pe_tokens], [mt_pe_alignments])[0] assert len(predicted_tags) == len(true_mt_tags) - for predicted_tags, true_tags in zip(predicted_tags, true_mt_tags): - assert predicted_tags == {t.value for t in true_tags} - - @pytest.mark.parametrize("mt_tokens, pe_tokens, mt_pe_alignments, true_mt_tags", [ - # Have same BBB token, so should filter CCC and TTT out as Deletion error and BBB as Ok - (["AAA", "BBB"], ["AAA", "BBB", "CCC", "TTT"], [(0, 0, 0.9), (1, 1, 0.9), (1, 2, 0.1), (1, 3, 0.1)], [{Tags.OK}, {Tags.OK, Tags.BAD_DELETION_RIGHT}]), - (["AAA", "BBB"], ["AAA", "TTT", "BBB", "CCC"], [(0, 0, 0.9), (1, 1, 0.1), (1, 2, 0.9), (1, 3, 0.1)], [{Tags.OK, Tags.BAD_DELETION_RIGHT}, {Tags.OK, Tags.BAD_DELETION_RIGHT, Tags.BAD_DELETION_LEFT}]), - # XXX, TTT and CCC >threshold are same BBB token, so its bad Contradiction - (["AAA", "BBB"], ["AAA", "XXX", "CCC", "TTT"], [(0, 0, 0.9), (1, 1, 0.9), (1, 2, 0.9), (1, 3, 0.9)], [{Tags.OK}, {Tags.BAD_CONTRACTION}]), - # BBX is >threshold, CCC/TTT threshold, so all are Contractions - (["AAA", "BBB"], ["AAA", "BBX", "XBB"], [(0, 0, 0.9), (1, 1, 0.9), (1, 2, 0.9)], [{Tags.OK}, {Tags.BAD_CONTRACTION}]), - # BBX and XBB >threshold while TTT is threshold are same BBB token, so its bad Contradiction + ( + ["AAA", "BBB"], + ["AAA", "XXX", "CCC", "TTT"], + [(0, 0, 0.9), (1, 1, 0.9), (1, 2, 0.9), (1, 3, 0.9)], + [{Tags.OK}, {Tags.BAD_CONTRACTION}], + ), + # BBX is >threshold, CCC/TTT threshold, so all are Contractions + ( + ["AAA", "BBB"], + ["AAA", "BBX", "XBB"], + [(0, 0, 0.9), (1, 1, 0.9), (1, 2, 0.9)], + [{Tags.OK}, {Tags.BAD_CONTRACTION}], + ), + # BBX and XBB >threshold while TTT is None: predicted_tags = tagger.tags_from_edits([mt_tokens], [pe_tokens], [mt_pe_alignments])[0] assert len(predicted_tags) == len(true_mt_tags) - for predicted_tags, true_tags in zip(predicted_tags, true_mt_tags): - assert predicted_tags == {t.value for t in true_tags} - - @pytest.mark.parametrize("mt_tokens, pe_tokens, mt_pe_alignments, true_mt_tags", [ - # BB token is same, so CCC and TTT are insertions - (["AAA", "BBB", "CCC", "TTT"], ["AAA", "BBB"], [(0, 0, 0.9), (1, 1, 0.9), (2, 1, 0.1), (3, 1, 0.1)], [{Tags.OK}, {Tags.OK}, {Tags.BAD_INSERTION}, {Tags.BAD_INSERTION}]), - (["AAA", "TTT", "BBB", "CCC"], ["AAA", "BBB"], [(0, 0, 0.9), (1, 1, 0.1), (2, 1, 0.9), (3, 1, 0.1)], [{Tags.OK}, {Tags.BAD_INSERTION}, {Tags.OK}, {Tags.BAD_INSERTION}]), - # XXX, TTT and CCC >threshold are same BBB token, so its bad Expansion - (["AAA", "XXX", "CCC", "TTT"], ["AAA", "BBB"], [(0, 0, 0.9), (1, 1, 0.9), (2, 1, 0.9), (3, 1, 0.9)], [{Tags.OK}, {Tags.BAD_EXPANSION}, {Tags.BAD_EXPANSION}, {Tags.BAD_EXPANSION}]), - # BBX is >threshold, CCC/TTT threshold, so all are Expansion - (["AAA", "BBX", "XBB"], ["AAA", "BBB"], [(0, 0, 0.9), (1, 1, 0.9), (2, 1, 0.9)], [{Tags.OK}, {Tags.BAD_EXPANSION}, {Tags.BAD_EXPANSION}]), - # BBX and XBB >threshold while TTT is threshold are same BBB token, so its bad Expansion + ( + ["AAA", "XXX", "CCC", "TTT"], + ["AAA", "BBB"], + [(0, 0, 0.9), (1, 1, 0.9), (2, 1, 0.9), (3, 1, 0.9)], + [{Tags.OK}, {Tags.BAD_EXPANSION}, {Tags.BAD_EXPANSION}, {Tags.BAD_EXPANSION}], + ), + # BBX is >threshold, CCC/TTT threshold, so all are Expansion + ( + ["AAA", "BBX", "XBB"], + ["AAA", "BBB"], + [(0, 0, 0.9), (1, 1, 0.9), (2, 1, 0.9)], + [{Tags.OK}, {Tags.BAD_EXPANSION}, {Tags.BAD_EXPANSION}], + ), + # BBX and XBB >threshold while TTT is None: predicted_tags = tagger.tags_from_edits([mt_tokens], [pe_tokens], [mt_pe_alignments])[0] assert len(predicted_tags) == len(true_mt_tags) - for predicted_tags, true_tags in zip(predicted_tags, true_mt_tags): - assert predicted_tags == {t.value for t in true_tags} - - @pytest.mark.parametrize("mt_tokens, pe_tokens, mt_pe_alignments, true_mt_tags", [ - # simple case - (["A", "B"], ["B", "A"], [(0, 1, 0.9), (1, 0, 0.9)], [{Tags.OK, Tags.BAD_SHIFTING}, {Tags.OK, Tags.BAD_SHIFTING}]), - # middle intact, but crossing edges, so shifted - (["A", "X", "Y", "B"], ["B", "X", "Y", "A"], [(0, 3, 0.9), (1, 1, 0.9), (2, 2, 0.9), (3, 0, 0.9)], [{Tags.OK, Tags.BAD_SHIFTING}, {Tags.OK, Tags.BAD_SHIFTING}, {Tags.OK, Tags.BAD_SHIFTING}, {Tags.OK, Tags.BAD_SHIFTING}]), - # node inserted, so should not be marked as shifted - (["A", "X", "B"], ["B", "A"], [(0, 1, 0.9), (1, None, None), (2, 0, 0.9)], [{Tags.OK, Tags.BAD_SHIFTING}, {Tags.BAD_INSERTION}, {Tags.OK, Tags.BAD_SHIFTING}]), - # node deleted, nothing to mark as shifted - (["A", "B"], ["B", "X", "A"], [(0, 2, 0.9), (None, 1, None), (1, 0, 0.9)], [{Tags.OK, Tags.BAD_SHIFTING, Tags.BAD_DELETION_RIGHT}, {Tags.OK, Tags.BAD_SHIFTING, Tags.BAD_DELETION_LEFT}]), - ]) + for pred_tags, true_tags in zip(predicted_tags, true_mt_tags): + assert pred_tags == {t.value for t in true_tags} + + @pytest.mark.parametrize( + "mt_tokens, pe_tokens, mt_pe_alignments, true_mt_tags", + [ + # simple case + ( + ["A", "B"], + ["B", "A"], + [(0, 1, 0.9), (1, 0, 0.9)], + [{Tags.OK, Tags.BAD_SHIFTING}, {Tags.OK, Tags.BAD_SHIFTING}], + ), + # middle intact, but crossing edges, so shifted + ( + ["A", "X", "Y", "B"], + ["B", "X", "Y", "A"], + [(0, 3, 0.9), (1, 1, 0.9), (2, 2, 0.9), (3, 0, 0.9)], + [ + {Tags.OK, Tags.BAD_SHIFTING}, + {Tags.OK, Tags.BAD_SHIFTING}, + {Tags.OK, Tags.BAD_SHIFTING}, + {Tags.OK, Tags.BAD_SHIFTING}, + ], + ), + # node inserted, so should not be marked as shifted + ( + ["A", "X", "B"], + ["B", "A"], + [(0, 1, 0.9), (1, None, None), (2, 0, 0.9)], + [{Tags.OK, Tags.BAD_SHIFTING}, {Tags.BAD_INSERTION}, {Tags.OK, Tags.BAD_SHIFTING}], + ), + # node deleted, nothing to mark as shifted + ( + ["A", "B"], + ["B", "X", "A"], + [(0, 2, 0.9), (None, 1, None), (1, 0, 0.9)], + [ + {Tags.OK, Tags.BAD_SHIFTING, Tags.BAD_DELETION_RIGHT}, + {Tags.OK, Tags.BAD_SHIFTING, Tags.BAD_DELETION_LEFT}, + ], + ), + ], + ) def test_single_error_shifted( - self, - mt_tokens: List[str], - pe_tokens: List[str], - mt_pe_alignments: List[Tuple[int, int]], - true_mt_tags: List[Set[StrEnum]], + self, + mt_tokens: List[str], + pe_tokens: List[str], + mt_pe_alignments: List[Tuple[int, int]], + true_mt_tags: List[Set[StrEnum]], ) -> None: predicted_tags = tagger.tags_from_edits([mt_tokens], [pe_tokens], [mt_pe_alignments])[0] assert len(predicted_tags) == len(true_mt_tags) - for predicted_tags, true_tags in zip(predicted_tags, true_mt_tags): - assert predicted_tags == {t.value for t in true_tags} + for pred_tags, true_tags in zip(predicted_tags, true_mt_tags): + assert pred_tags == {t.value for t in true_tags} class TestTagsToSource: - @pytest.mark.parametrize("src_tokens, mt_tokens, mt_pe_alignments, mt_tags, true_src_tags", [ - # ok cases - (["A", "B"], ["A", "B"], [(0, 0, 0.9), (1, 1, 0.9)], [{Tags.OK}, {Tags.OK}], [{Tags.OK}, {Tags.OK}]), - (["A", "B", "C", "D"], ["A", "B", "C", "D"], [(0, 0, 0.9), (1, 1, 0.9), (2, 2, 0.9), (3, 3, 0.9)], [{Tags.OK}, {Tags.OK}, {Tags.OK}, {Tags.OK}], [{Tags.OK}, {Tags.OK}, {Tags.OK}, {Tags.OK}]), - ([], [], [], [], []), - # substitution cases - (["A", "B"], ["A", "C"], [(0, 0, 0.9), (1, 1, 0.9)], [{Tags.OK}, {Tags.BAD_SUBSTITUTION}], [{Tags.OK}, {Tags.BAD_SUBSTITUTION}]), - (["A", "B", "C", "D"], ["A", "B", "X", "D"], [(0, 0, 0.9), (1, 1, 0.9), (2, 2, 0.1), (3, 3, 0.9)], [{Tags.OK}, {Tags.OK}, {Tags.BAD_SUBSTITUTION}, {Tags.OK}], [{Tags.OK}, {Tags.OK}, {Tags.BAD_SUBSTITUTION}, {Tags.OK}]), - # multiple tags - (["A", "B"], ["A", "C"], [(0, 0, 0.9), (1, 1, 0.9)], [{Tags.OK, Tags.BAD_SHIFTING}, {Tags.BAD_SUBSTITUTION, Tags.BAD_DELETION_RIGHT}], [{Tags.OK, Tags.BAD_SHIFTING}, {Tags.BAD_SUBSTITUTION, Tags.BAD_DELETION_RIGHT}]), - ]) + @pytest.mark.parametrize( + "src_tokens, mt_tokens, mt_pe_alignments, mt_tags, true_src_tags", + [ + # ok cases + (["A", "B"], ["A", "B"], [(0, 0, 0.9), (1, 1, 0.9)], [{Tags.OK}, {Tags.OK}], [{Tags.OK}, {Tags.OK}]), + ( + ["A", "B", "C", "D"], + ["A", "B", "C", "D"], + [(0, 0, 0.9), (1, 1, 0.9), (2, 2, 0.9), (3, 3, 0.9)], + [{Tags.OK}, {Tags.OK}, {Tags.OK}, {Tags.OK}], + [{Tags.OK}, {Tags.OK}, {Tags.OK}, {Tags.OK}], + ), + ([], [], [], [], []), + # substitution cases + ( + ["A", "B"], + ["A", "C"], + [(0, 0, 0.9), (1, 1, 0.9)], + [{Tags.OK}, {Tags.BAD_SUBSTITUTION}], + [{Tags.OK}, {Tags.BAD_SUBSTITUTION}], + ), + ( + ["A", "B", "C", "D"], + ["A", "B", "X", "D"], + [(0, 0, 0.9), (1, 1, 0.9), (2, 2, 0.1), (3, 3, 0.9)], + [{Tags.OK}, {Tags.OK}, {Tags.BAD_SUBSTITUTION}, {Tags.OK}], + [{Tags.OK}, {Tags.OK}, {Tags.BAD_SUBSTITUTION}, {Tags.OK}], + ), + # multiple tags + ( + ["A", "B"], + ["A", "C"], + [(0, 0, 0.9), (1, 1, 0.9)], + [{Tags.OK, Tags.BAD_SHIFTING}, {Tags.BAD_SUBSTITUTION, Tags.BAD_DELETION_RIGHT}], + [{Tags.OK, Tags.BAD_SHIFTING}, {Tags.BAD_SUBSTITUTION, Tags.BAD_DELETION_RIGHT}], + ), + ], + ) def test_one_to_one( - self, - src_tokens: List[str], - mt_tokens: List[str], - mt_pe_alignments: List[Tuple[int, int]], - mt_tags: List[Set[StrEnum]], - true_src_tags: List[Set[StrEnum]], + self, + src_tokens: List[str], + mt_tokens: List[str], + mt_pe_alignments: List[Tuple[int, int]], + mt_tags: List[Set[StrEnum]], + true_src_tags: List[Set[StrEnum]], ) -> None: - predicted_tags = tagger.tags_to_source([src_tokens], [mt_tokens], [mt_pe_alignments], [[{i.value for i in t} for t in mt_tags]])[0] + predicted_tags = tagger.tags_to_source( + [src_tokens], [mt_tokens], [mt_pe_alignments], [[{i.value for i in t} for t in mt_tags]] + )[0] assert len(predicted_tags) == len(true_src_tags) - for predicted_tags, true_tags in zip(predicted_tags, true_src_tags): - assert predicted_tags == {t.value for t in true_tags} + for pred_tags, true_tags in zip(predicted_tags, true_src_tags): + assert pred_tags == {t.value for t in true_tags} - @pytest.mark.parametrize("src_tokens, mt_tokens, mt_pe_alignments, mt_tags, true_src_tags", [ - (["A"], ["A", "B"], [(0, 0, 0.9), (None, 1, None)], [{Tags.OK}, {Tags.BAD_SUBSTITUTION}], [{Tags.OK}]), - (["A", "B"], ["A", "B", "C"], [(0, 0, 0.9), (1, 1, 0.9), (None, 2, None)], [{Tags.BAD_SUBSTITUTION}, {Tags.OK}, {Tags.OK}], [{Tags.BAD_SUBSTITUTION}, {Tags.OK}]), - ]) + @pytest.mark.parametrize( + "src_tokens, mt_tokens, mt_pe_alignments, mt_tags, true_src_tags", + [ + (["A"], ["A", "B"], [(0, 0, 0.9), (None, 1, None)], [{Tags.OK}, {Tags.BAD_SUBSTITUTION}], [{Tags.OK}]), + ( + ["A", "B"], + ["A", "B", "C"], + [(0, 0, 0.9), (1, 1, 0.9), (None, 2, None)], + [{Tags.BAD_SUBSTITUTION}, {Tags.OK}, {Tags.OK}], + [{Tags.BAD_SUBSTITUTION}, {Tags.OK}], + ), + ], + ) def test_src_deleted( - self, - src_tokens: List[str], - mt_tokens: List[str], - mt_pe_alignments: List[Tuple[int, int]], - mt_tags: List[Set[StrEnum]], - true_src_tags: List[Set[StrEnum]], + self, + src_tokens: List[str], + mt_tokens: List[str], + mt_pe_alignments: List[Tuple[int, int]], + mt_tags: List[Set[StrEnum]], + true_src_tags: List[Set[StrEnum]], ) -> None: - predicted_tags = tagger.tags_to_source([src_tokens], [mt_tokens], [mt_pe_alignments], [[{i.value for i in t} for t in mt_tags]])[0] + predicted_tags = tagger.tags_to_source( + [src_tokens], [mt_tokens], [mt_pe_alignments], [[{i.value for i in t} for t in mt_tags]] + )[0] assert len(predicted_tags) == len(true_src_tags) - for predicted_tags, true_tags in zip(predicted_tags, true_src_tags): - assert predicted_tags == {t.value for t in true_tags} + for pred_tags, true_tags in zip(predicted_tags, true_src_tags): + assert pred_tags == {t.value for t in true_tags} - @pytest.mark.parametrize("src_tokens, mt_tokens, mt_pe_alignments, mt_tags, true_src_tags", [ - (["A", "B", "C"], ["A", "B"], [(0, 0, 0.9), (1, 1, 0.9), (2, None, None)], [{Tags.BAD_SUBSTITUTION}, {Tags.OK}], [{Tags.BAD_SUBSTITUTION}, {Tags.OK}, set()]), - (["A", "B", "C", "D"], ["B"], [(0, None, None), (1, 0, 0.9), (2, None, None), (3, None, None)], [{Tags.BAD_SUBSTITUTION}], [set(), {Tags.BAD_SUBSTITUTION}, set(), set()]), - ]) + @pytest.mark.parametrize( + "src_tokens, mt_tokens, mt_pe_alignments, mt_tags, true_src_tags", + [ + ( + ["A", "B", "C"], + ["A", "B"], + [(0, 0, 0.9), (1, 1, 0.9), (2, None, None)], + [{Tags.BAD_SUBSTITUTION}, {Tags.OK}], + [{Tags.BAD_SUBSTITUTION}, {Tags.OK}, set()], + ), + ( + ["A", "B", "C", "D"], + ["B"], + [(0, None, None), (1, 0, 0.9), (2, None, None), (3, None, None)], + [{Tags.BAD_SUBSTITUTION}], + [set(), {Tags.BAD_SUBSTITUTION}, set(), set()], + ), + ], + ) def test_mt_deleted( - self, - src_tokens: List[str], - mt_tokens: List[str], - mt_pe_alignments: List[Tuple[int, int]], - mt_tags: List[Set[StrEnum]], - true_src_tags: List[Set[StrEnum]], + self, + src_tokens: List[str], + mt_tokens: List[str], + mt_pe_alignments: List[Tuple[int, int]], + mt_tags: List[Set[StrEnum]], + true_src_tags: List[Set[StrEnum]], ) -> None: - predicted_tags = tagger.tags_to_source([src_tokens], [mt_tokens], [mt_pe_alignments], [[{i.value for i in t} for t in mt_tags]])[0] + predicted_tags = tagger.tags_to_source( + [src_tokens], [mt_tokens], [mt_pe_alignments], [[{i.value for i in t} for t in mt_tags]] + )[0] assert len(predicted_tags) == len(true_src_tags) - for predicted_tags, true_tags in zip(predicted_tags, true_src_tags): - assert predicted_tags == {t.value for t in true_tags} + for pred_tags, true_tags in zip(predicted_tags, true_src_tags): + assert pred_tags == {t.value for t in true_tags} From fa7045d1a6bc12fea4c2cdf7fbc64aaa095401da Mon Sep 17 00:00:00 2001 From: Konstantin Chernyshev Date: Tue, 9 May 2023 13:24:18 +0200 Subject: [PATCH 11/23] chore: gitignore .ruff_cache/ --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index 6bfc3dd..29a2e87 100644 --- a/.gitignore +++ b/.gitignore @@ -66,6 +66,7 @@ coverage.xml *.py,cover .hypothesis/ .pytest_cache/ +.ruff_cache/ # Translations *.mo From 7b44a98b613b263b6123a20c035934d933e2a199 Mon Sep 17 00:00:00 2001 From: Konstantin Chernyshev Date: Tue, 9 May 2023 14:05:40 +0200 Subject: [PATCH 12/23] refactor: move taggers to separate module --- divemt/cer.py | 4 +- divemt/qe_taggers/__init__.py | 13 + divemt/qe_taggers/base.py | 104 +++++ divemt/{ => qe_taggers}/custom_simalign.py | 0 .../name_tbd_tagger.py} | 412 +----------------- divemt/qe_taggers/wmt22_tagger.py | 313 +++++++++++++ divemt/{ => qe_taggers}/wmt22qe_utils.py | 0 tests/test_qe_taggers_name_tbd_tagger.py | 4 +- 8 files changed, 440 insertions(+), 410 deletions(-) create mode 100644 divemt/qe_taggers/__init__.py create mode 100644 divemt/qe_taggers/base.py rename divemt/{ => qe_taggers}/custom_simalign.py (100%) rename divemt/{qe_taggers.py => qe_taggers/name_tbd_tagger.py} (51%) create mode 100644 divemt/qe_taggers/wmt22_tagger.py rename divemt/{ => qe_taggers}/wmt22qe_utils.py (100%) diff --git a/divemt/cer.py b/divemt/cer.py index 70bf57b..21a74eb 100644 --- a/divemt/cer.py +++ b/divemt/cer.py @@ -15,7 +15,7 @@ import ctypes import itertools -import Levenshtein +import Levenshtein as levenshtein class EditDistance: @@ -86,7 +86,7 @@ def cer(hyp_words, ref_words, ed_wrapper): if len(shifted_chars) == 0: return 1.0 - edit_cost = Levenshtein.distance(shifted_chars, ref_chars) + shift_cost + edit_cost = levenshtein.distance(shifted_chars, ref_chars) + shift_cost return min(1.0, edit_cost / len(shifted_chars)) diff --git a/divemt/qe_taggers/__init__.py b/divemt/qe_taggers/__init__.py new file mode 100644 index 0000000..b5ae9e7 --- /dev/null +++ b/divemt/qe_taggers/__init__.py @@ -0,0 +1,13 @@ +from .base import QETagger, TTag, TTag +from .name_tbd_tagger import NameTBDGeneralTags, NameTBDTagger +from .wmt22_tagger import WMT22QETags, WMT22QETagger + +__all__ = [ + "QETagger", + "TTag", + "TTag", + "NameTBDGeneralTags", + "NameTBDTagger", + "WMT22QETags", + "WMT22QETagger", +] diff --git a/divemt/qe_taggers/base.py b/divemt/qe_taggers/base.py new file mode 100644 index 0000000..cd4b095 --- /dev/null +++ b/divemt/qe_taggers/base.py @@ -0,0 +1,104 @@ +from abc import ABC, abstractmethod +from typing import Any, List, Optional, Set, Tuple, Union + +from ..parse_utils import tokenize + +TTag = Union[str, Set[str]] +TAlignment = Union[Tuple[Optional[int], Optional[int]], Tuple[Optional[int], Optional[int], Optional[float]]] + + +class QETagger(ABC): + """An abstract class to produce quality estimation tags from src-mt-pe triplets.""" + + ID = "qe" + + def align_source_mt( + self, + src_tokens: List[List[str]], + mt_tokens: List[List[str]], + **align_source_mt_kwargs: Any, + ) -> List[List[TAlignment]]: + """Align source and machine translation tokens.""" + raise NotImplementedError(f"{self.__class__.__name__} does not implement align_source_mt()") + + def align_source_pe( + self, + src_tokens: List[List[str]], + pe_tokens: List[List[str]], + **align_source_pe_kwargs: Any, + ) -> List[List[TAlignment]]: + """Align source and post-edited tokens.""" + raise NotImplementedError(f"{self.__class__.__name__} does not implement align_source_pe()") + + @abstractmethod + def align_mt_pe( + self, + mt_tokens: List[List[str]], + pe_tokens: List[List[str]], + **align_mt_pe_kwargs: Any, + ) -> List[List[TAlignment]]: + """Align machine translation and post-editing tokens.""" + pass + + @staticmethod + @abstractmethod + def tags_from_edits( + mt_tokens: List[List[str]], + pe_tokens: List[List[str]], + alignments: List[List[TAlignment]], + **mt_tagging_kwargs: Any, + ) -> List[List[TTag]]: + """Produce tags on MT tokens from edits found in the PE tokens.""" + pass + + @staticmethod + @abstractmethod + def tags_to_source( + src_tokens: List[List[str]], + tgt_tokens: List[List[str]], + **src_tagging_kwargs: Any, + ) -> List[List[TTag]]: + """Propagate tags from MT to source.""" + pass + + @staticmethod + def get_tokenized( + sents: List[str], lang: Union[str, List[str]] + ) -> Tuple[List[List[str]], Union[List[str], List[List[str]]]]: + """Tokenize sentences.""" + if isinstance(lang, str): + lang = [lang] * len(sents) + tok: List[List[str]] = [tokenize(sent, curr_lang, keep_tokens=True) for sent, curr_lang in zip(sents, lang)] + assert len(tok) == len(lang) + return tok, lang + + @abstractmethod + def generate_tags( + self, + srcs: List[str], + mts: List[str], + pes: List[str], + src_langs: Union[str, List[str]], + tgt_langs: Union[str, List[str]], + ) -> Tuple[List[TTag], List[TTag]]: + """Generate word-level quality estimation tags from source-mt-pe triplets. + + Args: + srcs (`List[str]`): + List of untokenized source sentences. + mts (`List[str]`): + List of untokenized machine translated sentences. + pes (`List[str]`): + List of untokenized post-edited sentences. + src_langs (`Union[str, List[str]]`): + Either a single language code for all source sentences or a list of language codes + (one per source sentence). + tgt_langs (`Union[str, List[str]]`): + Either a single language code for all target sentences or a list of language codes + (one per machine translation). + + Returns: + `Tuple[List[TTag], List[TTag]]`: A tuple containing the lists of quality tags for all source and the + machine translation sentence, respectively. + """ + pass diff --git a/divemt/custom_simalign.py b/divemt/qe_taggers/custom_simalign.py similarity index 100% rename from divemt/custom_simalign.py rename to divemt/qe_taggers/custom_simalign.py diff --git a/divemt/qe_taggers.py b/divemt/qe_taggers/name_tbd_tagger.py similarity index 51% rename from divemt/qe_taggers.py rename to divemt/qe_taggers/name_tbd_tagger.py index 924a9cb..5e2b9fb 100644 --- a/divemt/qe_taggers.py +++ b/divemt/qe_taggers/name_tbd_tagger.py @@ -1,15 +1,7 @@ -import codecs import logging -import subprocess import sys -from abc import ABC, abstractmethod -from collections import defaultdict from itertools import groupby -from pathlib import Path -from typing import Any, Generator, List, Optional, Set, Tuple, Union -from xml.sax.saxutils import escape - -from simalign import SentenceAligner +from typing import Generator, List, Optional, Set, Tuple, Union if sys.version_info < (3, 11): from strenum import StrEnum @@ -17,406 +9,14 @@ from enum import StrEnum from tqdm import tqdm +from ..cache_utils import CacheDecorator from .custom_simalign import SentenceAligner as CustomSentenceAligner -from .parse_utils import clear_nlp_cache, tokenize -from .wmt22qe_utils import align_sentence_tercom, parse_tercom_xml_file +from ..parse_utils import clear_nlp_cache +from .base import QETagger, TAlignment, TTag logger = logging.getLogger(__name__) -TTag = Union[str, Set[str]] -TAlignment = Union[Tuple[Optional[int], Optional[int]], Tuple[Optional[int], Optional[int], Optional[float]]] - - -class QETagger(ABC): - """An abstract class to produce quality estimation tags from src-mt-pe triplets.""" - - ID = "qe" - - def align_source_mt( - self, - src_tokens: List[List[str]], - mt_tokens: List[List[str]], - **align_source_mt_kwargs: Any, - ) -> List[List[TAlignment]]: - """Align source and machine translation tokens.""" - raise NotImplementedError(f"{self.__class__.__name__} does not implement align_source_mt()") - - def align_source_pe( - self, - src_tokens: List[List[str]], - pe_tokens: List[List[str]], - **align_source_pe_kwargs: Any, - ) -> List[List[TAlignment]]: - """Align source and post-edited tokens.""" - raise NotImplementedError(f"{self.__class__.__name__} does not implement align_source_pe()") - - @abstractmethod - def align_mt_pe( - self, - mt_tokens: List[List[str]], - pe_tokens: List[List[str]], - **align_mt_pe_kwargs: Any, - ) -> List[List[TAlignment]]: - """Align machine translation and post-editing tokens.""" - pass - - @staticmethod - @abstractmethod - def tags_from_edits( - mt_tokens: List[List[str]], - pe_tokens: List[List[str]], - alignments: List[List[TAlignment]], - **mt_tagging_kwargs: Any, - ) -> List[List[TTag]]: - """Produce tags on MT tokens from edits found in the PE tokens.""" - pass - - @staticmethod - @abstractmethod - def tags_to_source( - src_tokens: List[List[str]], - tgt_tokens: List[List[str]], - **src_tagging_kwargs: Any, - ) -> List[List[TTag]]: - """Propagate tags from MT to source.""" - pass - - @staticmethod - def get_tokenized( - sents: List[str], lang: Union[str, List[str]] - ) -> Tuple[List[List[str]], Union[List[str], List[List[str]]]]: - """Tokenize sentences.""" - if isinstance(lang, str): - lang = [lang] * len(sents) - tok: List[List[str]] = [tokenize(sent, curr_lang, keep_tokens=True) for sent, curr_lang in zip(sents, lang)] - assert len(tok) == len(lang) - return tok, lang - - @abstractmethod - def generate_tags( - self, - srcs: List[str], - mts: List[str], - pes: List[str], - src_langs: Union[str, List[str]], - tgt_langs: Union[str, List[str]], - ) -> Tuple[List[TTag], List[TTag]]: - """Generate word-level quality estimation tags from source-mt-pe triplets. - - Args: - srcs (`List[str]`): - List of untokenized source sentences. - mts (`List[str]`): - List of untokenized machine translated sentences. - pes (`List[str]`): - List of untokenized post-edited sentences. - src_langs (`Union[str, List[str]]`): - Either a single language code for all source sentences or a list of language codes - (one per source sentence). - tgt_langs (`Union[str, List[str]]`): - Either a single language code for all target sentences or a list of language codes - (one per machine translation). - - Returns: - `Tuple[List[TTag], List[TTag]]`: A tuple containing the lists of quality tags for all source and the - machine translation sentence, respectively. - """ - pass - - -class FluencyRule(StrEnum): - """Fluency rules used in the WMT22 QE task.""" - - NORMAL = "normal" - MISSING = "missing-only" - IGNORE_SHF = "ignore-shift-set" - - -class OmissionRule(StrEnum): - """Omission rules used in the WMT22 QE task.""" - - NONE = "none" - LEFT = "left" - RIGHT = "right" - - -class WMT22QETags(StrEnum): - """WMT22 QE tags""" - - OK = "OK" - BAD = "BAD" - - -class WMT22QETagger(QETagger): - """Mimics the word-level QE tagging process used for WMT22.""" - - ID = "wmt22_qe" - - def __init__( - self, - aligner: Optional[SentenceAligner] = None, - tmp_dir: Optional[str] = None, - tercom_out: Optional[str] = None, - tercom_path: Optional[str] = None, - ): - """Initialize the WMT22QETagger.""" - self.aligner = aligner if aligner else SentenceAligner(model="xlmr", token_type="bpe", matching_methods="mai") - self.tmp_dir = Path(tmp_dir) if tmp_dir is not None else Path("tmp") - self.tmp_dir.mkdir(parents=True, exist_ok=True) - self.tercom_out = Path(tercom_out) if tercom_out is not None else self.tmp_dir / "tercom" - self.tercom_path = tercom_path if tercom_path is not None else "scripts/tercom.7.25.jar" - - def align_source_pe( - self, - src_tokens: List[List[str]], - pe_tokens: List[List[str]], - pe_langs: List[str], - ) -> List[List[TAlignment]]: - return [ - self.aligner.get_word_aligns(src_tok, mt_tok)["itermax" if mt_lang not in ["de", "cs"] else "inter"] - for src_tok, mt_tok, mt_lang in tqdm( - zip(src_tokens, pe_tokens, pe_langs), - total=len(src_tokens), - desc="Aligning src-pe", - ) - ] - - def align_mt_pe( - self, - mt_tokens: List[List[str]], - pe_tokens: List[List[str]], - ) -> List[List[TAlignment]]: - ref_fname = self.tmp_dir / "ref.txt" - hyp_fname = self.tmp_dir / "hyp.txt" - # Adapted from https://github.com/deep-spin/qe-corpus-builder/corpus_generation/tools/format_tercom.py - with codecs.open(str(ref_fname), "w", encoding="utf-8") as rf: - with codecs.open(str(hyp_fname), "w", encoding="utf-8") as hf: - for idx, (ref, hyp) in enumerate(zip(mt_tokens, pe_tokens)): - _ref = " ".join(ref).rstrip() - _ref = escape(_ref).replace('"', '\\"') - rf.write(f"{_ref}\t({idx})\n") - _hyp = " ".join(hyp).rstrip() - _hyp = escape(_hyp).replace('"', '\\"') - hf.write(f"{_hyp}\t({idx})\n") - ps = [ - "java", - "-jar", - self.tercom_path, - "-r", - ref_fname, - "-h", - hyp_fname, - "-n", - self.tercom_out, - "-d", - "0", - ] - try: - _ = subprocess.run(ps, capture_output=True, check=True) - except subprocess.CalledProcessError as e: - logger.warning( - f"Error while running tercom: {e.stderr}.\nPlease make sure you have java installed and that the .jar " - f"file is found at {self.tercom_path}" - ) - # Parse tercom HTML - pe_parse_tokens, mt_parse_tokens, edits = parse_tercom_xml_file(f"{self.tercom_out}.xml") - - # Sanity check: Original and tercom files match in number of tokens - # Note that we will not use the tokenized tercom outputs only the alignments - for mt_par_toks, pe_par_toks, mt_toks, pe_toks in zip(mt_parse_tokens, pe_parse_tokens, mt_tokens, pe_tokens): - # Inserted tokens correspond to empty strings in the XLM tercom output - assert len([t for t in mt_par_toks if t]) == len(mt_toks), f"{mt_par_toks} != {mt_toks}" - assert len([t for t in pe_par_toks if t]) == len(pe_toks), f"{pe_par_toks} != {pe_toks}" - - return [align_sentence_tercom(mt, pe, edit) for mt, pe, edit in zip(mt_tokens, pe_tokens, edits)] - - @staticmethod - def tags_from_edits( - mt_tokens: List[List[str]], - pe_tokens: List[List[str]], - alignments: List[List[TAlignment]], - use_gaps: bool = False, - omissions: str = OmissionRule.RIGHT.value, - ) -> List[List[TTag]]: - """Produce tags on MT tokens from edits found in the PE tokens.""" - if use_gaps: - omissions = OmissionRule.NONE.value - - mt_tags = [] - for mt_tok, pe_tok, align in tqdm( - zip(mt_tokens, pe_tokens, alignments), - desc="Tagging MT", - total=len(mt_tokens), - ): - sent_tags = [] - sent_deletion_indices = [] - mt_position = 0 - - # Loop over alignments. This has the length of the edit-distance aligned sequences. - for mt_idx, pe_idx in align: - if mt_idx is None: - # Deleted word error (need to store for later) - if omissions == OmissionRule.LEFT or omissions == OmissionRule.NONE: - sent_deletion_indices.append(mt_position - 1) - else: - sent_deletion_indices.append(mt_position) - elif pe_idx is None: - # Insertion error - sent_tags.append(WMT22QETags.BAD.value) - mt_position += 1 - elif mt_tok[mt_idx] != pe_tok[pe_idx]: - # Substitution error - sent_tags.append(WMT22QETags.BAD.value) - mt_position += 1 - else: - # OK - sent_tags.append(WMT22QETags.OK.value) - mt_position += 1 - - # Insert deletion errors as gaps - word_and_gaps_tags = [] - if use_gaps: - # Add starting OK/BAD - if -1 in sent_deletion_indices: - word_and_gaps_tags.append(WMT22QETags.BAD.value) - else: - word_and_gaps_tags.append(WMT22QETags.OK.value) - # Add rest of OK/BADs - for index, tag in enumerate(sent_tags): - if index in sent_deletion_indices: - word_and_gaps_tags.extend([tag, WMT22QETags.BAD.value]) - else: - word_and_gaps_tags.extend([tag, WMT22QETags.OK.value]) - mt_tags.append(word_and_gaps_tags) - else: - if omissions == OmissionRule.NONE: - mt_tags.append(sent_tags) - elif omissions == OmissionRule.RIGHT: - for index, tag in enumerate(sent_tags): - if index in sent_deletion_indices: - word_and_gaps_tags.append(WMT22QETags.BAD.value) - else: - word_and_gaps_tags.append(tag) - if len(sent_tags) in sent_deletion_indices: - word_and_gaps_tags.append(WMT22QETags.BAD.value) - else: - word_and_gaps_tags.append(WMT22QETags.OK.value) - elif omissions == OmissionRule.LEFT: - if -1 in sent_deletion_indices: - word_and_gaps_tags.append(WMT22QETags.BAD.value) - else: - word_and_gaps_tags.append(WMT22QETags.OK.value) - for index, tag in enumerate(sent_tags): - if index in sent_deletion_indices: - word_and_gaps_tags.append(WMT22QETags.BAD.value) - else: - word_and_gaps_tags.append(tag) - mt_tags.append(word_and_gaps_tags) - - # Basic sanity checks - if use_gaps: - assert all(len(aa) * 2 + 1 == len(bb) for aa, bb in zip(mt_tokens, mt_tags)), "MT tag creation failed" - else: - if omissions == OmissionRule.NONE: # noqa: PLR5501 - assert all(len(aa) == len(bb) for aa, bb in zip(mt_tokens, mt_tags)), "MT tag creation failed" - else: - assert all(len(aa) + 1 == len(bb) for aa, bb in zip(mt_tokens, mt_tags)), "MT tag creation failed" - return mt_tags - - @staticmethod - def tags_to_source( - src_tokens: List[List[str]], - pe_tokens: List[List[str]], - mt_tokens: List[List[str]], - src_pe_alignments: List[List[TAlignment]], - mt_pe_alignments: List[List[TAlignment]], - fluency_rule: str = FluencyRule.NORMAL.value, - ) -> List[List[TTag]]: - """Propagate tags from MT to source.""" - # Reorganize source-target alignments as a dict - pe2source = [] - for sent in src_pe_alignments: - pe2source_sent = defaultdict(list) - for src_idx, pe_idx in sent: - pe2source_sent[pe_idx].append(src_idx) - pe2source.append(pe2source_sent) - - src_tags = [] - for ( - src_sent_tok, - mt_sent_tok, - pe_sent_tok, - sent_pe2src, - sent_mt_pe_aligns, - ) in tqdm( - zip(src_tokens, mt_tokens, pe_tokens, pe2source, mt_pe_alignments), - desc="Tagging source", - total=len(src_tokens), - ): - source_sentence_bad_indices = set() - mt_position = 0 - for mt_idx, pe_idx in sent_mt_pe_aligns: - if mt_idx is None or ( - mt_idx is not None and pe_idx is not None and mt_sent_tok[mt_idx] != pe_sent_tok[pe_idx] - ): - if fluency_rule == FluencyRule.NORMAL: - source_positions = sent_pe2src[pe_idx] - source_sentence_bad_indices |= set(source_positions) - elif fluency_rule == FluencyRule.IGNORE_SHF: - if pe_sent_tok[pe_idx] not in mt_sent_tok: - source_positions = sent_pe2src[pe_idx] - source_sentence_bad_indices |= set(source_positions) - elif fluency_rule == FluencyRule.MISSING: - if mt_idx is None: - source_positions = sent_pe2src[pe_idx] - source_sentence_bad_indices |= set(source_positions) - else: - raise Exception(f"Unknown fluency rule {fluency_rule}") - else: - mt_position += 1 - source_sentence_bad_tags = [WMT22QETags.OK.value] * len(src_sent_tok) - for index in list(source_sentence_bad_indices): - source_sentence_bad_tags[index] = WMT22QETags.BAD.value - src_tags.append(source_sentence_bad_tags) - - # Basic sanity checks - assert all(len(aa) == len(bb) for aa, bb in zip(src_tokens, src_tags)), "SRC tag creation failed" - return src_tags - - def generate_tags( - self, - srcs: List[str], - mts: List[str], - pes: List[str], - src_langs: Union[str, List[str]], - tgt_langs: Union[str, List[str]], - use_gaps: bool = False, - omissions: str = OmissionRule.RIGHT.value, - fluency_rule: str = FluencyRule.NORMAL.value, - ) -> Tuple[List[List[TTag]], List[List[TTag]]]: - src_tokens, src_langs = self.get_tokenized(srcs, src_langs) - mt_tokens, tgt_langs = self.get_tokenized(mts, tgt_langs) - pe_tokens, _ = self.get_tokenized(pes, tgt_langs) - - src_pe_alignments = self.align_source_pe(src_tokens, pe_tokens, tgt_langs) - mt_pe_alignments = self.align_mt_pe(mt_tokens, pe_tokens) - - mt_tags = self.tags_from_edits(mt_tokens, pe_tokens, mt_pe_alignments, use_gaps, omissions) - src_tags = self.tags_to_source( - src_tokens, - pe_tokens, - mt_tokens, - src_pe_alignments, - mt_pe_alignments, - fluency_rule, - ) - - clear_nlp_cache() - - return src_tags, mt_tags - - class NameTBDGeneralTags(StrEnum): """Error types tags for NameTBD.""" @@ -468,7 +68,7 @@ def _fill_deleted_inserted_tokens( return new_alignments - # @CacheDecorator() + @CacheDecorator() def align_source_mt( self, src_tokens: List[List[str]], @@ -481,7 +81,7 @@ def align_source_mt( for src_tok, mt_tok in tqdm(zip(src_tokens, mt_tokens), total=len(src_tokens), desc="Aligning src-mt") ] - # @CacheDecorator() + @CacheDecorator() def align_mt_pe( self, mt_tokens: List[List[str]], diff --git a/divemt/qe_taggers/wmt22_tagger.py b/divemt/qe_taggers/wmt22_tagger.py new file mode 100644 index 0000000..1455e5a --- /dev/null +++ b/divemt/qe_taggers/wmt22_tagger.py @@ -0,0 +1,313 @@ +import codecs +import logging +import subprocess +import sys +from collections import defaultdict +from pathlib import Path +from typing import List, Optional, Tuple, Union +from xml.sax.saxutils import escape + +if sys.version_info < (3, 11): + from strenum import StrEnum +else: + from enum import StrEnum +from simalign import SentenceAligner +from tqdm import tqdm + +from ..parse_utils import clear_nlp_cache +from .base import QETagger, TAlignment, TTag +from .wmt22qe_utils import align_sentence_tercom, parse_tercom_xml_file + +logger = logging.getLogger(__name__) + + +class FluencyRule(StrEnum): + """Fluency rules used in the WMT22 QE task.""" + + NORMAL = "normal" + MISSING = "missing-only" + IGNORE_SHF = "ignore-shift-set" + + +class OmissionRule(StrEnum): + """Omission rules used in the WMT22 QE task.""" + + NONE = "none" + LEFT = "left" + RIGHT = "right" + + +class WMT22QETags(StrEnum): + """WMT22 QE tags""" + + OK = "OK" + BAD = "BAD" + + +class WMT22QETagger(QETagger): + """Mimics the word-level QE tagging process used for WMT22.""" + + ID = "wmt22_qe" + + def __init__( + self, + aligner: Optional[SentenceAligner] = None, + tmp_dir: Optional[str] = None, + tercom_out: Optional[str] = None, + tercom_path: Optional[str] = None, + ): + """Initialize the WMT22QETagger.""" + self.aligner = aligner if aligner else SentenceAligner(model="xlmr", token_type="bpe", matching_methods="mai") + self.tmp_dir = Path(tmp_dir) if tmp_dir is not None else Path("tmp") + self.tmp_dir.mkdir(parents=True, exist_ok=True) + self.tercom_out = Path(tercom_out) if tercom_out is not None else self.tmp_dir / "tercom" + self.tercom_path = tercom_path if tercom_path is not None else "scripts/tercom.7.25.jar" + + def align_source_pe( + self, + src_tokens: List[List[str]], + pe_tokens: List[List[str]], + pe_langs: List[str], + ) -> List[List[TAlignment]]: + return [ + self.aligner.get_word_aligns(src_tok, mt_tok)["itermax" if mt_lang not in ["de", "cs"] else "inter"] + for src_tok, mt_tok, mt_lang in tqdm( + zip(src_tokens, pe_tokens, pe_langs), + total=len(src_tokens), + desc="Aligning src-pe", + ) + ] + + def align_mt_pe( + self, + mt_tokens: List[List[str]], + pe_tokens: List[List[str]], + ) -> List[List[TAlignment]]: + ref_fname = self.tmp_dir / "ref.txt" + hyp_fname = self.tmp_dir / "hyp.txt" + # Adapted from https://github.com/deep-spin/qe-corpus-builder/corpus_generation/tools/format_tercom.py + with codecs.open(str(ref_fname), "w", encoding="utf-8") as rf: + with codecs.open(str(hyp_fname), "w", encoding="utf-8") as hf: + for idx, (ref, hyp) in enumerate(zip(mt_tokens, pe_tokens)): + _ref = " ".join(ref).rstrip() + _ref = escape(_ref).replace('"', '\\"') + rf.write(f"{_ref}\t({idx})\n") + _hyp = " ".join(hyp).rstrip() + _hyp = escape(_hyp).replace('"', '\\"') + hf.write(f"{_hyp}\t({idx})\n") + ps = [ + "java", + "-jar", + self.tercom_path, + "-r", + ref_fname, + "-h", + hyp_fname, + "-n", + self.tercom_out, + "-d", + "0", + ] + try: + _ = subprocess.run(ps, capture_output=True, check=True) + except subprocess.CalledProcessError as e: + logger.warning( + f"Error while running tercom: {e.stderr}.\nPlease make sure you have java installed and that the .jar " + f"file is found at {self.tercom_path}" + ) + # Parse tercom HTML + pe_parse_tokens, mt_parse_tokens, edits = parse_tercom_xml_file(f"{self.tercom_out}.xml") + + # Sanity check: Original and tercom files match in number of tokens + # Note that we will not use the tokenized tercom outputs only the alignments + for mt_par_toks, pe_par_toks, mt_toks, pe_toks in zip(mt_parse_tokens, pe_parse_tokens, mt_tokens, pe_tokens): + # Inserted tokens correspond to empty strings in the XLM tercom output + assert len([t for t in mt_par_toks if t]) == len(mt_toks), f"{mt_par_toks} != {mt_toks}" + assert len([t for t in pe_par_toks if t]) == len(pe_toks), f"{pe_par_toks} != {pe_toks}" + + return [align_sentence_tercom(mt, pe, edit) for mt, pe, edit in zip(mt_tokens, pe_tokens, edits)] + + @staticmethod + def tags_from_edits( + mt_tokens: List[List[str]], + pe_tokens: List[List[str]], + alignments: List[List[TAlignment]], + use_gaps: bool = False, + omissions: str = OmissionRule.RIGHT.value, + ) -> List[List[TTag]]: + """Produce tags on MT tokens from edits found in the PE tokens.""" + if use_gaps: + omissions = OmissionRule.NONE.value + + mt_tags = [] + for mt_tok, pe_tok, align in tqdm( + zip(mt_tokens, pe_tokens, alignments), + desc="Tagging MT", + total=len(mt_tokens), + ): + sent_tags = [] + sent_deletion_indices = [] + mt_position = 0 + + # Loop over alignments. This has the length of the edit-distance aligned sequences. + for mt_idx, pe_idx in align: + if mt_idx is None: + # Deleted word error (need to store for later) + if omissions == OmissionRule.LEFT or omissions == OmissionRule.NONE: + sent_deletion_indices.append(mt_position - 1) + else: + sent_deletion_indices.append(mt_position) + elif pe_idx is None: + # Insertion error + sent_tags.append(WMT22QETags.BAD.value) + mt_position += 1 + elif mt_tok[mt_idx] != pe_tok[pe_idx]: + # Substitution error + sent_tags.append(WMT22QETags.BAD.value) + mt_position += 1 + else: + # OK + sent_tags.append(WMT22QETags.OK.value) + mt_position += 1 + + # Insert deletion errors as gaps + word_and_gaps_tags = [] + if use_gaps: + # Add starting OK/BAD + if -1 in sent_deletion_indices: + word_and_gaps_tags.append(WMT22QETags.BAD.value) + else: + word_and_gaps_tags.append(WMT22QETags.OK.value) + # Add rest of OK/BADs + for index, tag in enumerate(sent_tags): + if index in sent_deletion_indices: + word_and_gaps_tags.extend([tag, WMT22QETags.BAD.value]) + else: + word_and_gaps_tags.extend([tag, WMT22QETags.OK.value]) + mt_tags.append(word_and_gaps_tags) + else: + if omissions == OmissionRule.NONE: + mt_tags.append(sent_tags) + elif omissions == OmissionRule.RIGHT: + for index, tag in enumerate(sent_tags): + if index in sent_deletion_indices: + word_and_gaps_tags.append(WMT22QETags.BAD.value) + else: + word_and_gaps_tags.append(tag) + if len(sent_tags) in sent_deletion_indices: + word_and_gaps_tags.append(WMT22QETags.BAD.value) + else: + word_and_gaps_tags.append(WMT22QETags.OK.value) + elif omissions == OmissionRule.LEFT: + if -1 in sent_deletion_indices: + word_and_gaps_tags.append(WMT22QETags.BAD.value) + else: + word_and_gaps_tags.append(WMT22QETags.OK.value) + for index, tag in enumerate(sent_tags): + if index in sent_deletion_indices: + word_and_gaps_tags.append(WMT22QETags.BAD.value) + else: + word_and_gaps_tags.append(tag) + mt_tags.append(word_and_gaps_tags) + + # Basic sanity checks + if use_gaps: + assert all(len(aa) * 2 + 1 == len(bb) for aa, bb in zip(mt_tokens, mt_tags)), "MT tag creation failed" + else: + if omissions == OmissionRule.NONE: # noqa: PLR5501 + assert all(len(aa) == len(bb) for aa, bb in zip(mt_tokens, mt_tags)), "MT tag creation failed" + else: + assert all(len(aa) + 1 == len(bb) for aa, bb in zip(mt_tokens, mt_tags)), "MT tag creation failed" + return mt_tags + + @staticmethod + def tags_to_source( + src_tokens: List[List[str]], + pe_tokens: List[List[str]], + mt_tokens: List[List[str]], + src_pe_alignments: List[List[TAlignment]], + mt_pe_alignments: List[List[TAlignment]], + fluency_rule: str = FluencyRule.NORMAL.value, + ) -> List[List[TTag]]: + """Propagate tags from MT to source.""" + # Reorganize source-target alignments as a dict + pe2source = [] + for sent in src_pe_alignments: + pe2source_sent = defaultdict(list) + for src_idx, pe_idx in sent: + pe2source_sent[pe_idx].append(src_idx) + pe2source.append(pe2source_sent) + + src_tags = [] + for ( + src_sent_tok, + mt_sent_tok, + pe_sent_tok, + sent_pe2src, + sent_mt_pe_aligns, + ) in tqdm( + zip(src_tokens, mt_tokens, pe_tokens, pe2source, mt_pe_alignments), + desc="Tagging source", + total=len(src_tokens), + ): + source_sentence_bad_indices = set() + mt_position = 0 + for mt_idx, pe_idx in sent_mt_pe_aligns: + if mt_idx is None or ( + mt_idx is not None and pe_idx is not None and mt_sent_tok[mt_idx] != pe_sent_tok[pe_idx] + ): + if fluency_rule == FluencyRule.NORMAL: + source_positions = sent_pe2src[pe_idx] + source_sentence_bad_indices |= set(source_positions) + elif fluency_rule == FluencyRule.IGNORE_SHF: + if pe_sent_tok[pe_idx] not in mt_sent_tok: + source_positions = sent_pe2src[pe_idx] + source_sentence_bad_indices |= set(source_positions) + elif fluency_rule == FluencyRule.MISSING: + if mt_idx is None: + source_positions = sent_pe2src[pe_idx] + source_sentence_bad_indices |= set(source_positions) + else: + raise Exception(f"Unknown fluency rule {fluency_rule}") + else: + mt_position += 1 + source_sentence_bad_tags = [WMT22QETags.OK.value] * len(src_sent_tok) + for index in list(source_sentence_bad_indices): + source_sentence_bad_tags[index] = WMT22QETags.BAD.value + src_tags.append(source_sentence_bad_tags) + + # Basic sanity checks + assert all(len(aa) == len(bb) for aa, bb in zip(src_tokens, src_tags)), "SRC tag creation failed" + return src_tags + + def generate_tags( + self, + srcs: List[str], + mts: List[str], + pes: List[str], + src_langs: Union[str, List[str]], + tgt_langs: Union[str, List[str]], + use_gaps: bool = False, + omissions: str = OmissionRule.RIGHT.value, + fluency_rule: str = FluencyRule.NORMAL.value, + ) -> Tuple[List[List[TTag]], List[List[TTag]]]: + src_tokens, src_langs = self.get_tokenized(srcs, src_langs) + mt_tokens, tgt_langs = self.get_tokenized(mts, tgt_langs) + pe_tokens, _ = self.get_tokenized(pes, tgt_langs) + + src_pe_alignments = self.align_source_pe(src_tokens, pe_tokens, tgt_langs) + mt_pe_alignments = self.align_mt_pe(mt_tokens, pe_tokens) + + mt_tags = self.tags_from_edits(mt_tokens, pe_tokens, mt_pe_alignments, use_gaps, omissions) + src_tags = self.tags_to_source( + src_tokens, + pe_tokens, + mt_tokens, + src_pe_alignments, + mt_pe_alignments, + fluency_rule, + ) + + clear_nlp_cache() + + return src_tags, mt_tags diff --git a/divemt/wmt22qe_utils.py b/divemt/qe_taggers/wmt22qe_utils.py similarity index 100% rename from divemt/wmt22qe_utils.py rename to divemt/qe_taggers/wmt22qe_utils.py diff --git a/tests/test_qe_taggers_name_tbd_tagger.py b/tests/test_qe_taggers_name_tbd_tagger.py index 0350f5f..0b13ab4 100644 --- a/tests/test_qe_taggers_name_tbd_tagger.py +++ b/tests/test_qe_taggers_name_tbd_tagger.py @@ -8,8 +8,8 @@ else: from enum import StrEnum -from divemt.qe_taggers import NameTBDGeneralTags as Tags -from divemt.qe_taggers import NameTBDTagger +from divemt.qe_taggers.name_tbd_tagger import NameTBDGeneralTags as Tags +from divemt.qe_taggers.name_tbd_tagger import NameTBDTagger tagger = NameTBDTagger() From c13798e4d4ce7051a6c37cd6c0b7a72c06579324 Mon Sep 17 00:00:00 2001 From: Konstantin Chernyshev Date: Tue, 9 May 2023 14:35:39 +0200 Subject: [PATCH 13/23] fix: remove debug print in cache --- divemt/cache_utils.py | 1 - 1 file changed, 1 deletion(-) diff --git a/divemt/cache_utils.py b/divemt/cache_utils.py index c9ed2b4..c1af5be 100644 --- a/divemt/cache_utils.py +++ b/divemt/cache_utils.py @@ -117,7 +117,6 @@ def wrapper(*args: Any, **kwargs: Any) -> Any: with open(cache_file, "rb") as f: return pickle.load(f) else: - print(len(args), len(kwargs.items())) result = function(*args, **kwargs) print(f"CREATE CACHE: {cache_file}") cache_file.parent.mkdir(parents=True, exist_ok=True) From 4233114d36ca3f5a07b2114a43ff926a844215a3 Mon Sep 17 00:00:00 2001 From: Konstantin Chernyshev Date: Thu, 11 May 2023 10:49:45 +0200 Subject: [PATCH 14/23] style: add cache for wmt22 and fix style for tests --- divemt/qe_taggers/name_tbd_tagger.py | 4 +-- divemt/qe_taggers/wmt22_tagger.py | 3 ++ tests/test_qe_taggers_name_tbd_tagger.py | 44 ++++++++++++++++++++---- 3 files changed, 43 insertions(+), 8 deletions(-) diff --git a/divemt/qe_taggers/name_tbd_tagger.py b/divemt/qe_taggers/name_tbd_tagger.py index 5e2b9fb..a6cce6f 100644 --- a/divemt/qe_taggers/name_tbd_tagger.py +++ b/divemt/qe_taggers/name_tbd_tagger.py @@ -95,7 +95,7 @@ def align_mt_pe( @staticmethod def _group_by_node( - alignments: List[Tuple[Optional[int], Optional[int]]], by_start_node: bool = True, sort: bool = False + alignments: List[TAlignment], by_start_node: bool = True, sort: bool = False ) -> Generator[Tuple[int, List[int], List[float]], None, None]: """Yield a node id and a list of connected nodes.""" _by_index = 0 if by_start_node else 1 @@ -109,7 +109,7 @@ def _group_by_node( @staticmethod def _detect_crossing_edges( - mt_tokens: List[str], pe_tokens: List[str], alignments: List[Tuple[Optional[int], Optional[int], float]] + mt_tokens: List[str], pe_tokens: List[str], alignments: List[TAlignment] ) -> List[bool]: """Detect crossing edges in the alignments. Return mask list of nodes that cross some other node.""" # TODO: optimize from n^2 to n as 2 pointers diff --git a/divemt/qe_taggers/wmt22_tagger.py b/divemt/qe_taggers/wmt22_tagger.py index 1455e5a..3735ec7 100644 --- a/divemt/qe_taggers/wmt22_tagger.py +++ b/divemt/qe_taggers/wmt22_tagger.py @@ -17,6 +17,7 @@ from ..parse_utils import clear_nlp_cache from .base import QETagger, TAlignment, TTag from .wmt22qe_utils import align_sentence_tercom, parse_tercom_xml_file +from ..cache_utils import CacheDecorator logger = logging.getLogger(__name__) @@ -63,6 +64,7 @@ def __init__( self.tercom_out = Path(tercom_out) if tercom_out is not None else self.tmp_dir / "tercom" self.tercom_path = tercom_path if tercom_path is not None else "scripts/tercom.7.25.jar" + @CacheDecorator() def align_source_pe( self, src_tokens: List[List[str]], @@ -78,6 +80,7 @@ def align_source_pe( ) ] + @CacheDecorator() def align_mt_pe( self, mt_tokens: List[List[str]], diff --git a/tests/test_qe_taggers_name_tbd_tagger.py b/tests/test_qe_taggers_name_tbd_tagger.py index 0b13ab4..c2149a1 100644 --- a/tests/test_qe_taggers_name_tbd_tagger.py +++ b/tests/test_qe_taggers_name_tbd_tagger.py @@ -48,7 +48,12 @@ class TestTagsFromEdits: @pytest.mark.parametrize( "mt_tokens, pe_tokens, mt_pe_alignments, true_mt_tags", [ - (["A", "B"], ["A", "B"], [(0, 0, 0.9), (1, 1, 0.9)], [{Tags.OK}, {Tags.OK}]), + ( + ["A", "B"], + ["A", "B"], + [(0, 0, 0.9), (1, 1, 0.9)], + [{Tags.OK}, {Tags.OK}], + ), ( ["A", "B", "C", "D"], ["A", "B", "C", "D"], @@ -79,7 +84,12 @@ def test_single_error_ok( [(0, 0, 0.9), (1, 1, 0.9), (2, 2, 0.9)], [{Tags.OK}, {Tags.BAD_SUBSTITUTION}, {Tags.BAD_SUBSTITUTION}], ), - (["A", "B"], ["Z", "X"], [(0, 0, 0.9), (1, 1, 0.9)], [{Tags.BAD_SUBSTITUTION}, {Tags.BAD_SUBSTITUTION}]), + ( + ["A", "B"], + ["Z", "X"], + [(0, 0, 0.9), (1, 1, 0.9)], + [{Tags.BAD_SUBSTITUTION}, {Tags.BAD_SUBSTITUTION}], + ), # For 1-n and n-1 cases see contraction and expansion tests ], ) @@ -119,8 +129,18 @@ def test_single_error_insertion( @pytest.mark.parametrize( "mt_tokens, pe_tokens, mt_pe_alignments, true_mt_tags", [ - (["A"], ["A", "X"], [(0, 0, 0.9), (None, 1, None)], [{Tags.OK, Tags.BAD_DELETION_RIGHT}]), - (["A"], ["X", "A"], [(None, 0, None), (0, 1, 0.9)], [{Tags.OK, Tags.BAD_DELETION_LEFT}]), + ( + ["A"], + ["A", "X"], + [(0, 0, 0.9), (None, 1, None)], + [{Tags.OK, Tags.BAD_DELETION_RIGHT}], + ), + ( + ["A"], + ["X", "A"], + [(None, 0, None), (0, 1, 0.9)], + [{Tags.OK, Tags.BAD_DELETION_LEFT}], + ), ( ["A", "B"], ["A", "X", "B"], @@ -361,7 +381,13 @@ class TestTagsToSource: "src_tokens, mt_tokens, mt_pe_alignments, mt_tags, true_src_tags", [ # ok cases - (["A", "B"], ["A", "B"], [(0, 0, 0.9), (1, 1, 0.9)], [{Tags.OK}, {Tags.OK}], [{Tags.OK}, {Tags.OK}]), + ( + ["A", "B"], + ["A", "B"], + [(0, 0, 0.9), (1, 1, 0.9)], + [{Tags.OK}, {Tags.OK}], + [{Tags.OK}, {Tags.OK}], + ), ( ["A", "B", "C", "D"], ["A", "B", "C", "D"], @@ -413,7 +439,13 @@ def test_one_to_one( @pytest.mark.parametrize( "src_tokens, mt_tokens, mt_pe_alignments, mt_tags, true_src_tags", [ - (["A"], ["A", "B"], [(0, 0, 0.9), (None, 1, None)], [{Tags.OK}, {Tags.BAD_SUBSTITUTION}], [{Tags.OK}]), + ( + ["A"], + ["A", "B"], + [(0, 0, 0.9), (None, 1, None)], + [{Tags.OK}, {Tags.BAD_SUBSTITUTION}], + [{Tags.OK}], + ), ( ["A", "B"], ["A", "B", "C"], From 2fdba177d59039d7ba82e85a08401824a7ca00ec Mon Sep 17 00:00:00 2001 From: Konstantin Chernyshev Date: Fri, 12 May 2023 13:32:24 +0200 Subject: [PATCH 15/23] feat: add deletions (None, j) to _fill_deleted_inserted_tokens --- divemt/qe_taggers/__init__.py | 4 +- divemt/qe_taggers/name_tbd_tagger.py | 56 +++++++++++---- tests/test_qe_taggers_name_tbd_tagger.py | 92 +++++++++++++++++++++--- 3 files changed, 125 insertions(+), 27 deletions(-) diff --git a/divemt/qe_taggers/__init__.py b/divemt/qe_taggers/__init__.py index b5ae9e7..11c5e27 100644 --- a/divemt/qe_taggers/__init__.py +++ b/divemt/qe_taggers/__init__.py @@ -1,11 +1,11 @@ -from .base import QETagger, TTag, TTag +from .base import QETagger, TTag, TAlignment from .name_tbd_tagger import NameTBDGeneralTags, NameTBDTagger from .wmt22_tagger import WMT22QETags, WMT22QETagger __all__ = [ "QETagger", "TTag", - "TTag", + "TAlignment", "NameTBDGeneralTags", "NameTBDTagger", "WMT22QETags", diff --git a/divemt/qe_taggers/name_tbd_tagger.py b/divemt/qe_taggers/name_tbd_tagger.py index a6cce6f..7f368b1 100644 --- a/divemt/qe_taggers/name_tbd_tagger.py +++ b/divemt/qe_taggers/name_tbd_tagger.py @@ -3,6 +3,8 @@ from itertools import groupby from typing import Generator, List, Optional, Set, Tuple, Union +import numpy as np + if sys.version_info < (3, 11): from strenum import StrEnum else: @@ -46,25 +48,51 @@ def __init__( else CustomSentenceAligner(model="bert", token_type="bpe", matching_methods="mai", return_similarity="avg") ) + @staticmethod def _fill_deleted_inserted_tokens( - self, len_from: int, len_to: int, alignments: List[TAlignment] + len_from: int, len_to: int, alignments: List[TAlignment] ) -> List[TAlignment]: - """As aligner provides only actual alignments, add required (None, i), (i, None) tokens""" + """ + As aligner provides only actual alignments, add required i, None), (None, j) tokens + * (i, None) just inserted in places to maintain order by i + * (None, j) inserted in estimated places + - if + """ new_alignments: List[TAlignment] = [] # Add (i, None) in correct place (ordered by i) - current_alignment_index = 0 + current_i_alignment_index = 0 for align in alignments: - # Add missing index pairs with None - while current_alignment_index < align[0]: - new_alignments.append((current_alignment_index, None)) - current_alignment_index += 1 + # Add missing index pairs before current one with (i, None) + while current_i_alignment_index < align[0]: + new_alignments.append((current_i_alignment_index, None, None)) + current_i_alignment_index += 1 # Add the current alignment pair new_alignments.append(align) - current_alignment_index += 1 - - raise NotImplementedError() + current_i_alignment_index += 1 + # add last (i, None) + while current_i_alignment_index < len_from: + new_alignments.append((current_i_alignment_index, None, None)) + current_i_alignment_index += 1 + + # Add (None, j) in correct places + missed_j_tokens = set(range(len_to)) - {j[1] for j in new_alignments} + for current_j_alignment_index in missed_j_tokens: + # select the closest (*, j) by j: obtain index in the list and j value + closest_value_index = min( + range(len(new_alignments)), + key=lambda i: abs(new_alignments[i][1] - current_j_alignment_index) if new_alignments[i][1] is not None else np.inf + ) + closest_value_j = new_alignments[closest_value_index][1] + # insert position of the (None, current_j_alignment_index) - before of after the closes value + if closest_value_j < current_j_alignment_index: + insert_index = closest_value_index + 1 + else: + insert_index = closest_value_index # - 1 + insert_index = max(0, min(insert_index, len(new_alignments))) + # insert it in right place + new_alignments.insert(insert_index, (None, current_j_alignment_index, None)) return new_alignments @@ -173,12 +201,12 @@ def tags_from_edits( """ # TODO: check. now - if embeddings are not provided, use Lev distance - mt_tags: List[List[Set[str]]] = [] + mt_tags: List[List[TTag]] = [] for mt_sent_tok, pe_sent_tok, mt_pe_sent_align in tqdm( zip(mt_tokens, pe_tokens, mt_pe_alignments), desc="Tagging MT", total=len(mt_tokens) ): - mt_sent_tags: List[Set[str]] = [set() for _ in range(len(mt_sent_tok))] + mt_sent_tags: List[TTag] = [set() for _ in range(len(mt_sent_tok))] # clear 1-n and n-1 nodes with low threshold # e.g. if 1-n or n-1 have same token or high similarity, remove low similarity as deletions/insertions @@ -310,12 +338,12 @@ def tags_to_source( - Copy tags from top match in MT and ignore other matches """ - src_tags: List[List[Set[str]]] = [] + src_tags: List[List[TTag]] = [] for src_sent_tok, _mt_sent_tok, mt_sent_tags, mt_pe_sent_align in tqdm( zip(src_tokens, mt_tokens, mt_tags, src_mt_alignments), desc="Transfer to source", total=len(src_tokens) ): - src_sent_tags: List[Set[str]] = [set() for _ in range(len(src_sent_tok))] + src_sent_tags: List[TTag] = [set() for _ in range(len(src_sent_tok))] # Solve all as 1-n matches for src_node_id, connected_mt_nodes_ids, connected_mt_similarity in NameTBDTagger._group_by_node( diff --git a/tests/test_qe_taggers_name_tbd_tagger.py b/tests/test_qe_taggers_name_tbd_tagger.py index c2149a1..d896095 100644 --- a/tests/test_qe_taggers_name_tbd_tagger.py +++ b/tests/test_qe_taggers_name_tbd_tagger.py @@ -8,6 +8,7 @@ else: from enum import StrEnum +from divemt.qe_taggers import TTag, TAlignment from divemt.qe_taggers.name_tbd_tagger import NameTBDGeneralTags as Tags from divemt.qe_taggers.name_tbd_tagger import NameTBDTagger @@ -36,13 +37,82 @@ class TestUtils: ], ) def test_detect_crossing_edges( - self, mt_len: int, mt_pe_alignments: List[Tuple[int, int]], true_mt_shifts_mask: List[bool] + self, mt_len: int, mt_pe_alignments: List[TAlignment], true_mt_shifts_mask: List[bool] ) -> None: mt_shifts_mask = tagger._detect_crossing_edges( [str(i) for i in range(mt_len)], [str(i) for i in range(mt_len)], mt_pe_alignments ) assert mt_shifts_mask == true_mt_shifts_mask + @pytest.mark.parametrize( + "mt_len, pe_len, mt_pe_alignments, true_mt_pe_alignments", + [ + # Nothing to add + ( + 3, + 3, + [(0, 0, 0.5), (1, 2, 0.5), (2, 1, 0.5)], + [(0, 0, 0.5), (1, 2, 0.5), (2, 1, 0.5)], + ), + ( + 3, + 3, + [(0, 2, 0.5), (1, 1, 0.5), (2, 0, 0.5)], + [(0, 2, 0.5), (1, 1, 0.5), (2, 0, 0.5)], + ), + # Add (i, None) - insertions + ( + 3, + 2, + [(0, 1, 0.5), (2, 0, 0.5)], + [(0, 1, 0.5), (1, None, None), (2, 0, 0.5)], + ), + ( + 3, + 1, + [(0, 0, 0.5)], + [(0, 0, 0.5), (1, None, None), (2, None, None)], + ), + # Add (None, i) - deletions in the right places + ( + 2, + 3, + [(0, 0, 0.5), (1, 2, 0.5)], + [(0, 0, 0.5), (None, 1, None), (1, 2, 0.5)], + ), + ( + 2, + 4, + [(0, 0, 0.5), (1, 3, 0.5)], + [(0, 0, 0.5), (None, 1, None), (None, 2, None), (1, 3, 0.5)], + ), + ( + 1, + 3, + [(0, 0, 0.5)], + [(0, 0, 0.5), (None, 1, None), (None, 2, None)], + ), + ( + 2, + 4, + [(0, 0, 0.5), (1, 3, 0.5)], + [(0, 0, 0.5), (None, 1, None), (None, 2, None), (1, 3, 0.5)], + ), + # mixed insert/delete - first add (1, None), then (None, j) + ( + 2, + 3, + [(0, 0, 0.5)], + [(0, 0, 0.5), (None, 1, None), (None, 2, None), (1, None, None)], + ), + ] + ) + def test_fill_deleted_inserted_tokens( + self, mt_len: int, pe_len: int, mt_pe_alignments: List[TAlignment], true_mt_pe_alignments: List[TAlignment] + ) -> None: + filled_mt_pe_alignments = tagger._fill_deleted_inserted_tokens(mt_len, pe_len, mt_pe_alignments) + assert filled_mt_pe_alignments == true_mt_pe_alignments + class TestTagsFromEdits: @pytest.mark.parametrize( @@ -67,7 +137,7 @@ def test_single_error_ok( self, mt_tokens: List[str], pe_tokens: List[str], - mt_pe_alignments: List[Tuple[int, int]], + mt_pe_alignments: List[TAlignment], true_mt_tags: List[Set[StrEnum]], ) -> None: predicted_tags = tagger.tags_from_edits([mt_tokens], [pe_tokens], [mt_pe_alignments])[0] @@ -97,7 +167,7 @@ def test_single_error_substitution( self, mt_tokens: List[str], pe_tokens: List[str], - mt_pe_alignments: List[Tuple[int, int]], + mt_pe_alignments: List[TAlignment], true_mt_tags: List[Set[StrEnum]], ) -> None: predicted_tags = tagger.tags_from_edits([mt_tokens], [pe_tokens], [mt_pe_alignments])[0] @@ -118,7 +188,7 @@ def test_single_error_insertion( self, mt_tokens: List[str], pe_tokens: List[str], - mt_pe_alignments: List[Tuple[int, int]], + mt_pe_alignments: List[TAlignment], true_mt_tags: List[Set[StrEnum]], ) -> None: predicted_tags = tagger.tags_from_edits([mt_tokens], [pe_tokens], [mt_pe_alignments])[0] @@ -181,7 +251,7 @@ def test_single_error_deletion( self, mt_tokens: List[str], pe_tokens: List[str], - mt_pe_alignments: List[Tuple[int, int]], + mt_pe_alignments: List[TAlignment], true_mt_tags: List[Set[StrEnum]], ) -> None: predicted_tags = tagger.tags_from_edits([mt_tokens], [pe_tokens], [mt_pe_alignments])[0] @@ -249,7 +319,7 @@ def test_single_error_contraction( self, mt_tokens: List[str], pe_tokens: List[str], - mt_pe_alignments: List[Tuple[int, int]], + mt_pe_alignments: List[TAlignment], true_mt_tags: List[Set[str]], ) -> None: predicted_tags = tagger.tags_from_edits([mt_tokens], [pe_tokens], [mt_pe_alignments])[0] @@ -314,7 +384,7 @@ def test_single_error_expansion( self, mt_tokens: List[str], pe_tokens: List[str], - mt_pe_alignments: List[Tuple[int, int]], + mt_pe_alignments: List[TAlignment], true_mt_tags: List[Set[str]], ) -> None: predicted_tags = tagger.tags_from_edits([mt_tokens], [pe_tokens], [mt_pe_alignments])[0] @@ -367,7 +437,7 @@ def test_single_error_shifted( self, mt_tokens: List[str], pe_tokens: List[str], - mt_pe_alignments: List[Tuple[int, int]], + mt_pe_alignments: List[TAlignment], true_mt_tags: List[Set[StrEnum]], ) -> None: predicted_tags = tagger.tags_from_edits([mt_tokens], [pe_tokens], [mt_pe_alignments])[0] @@ -425,7 +495,7 @@ def test_one_to_one( self, src_tokens: List[str], mt_tokens: List[str], - mt_pe_alignments: List[Tuple[int, int]], + mt_pe_alignments: List[TAlignment], mt_tags: List[Set[StrEnum]], true_src_tags: List[Set[StrEnum]], ) -> None: @@ -459,7 +529,7 @@ def test_src_deleted( self, src_tokens: List[str], mt_tokens: List[str], - mt_pe_alignments: List[Tuple[int, int]], + mt_pe_alignments: List[TAlignment], mt_tags: List[Set[StrEnum]], true_src_tags: List[Set[StrEnum]], ) -> None: @@ -493,7 +563,7 @@ def test_mt_deleted( self, src_tokens: List[str], mt_tokens: List[str], - mt_pe_alignments: List[Tuple[int, int]], + mt_pe_alignments: List[TAlignment], mt_tags: List[Set[StrEnum]], true_src_tags: List[Set[StrEnum]], ) -> None: From 270491a8bfff8dd826a458d46c91be638e20c863 Mon Sep 17 00:00:00 2001 From: Konstantin Chernyshev Date: Fri, 12 May 2023 13:40:25 +0200 Subject: [PATCH 16/23] fix: make _fill_deleted_inserted_tokens use lists as input --- divemt/qe_taggers/name_tbd_tagger.py | 75 +++++++++++++----------- tests/test_qe_taggers_name_tbd_tagger.py | 5 +- 2 files changed, 43 insertions(+), 37 deletions(-) diff --git a/divemt/qe_taggers/name_tbd_tagger.py b/divemt/qe_taggers/name_tbd_tagger.py index 7f368b1..b6983f2 100644 --- a/divemt/qe_taggers/name_tbd_tagger.py +++ b/divemt/qe_taggers/name_tbd_tagger.py @@ -50,51 +50,56 @@ def __init__( @staticmethod def _fill_deleted_inserted_tokens( - len_from: int, len_to: int, alignments: List[TAlignment] - ) -> List[TAlignment]: + len_from_list: List[int], len_to_list: List[int], alignments_list: List[List[TAlignment]] + ) -> List[List[TAlignment]]: """ As aligner provides only actual alignments, add required i, None), (None, j) tokens * (i, None) just inserted in places to maintain order by i * (None, j) inserted in estimated places - if """ - new_alignments: List[TAlignment] = [] + full_new_alignments: List[List[TAlignment]] = [] + + for len_from, len_to, alignments in zip(len_from_list, len_to_list, alignments_list): + new_alignments: List[TAlignment] = [] - # Add (i, None) in correct place (ordered by i) - current_i_alignment_index = 0 - for align in alignments: - # Add missing index pairs before current one with (i, None) - while current_i_alignment_index < align[0]: + # Add (i, None) in correct place (ordered by i) + current_i_alignment_index = 0 + for align in alignments: + # Add missing index pairs before current one with (i, None) + while current_i_alignment_index < align[0]: + new_alignments.append((current_i_alignment_index, None, None)) + current_i_alignment_index += 1 + + # Add the current alignment pair + new_alignments.append(align) + current_i_alignment_index += 1 + # add last (i, None) + while current_i_alignment_index < len_from: new_alignments.append((current_i_alignment_index, None, None)) current_i_alignment_index += 1 - # Add the current alignment pair - new_alignments.append(align) - current_i_alignment_index += 1 - # add last (i, None) - while current_i_alignment_index < len_from: - new_alignments.append((current_i_alignment_index, None, None)) - current_i_alignment_index += 1 - - # Add (None, j) in correct places - missed_j_tokens = set(range(len_to)) - {j[1] for j in new_alignments} - for current_j_alignment_index in missed_j_tokens: - # select the closest (*, j) by j: obtain index in the list and j value - closest_value_index = min( - range(len(new_alignments)), - key=lambda i: abs(new_alignments[i][1] - current_j_alignment_index) if new_alignments[i][1] is not None else np.inf - ) - closest_value_j = new_alignments[closest_value_index][1] - # insert position of the (None, current_j_alignment_index) - before of after the closes value - if closest_value_j < current_j_alignment_index: - insert_index = closest_value_index + 1 - else: - insert_index = closest_value_index # - 1 - insert_index = max(0, min(insert_index, len(new_alignments))) - # insert it in right place - new_alignments.insert(insert_index, (None, current_j_alignment_index, None)) - - return new_alignments + # Add (None, j) in correct places + missed_j_tokens = set(range(len_to)) - {j[1] for j in new_alignments} + for current_j_alignment_index in missed_j_tokens: + # select the closest (*, j) by j: obtain index in the list and j value + closest_value_index = min( + range(len(new_alignments)), + key=lambda i: abs(new_alignments[i][1] - current_j_alignment_index) if new_alignments[i][1] is not None else np.inf + ) + closest_value_j = new_alignments[closest_value_index][1] + # insert position of the (None, current_j_alignment_index) - before of after the closes value + if closest_value_j < current_j_alignment_index: + insert_index = closest_value_index + 1 + else: + insert_index = closest_value_index # - 1 + insert_index = max(0, min(insert_index, len(new_alignments))) + # insert it in right place + new_alignments.insert(insert_index, (None, current_j_alignment_index, None)) + + full_new_alignments.append(new_alignments) + + return full_new_alignments @CacheDecorator() def align_source_mt( diff --git a/tests/test_qe_taggers_name_tbd_tagger.py b/tests/test_qe_taggers_name_tbd_tagger.py index d896095..e2cef1d 100644 --- a/tests/test_qe_taggers_name_tbd_tagger.py +++ b/tests/test_qe_taggers_name_tbd_tagger.py @@ -110,8 +110,9 @@ def test_detect_crossing_edges( def test_fill_deleted_inserted_tokens( self, mt_len: int, pe_len: int, mt_pe_alignments: List[TAlignment], true_mt_pe_alignments: List[TAlignment] ) -> None: - filled_mt_pe_alignments = tagger._fill_deleted_inserted_tokens(mt_len, pe_len, mt_pe_alignments) - assert filled_mt_pe_alignments == true_mt_pe_alignments + filled_mt_pe_alignments = tagger._fill_deleted_inserted_tokens([mt_len], [pe_len], [mt_pe_alignments]) + for pred_alignments, true_alignments in zip(filled_mt_pe_alignments, [true_mt_pe_alignments]): + assert pred_alignments == true_alignments class TestTagsFromEdits: From 1734e313894f73b50f7e8750ef0e0d6c9fd8858e Mon Sep 17 00:00:00 2001 From: Konstantin Chernyshev Date: Fri, 12 May 2023 16:11:48 +0200 Subject: [PATCH 17/23] fix: _fill_deleted_inserted_tokens insertions error --- divemt/qe_taggers/name_tbd_tagger.py | 27 +++++++++++++++++------- tests/test_qe_taggers_name_tbd_tagger.py | 18 ++++++++++++++++ 2 files changed, 37 insertions(+), 8 deletions(-) diff --git a/divemt/qe_taggers/name_tbd_tagger.py b/divemt/qe_taggers/name_tbd_tagger.py index b6983f2..25de0df 100644 --- a/divemt/qe_taggers/name_tbd_tagger.py +++ b/divemt/qe_taggers/name_tbd_tagger.py @@ -73,7 +73,8 @@ def _fill_deleted_inserted_tokens( # Add the current alignment pair new_alignments.append(align) - current_i_alignment_index += 1 + if align[0] == current_i_alignment_index: + current_i_alignment_index += 1 # add last (i, None) while current_i_alignment_index < len_from: new_alignments.append((current_i_alignment_index, None, None)) @@ -318,6 +319,9 @@ def tags_from_edits( assert all( len(mt_sent_tokens) == len(mt_sent_tags) for mt_sent_tokens, mt_sent_tags in zip(mt_tokens, mt_tags) ), "MT tags creation failed, number of tokens and tags do not match" + assert all( + len(tags) > 0 for mt_sent_tags in mt_tags for tags in mt_sent_tags + ), "At least 1 tag in the set should be present for each token" return mt_tags @staticmethod @@ -350,13 +354,18 @@ def tags_to_source( ): src_sent_tags: List[TTag] = [set() for _ in range(len(src_sent_tok))] + # Filter all (i, None), (None, j) + cleared_mt_pe_sent_align = [ + alignment + for alignment in mt_pe_sent_align + if alignment[0] is not None and alignment[1] is not None + ] + # Solve all as 1-n matches for src_node_id, connected_mt_nodes_ids, connected_mt_similarity in NameTBDTagger._group_by_node( - mt_pe_sent_align, by_start_node=True, sort=True + cleared_mt_pe_sent_align, by_start_node=True, sort=True ): - if src_node_id is None: - continue - elif len(connected_mt_nodes_ids) == 0: + if len(connected_mt_nodes_ids) == 0: continue elif len(connected_mt_nodes_ids) > 1: # n-1 match, find best match @@ -370,9 +379,6 @@ def tags_to_source( else: # copy tags from best match src_sent_tags[src_node_id].update(mt_sent_tags[best_mt_node_id]) - elif connected_mt_nodes_ids[0] is None: - # nothing to copy from MT - continue else: # 1-1 match, copy tags src_sent_tags[src_node_id].update(mt_sent_tags[connected_mt_nodes_ids[0]]) @@ -400,6 +406,11 @@ def generate_tags( src_mt_alignments = self.align_source_mt(src_tokens, mt_tokens, src_langs, tgt_langs) mt_pe_alignments = self.align_mt_pe(mt_tokens, pe_tokens, tgt_langs) + mt_pe_alignments = self._fill_deleted_inserted_tokens( + [len(i) for i in mt_tokens], + [len(i) for i in pe_tokens], + mt_pe_alignments, + ) mt_tags = self.tags_from_edits(mt_tokens, pe_tokens, mt_pe_alignments) src_tags = self.tags_to_source(src_tokens, pe_tokens, src_mt_alignments, mt_tags) diff --git a/tests/test_qe_taggers_name_tbd_tagger.py b/tests/test_qe_taggers_name_tbd_tagger.py index e2cef1d..043a824 100644 --- a/tests/test_qe_taggers_name_tbd_tagger.py +++ b/tests/test_qe_taggers_name_tbd_tagger.py @@ -73,6 +73,18 @@ def test_detect_crossing_edges( [(0, 0, 0.5)], [(0, 0, 0.5), (1, None, None), (2, None, None)], ), + ( + 6, + 3, + [(0, 0, 0.5), (2, 0, 0.5), (3, 1, 0.5), (5, 2, 0.5), (6, 0, 0.5)], + [(0, 0, 0.5), (1, None, None), (2, 0, 0.5), (3, 1, 0.5), (4, None, None), (5, 2, 0.5), (6, 0, 0.5)], + ), + ( + 3, + 3, + [(0, 0, 0.5), (0, 1, 0.5), (0, 2, 0.5), (2, 0, 0.5)], + [(0, 0, 0.5), (0, 1, 0.5), (0, 2, 0.5), (1, None, None), (2, 0, 0.5)], + ), # Add (None, i) - deletions in the right places ( 2, @@ -105,6 +117,12 @@ def test_detect_crossing_edges( [(0, 0, 0.5)], [(0, 0, 0.5), (None, 1, None), (None, 2, None), (1, None, None)], ), + ( + 11, + 11, + [(0, 0, 0.5), (1, 1, 0.5), (2, 2, 0.5), (4, 4, 0.5), (5, 5, 0.5), (5, 6, 0.5), (6, 7, 0.5), (7, 8, 0.5), (8, 9, 0.5), (10, 10, 0.5)], + [(0, 0, 0.5), (1, 1, 0.5), (2, 2, 0.5), (None, 3, None), (3, None, None), (4, 4, 0.5), (5, 5, 0.5), (5, 6, 0.5), (6, 7, 0.5), (7, 8, 0.5), (8, 9, 0.5), (9, None, None), (10, 10, 0.5)], + ), ] ) def test_fill_deleted_inserted_tokens( From 5aedb48641cbb8deb67712ffd95a23325a8d00a1 Mon Sep 17 00:00:00 2001 From: Konstantin Chernyshev Date: Fri, 12 May 2023 16:17:13 +0200 Subject: [PATCH 18/23] style: apply black --- divemt/qe_taggers/__init__.py | 4 ++-- divemt/qe_taggers/name_tbd_tagger.py | 16 ++++++++-------- divemt/qe_taggers/wmt22_tagger.py | 2 +- tests/test_qe_taggers_name_tbd_tagger.py | 24 +++++++++++++++++------- 4 files changed, 28 insertions(+), 18 deletions(-) diff --git a/divemt/qe_taggers/__init__.py b/divemt/qe_taggers/__init__.py index 11c5e27..887c76b 100644 --- a/divemt/qe_taggers/__init__.py +++ b/divemt/qe_taggers/__init__.py @@ -1,6 +1,6 @@ -from .base import QETagger, TTag, TAlignment +from .base import QETagger, TAlignment, TTag from .name_tbd_tagger import NameTBDGeneralTags, NameTBDTagger -from .wmt22_tagger import WMT22QETags, WMT22QETagger +from .wmt22_tagger import WMT22QETagger, WMT22QETags __all__ = [ "QETagger", diff --git a/divemt/qe_taggers/name_tbd_tagger.py b/divemt/qe_taggers/name_tbd_tagger.py index 25de0df..64e64c6 100644 --- a/divemt/qe_taggers/name_tbd_tagger.py +++ b/divemt/qe_taggers/name_tbd_tagger.py @@ -12,9 +12,9 @@ from tqdm import tqdm from ..cache_utils import CacheDecorator -from .custom_simalign import SentenceAligner as CustomSentenceAligner from ..parse_utils import clear_nlp_cache from .base import QETagger, TAlignment, TTag +from .custom_simalign import SentenceAligner as CustomSentenceAligner logger = logging.getLogger(__name__) @@ -86,7 +86,11 @@ def _fill_deleted_inserted_tokens( # select the closest (*, j) by j: obtain index in the list and j value closest_value_index = min( range(len(new_alignments)), - key=lambda i: abs(new_alignments[i][1] - current_j_alignment_index) if new_alignments[i][1] is not None else np.inf + key=lambda i: ( + abs(new_alignments[i][1] - current_j_alignment_index) + if new_alignments[i][1] is not None + else np.inf + ), ) closest_value_j = new_alignments[closest_value_index][1] # insert position of the (None, current_j_alignment_index) - before of after the closes value @@ -142,9 +146,7 @@ def _group_by_node( ], [similarity for _, _, similarity in connected_alignments] @staticmethod - def _detect_crossing_edges( - mt_tokens: List[str], pe_tokens: List[str], alignments: List[TAlignment] - ) -> List[bool]: + def _detect_crossing_edges(mt_tokens: List[str], pe_tokens: List[str], alignments: List[TAlignment]) -> List[bool]: """Detect crossing edges in the alignments. Return mask list of nodes that cross some other node.""" # TODO: optimize from n^2 to n as 2 pointers shifted_mt_mask = [False] * len(mt_tokens) @@ -356,9 +358,7 @@ def tags_to_source( # Filter all (i, None), (None, j) cleared_mt_pe_sent_align = [ - alignment - for alignment in mt_pe_sent_align - if alignment[0] is not None and alignment[1] is not None + alignment for alignment in mt_pe_sent_align if alignment[0] is not None and alignment[1] is not None ] # Solve all as 1-n matches diff --git a/divemt/qe_taggers/wmt22_tagger.py b/divemt/qe_taggers/wmt22_tagger.py index 3735ec7..15b36d9 100644 --- a/divemt/qe_taggers/wmt22_tagger.py +++ b/divemt/qe_taggers/wmt22_tagger.py @@ -14,10 +14,10 @@ from simalign import SentenceAligner from tqdm import tqdm +from ..cache_utils import CacheDecorator from ..parse_utils import clear_nlp_cache from .base import QETagger, TAlignment, TTag from .wmt22qe_utils import align_sentence_tercom, parse_tercom_xml_file -from ..cache_utils import CacheDecorator logger = logging.getLogger(__name__) diff --git a/tests/test_qe_taggers_name_tbd_tagger.py b/tests/test_qe_taggers_name_tbd_tagger.py index 043a824..cefaa65 100644 --- a/tests/test_qe_taggers_name_tbd_tagger.py +++ b/tests/test_qe_taggers_name_tbd_tagger.py @@ -1,5 +1,5 @@ import sys -from typing import List, Set, Tuple +from typing import List, Set import pytest @@ -8,7 +8,7 @@ else: from enum import StrEnum -from divemt.qe_taggers import TTag, TAlignment +from divemt.qe_taggers import TAlignment from divemt.qe_taggers.name_tbd_tagger import NameTBDGeneralTags as Tags from divemt.qe_taggers.name_tbd_tagger import NameTBDTagger @@ -118,12 +118,22 @@ def test_detect_crossing_edges( [(0, 0, 0.5), (None, 1, None), (None, 2, None), (1, None, None)], ), ( - 11, - 11, - [(0, 0, 0.5), (1, 1, 0.5), (2, 2, 0.5), (4, 4, 0.5), (5, 5, 0.5), (5, 6, 0.5), (6, 7, 0.5), (7, 8, 0.5), (8, 9, 0.5), (10, 10, 0.5)], - [(0, 0, 0.5), (1, 1, 0.5), (2, 2, 0.5), (None, 3, None), (3, None, None), (4, 4, 0.5), (5, 5, 0.5), (5, 6, 0.5), (6, 7, 0.5), (7, 8, 0.5), (8, 9, 0.5), (9, None, None), (10, 10, 0.5)], + 7, + 7, + [(0, 0, 0.5), (2, 2, 0.5), (3, 3, 0.5), (3, 4, 0.5), (4, 5, 0.5), (6, 6, 0.5)], + [ + (0, 0, 0.5), + (None, 1, None), + (1, None, None), + (2, 2, 0.5), + (3, 3, 0.5), + (3, 4, 0.5), + (4, 5, 0.5), + (5, None, None), + (6, 6, 0.5), + ], ), - ] + ], ) def test_fill_deleted_inserted_tokens( self, mt_len: int, pe_len: int, mt_pe_alignments: List[TAlignment], true_mt_pe_alignments: List[TAlignment] From e577f92fc3945fa81a8e3d4865c1b6615ba6a753 Mon Sep 17 00:00:00 2001 From: Konstantin Chernyshev Date: Sun, 27 Aug 2023 17:31:18 +0200 Subject: [PATCH 19/23] feat: optimize simalign to load models faster (much faster) --- divemt/qe_taggers/custom_simalign.py | 103 +++++++++++++++++++-------- 1 file changed, 73 insertions(+), 30 deletions(-) diff --git a/divemt/qe_taggers/custom_simalign.py b/divemt/qe_taggers/custom_simalign.py index 579c402..67ebadd 100644 --- a/divemt/qe_taggers/custom_simalign.py +++ b/divemt/qe_taggers/custom_simalign.py @@ -31,43 +31,78 @@ XLMRobertaModel, XLMRobertaTokenizer, XLMTokenizer, + PreTrainedTokenizer, + PreTrainedModel, ) +_LOADED_MODELS: Dict[str, Tuple[PreTrainedModel, PreTrainedTokenizer]] = {} + + class EmbeddingLoader: - def __init__(self, model: str = "bert-base-multilingual-cased", device=torch.device("cpu"), layer: int = 8): - TR_Models = { - "bert-base-uncased": (BertModel, BertTokenizer), - "bert-base-multilingual-cased": (BertModel, BertTokenizer), - "bert-base-multilingual-uncased": (BertModel, BertTokenizer), - "xlm-mlm-100-1280": (XLMModel, XLMTokenizer), - "roberta-base": (RobertaModel, RobertaTokenizer), - "xlm-roberta-base": (XLMRobertaModel, XLMRobertaTokenizer), - "xlm-roberta-large": (XLMRobertaModel, XLMRobertaTokenizer), - } + TR_MODELS = { + "bert-base-uncased": (BertModel, BertTokenizer), + "bert-base-multilingual-cased": (BertModel, BertTokenizer), + "bert-base-multilingual-uncased": (BertModel, BertTokenizer), + "xlm-mlm-100-1280": (XLMModel, XLMTokenizer), + "roberta-base": (RobertaModel, RobertaTokenizer), + "xlm-roberta-base": (XLMRobertaModel, XLMRobertaTokenizer), + "xlm-roberta-large": (XLMRobertaModel, XLMRobertaTokenizer), + } + def __init__( + self, + model: str = "bert-base-multilingual-cased", + device="cpu", + layer: int = 8, + lazy_loading: bool = True, + ): self.model = model self.device = device self.layer = layer - self.emb_model = None - self.tokenizer = None - - if model in TR_Models: - model_class, tokenizer_class = TR_Models[model] - self.emb_model = model_class.from_pretrained(model, output_hidden_states=True) - self.emb_model.eval() - self.emb_model.to(self.device) - self.tokenizer = tokenizer_class.from_pretrained(model) + self.lazy_loading = lazy_loading + self._emb_model = None + self._tokenizer = None + + if not self.lazy_loading: + self._load_model() + + @property + def tokenizer(self) -> PreTrainedTokenizer: + if self.lazy_loading and self._tokenizer is None: + self._load_model() + return self._tokenizer + + @property + def emb_model(self) -> PreTrainedModel: + if self.lazy_loading and self._emb_model is None: + self._load_model() + return self._emb_model + + def _load_model(self) -> None: + if self.model in _LOADED_MODELS: + self._emb_model, self._tokenizer = _LOADED_MODELS[self.model] else: - # try to load model with auto-classes - config = AutoConfig.from_pretrained(model, output_hidden_states=True) - self.emb_model = AutoModel.from_pretrained(model, config=config) - self.emb_model.eval() - self.emb_model.to(self.device) - self.tokenizer = AutoTokenizer.from_pretrained(model) + if self.model in self.TR_MODELS: + model_class, tokenizer_class = self.TR_MODELS[self.model] + self._emb_model = model_class.from_pretrained(self.model, output_hidden_states=True) + self._tokenizer = tokenizer_class.from_pretrained(self.model) + else: + # try to load model with auto-classes + config = AutoConfig.from_pretrained(self.model, output_hidden_states=True) + self._emb_model = AutoModel.from_pretrained(self.model, config=config) + self._tokenizer = AutoTokenizer.from_pretrained(self.model) + _LOADED_MODELS[self.model] = (self._emb_model, self._tokenizer) + + self._emb_model.eval() + # self._emb_model.half() + self._emb_model.to(self.device) def get_embed_list(self, sent_batch: List[List[str]]) -> torch.Tensor: - if self.emb_model is not None: + if self.lazy_loading and self._emb_model is None: + self._load_model() + + if self._emb_model is not None: with torch.no_grad(): if not isinstance(sent_batch[0], str): inputs = self.tokenizer( @@ -77,7 +112,10 @@ def get_embed_list(self, sent_batch: List[List[str]]) -> torch.Tensor: inputs = self.tokenizer( sent_batch, is_split_into_words=False, padding=True, truncation=True, return_tensors="pt" ) - hidden = self.emb_model(**inputs.to(self.device))["hidden_states"] + + # with torch.autocast(device_type=self.device, dtype=torch.bfloat16 if self.device == 'cpu' else torch.float16): + # hidden = self.emb_model(**inputs.to(self.device))["hidden_states"] + hidden = self._emb_model(**inputs.to(self.device))["hidden_states"] if self.layer >= len(hidden): raise ValueError( f"Specified to take embeddings from layer {self.layer}, but model has only" @@ -101,8 +139,12 @@ def __init__( ] = None, # new: ["max", "avg"] type of average similarity for words from tokens device: str = "cpu", layer: int = 8, + lazy_loading: bool = True, ): - model_names = {"bert": "bert-base-multilingual-cased", "xlmr": "xlm-roberta-base"} + model_names = { + "bert": "bert-base-multilingual-cased", + "xlmr": "xlm-roberta-base" + } all_matching_methods = {"a": "inter", "m": "mwmf", "i": "itermax", "f": "fwd", "r": "rev"} self.model = model @@ -112,9 +154,10 @@ def __init__( self.distortion = distortion self.matching_methods = [all_matching_methods[m] for m in matching_methods] self.return_similarity = return_similarity - self.device = torch.device(device) + self.device = device + self.lazy_loading = lazy_loading - self.embed_loader = EmbeddingLoader(model=self.model, device=self.device, layer=layer) + self.embed_loader = EmbeddingLoader(model=self.model, device=self.device, layer=layer, lazy_loading=lazy_loading) @staticmethod def get_max_weight_match(sim: np.ndarray) -> np.ndarray: From 01092bb3f3dbcce4344f921d2bccdbfb608b889b Mon Sep 17 00:00:00 2001 From: Konstantin Chernyshev Date: Sun, 27 Aug 2023 17:32:07 +0200 Subject: [PATCH 20/23] feat: save alignments --- divemt/parse_utils.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/divemt/parse_utils.py b/divemt/parse_utils.py index 8d15935..a30c22f 100644 --- a/divemt/parse_utils.py +++ b/divemt/parse_utils.py @@ -351,7 +351,7 @@ def texts2qe( ) -> pd.DataFrame: """Add quality tags to a dataframe.""" pe_texts = data.copy()[data.mt_text.notnull()] - src_tags, mt_tags = tagger.generate_tags( + src_tags, mt_tags, src_mt_alignments, mt_pe_alignments = tagger.generate_tags( pe_texts["src_text"].tolist(), pe_texts["mt_text"].tolist(), pe_texts["tgt_text"].tolist(), @@ -361,6 +361,10 @@ def texts2qe( pe_texts[f"src_{tagger.ID}"] = src_tags pe_texts[f"mt_{tagger.ID}"] = mt_tags pe_texts = pe_texts[["unit_id", f"src_{tagger.ID}", f"mt_{tagger.ID}"]] + if src_mt_alignments: + pe_texts[f"src_mt_{tagger.ID}_alignments"] = src_mt_alignments + if mt_pe_alignments: + pe_texts[f"mt_pe_{tagger.ID}_alignments"] = mt_pe_alignments data = data.join(pe_texts.set_index("unit_id"), on="unit_id") return data @@ -409,7 +413,7 @@ def parse_from_folder( if add_annotations_information: texts_df = texts2annotations(texts_df) # TODO: make cache optional if add_wmt22_quality_tags: - tagger = WMT22QETagger() + tagger = WMT22QETagger() # TODO: make cache optional texts_df = texts2qe(texts_df, tagger) if add_name_tbd_quality_tags: tagger = NameTBDTagger() # TODO: make cache optional From b53d28c601ed196b5ea4289df14ad0abe5484496 Mon Sep 17 00:00:00 2001 From: Konstantin Chernyshev Date: Sun, 27 Aug 2023 17:33:06 +0200 Subject: [PATCH 21/23] fix: add some typings, fix cache for ald tags --- divemt/cache_utils.py | 8 ++++++-- divemt/qe_taggers/base.py | 2 +- divemt/qe_taggers/name_tbd_tagger.py | 20 +++++++++++--------- divemt/qe_taggers/wmt22_tagger.py | 10 +++++----- pyproject.toml | 2 ++ 5 files changed, 25 insertions(+), 17 deletions(-) diff --git a/divemt/cache_utils.py b/divemt/cache_utils.py index c1af5be..08571aa 100644 --- a/divemt/cache_utils.py +++ b/divemt/cache_utils.py @@ -95,8 +95,9 @@ def calc_args_hash(*args: Any, **kwargs: any) -> bytes: class CacheDecorator: - def __init__(self, cache_dir: Optional[Path] = None, version: int = 0): + def __init__(self, cache_dir: Optional[Path] = None, version: int = 0, name: Optional[str] = None): self.version = version + self.name = name or '' self.cache_dir = cache_dir or Path(".cache") @staticmethod @@ -104,11 +105,14 @@ def _is_bound_method(function: Callable, arg: Any): return inspect.ismethod(function) or (hasattr(arg, "__class__") and function.__name__ in dir(arg.__class__)) def __call__(self, function: Callable) -> Any: + cached_function_name = function.__qualname__.replace(".", "_") + if self.name: + cached_function_name = cached_function_name + "_" + self.name @functools.wraps(function) def wrapper(*args: Any, **kwargs: Any) -> Any: cache_key_args = args[1:] if self._is_bound_method(function, args[0]) else args hash_val = calc_args_hash(*cache_key_args, **kwargs) - cache_file = self.cache_dir / f"{function.__name__}_v{self.version}_{hash_val.hex()}.pkl" + cache_file = self.cache_dir / f"{cached_function_name}_v{self.version}_{hash_val.hex()}.pkl" # TODO: add logging, not printing diff --git a/divemt/qe_taggers/base.py b/divemt/qe_taggers/base.py index cd4b095..bb631a0 100644 --- a/divemt/qe_taggers/base.py +++ b/divemt/qe_taggers/base.py @@ -80,7 +80,7 @@ def generate_tags( pes: List[str], src_langs: Union[str, List[str]], tgt_langs: Union[str, List[str]], - ) -> Tuple[List[TTag], List[TTag]]: + ) -> Tuple[List[TTag], List[TTag], List[TAlignment], List[TAlignment]]: """Generate word-level quality estimation tags from source-mt-pe triplets. Args: diff --git a/divemt/qe_taggers/name_tbd_tagger.py b/divemt/qe_taggers/name_tbd_tagger.py index 64e64c6..3c4cece 100644 --- a/divemt/qe_taggers/name_tbd_tagger.py +++ b/divemt/qe_taggers/name_tbd_tagger.py @@ -42,10 +42,12 @@ def __init__( self, aligner: Optional[CustomSentenceAligner] = None, ): + # TODO: check with xlmr amth other trained with semanting sim + # version 0 for bert and 1 for xlmr self.aligner = ( aligner if aligner - else CustomSentenceAligner(model="bert", token_type="bpe", matching_methods="mai", return_similarity="avg") + else CustomSentenceAligner(model="xlmr", token_type="bpe", matching_methods="mai", return_similarity="avg") ) @staticmethod @@ -106,7 +108,7 @@ def _fill_deleted_inserted_tokens( return full_new_alignments - @CacheDecorator() + @CacheDecorator(version=0, name="xlmr") def align_source_mt( self, src_tokens: List[List[str]], @@ -119,7 +121,7 @@ def align_source_mt( for src_tok, mt_tok in tqdm(zip(src_tokens, mt_tokens), total=len(src_tokens), desc="Aligning src-mt") ] - @CacheDecorator() + @CacheDecorator(version=0, name="xlmr") def align_mt_pe( self, mt_tokens: List[List[str]], @@ -187,11 +189,11 @@ def tags_from_edits( The following situations are considered: 1:1 match: OK if same, SUB if different 1:n match: - - Obtain similarity between 1 and n (lexical, LaBSE if not found) + - Obtain similarity between 1 and n (align scores, TODO: lexical if not found) - If all matches are threshold, tag as CON (contraction) - Else, tackle the highest match as 1:1 (OK/SUB) and the rest as None:1 (deletions) n:1 match: - - Obtain similarity between n and 1 (lexical, LaBSE if not found) + - Obtain similarity between n and 1 (align scores, TODO: lexical if not found) - If all matches are threshold, tag as EXP (expansion) - Else, tackle the highest match as 1:1 (OK/SUB) and the rest as 1:None (insertions) n:m match: @@ -339,12 +341,12 @@ def tags_to_source( The following cases are considered: 1:1 match: copy tags from MT 1:n match: - - Find highest match for 1 in n (lexical, LaBSE if not found) + - Find highest match for 1 in n (align scores, TODO: lexical if not found) - If all matches are threshold, TBD - Else, copy tags from top match in MT and ignore other matches n:1 match: copy tags from 1 to all n n:m match: - - For each 1 in n, find highest match for 1 in m (lexical, LaBSE if not found) + - For each 1 in n, find highest match for 1 in m (align scores, TODO: lexical if not found) - If all matches are threshold, ignore and continue - Copy tags from top match in MT and ignore other matches """ @@ -399,7 +401,7 @@ def generate_tags( pes: List[str], src_langs: Union[str, List[Set[str]]], tgt_langs: Union[str, List[Set[str]]], - ) -> Tuple[List[TTag], List[TTag]]: + ) -> Tuple[List[TTag], List[TTag], List[TAlignment], List[TAlignment]]: src_tokens, src_langs = self.get_tokenized(srcs, src_langs) mt_tokens, tgt_langs = self.get_tokenized(mts, tgt_langs) pe_tokens, _ = self.get_tokenized(pes, tgt_langs) @@ -417,4 +419,4 @@ def generate_tags( clear_nlp_cache() - return src_tags, mt_tags + return src_tags, mt_tags, src_mt_alignments, mt_pe_alignments diff --git a/divemt/qe_taggers/wmt22_tagger.py b/divemt/qe_taggers/wmt22_tagger.py index 15b36d9..885bc28 100644 --- a/divemt/qe_taggers/wmt22_tagger.py +++ b/divemt/qe_taggers/wmt22_tagger.py @@ -11,13 +11,13 @@ from strenum import StrEnum else: from enum import StrEnum -from simalign import SentenceAligner from tqdm import tqdm from ..cache_utils import CacheDecorator from ..parse_utils import clear_nlp_cache from .base import QETagger, TAlignment, TTag from .wmt22qe_utils import align_sentence_tercom, parse_tercom_xml_file +from .custom_simalign import SentenceAligner logger = logging.getLogger(__name__) @@ -64,7 +64,7 @@ def __init__( self.tercom_out = Path(tercom_out) if tercom_out is not None else self.tmp_dir / "tercom" self.tercom_path = tercom_path if tercom_path is not None else "scripts/tercom.7.25.jar" - @CacheDecorator() + @CacheDecorator(version=0, name="xlmr") def align_source_pe( self, src_tokens: List[List[str]], @@ -80,7 +80,7 @@ def align_source_pe( ) ] - @CacheDecorator() + @CacheDecorator(version=0, name="") def align_mt_pe( self, mt_tokens: List[List[str]], @@ -293,7 +293,7 @@ def generate_tags( use_gaps: bool = False, omissions: str = OmissionRule.RIGHT.value, fluency_rule: str = FluencyRule.NORMAL.value, - ) -> Tuple[List[List[TTag]], List[List[TTag]]]: + ) -> Tuple[List[List[TTag]], List[List[TTag]], List[TAlignment], List[TAlignment]]: src_tokens, src_langs = self.get_tokenized(srcs, src_langs) mt_tokens, tgt_langs = self.get_tokenized(mts, tgt_langs) pe_tokens, _ = self.get_tokenized(pes, tgt_langs) @@ -313,4 +313,4 @@ def generate_tags( clear_nlp_cache() - return src_tags, mt_tags + return src_tags, mt_tags, None, None diff --git a/pyproject.toml b/pyproject.toml index e8b0f4a..714d15a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -17,7 +17,9 @@ dependencies = [ "numpy<1.19.5", # as simalign is not compatible with numpy >=1.20.0 (np.int is deprecated), 1.19.5 vulnerable "pandas", "sacrebleu", + "tokenizers", "Levenshtein", + "astred[stanza]", "stanza", "simalign", "strenum", From a90764cc115d3fb46c85445a461b43817e3274b9 Mon Sep 17 00:00:00 2001 From: Konstantin Chernyshev Date: Sun, 27 Aug 2023 18:23:35 +0200 Subject: [PATCH 22/23] feat: add analyze notebooks --- notebooks/fine-grained-analysis.ipynb | 1274 +++++++++++++++++++++++++ notebooks/qe_visualize.ipynb | 490 ++++++++++ 2 files changed, 1764 insertions(+) create mode 100644 notebooks/fine-grained-analysis.ipynb create mode 100644 notebooks/qe_visualize.ipynb diff --git a/notebooks/fine-grained-analysis.ipynb b/notebooks/fine-grained-analysis.ipynb new file mode 100644 index 0000000..6236796 --- /dev/null +++ b/notebooks/fine-grained-analysis.ipynb @@ -0,0 +1,1274 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "## Imports" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 1, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip available: \u001B[0m\u001B[31;49m22.3.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m23.2.1\u001B[0m\r\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpip install --upgrade pip\u001B[0m\r\n" + ] + } + ], + "source": [ + "!pip install -q seaborn pandas " + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "start_time": "2023-08-27T13:14:02.233295Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 2, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "import ast\n", + "from collections import defaultdict\n", + "\n", + "from tqdm import tqdm\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "from stanza.models.common.doc import Sentence as StanzaSentence, Word as StanzaWord, Token as StanzaToken\n", + "from astred import Sentence, AlignedSentences" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-08-27T13:14:08.022332Z", + "start_time": "2023-08-27T13:14:07.457666Z" + } + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "import warnings\n", + "warnings.filterwarnings(\"error\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## Load data" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 3, + "outputs": [ + { + "data": { + "text/plain": "True" + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "DATASET_FOLDER = Path() / '..' / 'data' / 'processed'\n", + "MERGED_FOLDER = DATASET_FOLDER / 'merged'\n", + "MERGED_FOLDER.exists()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-08-27T13:14:08.053094Z", + "start_time": "2023-08-27T13:14:08.034709Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 5, + "outputs": [ + { + "data": { + "text/plain": " src_text \\\nunit_id \nflores101-main-ukr-66-pe2-3 The qualities that determine a subculture as d... \nflores101-main-nld-9-pe2-4 A course will normally be from 2-5 days and wi... \nflores101-main-ara-41-pe2-3 For example, one might say that the motor car ... \nflores101-main-ita-3-pe1-3 Most modern research telescopes are enormous f... \nflores101-main-ukr-98-pe1-4 Searches at security checkpoints have also bec... \nflores101-main-vie-19-pe1-1 South Africa have defeated the All Blacks (New... \nflores101-main-ara-102-ht-4 Some cruises feature Berlin, Germany in the br... \nflores101-main-ukr-31-ht-2 It is still produced today, but more important... \nflores101-main-ukr-9-pe2-5 Books and magazines dealing with wilderness su... \nflores101-main-tur-54-ht-1 Murray lost the first set in a tie break after... \nflores101-main-tur-53-ht-5 They all ran back from where the accident had ... \nflores101-main-ukr-16-ht-3 An up-bow usually generates a softer sound, wh... \nflores101-main-tur-68-pe1-4 He was initially hospitalised in the James Pag... \nflores101-main-tur-42-pe1-4 The presence of a true “invisible team” (Larso... \nflores101-main-nld-39-pe2-1 After its adoption by Congress on July 4, a ha... \nflores101-main-vie-80-ht-2 Accepted were Aristotle's views on all matters... \nflores101-main-vie-29-ht-1 The satellites, both of which weighed in exces... \nflores101-main-nld-91-ht-1 The use of video recording has led to importan... \nflores101-main-vie-42-pe2-2 Virtual team members often function as the poi... \nflores101-main-ukr-44-ht-2 The definition has geographic variations, wher... \n\n mt_text \\\nunit_id \nflores101-main-ukr-66-pe2-3 Якістю, яка визначає субкультуру як індивідуал... \nflores101-main-nld-9-pe2-4 Een cursus zal normaal van 2-5 dagen zijn en r... \nflores101-main-ara-41-pe2-3 على سبيل المثال، يمكن القول أن السيارة النارية... \nflores101-main-ita-3-pe1-3 I telescopi di ricerca più moderni sono enormi... \nflores101-main-ukr-98-pe1-4 Обшуки на блокпостах безпеки також стали набаг... \nflores101-main-vie-19-pe1-1 Nam Phi đã đánh bại All Blacks (New Zealand) t... \nflores101-main-ara-102-ht-4 NaN \nflores101-main-ukr-31-ht-2 NaN \nflores101-main-ukr-9-pe2-5 Бібліотеки та журнали про виживання в дикій пр... \nflores101-main-tur-54-ht-1 NaN \nflores101-main-tur-53-ht-5 NaN \nflores101-main-ukr-16-ht-3 NaN \nflores101-main-tur-68-pe1-4 Başlangıçta Great Yarmouth'daki James Paget Ha... \nflores101-main-tur-42-pe1-4 Gerçek bir “görünmez ekibin” varlığı (Larson v... \nflores101-main-nld-39-pe2-1 Nadat het 4 juli door het Congres werd aangeno... \nflores101-main-vie-80-ht-2 NaN \nflores101-main-vie-29-ht-1 NaN \nflores101-main-nld-91-ht-1 NaN \nflores101-main-vie-42-pe2-2 Các thành viên trong nhóm ảo thường là điểm ti... \nflores101-main-ukr-44-ht-2 NaN \n\n tgt_text \\\nunit_id \nflores101-main-ukr-66-pe2-3 Рисами, які визначають субкультуру як відмінну... \nflores101-main-nld-9-pe2-4 Een cursus duurt normaal gesproken 2-5 dagen z... \nflores101-main-ara-41-pe2-3 على سبيل المثال، قد يقول الفرد أن السيارات تؤد... \nflores101-main-ita-3-pe1-3 I telescopi di ricerca più avanzati sono costi... \nflores101-main-ukr-98-pe1-4 Обшуки на безпекових блокпостах також стали на... \nflores101-main-vie-19-pe1-1 Đội tuyển Nam Phi đã đánh bại đội All Blacks (... \nflores101-main-ara-102-ht-4 تعرض بعض الرحلات البحرية زيارة برلين في ألماني... \nflores101-main-ukr-31-ht-2 Їх виготовляють і зараз, але, що важливіше, ві... \nflores101-main-ukr-9-pe2-5 Книги та журнали про виживання в дикій природі... \nflores101-main-tur-54-ht-1 Her iki adamın setteki her bir servisi kazanma... \nflores101-main-tur-53-ht-5 Hepsi kaza yerinden koşarak gelmişti. \nflores101-main-ukr-16-ht-3 Рух смичком угору зазвичай дає м’якший звук, т... \nflores101-main-tur-68-pe1-4 Sürücü önce Great Yarmouth'daki James Paget Ha... \nflores101-main-tur-42-pe1-4 Gerçek bir \"görünmez ekibin\" varlığı (Larson v... \nflores101-main-nld-39-pe2-1 Nadat het op 4 juli door het Congres werd aang... \nflores101-main-vie-80-ht-2 Các quan điểm của Aristotle về tất cả các lĩnh... \nflores101-main-vie-29-ht-1 Hai vệ tinh đã va chạm trong vũ trụ cách Trái ... \nflores101-main-nld-91-ht-1 Het gebruik van video-opnamen heeft geleid tot... \nflores101-main-vie-42-pe2-2 Các thành viên trong đội ngũ ảo thường là đầu ... \nflores101-main-ukr-44-ht-2 Визначення залежить від регіону: для деяких мі... \n\n aligned_edit \\\nunit_id \nflores101-main-ukr-66-pe2-3 REF: якістю , яка визначає субкультуру як *... \nflores101-main-nld-9-pe2-4 REF: een cursus zal normaal van 2-5 d... \nflores101-main-ara-41-pe2-3 REF: على سبيل المثال ، يمكن القول أن السي... \nflores101-main-ita-3-pe1-3 REF: i telescopi di ricerca più moderni sono... \nflores101-main-ukr-98-pe1-4 REF: обшуки на блокпостах безпеки та... \nflores101-main-vie-19-pe1-1 REF: *** ***** nam phi đã đánh bại *** all_bl... \nflores101-main-ara-102-ht-4 NaN \nflores101-main-ukr-31-ht-2 NaN \nflores101-main-ukr-9-pe2-5 REF: бібліотеки та журнали про виживання в ди... \nflores101-main-tur-54-ht-1 NaN \nflores101-main-tur-53-ht-5 NaN \nflores101-main-ukr-16-ht-3 NaN \nflores101-main-tur-68-pe1-4 REF: ****** başlangıçta great yarmouth'daki j... \nflores101-main-tur-42-pe1-4 REF: gerçek bir “görünmez ekibin” varlığı (... \nflores101-main-nld-39-pe2-1 REF: nadat het ** 4 juli door het congres wer... \nflores101-main-vie-80-ht-2 NaN \nflores101-main-vie-29-ht-1 NaN \nflores101-main-nld-91-ht-1 NaN \nflores101-main-vie-42-pe2-2 REF: các thành_viên trong nhóm ảo_thường l... \nflores101-main-ukr-44-ht-2 NaN \n\n lang_id \\\nunit_id \nflores101-main-ukr-66-pe2-3 ukr \nflores101-main-nld-9-pe2-4 nld \nflores101-main-ara-41-pe2-3 ara \nflores101-main-ita-3-pe1-3 ita \nflores101-main-ukr-98-pe1-4 ukr \nflores101-main-vie-19-pe1-1 vie \nflores101-main-ara-102-ht-4 ara \nflores101-main-ukr-31-ht-2 ukr \nflores101-main-ukr-9-pe2-5 ukr \nflores101-main-tur-54-ht-1 tur \nflores101-main-tur-53-ht-5 tur \nflores101-main-ukr-16-ht-3 ukr \nflores101-main-tur-68-pe1-4 tur \nflores101-main-tur-42-pe1-4 tur \nflores101-main-nld-39-pe2-1 nld \nflores101-main-vie-80-ht-2 vie \nflores101-main-vie-29-ht-1 vie \nflores101-main-nld-91-ht-1 nld \nflores101-main-vie-42-pe2-2 vie \nflores101-main-ukr-44-ht-2 ukr \n\n src_tokens \\\nunit_id \nflores101-main-ukr-66-pe2-3 ['The', 'qualities', 'that', 'determine', 'a',... \nflores101-main-nld-9-pe2-4 ['A', 'course', 'will', 'normally', 'be', 'fro... \nflores101-main-ara-41-pe2-3 ['For', 'example', ',', 'one', 'might', 'say',... \nflores101-main-ita-3-pe1-3 ['Most', 'modern', 'research', 'telescopes', '... \nflores101-main-ukr-98-pe1-4 ['Searches', 'at', 'security', 'checkpoints', ... \nflores101-main-vie-19-pe1-1 ['South', 'Africa', 'have', 'defeated', 'the',... \nflores101-main-ara-102-ht-4 ['Some', 'cruises', 'feature', 'Berlin', ',', ... \nflores101-main-ukr-31-ht-2 ['It', 'is', 'still', 'produced', 'today', ','... \nflores101-main-ukr-9-pe2-5 ['Books', 'and', 'magazines', 'dealing', 'with... \nflores101-main-tur-54-ht-1 ['Murray', 'lost', 'the', 'first', 'set', 'in'... \nflores101-main-tur-53-ht-5 ['They', 'all', 'ran', 'back', 'from', 'where'... \nflores101-main-ukr-16-ht-3 ['An', 'up', '-', 'bow', 'usually', 'generates... \nflores101-main-tur-68-pe1-4 ['He', 'was', 'initially', 'hospitalised', 'in... \nflores101-main-tur-42-pe1-4 ['The', 'presence', 'of', 'a', 'true', '“', 'i... \nflores101-main-nld-39-pe2-1 ['After', 'its', 'adoption', 'by', 'Congress',... \nflores101-main-vie-80-ht-2 ['Accepted', 'were', 'Aristotle', \"'s\", 'views... \nflores101-main-vie-29-ht-1 ['The', 'satellites', ',', 'both', 'of', 'whic... \nflores101-main-nld-91-ht-1 ['The', 'use', 'of', 'video', 'recording', 'ha... \nflores101-main-vie-42-pe2-2 ['Virtual', 'team', 'members', 'often', 'funct... \nflores101-main-ukr-44-ht-2 ['The', 'definition', 'has', 'geographic', 'va... \n\n src_annotations \\\nunit_id \nflores101-main-ukr-66-pe2-3 [{'lemma': 'the', 'upos': 'DET', 'feats': 'Def... \nflores101-main-nld-9-pe2-4 [{'lemma': 'a', 'upos': 'DET', 'feats': 'Defin... \nflores101-main-ara-41-pe2-3 [{'lemma': 'for', 'upos': 'ADP', 'feats': '', ... \nflores101-main-ita-3-pe1-3 [{'lemma': 'most', 'upos': 'ADJ', 'feats': 'De... \nflores101-main-ukr-98-pe1-4 [{'lemma': 'search', 'upos': 'NOUN', 'feats': ... \nflores101-main-vie-19-pe1-1 [{'lemma': 'South', 'upos': 'PROPN', 'feats': ... \nflores101-main-ara-102-ht-4 [{'lemma': 'some', 'upos': 'DET', 'feats': '',... \nflores101-main-ukr-31-ht-2 [{'lemma': 'it', 'upos': 'PRON', 'feats': 'Cas... \nflores101-main-ukr-9-pe2-5 [{'lemma': 'book', 'upos': 'NOUN', 'feats': 'N... \nflores101-main-tur-54-ht-1 [{'lemma': 'Murray', 'upos': 'PROPN', 'feats':... \nflores101-main-tur-53-ht-5 [{'lemma': 'they', 'upos': 'PRON', 'feats': 'C... \nflores101-main-ukr-16-ht-3 [{'lemma': 'a', 'upos': 'DET', 'feats': 'Defin... \nflores101-main-tur-68-pe1-4 [{'lemma': 'he', 'upos': 'PRON', 'feats': 'Cas... \nflores101-main-tur-42-pe1-4 [{'lemma': 'the', 'upos': 'DET', 'feats': 'Def... \nflores101-main-nld-39-pe2-1 [{'lemma': 'after', 'upos': 'ADP', 'feats': ''... \nflores101-main-vie-80-ht-2 [{'lemma': 'accept', 'upos': 'VERB', 'feats': ... \nflores101-main-vie-29-ht-1 [{'lemma': 'the', 'upos': 'DET', 'feats': 'Def... \nflores101-main-nld-91-ht-1 [{'lemma': 'the', 'upos': 'DET', 'feats': 'Def... \nflores101-main-vie-42-pe2-2 [{'lemma': 'virtual', 'upos': 'ADJ', 'feats': ... \nflores101-main-ukr-44-ht-2 [{'lemma': 'the', 'upos': 'DET', 'feats': 'Def... \n\n mt_tokens \\\nunit_id \nflores101-main-ukr-66-pe2-3 ['Якістю', ',', 'яка', 'визначає', 'субкультур... \nflores101-main-nld-9-pe2-4 ['Een', 'cursus', 'zal', 'normaal', 'van', '2-... \nflores101-main-ara-41-pe2-3 ['على', 'سبيل', 'المثال', '،', 'يمكن', 'القول'... \nflores101-main-ita-3-pe1-3 ['I', 'telescopi', 'di', 'ricerca', 'più', 'mo... \nflores101-main-ukr-98-pe1-4 ['Обшуки', 'на', 'блокпостах', 'безпеки', 'так... \nflores101-main-vie-19-pe1-1 ['Nam', 'Phi', 'đã', 'đánh', 'bại', 'All Black... \nflores101-main-ara-102-ht-4 NaN \nflores101-main-ukr-31-ht-2 NaN \nflores101-main-ukr-9-pe2-5 ['Бібліотеки', 'та', 'журнали', 'про', 'вижива... \nflores101-main-tur-54-ht-1 NaN \nflores101-main-tur-53-ht-5 NaN \nflores101-main-ukr-16-ht-3 NaN \nflores101-main-tur-68-pe1-4 ['Başlangıçta', 'Great', \"Yarmouth'daki\", 'Jam... \nflores101-main-tur-42-pe1-4 ['Gerçek', 'bir', '“görünmez', 'ekibin”', 'var... \nflores101-main-nld-39-pe2-1 ['Nadat', 'het', '4', 'juli', 'door', 'het', '... \nflores101-main-vie-80-ht-2 NaN \nflores101-main-vie-29-ht-1 NaN \nflores101-main-nld-91-ht-1 NaN \nflores101-main-vie-42-pe2-2 ['Các', 'thành viên', 'trong', 'nhóm', 'ảo thư... \nflores101-main-ukr-44-ht-2 NaN \n\n mt_annotations \\\nunit_id \nflores101-main-ukr-66-pe2-3 [{'lemma': 'якість', 'upos': 'NOUN', 'feats': ... \nflores101-main-nld-9-pe2-4 [{'lemma': 'een', 'upos': 'DET', 'feats': 'Def... \nflores101-main-ara-41-pe2-3 [{'lemma': 'عَلَى', 'upos': 'ADP', 'feats': 'A... \nflores101-main-ita-3-pe1-3 [{'lemma': 'il', 'upos': 'DET', 'feats': 'Defi... \nflores101-main-ukr-98-pe1-4 [{'lemma': 'обшук', 'upos': 'NOUN', 'feats': '... \nflores101-main-vie-19-pe1-1 [{'lemma': 'Nam', 'upos': 'NOUN', 'feats': '',... \nflores101-main-ara-102-ht-4 NaN \nflores101-main-ukr-31-ht-2 NaN \nflores101-main-ukr-9-pe2-5 [{'lemma': 'бібліотека', 'upos': 'NOUN', 'feat... \nflores101-main-tur-54-ht-1 NaN \nflores101-main-tur-53-ht-5 NaN \nflores101-main-ukr-16-ht-3 NaN \nflores101-main-tur-68-pe1-4 [{'lemma': 'başlangıç', 'upos': 'NOUN', 'feats... \nflores101-main-tur-42-pe1-4 [{'lemma': 'gerçek', 'upos': 'ADJ', 'feats': '... \nflores101-main-nld-39-pe2-1 [{'lemma': 'nadat', 'upos': 'SCONJ', 'feats': ... \nflores101-main-vie-80-ht-2 NaN \nflores101-main-vie-29-ht-1 NaN \nflores101-main-nld-91-ht-1 NaN \nflores101-main-vie-42-pe2-2 [{'lemma': 'Các', 'upos': 'DET', 'feats': '', ... \nflores101-main-ukr-44-ht-2 NaN \n\n tgt_tokens \\\nunit_id \nflores101-main-ukr-66-pe2-3 ['Рисами', ',', 'які', 'визначають', 'субкульт... \nflores101-main-nld-9-pe2-4 ['Een', 'cursus', 'duurt', 'normaal', 'gesprok... \nflores101-main-ara-41-pe2-3 ['على', 'سبيل', 'المثال', '،', 'قد', 'يقول', '... \nflores101-main-ita-3-pe1-3 ['I', 'telescopi', 'di', 'ricerca', 'più', 'av... \nflores101-main-ukr-98-pe1-4 ['Обшуки', 'на', 'безпекових', 'блокпостах', '... \nflores101-main-vie-19-pe1-1 ['Đội', 'tuyển', 'Nam', 'Phi', 'đã', 'đánh', '... \nflores101-main-ara-102-ht-4 ['تعرض', 'بعض', 'الرحلات', 'البحرية', 'زيارة',... \nflores101-main-ukr-31-ht-2 ['Їх', 'виготовляють', 'і', 'зараз', ',', 'але... \nflores101-main-ukr-9-pe2-5 ['Книги', 'та', 'журнали', 'про', 'виживання',... \nflores101-main-tur-54-ht-1 ['Her', 'iki', 'adamın', 'setteki', 'her', 'bi... \nflores101-main-tur-53-ht-5 ['Hepsi', 'kaza', 'yerinden', 'koşarak', 'gelm... \nflores101-main-ukr-16-ht-3 ['Рух', 'смичком', 'угору', 'зазвичай', 'дає',... \nflores101-main-tur-68-pe1-4 ['Sürücü', 'önce', 'Great', \"Yarmouth'daki\", '... \nflores101-main-tur-42-pe1-4 ['Gerçek', 'bir', '\"görünmez', 'ekibin\"', 'var... \nflores101-main-nld-39-pe2-1 ['Nadat', 'het', 'op', '4', 'juli', 'door', 'h... \nflores101-main-vie-80-ht-2 ['Các', 'quan điểm', 'của', 'Aristotle', 'về',... \nflores101-main-vie-29-ht-1 ['Hai', 'vệ', 'tinh', 'đã', 'va', 'chạm', 'tro... \nflores101-main-nld-91-ht-1 ['Het', 'gebruik', 'van', 'video-opnamen', 'he... \nflores101-main-vie-42-pe2-2 ['Các', 'thành viên', 'trong', 'đội ngũ', 'ảo ... \nflores101-main-ukr-44-ht-2 ['Визначення', 'залежить', 'від', 'регіону', '... \n\n ... doc_id time_s time_m time_h \\\nunit_id ... \nflores101-main-ukr-66-pe2-3 ... 66 86.563 1.4427 0.0240 \nflores101-main-nld-9-pe2-4 ... 9 19.836 0.3306 0.0055 \nflores101-main-ara-41-pe2-3 ... 41 52.346 0.8724 0.0145 \nflores101-main-ita-3-pe1-3 ... 3 137.284 2.2881 0.0381 \nflores101-main-ukr-98-pe1-4 ... 98 28.684 0.4781 0.0080 \nflores101-main-vie-19-pe1-1 ... 19 182.751 3.0458 0.0508 \nflores101-main-ara-102-ht-4 ... 102 74.626 1.2438 0.0207 \nflores101-main-ukr-31-ht-2 ... 31 91.587 1.5264 0.0254 \nflores101-main-ukr-9-pe2-5 ... 9 45.923 0.7654 0.0128 \nflores101-main-tur-54-ht-1 ... 54 142.789 2.3798 0.0397 \nflores101-main-tur-53-ht-5 ... 53 26.832 0.4472 0.0075 \nflores101-main-ukr-16-ht-3 ... 16 97.448 1.6241 0.0271 \nflores101-main-tur-68-pe1-4 ... 68 19.456 0.3243 0.0054 \nflores101-main-tur-42-pe1-4 ... 42 30.930 0.5155 0.0086 \nflores101-main-nld-39-pe2-1 ... 39 73.994 1.2332 0.0206 \nflores101-main-vie-80-ht-2 ... 80 42.489 0.7081 0.0118 \nflores101-main-vie-29-ht-1 ... 29 178.737 2.9789 0.0496 \nflores101-main-nld-91-ht-1 ... 91 35.700 0.5950 0.0099 \nflores101-main-vie-42-pe2-2 ... 42 101.728 1.6955 0.0283 \nflores101-main-ukr-44-ht-2 ... 44 226.385 3.7731 0.0629 \n\n time_per_char time_per_word key_per_char \\\nunit_id \nflores101-main-ukr-66-pe2-3 0.5549 4.1220 1.9872 \nflores101-main-nld-9-pe2-4 0.1681 0.9016 0.6102 \nflores101-main-ara-41-pe2-3 0.5690 3.2716 0.4022 \nflores101-main-ita-3-pe1-3 1.2480 9.8060 1.3182 \nflores101-main-ukr-98-pe1-4 0.2758 1.7928 1.0096 \nflores101-main-vie-19-pe1-1 1.2265 7.3100 1.6174 \nflores101-main-ara-102-ht-4 0.3969 1.9638 1.1330 \nflores101-main-ukr-31-ht-2 0.7386 4.5794 1.5161 \nflores101-main-ukr-9-pe2-5 0.4064 2.7014 2.2478 \nflores101-main-tur-54-ht-1 1.5354 7.1394 2.3226 \nflores101-main-tur-53-ht-5 0.4879 2.6832 0.7273 \nflores101-main-ukr-16-ht-3 1.0592 6.4965 1.3913 \nflores101-main-tur-68-pe1-4 0.2560 1.6213 0.7500 \nflores101-main-tur-42-pe1-4 0.2621 1.4729 0.5339 \nflores101-main-nld-39-pe2-1 0.3474 1.9472 0.0329 \nflores101-main-vie-80-ht-2 0.5311 3.8626 2.6375 \nflores101-main-vie-29-ht-1 1.1606 7.1495 2.6299 \nflores101-main-nld-91-ht-1 0.2364 1.6227 1.2914 \nflores101-main-vie-42-pe2-2 1.0708 6.7819 1.8421 \nflores101-main-ukr-44-ht-2 1.7967 10.7802 2.4762 \n\n words_per_hour words_per_minute \\\nunit_id \nflores101-main-ukr-66-pe2-3 873.3524 14.5559 \nflores101-main-nld-9-pe2-4 3992.7405 66.5457 \nflores101-main-ara-41-pe2-3 1100.3706 18.3395 \nflores101-main-ita-3-pe1-3 367.1222 6.1187 \nflores101-main-ukr-98-pe1-4 2008.0881 33.4681 \nflores101-main-vie-19-pe1-1 492.4734 8.2079 \nflores101-main-ara-102-ht-4 1833.1413 30.5524 \nflores101-main-ukr-31-ht-2 786.1378 13.1023 \nflores101-main-ukr-9-pe2-5 1332.6655 22.2111 \nflores101-main-tur-54-ht-1 504.2405 8.4040 \nflores101-main-tur-53-ht-5 1341.6816 22.3614 \nflores101-main-ukr-16-ht-3 554.1417 9.2357 \nflores101-main-tur-68-pe1-4 2220.3947 37.0066 \nflores101-main-tur-42-pe1-4 2444.2289 40.7371 \nflores101-main-nld-39-pe2-1 1848.7986 30.8133 \nflores101-main-vie-80-ht-2 932.0059 15.5334 \nflores101-main-vie-29-ht-1 503.5331 8.3922 \nflores101-main-nld-91-ht-1 2218.4874 36.9748 \nflores101-main-vie-42-pe2-2 530.8273 8.8471 \nflores101-main-ukr-44-ht-2 333.9444 5.5657 \n\n per_subject_visit_order \nunit_id \nflores101-main-ukr-66-pe2-3 284 \nflores101-main-nld-9-pe2-4 388 \nflores101-main-ara-41-pe2-3 181 \nflores101-main-ita-3-pe1-3 424 \nflores101-main-ukr-98-pe1-4 421 \nflores101-main-vie-19-pe1-1 74 \nflores101-main-ara-102-ht-4 20 \nflores101-main-ukr-31-ht-2 132 \nflores101-main-ukr-9-pe2-5 385 \nflores101-main-tur-54-ht-1 235 \nflores101-main-tur-53-ht-5 234 \nflores101-main-ukr-16-ht-3 67 \nflores101-main-tur-68-pe1-4 294 \nflores101-main-tur-42-pe1-4 187 \nflores101-main-nld-39-pe2-1 93 \nflores101-main-vie-80-ht-2 429 \nflores101-main-vie-29-ht-1 119 \nflores101-main-nld-91-ht-1 394 \nflores101-main-vie-42-pe2-2 178 \nflores101-main-ukr-44-ht-2 194 \n\n[20 rows x 66 columns]", + "text/html": "
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src_textmt_texttgt_textaligned_editlang_idsrc_tokenssrc_annotationsmt_tokensmt_annotationstgt_tokens...doc_idtime_stime_mtime_htime_per_chartime_per_wordkey_per_charwords_per_hourwords_per_minuteper_subject_visit_order
unit_id
flores101-main-ukr-66-pe2-3The qualities that determine a subculture as d...Якістю, яка визначає субкультуру як індивідуал...Рисами, які визначають субкультуру як відмінну...REF: якістю , яка визначає субкультуру як *...ukr['The', 'qualities', 'that', 'determine', 'a',...[{'lemma': 'the', 'upos': 'DET', 'feats': 'Def...['Якістю', ',', 'яка', 'визначає', 'субкультур...[{'lemma': 'якість', 'upos': 'NOUN', 'feats': ...['Рисами', ',', 'які', 'визначають', 'субкульт......6686.5631.44270.02400.55494.12201.9872873.352414.5559284
flores101-main-nld-9-pe2-4A course will normally be from 2-5 days and wi...Een cursus zal normaal van 2-5 dagen zijn en r...Een cursus duurt normaal gesproken 2-5 dagen z...REF: een cursus zal normaal van 2-5 d...nld['A', 'course', 'will', 'normally', 'be', 'fro...[{'lemma': 'a', 'upos': 'DET', 'feats': 'Defin...['Een', 'cursus', 'zal', 'normaal', 'van', '2-...[{'lemma': 'een', 'upos': 'DET', 'feats': 'Def...['Een', 'cursus', 'duurt', 'normaal', 'gesprok......919.8360.33060.00550.16810.90160.61023992.740566.5457388
flores101-main-ara-41-pe2-3For example, one might say that the motor car ...على سبيل المثال، يمكن القول أن السيارة النارية...على سبيل المثال، قد يقول الفرد أن السيارات تؤد...REF: على سبيل المثال ، يمكن القول أن السي...ara['For', 'example', ',', 'one', 'might', 'say',...[{'lemma': 'for', 'upos': 'ADP', 'feats': '', ...['على', 'سبيل', 'المثال', '،', 'يمكن', 'القول'...[{'lemma': 'عَلَى', 'upos': 'ADP', 'feats': 'A...['على', 'سبيل', 'المثال', '،', 'قد', 'يقول', '......4152.3460.87240.01450.56903.27160.40221100.370618.3395181
flores101-main-ita-3-pe1-3Most modern research telescopes are enormous f...I telescopi di ricerca più moderni sono enormi...I telescopi di ricerca più avanzati sono costi...REF: i telescopi di ricerca più moderni sono...ita['Most', 'modern', 'research', 'telescopes', '...[{'lemma': 'most', 'upos': 'ADJ', 'feats': 'De...['I', 'telescopi', 'di', 'ricerca', 'più', 'mo...[{'lemma': 'il', 'upos': 'DET', 'feats': 'Defi...['I', 'telescopi', 'di', 'ricerca', 'più', 'av......3137.2842.28810.03811.24809.80601.3182367.12226.1187424
flores101-main-ukr-98-pe1-4Searches at security checkpoints have also bec...Обшуки на блокпостах безпеки також стали набаг...Обшуки на безпекових блокпостах також стали на...REF: обшуки на блокпостах безпеки та...ukr['Searches', 'at', 'security', 'checkpoints', ...[{'lemma': 'search', 'upos': 'NOUN', 'feats': ...['Обшуки', 'на', 'блокпостах', 'безпеки', 'так...[{'lemma': 'обшук', 'upos': 'NOUN', 'feats': '...['Обшуки', 'на', 'безпекових', 'блокпостах', '......9828.6840.47810.00800.27581.79281.00962008.088133.4681421
flores101-main-vie-19-pe1-1South Africa have defeated the All Blacks (New...Nam Phi đã đánh bại All Blacks (New Zealand) t...Đội tuyển Nam Phi đã đánh bại đội All Blacks (...REF: *** ***** nam phi đã đánh bại *** all_bl...vie['South', 'Africa', 'have', 'defeated', 'the',...[{'lemma': 'South', 'upos': 'PROPN', 'feats': ...['Nam', 'Phi', 'đã', 'đánh', 'bại', 'All Black...[{'lemma': 'Nam', 'upos': 'NOUN', 'feats': '',...['Đội', 'tuyển', 'Nam', 'Phi', 'đã', 'đánh', '......19182.7513.04580.05081.22657.31001.6174492.47348.207974
flores101-main-ara-102-ht-4Some cruises feature Berlin, Germany in the br...NaNتعرض بعض الرحلات البحرية زيارة برلين في ألماني...NaNara['Some', 'cruises', 'feature', 'Berlin', ',', ...[{'lemma': 'some', 'upos': 'DET', 'feats': '',...NaNNaN['تعرض', 'بعض', 'الرحلات', 'البحرية', 'زيارة',......10274.6261.24380.02070.39691.96381.13301833.141330.552420
flores101-main-ukr-31-ht-2It is still produced today, but more important...NaNЇх виготовляють і зараз, але, що важливіше, ві...NaNukr['It', 'is', 'still', 'produced', 'today', ','...[{'lemma': 'it', 'upos': 'PRON', 'feats': 'Cas...NaNNaN['Їх', 'виготовляють', 'і', 'зараз', ',', 'але......3191.5871.52640.02540.73864.57941.5161786.137813.1023132
flores101-main-ukr-9-pe2-5Books and magazines dealing with wilderness su...Бібліотеки та журнали про виживання в дикій пр...Книги та журнали про виживання в дикій природі...REF: бібліотеки та журнали про виживання в ди...ukr['Books', 'and', 'magazines', 'dealing', 'with...[{'lemma': 'book', 'upos': 'NOUN', 'feats': 'N...['Бібліотеки', 'та', 'журнали', 'про', 'вижива...[{'lemma': 'бібліотека', 'upos': 'NOUN', 'feat...['Книги', 'та', 'журнали', 'про', 'виживання',......945.9230.76540.01280.40642.70142.24781332.665522.2111385
flores101-main-tur-54-ht-1Murray lost the first set in a tie break after...NaNHer iki adamın setteki her bir servisi kazanma...NaNtur['Murray', 'lost', 'the', 'first', 'set', 'in'...[{'lemma': 'Murray', 'upos': 'PROPN', 'feats':...NaNNaN['Her', 'iki', 'adamın', 'setteki', 'her', 'bi......54142.7892.37980.03971.53547.13942.3226504.24058.4040235
flores101-main-tur-53-ht-5They all ran back from where the accident had ...NaNHepsi kaza yerinden koşarak gelmişti.NaNtur['They', 'all', 'ran', 'back', 'from', 'where'...[{'lemma': 'they', 'upos': 'PRON', 'feats': 'C...NaNNaN['Hepsi', 'kaza', 'yerinden', 'koşarak', 'gelm......5326.8320.44720.00750.48792.68320.72731341.681622.3614234
flores101-main-ukr-16-ht-3An up-bow usually generates a softer sound, wh...NaNРух смичком угору зазвичай дає м’якший звук, т...NaNukr['An', 'up', '-', 'bow', 'usually', 'generates...[{'lemma': 'a', 'upos': 'DET', 'feats': 'Defin...NaNNaN['Рух', 'смичком', 'угору', 'зазвичай', 'дає',......1697.4481.62410.02711.05926.49651.3913554.14179.235767
flores101-main-tur-68-pe1-4He was initially hospitalised in the James Pag...Başlangıçta Great Yarmouth'daki James Paget Ha...Sürücü önce Great Yarmouth'daki James Paget Ha...REF: ****** başlangıçta great yarmouth'daki j...tur['He', 'was', 'initially', 'hospitalised', 'in...[{'lemma': 'he', 'upos': 'PRON', 'feats': 'Cas...['Başlangıçta', 'Great', \"Yarmouth'daki\", 'Jam...[{'lemma': 'başlangıç', 'upos': 'NOUN', 'feats...['Sürücü', 'önce', 'Great', \"Yarmouth'daki\", '......6819.4560.32430.00540.25601.62130.75002220.394737.0066294
flores101-main-tur-42-pe1-4The presence of a true “invisible team” (Larso...Gerçek bir “görünmez ekibin” varlığı (Larson v...Gerçek bir \"görünmez ekibin\" varlığı (Larson v...REF: gerçek bir “görünmez ekibin” varlığı (...tur['The', 'presence', 'of', 'a', 'true', '“', 'i...[{'lemma': 'the', 'upos': 'DET', 'feats': 'Def...['Gerçek', 'bir', '“görünmez', 'ekibin”', 'var...[{'lemma': 'gerçek', 'upos': 'ADJ', 'feats': '...['Gerçek', 'bir', '\"görünmez', 'ekibin\"', 'var......4230.9300.51550.00860.26211.47290.53392444.228940.7371187
flores101-main-nld-39-pe2-1After its adoption by Congress on July 4, a ha...Nadat het 4 juli door het Congres werd aangeno...Nadat het op 4 juli door het Congres werd aang...REF: nadat het ** 4 juli door het congres wer...nld['After', 'its', 'adoption', 'by', 'Congress',...[{'lemma': 'after', 'upos': 'ADP', 'feats': ''...['Nadat', 'het', '4', 'juli', 'door', 'het', '...[{'lemma': 'nadat', 'upos': 'SCONJ', 'feats': ...['Nadat', 'het', 'op', '4', 'juli', 'door', 'h......3973.9941.23320.02060.34741.94720.03291848.798630.813393
flores101-main-vie-80-ht-2Accepted were Aristotle's views on all matters...NaNCác quan điểm của Aristotle về tất cả các lĩnh...NaNvie['Accepted', 'were', 'Aristotle', \"'s\", 'views...[{'lemma': 'accept', 'upos': 'VERB', 'feats': ...NaNNaN['Các', 'quan điểm', 'của', 'Aristotle', 'về',......8042.4890.70810.01180.53113.86262.6375932.005915.5334429
flores101-main-vie-29-ht-1The satellites, both of which weighed in exces...NaNHai vệ tinh đã va chạm trong vũ trụ cách Trái ...NaNvie['The', 'satellites', ',', 'both', 'of', 'whic...[{'lemma': 'the', 'upos': 'DET', 'feats': 'Def...NaNNaN['Hai', 'vệ', 'tinh', 'đã', 'va', 'chạm', 'tro......29178.7372.97890.04961.16067.14952.6299503.53318.3922119
flores101-main-nld-91-ht-1The use of video recording has led to importan...NaNHet gebruik van video-opnamen heeft geleid tot...NaNnld['The', 'use', 'of', 'video', 'recording', 'ha...[{'lemma': 'the', 'upos': 'DET', 'feats': 'Def...NaNNaN['Het', 'gebruik', 'van', 'video-opnamen', 'he......9135.7000.59500.00990.23641.62271.29142218.487436.9748394
flores101-main-vie-42-pe2-2Virtual team members often function as the poi...Các thành viên trong nhóm ảo thường là điểm ti...Các thành viên trong đội ngũ ảo thường là đầu ...REF: các thành_viên trong nhóm ảo_thường l...vie['Virtual', 'team', 'members', 'often', 'funct...[{'lemma': 'virtual', 'upos': 'ADJ', 'feats': ...['Các', 'thành viên', 'trong', 'nhóm', 'ảo thư...[{'lemma': 'Các', 'upos': 'DET', 'feats': '', ...['Các', 'thành viên', 'trong', 'đội ngũ', 'ảo ......42101.7281.69550.02831.07086.78191.8421530.82738.8471178
flores101-main-ukr-44-ht-2The definition has geographic variations, wher...NaNВизначення залежить від регіону: для деяких мі...NaNukr['The', 'definition', 'has', 'geographic', 'va...[{'lemma': 'the', 'upos': 'DET', 'feats': 'Def...NaNNaN['Визначення', 'залежить', 'від', 'регіону', '......44226.3853.77310.06291.796710.78022.4762333.94445.5657194
\n

20 rows × 66 columns

\n
" + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.DataFrame()\n", + "for lang in ['ukr', 'ara', 'ita', 'nld', 'tur', 'vie']:\n", + " if lang == 'ita':\n", + " lang_df_main_texts = pd.read_csv(MERGED_FOLDER / f'{lang}_t1_t4_t5_main_texts.tsv', sep='\\t', index_col=0)\n", + " lang_df_main = pd.read_csv(MERGED_FOLDER / f'{lang}_t1_t4_t5_main.tsv', sep='\\t', index_col=0)\n", + " else:\n", + " lang_df_main_texts = pd.read_csv(MERGED_FOLDER / f'{lang}_t1_t2_t3_main_texts.tsv', sep='\\t', index_col=0)\n", + " lang_df_main = pd.read_csv(MERGED_FOLDER / f'{lang}_t1_t2_t3_main.tsv', sep='\\t', index_col=0)\n", + "\n", + " lang_df = pd.merge(lang_df_main_texts, lang_df_main, how='inner', on=['unit_id', 'lang_id'])\n", + "\n", + " df = pd.concat([df, lang_df], ignore_index=False)\n", + "\n", + "df.sample(20)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-08-27T13:17:13.399083Z", + "start_time": "2023-08-27T13:17:11.128478Z" + } + } + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 8, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 79%|███████▉ | 4098/5160 [23:06<07:44, 2.29it/s] " + ] + } + ], + "source": [ + "# process data: read files, read lists to python lists, read alignments \n", + "\n", + "df_overlap = pd.DataFrame()\n", + "\n", + "for _id, x in tqdm(df.iterrows(), total=len(df)):\n", + " pe_tokens = ast.literal_eval(x['tgt_tokens'])\n", + " mt_tokens = ast.literal_eval(x['mt_tokens'])\n", + " mt_tbd_qe = ast.literal_eval(x['mt_tbd_qe'])\n", + " mt_wmt22_qe = ast.literal_eval(x['mt_wmt22_qe'])[:-1] # as omission rule right\n", + "\n", + " mt_pe_alignments_raw = ast.literal_eval(x['mt_pe_tbd_qe_alignments'])\n", + " mt_pe_alignments_dict = defaultdict(list)\n", + "\n", + " for k, v, score in mt_pe_alignments_raw:\n", + " if k is not None:\n", + " mt_pe_alignments_dict[k].append(v)\n", + "\n", + " for i, mt_tok in enumerate(mt_tokens):\n", + "\n", + " paired_pe_tok_i = mt_pe_alignments_dict[i][0] if mt_pe_alignments_dict[i] else None # SUB have to be paired with one PE token\n", + " if paired_pe_tok_i is None:\n", + " continue\n", + "\n", + " tbd_qe_tags = mt_tbd_qe[i]\n", + "\n", + " for tbd_qe_tag in tbd_qe_tags:\n", + " _df_tok_stats = pd.DataFrame([{\n", + " 'unit_id': _id,\n", + " 'lang_id': x['lang_id'],\n", + " 'mt_tok': mt_tok,\n", + " 'pe_tok': pe_tokens[paired_pe_tok_i],\n", + " 'mt_tbd_qe': tbd_qe_tag,\n", + " 'mt_wmt22_qe': mt_wmt22_qe[i],\n", + " }])\n", + " df_overlap = pd.concat([df_overlap, _df_tok_stats], ignore_index=True)\n", + "\n", + "df_overlap" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-08-27T13:52:55.913558Z", + "start_time": "2023-08-27T13:17:51.561633Z" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "## Check overlap with wmt22" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 9, + "outputs": [], + "source": [ + "print('Note: BAD-DEL and BAD-SHF is overlapping with other cats')\n", + "pd.crosstab(df_overlap['mt_tbd_qe'], [df_overlap['lang_id'], df_overlap['mt_wmt22_qe']], rownames=['mt_tbd_qe'], colnames=['lang_id', 'mt_wmt22_qe'])" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 10, + "outputs": [], + "source": [ + "df_overlap[(df_overlap['mt_tbd_qe'] == 'OK') & (df_overlap['mt_wmt22_qe'] == 'BAD')].sample(10)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 11, + "outputs": [], + "source": [ + "df_overlap[(df_overlap['mt_tbd_qe'] == 'BAD-EXP') & (df_overlap['mt_wmt22_qe'] == 'OK')].sample(5)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 12, + "outputs": [], + "source": [ + "df_overlap" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 13, + "outputs": [], + "source": [ + "pd.crosstab(df_overlap['mt_tbd_qe'], df_overlap['mt_wmt22_qe']).T" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 14, + "outputs": [], + "source": [ + "df_overlap" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 15, + "outputs": [], + "source": [ + "plt.figure(figsize=(12, 4))\n", + "sns.countplot(\n", + " df_overlap,\n", + " x='mt_tbd_qe',\n", + " hue='mt_wmt22_qe',\n", + ")\n", + "plt.show()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## Analyse BAD-SUB" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 162, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5160/5160 [00:58<00:00, 88.93it/s] \n" + ] + }, + { + "data": { + "text/plain": " unit_id lang_id mt_tok pe_tok mt_pos \\\n0 flores101-main-ukr-100-pe1-1 ukr при від ADP \n1 flores101-main-ukr-100-pe1-1 ukr вступі повернення NOUN \n2 flores101-main-ukr-100-pe1-1 ukr фази фаза NOUN \n3 flores101-main-ukr-100-pe1-1 ukr бути проходити AUX \n4 flores101-main-ukr-100-pe1-3 ukr Повернувшись Проживши VERB \n... ... ... ... ... ... \n14804 flores101-main-vie-48-pe1-3 vie bằng trên ADP \n14805 flores101-main-vie-48-pe1-3 vie vận chuyển tàu thuyền VERB \n14806 flores101-main-vie-48-pe1-3 vie cuộc đoàn NOUN \n14807 flores101-main-vie-48-pe1-3 vie thoại truyền NOUN \n14808 flores101-main-vie-48-pe1-4 vie điện thoại thông NOUN \n\n pe_pos same_word same_pos same_lemma same_morf same_deprel \n0 ADP False True False False True \n1 NOUN False True False False False \n2 NOUN False True True False False \n3 VERB False False False True False \n4 VERB False True False True True \n... ... ... ... ... ... ... \n14804 ADP False True False True True \n14805 NOUN False False False True False \n14806 NOUN False True False True True \n14807 VERB False False False True True \n14808 ADJ False False False True False \n\n[14809 rows x 11 columns]", + "text/html": "
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unit_idlang_idmt_tokpe_tokmt_pospe_possame_wordsame_possame_lemmasame_morfsame_deprel
0flores101-main-ukr-100-pe1-1ukrпривідADPADPFalseTrueFalseFalseTrue
1flores101-main-ukr-100-pe1-1ukrвступіповерненняNOUNNOUNFalseTrueFalseFalseFalse
2flores101-main-ukr-100-pe1-1ukrфазифазаNOUNNOUNFalseTrueTrueFalseFalse
3flores101-main-ukr-100-pe1-1ukrбутипроходитиAUXVERBFalseFalseFalseTrueFalse
4flores101-main-ukr-100-pe1-3ukrПовернувшисьПрожившиVERBVERBFalseTrueFalseTrueTrue
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14804flores101-main-vie-48-pe1-3viebằngtrênADPADPFalseTrueFalseTrueTrue
14805flores101-main-vie-48-pe1-3vievận chuyểntàu thuyềnVERBNOUNFalseFalseFalseTrueFalse
14806flores101-main-vie-48-pe1-3viecuộcđoànNOUNNOUNFalseTrueFalseTrueTrue
14807flores101-main-vie-48-pe1-3viethoạitruyềnNOUNVERBFalseFalseFalseTrueTrue
14808flores101-main-vie-48-pe1-4vieđiện thoạithôngNOUNADJFalseFalseFalseTrueFalse
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14809 rows × 11 columns

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" + }, + "execution_count": 162, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# collect all BAD-SUB: read files, filter sents with BAD-SUB token, process to python lists, read alignments\n", + "df_stats = pd.DataFrame()\n", + "\n", + "for _id, x in tqdm(df.iterrows(), total=len(df)):\n", + " pe_tokens = ast.literal_eval(x['tgt_tokens'])\n", + " pe_annotations = ast.literal_eval(x['tgt_annotations'])\n", + " mt_annotations = ast.literal_eval(x['mt_annotations'])\n", + " mt_tokens = ast.literal_eval(x['mt_tokens'])\n", + " mt_tbd_qe = ast.literal_eval(x['mt_tbd_qe'])\n", + " mt_pe_alignments_raw = ast.literal_eval(x['mt_pe_tbd_qe_alignments'])\n", + " mt_pe_alignments_dict = defaultdict(list)\n", + "\n", + " for k, v, score in mt_pe_alignments_raw:\n", + " if k is not None:\n", + " mt_pe_alignments_dict[k].append(v)\n", + "\n", + " for i, mt_tok in enumerate(mt_tokens):\n", + " if 'BAD-SUB' in mt_tbd_qe[i]:\n", + " paired_pe_tok_i = mt_pe_alignments_dict[i][0] if mt_pe_alignments_dict[i] else None # SUB have to be paired with one PE token\n", + " if paired_pe_tok_i is None:\n", + " continue\n", + "\n", + " _df_tok_stats = pd.DataFrame([{\n", + " 'unit_id': _id,\n", + " 'lang_id': x['lang_id'],\n", + " 'mt_tok': mt_tok,\n", + " 'pe_tok': pe_tokens[paired_pe_tok_i],\n", + " 'mt_pos': mt_annotations[i]['upos'],\n", + " 'pe_pos': pe_annotations[paired_pe_tok_i]['upos'],\n", + " 'same_word': mt_tok.lower() == pe_tokens[paired_pe_tok_i].lower(),\n", + " 'same_pos': mt_annotations[i]['upos'] == pe_annotations[paired_pe_tok_i]['upos'],\n", + " 'same_lemma': mt_annotations[i]['lemma'] == pe_annotations[paired_pe_tok_i]['lemma'],\n", + " 'same_morf': mt_annotations[i]['feats'] == pe_annotations[paired_pe_tok_i]['feats'],\n", + " 'same_deprel': mt_annotations[i]['deprel'] == pe_annotations[paired_pe_tok_i]['deprel'],\n", + " }])\n", + " df_stats = pd.concat([df_stats, _df_tok_stats], ignore_index=True)\n", + "\n", + "\n", + "df_stats = df_stats.astype({'same_word': bool, 'same_pos': bool, 'same_lemma': bool, 'same_morf': bool, 'same_deprel': bool})\n", + "\n", + "df_stats" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-07-18T21:01:51.313256Z", + "start_time": "2023-07-18T21:00:53.213021Z" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "---" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 413, + "outputs": [ + { + "data": { + "text/plain": " total_sub diff_pos same_pos \\\nunit_id lang_id \nflores101-main-ara-1-pe1-1 ara 3 1 2 \nflores101-main-ara-1-pe1-4 ara 2 2 0 \nflores101-main-ara-1-pe2-2 ara 5 3 2 \nflores101-main-ara-1-pe2-3 ara 1 0 1 \nflores101-main-ara-1-pe2-4 ara 3 2 1 \n... ... ... ... \nflores101-main-vie-99-pe1-4 vie 1 1 0 \nflores101-main-vie-99-pe2-1 vie 3 0 3 \nflores101-main-vie-99-pe2-2 vie 3 1 2 \nflores101-main-vie-99-pe2-3 vie 7 1 6 \nflores101-main-vie-99-pe2-4 vie 4 0 4 \n\n diff_pos_percent \nunit_id lang_id \nflores101-main-ara-1-pe1-1 ara 0.333333 \nflores101-main-ara-1-pe1-4 ara 1.000000 \nflores101-main-ara-1-pe2-2 ara 0.600000 \nflores101-main-ara-1-pe2-3 ara 0.000000 \nflores101-main-ara-1-pe2-4 ara 0.666667 \n... ... \nflores101-main-vie-99-pe1-4 vie 1.000000 \nflores101-main-vie-99-pe2-1 vie 0.000000 \nflores101-main-vie-99-pe2-2 vie 0.333333 \nflores101-main-vie-99-pe2-3 vie 0.142857 \nflores101-main-vie-99-pe2-4 vie 0.000000 \n\n[4088 rows x 4 columns]", + "text/html": "
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total_subdiff_possame_posdiff_pos_percent
unit_idlang_id
flores101-main-ara-1-pe1-1ara3120.333333
flores101-main-ara-1-pe1-4ara2201.000000
flores101-main-ara-1-pe2-2ara5320.600000
flores101-main-ara-1-pe2-3ara1010.000000
flores101-main-ara-1-pe2-4ara3210.666667
..................
flores101-main-vie-99-pe1-4vie1101.000000
flores101-main-vie-99-pe2-1vie3030.000000
flores101-main-vie-99-pe2-2vie3120.333333
flores101-main-vie-99-pe2-3vie7160.142857
flores101-main-vie-99-pe2-4vie4040.000000
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4088 rows × 4 columns

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" + }, + "execution_count": 413, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# precalculate df for charts: count same_pos, diff_pos, total_sub\n", + "df_stats_ext = df_stats[['unit_id', 'lang_id', 'same_pos']].copy()\n", + "df_stats_ext['diff_pos'] = ~df_stats_ext['same_pos']\n", + "df_stats_ext['total_sub'] = 1\n", + "df_stats_ext_sum = df_stats_ext.groupby(['unit_id', 'lang_id'])[['total_sub', 'diff_pos', 'same_pos']].sum()\n", + "df_stats_ext_sum = df_stats_ext_sum[(df_stats_ext_sum['total_sub'] < 10) & (df_stats_ext_sum['diff_pos'] < 6)]\n", + "df_stats_ext_sum['diff_pos_percent'] = df_stats_ext_sum['diff_pos'] / df_stats_ext_sum['total_sub']\n", + "df_stats_ext_sum" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-07-19T10:23:35.738184Z", + "start_time": "2023-07-19T10:23:35.680165Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 414, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "f, axes = plt.subplots(2, 3, figsize=(9, 6), sharex=True, sharey=True)\n", + "\n", + "for ax, lang in zip(axes.flat, df['lang_id'].unique()):\n", + "\n", + " sns.histplot(\n", + " df_stats_ext_sum.loc[(slice(None),lang), :],\n", + " x=\"diff_pos_percent\",\n", + " kde=True,\n", + " ax=ax,\n", + " )\n", + " ax.set_title(lang)\n", + " # ax.set_axis_off()\n", + "\n", + "ax.set(xlim=(0, 1))\n", + "f.suptitle(\"Percent of diff_pos of all BAD-SUB\", fontsize=12)\n", + "plt.show()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-07-19T10:23:59.005619Z", + "start_time": "2023-07-19T10:23:54.861742Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 416, + "outputs": [ + { + "data": { + "text/plain": "" + }, + "execution_count": 416, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": "
", + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.displot(\n", + " df_stats_ext_sum,\n", + " x=\"diff_pos_percent\",\n", + " kde=True,\n", + ")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-07-19T10:28:23.548919Z", + "start_time": "2023-07-19T10:28:22.673989Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 415, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(6, 6))\n", + "sns.heatmap(\n", + " pd.crosstab(df_stats_ext_sum['total_sub'], df_stats_ext_sum['diff_pos']),\n", + " annot=True,\n", + " fmt=\".0f\",\n", + ").set(title='#total_sub vs #diff_pos in sentences')\n", + "plt.show()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-07-19T10:24:00.116236Z", + "start_time": "2023-07-19T10:23:59.016911Z" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "## Analyse Syntax Trees (using Stanza & Tree Edit Distance)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "# process saved stanza sentences to original format\n", + "\n", + "def list_to_stanza_sentence(token_list, annotations_list) -> StanzaSentence:\n", + " tokens = []\n", + " for i, (token, anotation) in enumerate(zip(token_list, annotations_list), start=1):\n", + " token_dict = dict(\n", + " id=i,\n", + " text=token,\n", + " lemma=anotation.get('lemma'),\n", + " upos=anotation.get('upos'),\n", + " xpos=anotation.get('xpos'),\n", + " feats=anotation.get('feats'),\n", + " head=anotation.get('head'),\n", + " deprel=anotation.get('deprel'),\n", + " misc='start_char={}|end_char={}'.format(anotation.get('start_char'), anotation.get('end_char')),\n", + " ner=anotation.get('ner'),\n", + " )\n", + "\n", + " tokens.append(token_dict)\n", + "\n", + " sentence = StanzaSentence(tokens=tokens)\n", + " return sentence" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 304, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 22/5160 [00:01<06:32, 13.08it/s]19-Jul 11:30:28 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 2%|▏ | 109/5160 [00:06<06:06, 13.76it/s]19-Jul 11:30:33 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 7%|▋ | 386/5160 [00:33<05:47, 13.72it/s]19-Jul 11:31:00 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + "19-Jul 11:31:00 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 9%|▉ | 462/5160 [00:42<04:36, 17.01it/s]19-Jul 11:31:09 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 14%|█▍ | 724/5160 [01:06<14:20, 5.15it/s]19-Jul 11:31:32 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + "19-Jul 11:31:32 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 14%|█▍ | 738/5160 [01:06<04:37, 15.93it/s]19-Jul 11:31:33 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + "19-Jul 11:31:33 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 19%|█▉ | 974/5160 [01:21<02:10, 31.98it/s]19-Jul 11:31:48 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 20%|█▉ | 1015/5160 [01:23<02:57, 23.34it/s]19-Jul 11:31:49 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 22%|██▏ | 1123/5160 [01:26<02:24, 27.99it/s]19-Jul 11:31:53 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 23%|██▎ | 1175/5160 [01:29<02:29, 26.71it/s]19-Jul 11:31:55 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 24%|██▍ | 1242/5160 [01:31<02:34, 25.42it/s]19-Jul 11:31:58 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 24%|██▍ | 1260/5160 [01:32<03:43, 17.42it/s]19-Jul 11:31:59 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 27%|██▋ | 1394/5160 [01:39<03:37, 17.31it/s]19-Jul 11:32:06 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + "19-Jul 11:32:06 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 30%|██▉ | 1538/5160 [01:46<02:30, 24.05it/s]19-Jul 11:32:13 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + "19-Jul 11:32:13 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 38%|███▊ | 1935/5160 [02:11<04:04, 13.19it/s]19-Jul 11:32:37 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 41%|████ | 2090/5160 [02:20<02:30, 20.35it/s]19-Jul 11:32:47 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + "19-Jul 11:32:47 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 43%|████▎ | 2228/5160 [02:27<02:17, 21.36it/s]19-Jul 11:32:54 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + "19-Jul 11:32:54 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 49%|████▊ | 2512/5160 [02:46<04:09, 10.62it/s]19-Jul 11:33:12 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + "19-Jul 11:33:12 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 60%|█████▉ | 3092/5160 [03:29<02:34, 13.40it/s]19-Jul 11:33:56 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 65%|██████▌ | 3373/5160 [03:52<02:09, 13.85it/s]19-Jul 11:34:19 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + "19-Jul 11:34:19 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 67%|██████▋ | 3443/5160 [03:57<01:31, 18.77it/s]19-Jul 11:34:24 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 84%|████████▎ | 4309/5160 [04:38<00:50, 16.71it/s]19-Jul 11:35:05 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 84%|████████▍ | 4339/5160 [04:41<01:39, 8.23it/s]19-Jul 11:35:07 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 86%|████████▌ | 4427/5160 [04:50<01:11, 10.23it/s]19-Jul 11:35:17 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + "19-Jul 11:35:17 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 87%|████████▋ | 4473/5160 [04:54<00:52, 13.00it/s]19-Jul 11:35:20 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + "19-Jul 11:35:20 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 88%|████████▊ | 4520/5160 [04:58<01:05, 9.77it/s]19-Jul 11:35:25 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 89%|████████▉ | 4613/5160 [05:08<00:47, 11.50it/s]19-Jul 11:35:35 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 91%|█████████ | 4693/5160 [05:16<00:50, 9.33it/s]19-Jul 11:35:42 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + "19-Jul 11:35:42 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 91%|█████████ | 4702/5160 [05:16<00:42, 10.67it/s]19-Jul 11:35:43 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 91%|█████████▏| 4721/5160 [05:19<00:46, 9.50it/s]19-Jul 11:35:46 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + "19-Jul 11:35:46 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 92%|█████████▏| 4749/5160 [05:22<00:34, 11.84it/s]19-Jul 11:35:49 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + "19-Jul 11:35:49 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 93%|█████████▎| 4815/5160 [05:28<00:38, 8.94it/s]19-Jul 11:35:55 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 97%|█████████▋| 4985/5160 [05:42<00:14, 11.82it/s]19-Jul 11:36:09 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 97%|█████████▋| 5030/5160 [05:47<00:18, 7.12it/s]19-Jul 11:36:14 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 99%|█████████▉| 5133/5160 [05:56<00:02, 9.15it/s]19-Jul 11:36:22 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + "100%|██████████| 5160/5160 [05:58<00:00, 14.41it/s]\n" + ] + }, + { + "data": { + "text/plain": " unit_id lang_id ted\n0 flores101-main-ukr-100-pe1-1 ukr 14\n1 flores101-main-ukr-100-pe1-2 ukr 4\n2 flores101-main-ukr-100-pe1-3 ukr 9\n3 flores101-main-ukr-100-pe1-4 ukr 12\n4 flores101-main-ukr-100-pe1-5 ukr 14\n... ... ... ...\n5155 flores101-main-vie-106-pe2-4 vie 16\n5156 flores101-main-vie-48-pe1-1 vie 11\n5157 flores101-main-vie-48-pe1-2 vie 27\n5158 flores101-main-vie-48-pe1-3 vie 16\n5159 flores101-main-vie-48-pe1-4 vie 10\n\n[5160 rows x 3 columns]", + "text/html": "
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unit_idlang_idted
0flores101-main-ukr-100-pe1-1ukr14
1flores101-main-ukr-100-pe1-2ukr4
2flores101-main-ukr-100-pe1-3ukr9
3flores101-main-ukr-100-pe1-4ukr12
4flores101-main-ukr-100-pe1-5ukr14
............
5155flores101-main-vie-106-pe2-4vie16
5156flores101-main-vie-48-pe1-1vie11
5157flores101-main-vie-48-pe1-2vie27
5158flores101-main-vie-48-pe1-3vie16
5159flores101-main-vie-48-pe1-4vie10
\n

5160 rows × 3 columns

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" + }, + "execution_count": 304, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# use astred to calculate tree edit distance\n", + "\n", + "df_synt_scores = pd.DataFrame()\n", + "\n", + "langs = {\n", + " 'vie': 'vi',\n", + " 'tur': 'tr',\n", + " 'ukr': 'uk',\n", + " 'ara': 'ar',\n", + " 'ita': 'it',\n", + " 'nld': 'nl',\n", + "}\n", + "\n", + "for _id, x in tqdm(df.iterrows(), total=len(df)):\n", + " pe_tokens = eval(x['tgt_tokens'])\n", + " pe_annotations = eval(x['tgt_annotations'])\n", + " mt_tokens = eval(x['mt_tokens'])\n", + " mt_annotations = eval(x['mt_annotations'])\n", + " mt_tbd_qe = eval(x['mt_tbd_qe'])\n", + " mt_pe_alignments_raw = eval(x['mt_pe_tbd_qe_alignments'])\n", + " mt_pe_alignments_dict = defaultdict(list)\n", + "\n", + " for k, v, score in mt_pe_alignments_raw:\n", + " if k is not None:\n", + " mt_pe_alignments_dict[k].append(v)\n", + "\n", + " mt_pe_alignments_pairs = [(k, v[0]) for k, v in mt_pe_alignments_dict.items() if len(v) > 0 and v[0] is not None]\n", + "\n", + " # fix 2 sentences examples with 2 heads to be 1 headed (match to first head\n", + " def _to_int(x, max_value = None):\n", + " try:\n", + " values = [int(i) for i in x.split('+')]\n", + " if 0 in values:\n", + " val = 0\n", + " else:\n", + " val = values[0]\n", + " except AttributeError:\n", + " val = int(x)\n", + " if max_value:\n", + " # fix index error when some tree indexes greater than tree length\n", + " val = min(val, max_value)\n", + " return val\n", + "\n", + " try:\n", + " mt_annotations = [{**annotation, 'head': _to_int(annotation['head'], max_value=len(mt_annotations)-1)} for annotation in mt_annotations]\n", + " first_head, *other_heads = [i for i, annotation in enumerate(mt_annotations) if annotation['head'] == 0]\n", + " mt_annotations = [{**annotation, 'head': first_head} if i in other_heads else annotation for i, annotation in enumerate(mt_annotations)]\n", + "\n", + " pe_annotations = [{**annotation, 'head': _to_int(annotation['head'], max_value=len(pe_annotations)-1)} for annotation in pe_annotations]\n", + " first_head, *other_heads = [i for i, annotation in enumerate(pe_annotations) if annotation['head'] == 0]\n", + " pe_annotations = [{**annotation, 'head': first_head} if i in other_heads else annotation for i, annotation in enumerate(pe_annotations)]\n", + " except ValueError:\n", + " print('VALUE ERROR')\n", + " print('mt_annotations', [i['head'] for i in mt_annotations])\n", + " print('pe_annotations', [i['head'] for i in pe_annotations])\n", + " _df_synt_scores = pd.DataFrame([{\n", + " 'unit_id': _id,\n", + " 'lang_id': x['lang_id'],\n", + " 'ted': None,\n", + " }])\n", + " df_synt_scores = pd.concat([df_synt_scores, _df_synt_scores], ignore_index=True)\n", + " continue\n", + "\n", + " try:\n", + " sent_mt = Sentence.from_parser(list_to_stanza_sentence(mt_tokens, mt_annotations))\n", + " sent_pe = Sentence.from_parser(list_to_stanza_sentence(pe_tokens, pe_annotations))\n", + "\n", + " aligned = AlignedSentences(\n", + " sent_mt,\n", + " sent_pe,\n", + " word_aligns=mt_pe_alignments_pairs,\n", + " )\n", + " ted = aligned.ted\n", + " except IndexError:\n", + " print('INDEX ERROR')\n", + " print('mt_annotations', [i['head'] for i in mt_annotations])\n", + " print('pe_annotations', [i['head'] for i in pe_annotations])\n", + " ted = None\n", + "\n", + " _df_synt_scores = pd.DataFrame([{\n", + " 'unit_id': _id,\n", + " 'lang_id': x['lang_id'],\n", + " 'ted': int(ted),\n", + " }])\n", + " df_synt_scores = pd.concat([df_synt_scores, _df_synt_scores], ignore_index=True)\n", + "\n", + "df_synt_scores['ted'] = df_synt_scores['ted'].astype(int)\n", + "\n", + "df_synt_scores" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-07-19T09:36:35.619460Z", + "start_time": "2023-07-19T09:30:26.771815Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 419, + "outputs": [ + { + "data": { + "text/plain": "
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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "g = sns.displot(\n", + " df_synt_scores,\n", + " x=\"ted\",\n", + " hue=\"lang_id\",\n", + " kde=True,\n", + " # log_scale=(False, 2),\n", + " multiple=\"layer\",\n", + " alpha=0.15,\n", + " # facet_kws={'hist_kws':dict(alpha=0.1)}\n", + ")\n", + "plt.xlim(-1, 30)\n", + "plt.ylim(0, None)\n", + "plt.show()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-07-19T10:31:42.618669Z", + "start_time": "2023-07-19T10:31:39.680391Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 372, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "f, axes = plt.subplots(2, 3, figsize=(9, 6), sharex=True, sharey=True)\n", + "\n", + "for ax, lang in zip(axes.flat, df['lang_id'].unique()):\n", + " #\n", + " # # Create a cubehelix colormap to use with kdeplot\n", + " # cmap = sns.cubehelix_palette(start=s, light=1, as_cmap=True)\n", + "\n", + " sns.histplot(\n", + " df_synt_scores[df_synt_scores['lang_id'] == lang],\n", + " x=\"ted\",\n", + " kde=True,\n", + " # log_scale=(False, 2),\n", + " multiple=\"layer\",\n", + " # alpha=0.25,\n", + " # facet_kws={'hist_kws':dict(alpha=0.1)}\n", + " stat='count',\n", + " ax=ax,\n", + " )\n", + " ax.set_title(lang)\n", + " # ax.set_axis_off()\n", + "\n", + "ax.set(xlim=(-1, 30))\n", + "f.suptitle(\"Distribution of Tree Edit Distance (TED)\", fontsize=12)\n", + "plt.show()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-07-19T10:06:31.926535Z", + "start_time": "2023-07-19T10:06:27.991091Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 309, + "outputs": [ + { + "data": { + "text/plain": " unit_id lang_id ted mt_tbd_bad_count\n2864 flores101-main-nld-100-pe2-1 nld 11 4\n433 flores101-main-ukr-5-pe2-2 ukr 2 6\n4835 flores101-main-vie-93-pe1-5 vie 7 2\n4776 flores101-main-vie-69-pe1-4 vie 18 2\n2975 flores101-main-nld-39-pe1-5 nld 0 0", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
unit_idlang_idtedmt_tbd_bad_count
2864flores101-main-nld-100-pe2-1nld114
433flores101-main-ukr-5-pe2-2ukr26
4835flores101-main-vie-93-pe1-5vie72
4776flores101-main-vie-69-pe1-4vie182
2975flores101-main-nld-39-pe1-5nld00
\n
" + }, + "execution_count": 309, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_synt_scores['mt_tbd_bad_count'] = df['mt_tbd_qe'].apply(eval).apply(lambda x: sum(len(i - {'OK', 'BAD-DEL-L', 'BAD-DEL-R', 'BAD-SHF'}) for i in x)).values\n", + "df_synt_scores.sample(5)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-07-19T09:36:35.620924Z", + "start_time": "2023-07-19T09:36:30.127487Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 327, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pearson correlation\n" + ] + }, + { + "data": { + "text/plain": "lang_id \nara ted 0.556922\nita ted 0.434015\nnld ted 0.669098\ntur ted 0.689409\nukr ted 0.710757\nvie ted 0.642947\nName: mt_tbd_bad_count, dtype: float64" + }, + "execution_count": 327, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print('Pearson correlation')\n", + "df_synt_scores.groupby('lang_id')[['ted', 'mt_tbd_bad_count']].corr(method='pearson').loc[(slice(None),'ted'), 'mt_tbd_bad_count']" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-07-19T09:43:18.169352Z", + "start_time": "2023-07-19T09:43:18.104678Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 355, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: BAD count except for BAD-DEL and BAD-SHF\n" + ] + }, + { + "data": { + "text/plain": "
", + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print('Note: BAD count except for BAD-DEL and BAD-SHF')\n", + "sns.jointplot(df_synt_scores, x=\"ted\", y=\"mt_tbd_bad_count\", hue=\"lang_id\", kind=\"kde\", fill=Fl, marginal_kws={'hist_kws':dict(alpha=0.1)})\n", + "plt.xlim(-5, 60)\n", + "plt.ylim(-5, 30)\n", + "plt.show()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-07-19T09:59:48.479409Z", + "start_time": "2023-07-19T09:59:42.344089Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 374, + "outputs": [ + { + "data": { + "text/plain": "Text(0.5, 0.98, 'Correlation of Tree Edit Distance (TED) vs #BAD-X tags')" + }, + "execution_count": 374, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": "
", + "image/png": 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+ }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "f, axes = plt.subplots(2, 3, figsize=(9, 6), sharex=True, sharey=True)\n", + "\n", + "for ax, lang in zip(axes.flat, df['lang_id'].unique()):\n", + " #\n", + " # # Create a cubehelix colormap to use with kdeplot\n", + " # cmap = sns.cubehelix_palette(start=s, light=1, as_cmap=True)\n", + "\n", + " sns.kdeplot(\n", + " df_synt_scores[df_synt_scores['lang_id'] == lang],\n", + " x=\"ted\",\n", + " y=\"mt_tbd_bad_count\",\n", + " # cmap=None,\n", + " fill=True,\n", + " # clip=(-5, 5),\n", + " # cut=10,\n", + " # thresh=0,\n", + " # levels=15,\n", + " ax=ax,\n", + " )\n", + " ax.set_title(lang)\n", + " # ax.set_axis_off()\n", + "\n", + "ax.set(xlim=(-7, 40), ylim=(-5, 25))\n", + "f.suptitle(\"Correlation of Tree Edit Distance (TED) vs #BAD-X tags\", fontsize=12)\n", + "plt.show()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-07-19T10:07:10.961389Z", + "start_time": "2023-07-19T10:07:04.374076Z" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "---" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 35, + "outputs": [], + "source": [ + "import matplotlib.ticker as ticker\n", + "\n", + "\n", + "def plot_summary_df(summary_df, title=''):\n", + " try:\n", + " summary_df = summary_df.drop(['TOTAL', 'AVG'], axis=0)\n", + " except KeyError:\n", + " pass\n", + "\n", + " sns.set_theme(style=\"whitegrid\")\n", + " sns.set(font_scale=1.5)\n", + "\n", + " # Make the PairGrid\n", + " g = sns.PairGrid(\n", + " # summary_df.reset_index().sort_values(\"total (same pos)\", ascending=False),\n", + " summary_df.reset_index(),\n", + " x_vars=summary_df.columns,\n", + " y_vars=[\"lang_id\"],\n", + " height=10,\n", + " aspect=.3,\n", + " )\n", + "\n", + " # Draw a dot plot using the stripplot function\n", + " g.map(\n", + " sns.stripplot,\n", + " size=15,\n", + " orient=\"h\",\n", + " jitter=False,\n", + " palette=\"flare_r\",\n", + " linewidth=2,\n", + " edgecolor=\"w\",\n", + " )\n", + "\n", + " if title:\n", + " g.fig.subplots_adjust(top=0.9)\n", + " g.fig.suptitle(title)\n", + "\n", + " # Calculate the average for each column and draw a horizontal line\n", + " for ax, col in zip(g.axes.flat, summary_df.columns):\n", + " avg = summary_df[col].mean()\n", + " ax.axvline(avg, color='r', linestyle='--')\n", + "\n", + " step = 5000 if summary_df[col].max() > 10000 else 2500 if summary_df[col].max() > 5000 else 1000 if summary_df[col].max() > 2000 else 500 if summary_df[col].max() > 800 else 100 if summary_df[col].max() > 500 else 50\n", + " ax.set_xticks(np.arange(0, summary_df[col].max(), step=step))\n", + " ax.xaxis.set_major_formatter(ticker.EngFormatter())\n", + "\n", + "\n", + " # setup axis limits\n", + " g.set(xlim=(0, None), xlabel=\"Count\", ylabel=\"\")\n", + " for ax, title in zip(g.axes.flat, list(summary_df.columns)):\n", + " ax.set(title=title)\n", + " ax.xaxis.grid(True)\n", + " ax.yaxis.grid(True)\n", + "\n", + " sns.despine(left=True, bottom=True)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-07-18T17:00:42.468181Z", + "start_time": "2023-07-18T17:00:42.176945Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 190, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TOTAL:\t 14809\n", + "SAME POS:\t 10820\n", + "DIFF POS:\t 3989\n" + ] + }, + { + "data": { + "text/plain": " total same_pos diff_pos diff_deprel\nlang_id \nara 2055 1230 825 1027\nita 2368 1702 666 861\nnld 1728 1301 427 600\ntur 2217 1685 532 1079\nukr 4295 3425 870 1641\nvie 2146 1477 669 1217\nTOTAL 14809 10820 3989 6425\nAVG 2468 1803 664 1070", + "text/html": "
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totalsame_posdiff_posdiff_deprel
lang_id
ara205512308251027
ita23681702666861
nld17281301427600
tur221716855321079
ukr429534258701641
vie214614776691217
TOTAL148091082039896425
AVG246818036641070
\n
" + }, + "execution_count": 190, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print('TOTAL:\\t', len(df_stats))\n", + "print('SAME POS:\\t', len(df_stats[df_stats['same_pos']]))\n", + "print('DIFF POS:\\t', len(df_stats[~df_stats['same_pos']]))\n", + "\n", + "tmp = pd.DataFrame([\n", + " df_stats.groupby(['lang_id']).size(),\n", + " df_stats[df_stats['same_pos']].groupby(['lang_id']).size(),\n", + " df_stats[~df_stats['same_pos']].groupby(['lang_id']).size(),\n", + " df_stats[~df_stats['same_deprel']].groupby(['lang_id']).size(),\n", + "], index=[\n", + " 'total',\n", + " 'same_pos',\n", + " 'diff_pos',\n", + " 'diff_deprel',\n", + "]).fillna(0).astype('int').T\n", + "tmp.loc['TOTAL'] = tmp.sum(numeric_only=True)\n", + "tmp.loc['AVG'] = (tmp.loc['TOTAL'] / len(set(df_stats['lang_id']) - {'TOTAL'})).astype('int')\n", + "\n", + "# print(tmp)\n", + "tmp" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-07-13T11:05:51.373876Z", + "start_time": "2023-07-13T11:05:51.325488Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 193, + "outputs": [ + { + "data": { + "text/plain": " total same_pos diff_pos diff_deprel\nlang_id \nara 2055 1230 825 1027\nita 2368 1702 666 861\nnld 1728 1301 427 600\ntur 2217 1685 532 1079\nukr 4295 3425 870 1641\nvie 2146 1477 669 1217\nTOTAL 14809 10820 3989 6425\nAVG 2468 1803 664 1070", + "text/html": "
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totalsame_posdiff_posdiff_deprel
lang_id
ara205512308251027
ita23681702666861
nld17281301427600
tur221716855321079
ukr429534258701641
vie214614776691217
TOTAL148091082039896425
AVG246818036641070
\n
" + }, + "execution_count": 193, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tmp" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-07-13T11:06:01.710994Z", + "start_time": "2023-07-13T11:06:01.681673Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 191, + "outputs": [ + { + "data": { + "text/plain": "
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666239Mwzz+jUU081cESA6LV9+3ZdeeWV2rdvn5KTk9WrVy9J0tatW/X8889r7ty5ev7553XkkUcGvz569Gjt3r1bXq9XPXr0UNeuXbVz506tWbNGa9as0ZtvvqnXX39drVq1qvF+U6dO1QsvvKC2bduqZ8+e2rJli5YuXarPPvtMM2bM0P3336+NGzeqa9euOvTQQ7VhwwbNnz9fX3zxhd544w35/f7gtvbu3atx48bp22+/lcvlUpcuXZSenq6NGzdq1qxZmjdvnp555hkNGDAgbMervLxc48aN0/Lly9W+fXv16dNHmzZt0quvvqr//ve/evrpp3XiiSdWe01jM1GSvvjiC1133XUqLCxU69at1adPH5WUlGj9+vX69ttvNXfuXL388svB3AViwRtvvKHf/e53KisrU1pamvr06aOdO3dqypQpOuGEE0K+3ufz6Wc/+5ny8/O1fv16SdLPfvYzSQevNdavX6/8/Hx17tw5rP2HbADCL1ozYfny5brppptUUFCg5ORkZWRkaO/evXriiSfqvZHKpk2b9Otf/1o7d+6Ux+NR9+7dlZSUpI0bN+rpp5/WvHnzNGPGDB1++OE1Xpufn6+rrrpK69evV5cuXXT44Ydrw4YN+sc//qE333xTM2fOrJYllT777DP9/e9/V1pamg4//HBt2rRJRxxxhCQz11kxzwZayGmnnWZnZGTYr7zySvBrO3bssI899lg7IyPDvuGGG+ysrKzgc9u3b7evuOIKOyMjwx4yZIidm5tbbXsZGRl2RkaGvXTp0mpff/311+2MjAy7f//+9ocffljtuY0bN9rnnXeenZGRYV9yySXVnrvrrrvsjIwMe+LEieHaZSDm3HbbbXZGRoY9YcIEOz8/P/j1rKysYH+97rrrgl+/+uqr7YyMDPvyyy+39+zZE/y6ZVn23Llz7b59+9oZGRn2Cy+8UO19Kvt3RkaGPX36dLusrMy2bdvevXu3ffLJJ9sZGRl237597dNPP93+8ssvg6/7+OOP7SOOOMLOyMiw58yZU+39Kts3cuRIe9OmTcHncnNz7XvuucfOyMiwf/7zn9t79+5t9nH65JNPgu3PzMy0//GPf9iBQMC2bds+cOCAPX78eDsjI8MeNGhQtWxraiZedtlldkZGhv2nP/3JLikpqfaaoUOH2hkZGfYf/vCHZu8X4BTbt2+3jz76aDsjI8OeOnVq8LwvLy+3p0+fXi1Dtm7datv2weuDwYMHV9tW1f76U5UZ9uSTT4al3WQD0DKiNRNycnLsE0880c7IyLBvv/12Oy8vL/jcnDlz7KOOOqrW33kKCgrss846y87IyLDHjx9vf//998Hn9u7da48bN87OyMiwhw4dahcVFQWfq9znjIwM++ijj7YXLFgQfO77778PZsl5550XvPaybdt+8skng6+76aabgsd33759tm03/Tqr6rGu+n6owJoziKjp06ersLBQGRkZeuKJJ9SuXbvgc927d9f06dPVvn177dq1S88//3yDtrl06VJ5vV5dddVVNUbGHH744cHhgZUVcQANV3n3ggsuuKDaSJd27drp97//vQYPHqzevXtLkn744Yfgre7/9Kc/VRvK6nK5dNFFFwX/klXXkODBgwdr3LhxwUX3OnXqpIsvvlhSxbzvBx54QP379w9+/0knnRT8i8yaNWuCX3/vvff0xRdfqEOHDpo5c6YOO+yw4HOpqal64IEHdMwxx2j//v2aNWtW0w5OHUaPHq3Ro0fL7a74EZuWlqbHHntM3bp10759+zR79uzg9zY1Eys/l0svvbTaPPju3bvrrrvu0mmnnaauXbuGdb8Ak5577jmVlpbqhBNO0N133x087z0ej8aNGxec5uxkZAMQPtGaCS+//LKys7N16KGH6sEHH1RKSkrwuYsvvlg33HBDra979dVXtW3bNh111FGaNm1atanc7du31xNPPKGuXbtq69atmjNnTq3buPPOO3X++ecH/92xY0c988wzSk1N1YYNG/T222/X+rq77roreHzbtm0ryex1ViyjOIOI+vDDDyVJI0eOrHVhrbS0NF166aWSpEWLFjVom4888oi++uor3X777bU+XznNobS0NLioF4CGqVwH4eGHH9aiRYtUXFwcfK5fv36aOXOm7rnnHkkVBZtPPvlEX375pTIyMmpsKxAIBC9Cqm6nqiFDhtT4WuUvEn6/v9ZhypVFoIKCguDXKvPjzDPPVHJyco3XuFwuXXDBBZKkDz74oNa2NFXlulhV+Xy+YJHpvffeC369qZlY+blMmjRJy5cvV1lZWfC5008/XX/72990/fXXN39nAIeo7Ct1/cI1cuTICLamacgGIHyiNRMq2z18+PAat/SW6m53ZT8/99xz5fF4ajyflJSkYcOGSar9uiY5OVmXX355ja+3bdtWZ511lqTqGVSpffv26t69e53tMXGdFctYcwYRk5+frz179kiSjj766Dq/76ijjpIkbdmypcHb9ng8Kikp0apVq7R582Z999132rp1q9auXavdu3cHv8+yrOBfrACEduutt2rFihXasmWLbrrpJiUmJuq4447TSSedpFNPPVV9+/at8ZqkpCTt3LlTX3/9tbZv367vvvtOmzZt0rfffqvCwkJJqrNQ2qlTpxpfq7x4SU9Pr7X/Vj5v23bwa5Uj5T744IPgX5J/Kjc3V1LFOjm2bcvlctV5HBqqQ4cOtS5MLCl4rLZu3SqpeZl4xx13aPz48fryyy81evRoJScn6/jjj9cvfvELDRkyRIceemiz9wVwiuLi4uDP8j59+tT6PX379pXL5aqWA05CNgDhE82ZUNlf62r3IYccog4dOmjv3r3Vvl55XfPqq6/WWkSRKkYwS9LmzZtrPJeZmSmfz1fr6yrXmqnMoKrqWtDX1HVWrKM4g4ip+lftqkP4fqryucLCwgZ15LKyMj322GN68cUXq/013uPxKCMjQ/37969zmB6A+h1xxBF64403NH369ODq/itWrNCKFSv06KOPKiMjQ5MmTdLAgQMlVVwQTJo0SZ9++mm17aSkpGjgwIHau3dvnT/EJdX615dKjSms5ufnS5J2795drUBbm0AgoIKCgnpzqaHS09PrfK5y3/Ly8iQ1LxNPOeUUvfbaa5oxY4Y+/PBDFRQUaPHixVq8eLGmTp2qAQMGaMqUKcEpZ0A0y8nJCT6uKyMSExPl9/uDBWCnIRuA8InmTKgsWNR3vZOWllajOFN5XbN169ZaiyhVVWZJVfVlUOW09cq2VVVXQcfUdVasoziDiKm6XkVlh65NZeAmJyc3qMJ63333ac6cOfJ4PLriiit0/PHHq0+fPjr00EOVlJSkpUuXUpwBmqF79+66//77NWXKFH3zzTf69NNPtXz5cq1YsULr16/X2LFj9d///leJiYm6+uqrtW/fPnXp0kWXX365jjzySB122GHq1q2bXC6XJk6cWG9xJlwqpzP+4Q9/0NVXX93i71epvovAyoulyvnazc3EI444Qo8++qjKysr05ZdfasWKFVq2bJk+//xzrVq1SqNHj9Y777xT7wUgEA2q/lJRV1+xbVulpaURalHjkQ1A+ERzJqSnpysrK6vevl3b1G+/36+8vDz97W9/02mnndbo921IBlXeCbchTF1nxTrmdyBiUlJS1L59e0nSN998U+f3VT7XkKG3e/bs0dy5cyVVLEA6ZcoUDR8+XH379lVSUpIk6fvvv29my4H4ZNu2duzYoWXLlkmqGLnSv39/jR07Vs8995wWLFiglJQUFRUV6Z133tHrr7+uffv2KT09Xa+//rrGjx+vU089Vd27dw/+4lA5VL+lVd7yu3KB4trs3r1bq1evDmubdu/eXecF17fffitJwb9YNzUTA4GAtm3bps8++0xSxbSugQMH6qabbtKLL76oF198US6XS1lZWcHPDohmPp8vuPZUZT/6qc2bN6u8vDySzWoUsgEIn2jOhMrrk7raXVBQoF27dtX5uvqua7Zu3aqvv/5a2dnZNZ7btGlTnVO8fppBDWHqOivWUZxBi6n8ZaxqEJx++umSpNmzZ9dazc7JydG8efMkSaecckrI7e3atSv478q511VZllVtxfJAINCUXQHi0oEDBzRs2DD96le/0tdff13j+V69eqlLly6SKvrajh07JEldunQJ/gW4qo0bN2r16tWSWr4vVv5V6a233tK+fftq/Z7f/e53uuKKKzRx4sSwvW8gEAgWjKsqKirS/PnzJUlnn3128OtNycQNGzZo6NCh+uUvf6msrKwarznuuOOCf3lnEXTEiqFDh0qquNNJbfnx6quvRrpJjUI2AOEVrZlQ2e758+dXm8JYac6cObXuT+V1zWuvvVbryJry8nLdeOONGjFihB588MEaz//www+1Lsy7d+/e4OK+VTMoFFPXWbGO4gxaTOVw2Z07dwa/9utf/1qtWrXS+vXrdeutt1brzN99952uv/56/fDDD+rYsaN++ctf1rq9qtXknj17BlcsnzFjhoqKioLP7dq1S7feeqtWrlwZ/FrV5wHUr02bNho8eLCkih+wmzZtCj5nWZZefPFFrV+/Xm63W4MHDw7eRnHt2rXVphLatq0lS5Zo7NixwTuHtHRfPPfcc5WRkaHc3FyNGTOm2l928vPz9cc//lHLli2Ty+XSuHHjwvrejzzyiN59993gv7Ozs3XLLbdo165d6tWrV7U7SzQlE/v27auMjAwFAgH95je/qTY6sLS0VI899pjy8/OVnJwcXAsIiHZjxoxRWlqa1qxZo3vuuSc4CsW2bb300kv617/+ZbiFoZENQPhEayaMGDFCPXr00Pfff69bbrmlWt9+55139Mgjj9T6ulGjRql9+/batm2bxo8fX+33oezsbN12223atGmTEhISdN1119W6jd///vfVfi/asWOHxo8fr8LCQp1wwgk6+eSTG7wfJq+zYhlrzqDFHHnkkVq/fr1mzpypxYsXa+jQobrxxhv15JNP6tZbb9X777+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+ }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_summary_df(tmp, title='BAD-SUB types')" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-07-13T11:05:54.004076Z", + "start_time": "2023-07-13T11:05:52.391641Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 182, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TOTAL:\t 10820\n" + ] + }, + { + "data": { + "text/plain": " total (same pos) same_lemma same_morf same_lemma diff_morf \\\nlang_id \nara 1230 93 374 \nita 1702 100 500 \nnld 1301 153 100 \ntur 1685 115 623 \nukr 3425 128 1164 \nvie 1477 0 0 \nTOTAL 10820 589 2761 \nAVG 1803 98 460 \n\n diff_lemma same_morf diff_lemma diff_morf same_word (diff case) \nlang_id \nara 498 265 0 \nita 705 397 68 \nnld 867 181 114 \ntur 604 343 79 \nukr 1067 1066 124 \nvie 1468 9 160 \nTOTAL 5209 2261 545 \nAVG 868 376 90 ", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
total (same pos)same_lemma same_morfsame_lemma diff_morfdiff_lemma same_morfdiff_lemma diff_morfsame_word (diff case)
lang_id
ara1230933744982650
ita170210050070539768
nld1301153100867181114
tur168511562360434379
ukr3425128116410671066124
vie14770014689160
TOTAL10820589276152092261545
AVG18039846086837690
\n
" + }, + "execution_count": 182, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "_df_stats = df_stats[df_stats['same_pos']]\n", + "print('TOTAL:\\t', len(_df_stats))\n", + "\n", + "tmp = pd.DataFrame([\n", + " _df_stats.groupby(['lang_id']).size(),\n", + " _df_stats[_df_stats['same_lemma'] & _df_stats['same_morf']].groupby(['lang_id']).size(),\n", + " _df_stats[_df_stats['same_lemma'] & ~_df_stats['same_morf']].groupby(['lang_id']).size(),\n", + " _df_stats[~_df_stats['same_lemma'] & _df_stats['same_morf']].groupby(['lang_id']).size(),\n", + " _df_stats[~_df_stats['same_lemma'] & ~_df_stats['same_morf']].groupby(['lang_id']).size(),\n", + " _df_stats[_df_stats['same_word']].groupby(['lang_id']).size(),\n", + "], index=[\n", + " 'total (same pos)',\n", + " 'same_lemma same_morf',\n", + " 'same_lemma diff_morf',\n", + " 'diff_lemma same_morf',\n", + " 'diff_lemma diff_morf',\n", + " 'same_word (diff case)',\n", + "]).fillna(0).astype('int').T\n", + "tmp.loc['TOTAL'] = tmp.sum(numeric_only=True)\n", + "tmp.loc['AVG'] = (tmp.loc['TOTAL'] / len(set(df_stats['lang_id']) - {'TOTAL'})).astype('int')\n", + "\n", + "# print(tmp)\n", + "tmp" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-07-13T10:37:02.689878Z", + "start_time": "2023-07-13T10:37:02.633850Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 183, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": 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+ }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_summary_df(tmp, title='BAD-SUB same_pos types')" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-07-13T10:37:11.466933Z", + "start_time": "2023-07-13T10:37:08.060140Z" + } + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "---" + ], + "metadata": { + "collapsed": false + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks/qe_visualize.ipynb b/notebooks/qe_visualize.ipynb new file mode 100644 index 0000000..73fa658 --- /dev/null +++ b/notebooks/qe_visualize.ipynb @@ -0,0 +1,490 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "## Imports" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "ExecuteTime": { + "end_time": "2023-08-27T12:24:02.230745Z", + "start_time": "2023-08-27T12:24:02.185332Z" + } + }, + "outputs": [], + "source": [ + "from typing import List, Set, Tuple, Union, Optional\n", + "from pathlib import Path\n", + "import ast\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import networkx as nx\n", + "import pandas as pd\n", + "\n", + "from divemt.qe_taggers import NameTBDGeneralTags, NameTBDTagger\n", + "from divemt.qe_taggers.custom_simalign import SentenceAligner as CustomSentenceAligner" + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Load data" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "ExecuteTime": { + "end_time": "2023-08-27T12:24:03.212569Z", + "start_time": "2023-08-27T12:24:03.153402Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": "True" + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "DATASET_FOLDER = Path() / '..' / 'data' / 'processed'\n", + "DATASET_FOLDER.exists()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "def get_sample(df, idx):\n", + " sample = df.iloc[idx]\n", + " lang_id = sample['lang_id']\n", + "\n", + " src_tokens = ast.literal_eval(sample['src_tokens'])\n", + " mt_tokens = ast.literal_eval(sample['mt_tokens'])\n", + " tgt_tokens = ast.literal_eval(sample['tgt_tokens'])\n", + "\n", + " src_tbd_qe = ast.literal_eval(sample[f'src_tbd_qe'])\n", + " mt_tbd_qe = ast.literal_eval(sample[f'mt_tbd_qe'])\n", + "\n", + " return lang_id, src_tokens, mt_tokens, tgt_tokens, src_tbd_qe, mt_tbd_qe" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "# read pandas dataframe\n", + "df_it = pd.read_csv(DATASET_FOLDER / 'ita' / 't1_warmup_texts.tsv', sep='\\t')\n", + "df_it = df_it[pd.notna(df_it['mt_tokens'])]\n", + "len(df_it)\n", + "df_it" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## Aux functions for visualization" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "ExecuteTime": { + "end_time": "2023-08-27T12:24:05.676319Z", + "start_time": "2023-08-27T12:24:05.653381Z" + } + }, + "outputs": [], + "source": [ + "def custom_bipartite_layout(\n", + " G, top, bottom, aspect_ratio=4 / 3,\n", + "):\n", + " height = 1\n", + " width = aspect_ratio * height\n", + " offset = (width / 2, height / 2)\n", + "\n", + " nodes = list(top) + bottom\n", + "\n", + " left_xs = np.repeat(0, len(top))\n", + " right_xs = np.repeat(width, len(bottom))\n", + " left_ys = np.linspace(0, height, len(top))\n", + " right_ys = np.linspace(0, height, len(bottom))\n", + "\n", + " top_pos = np.column_stack([left_xs, left_ys]) - offset\n", + " bottom_pos = np.column_stack([right_xs, right_ys]) - offset\n", + "\n", + " pos = np.concatenate([top_pos, bottom_pos])\n", + " pos = pos[:, ::-1] # swap x and y coords for horizontal\n", + " pos = dict(zip(nodes, pos))\n", + " return pos\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "ExecuteTime": { + "end_time": "2023-08-27T12:24:06.775669Z", + "start_time": "2023-08-27T12:24:06.749448Z" + } + }, + "outputs": [], + "source": [ + "def draw_aligned_qe(\n", + " top_tokens: List[str],\n", + " bottom_tokens: List[str],\n", + " top_qe_tags: Optional[List[Union[str, Set[str]]]],\n", + " bottom_qe_tags: Optional[List[Union[str, Set[str]]]],\n", + " top_bottom_alignments: List[Union[Tuple[int, int], Tuple[int, int, float]]],\n", + " *,\n", + " title: str = None,\n", + "):\n", + " # create graph\n", + " G = nx.Graph()\n", + " top, bottom = [f'top_{i}' for i in range(len(top_tokens))], [f'bottom_{i}' for i in range(len(bottom_tokens))]\n", + " G.add_nodes_from(top, bipartite=0)\n", + " G.add_nodes_from(bottom, bipartite=1)\n", + " G.add_edges_from([(f'top_{alignment[0]}', f'bottom_{alignment[1]}') for alignment in top_bottom_alignments])\n", + "\n", + " # set words as nore names\n", + " custom_node_names = {}\n", + " custom_node_names.update({f'top_{i}': tok for i, tok in enumerate(top_tokens)})\n", + " custom_node_names.update({f'bottom_{i}': tok for i, tok in enumerate(bottom_tokens)})\n", + "\n", + " # set qe attributes as labels\n", + " custom_node_attrs = {}\n", + " if top_qe_tags:\n", + " custom_node_attrs.update({f'top_{i}': str(qe) for i, qe in enumerate(top_qe_tags)})\n", + " if bottom_qe_tags:\n", + " custom_node_attrs.update({f'bottom_{i}': str(qe) for i, qe in enumerate(bottom_qe_tags)})\n", + "\n", + " # connection weighs if any\n", + " if top_bottom_alignments and len(top_bottom_alignments[0]) == 3:\n", + " custom_edge_weights = {(f'top_{i}', f'bottom_{j}'): round(w, 2) for i, j, w in top_bottom_alignments}\n", + " else:\n", + " custom_edge_weights = None\n", + "\n", + " # get nodes and attributes positions\n", + " pos = custom_bipartite_layout(G, bottom, top)\n", + " pos_attrs = {node: (x, y+0.2) if node in top else (x, y-0.2) for node, (x, y) in pos.items()}\n", + "\n", + " # draw graph\n", + " fig, ax = plt.subplots()\n", + " width = max(3*max(len(top_tokens), len(bottom_tokens)), 12)\n", + " ax.margins(0.1, 0.2)\n", + " height = 6\n", + " fig.set_size_inches(width, height)\n", + " nx.draw_networkx(G, pos=pos, width=2, ax=ax, labels=custom_node_names, node_size=0, bbox=dict(facecolor='white', edgecolor='skyblue', boxstyle='round', pad=0.2), edgelist=custom_edge_weights.keys(), edge_color=custom_edge_weights.values(), edge_cmap=plt.cm.Blues, edge_vmin=0.5, edge_vmax=1)\n", + " nx.draw_networkx_labels(G, pos_attrs, labels=custom_node_attrs, font_size=9)\n", + " if custom_edge_weights:\n", + " nx.draw_networkx_edge_labels(G, pos, edge_labels=custom_edge_weights, font_size=8, label_pos=0.5, ax=ax)\n", + " if title:\n", + " ax.set_title(title)\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "ExecuteTime": { + "end_time": "2023-08-27T12:36:38.042486Z", + "start_time": "2023-08-27T12:36:37.009381Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": 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+ }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "alignments = [(1, 0, 0.9), (2, 2, 0.75), (2, 3, 0.6), (3, 4, 0.75), (4, 4, 0.9), (5, 5, 0.95)]\n", + "top_tokens = ['There', 'some', 'translated', 'the', 'words', 'here']\n", + "top_qe = [{'BAD-INS'}, {'OK', 'BAD-DEL-R'}, {'BAD-CON', 'BAD-DEL-L'}, {'BAD-EXP'}, {'BAD-EXP'}, {'OK'}]\n", + "bottom_tokens = ['Some', 'cool', 'post', 'edit', 'words', 'here']\n", + "# bottom_qe = [{'OK'}, {'BAD-INS'}, {'BAD-DEL-L', 'OK'}, {'BAD-DEL-R', 'OK'}]\n", + "bottom_qe = []\n", + "\n", + "draw_aligned_qe(top_tokens, bottom_tokens, top_qe, bottom_qe, alignments, title='Translated and Post-edited Sentences alignments with QE tags')" + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Load align models" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "ExecuteTime": { + "end_time": "2023-06-12T11:12:48.109430Z", + "start_time": "2023-06-12T11:12:02.024504Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Some weights of the model checkpoint at bert-base-multilingual-cased were not used when initializing BertModel: ['cls.seq_relationship.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.bias', 'cls.predictions.transform.dense.weight']\n", + "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n", + "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n", + "Some weights of the model checkpoint at xlm-roberta-base were not used when initializing XLMRobertaModel: ['lm_head.bias', 'lm_head.layer_norm.bias', 'lm_head.dense.weight', 'lm_head.layer_norm.weight', 'lm_head.decoder.weight', 'lm_head.dense.bias']\n", + "- This IS expected if you are initializing XLMRobertaModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n", + "- This IS NOT expected if you are initializing XLMRobertaModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n" + ] + } + ], + "source": [ + "aligner_bert = CustomSentenceAligner(model=\"bert\", token_type=\"bpe\", matching_methods=\"mai\", return_similarity=\"avg\")\n", + "aligner_xlmr = CustomSentenceAligner(model=\"xlmr\", token_type=\"bpe\", matching_methods=\"mai\", return_similarity=\"avg\")\n", + "tagger_bert = NameTBDTagger(aligner=aligner_bert)\n", + "tagger_xlmr = NameTBDTagger(aligner=aligner_xlmr)" + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Cherry pick samples and analyze alignments" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "ExecuteTime": { + "end_time": "2023-06-12T11:16:01.430049Z", + "start_time": "2023-06-12T11:16:01.392714Z" + } + }, + "outputs": [], + "source": [ + "# select index of a sample to visualize\n", + "idx = 1\n", + "# idx = 5\n", + "# idx = 8\n", + "# idx = 10\n", + "lang_id, src_tokens, mt_tokens, tgt_tokens, src_tbd_qe, mt_tbd_qe = get_sample(df_it, idx)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "xlmr" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "ExecuteTime": { + "end_time": "2023-06-12T11:16:02.881106Z", + "start_time": "2023-06-12T11:16:01.742546Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Aligning mt-pe: 100%|██████████| 1/1 [00:00<00:00, 4.84it/s]\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mt_pe_alignments = tagger_xlmr.align_mt_pe([mt_tokens], [tgt_tokens], lang_id)[0]\n", + "draw_aligned_qe(mt_tokens, tgt_tokens, mt_tbd_qe, None, mt_pe_alignments, title='MT - PE (XLMR)')" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "ExecuteTime": { + "end_time": "2023-06-12T11:16:03.680420Z", + "start_time": "2023-06-12T11:16:02.888963Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Aligning src-mt: 100%|██████████| 1/1 [00:00<00:00, 4.18it/s]\n" + ] + }, + { + "data": { + "image/png": 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6dOiQduzYIUmKjIyU2WxWdHS0pNzSX2RkpCIjI21lwG3btikxMTFP6Q+AY4qMjNSGDRt07tw5SblX7T/wwAOqXbu27b/5qKgomUymPONH2bJl85R8JWnOnDmKjIxUQEBAnnGrTZs2MpvNttnC/f39de7cOS1btixf9mHOnDmqUqWKIiIi8nxu69atJemy8bJly5aX/dic35kAOI5LS3DZ2dk6ffq0KlSoIH9/f23YsOGq77swY+9vv/2mtLS0K66zaNEiSdKLL76YZ/nw4cMlSb/++uttZQdQuP2zrPvss89Kujj2XDq+XbiLTMuWLXXgwAElJSVddbuLFi1S8eLF8/x46+XllefuDgBwPVc677qW641p+WH+/PmyWCzq1atXnnPH0NBQVaxY8aa/awOAf+IcEIAja9Omja3kK+Ve1ODr66sDBw7Ylt3q92gAAABwTMzoCwD/0LBhQ9WvXz/Pso8++kiSbEW1f/L19b2pz4iNjVWjRo0uW16lShXb69WrV1fdunXl5eWlqKgotWvXTlFRUXrrrbcUGhqqzz//XBkZGbby36WlPwCOKTIyUjk5OYqJiVGpUqV08uRJRUZGavv27XmKvlWrVlVgYKDtfWXLlr1sW3v37tWWLVtst0T9p5MnT0rKnclk9uzZat++vcLCwnTfffepV69euv/++29pH/bu3audO3de93OvlT2/MwFwHOnp6frggw80bdo0xcfHy2q12l67VhGubNmyevHFFzVu3Dh99913ioyMVKdOnfToo4/aSsCxsbFycnJShQoV8rw3NDRU/v7+io2NvTM7BaBQuHAHhQvKly8vJycnHTp0SJK0atUqvfnmm4qJibnsgoSkpCTbWPVPsbGxqlChgkwmU57llStXzr/wAAq8K513Xcv1xrT8sHfvXlmt1ss+6wJXV9d8+ywAhRPngAAcWXh4+GXLAgICdPbsWdvzW/0eDQAAAI6Joi8A3ACLxSJJmjlzpkJDQy973cXlzgynrq6uatSokf7880/t27dPx48fV2RkpIoVK6bs7Gz9/fffioqKUkRExFVLdQAcR/369eXh4aE///xT4eHhCgkJUaVKlRQZGakJEyYoMzNTUVFR6tq1a573XXrl/gUWi0Vt27bVyy+/fMXPqlSpkiQpJCREmzZt0m+//abFixdr8eLFmjZtmh577DHNmDHjpvfBYrGoRo0aGjdu3BVfL1Wq1HWz53cmAI7j2Wef1bRp0/T888+rSZMm8vPzk8lk0sMPP2w7Hruajz/+WP3799ePP/6opUuXatiwYfrggw+0evVqlSxZ0rbeP8tyAHAnXDrW7N+/X/fee68iIiI0btw4lSpVSm5ublq0aJE++eST645vAHC7rnTedTP+efx0teMps9l8w9u0WCwymUxavHixnJ2dL3vdx8fn5kICwFVwDgjAHtzs8dOVjo8k5Snz3s73aAAAAHA8FH0B4AZcuD1OSEiI2rRpc9vbK126tHbv3n3Z8l27dtlevyAyMlKjR4/W8uXLFRQUpIiICJlMJlWrVk1RUVGKiopSx44dbzsTAOO5ubmpYcOGioqKUnh4uCIjIyXljgOZmZn67rvvdOLECbVo0eK62ypfvrxSU1NvaMxyc3PTgw8+qAcffFAWi0VPP/20Jk+erDfeeOOKM8hd73M3b96se++997Z+SLleJgAF09y5c9WvXz99/PHHtmUZGRlKTEy8offXqFFDNWrU0Ouvv67o6Gg1a9ZMkyZN0rvvvqvSpUvLYrFo7969trsoSNKJEyeUmJiY5/gLAG7W3r1788yYuW/fPlksFpUpU0Y///yzMjMz9dNPP+WZlelGbktfunRpbdu2TVarNc+x1ZXOJwEUbvlZZLvWmCblziYn6bJjtCvNjnm1XOXLl5fValXZsmVtF6ICwK260ljDOSAAexIQEHDF77duZ3bx2/0eDQAAAI7FyegAAOAI2rVrJ19fX73//vvKzs6+7PVTp07d1PYeeOABrVmzRjExMbZl586d05QpU1SmTBlVrVrVtvxCwe/TTz9V8+bNbV9aRkZGaubMmTp69KitDAjA8UVGRurvv//WihUrbP9tBwUFqUqVKho9erRtnevp1auXYmJi9Ntvv132WmJionJyciRJp0+fzvOak5OTatasKUnKzMyUJHl7e9vedyOfGx8fr6lTp172Wnp6us6dO3fdbdxIJgAFk7Ozc56ZSSTp888/v+7scMnJybZx7YIaNWrIycnJNm488MADkqRPP/00z3oXZiDv0KHD7UQHUMh9+eWXeZ5//vnnkqT27dvbZmL6521Up02bdt3tPvDAAzp69Kjmzp1rW5aWlqYpU6bkR2wABcjNnLddz7XGNEny9fVVUFCQ/vzzzzzrTZgw4YZzdevWTc7OznrrrbcuO/6zWq2XnRcCwLVcaazhHBCAPSlfvrySkpK0ZcsW27Jjx45pwYIFt7zNW/0eDQDyU1pamnbt2qWEhASjowBAgceMvgBwA3x9fTVx4kT17dtXdevW1cMPP6zg4GAdPnxYv/76q5o1a6Yvvvjihrf3yiuv6Pvvv1f79u01bNgwBQYGasaMGTp48KDmzZsnJ6eL12E0adJELi4u2r17twYNGmRb3qJFC02cOFHSjZX+ADiGyMhIvffeezpy5Eie/7ZbtGihyZMnq0yZMnluQX81L730kn766Sd17NhR/fv3V7169XTu3Dlt3bpVc+fO1aFDhxQUFKQBAwbozJkzat26tUqWLKnY2Fh9/vnnql27tm22k9q1a8vZ2VmjR49WUlKS3N3d1bp1a4WEhFz2uX379tXs2bM1ZMgQrVixQs2aNZPZbNauXbs0e/Zs/fbbb6pfv/41s99IJgAFU8eOHTVz5kz5+fmpatWqiomJ0fLly1W0aNFrvu/333/X0KFD1bNnT1WqVEk5OTmaOXOmnJ2d1b17d0lSrVq11K9fP02ZMkWJiYlq2bKl1qxZoxkzZqhLly6655577sYuAiigDh48qE6dOun+++9XTEyMvv32W/Xp00e1atWSh4eH7W4FgwcPVmpqqqZOnaqQkBAdO3bsmtsdOHCgvvjiCz322GNav369ihcvrpkzZ8rLy+su7RkAR1GvXj1J0muvvaaHH35Yrq6uevDBB29pW9ca0y4YMGCAPvzwQw0YMED169fXn3/+qT179txwrvLly+vdd9/Vv/71Lx06dEhdunRRkSJFdPDgQS1YsECDBg3SiBEjbik/gMLnamMN54AA7rb//Oc/WrJkyWXL+/btq5EjR6pr164aNmyY0tLSNHHiRFWqVEkbNmy4pc+61e/RACA/rVmzRvfcc4/efPNNjRo1yug4AFCgUfQFgBvUp08flShRQh9++KHGjBmjzMxMhYWFKTIyUo8//vhNbatYsWKKjo7WyJEj9fnnnysjI0M1a9bUzz//fNlMAt7e3qpTp47Wrl2r5s2b25ZfKACWKlWK24wBBUjTpk3l7OwsLy+vPD+iRkZGavLkyTdc7Pfy8tIff/yh999/X3PmzNE333wjX19fVapUSW+99Zb8/PwkSY8++qimTJmiCRMmKDExUaGhoXrooYc0atQo20UHoaGhmjRpkj744AM9+eSTMpvNWrFixRWLvk5OTlq4cKE++eQTffPNN1qwYIG8vLxUrlw5Pffcczd0S9YbyQSgYPrss8/k7Oys7777ThkZGWrWrJmWL1+udu3aXfN9tWrVUrt27fTzzz8rPj7eNoYuXrxYjRs3tq331VdfqVy5cpo+fboWLFig0NBQ/etf/9Kbb755p3cNQAH3ww8/6N///rdeeeUVubi4aOjQoRozZowkqXLlypo7d65ef/11jRgxQqGhoXrqqacUHBysJ5544prb9fLy0v/+9z89++yz+vzzz+Xl5aVHHnlE7du31/333383dg2Ag2jQoIHeeecdTZo0SUuWLJHFYtHBgwdvaVvXGtMu+Pe//61Tp05p7ty5mj17ttq3b6/Fixdfdp54tVze3t565ZVXVKlSJX3yySd66623JOV+z3XfffepU6dOt/YPAkChdLWxhnNAAHfbhQl6/ql///5asGCBXnzxRb388ssqW7asPvjgA+3du/eWi763+j0aAAAAHJPJ+s/7OVxBcnKy/Pz8lJSUJF9f37uRCwDumBm7ExXi6az24UWMjnJHHU7N1qy9SRpUJUCBHs5GxwFwibGbEtSqhLfqh3gaHcWhnUrP0de7EtW3kp/CvF2NjgPgJn27J1H+7s7qWNo+j8lijqdpzal0PVeDWVAASBO2n1H1AHe1KOGdZ/moUaP01ltv6dSpUwoKCjIo3UWrT6Rp9Yl0PV+TsQsoqD7dclqNi3mqcbHCNbP3n0fPadvZTD1dLdDoKAAMtP5Uun6PP6eXauf/cVdiplmTdpzVwxV8VaaIW75vH0DBEH8uWzP3JOnJCH8FezrWnGrrTqZr5dFzGnEHxlAABd/UnWdVroir7i3pc0Prf7b1tBoGe6pJaOE6dwWAm3UzvVymRAMAAAAAAAAAAAAAAAAAAADsEEVfAAAAAAAAAAAAAAAAAAAAwA5R9AWAgspqdAAAAIArs1isSkzLNjoGAAAAAAAAUCjwsyGAu4pBBwDyHUVfAIWOi5OUZS74R5ZZltx9dGakB+yOi5PJ9t8obt2Ff4YuJpPBSQDcKIvFqsVbjqvL+GjtO5GqTDs+JsuyWBlfANi4mEzKvMLx26hRo2S1WhUUFGRAqstlmRm7gILOxWQqFN9r/VMmx2YAlPudmtkq5dyB79X4ngnAjbgwRjji9/tZFqtcnBjjANwaF9ONj30Wq1XZFqucGXMAIF9R/wJQ6IR5uepQSrYyzBajo9xRexIzVcTVSb6uDPWAvQnzdtHepCxZrI73ZaA92Z2YJXcnk4p6OBsdBcB1mC1WLdp8TJ3HR2v4f7do38lzOpmQqoPJWcrIsb9jMrPVqn1JWQrzdjE6CgA7UcLbRQeSs+5IqSS/mK1W7UvOUpgPYxdQkJXwdtG+5CyZC9H5ZI7FqgPJHJsBkEp45Y4De5Oy8n3buxMz5WKSQjwZawBcXVEPZ7k7mbQnMf/HoTvJwnddAG5TmLerDiRnK/sGvhs7mJytHKtUkjEHAPIVoyqAQqdmUQ9tPJ2hmXuSVCPQXT4FrAibY5EOpmRpd2KWWod5y8QMBIDdqR/sqdn7kzVrb5IiAtzl4cx/pzfDbJEOp2Zr+9lMNS3mySwEgB0zn5/Bd9KKAzpw6lye19yzsmWS9M2eRNUI9FARN/s4JkvPsWrn2UydzjSrbSkfo+MAsBN1gzy062ymvt2TpKqB7vJysa/jj/Qcq3YlZiohw6x7w7yNjgPgDqoX7KEfLpxP+rvL087Go/yWlmPVjjOZSsmyqE5pD6PjADBYsKeLyhVx1aLDKYo7l61ini663a+FzFYpLjVbW89kqn6wh9z4ng7ANbg4mVQv2EPRJ9KVmm1RuI+r3d9ZM8Ns1a6zmTqelqNe5X2NjgPAQdUO8tDWMxmauSdR1QLc5X2FjoXFKiVkmLU5IUMlvV1U3ItKGgDkJ5PVev1L/5OTk+Xn56ekpCT5+nLwB8DxHU/L0V/H03QoOUs5BXAClBBPZ9UJ8lCdIE+jowC4in1JWfr7ZJriUnNUAIehO66ou7NqFnVXwxBPLmgA7JDZYtWiLcc16ff9OpiQlue1uqX99fS95dWkfKBOppv11/E0HbSjYzInSaV8XNW4mKfK+roZHQeAHTmcmq2Y42mKTc2WvU3sa1Lu2NWEsQsoFA4mZynmRLqOpGYX+PNJJ5NU2sdVTUK9FO7janQcAHYg22LVH0fPaVdillKz8+cOMcnJ6Uo4elZf9KgmN1fuHAXg2qxWq9acTNeW07kXits7k6SSPi5qFOKlCn6cLwK4dfHnsrXqeJpiU7JlvsrJqLeLSZX93dWyhJfc7f1KCACwAzfTy6XoC6BQs1qtVz0IdVROJsmJ0hvgMIweh/639biemBAtSXqgbpgmDmxkXJgbxDgH2K8csyW34LvigA79o+Bbr4y/nrm3vBqVC7ysoG/0WHgpZ5O4gADANd3NMctqterfP+7U/3adkiSV8PPQ1/3qyM8r74+zjF1A4WRPx1BXs3LXKY2cvVWZObllvOphRfTFo3Xk731jJRPGNwDXYrZadf1fOa8uJS1Lbd9eqkMnc+9A83zHKnq9R618SgegMLBYrXf0QtBh32zQks3HJUlTn6yvllVDbnobHE8ByG+Xnou2ePt3HU/KUKifh6LebC1nxhsAuCk308tlnnQAhZrJZFIBv8MhADtn9Dg0b3WszOe/iezZOFwut3u/QwCFUo7Zol82H9PkFQcVezpvwbdB2QA93bq8GpYLuOqPCkaPhQBwM+7umGXSmw9GKO5sunYeS1Hc2XS9umCHvuxTSy7MigIUeo5wDNWmaogm96urZ2ZuVEpGjrYcSVa/qWs15fF6CgvgTlQAbo+zyZQ7VeUtCvBx15QhTfXAe8uVY7bqs193KrJKMbWsFpp/IQEUaE4mk+7kV+pd64Vp0cZjkqQF6+J1b/Vid+7DAOAGXXouarFYbX+UfAHgzuIXAQAAgEIqKS1Lv206KkkK9HHTPfyIAeAm5ZgtWrA+Xh0+WaVX527PU/JtWDZA0wfU14yBDdSo/OWz+AIAboyHq7PG9qyuoudnv1wfm6gxv+01OBUA3Lj6ZQM0c1ADBRdxlyQdTEjTo5PXaO+JVIOTAYBUt1xRvda9piTJapWemrJap5IzDE4FALlaVglR4PlzwaVbjys5PdvgRAAAADAKRV8AAIBC6pf18bbbp3ZrFC5XFw4NAdyYbLNF89blFnxfm7ddR86k215rVC5Q3wysr+kDG6hhuUADUwJAwVHM10Nje1aXq3PuRRPzNhzV3PXxBqcCgBtXKbSIZg1pqNJFvSRJJ5Iz1XfKGm2MTTQ2GABIeub+CLWunnsB/MmkDA2d+rcs5++ABQBGcnNxUqd6JSRJWTkWLd50zOBEAAAAMAptDgAAgEJqTkys7XGPxuEGJgHgKLLNFs1dG6cO41bpjfl5C75NKgRq5qAGmjagvuqXpeALAPmtRkk/vdYhwvZ8zJK9WnforIGJAODmhAV46tvBDVQtzFeSlJyeoyf/s05/7DplcDIAhZ2Tk0lfDGysEF8PSdL/th7ThN92G5wKAHJ1rV/S9nje2jgDkwAAAMBIFH0BAAAKodhT57R6b4IkqWLxIqpVOsDgRADsWVaORXPWxqn9x3/p3wt2KO7sxYJv0wpF9e3gBvr6ifqqV4axBADupI41Q9W3cSlJktlq1ch52xR3yUUXAGDvivq4a/qA+mpSIffCsIxsi4Z+u0kLNzBLOQBjhfh5aMKgxrbn787drA0HThuYCABy1Qz3U/liPpKkNfvP6MjpNIMTAQAAwAgUfQEAAAqheX9fnM23V5PSMplMBqYBYK+yciz64e8jaj/uL725YIeOJmbYXmtesahmDWmor56op7pcLAAAd83Q1uXV7HxBLik9Ry/O3qLUzByDUwHAjfN2d9HEx+qqfY1QSZLZYtWrc7fr6z8PGpwMQGHXqnqonutQRZKUY7Zq0MRopaRnG5wKQGFnMpnUrcHFWX0XruMCKQAAgMKIoi8AAEAhY7VaNSfmsCTJZJK6NQo3OBEAe5OVY9F//z6i+z/+S2/9uFPHLin4RlYK0vdDGmrK4/VUO9zfuJAAUEg5O5n0XpdqKhvkJUk6kJCmNxbukNliNTgZANw4NxcnjXmohvqcn6Vckj5esldjFu+WhfEMgIFe6VpD9csXlSQdOnVOw2esldXKuATAWF3qhdkez18bx7gEAABQCFH0BQAAKGTWHzijgydTJUnNKgcrLNDL4EQA7EVWjkXfrz6s+z+O0ts/7tTxpIsF3xaVg/Tfpxppcv+6qkXBFwAM5ePhonG9asjXw0WSFLX3tCasPGBwKgC4OU5OJr32YISGtalgWzYtKlavzdumbLPFwGQACjNXFydNHtJERTxdJUnzVx/W938x4zgAY4UFeqpJxdyLEA6eOqeNsYnGBgIAAMBdR9EXAACgkJmzOtb2uGeT0gYmAWAvMrPN+i7msNqNjdI7P+3S8aRM22utIoL0w9ONNKlfXdUs5WdgSgDApUoFeumDbtXkbDJJkmZEH9birccNTgUAN8dkMmlI63Ia1aWqnHKHM/248ZiGfbtJ6VlmY8MBKLRKB/vo08cb2J6/MnO99hxNMjARAEjdGpS0PV6wNs7AJAAAADACRV8AAIBCJDPbrB/XHJEkebo5q0PdsOu8A0BBlplt1rfRh9Xu47/03s+7dCL5YsH3nirBmvNMY014rK5qlKTgCwD2qFG5QL1w38WZMN/5Zbe2xScbmAgAbk2vhiX1Se9acnPJ/cnij90JevI/65SYlm1wMgCFVeeG4erbspwkKS3LrIETY5TBBQgADNS+VnF5uOYeK/284agycxiTAAAAChOKvgAAAIXI8q3HbT+UPlAnTD4ergYnAmCEjGyzZq6K1X1j/9L7v+zSyUsKvq2rBGvuM431Zd86qhbma2BKAMCNeKh+mLrWKS5JyjJbNGLO1jzjOgA4irbVi2lK/7rycXeRJG06nKS+U9boeFKGwckAFFbv9amryiVyz4u3H0nUmz9sMjYQgELNx8NF7WqGSpIS07K1YvtJgxMBAADgbqLoCwAAUIjMjYm1Pe7ZJNzAJACMkJ5l1oy/YnXf2Ch98OtunUq5WARrUzVE84Y21hd966gqBV8AcBgmk0kv319JdcNzZ19PSM3SiDlblZHN7E4AHE/DcoGaMbC+ivq4SZL2nzynPpPWaP/JVIOTASiMvNxd9NXTTeXh6ixJ+vp/e7VofZzBqQAUZt0alLQ9XrAu3sAkAAAAuNso+gIAABQSp1MytXzrMUlSMT8PRVYpZnAiAHdLepZZ0/86pPvGRmn0ot1KSMmyvXZftRDNf7aJxj9aW1VKUPAFAEfk6uyk0d2rq7ifhyRpx7EUvfPLLlmtVoOTAcDNq1LCV98NbqhSgZ6SpONJGXp08lptPpJobDAAhVKVkv56p3cd2/Nh/1mj+NPnDEwEoDBrVilIIb7ukqTft5/Q2XNZ13kHAAAACgqKvgAAAIXEj+uOKNucW/bo1ihczk4mgxMBuNPSsnL0n6hDum9MlD5atEenUy8p+FYvpgXPNtGnj9RWRPEiBqYEAOSHAG83jetVQ57nZ5z7bftJzYg+bHAqALg14UW99N3ghrbj1KT0bD3x1TpF7UkwOBmAwqj/PeX1YP3cWTQTz2Vp8OQY5ZgtBqcCUBi5ODupc70wSVK22apfNhw1OBEAAADuFoq+AAAAhcScmFjb415NShuYBMCddi4zR1//eVD3jYnS2MV7dPr87B4mk3R/jWL6cVgTfdqnlipT8AWAAqViMR+93bmK7fmXKw7oT0pxABxUUBF3zRhYXw3LBkiS0rMteuabjfp50zGDkwEobEwmkz55vKFKFvWSJK3ek6CxP203OBWAwqpbg5K2x/PWxhmYBAAAAHcTRV8AAIBCYN/xFG08eFaSVL2Uv6qU9DM4EYA74Vxmjr76I7fg+/GSvTpzLltSbsH3gZqhWjisqcb1rqWKoRR8AaCguiciWENalpUkWSW9vnCH9p1MNTYUANyiIh6umty/rtpWC5Ek5VisGjl7q75ZFXuddwJA/vL3dtOUIU1td8j6+Kft+mvnCYNTASiMqoT5qkqYryRpU2yiDnC+BwAAUChQ9AUAACgE5l4ym2+PxuEGJgFwJ5zLzNGUlQfUdkyUxv22V2fTLhZ8O9QM1Y/DmmrswzVVsZiPwUkBAHfDk81Lq23V3FJcWpZZw2dvVWJalsGpAODWuLs6a1zvWurV8OLsdR/+ulvjftsrq9VqYDIAhU3DikF6pWt1SZLVKg2ZvFqnUzINTgWgMOpWP8z2eMHaeAOTAAAA4G6h6AsAAFDAWSxWzVl9WJLkZJK6NaLoCxQUqRk5mrTigNp8FKVPl+5T4vmCr5NJ6li7uH5+rpnGPFxTFSj4AkChYjKZ9OaDEapSPHcG9/jEDI2ct105ZovByQDg1jg7mfRm5yp6qnU527Kv/jioN+bvYGwDcFcN61BFkVVyL6g6npiuZ7/6m4sOANx1neuF6fwE45q/Lk4WC+MQAABAQUfRFwAAoIBbvTdB8WfSJEmtqoUqxM/D4EQAbldKRrYm/X5Abcb8qfHL9ikp/WLBt1Od4vr5+Wb6qFcNlQvxNjgpAMAoHq7OGtuzuop6u0mS1scmasxvew1OBQC3zmQy6dk2FfRGpwiZLhRb1sfruVmblZFtNjYcgELD2clJEwY1UVARd0nS0s1HNWXZHoNTAShsQvw8FBkRLEmKP5OutQfOGJwIAAAAdxpFXwAAgAJuTkys7XHPxszmCziy5PRsTfjffrX9KErjl+9TcnqOpNyCb+c6xfXLC830Yc8aKhtMwRcAIBXz9dDYntXl6pzbiJu34ajmrue2rgAcW+/G4Rr7UE25nB/bVuw8pYHT1iv5/MVvAHCnFQ/w1BcDG9mevzV7s7bEnjUwEYDCqFv9krbH89fGGZgEAAAAdwNFXwAAgAIsLTNHP6/P/ZLPx8NF7WqXMDgRgFuRnJ6tL5bvU9sxUfrif/uVnJFb8HV2MqlL3RL69cXm+qBnDZUJouALAMirRkk/vdYhwvZ8zJK9WneIIgoAx9a+Zqgm96srLzdnSdL6Q4l6bOpanUzOMDgZgMKiTc0SeqpdZUlSVo5FAyZEK4ULDgDcRffVDJW3e+6x0KJNx5SRxR0OAAAACjKKvgAAAAXY0s3HlHq+EPhgvZLycncxOBGAm5GUnq3Pzxd8J/x+QCmXFHy71QvTry800/s9qqt0US+DkwIA7FnHmqHq27iUJMlstWrkvG2KO5NucCoAuD1NKhTVjIENFOjtKknaczxVj0xeo0MJ5wxOBqCweKNnTdUqEyBJOnAiRa98u97gRAAKE083Zz1wfmKPlIwcLdt2wuBEAAAAuJMo+gIAABRgs2NibY97NiltYBIANyMxLVvjl+1T24+iNPGSgq+Lk0nd64dp0YvN9G73agqn4AsAuEFDW5dXswqBkqSk9By9OHuLUjNzDE4FALenWpivvh3cUGEBHpKk+LMZenTyGm2PTzY4GYDCwM3FWVOfaipvj9wL639YdUizVx00OBWAwqRbgzDb4/lr4wxMAgAAgDuNoi8AAEABdTIpQyu3H5ckhQV6qXHFIIMTAbiexLQsfbp0r9qO+VOTVhywFbBcnEzq2SC34PtOt2oqFUjBFwBwc5ydTHqvSzWVOX+RyIGENL2xcIfMFqvByQDg9pQJ8tZ3gxuqUqiPJOnMuWz1m7pW0ftOG5wMQGFQrlgRfdyvvu35S9+s1/7jKQYmAlCYNCpfVGEBnpKkP3ed0qnkTIMTAQAA4E6h6AsAAFBALVhzWBd6Gz0bh8vJyWRsIABXdfZclj75ba/afBSlKSsP6lymWZLk4mxSr4YltXh4c73VtZpKUvAFANwGHw8XjXuohnzPzzoXtfe0Jq48YHAqALh9Ib4e+mZgA9Ur4y9JSssya8iMDVq85bixwQAUCj2alFHv5mUlSecyczRwYrQys80GpwJQGDg5mdSlfu6svmaLVT9tiDc4EQAAAO4Uir4AAAAF1JyYw7bHPZqUNjAJgKs5k5qlcUv2qM2YKE3946DSsi4WfB9qWFJLhjfXqC5VbTNzAABwu8IDvfRBt2pyNuVeBDY9+rAWb6UIB8Dx+Xq6aurj9dS6SrAkKcds1Ygftui7S86NAeBO+eDRuqoQWkSStCX2rN6Zu8XgRAAKi64NStoez18bZ2ASAAAA3EkUfQEAAAqgHXGJ2nYkUZJUp2yA7YcGAPbhdGqmxi7eo7Zjo/TVn4eUfr7g6+psUu9GpfTb8Ei92aWqSvhT8AUA5L9G5QL1wn0VbM/f+WW3tsUnG5gIAPKHh6uzPu1TS93q5c5sZ7VK7/28S58v2yer1WpwOgAFmY+Hq6Y+1VRuLrk/vU76bbeWbjpqcCoAhUGFYj6qFe4nSdoel6xdRzm3AwAAKIgo+gIAABRAl87m25PZfAG7cTo1U2MW79Z9Y6L0n6i8Bd8+jUvptxGReqNzFRX39zA4KQCgoHuofpi61ikuScoyWzRizladTM40OBUA3D4XZye9062qBrYsa1s2ccUBjVq4U2YLZV8Ad06N0gF666HatufPfv23jp1NMy4QgEKj2yWz+i5gVl8AAIACiaIvAABAAWO2WDX/79yir6uzSZ3rlzI4EYBTKZkavWi32o6J0rSoWKVnWyRJbi5OeqRJuJaOiNTrnaoo1I+CLwDg7jCZTHr5/kqqe37mp4TULI2Ys1UZ2WaDkwHA7TOZTHqhXUX9q0Nl27I5a+P04veblck4B+AOGtCmou6vkzur+OmUTA2ZvFpmi8XgVAAKugfrhsnFySRJWrg+noubAAAACiCKvgAAAAVM1M4TOpGUIUm6t0ZxFS3ibnAioPA6lZypD3/dpfvGRGnGX7HKOF/wdXdxUt+m4Vo6orleezBCxSj4AgAM4OrspNHdq6v4+f8/tONYit75ZRe3twdQYPRtVlof9aphK74s235Sg6dvUEpGtsHJABRUJpNJ459sqBKBnpKkVbtO6tNfdhqcCkBBF+jjplZVQyRJJ5IyFb0nweBEAAAAyG8UfQEAAAqYOTGHbY97NiltYBKg8DqVnKkPftml+8ZG6ZtVh5WZc7Hg+1iz3Bl8/9UxQiG+FHwBAMYK8HbTuF415OnqLEn6bftJzYg+fJ13AYDj6Fi7uCY8Vkeebrnj3JqDZ9Vv6jqdSsk0OBmAgirQx12TBjeRkyn3IoOPFm7T6j2nDE4FoKDr3qCk7fH8tXEGJgEAAMCdQNEXAACgAEnNyNaijfGSJH8vV7WpEWpwIqBwOZGUofd+3qW2Y6M0M/piwdfD1Un9mpfW0pci9UqHCAX7MtM2AMB+VCzmo7c7V7E9/3LFAf3JDFAACpDmlYI07cn68vdylSTtOpaiRyev0eHTaQYnA1BQNa0cohGdq0mSzBarBk+K0dlULjAAcOe0rh4iX8/cY50lW47rXGaOwYkAAACQnyj6AgAAFCC/bohXepZZktSpQSm5n5+ZDcCddTwpQ+/+tFPtPv5L38UcVtb5gq+nq5MejyytpSMiNfKBygouQsEXAGCf7okI1pCWZSVJVkmvL9yhfSdTjQ0FAPmoZik/fTuooYr7595V48iZdD0yeY12HE02OBmAgmp4p6pqUjlYkhR/Jk3PT1srq9VqcCoABZW7i7M61ikuSUrPMmvJ5uMGJwIAAEB+ougLAABQgMyJibU97tWktIFJgMLhWGKG3vlxp9qNjdKs1UfyFHyfiCyjpS9F6qX2lRVEwRcA4ACebF5abauGSJLSsswaPnurEtOyDE4FAPmnXIi3vhvcUBVCvCVJp1Oz1G/qWq05cMbgZAAKImcnJ00e3EQB3m6SpF/Xx2na7/sMTgWgIOvesKTt8fy1cQYmAQAAQH6j6AsAAFBAxJ9J06rdpyRJZUN8VK9coMGJgILraGK63lq4Q/d/HKXv/z6ibHPujDyebs4a0KKMlr3UQiPaV1JRHwq+AADHYTKZ9OaDEYoI9ZEkxSdmaOS87coxWwxOBgD5J9TPQ98Maqg64f6SpHOZZg2ctl5Lt50wNhiAAqlEoJfGD2hke/7G9xu1/UiicYEAFGh1ywSodJCXJCl6b4KOJaYbnAgAAAD5haIvAABAATH/78O6cPe/nk3CZTKZjA0EFEDxZ9M1auEO3f/xX/phTZyt4Ovl5qyBLctq+UuRevH+Sgr0cTM4KQAAt8bD1Vlje9ZQ0fMzz62PTdTYpXsNTgUA+cvfy1VfPVFPLSsHSZKyzVa98P1m/fD3EYOTASiI2tcJ08A2FSVJmTkWDZwYrXOZOQanAlAQmUwmdWuQO6uv1SotXBdvcCIAAADkF4q+AAAABYDVatXsmFjb8+6NShuYBih44s+m680F29V+3F+avSZOOecLvt7uzhrUqqyWvxypF9pVtN2OEwAARxbq56GxPavL1Tn3wrG5649q7np+IAZQsHi6OWv8o7XVpW4JSbllmLd+3KkJ/9sv64WraAEgn7zZq7ZqnJ9JfM/RZL363QZjAwEosLrWD7M9nr82juMaAACAAoKiLwAAQAGwOfas9h5LkSQ1rhik0sHeBicCCoYjZ9L0xvztav/xX5qzNj5PwXfIPeW07KUWev6+ivL3ouALAChYapT002sdImzPxyzZq3WHzhqYCADyn6uzk97rXk1PRJaxLfvif/v13s+7ZLZQigGQfzzcnDXlqabydneRJH335wEt+PuwwakAFEThQd5qUC5QkrT3eKq2xSUZnAgAAAD5gaIvAABAATB39cUfBno2YTZf4HYdOZOm1+dt1wPjVmneunjlnP+R38fdRU+1LqflL7fQsLYV5O/lanBSAADunI41Q9W3cSlJktlq1ch52xR3Jt3gVACQv0wmk0a0r6QR7SvZls1afUQv/bBFWTkWA5MBKGgqFvfVh4/WtT1/cfpaHTqZamAiAAVVtwaXzurL3VkAAAAKAoq+AAAADi47x6L552cAcXdxUsd6Ydd5B4CriT2dplfnbtMD41Zp/vp42yxeRTxc9HTrclr2cqSebVNBfp4UfAEAhcPQ1uXVtHzubFBJ6Tl6cfYWpWbmGJwKAPLfE5Fl9EGP6nJ2MkmSlmw9oSEzNugcYx6AfPRw87Lqcf4i/ZT0bA2aFK1sLioAkM8eqF1Cbi65VZCf1scr28w4AwAA4Ogo+gIAADi4FduP60xqliSpXe0S8vNyMzgR4HgOJZzTv+ZsVcdPVmnhhqO2gq+vh4uG3ltey16K1FAKvgCAQsjZyaT3u1ZTmaJekqQDCWl6Y+EObmkPoEDqXLeEvni0tjxcc386Wb3/jPp/tU6nUzMNTgagoDCZTBrzWH2VDfGRJG04cEbvz99icCoABY2fl6vaVi8mSTqdmqU/d54yOBEAAABuF0VfAAAABzcn5rDtcc/zM4IAuDGHEs7plfMF3x83HrtY8PV00bNtymvZy5F6+t7y8qXgCwAoxHw8XDTuoRry9XCRJEXtPa2JKw8YnAoA7oyWEcH6+on68vXMHfO2xyfr0clrFXcmzeBkAAqKIp6umvJUU7k65/5M+/miXfp96zGDUwEoaLo1KGl7PH9dnIFJAAAAkB8o+gIAADiwxHNZWrr5qCSpaBF3tapazOBEgGM4cPKcXp6dW/D9aeMxXZiU0M/TVcPaVtDyl1roqdblVcSDgi8AAJIUHuilD7pVk7Mp95b206MPa/HW4wanAoA7o05pf80c1FDFfN0lSbGn0/TI5LXafSzF4GQACoo6ZQP1Rs+atufPTF2tE4npBiYCUNC0qBKsoj65d/9btvWEktKyDU4EAACA20HRFwAAwIH9vD5OmTkWSVK3hqXk6sLhHXAt+0+m6qX/btGDn63SL5suFnz9vVz1/H0VtPzlSA25p5x8zs9YCAAALmpULlAv3FfB9vydX3ZrW3yygYkA4M6pWMxH3w1pqHLB3pKkUymZemzqWq07eNbgZAAKiiH3VVabmsUlSaeSM/XM1NWyXPiiAgBuk6uzkx6sW0KSlJVj0aJNRw1OBAAAgNtBEwQAAMCBzY2JtT3u2aS0gUkA+7bvRKpG/HeLOn0WrV+3HJf1koLvC+0qatlLkRrUqpy83Sn4AgBwLQ/VD1PXOrmFlCyzRSPmbNXJ5EyDUwHAnVHC31MzBzVQzVJ+kqSUjBwNnLZev+88aXAyAAWBk5NJnw9opGL+HpKkldtP6IvFuwxOBaAg6d6wpO3xgnXxBiYBAADA7aLoCwAA4KAOnUzV3/tOS5IqFfdVjXB/YwMBdmjv8RS9+P1mdR4frUWXFHwDvFw1/P7cgu/AlmUp+AIAcINMJpNevr+S6pwvvSWkZmnEnK3KyDYbnAwA7owAbzf958l6al6xqCQpM8eiYd9u0jzKMgDyQbCvhyYOaiKTKff5+/O3aN2+BGNDASgwqpf0U4ViPpKkNfvP6HDCOYMTAQAA4FZR9AUAAHBQc1cftj3u2SRcpgu/CADQnuMpen7WZnUeH6MlW0/YCr5Fvd00on0lLXs5Uk+2oOALAMCtcHV20kc9qqu4X+7sczuOpejdX3bLauVW0wAKJi83F33Rt4461AqVJFms0hvzt2vKygOMfQBuW4uqxfRCx6qSpByzVYMmxSjpXJbBqQAUBCaTSd0aMKsvAABAQUDRFwAAwAFZrVbNXR0rSTKZpO6Nwg1OBNiH3cdS9Px3m9RlfIyWbjthW17U200vta+k315qriciy8jLjYIvAAC3I8DbTeN61ZCnq7Mkacn2E5oRffg67wIAx+Xm4qTRPWvosWYXz78/XbpPH/66WxYLZV8At+flLtXVsEKQJOlwwjm9OGMtFxIAyBdd6ofZZg1fsDaOsQUAAMBBUfQFAABwQOv2n9ahU7m32YqMCFGJQC+DEwHG2nk0WcO+3aSun8do6faTtuVBRdw08oHKWvpSpB6n4AsAQL6qWMxHb3euYnv+5YoD+nMPt5oGUHA5OZk08oHKeqFdRduymdGHNXLOVmXlWAxMBsDRuTg7afKQJvLzcpUk/bjmiL7984DBqQAUBCUCPNW0Yu6FBIcS0rThUKKxgQAAAHBLKPoCAAA4oDkxF2dL69G4tIFJAGPtOJqsoTM3qvsXq7V8x8WCb3ARd/2rQ2UtHRGpfs1Ly9PN2cCUAAAUXPdEBGtIy7KSJKuk1xfu0L6TqcaGAoA7yGQyaWDLsnqnW1U5nZ8d79fNxzV05kady8wxNhwAh1YqyFufPt7Q9vzV7zZod3ySgYkAFBRdG4TZHs9fG2dgEgAAANwqir4AAAAOJiPbrB/XHZEkebo5q0PdsOu8Ayh4tscn65mZG9Xji9X6fecp2/LgIu76V8fK+m1Ec/VtVloerhR8AQC4055sXlptq4ZIktKyzBo+e6sS07IMTgUAd1b3+iX12SO15e6S+zPLX3tP64mv1+nsOcY/ALfuwQal1P+eCpKk9CyzBkyMVnoWFxEAuD331yxumwjhl41HlZljNjgRAAAAbhZFXwAAAAezfMsxJaVlS5I61A2Tt4eLwYmAu2dbXJKe/maDen65WisuKfgW83XXaw9GaOmI5urblIIvAAB3k8lk0psPRigi1EeSFJ+YoZHztivHzG3sARRs91YN0dTH66nI+fPyrXHJenTyGh1NTDc4GQBH9k7v2qpS0k+StDMuSW98v8nYQAAcno+Hi+6vGSpJSkrL1ortJ6/zDgAAANgbir4AAAAOZk5MrO1xzyalDUwC3D1bjiRpyIwN6jXhb63clWBbHurnrjc6RWjJ8OZ6pEm43Cn4AgBgCA9XZ43tWUNFvd0kSetjEzV26V6DUwHAnVe/bIC+GdhAwUXcJUkHE9L0yKQ12nsi1eBkAByVp5uLvnqqqW32zekr9unntUcMTgXA0XVtUNL2eN7aOAOTAAAA4FZQ9AUAAHAgCSmZ+t+245KkUH8PNY8IMTgRcGdtPpKowdM36OGJf+vP3ZcWfD30705VtGR4pHo3puALAIA9CPXz0Nie1eXqbJIkzV1/VHPXxxucCgDuvMrFi2jWkIYqXdRLknQiOVN9p6zRxthEY4MBcFiVw/z0Xp+6tufPT1ujIwnnDEwEwNE1qxSkEN/cC5NWbD+pM6lZBicCAADAzaDoCwAA4EB+XHtEOWarJKl7o3A5O5kMTgTcGZsOJ2rQtPXqPXGNovZcLPgW9/fQm52raMnw5nq4cSm5uXBKAwCAPalR0k+vdYiwPR+zZK/WHTprYCIAuDvCAjz17eAGqhbmK0lKTs/Rk/9Zpz92nTI4GQBH1bdlOXVuWEqSlJSWrcGTYpRjthicCoCjcnYyqWv9MElSjsWqXzYeNTgRAAAAbga/igMAADiQOTGxtsc9mpQ2MAlwZ2yMTdTAaevVZ9Ia/bX3tG15CX8PvdW1qha/2FwPNaLgCwCAPetYM1R9G+eWUsxWq0bO26a4M+kGpwKAO6+oj7umD6ivJhUCJUkZ2RYN/XaTftxAkQbAzTOZTPqkfwOFB3lLktbsS9BHC7cZnAqAI+vaoKTt8by1cQYmAQAAwM3i13EAAAAHsfdYsjadnw2tRri/qoT5GZwIyD/rD53Vk/9Zp0cmr9GqSwq+YQEeertrVS16sbl6NihJwRcAAAcxtHV5NS2fW3RLSs/Ri7O3KDUzx+BUAHDnebu7aOJjddW+RqgkyWyx6l9zt+k/UYeMDQbAIfl6uWnKkCa2u3p98ssO/bnjhMGpADiqiBK+qnr+7gObYxO1/0SqwYkAAABwo/iVHAAAwEHMXX3Y9rgns/migFh38Iwe/2qd+k5Zq5h9Z2zLSwV66p1u1bToxebqQcEXAACH4+xk0vtdq6lMUS9J0oGENL2xcIfMFqvByQDgznNzcdKYh2qoz/nZzSVp7OI9GrN4tyyMgwBuUv0KQXqte01JktUqPTUlRgnJGQanAuCoul0yq++CdczqCwAA4Cj4tRwAAMABWCxWzV0dKym3NNG1YanrvAOwb2sPnNHjX63VY1PX6e8DeQu+73Wvpl9eaKbu9cPk6swpCwAAjsrHw0XjHqohXw8XSVLU3tOauPKAwakA4O5wcjLptQcj9Gyb8rZl06Ji9dq8bco2WwxMBsARDW0foVbVikmSTiRmaOhXf3PhAIBb0qleCZ2fJFwL1sYzlgAAADgIfjUHAABwANF7Tin+TLokqVW1Ygr29TA4EXDzrFar/t5/Rv2mrlW/r9bp7wNnba+FF/XS+z2q6dcXmqlrPQq+AAAUFOGBXvqgWzU5m3J/SZ4efViLtx43OBUA3B0mk0lPtS6vNztXsRVqftx4TMO+3aT0LLOx4QA4FCcnk74c2FjBvu6SpOVbjmnS0t0GpwLgiEJ8PdQiIliSFH82XX/vP21wIgAAANwIfj0HAABwAHNXH7Y97tmktIFJgJtntVq1ev9pPTZ1rR7/ep3WHrxY8C0T5KUPe1bXL883VZe6YXKh4AsAQIHTqFygXrivgu35O7/s1rb4ZAMTAcDd9VCjUvqkdy25Oue2ff/YnaAn/7NOiWnZBicD4EiK+XtqwqDGtufvzNmiTYfOXOMdAHBl3RqWtD1esDbewCQAAAC4UfyKDgAAYOfSMnP087o4SVIRTxe1q1XC4ETAjbFarYred1p9p6zVE1+v1/pDibbXygZ5aXSvGvr5+WbqVKcEBV8AAAq4h+qHqWud4pKkLLNFI+Zs1cnkTINTAcDd07Z6MU3pX0/e7s6SpE2Hk9R3yhodT8owOBkAR3JP9eIa2j5CkpRttmjghGilpHPRAICbc1/1UBXxcJEkLdp0jDsNAAAAOAB+TQcAALBzSzYd1bnMHEnSg/VKytPN2eBEwLVZrVat2pugRyev1YD/rNeG2ETba+WCvfVRrxr66flmerB2cTlfuH8tAAAo0Ewmk16+v5LqlPKTJCWkZmnE3K3KyOYHZQCFR6PygZoxsIGK+rhJkvafPKc+k9bowMlzBicD4Ehe7V5DdcsFSpIOnkzVS9+sk9VqNTgVAEfi4eas9rVyL8RMzczR0q3HDU4EAACA66HoCwAAYOfmxMTaHvdsUtrAJMC1Wa1W/bUnQX0mrdHAaRu08XCi7bXyId4a+1AN/fhcU3Wk4AsAQKHk6uykj3pUV3E/D0nSjqMpeveX3RRTABQqVUv46rvBDVUq0FOSdDwpQ49OWaPNRxKNDQbAYbi5OGvKkKYq4ukqSZobE6sfVh0yNhQAh9OtYUnb4/lr4wxMAgAAgBtB0RcAAMCOnUhM1x87TkiSShX1UqMKQQYnAi5ntVr15+5T6j1pjQZN36DNR5Jsr1UI8dbHD9fUj8Oa6oFaFHwBACjsArzdNK5XDXm65t6lYsn2E5oRfdjgVABwd4UX9dK3gxsqongRSVJiWrae+Gqd/tqTYHAyAI6iTIiPxvVvYHv+8jfrtPdYsoGJADiahuUCFXb+wqOoXad0MjnD4EQAAAC4Foq+AAAAdmz+miOynJ/grEfjcDlRkoQdsVqt+mPXKT088W8NmbFRWy4p+FYs5qNxvWtq4bCmal8zlP/tAgAAm4rFfPR25yq251+uOKA/KbcBKGSCi7hrxsD6alg2QJKUnm3R099s1C+bjhmcDICj6NooXI+0KCdJSssya9DEaGVmmw1OBcBRODmZ1LV+mCTJYpV+Wn/U4EQAAAC4Foq+AAAAdmxOTKztcY/GpQ1MAlxktVq1ctcpPTThbz31zUZtjbs4Y0zlUB992qeWFjzbRPfXoOALAACu7J6IYA1pWVaSZJX0+sId2ncy1dhQAHCXFfFw1eT+ddW2WogkKcdi1cuzt2rmqtjrvBMAcr3/SF1VLO4rSdp6OFFvzd5scCIAjqRr/ZK2x/PWxhmYBAAAANdD0RcAAMBObT+SqB1xuTOk1isXqPKhRQxOhMLOarXq950n1WvC33r6m43aFn9Jwbd4EY1/pJbmDW2i+6oXo+ALAACu68nmpdW2am65LS3LrOGztyoxLcvgVABwd7m7Omtc71rq1fBi0eaDX3frk9/2ymq1GpgMgCPwdnfRV083lbtL7k++U5bt0eKN8QanAuAoyhfzUe3S/pKknfHJ2nnJ970AAACwLxR9AQAA7NSc1czmC/tgtVq1fMdJ9fhytYbO3KTtl3zhG1G8iMY/WlvznmmsNtUo+AIAgBtnMpn05oMRigj1kSTFJ2Zo5LztyjFbDE4GAHeXs5NJb3auoqdal7Mtm/rHQf17wQ7GRADXVa2Uv97pXcf2fNhXf+vomTQDEwFwJN0aXLzYaME6ZvUFAACwVxR9AQAA7FCO2aL5fx+RJLk6m9T5ki/bgLvFYrFq+fYT6vbFag37dpN2Hk2xvVa1RBF90be25g1trDZVQyj4AgCAW+Lh6qyxPWuoqLebJGl9bKLGLt1rcCoAuPtMJpOebVNBrz8YIdP506t56+L1/KzNysg2GxsOgN17vHUFdaiX+/3h2XNZGjw5RmYLFwoAuL6OdUrI1Tn34GPhuniZLdxRAAAAwB5R9AUAALBDUTtP6mRShiSpbc3iCvRxNzgRChOLxaql206o+xcxGvbdZu0+drHgWy3MVxMeq6M5zzRW6yohMpko+AIAgNsT6uehsT2r235cnrv+qOau55bTAAqnPk3CNfahmnI5Pyb+vvOUBk5br+T0bIOTAbBnJpNJnz7eQGGBXpKkmN2n9PFPOwxOBcARBPq46Z6qIZKkk8mZWrUnweBEAAAAuBKKvgAAAHZoTkys7XGPxqUNTILCxGKxasnW4+r2RYyen7VZu4+n2l6rUdJXEx+ro9lPN1KriGAKvgAAIF/VKOmn1zpE2J6PWbJX6w6dNTARABinfc1QTepXV15uzpKk9YcS9djUtTqZnGFwMgD2LMDHXZOHNJHT+e9sxv64XdG7TxqcCoAj6HbJHQXnr4kzMAkAAACuhqIvAACAnUnNyNbiTUclSQHebrq3RqjBiVDQWSxWLd5yXF3GR+vF77dozyUF35ql/DSpXx3996lGaknBFwAA3EEda4aqb+NSkiSz1aqR87Yp7ky6wakAwBhNKxTV9AH1FejtKknaczxVj0xeo0MJ5wxOBsCeNa4UrJFdq0uSLFarBk+K0ZnUTINTAbB391QLkZ9X7jHHki3HlJqRY3AiAAAA/BNFXwAAADvzy/p4pWeZJUmdG5SSu6uzwYlQUJktVi3afEydx0dr+H+3aN/Jiz8Y1yrlpyn96+r7IQ3VojIFXwAAcHcMbV1eTcsHSpKS0nP04uwtSs3kR2YAhVP1kn76dnBDlfD3kCTFn83Qo5PXaHt8ssHJANiz5ztWUbOIEEnSsbPpGvb1GlmtVoNTAbBn7i7OerBOCUlSRrZFS7YcMzgRAAAA/omiLwAAgJ2ZExNre9yrSbiBSVBQmS1W/bLpmDp/Fq0RP2zV/ksKvnXC/TX18bqaNaShmlcKouALAADuKmcnk97vWk1linpJkg4kpOmNhTtktlBOAVA4lQny1qwhDVUp1EeSdOZctvpNXavofacNTgbAXjk7OWnS4MYK9HGTJC3ZGK+vlu81OBUAe9etYUnb4/lr4gxMAgAAgCuh6AsAAGBHjpw+p1W7T0mSyoX4qE7ZQIMToSAxW6z6edMxdfp0lV6evVUHTl0s+NYt7a+vnqinbwc3ULOKFHwBAIBxfDxcNO6hGvL1cJEkRe09rYkrDxicCgCME+LroW8GNlC9Mv6SpLQss4bM2KDFW44bGwyA3Soe4KUvBjS2PX/zh03aGnvWwEQA7F2d0v4qG+wtSYrZd1rxZ9INTgQAAIBLUfQFAACwI/NXH7Y97tmkNGVL5Iscs0U/bTyqBz9dpZGzt+pgQprttXpl/PX1E/U0c1ADNa1QlP/NAQAAuxAe6KUPulWT8/ljk+nRh7V4K4U2AIWXr6erpj5eT62rBEuScsxWjfhhi2bFHL7OOwEUVvfVLqHB91WSJGXlWDRwYrRSM7INTgXAXplMJnVtECZJslqlhevjDU4EAACAS1H0BQAAsBNWq1VzLyn6dm8cbmAaFAQ5ZosWbojXg59G65U523TokoJvg7IBmvZkfX0zsIGaUPAFAAB2qFG5QL1wXwXb83d+2a1t8ckGJgIAY3m4OuvTPrXUrd7FEs67P+/S58v3yWq1GpwOgD36d89aqlk6QJK073iK/vXtBoMTAbBnXeuXtD1esDaO4wsAAAA7QtEXAADATmw6dFZ7j6dIkhpXClJ4kLfBieCocswWLVgfrw6frNKrc7cr9vTFgm/DsgGaPqC+ZgxsoEblAyn4AgAAu/ZQ/TB1rVNckpRltmjEnK06mZxpcCoAMI6Ls5Pe6VZVA1uWtS2b+PsBvfXjTpktlHEA5OXu6qypTzWVt4eLJOn7vw5qbswhY0MBsFulinqpYflASdK+E6naeiTJ4EQAAAC4gKIvAACAnZgTE2t73KtxaQOTwFFlmy2aty634PvavO06cibd9lqjcoH6ZmB9TR/YQA3LBRqYEgAA4MaZTCa9fH8l1SnlJ0lKSM3SiLlblZFtNjgZABjHZDLphXYV9a8OlW3LZq+J0/DvNyuT8RHAP5QPLaIxfevZno+YsU4HTqQYmAiAPevW4OKsvvPXxhmYBAAAAJei6AsAAGAHsnIsWrj2iCTJw9VJHeuVvM47gIuyzRbNXRunDuNW6Y35eQu+TSoEauagBpo2oL7ql6XgCwAAHI+rs5M+6lFdxf08JEk7jqbo3V92cxtZAIVe32al9VGvGnJxyr1Ty9LtJzV4+galZGQbnAyAvenVrKwealZGkpSakaNBk2KUlcOFAQAu90Dt4nJzya2R/LT+qLLNFoMTAQAAQKLoCwAAYBdWbDuuM6lZkqR2tUvI18vV4ERwBFk5Fs1ZG6f2H/+lfy/YobizFwu+TSsU1beDG+jrJ+qrXpkAA1MCAADcvgBvN43rVUOers6SpCXbT2hG9GGDUwGA8TrWLq4vH6sjT9fcn3vWHDyrflPX6VRKpsHJANib0X3rqVyxIpKkTQfP6N25WwxOBMAe+Xq6qm2NYpKkM+ey9MfOUwYnAgAAgETRFwAAwC7Mjom1Pe7ZpLSBSeAIsnIs+uHvI2o/7i+9uWCHjiZm2F5rXrGoZg1pqK+eqKe6pSn4AgCAgqNiMR+93bmK7fmXKw7ozz0JBiYCAPsQWSlI/xlQX/7nLxredSxFj05eo8On0wxOBsCe+Hi4aupTTWwzdU5YslvLtxw1OBUAe9S9wcU7Ds5fG2dgEgAAAFxA0RcAAMBgieeytGzLMUlSUBF3tapazOBEsFdZORb9d/UR3f/xX3rrx506dknBN7JSkL4f0lBTHq+n2uH+xoUEAAC4g+6JCNaQlmUlSVZJry/coX0nU40NBQB2oFYpf307qKFC/TwkSUfOpOuRyWu042iywckA2JNaZQL1Zq9atudDp/6t44np13gHgMIoMiJYQT5ukqTl204oKS3L4EQAAACg6AsAAGCwn9bFKSvHIknq1qiUXJw5RENeWTkWfb/6sO7/OEpv/7RTx5MuFnxbVA7Sf59qpMn966oWBV8AAFAIPNm8tNpWDZEkpWWZNXz2ViWmZRucCgCMVy7EW7OGNFT5EG9J0unULPWbulZrDpwxOBkAezKobSXdV6uEJCkhJVNPTY6R2WIxOBUAe+Lq7KQH64VJyv1u+tdNxwxOBAAAAFokAAAABpsTE2t73LNJaQOTwN5kZpv1XcxhtRsbpXd+2qXjSZm211pFBOmHpxtpUr+6qlnKz8CUAAAAd5fJZNKbD0YoItRHkhSfmKGR87Ypx0xBBQBC/Tw0c1BD1Q7PPU88l2nWwGnrtWzbCYOTAbAXJpNJnw9opFB/T0lS1M6TGv/rToNTAbA33RuUtD2evybOwCQAAACQKPoCAAAY6tDJVK3df1qSFBHmq+ql/I0NBLuQkW3WzOhYtfv4L7338y6dSL5Y8L2nSrDmPNNYEx6rqxolKfgCAIDCycPVWWN71lBR79zbya6PTdTYpXsNTgUA9sHfy1VfP1FfLSoHSZKyzVa98P1mzaakA+C8okXcNWlwY5lMuc8/XLBNa/YmGBsKgF2pVtJXlUKLSJLWHTyr2IRzBicCAAAo3Cj6AgAAGGjO6ryz+ZoufLuOQikj26yZq2LVbuxf+uCX3Tp5ScG3dZVgzX2msb7sW0fVwnwNTAkAAGAfQv08NLZndbk65x5Dz11/VHPXxxucCgDsg6ebsz5/tLY61ykuSbJYpVELd2ji7/tltVoNTgfAHjSvUkzDO1WTJJktVg2aFK3Ec1kGpwJgL0wmk7o1DLM9X7CWcy0AAAAjUfQFAAAwiNVq1ZyYw5Ikk0nq1jDc4EQwSnqWWTP+itV9Y6P0wa+7dSrlYsG3TdUQzRvaWF/0raOqFHwBAADyqFHST691iLA9H7Nkr9YdOmtgIgCwH67OTnq/R3U9EVnGtuzz5fv13s+7ZLFQ9gUgjehUTY0rBUuS4k6n6YVpa7gYAIBN53phtpm/56+NY3wAAAAwEEVfAAAAg6zZd1qHz9/uqkWVEBUP8DQ4Ee62tKwcTYs6pPvGRmn0ot1KSLk4a8p91UI0/9kmGv9obVUpQcEXAADgajrWDFXfxqUkSWarVSPnbVPcmXSDUwGAfTCZTBrRvpJGtK9kWzZr9RG99MNWZeVYDEwGwB64ODtp8uDG8vd2kyT9n737jI+q3Lc4vmYmvUJCSUhI6L1D6KiooKB0EBUVlK7YQEWv7Xg8VoodpAtY6SqKgIIaegi9l0BCAoFQ0uuU+2LCAOdYAIGd8vu+uc/eQ2B57ieTyd5r/5/vNydq9q+HDU4FoKgILeOtdjXLSZISzmQr9ggPVQIAABiFoi8AAIBBFmyId637tYk0MAlutOx8q2ZGH9Ud49Zo3LIDOpN5UcG3QUUtfryN3h/QRHVC/Q1MCQAAUHyMurW62lYPkiSl5Vg1et4OZeZZDU4FAEXHIx2q6K2+DWQxO8fyLduZrJFztiiL90qg1AsL9tWHj7R0Hb/05VbtTUw1LhCAIqV3y3DXelFMooFJAAAASjeKvgAAAAbILbDp28KLYj6eFnVpGmZwItwIWXlWzfj9iDqPi9b4ZQd0JstZ8DWZpDsbVtS3T7TR+/c3Vm0KvgAAAFfEYjbpzV71VSXYR5IUdzpbLy/ZIxtb0wOAS49mlfTxA03k5e68NbT+0Fk9PH2zzl708CmA0qlr83ANvq2mJOd1yyGT1imbBwEASLqjUYi8PSySpKVbTyi3wGZwIgAAgNKJoi8AAIABVmw/ofScAknSXc3C5evpZnAiXE9ZeVZN+81Z8J3w00GdzXL+/95kkro2CtGSJ9pq4n2NVTOEgi8AAMDV8vNy08T+DRXg5fxsHX3wjCb/GmdwKgAoWm6uU14zHmmhAG/ne+WupHQNmLJJSedyDE4GwGiv9W+i+pXLSJL2H0/Xi19uMTYQgCLB19NNXRqHSJLScwq0avdJgxMBAACUThR9AQAADLBgQ7xrfU+bCAOT4HrKyrNq6q9x6jQuWu8tP6hz2RcKvnc1CtG3T7TV+HsbqWZFP4OTAgAAlAwRQT56q3d9WUzOrek/W5egZTuTDU4FAEVL08gymjuspSoGeEqS4s9k6/5PN2n/iQyDkwEwkpeHRdNHtpVP4eTOub/F6dtNCQanAlAU9I4Kd60XxSQZmAQAAKD0ougLAABwg6Wk52rVLmfZILSMt9rWrmBwIlxrmblWfbo6Tre/G633VxxSamHB12yS7m4Squ+fbKdx9zZSDQq+AAAA11yrakF6unMN1/HrS/drV1K6gYkAoOipWdFPX4xoqarlfCRJKRl5emhajDYfOWdwMgBGqlkpQG890Nx1/PSsGMWnZBqYCEBR0KZmOVUMdD4g9OueUzqTmWdwIgAAgNKHoi8AAMAN9m3MMVltDklSn9YRsphNBifCtZKRW6DJqw7r9nG/68OVh5SWc6Hg271pqL5/qp3evaehqlXwNTgpAABAyda/RZh6NgmVJOXb7Hpm/k6lZHAzGgAuVqmMtz4f3lINwwMkSRm5Vg2dFatVe08ZnAyAke7vUFW9Wzt3IEvPKdCwyetVYLUbnAqAkSxmk3q2cE71tdod+n7LcYMTAQAAlD4UfQEAAG6w+esvbHnXt/CiOYq39JwCTfrlsDq9G62Pfj6s9ByrJGfBt0fTUC19up3e7tdQVctT8AUAALgRTCaTxnappaaVAyVJpzPzNWb+TuUW2AxOBgBFS1lfD80c3ELtawZLkvKsdj3x+TYt3My23EBpZTKZNGFglKoUXseKjTujtxfvNDgVAKP1bhHmWi+OSTQwCQAAQOlE0RcAAOAG2n88XdvjndtgNoosqzphgQYnwj+RnlOgj38+pE7jovXxL4eVnuss+FrMJvVsVkk/jG6vt/o1VJVyFHwBAABuNHeLWe/2baDQQC9J0p7jGfrP0v1yOBwGJwOAosXX000fP9hUdzUKkSTZHdLLi3Zr2m9HeM8ESil/b3dNHdlWbhbnTmQf/LBXv+5KNjgVACPVrhSgBuHO+xnbE9J0KDnD4EQAAAClC0VfAACAG2jBhnjXuh/TfIuttJwCfVRY8J20Kk4ZFxV8ezcP0w9Pt9ObfRsoMtjH4KQAAAClW1lfD028p6G83S2SpJ92n9TsdQl/81UAUPp4uJn1zj0N9WDbC9cq3lt+UO/8uF92O2VfoDRqVi1YL/Vt7Dp+dOoGnUrLNTARAKP1irow1XdRDNP/AQAAbiSKvgAAADeI3e7Qwg3OUoHFbFLPlpUNToQrlZpdoA9XHlKnd6M1+aKCr5vZpD4twvTj6Hb6T5/6iqDgCwAAUGTUrOinf/eo6zr+ZHWcfj9w2sBEAFA0mc0mPX9XbT19R03XuTlrE/T8gl3Kt9oNTAbAKI/eUVu3NnBO+z6VnqtR0zZQ/gdKse7NwmQxOyd9L9mcyPsBAADADUTRFwAA4AZZtz9Fx8/lSJJubRCi8gFeBifC5UrNztf7Kw6q07jf9enqOGXmXSj49otyFnxf711flYMo+AIAABRFHeuU14ibq0qSHJJeWrJHh05lGhsKAIogk8mkoTdX1eu966mwx6Ol205o1Nytys63GhsOwA1nNpv0ybDWqhDovI65aleyPvlpn8GpABilfICnbq5TXpJ0PDVXGw6fMTgRAABA6UHRFwAA4AaZtz7ete7bOuIv/iSKinNZ+Xpv+UHd/m60pv56RFl5NkmSm8Wke1qGa9mY9nqtV32FU/AFAAAo8ga3j1SnehUkSdn5No2Zt1Op2QUGpwKAoqlPi3B9MKCJPN2ct5HWHDyjR2bEKjU73+BkAG608gFemjystUyF5f83Fu7QljjKfUBp1Ssq3LVetCnRwCQAAAClC0VfAACAGyArz6qlW5wXvQK83dW5cSWDE+GvnM3M18SfDuj2cdGa9tsRZedfKPj2bxmun8a017961lNYWW+DkwIAAOBymUwmvdqtjuqE+EmSklJzNXbhLlltbEcPAH/ktnoVNO3h5vL3cpMk7TiWpgemxOh4ao7ByQDcaDfXD9ETXetKkqw2h4ZNXqd0iv9AqdSpQUXXZ4Nl208op/DaOQAAAK4vir4AAAA3wE9bjyu7cBps9xbh8vawGJwIf+RMZp7GLzugTuOjNf33o66LlO4Wk+5rVVnLx3TQqz3rqVIZCr4AAADFkZe7ReP7NVSwr4ckKTY+VeNXHDQ4FQAUXS2qltWcoVEq7+8pSYpLydIDUzbp0MlMg5MBuNGe79VQLaoHS5KOpmRpzOzNcjgcBqcCcKN5eVh0V9NQSVJWnk0rdiQbnAgAAKB0oOgLAABwA8xfH+9a920dYWAS/JHTGXkat2y/Oo+L1szoSwu+97eurOXPdNDLPeoqtIyXwUkBAADwT4UEeml8vwZytzj3n14Qe1wLYpMMTgUARVftUH99MTxKkcE+kqTktDw9MHWTtiWkGhsMwA3l7mbW1BFtFODtLklavDFBX0YfMTgVACP0ahHuWi+MSTQwCQAAQOlB0RcAAOA6S07N0e97T0qSIsr5qmWNcgYnwnkpGXl658f96jw+WrOi45VT4Ny22cPNrAFtIrTimQ56qXtdhQRS8AUAAChJGoYH6sWutV3H4346qM1HzxmYCACKtvAgH30+PEr1wwIkSek5Vj0yY7N+25dicDIAN1JEeT+993CU6/j5z2N14HiagYkAGCGqWpDCg5y73q3Zn6JTabkGJwIAACj5KPoCAABcZ4s2JsheuItd39YRMptNxgaCUtLz9PYP+9R5XLRmr4lXbmHB19PNrAfaRGjFM+31Yrc6qkjBFwAAoMS6u3GoHmhdWZJkczg0duEuJZ7LMTgVABRdwX6e+mxIC7WuHiRJyi2wa9Tn2/TtluMGJwNwI/VoGaGHbqkuScrJt2no5PXKLdwdC0DpYDab1DvKOdXX7pC+ZYcUAACA646iLwAAwHXkcDg0b32867hv6wgD0yAlPU9vLd2nzuOjNWdtgvKsFwq+D7VzTvD9v251VCGAgi8AAEBp8Pit1dW2sLCWlmPV6Hk7lZlnNTgVABRdvp5u+nRgM93ZsKIkyWZ36IUFuzQz+qixwQDcUP+5r6nqhAVKknYfS9Wr32wzNhCAG65XizDXemFMooFJAAAASgeKvgAAANfR7mNp2peULklqUT1I1Sr6G5yodDqZlqs3vt+nTuOjNXfdhYKvl7tZA9tHasWzHfT8XXVUPsDT4KQAAAC4kSxmk97sVV9Vgn0kSXEpWXp5yR7Zzm/JAQD4Hx5uZo3r30j3F05Fl6Txyw5o/LIDcjh4/wRKAx9PN00b2UZe7hZJ0oxfDuqHWIp+QGlStYKfmlYpI0nadzxDewrvgwAAAOD6oOgLAABwHc3fcGGab7/WkQYmKZ2S03L1n+/26o4Ja/TF+gTlX1TwHdQ+Uiue6aCxXWurvD8FXwAAgNLKz8tNE/s3VICXmyQp+uAZTf41zuBUAFC0WcwmvditjkbdVt11bmb0Ub24cLcKbHYDkwG4UeqGl9F/7m/qOn5y5iYlnskyMBGAG613VLhrvZipvgAAANcVRV8AAIDrxGqza9HGBEnOaTfdoyr/zVfgWjmRmqvXv92rO8ZH68sNx1wFX293sx7pUEUrn+2g57rWVjkKvgAAAJAUEeSjt3rXl8VkkiR9ti5By3YmG5wKAIo2k8mkR2+rrld71JXZ+fapJVuO64nPtykn32ZsOAA3xMBbqqt7C+c1z9SsfA3/dL2slP2BUqNb00pytzg/BCyJTeL7HwAA4Dqi6AsAAHCd/LbnpFLS8yRJnRqFqqyvh8GJSr7jqTl6bcke3TkhWl9tPKYCm3PLUG8Pi4bcVEUrn71Jz3SppWA/Cr4AAAC4VKtqQXq6Uw3X8etL92sX288CwN/q36qy3ruvsavo89v+0xoyM1ap2QUGJwNwvZlMJr33cJQqB/tIkjYePK1x3+42OBWAG6WMr4durV9RkpSSnqc1B04bnAgAAKDkougLAABwncxfn+Ba92sTaWCSki/pXI7+tWSP7pywRt9sSnQVfH08LBpyc1X9/GwHjb6zloL8KFsDAADgz/WPClPPJqGSpHybXc/M36mUjDyDUwFA0depQUVNHdRcvp4WSdLWhFQ9NHWTktNyDU4G4HoL9PXQ1JFtZSkc7T3x+92K3nvS4FQAbpTeUeGu9aJNiQYmAQAAKNko+gIAAFwHGTkF+mlbkiQpyM9DtzYIMThRyZR0LkevLt6tLhPXaN6mRFkLC76+nhYNu6Wqfn6ug0bfUZNpygAAALgsJpNJY7vUUtPKgZKk05n5GjN/p3IL2IIeAP5Oq+pBmj00SsGFD9keOpWlAVM2Ke5UlsHJAFxvUTXK6YXeDSVJDoc0csoGneFhKaBU6Fivgsr4uEuSVuxMVkYuE/0BAACuB4q+AAAA18H3sYnKLbBLknpEVZaHGx+7rqVjZ7P18qLd6jJhjebHJF1S8B3RsZpWPnuTnupcU2V8KPgCAADgyrhbzHq3bwOFBnpJkvYcz9B/lu6Xw+EwOBkAFH31KgXoi+EtVTnIW5J0IjVXD0zdpB3H0gxOBuB6e6JrXd1Ur6IkKTk1R6Omb+TzE1AKeLiZ1a1ZJUlSboFdy7YlG5wIAACgZKJxAgAAcB3MXx/vWvdrE2lgkpLl2NlsvbRwt7pOXKuFm5NktTtvFvh5umlkYcH3iU41XBMEAAAAgKtR1tdDE+9pKG935xb0P+0+qdnrEgxOBQDFQ0Swjz4f3lJ1Qv0lSanZBXp4xmatOXDa4GQAriez2aRJw1qrnL+nJGnl9uOasuKAwakA3Ai9o8Jd60UxiQYmAQAAKLko+gIAAFxjx85kaX3hzasaIf5qWqWswYmKv/gz2fq/BbvUdeJaLYpNkq2w4Ovv5aZHb62mlc910OMUfAEAAHAN1azop3/3qOs6/mR1nH6npAYAl6W8v6dmD22hllWd10Ry8m16dM5WLd12wuBkAK6nkDLe+nhoK9fxa/O2a/vRswYmAnAjNIkso2oVfCVJGw6dUeLZbIMTAQAAlDwUfQEAAK6xhRsuTPrq2zpCJpPJwDTF29HTWXph/k7d/d5aLdly3FXwDfBy06jbqmvlsx006vYaCvSm4AsAAIBrr2Od8hpxc1VJkkPSS0v26HBKlrGhAKCY8Pdy15RBzdSpfgVJktXu0HPzdmru2vi/+UoAxdntjSrp0TtrS5IKbHYNnbxeGTkFBqcCcD2ZTCb1anFhqu+3m5MMTAMAAFAyUfQFAAC4hhwOh+avv3DDqk/rCAPTFF9HT2fp+cKC77dbT1wo+Hq76fHbq2vlcx306G3VFUDBFwAAANfZ4PaR6lTPWVLLzrdp9Dc7lJpNWQUALoenu0UT72usfhdt6f3WD/v13vKDcjgcBiYDcD291LeRmlQNkiTFnczQ2LmxBicCcL31bBHmWi+MSeTnPAAAwDVG0RcAAOAa2nr0nA6fzJQkta1dXpWDfQ1OVLzEncrSc/OcBd/vtp5QYb9Xgd7ueqJTDf387E0aeWt1+XtR8AUAAMCNYTKZ9Gq3OqoT4idJSkrN1diFu2S12Q1OBgDFg8Vs0r961tXIjtVc56b9dkSvLN7DeylQQnm4WTRtZBv5eblJkuatO6p5a48YnArA9VQ52EetahQW/E9laeexNIMTAbgRHmwfqRG3VdeD7SONjgIAJZ6b0QEAAABKkoun+fZlmu9lO3wqU5+uitOPO5N18YP+ZXzcNah9pAa0iZCvJx9dAQAAYAwvd4vG92uogTNjdSYrX7HxqRq/4qCe71Lb6GgAUCyYTCY93qmGgvw89ObSfXI4pIWbk3QuK1/j720kL3eL0REBXGNVK/hr/MAWGjFlgyTp2TmxalY9WDVCAgxOBuB66d0iXHa71DsqXHUq8b0OlAbDb6suhyST0UEAoBQwOS5jz4T09HQFBgYqLS1NAQF8IAMAAPgj+Va7Gj+zVOey8uXlbtbOCd3k783k2b9y6GSmPl0dp2V/UPB9uEMV3d+6MgVfAAAAFBk7EtM0fO5WFdicH16f71JLfZuH/c1XAQAutmxHssbO3ylr4Xtp8ypl9MmDTRXANRSgRHpixkZ9Ge2c5tswsqxWvHy73N0o9wMlkd3ukNlsUoHNLncLm0sDAAD8nSvp5fLpCgAA4BpZtStZ57LyJUldmoZR8v0LB5MzNPqr7erx4Tr9uONCybesj7vG3FlTK5/toKE3V6XkCwAAgCKlUXigXux6YYrvFxuPKafAZmAiACh+ujQK0acDm8nHw1n0iz2aqoHTYpSSnmdwMgDXw1sPNFeNEH95uVv0wE3VKPkCJZjZ7JzpSckXAADg2qM5AQAAcI2YJNUNC9TepDT1axNpdJwi6UByhiatitOKXScvOR/s66GHb6qie1uFy8eDj6gAAAAouu5uHKpDKVnaeyJD4/s1lLuFDSoB4Eq1rRGsz4a00IjZW3Q2q0D7kzN1/5SNmj0kSpXKehsdD8A15OvppjmPt5dMUvUQdo4FAAAAgKthcjgu3iT5j13JiGAAAIDSymqzy81i1sET6apW0U8WM0+tn7f/RIYmrzqsFbtPXXI+2NdDj9xURf0p+AIAAKAYsdudl1Qdkixmir4AcLWOns7SkJmxOp6aq8pB3vrm0VYK9HaXycR7K1DS2O0O17RPAAAAAMCV9XJpUwAAAFwjboXbUdUI8eeGVKG9x9M1eVWcft5zacG3nL+HBneoqntahsvbg+36AAAAULxcTkklIyNDXl5eMplMcnPjMiwA/JEq5Xz15YiWem7eTr3Zp4H8PN3+9JpKdna20tPTFRIScoNTArgW/urzU15ensaPH6/g4GAFBgbqvvvuu4HJAFxPOTk5OnLkiNasWaOEhAQ99NBDqlGjhswMSgFKlJSUFB07dkySVLlyZZUvX97gRABQ8nCFGQAA4Bqj5CvtOZ6uSb8c1qq9KZecL+/vqSE3VVG/luHycqfgCwAAgJKnoKBAn3/+uSZOnKgmTZooNzdX8+fPNzoWABRZFQK8NOORFpL+fEr6448/rqSkJB07dkz9+/fXM888cyMjArhOHA6HlixZon/961/y9fXVPffco48++kiSKPsCJcB3332nGTNm6PDhw/Lz81Pr1q01cuRIPfDAA3r44YeNjgfgGjhw4IBGjBihLVu2KDQ0VJJ04sQJNW/eXFOmTFGNGjUMTggAJQdFXwAAAFyxrKws+fr6yuFw/E+xefyy/ZoZHX/JufL+nhpycxX1i6LgCwAAgJIrLS1Nr776qpYuXaq3335bbdu21ejRo/Xoo49q0qRJRscDgCLrzwq+586d0/PPP681a9Zo/vz5slgsuueee1S2bFkNHjz4BqcEcK298847+uyzz/Too4/qiSeekCQ1a9ZMTz31lLp37y5fX1+DEwK4Wm+//bYmTZqkESNGaNKkSQoLC5Mk/fjjj3r77bcp+gIlxKBBgzR8+HD9/PPPrknddrtdX3zxhQYOHKi1a9canBAASg6KvgAAANeA3W4vFVtN/fzzz/r0009111136eGHH/6fkq/VZlebGsGuom/FAE8Nubmq+rYIkycFXwAAAJRwDz74oNLS0vTLL78oMjJSkvTCCy9o4sSJpeZ3BgC4lt5++23t3r1bv/32m8qVKydJeuSRR2S32w1OBuCfmjlzpj755BMtXrxYLVq0cJ23Wq1q1qwZJV+gGDt69KjWrl2rlStXqnbt2pe8VrFiRfn4+CgzM1N+fn4GJQRwrZw5c0YDBw685JzZbNaDDz6o//znPwalAoCSiSvLAAAAV8jhcCg3N1c///yzduzYIUml4ob9Cy+8oBdeeEHdu3fXvffeK5vN9j9/xs1iVrua5dSxbjm93L2OfhrTXgPaRFDyBQAAQIn3/vvv6/Tp0/rtt99cJV9J+vDDD2W32//nITkAwF9buHChZsyYoTlz5rhKvpL06aefqqCgwMBkAK6FEydO6PXXX7+k5LtixQo99NBDqlWrloHJAPxToaGh2rdvnypVquQ6l5aWpqlTp+r+++9X//79KfkCJURgYKAWLFjwP+cXLlyoMmXK3PhAAFCCMdEXAADgCk2fPl0rV66Um5ubVq9ereDgYN17770aOXKkgoODjY53zdlsNp09e1bHjh3Tjz/+qPLly//ln7c7HPpoQFOZ/2TbTQAAAKAkSktLu2QbeavVqieffFI7duzQkiVLKPoCwBXav3+/xo4dq2rVqrnOPfzww6pXr54effRRSc6HsSXxHgsUQ4mJiUpMTNSgQYMkSf/+97+1cuVKjR07Vo8//rix4QD8I56enoqKitKTTz6pxo0ba8OGDUpMTJSPj49eeuklPfjgg0ZHBHCNfPbZZxo0aJAef/xxRURESJISEhJUuXJlzZ492+B0AFCyUPQFAAC4Qu+//74mTZqkatWq6fPPP9dvv/2m33//XSkpKXr33Xfl6elpdMR/bNWqVTp58qRuueUWhYaGKiMjQ5s2bVJgYKBmz56tL7/8Uh06dFDjxo3VrVu3S7YhNptMEvfXAAAAUMoEBARo7ty56tOnj5KSkvTkk0+qXLlymjRpksLCwmSz2WSxWC757AwA+HP5+fk6cOCA63jw4ME6d+6cXnnlFUn6n/dT3l+B4uWDDz5QVFSUHnnkEa1bt06hoaF64YUX1KFDB0lyfXYCUDxNnDhRv/76q+bMmaOoqCgNGjRI9evXV3h4uNHRAFxD9erV06ZNmxQXF6eEhARJUkRExCUP6wEArg2T4/zjzn8hPT1dgYGBSktLU0BAwI3IBQAAUCRt3LhRzz//vFavXi3JedNp4MCBevnllzV06FCNGjVK9913n8Epr97WrVs1evRoFRQUqEaNGjp16pSmTJkib29vPfnkkzKbzfL391fPnj21c+dOvfrqq0pJSZG3t7fR0QEAAADDDR48WGfOnNHu3bvVo0cPjRo1ShERERTPAOAqZGZmqnv37vLy8pLNZlNaWppmzpyp2rVrX1L+27Rpk1atWuV6yKJGjRoGpgZwJRISEpScnKzk5GR17979fwr7ubm5ys3NlZubm/z8/AxMCuCfcjgc/zOB/4/OAShehg4dqmnTphkdAwCKrSvp5TLRFwAA4Ar4+vrKYrFo1qxZatu2rX799Vfl5OSoXr16eumll/T5558X66LvzJkz1bVrVz377LM6dOiQXnrpJZnNZjkcDkVGRuqzzz7TunXrVKVKFXXu3FnffPONpk2bpieeeMLo6AAAAIDhZsyYoaysLOXn56ts2bKu8+dvYCclJWnhwoWKi4tT7dq1NXLkSAPTAkDR5ufnp1WrVmnhwoUKCQlRu3btJDkn955ntVr18ccf69SpU7rtttvUu3dvzZw5Uy1atDAqNoArEBERoYiICO3evVuSXNchTSaTEhIS9NFHH+ngwYOy2+3q1q2bhg4danBiAFfiyy+/VHZ2toYMGeL63j7/f61Wq9zcqKsAxV2PHj2MjgAApQajJAAAAK5AgwYNNGDAAP3yyy8aOnSofv31V73wwguSpBUrVqhKlSrGBvwHMjMzde7cOdfWOnPnzlVCQoJOnTqlgIAA9enTRxEREVq2bJkkKTs7W7Vq1VL79u2NjA0AAAAUKb6+vlq3bp1+++0317nzU6q2b9+ujz76SLVq1dJnn32mmTNnGhUTAIqNPn36qF27dq6y0Plpn+fOndP27dtltVo1YcIEPfvssxo6dKheffVV5ebmGpwawJWYN2+eawe185+b1q5dq7i4ON133336z3/+o/HjxysmJsbImACu0O23366aNWtKkuvnt8lk0p49ezRx4kTNmTPHdT8CQPF09913Gx0BAEoNHpECAAC4QgMGDFCNGjXk5eWlJk2ayN3dXdnZ2VqyZIlWrlxpdLzLcvLkSVWsWFGSZLPZZLFY5OPjo/79++vrr79WeHi46tWrp27duumll15SeHi4pkyZopdeeklPPfWUTpw4oR9//FGNGjVSrVq1DP6vAQAAAIqW6tWra/PmzZKk1NRULVq0SKdPn9ahQ4fUo0cPPfroo+rYsaP69eunm2++WdWrVzc4MQAUfdnZ2YqNjVWHDh20cOFCLV++XImJibLb7brvvvv05ZdfSpIqVKhwyVRQAEXfiy++qLS0NEnOSd1ms1lbt25VmzZt1L9/f0nSoEGD9PnnnysqKsrIqACuQIUKFVShQgWlpqZq5syZ8vX1Vc2aNbV06VKtWbNGNWvW1MqVKzV37lyjowK4Svn5+frkk08UFxenhx56SC1atND3338vh8Oh7t2783kcAK4hir4AAABXwOFwyMPDQx06dHCds9lscnNz07x581SjRg0D0/29pUuX6s0335Svr68aNGigd955Rx4eHpKcT9R369ZN/v7+kqQvvvhCkrRjxw716NFDCQkJuvvuu1W9enWtX79eU6dOVbNmzQz7bwEAAACKqjp16rh2+/juu++0fPlyNWjQQG3atNHjjz+ubt26yW63q1GjRvLz8zM2LAAUE0OGDFFeXp4k54R0Nzc3TZs2TWFhYRo2bJgeeughtWrVSvXq1XNd6wBQPHh4eKh8+fI6ffq0rFarQkJCFBgYqJ07d0qSkpKSlJycrHbt2hmcFMCVOP/QzdKlS7VixQq1aNFC0dHR2rhxow4ePChJatSokVJTU1WmTBljwwK4KqNGjdLRo0d166236sUXX1T9+vX122+/ydPTU7///rsmTJhgdEQAKDEo+gIAAFyB/37y1OFwyGKxyGKxFPlpEikpKXrnnXc0evRotWvXTg899JAef/xxffDBB/Ly8nJddMvIyFBwcLDS0tIUGBioc+fOqX379q4LbXXr1lXdunWN/Y8BAAAAijhPT09JUmxsrGrVqqWXX35ZknN3jccee0zNmze/5M8BAP7e+QJvSkqKatasqbCwMEnSbbfdpvLly+uNN96Q5Lxe88EHH6hs2bKyWq0aPHiwYZkBXL65c+fq+++/16pVq/Tiiy/q4Ycf1pgxY7R+/XrZ7XYNHDjQ6IgAroDJZFJOTo7mzJmjYcOGqU+fPjKZTKpRo4ZWr16tjh07KiQkRCtXrlS/fv2MjgvgKqxZs0bbt2+Xu7u7HnvsMYWGhio5OVk+Pj5q2LAhRV8AuIbMRgcAAAAoLuLj43XmzBllZWUpOztbkvNCVV5enpYuXSqHw2Fwwr/2+++/q2LFiurbt69CQ0M1ffp0bd68Wb/++usl21nm5OToyJEjevPNNzVp0iQ9/fTTqlmzpgICAgz+LwAAAACKj/Ofr93d3ZWenu46HxAQoIEDB2r69OmaNWuWypQpI4fDUeR/nwCAouD8e2uPHj00ffp0/fjjj9qzZ4/mz5+vM2fOSHJOUh8yZIgWL16svLw8zZ49W8OGDTMyNoDL9PTTT8vNzU3vvPOOdu7cqX79+mn16tU6ffq03nvvPTVr1kwOh0Nz5szRkSNHjI4L4DJ4e3vLzc1NQUFBrp/jzZo10+uvv64hQ4aofPnyuuWWW4wNCeCqeXh4yN3dXZLk7++vgIAA+fr6ymw2y82N2ZMAcC3xrgoAAHAZ/u///k+7du3S+vXr1ahRI3Xs2FHdunVT48aNtX//fm3ZskV333230TH/Up06dbRq1SpZrVa5ubkpMjJSt9xyixYuXKjbbrvN9Yt4r169VKlSJX3++eeKjY3VnDlz1KBBA4PTAwAAAMXTu+++q1tvvVVPPvmkatasqQ8++EBjx451ff6WLhTXCgoKLjkPAPhjd955p7y9vTV27FhVqVJFGRkZ+vLLL7Vz5059/PHH8vHx0RtvvKH27dtr2LBh6tevn+bOnasHH3zQ6OgA/sa0adP05ptvatSoUQoNDVWjRo306aefunYkS05O1s6dO/XBBx8oNjbW6LgALsPDDz+s119/XR4eHjp58qRsNptmzJihRYsWqUaNGipXrpzREQFcpZCQEL3zzjvq1q2bZs+erbCwMD3//PMKCAjgexsArjGT4zJGRaSnpyswMFBpaWlMcgMAAKVObGysBg0apJUrVyogIEDfffedvv32W61du1Y9e/bUhx9+qPz8fNf2kUWVw+HQXXfdpZtvvlljx46VJB0+fFjt2rXTgQMHFBAQoOjoaDVs2FBlypShZAAAAABcI4mJifroo4+UnZ2txo0ba/DgwVqxYoWOHDmi/Px8bd++Xe3bt9fSpUt1//33q0+fPkZHBoBi4cyZM/L29pYk+fj46KmnntKKFSs0cuRIff/99+rTp4+GDx+utLQ0nTlzRtWqVTM4MYDLkZ+fL4fDodzcXAUGBkq69IGobdu2qVmzZpo5c6YGDRpkYFIAl8PhcOidd97R6tWrFRcXp1GjRunJJ5+85PXzDz8CKF6OHj2qkSNHateuXbrllls0ZcoUvfLKK0pMTNSbb77J528A+BtX0sul6AsAAPA3XnnlFZ08eVJTpky55PyePXv00ksvacyYMWrXrp1B6f7c+Y95F18gW7hwoZ5//nnt27dPFotFktSxY0d98sknOnnypGJjY/XYY4+5bpIBAAAAuHbO38CeNm2a3nnnHQ0ePFiNGjWSp6enkpOT9dBDD+m+++7TF198YXRUACh29u7dqy5dumjFihWqVauWNm3apNdee02ff/65ypQpQ4EIKMZsNpvrWua4ceP07rvvatiwYXrjjTcMTgbgSiQnJyskJMR1nJqaqieeeELx8fHq2bOnnn76aQPTAQAA3HhX0ss136BMAAAAxVafPn109uzZ/9kKrl69egoLC9N3331nULI/5nA49Nu+FA2cFqP0XKsufq6rT58+ql69ugYOHKgdO3bo5ZdflpeXl+rUqaOOHTvqmWeeoeQLAAAAXGdDhw5V5cqVZbPZdNddd8lms2n48OF6/vnnKfkCwFVyd3dX+/btVatWLUlSlSpVlJ2drdzcXEq+QDGVmJiopUuXymKxKDExUb169dLHH3+sefPmuUq+NpvN4JQALldISIi++uorjRw5Uvfff7+mTZumM2fO6OWXX9aCBQu0evVqoyMCuAqLFy/WhAkTdOTIEUnSjh07tH37doNTAUDJQ9EXAADgb9SrV0+1atVS586d1aVLF82aNUvp6ek6ePCgfv31V/Xr18/oiJKcBd9f96Wo/6SNGjlnqzYfTdW0X4/ov7dvmDVrlurVq6eRI0cqPj5eH330kcxmPhYCAAAA15vJZHKVUb777jt98803uvfee3XXXXdp3LhxevPNNyVJl7EJGwDgv1SvXl3nzp3TwIEDtWHDBt13332y2WwKDQ01OhqAq3Tu3DkNGjRIU6dOVadOnZSfn699+/apY8eOstvtcjgcrkm/AIq+DRs26K233lJYWJj69++vH374Qa1atdLtt9+ue+65R1999ZXREQFcoRdffFGvvPKKDh48qAEDBmj8+PHq2rWrevTooXfeecfoeABQopgcl3HV+EpGBAMAAJRUiYmJ+vrrr/X9999rx44duvXWW9WgQQO99tprhuZyOBxavS9Fk1fFaXdS+iWvNY4I1JyhUXK3/G+RNyMjQ/7+/jcqJgAAAID/ctddd2nZsmWaOnWqhgwZIofDwdRJAPiHHn/8cUlSSkqKvvrqK5lMJtntdh5yBoqpSZMmadSoUXrvvff05JNPSpKsVqvc3NwMTgbgSk2ePFmxsbGaPn26JOnXX3/VqFGj9NNPP2n16tVasWKF5s6da3BKAFeibt262rx5s3x9fXXy5ElVq1ZNR44ckb+/v1q0aKHdu3cbHREAirQr6eXyGxAAAMBlCg8P15gxY/TMM88oPT1d6enpCg8PNyyPw+HQL3tTNHnVYe09nnHJa3VC/fXobdV1a53yMpv/uChAyRcAAAAwTrdu3bRv3z6NHz9emZmZstlsTKQDgGvgo48+uuT4fMnXXjj3xswDFUCx8uijj2rFihVKS0uTJNlsNkq+QDF16623atasWa7jvLw8BQUF6ZVXXlF0dLQ++OADA9MBuBpeXl7y9fWVJFWsWFHBwcGqUKGCJMnDw8PIaABQ4vBbEAAAwGU4P1nr/HStgIAAw3Y6sNsdWrX3lD5ZFaf9Jy4t+Nat5K/HbquujnXKMwkMAAAAKMImT57senAwJyeHki8AXCdms1kOh0NySGO+3qFuTUN1a90KRscCcAW+/vprjRs3TtnZ2fLx8TE6DoCrVLt2bQUFBenpp5+Wm5ubvv76a82cOVN169ZVVlaWateubXREAFfI19dX33zzjbp166ZZs2bJ09NTn3zyicqUKSNvb2+j4wFAiWJyOAofYf4LVzIiGAAAoCTafzxdVcr7ysPNLIdDfzol93qy2x36ec8pTV51WPuTMy95rX5YgB69tZpuoeALAAAAFCsXbydvtztkNptUYLPL3cIW8wBwLb28aLcWbk6SxWzSa73qqXfzMKMjAQBQ6sTHx2vp0qX68ccf9cgjj6hPnz5GRwLwD2zdulX333+/9u/fr3bt2mnevHl6/PHHdezYMX388ceKiooyOiIAFGlX0sul6AsAAPA3HA6HbvnXSiWeydbdzcP0zoCm8vK4cRsj2O0Ordh9UpNXxengyUsLvg3DA/TordV1U+1yFHwBAACAYsxmd6jAZtdLS/bIy82i13vW5TM+AFwjVptdL8zfpR92JLvOjb6jpgbfVIX3WqAYstrskqQfYhPVo2WEwWkAAIDVapWbG5vKA8CVupJeLu+yAAAAf2PXsVTtP54uSTpyKvOGlXztdoeW7zqpyasO69CprEteaxgeoMduq64OtSj4AgAAACWB3eHQiLnbtKvwd48aFXw1qF2kwakAoGRws5j1zj0NFeTnobnrEiRJE5cf1JnMfD3bpZYhOzcBuDoOh0NJZ7P18MdrtSP+nMwmk7pFVTY6FoArZLfbZTKZuL8BlBCUfAHg+uOdFgAA4G/MXx/vWvdtff1vtNvsDi3fmazJq+N0+L8Kvo0rB+rR26qrfc1gLoABAAAAJYi7xayBbSP07IJdkqRPVsepWnlf3VSrnMHJAKBkMJtNev6u2gr289D7Kw5JkmavjdeZrHz9p3d9ebiZDU4I4HKYTCat239KO+LPSZKenLlJjauUVUR5P4OTAbgSZrNZdrtDDodDMklm7ncAAAD8Ja5aAAAA/AWrza5FG49JkjzczOreIvy6/Vs2u0NLt51Qjw/W6Zlvdl5S8m0aUUbTHm6mL0e0ZIovAAAAUEJ1rFNeI26uKklySHppyR4dTsn66y8CAFw2k8mkYbdU07971dP5Ib5Lt53QqLlblZ1vNTYcgMt2b7uq6tkyQpKUnlOg4VPWq8BqNzgVgCu1/0SGXl+yR3e8/ZusNr6HgeLIarOrwGbnexgAbgCKvgAAAH/h1z0ndTojT5LUuXGoyvh6XPN/w2Z36PttJ9T9/bV6bt5OxV10I79ZZBlNf6S5Ph8epXY1KfgCAAAAJd3g9pHqVK+CJCk736bR3+xQanaBwakAoGTpGxWu9wc0cU3xXXPwjB6ZEavU7HyDkwG4HCaTSRMHtVBkeV9JUsyhM3p3yS6DUwG4Uh8uP6BZvx3RweRMRe8/bXQcAFdhyi+HNeGH/Zryy2GjowBAiUfRFwAA4C/MXx/vWvdrHXlN/26rza7vth5Xt/fXauy8nTpyOtv1WvMqZTTjkeaaOyxKbWsEU/AFAAAASgmTyaRXu9VRnRDn9tNJqbkau3AX03EA4Bq7vV4FTX+4mfy93CRJO46l6YEpMTqemmNwMgCXI8DHQ1NHtJWbxXnd9P0f9uj3PckGpwJwJXpFXdhBcVFMooFJAFytuWvi9ekvhzV3Tfzf/2EAwD9C0RcAAOBPpGcX6KetxyVJQX4e6tgg5Jr8vVabXUu2JOnu99fp+fm7dPSigm9U1bKaNbiF5gyNUhsKvgAAAECp5OVu0fh+DRVcuKNIbHyqxq84aHAqACh5WlQN0pyhUSrn73y/jUvJ0gNTNunQyUyDkwG4HM2rB+v/+jSSJDkc0ogpG5SSnmtwKgCX65a6FVTW112StGJnstJz2MkEAADgz1D0BQAA+BNLYxOVZ3VOzerVMsK1nePVstrsWhybpLveW6v/W7BbCWcuFHxbVi2rz4a00OyhUWpVPYiCLwAAAFDKhQR6aVy/BnIvnFK3IPa4FsQmGZwKAEqe2qH++nJ4S0UE+0iSktPy9MDUTdqWkGpsMACXZdSddVwDGk6l5erx6RtltzsMTgXgcni4mdW9WZgkKa/Arp+2nzA4EQAAQNFF0RcAAOBPzNtwYZuZvq0jrvrvKbDZtXCzs+D74sLdOnb2whaQraoFac7QFvpsaJRaVgv6R3kBAAAAlCyNwgP1YtfaruNxyw9q89FzBiYCgJIpPMhHXwyPUv2wAElSeo5Vj8zYrN/2pxicDMDfMZtN+nhIK5UP8JQk/bzjhCYv329wKgCXq1dUmGu9MCbRwCQAAABFG0VfAACAP5BwOksbDpyWJNUM8VeTKmWv+O8osNm1ICZRd01cq5cXXVrwbVMjSHOHRWnWkBZqUZWCLwAAAIA/dnfjUD3QurIkyWZ3aOzCXUo8l/M3XwUAuFLBfp76bEgLta7uvE6TW2DXqLnb9N3W4wYnA/B3Kpbx1qRhrV3H/1mwQ1uPnDUwEYDL1TiijKpV8JUkbTx0Vscu2gkRAAAAF1D0BQAA+AMLNyS41v3aRMpkMl321+Zb7Zofk6guE9bolcV7LrkJ37ZGsD4fHqUZj7RQ86soDwMAAAAofR6/tbraFhbP0nKsGj1vpzLzrAanAoCSx9fTTZ8ObKY7G1aU5HzA4vn5uzQr+qixwQD8rY4NQvV41zqSnAMYhk1ep4ycAoNTAfg7JpNJfaLCXcffxiYZmAYAAKDoougLAADwXxwOh+avj3cd924VcVlfl2+165uNx9Rl4hq9uniPjqfmul5rXzNYX45oqemPNFezSAq+AAAAAC6fxWzSm73qKzLYR5IUl5Kll5fskc3uMDgZAJQ8Hm5mjevfSPcXTlOXpHHLDmj8sgNyOHjfBYqy/+vdSM2rBUuSjpzK1DOzN/N9CxQDPVqEudaLYhL5vgUAAPgDFH0BAAD+y5a4s4o7lSlJale7vMILb6b/mXyrXV9vOKY7J6zRa9/u1YmLCr4dapXTVyNaaurDzdUkosz1jA0AAACgBPPzctN7/RvK38tNkhR98Iwm/xpncCoAKJksZpNe7FZHo26r7jo3M/qoXly4W1ab3cBkAP6Ku5tZU0e2kb+3uyRp4YZ4fb3miMGpAPyd8CAfta7hLOnHncrStvhUYwMBAAAUQRR9AQAA/sv8DQmudb82kX/65/Ktdn21IUF3TojWv7/bq+S0CwXfm2qX09cjW2nKoGZqTMEXAAAAwDUQEeSjt3rXl8VkkiR9ti5BP+06aXAqACiZTCaTHr2tul7pUVeFb7tasuW4nvhim3LybcaGA/CnIsv7aeKgKNfx2LmxOngi3cBEAC5H76hw13rx5kQDkwAAABRNFH0BAAAukldg07cxxyRJ3h4W3d087A//zBfrE3TH+Gi9/t0+JafluV67pU45ffNoK306sJkaVQ68YbkBAAAAlA6tqwXp6U41XMevL92n3ccprwDA9XJvq8p6777Gcrc4276/7jutITNjlZpdYHAyAH+mV6sIPXBTNUlSdr5NQyetUy4FfaBI69IkRF7uzvrKd7HHlW9lgj4AAMDFKPoCAABc5JedyTqXlS9J6tKkkvy83F2v5RbYNHddvO6YsEZvfL9PJ9MvFHw71i2v+Y+11qSHmqlhOAVfAAAAANdP/6gw9WwSKknKs9o1Zt5OpWTk/c1XAQCuVucGFTV1UHP5elokSVsTUvXQ1E2X7O4EoGh5c0Az1aoUIEnadSxVr83bZmwgAH/J38tdnRuGSJJSswu0es8pgxMBAAAULRR9AQAALjJ/fbxr3a9NpKTCgu/aeN0xfo3eWrpfpy4q+N5at7wWPNZanzzYVPXDAm54XgAAAAClj8lk0tgutdS0cBeR05n5GjN/p3ILmFQHANdLq+pBmj00SsG+HpKkQ6eyNGDKJsWdyjI4GYA/4uPppukj28rTzXk7fNrPB/XjlkSDUwH4K71bhrvWi2L4fgUAALgYRV8AAIBCZzPz9PPOE5KkioFealE9WLPXxKvz+Gi99cP+SyZk3V6vghaOaq2PH2yqehR8AQAAANxg7haz3u3bQKGBXpKkPccz9J+l++VwOAxOBgAlV71KAfp8REtVDvKWJJ1IzdUDUzdpx7E0g5MB+CP1KpfR6/c3dR0/OWOTjp/NNjARgL/SvlY5lQ/wlCSt2n1SqYW7LwIAAICiLwAAgMu3MYkqsDlviterEqSu763VOz/u1+mMCxeTOtevoEWPt9GHDzRR3UoUfAEAAAAYp6yvhybc01De7s6t5H/afVKz1ycYnAoASrbIYB99Prylaof6S3JuL/7wjM1ae/C0wckA/JGHO9bQXc2dU0LPZeVr2KfrZbXZDU4F4I+4Wczq0SxMklRgc+j7rccNTgQAAFB0UPQFAAAo9M26o/Ly9lCZYD9tP56pM5kXFXwbVNTix9vo/QFNVKfwRg4AAAAAGK1WRT/9u0dd1/Enq+L0+wHKZgBwPZX399ScoS0UVbWsJCkn36aRs7fqh+0nDE4G4L+ZTCZ98EhLhQf7SJI2HEjRhO92G5wKwJ/p3TLctV4Uk2hgEgAAgKKFoi8AACj1svKseveHfTqaUSAfPy+Zzc6PSCaTdGfDivr2iTZ6//7GrkktAAAAAFCUdKxTXiNuripJckh6ackeHU7JMjYUAJRw/l7umjqomTrVryBJstodevabnZq7Lt7gZAD+WxlfD00Z0UYWs0mSNOG7PVq775TBqQD8kXphAapTyXkvZuvRVB05lWlwIgAAgKKBoi8AACi1svKsmvbbEXUeF63P1ia4Cr6S1LVRiJY80VYT72usmiEUfAEAAAAUbYPbR6pTPWfZLDvfptHf7FBqdoHBqQCgZPN0t2jifY3VL+rC9MG3lu7X+ysOyuFwGJgMwH9rVbO8xvZsIEmyOxwaMWW9zmbmGZwKwB/pfdHP1cWbkwxMAgAAUHRQ9AUAAKVOVp5VU3+NU6dx0Xpv+UGdK7z57XA4lJ9boFmPNNP4exupZkU/g5MCAAAAwOUxmUx6tVsd1Q5x/h6TlJqrsQt3yWqzG5wMAEo2i9mkf/Wsq5Edq7nOTf31iF5dvIf3YKCIefLuumpf1/lg1IlzOXp8+kZK+UAR1KN5mAoHcGtRTKLsdr5PAQAAKPoCAIBSIzPXqk9Xx+n2d6P1/opDrulWJpOUl5uvtHNZiooMUKsa5QxOCgAAAABXzsvdogn9GirY10OSFBufqvErDhqcCgBKPpPJpMc71dCL3erIVFhMWrA5SU9/tUO5BTZjwwFwsZjNmjysjYL9PSVJy7cd1/Sf+awEFDUVA73UvnZ5SVLi2RxtPnLW4EQAAADGo+gLAABKvIzcAk1edVi3j/tdH648pLQcZ8HXbJK6Nw1VuyqBysrIld1m1z2tIw1OCwAAAABXLyTQS+P6NZC7xdk0WxB7XAti2e4WAG6EAW0iNL5/I7kVvgf/sueUhs3aovTCa1EAjBda1lsfDW7lOn71m23aEX/OwEQA/kifqHDXelFMooFJAAAAigaKvgAAoMRKzynQpF8Oq9O70fro58NKz7FKchZ8ezQN1dKn2+nVHvW0eucJSZKfl5vuaFLJyMgAAAAA8I81Cg/Ui11ru47HLT+ozUcpsADAjdClUYg+faiZvD0skqTNR89p4LQYpaTnGZwMwHmdm1TSiDucn5XyrXYNnbxOmbkU8oGipHOjEPl6On+W/rD1hHLzmZAPAABKN4q+AACgxEnPKdDHPx9Sp3HR+viXw0rPdRZ8LWaTejarpB9Gt9db/RqqSjlfLd9+XBmFBeC7m4fLx9PNyOgAAAAAcE3c3ThUD7SuLEmy2R0au3CXEs/lGJwKAEqHtjWDNXtIC5X1cZck7U/O1P1TNuro6SyDkwE47+W+jdQosqwk6XByhp6fG2twIgAX8/awqEvjUElSRq5VP+8+aXAiAAAAY1H0BQAAJUZaToE+Kiz4TloVp4yLCr69mlfSD0+305t9Gygy2Mf1NfPXx7vW/dpE3PDMAAAAAHC9PH5rdbWtHiRJSsuxavS8ncrMsxqcCgBKhwbhgfpiREtVKuMlSUo6l6sHpmzS7qR0g5MBkCRPd4umjWwrXy/n4Iev1x7V/HVHjQ0F4BK9W4a71os2JRqYBAAAwHgUfQEAQLGXml2gD1ceUqd3ozX5ooKvm9mkPi3C9OPodnqjTwNFXFTwlaRTabn6tfAp8LAgH7WpWf6GZwcAAACA68ViNunNXvVdDzvGpWTp5SV7ZLM7DE4GAKVDlXK++nJES9Ws6CdJOptVoIHTYrT+0BmDkwGQpOoh/hr3UAvX8TNzNivuZIaBiQBcrHX1YNcDM7/tS1FKep7BiQAAAIxD0RcAABRbqdn5en/FQXUa97s+XR3nmkzlZjapX5Sz4Pt67/qqHOTzh1+/eFOC6wZ339YRMptNNyw7AAAAANwIfl5ueq9/Q/kXTquLPnhGn/4aZ3AqACg9KgR4ac6wKDWLLCNJys63acTsLfppZ7KxwQBIku5pW0X3tqsiScrKtWro5HXKt9qMDQVAkmQ2m9SzhXOqr83u0HdbkgxOBAAAYByKvgAAoNg5l5Wv95Yf1O3vRmvqr0eUlee88OpmMalfVLiWjWmv13rVV/ifFHzPW7AhwbXu2zriumYGAAAAAKNEBPnord71ZTE5H26ctS5BP+06aXAqACg9Ar3dNf2R5upYx7mbVIHNoTFf79BXF12bAmCctx9sruoh/pKk7UfP6fX5OwxOBOC83lFhrvXiGIq+AACg9KLoCwAAio2zmfma+NMB3T4uWtN+O6Ls/AsF3/4tw/XTmPZ6rVc9hZX1/tu/a29imnYmpEqSmlQpq5qhAdczOgAAAAAYqnW1ID3dqYbr+PWl+7T7eLqBiQCgdPFyt+iDAY3Vu7mzsORwSK9/t08f/3xIDofD4HRA6ebn5a5pI9vKw81563zy8v1auf24wakASFKNEH81qhwoSdqVmKb9/A4DAABKKYq+AACgyDuTmafxyw6o0/hoTf/9qHIKC77uFpPua1VZy8d00Ks966lSmb8v+J43f0O8a92vTeQ1zwwAAAAARU3/qDD1bBIqScqz2jVm3k6lZOQZnAoASg83i1mv966nITdXdZ2btCpO//52r2x2yr6AkRpFltW/+jdxHY+avlEnzuUYFwiAS++W4a71os1M9QUAAKUTRV8AAFBknc7I07hl+9V5XLRmRl9a8L2/dWUtf6aDXu5RV6FlvK7o77XZHVq00bk1opvFpJ5Rla95dgAAAAAoakwmk8Z2qaUmhROxTmfma8z8ncotsBmcDABKD5PJpNF31NTzd9V2nftmU6LGfL1D+Va7gckADL29pu5oUkmSdCYjT49OXS+bne9LwGjdmlWSm9kkSfp2cxIPxwAAgFKJoi8AAChyUjLy9M6P+9V5fLRmRccrp8B5MdXDzawBbSK04pkOeql7XYUEXlnB97w1+04pOTVXknRbgxAF+3tes+wAAAAAUJS5W8x6t28DhQQ4fw/aczxDb/ywn23jAeAGe6hdpN65p6GruLRi10kN/yxWmblWg5MBpZfJZNKHg1sptKxz57jovaf0wdK9BqcCEOznqVvqVZAkJaflav3B0wYnAgAAuPEo+gIAgCIjJT1Pb/+wT53HRWv2mnjlFhZ8Pd3MeqBNhFY8014vdqujildZ8D1v/vp417pfm8h/9HcBAAAAQHET5Ouhif0bydvdIklatuukZq9PMDgVAJQ+3ZqE6pOHmsrb3Xm7bmPcOQ2cHqPTGXkGJwNKr2B/T306vI3MJmcJ/50lu7SJUiFguN5RYa71ophEA5MAAAAYg6IvAAAw3Kn0XL21dJ86j4/WnLUJyrNeKPg+1M45wff/utVRhYB/VvCVpKxcq37YkiRJCvRxV6dGof/47wQAAACA4qZWRT/9u0dd1/Enq+L0+wFKLABwo3WoVU4zh7RQoLe7JGnv8QwNmLJJx85mG5wMKL3a1amg0d3rSZJsdoeGfbpOqVn5BqcCSrdb61dUQOHPyp+2Jysrjwn4AACgdKHoCwAADHMyLVdvfL9Pncev0dx1Fwq+Xu5mDWwfqRXPdtDzd9VR+cItZa+FH7YkKSffJknqEVVZnoUTrAAAAACgtOlYp7xG3FxVkuSQ9NKSPTqckmVsKAAohRpXLqPPh0cppHAXq2Nnc3T/p5u093i6wcmA0uuZ7vXVulZ5SVLimWw9NXOTHA6HwamA0svL3aK7mzoHt2Tn27R8R7LBiQAAAG4sir4AAOCGS07L1X++26s7JqzRF+sTlH9RwXdQ+0iteKaDxnatrfL+167ge978DfGudb/WEdf87wcAAACA4mRw+0h1qldBkvOG+ehvdig1u8DgVABQ+lSv4KcvhrdUtfK+kqQzmfkaOG2zNsWdNTgZUDq5WcyaMry1yvh6SJKWxibqs9WHDU4FlG69o8Jd60WbEg1MAgAAcONR9AUAADfMidRcvf7tXt0xPlpfbjjmKvh6u5v1SIcqWvlsBz3XtbbKXYeCryQdP5utNftOSZKqlPdVi+rB1+XfAQAAAIDiwmQy6dVudVQ7xE+SlJSaq7ELd8lqsxucDABKn9AyXvp8eJQaVw6UJGXmWTXssy1aueukwcmA0iks2FcfDm7pOn7pyy3acyzVuEBAKde8allFBPtIktYePK0TqTkGJwIAALhxKPoCAIDr7nhqjl5bskd3TIjWVxuPqcDm3OLM28OiITdV0cpnb9IzXWop2O/6FHzPW7gxQed3V+vXJlImk+m6/nsAAAAAUBx4uVs0oV9DBRdOrIuNT9WEFYcMTgUApVMZHw/NGNxcHWqVkyTlW+16+qvtmh/D5ELACF2bhWvIbTUlSXlWu4ZMXqfsPKvBqYDSyWQyuab6OhzSt7FJBicCAAC4cSj6AgCA6ybpXI7+tWSP7pywRt9sSpS1sODr42HRkJur6udnO2j0nbUU5Odx3bM4HA7NX5/gOu7bOuK6/5sAAAAAUFyEBHppXL8Gcrc4H4icH5ukBdw4BwBD+Hi46eMHm6h701BJkt0hvbp4jz5dFSfH+afYAdww/+rfRA0ql5EkHTierhe/3GJsIKAU6xUV5lov3JTIz0UAAFBqUPQFAADXXNK5HL26eLe6TFyjeRcVfH09LRp2S1X9/FwHjb6jpsr6Xv+C73k7ElJ14ES6JKlVjWBFlve7Yf82AAAAABQHjcID9WLX2q7jccsPavPRcwYmAoDSy91i1pt9GmhQ+0jXuQ9/PqQ3vt8nu51SE3AjeXlYNG1kW/l4WCRJc3+L0+KNCX/zVQCuh8hyvmpRtawk6WBypnYnphucCAAA4Mag6AsAAK6ZY2ez9fKi3eoyYY3mxyRdUvAd0bGaVj57k57qXFNlfG5cwfe8BevjXet+bSL/4k8CAAAAQOl1d+NQPdC6siTJZndo7MJdSjyXY3AqACidzGaTnutaW2PurOk69+WGY3pu3k7lW+0GJgNKn5qVAvT2g81dx6M/i1F8SqaBiYDSq3fLcNd6UUyigUkAAABuHIq+AADgHzt2NlsvLdytrhPXauHmJFkLp4r4ebppZGHB94lONVTGx92QfAVWuxZtOiZJ8nQzq1uL8L/5CgAAAAAovR6/tbraVg+SJKXlWDV63k5l5lkNTgUApdfgm6rqzb71ZTGbJEk/7kjWo3O2Kov3ZuCGuq99VfVp7RwikZFToGGT16uA0j1ww93VJFQeFmfV5bvYJBXY+D4EAAAlH0VfAABw1eLPZOv/FuxS14lrtSg2SbbCgq+/l5sevbWaVj7XQY8bWPA9b/XuZJ3JyJMkdW5cSYEGTBQGAAAAgOLCYjbpjV71FBnsI0mKS8nSy0v2yO5gq3gAMErPZmH66IEm8nJ33tpbd+iMHp6+WWcz8w1OBpQeJpNJ4we2UNUKfpKk2LgzemvRToNTAaVPoI+Hbm9YUZJ0OjNf0ftSDE4EAABw/VH0BQAAV+zo6Sy9MH+n7n5vrZZsOe4q+AZ4uWnUbdW18tkOGnV7DQV6G1vwPW/BhgTXul+bCAOTAAAAAEDx4O/lrvf6N5S/l5skKfrgGU1eHWdwKgAo3W6pU14zHmmhgML35l1J6Xpg6iYlncsxOBlQevh7u2vKiDZyszgnbH/4416t3nXC4FRA6dM76sLOjYtikgxMAgAAcGNQ9AUAAJft6OksPV9Y8P1264kLBV9vNz1+e3WtfK6DHr2tugKKSMFXktKy87V823FJUrC/pzrWDzE4EQAAAAAUDxFBPnqrd31ZTM4iy6x1Cfpp10mDUwFA6dY0sozmDotShQBPSdLR09kaMGWTDiRnGJwMKD2aVQvWy30bu44fm7pRp9JyDUwElD431y2vIF/n7o0rdiYrLbvA4EQAAADXF0VfAADwt+JOZem5ec6C73dbT6iw36tAb3c90amGfn72Jo28tbr8vYpOwfe87zcnKs9qlyT1allZ7m58/AEAAACAy9W6WpCe7lTDdfz60n3afTzdwEQAgJoh/vpieEtVLecjSTqVnqeHpsYo9ug5g5MBpcfIO2rrtoahkqRT6bl6bNoG2c9fOAdw3blbzOrevJIkKd9q17LtTNYGAAAlG00XAADwpw6fytSzX+9Qtw/Waum2CwXfMj7ueqpzDf38XAeN6FhNfoXbBRZFCzYkuNb92kQamAQAAAAAiqf+UWHq2cRZZMmz2jVm3k6lZOQZnAoASrewst76fHhLNQwPkCSl51o1ZGasVu09ZXAyoHQwm036eGgrVQj0kiSt3pWsj3/aZ3AqoHTpHRXuWi+KSTQwCQAAwPVH0RcAAPyPQycz9czXO9T9g3X6YUeyHBcVfJ++o6ZWPttBw26pJl/PolvwlaT4lExtOHhaklQrNECNIsoYGwgAAAAAiiGTyaSxXWqpSeVASdLpzHyNmb9TuQU2g5MBQOlW1tdDMwe3ULuawZKcD2M8+cV2LYpNMjgZUDqUD/DS5GGtZTI5j99cuEOxh88YGwooRRpWDlSNin6SpE2Hz+rYmWyDEwEAAFw/FH0BAIDLweQMjf5qu3p8uE4/XlTwLevjrjF3Ogu+Q2+uWuQLvuddOs03QqbzV1wBAAAAAFfE3WLWu30bKCTAU5K053iG3vhhvxwOtqgGACP5errpkweb6q5GIZIkm92hlxbu1vTfjvAeDdwAN9cP0ZN31ZUkWW0ODft0ndKz8w1OBZQOJpNJvS6a6rt4M1N9AQBAyUXRFwAA6EByhp76crt6fLheP+086Sr4Bvt66JkutbTyuQ4afFPxKfhKksPh0Pz18ZIkk0nq3SrC4EQAAAAAULwF+XpoYv9G8na3SJKW7Tqp2esT/uarAADXm4ebWe/c01APtLlw/Wvi8oN698cDstsp+wLX29ieDRVVwzlZOz4lS6M/20zRHrhBejYPc03VXrQpke89AABQYlH0BQCgFNt/IkNPfbFNPT9crxW7TrrOB/t66NkutbT82fZ6pEMV+XgUn4LvebFxZ3U0JUuS1L5OBYUF+RicCAAAAACKv1oV/fTvHnVdx5+sitPvB04bmAgAIElms0kv3F1bT3au4To3e228XliwSwU2u4HJgJLP3c2sKcPbKMDbXZK0ZFOCvvg9zuBUQOkQFuStNoVF+6Ons7U1PtXYQAAAANcJRV8AAEqhvcfT9cTn29Tro/VasfuU63w5fw+N7VpbK57toIeLacH3vHmF03wlqW9rpvkCAAAAwLXSsU55jbi5qiTJIemlJXt0uPBBSwCAcUwmk4bfUk3/7lVP5sLpht9vO6HH5m5Vdr7V2HBACRdR3k/vP9LSdfzCF1t04HiagYmA0qNXVLhrvWhTooFJAAAArh+KvgAAlCJ7jqdr1Nyt6vPxBv2850LBt7y/p164q7ZWPNNBA9tHytvDYmDKfy6vwKbvYo5Jkrw9LLqrWZjBiQAAAACgZBncPlK31y0vScrOt2n0NzuUml1gcCoAgCT1jQrX+wOayMPNeRtwzYEzemRGrFKz8w1OBpRs3aMqa+At1SVJOfk2DZm0TjmU7IHrrkvjUHm5O3/mfb/1uPKsNoMTAQAAXHsUfQEAKAV2J6Xrsblb1ffjDVq1N8V1vry/p164u7aWP9NeD7aLlJd78S74nvfzzmTXDea7moXJz8vd4EQAAAAAULKYTCb9q3td1Q7xkyQlpebq+YW7ZGV7eAAoEm6vV0HTH24mfy/njl07jqXpgSkxOpGaa3AyoGT7z/1NVScsUJK0JzFNr369zdhAQCng5+WmOxuFSpLSsgu0+qKdLAEAAEoKir4AAJRguxLT9OicLer3yQatvqjgWzHAUy92q6MVz7TXg21LTsH3vPnr413rvq0jDEwCAAAAACWXl7tFE/o1VLCvhyRpc3yqJqw4ZHAqAMB5LaoGac7QKJXzd75Px6VkacCUjTp0MtPgZEDJ5e3hpumPtnXtmjdz1SEt3XzM4FRAydcr6sLOjotiEg1MAgAAcH1Q9AUAoATacSxNI2Zv0T2TNurXfadd50MCPfVy9zr6aUx7DWgTIc8SVvCVpDMZefp55wlJUsVAL3WoW9HgRAAAAABQcoUEemlcvwZyt5gkSfNjk7QgNsngVACA82qH+uvL4S0VEewjSUpOy9ODU2O0LSHV2GBACVYnLFD/ub+p6/jJmZuUeCbLwERAydeuVjlVCPCUJK3ec0rnsvINTgQAAHBtUfQFAKAE2X4sVcM/26J7J2/U7/svLvh66ZXudfXTmA66r3XJLPie9+3mY7LaHJKkPq0jZDGbDE4EAAAAACVbo/BAvdi1tut43PKD2nz0nIGJAAAXCw/y0RfDo1Svkr8kKS2nQI/M2Kzf9qf8zVcCuFoP3VxdPaIqS5LSsgs0/NP1strsBqcCSi43i1k9Wzin+hbYHFq65bjBiQAAAK4tir4AAJQA2xJSNWxWrO6bvEnRBy4UfEPLeOnVHnX105j2urd1ZXm4lfwf/fPXx7vW/VpHGpgEAAAAAEqPuxuH6oHWzjKLze7Q2IW7lHgux+BUAIDzgv089dmQKLWqFiRJyi2wa9TcbfpuK0Uo4HowmUyaOChKlQunaW88eFrjluwyOBVQsvVqEe5aL4xJNDAJAADAtVfy2z4AAJRgW+LPacjMWN3/6SatOXjGdb5SGS/9q2c9LRvdXv1blY6CryQdSs7Q1iPOqVENKpdR3fBAgxMBAAAAQOnx+K3V1ba6s0CWlmPV6Hk7lZlnNTgVAOA8Py83TRnUTHc0qCjJ+WDG8/N3aVb0UWODASVUoK+Hpo5s69p1buLSPYree9LgVEDJVTcsQHXDAiRJ2+JTdfhkpsGJAAAArp3S0foBAKCEiT16ToNnbtYDU2K07tCFgm9YWS/9u1c9/Ti6ve5pGV5qCr7nLbh4mm+bCAOTAAAAAEDpYzGb9EaveoosnFwXl5Kll5fskd3hMDgZAOA8Dzezxt/bSPe1quw6N27ZAY1fdkAO3q+Bay6qRjn9X++GkiSHQxoxZb1Op+canAooufpEXZjqu2RzkoFJAAAArq3S1f4BAKCY23zkrB6evlkPTo3R+kNnXefDy3rr9d719ePo9uobVfoKvpJktzs0f0OCJOfN5V4tKfoCAAAAwI3m7+Wu9/o3lL+XmyQp+uAZTV4dZ3AqAMDFLGaTXupeR4/dVt11bmb0Ub24cLesNruByYCS6fGudXVzfeck7ZOpuXp8xiaK9cB10r15JRUO0daizYmy2/leAwAAJUPpawEBAFAMbYo7q0HTYvTQtM3aGHeh4Fs5yFv/6VNfP4xupz4twuRuKb0/2tcfTFHS2WxJ0s31KqpCoJfBiQAAAACgdIoI8tFbvevLYnLeYZ+1LkE/7WKbagAoSkwmkx67rbpe6VFXhW/XWrLluJ74Ypty8m3GhgNKGLPZpE+GtlY5f09J0srtxzVlxQGDUwElU4UAL3WoU16SlHQ2R5suuqcGAABQnJXeNhAAAEWcw+HQxsNnNXBajAZN36xNR865XosI9tGbfevrh6fbqXfz0l3wPW/++gTX+p42kQYmAQAAAAC0rhakpzvVcB2/vnSfdh9PNzARAOCP3Nuqsibe20juFmfb99d9pzV0VqzScgoMTgaULCFlvPXJ0Nau49fmbde2oxQQgeuhd1S4a71oU6KBSQAAAK4dWkEAABQxDodDGw6f0UPTYvTwjM2KuajgGxnso7f7NdDSp9qqZ7MwuVHwlSRl51m1NNZ5scbf2013NKlkcCIAAAAAQP+oMPVsEipJyrPaNWbeTqVk5BmcCgDw3+5oGKIpg5rJ19MiSdoSn6oHp2zSybRcg5MBJcttjUL12J11JEkFNruGTl6nDEr1wDXXuWGI/DzdJEk/bjuhXCbVAwCAEoB2EAAARYTD4dC6Q2f04NQYPTIjVrFHU12vVS3nLPh+/1RbdW9aiYLvf1m+7bgyc62SpLubhcvbw2JwIgAAAACAyWTS2C611KRyoCTpdGa+xszfqdwCbrQDQFHTunqwZg+NUrCvhyTp0Kks3T9lk46kZBmcDChZXuzbUE2rBkmSjpzM1HNzNhucCCh5vD0s6lL4wGFmnlUrdiUbnAgAAOCfoyUEAIDBHA6H1h48rQemxGjIzFhtiU91vVatvK/evaehvnuqHQXfvzBvQ7xr3a9NpIFJAAAAAAAXc7eY9W7fBgoJ8JQk7TmeoTd+2C+Hw2FwMgDAf6tXKUCfj2ipykHekqQTqbl6YMom7UxMMzgZUHJ4uFk0dWQb+Xk5p43OXx+vb9YeMTgVUPL0jgpzrRdtSjQwCQAAwLVBWwgAAIM4HA6tOXBa93+6SUNnbdHWhFTXa9XK+2p8/4b69sm2urtJqCxmk3FBi7hTabn6bfdJSVJ4sI9a1yxncCIAAAAAwMWCfD00sX8jebs7d19ZtuukZq9PMDgVAOCPRAb76PPhLVU71F+SdC67QIOmb9bag6cNTgaUHFUr+GvCoCjX8XNzYnUoOd3AREDJ06p6sMLKOh9cid5/WqfScw1OBAAA8M9Q9AUA4AZzOBz6fX+K7vt0k4Z9tkXbj12YiFGjgq8m3NtI3z7ZVl0bU/C9HIs2JsheOAiqb6sImfnfDAAAAACKnFoV/fTvHnVdx5+sitPvByiNAUBRVN7fU3OGtlBU1bKSpJx8m0bO2aoftp8wOBlQcvRpHakBHapKkrLyrBo6eb3yCmwGpwJKDrPZpJ4tnFN9bXaHvos9bnAiAACAf4aiLwAAN4jD4dBv+1J07+SNGjF7q3ZcVPCtWdFPE+9rpCVPtFWXRiEUfK/A/PXxrnXfNpEGJgEAAAAA/JWOdcprxM3OQotD0ktL9uhwSpaxoQAAf8jfy11TBzXT7fUqSJKsNoee/Wan5q6L/5uvBHC53nyguWqGBkiSdsaf07/nbTc4EVCy9IoKd60XxSQamAQAAOCfo+gLAMB15nA49Ou+FPWftFEj52zVzsQLW3DVCvHT+/c31uLH2+jOhiFMo71CexJTtTvRWZhuVjVINUL8DU4EAAAAAPgrg9tH6va65SVJ2fk2jf5mh1KzCwxOBQD4I57uFr13f2P1iwpznXtr6X69v+KgHA6HgcmAksHX003TRraRp5vzlv2UlQf009Ykg1MBJUeNin5qHFlGkrQnKV37jqf/9RcAAAAUYRR9AQC4ThwOh1btPaV7Jm3Uo3O2alfShQsItUP99cH9jbVoVBt1blCRgu9Vmr8+wbXu2ybCwCQAAAAAgMthMpn0r+51VTvET5KUlJqr5xfuktVmNzgZAOCPWMwm/atnPY3oWM11buqvR/Tq4j28dwPXQIOIsvr3vU1dx0/M2KgT57INTASULL1bXHhYZTFTfQEAQDFG0RcAgGvM4XDo5z2n1PeTDRo1d5t2X1TwrRPqrw8faKKFj7VWJwq+/4jN7tCijc6ir7vFpB4tKhucCAAAAABwObzcLZrQr6GCfT0kSZvjUzVhxSGDUwEA/ozJZNITnWro/+6uI1Ph5cwFm5P09Fc7lFtgMzYcUAI8clsNdW3mLCOezczX8E/Xy2anSA9cC92ahcmt8F7cktgk2exMpAcAAMUTRV8AAK4Ru92hn3efVO+PN+iJz7dp7/EM12t1K/nr4webaOGo1rq9XgUKvtdA9N6TOpmWK0m6vWGogv09DU4EAAAAALhcIYFeGtevgdwtzt+P58cmaUEsW1UDQFH2QNsIjbunodwK37t/2XNKw2ZtUXpOgcHJgOLNZDLpg0daKizIR5K0bn+KJn6/x+BUQMkQ5OehjvUrSJJOpuVp3YHTBicCAAC4OhR9AQD4h+x2h1bsOqk+H6/XE19s1/4TFwq+9cMC9MmDTbTgsda6tW4FmUwUfK+V+esTXOt+bSINTAIAAAAAuBqNwgP1YtfaruNxyw9q89FzBiYCAPydro1D9elDzeTtYZEkbT56TgOnxSglPc/gZEDxVtbPU58Oby1z4T2EcUt2a/3+UwanAkqGPlHhrvWimEQDkwAAAFw9ir4AAFwlu92hn3Ymq9dH6/XUl9u1PznT9VrD8ABNfqip5j3aSh0p+F5zmbkF+nGrc9JTGR933dYwxOBEAAAAAICrcXfjUD3QurIkyWZ3aOzCXUo8l2NwKgDAX2lbM1ifDWmhsj7ukqT9yZkaMGWTjp7OMjgZULy1qV1Bz/asL0myOxwaPmW9zmVSogf+qY71Kyiw8GfWTzuSlZlrNTgRAADAlaPoCwDAFbLbHVq2I1k9P1yn0V/t0MGTlxZ8Px3YVF+PbKWb65Sn4HudLI1NUk6+TZLUo2VlebpbDE4EAAAAALhaj99aXW2rB0mS0nKsGj1vp7LyuPkOAEVZw/BAfTGipSqV8ZIkJZ7L0QNTNmlPUrrByYDibXS3empbu7wk6fjZHD0xY5McDofBqYDizdPNorubVpIk5eTb9NOOEwYnAgAAuHIUfQEAuEw2u0M/bj+hHh+u05ivd+jQqQsTKhpXDtSUQc309chWuqk2Bd/rbcGGeNf6ntaRBiYBAAAAAPxTFrNJb/Sqp8hgH0lSXEqWXv52r+yUWgCgSKtSzldfjmipmhX9JElnswo0cHqMNhw+Y3AyoPiymM2aMqKNgvw8JEnLtiZp5i+HDE4FFH+9o8Jd68UxSQYmAQAAuDoUfQEA+Bs2u0NLt51Qjw/W6ZlvdurwRQXfphFlNO3hZvpyREt1qFWOgu8NkHgmW2v3p0iSqlXwU7NqQQYnAgAAAAD8U/5e7nqvf0P5e7lJkn4/cFqTV8cZnAoA8HcqBHhpzrAoNYssI0nKyrNp+GdbtHxnsrHBgGIstKyPPhrSynX8ytdbtSvhnIGJgOKvWZUyqlLO+WDhuoOndfxcjsGJAAAArgxFXwAA/oTN7tD3206o+/tr9dy8nYpLuVDwbRZZRtMfaa7Ph0epXU0KvjfSoo0JOj/UqW+bCP63BwAAAIASIiLIR2/1ri9L4e95s9Yl6KddJw1OBQD4O4He7pr+SHN1rFNeklRgc2j01zv09YZjBicDiq87moRpeKdakqQ8q11DJq1TVp7V4FRA8WUymdSrcKqvwyEt2cxUXwAAULxQ9AUA4L9YbXZ9t/W4ur2/VmPn7dSR09mu15pXKaMZjzTX3GFRalsjmJLpDeZwODR/Q7zruE+rSAPTAAAAAACutdbVgvR0pxqu49eX7tPu4+kGJgIAXA4vd4s+GNBYvZpXkuQsUf37u736+OdDcpx/ah/AFXnlnsZqGFlWknQoOUP/93mswYmA4q1XizDXevHmRH4+AQCAYoWiLwAAhaw2u5ZsSdLd76/T8/N36ehFBd+oqmU1a3ALzRkapTYUfA2zPf6cDp7IkCS1rllOkeV9DU4EAAAAALjW+keFqUeTUEnOCXZj5u1USkaewakAAH/HzWLWf3rX15CbqrjOTVoVp39/u1c2O2Uq4Ep5uls0bWQb+Xq6SZK+iD6ihRcNwgBwZSLK+SqqWpAk6WBypnYlphmcCAAA4PJR9AUAlHpWm12LY5N013tr9X8LdivhzIWCb8uqZfXZkBaaPTRKraoHUfA12Pz1Ca51vzZM8wUAAACAkshkMun5LrXUpHKgJOl0Zr7GzN+p3AKbwckAAH/HZDJp9J21NLZrbde5bzYlaszXO5RvtRuYDCieaoQE6N2HmruOx3wWoyOnMgxMBBRvvaMuTPVduCnRwCQAAABXhqIvAKDUKrDZtXCzs+D74sLdOnY2x/Vaq2pBmjO0hT4bGqWWhU/3wlgFVrsWb3IWfT3dzOrWPNzgRAAAAACA68XdYta7fRsoJMBTkrTneIbe+GE/2+sCQDExsH2k3u7XQG5m5+CEFbtOavhnscrMtRqcDCh++rerqnvaVpEkZeZaNXTyeuVbeQAKuBpdm1SSh5uzJvP9luMqsPEQCgAAKB4o+gIASp0Cm10LYhJ118S1ennRpQXfNjWCNHdYlGYNaaEWVSn4FiWrdyfrbGa+JOmOJpUU4ONucCIAAAAAwPUU5Ouhif0bydvdIklatuukZl+00wsAoGjr3rSSPn6wibzdnbcjN8ad08DpMTqdkWdwMqD4eefB5qpa0U+StO3IWb2xYKfBiYDiKdDHXZ0aVJQkncnM1+97UwxOBAAAcHko+gIASo18q13zYxLVZcIavbJ4jxLPXSj4tq0RrM+HR2nGIy3UvEpZA1Piz8xbH+9a92sTaWASAAAAAMCNUquin/7do67r+JNVcfr9wGkDEwEArsRNtctr5uAWCvR2PrS/93iGBkzZpGNnsw1OBhQv/t7umjayrdwtztv7n/y0T7/sOGFwKqB46h11YcfIRTGJBiYBAAC4fBR9AQAlXr7Vrm82HlOXiWv06uI9Op6a63qtfc1gfTmipaY/0lzNIin4FlWpWflaud150bKcv6duqVfR4EQAAAAAgBulY53yGnFzVUmSQ9JLS/bocEqWsaEAAJetcUQZfT48SiGBnpKkY2dzNGDKJu07kWFwMqB4aVIlSK/e09h1/Ni0DUpOzfmLrwDwR26qW17Bfh6SpJW7Tiotu8DgRAAAAH+Poi8AoMTKt9r19YZjunPCGr327V6duKjg26FWOX01oqWmPtxcTSLKGBcSl+X72ETlWe2SpN6tKsvdjY8wAAAAAFCaDG4fqdvrlpckZefbNPqbHUrlhjwAFBvVK/jpi+GtVK28ryTpdEa+Hpoao5i4swYnA4qX4Z1rqVPjSpKk0xl5emzaBtntDoNTAcWLu8Ws7s3DJDnvJf647bjBiQAAAP4eLRkAQImTb7Xrqw0JunNCtP793V4lp10o+N5Uu5y+HtlKUwY1U2MKvsXG/PXxrnXf1pEGJgEAAAAAGMFkMulf3euqdoifJCkpNVfPL9wlq81ucDIAwOUKLeOlz4dHqXHlQElSZp5VQz/bop93nzQ4GVB8mEwmfTS4pSqW8ZIk/bb7pD76ca/BqYDip3dUmGu9KCbJwCQAAACXh6IvAKDEyCuw6Yv1CbpjfLRe/26fktPyXK/dUqecvnm0lT4d2EyNCi8ko3g4eipTmw6dkSTVrhSghhS0AQAAAKBU8nK3aEK/hgr2dW6zuzk+VRNWHDI4FQDgSpTx8dCMwc3VoVY5Sc6hDU99uV0LYhINTgYUH+UCvPTp8DYymZzHby7aqZhDp40NBRQzDcIDVbPwIcKYuLNKOJ1lcCIAAIC/RtEXAFDs5RbYNHddvO6YsEZvfL9PJ9MvFHw71imveY+20qSHmqlhOAXf4mjBhgTXul+bSJnOX70EAAAAAJQ6IYFeGtevgdwtzt8N58cmaUEsE7gAoDjx8XDTxw82UfemoZIku0N6ZfEefbo6Tg6Hw+B0QPHQoW5Fjb67niTJZndo2OR1SsvKNzgVUHyYTCb1jgp3HS/ezO8UAACgaKPoCwAotnILbJq7Nl53jF+jt5bu16mLCr631i2vBY+11icPNVUDCr7FlsPh0PwN8ZIkk0nq3bKywYkAAAAAAEZrFB6oF7vWdh2PW35Qm4+eMzARAOBKuVvMerNPAw1qH+k69+HKQ3pz6X7Z7ZR9gcvxbM8GalXTOR372JlsPT0rhrI8cAV6tghzTcZeFJPI9w8AACjSKPoCAIqdnHybZq+JV+fx0Xrrh/1KybhQ8L29XgUtHNVaHz/YVPXCAgxMiWsh5vAZxac4t0vqUKeCKgX5GJwIAAAAAFAU3N04VANaOR8GtdkdGrtwtxLP5RicCgBwJcxmk57rWltj7qzpOvfF+gQ9N2+n8q12A5MBxYObxawpI9qojK+HJOm7zcc057fDBqcCio/QMt5qW1iWjz+drS08PAgAAIowir4AgGIjO9+qWdFH1Xl8tN75cb9OZ1zYhqpT/Qpa9HgbffhAE9WtRMG3pFiwPsG17tcm8i/+JAAAAACgtHnitupqWz1IkpSWU6Ax83YqK89qcCoAwJUafFNVvdGnvixm51jFH3ck69E5W3lPBy5DeLCvPnikpev4xS+2al9SmoGJgOKld1S4a70oJsnAJAAAAH+Noi8AoMjLzrdqZvRR3TFujcYtO6AzmRcKvp0bVNTix9vogwFNVCfU38CUuNZyC2z6dvMxSZK3h0Vdm4YZnAgAAAAAUJRYzCa90aueIoOdu78cTsnSy9/ulZ0tdwGg2OnVPEwfDmgsTzfnrct1h87o4embdfaia8EA/thdzcP1yK01JDmvqw+ZtE7ZFOWBy3Jn4xB5e1gkSd9vOa48q83gRAAAAH+Moi8AoMjKyrNqxu9H1HlctMYvO6AzWc6LuiaTdGfDivr2iTZ6//7Gqk3Bt0RaueOE0rILJEl3NQuTr5ebwYkAAAAAAEWNv5e73uvfUP6FvzP+fuC0Jq+OMzgVAOBqdKxbQTMGN1dA4Xv6rqR0PTB1k5LO5RicDCj6Xru3ieqFB0qS9iWl6eWvthqcCCgefD3ddGejEElSek6BVu06ZXAiAACAP0bRFwBQ5GTlWTXtN2fBd8JPB3U2y1n2NJmkLg1DtOSJtpp4X2PVDKHgW5ItWB/vWt/TJtLAJAAAAACAoiwiyEdv9a4vi8m55fusdQn6addJg1MBAK5Gs8iymjssShUCPCVJR09na8CUTTqQnGFwMqBo8/Zw0/RH28qncDLp7F8P69tNCQanAoqH3lHhrvWizYkGJgEAAPhzFH0BAEVGVp5VU3+NU6dx0Xpv+UGdy75Q8L2rUYi+faKtJtzXSDUr+hmcFNfb6Yw8/bIrWZIUWsZb7epUMDgRAAAAAKAoa10tSE93quE6fn3pPu0+nm5gIgDA1aoZ4q8vhrdUlXI+kqRT6Xl6aGqMYo+eMzgZULTVqhSoNwc0cx0/PStGCSmZBiYCioe2tcqpYqDzAZPVu0/pbGa+wYkAAAD+F0VfAIDhMnOt+nR1nG5/N1rvrzik1MKCr9kk3d0kVN8/2U7j7m2kGhR8S40lm47JanNIknq3qiyL2WRwIgAAAABAUdc/Kkw9moRKkvKsdj0zf6dSMvIMTgUAuBphZb31+bCWahgeIElKz7VqyMxYrd7LlurAXxlwUzX1bBkhSUrPKdDwKetVYLUbnAoo2ixmk3o2D5MkWe0Ofb8lyeBEAAAA/4uiLwDAMBm5BZq86rBuH/e7Plx5SGk5Fwq+3ZqE6vun2undexqqWgVfg5PiRluwId617tsm0sAkAAAAAIDiwmQy6fkutdSkcqAkKSUjX8/M36ncApvByQAAVyPIz0MzB7dQu5rBkpwPcTzxxXYtiqWABfwZk8mkiYNaKLK8875KzKEzemfJToNTAUVf76hw13rRZn7OAACAooeiLwDghkvPKdCkXw6r07vR+ujnw0rPsUpyFnx7NA3V0qfb6Z17GqpqeQq+pdGBE+naVrgNX6OIMqobFmhwIgAAAABAceFuMevdvg0UEuDcenf38Qy98cN+ORwOg5MBAK6Gr6ebPnmwqe5qFCJJstkdemnhbs34/Qjv7cCfCPDx0LSRbeVmce6U98EPe/Xb7mSDUwFFW+1KAapfOEV+e3yqDp3MNDgRAADApSj6AgBumPScAn388yF1Ghetj385rPRcZ8HXYjapZ7NK+mF0e73Vr6GqlKPgW5otWM80XwAAAADA1Qvy9dDE/o3k7W6RJC3bdVKz1ycYnAoAcLU83Mx6556GeqBNhOvchJ8O6t1lB2S3U/YF/kizasF6sU8jSZLDIY2cukEp6bkGpwKKtl4tLkz1XRyTaGASAACA/0XRFwBw3aXlFOijlYd0+7vRmrQqThkXFXx7Na+kH55upzf7NlBksI/BSWE0u92hhRudN18tZpN6taxscCIAAAAAQHFUq6KfXutR13X8yao4/X7gtIGJAAD/hNls0gt319aTnWu4zs1eE68XFuxSgc1uYDKg6Hrszjq6tYFzGvaptFyNmraRcjzwF7o3rySL2TkJe8nmJL5fAABAkULRFwBw3aRmF+jDlYfU6d1oTV4dp8w8Z8HXzWxSnxZh+nF0O73Rp4EiKPii0LoDKUo6myNJ6li/osoHeBmcCAAAAABQXN1ap7yG31RFkuSQ9NKSPTqckmVoJgDA1TOZTBp+SzX9u1c9Ffaw9P22Exo1d5uy863GhgOKILPZpI+HtlaFwuvsv+w8ocnL9xucCii6KgR46aY65SVJSedytPHwGYMTAQAAXEDRFwBwzaVm5+v9FQfVadzv+vS/Cr79opwF39d711flIAq+uNT89fGudd82kQYmAQAAAACUBEM6VNHtdZ0367PzbRr9zQ6lZhcYnAoA8E/0jQrX+wOayMPNeZsz+sBpDZ4Rq9TsfIOTAUVPhUAvTRrW2nX8+oLt2hJHeRH4M72iwlzrRTFJBiYBAAC4FEVfAMA1cy4rXxOXH9Tt70Zr6q9HlJVnkyS5WUzqFxWuZWPa67Ve9RVOwRd/IDvPqqWxzosm/t5uuqNxJYMTAQAAAACKO5PJpH91r6vaIX6SpKTUXD2/cJesbPMOAMXa7fUqaNqgZvLzdJMkbT+WpgemxOhEaq7ByYCi55YGIXqia11JktXm0LDJ65SRw4NPwB/p3CBE/l7Ony0/bjuunHybwYkAAACcKPoCAP6xs5n5mvjTAd0+LlrTfzui7PwLBd/+LcP105j2eq1XPYWV9TY4KYqyn7YdV1bh9OfuLSrL28NicCIAAAAAQEng5W7RhH4NFezrIUnaHJ+qCSsOGZwKAPBPRVUL0pxhUSrn73x/j0vJ0oApG3XoZKbByYCi54XeDdW8WrAk6WhKlsbMjpHD4TA4FVD0eHlY1LVJqCQpK8+mFTuTDU4EAADgRNEXAHDVzmTmafyyA+o0PlrTfz/qeqrV3WLSfa0qa/mYDnq1Zz1VKkPBF39v/vp417pv6wgDkwAAAAAASpqQQC+N69dA7haTJGl+bJIWxLIVLwAUd3VC/fXF8JaKCHbuIpeclqcHp8ZoW0KqscGAIsbdzaypI9vI39tdkrRoQ4K+WnPE4FRA0dQrKty1XhSTaGASAACACyj6AgCu2OmMPI1btl+dx0VrZvSlBd/7W1fW8mc66OUedRVaxsvgpCguTqbm6Lc9JyVJlYN91KpGOYMTAQAAAABKmkbhgXqxa23X8bjlB7X56DkDEwEAroXKQT76fFiU6lbylySl5RTokRmb9fv+FIOTAUVLZHk/vfdwlOv4+bmxOng83cBEQNHUslqQwoKcQ4yi96XoVFquwYkAAAAo+gIArkBKRp7e+XG/Oo+P1qzoeOUU2CVJHm5mDWgToRXPdNBL3esqJJCCL67Mok3HZC/cJaxvm0iZzSZjAwEAAAAASqS7G4dqQKvKkiSb3aGxC3cr8VyOwakAAP9UOX9PzR4SpVbVgiRJuQV2jZq7Td9tPW5wMqBo6dkyQg/eXE2SlJ1v05DJ65RbOMwFgJPZbFLvFs6pvnaH9N0WfpYAAADjUfQFAPytlPQ8vf3DPnUeF63Za+KVW1jw9XQz64E2EVrxTHu92K2OKlLwxVWavz7ete7XOsLAJAAAAACAku6J26qrbXVnESwtp0Bj5u1UVp7V4FQAgH/Kz8tNUwY1U+cGFSVJVrtDz8/fpc/WHDU2GFDEvHF/M9WuFCBJ2n0sVf/6ZpuxgYAiqFdUmGu9MCbRwCQAAABOFH0BAH/qVHqu3lq6T53HR2vO2gTlWS8UfB9q55zg+3/d6qhCAAVfXL3dx1K1JzFNktS8WpCqVfQ3OBEAAAAAoCSzmE16o1c9RQb7SJIOp2Tp5W/3yu5wGJwMAPBPebiZNeHeRrqvcHq7JL374wFN+OmAHLzPA5IkH083TRvZVl7uFknS9F8O6sctFBmBi1Wr4KcmkWUkSXuT0rUnKd3YQAAAoNSj6AsA+B8n03L1xvf71Hn8Gs1dd6Hg6+Vu1sD2kVrxbAc9f1cdlQ/wNDgpSoJ5F0/zbRNpYBIAAAAAQGnh7+Wu9/o3lL+XmyTp9wOnNXl1nMGpAADXgsVs0kvd6+ix26q7zs34/aheWrRbVpvdwGRA0VGvchm9fl9T1/ETMzYp6UyWgYmAoqdPVLhrvZipvgAAwGAUfQEALslpufrPd3t1x4Q1+mJ9gvIvKvgOah+pFc900NiutVXen4Ivrg2rza7Fm45JktwtJvWIqvw3XwEAAAAAwLUREeSjt3rXl9nkPJ61LkE/7TppbCgAwDVhMpn02G3V9UqPujIVvs8vjj2uJ7/Yrpx8m7HhgCJiUMfq6tbCWWRMzcrX8CkbKMMDF7m7WSW5W5w/RJbEJvH9AQAADEXRFwCgE6m5ev3bvbpjfLS+3HDMVfD1djfrkQ5VtPLZDnqua22Vo+CLa+z3vad0Ki1XktSpUajK+noYnAgAAAAAUJq0rhakpzvVcB2/vnSfdh9nW14AKCnubVVZE+9t5Cpqrd6XoqGzYpWWU2BwMsB4JpNJ7z3cUuHBPpKkDQdSNP673QanAoqOsr4e6livgiQpJT1Paw+cNjgRAAAozSj6AkApdjw1R68t2aM7JkTrq43HVGBzSJK8PSwaclMVrXz2Jj3TpZaC/Sj44vqYvz7ete7XJtLAJAAAAACA0ureqHD1aBIqScqz2vXM/J1KycgzOBUA4Fq5o2GIpgxqJl9PiyRpS3yqHpoao5OFAwiA0qyMr4emjmgrS+EWBxO/26O1+04ZnAooOnpHhbvWi2ISDUwCAABKO4q+AFAKJZ3L0b+W7NGdE9bom02JshYWfH08LBpyc1X9/GwHjb6zloL8mK6K6ycjp0A/bTsuyflU9G0NQw1OBAAAAAAojUwmk57vUktNKgdKklIy8vXM/J3KLWBrdwAoKVpXD9bsIVEKLtxR7ODJTN0/ZZOOpGQZnAwwXsua5TS2ZwNJkt3h0Igp63WGh54ASVLH+hVUxsddkrR8R7Iyc60GJwIAAKUVRV8AKEWSzuXo1cW71WXiGs27qODr62nRsFuq6ufnOmj0HTVV1peCL66/pVuSlJPvvGnaI6qyPNz4WAIAAAAAMIa7xax3+zZQSIBzV6PdxzP0xg/75XA4DE4GALhW6oUF6PMRLRVe1luSdCI1Vw9M2aSdiWkGJwOM9+TdddWhbgVJ0olzOXpixkY+BwGSPN0surtpJUlSboFdy7afMDgRAAAorWjUAEApcOxstl5etFtdJqzR/JikSwq+IzpW08pnb9JTnWuqjA8FX9w489fHu9b3tIkwMAkAAAAAAFKQr4cm9m8kb3fn1u7Ldp3U7PUJBqcCAFxLkcE++mJ4S9UO9Zckncsu0KDpm7X24GmDkwHGspjNmjSsjYL9nQ89Ld92XNN+PmhwKqBo6N0y3LVeFJNoYBIAAFCaUfQFgBLs2Nls/X97dx4dRZX2cfxX3Uk6SWdPGjBsCVFWBxdEBGVzWFwRHGBwBR0GB1TccFcWZ0Zm1FcdmRGFQUCMiqI4MoqICoosigqCqKwJWxCykH1P1/sHpoeQBDohSXenv59zOIe+VV39VM69t+6teqrqsXe26Ypn1+qdbw6q3HkswTfMFqCJvyb4Th58puuVM0BT2Z9ZoHXb0yVJSS3DdF5ijIcjAgAAAABA6tgyTDOu6eL6/K/P9uiLHSR/AUBz4oiw6dU/XqALEqIlSUWlFZr46iZ9wFMa4efOiA7RP8f3cn2evniztuw96sGIAO9wXvsoJTrskqQNuzJ1MKvIwxEBAAB/RKIvADRDezML9ciSH3TFs2v17rcHVfFrgm94cIAmXdpBKx/oqztJ8IUHvbvhf09EGnlRexmG4cFoAAAAAAD4n0s7O3RbvwRJkinpsfd+1O70Ao/GBABoWOHBgZp7y/n6bdcWkqTyClP3L96qRev2nuKbQPM2+Jx4TRzaSZJUWu7U+BfXKb+4zMNRAZ5lGIZG9GwtSTJN6b1vD3o4IgAA4I9I9AWAZiQ1o0APv71VVz23Vu99l+ZK8I0IDtAdv03Syvv76o5BZyoyhARfeI5pmnr7uETf313UzoPRAAAAAABQ3fi+CRrUxSFJKiyt0L2Ltyi7kCQXAGhObIFWPXddd436NXlLkmb+d7v+8fFOmabpwcgAz3p8VHed8+sTr/ccztNDi771cESA5424oI3r/+9uPMBxAgAANLkATwcAAJ5kmqYqmsE8bG9mgeauTtGHW35R+XE7FBESoLEXt9eNfdopPJjkXjSt2trXltQs7folT5LUu2Oc2sXZmzgyNCcVpinOp50+w5CsPFkb9XC6YymLIVmoe/AiTtOU00uPK1ZDvAUBp625zIGPR9tAQ6hwmqqpaTx2VWcdyC7SjsMFOpRboofe/UHP/767AqxN+/wM6jk8rbHGSNRtNDa3zhsZhh4b1kUxYTbN/TxFkjT3i1RlFZTq4as6N3mfXxfMqdFYggKsmjuxjwZOW6GC4nK9uTZV/bq10ug+CZJqHzudyNCxekpfD29Tn+sKZ0SHqNeZsdq4J0sp6QXavC9bv2kb1SjxSYyT4N2OP79ksRiufxWmybUmAGhEhunGrUa5ubmKjIxUTk6OIiIimiIuAGhUGcXl+iKtUCl5pSpzejqahmWapvLzitXSKt16wRkKC+aeDjSt7dkl+vpIkdIKyms+2ec0tWNPutZu2KNpI7rp+ksSmzpE+Lgyp6nP0wr0c3ap8ptbJ+5B4YEWdYm2qd8ZoQqwcCIGJ7crp1RfHSnUgfxa+vo6iLVZ1T3WpgtbhHDyGh5hmqa+SS/W95nFyiiu8HQ4tQowpMSIIPU7I1SOEMb4qJudOSX66nCRDtY2RvdhVkNqHx6oi1uFqrWdG1zhvqJyp1anFWhHTqmKyr27ZQRZDCVFBKp/vF1RNqunw4GfcJqm1v5SqG1ZJcoubZy5d+X4pu8ZoWrB+AYNpKTCqc/TCrU9u0QFXt6/N4TYYKu6xzCnRuN4e12qJs7ZIEmKiw7RtNv66nCpqeI63D1osxo6MyJI/eNDFRHEOAae42vXFRgnwRsdLCjT2kOF2ptfVuuN5PYAQ52ibOofHyqbF98wBQDeoi55uST6AvA7uaUVWrA9W0EWQ91jgxUR1LwGmGVOU7tySrU7t0yXtwvTObHBng4JfmR7domWpuSpfVigOkUFKcha9eSyaUq5ZU59d6RIGXklur17rFqE2zwULXyRaZp6a3euDhSU6ZzYYLUICRA5qafPaUq/FJZrS2axOkQE6doOjPlRuz25pXp7d65a2wPUOdqmYGv9G2GFU9qXX6ZtR0vUp2WI+sXzlHc0vbW/FGrNoUJ1jbapfVigvPX8c16pU1uyilVcYWpcpyhFcoEUbtqdU6ole3LVJixAnaNssp1Gv+2NCsqc+iGrRDmlTt3YMZILoHCL0zS1cHu2ckqdOjc2WLHBVnlrbpRpSjmlTm3JLJYkjescpdAALz1YoVlZvi9PWzJL1D3Wpnh7oBrj8JFX6tTWrBIVlTs1tlMUiew4baZpKnlnjtKLKnROXLAcXty/N4Qqc+pWIep3BnNqNLw75m7Qkg37NO7Gi9QyJlQ9HCGKtrnXtkxTyi6t0PcZJQq0SuM6RZH0BY9ZvCvHp64rVI6TCsudGsc4CV7gSFG5Fu3IVrTNqm7RNtkDq/fnpimlF1fo+4xiOUKsuuGsSG5EAoBTqEteLme+AfidbVklKnOaGt85WqE1DECbg3Njg7U0JU8bjxSR6IsmtfFIkdqFBWrMmREnnbj9Jsam2duO6kCxUy3CmzBA+LyM4gql5JXpmoRwdYkmSbwhdY+VWoUG6MN9+couqeDEIWr1TXqRWoUG6PqzIhvkFaHnxAXLHmjRt+nF6tOKJ0qjaVWYpr45UqQejmANbhPm6XBO6dy4YM3edlQ/ZJXo4lahng4HPmJjepHi7QG67syG6be90XlxIZr701FtzijWkLbe35bhefvyynS4qELXnxWpdmG+8STo7rHH5tE/Hy3R+Y4QT4eDZq6o3KktmSUaEB+qXi0bd8xxXlywXvx1fHPJGYxvcHrSCst1oKBcozpEKCkyyNPhNIlz4oIVGmDo2/RiXdwyVFbm1Ghgf7uphw4UOxUVFarrz4pSy9C6pxd0jbZp7k/Z2pVTqm4xXLNC00svKvfJ6wrnxQVr9o9HtTWrWH25mQMetjmjWMFWi27qGKXAU4w32ocF6u09uTpUWK543r4EAA2meWa4AcBJHCwsVxt7YLNN8pUkwzDUMSpIGcUVKqnw/tfPoHkwTVNpheXqGBl0yrszI4KsahUaoIMF5U0UHZqLtF/rzFl+crGmqXX89e96sKDMw5HAm6UVlOusyKAGTRbrGBmkEqepzOKKBtsm4I7skgoVVZiu/s/bhQRY1DYsgH4addIY/ba3CbIaSgwPdI0VgVM5WFiuEKuhtnbfeQ5GRJBVZzCPRhP5pbBcpqSOUY2fCBMcYFG78EDGN2gQaQXlv77q3L8SSjpF2VRSYSqzhDk1Gl5YcKCuH9RJoRbVK8lXkmKDAxQXbGUcA4/x1esKwQEWtQsLpO3AKxwsKFOHiMBTJvlKx8ZiAYZ0gLoLAA2q+Wa5AUAtKpymgrzkVaXTp0+XYRjKyMho8G0H/TrILifPF03IaarG9rVgwQIZhqHU1FRXmc1qqNw0mzA6NAflpqkAQzzx8zStXr1ahmFo9erVVcor2285TRMnUV7HsVTleKdSQkKCxo0bV2Wd/9U9Kh+aVuVY+cRXh9Y0dvEWQRaDMT7qpNw0XfPD4zXmfNQTgphfoA4qzw3VdJPqxo0b1adPH9ntdhmGoc2bNzd9gLUIshqqoJ6jCVT2pzUdPxqDzUIfjoZR7jQVaDEa5AYnwzA0ffp012dvHju55tRO2hEaR3S4TSGn+fCcIPp6eFC5acrqo9cVjp0Hou3A88pN9+cHFsNQoMVQBXUXABoUib4AcIIXX3xRhmGoV69eng7ltDTjhzWhmaCKAt6HdglPoe6huXj99df1/PPPVytPS0vT9OnTGyRZ7FRvTgAA1F9ZWZlGjRqlrKwsPffcc1q0aJHat2/v1nfXrVun6dOnKzs7u9qyJ598Uu+9995px8cRAADq52R9tCd5a1xATZrzG0Lgvypv9P7mm288HUqtaHrwWdRdAGhwJPoCwAmSk5OVkJCgr7/+Wrt27fJ0OECzcNNNN6moqMjtC6QAGle/fv1UVFSkfv36eToU+KHt27dr7ty5ng4DaBQnS/SdMWOGVz0VEgBQ3e7du7V3715NmTJFEyZM0I033qjo6Gi3vrtu3TrNmDGjURN9AQD1c7I+ujZFRUV67LHHGi8o1S8uAAAAAIB/ItEXAI6TkpKidevW6dlnn5XD4VBycrKnQwKaBavVquDgYJ5AB3gJi8Wi4OBgWSxMB9D0bDabAgMDPR0GAABANUeOHJEkRUVFeTYQAIBHOJ1OFRcXS5KCg4MVEBDg4YgAAAAAADiGK/sAcJzk5GRFR0fryiuv1MiRI6sl+qampsowDD3zzDOaM2eOkpKSZLPZ1LNnT23cuLHa9n7++WeNHj1aDodDISEh6tSpkx599NFq62VnZ2vcuHGKiopSZGSkbrnlFhUWFlZb77XXXlOPHj0UEhKimJgYjRkzRvv372+4PwDQSCpff5SamurpUNCMHTx4UH/4wx8UHx8vm82mxMRETZw4UaWlpZKkPXv2aNSoUYqJiVFoaKguuugiffDBB1W2sXr1ahmGobfeekszZsxQ69atFR4erpEjRyonJ0clJSW6++671aJFC4WFhemWW25RSUlJlW0YhqE77rhDycnJ6tSpk4KDg9WjRw998cUXVdbbu3evJk2apE6dOikkJESxsbEaNWpUje1ky5Yt6t+/v0JCQtSmTRv95S9/0fz586u1q4SEBF111VX68ssvdeGFFyo4OFgdOnTQq6++WuN+rl69uv5/cKAGX375pXr27Kng4GAlJSXp5ZdfrrZOQkKCxo0b1/TBAafpxRdfVLdu3WSz2RQfH6/bb7+9ypO3BgwYoA8++EB79+6VYRgyDEMJCQlavXq1evbsKUm65ZZbXMsWLFggSVqzZo1GjRqldu3ayWazqW3btrrnnntUVFTkgb2EPzrVfHT+/Pm69NJL1aJFC9lsNnXt2lWzZ8+uth13xyGVc4O1a9fq3nvvlcPhkN1u14gRI5Senl5tu6dqe0BDGTdunPr37y9JGjVqlAzD0IABA7RlyxaNGzdOHTp0UHBwsFq1aqVbb71VmZmZru9Onz5d999/vyQpMTHR1ddXnkcqKCjQwoULXeXHj4U2bdqkyy+/XBEREQoLC9Nvf/tbbdiwoUn3HXDXuHHjlJCQUK18+vTpVW7urpwXv/feezr77LNls9nUrVs3ffTRR00YLXDMqfroynM4leONynpqGIamT59ebXsZGRkaPXq0IiIiFBsbq7vuusuVHCz97xpC5Xj/eMdv82RxSVJ5ebn+/Oc/u65BJCQk6JFHHql2HgrwlLqc2wR8lbtjH8n98Q9tBwAA1Be3ogLAcZKTk3XttdcqKChI1113nWbPnq2NGze6LsxXev3115WXl6fbbrtNhmHoqaee0rXXXqs9e/a4nlC3ZcsW9e3bV4GBgZowYYISEhK0e/duLVu2TH/961+rbG/06NFKTEzUzJkz9d133+nf//63WrRoob///e+udf7617/q8ccf1+jRozV+/Hilp6dr1qxZ6tevnzZt2sTTZgD4tbS0NF144YXKzs7WhAkT1LlzZx08eFBLlixRYWGhjh49qj59+qiwsFCTJ09WbGysFi5cqGHDhmnJkiUaMWJEle3NnDlTISEheuihh7Rr1y7NmjVLgYGBslgsOnr0qKZPn64NGzZowYIFSkxM1NSpU6t8//PPP9fixYs1efJk2Ww2vfjii7rsssv09ddf6+yzz5Ykbdy4UevWrdOYMWPUpk0bpaamavbs2RowYIB+/PFHhYaGSjqWwDxw4EAZhqGHH35Ydrtd//73v2Wz2Wr8W+zatUsjR47UH/7wB40dO1avvPKKxo0bpx49eqhbt26N8NcHjtm6dauGDBkih8Oh6dOnq7y8XNOmTVPLli09HRpw2qZPn64ZM2Zo0KBBmjhxorZv3+6aK6xdu1aBgYF69NFHlZOTowMHDui5556TJIWFhalLly564oknNHXqVE2YMEF9+/aVJPXp00eS9Pbbb6uwsFATJ05UbGysvv76a82aNUsHDhzQ22+/7bF9hv841Xx09uzZ6tatm4YNG6aAgAAtW7ZMkyZNktPp1O23315lW3UZh9x5552Kjo7WtGnTlJqaqueff1533HGHFi9e7FrHnbYHNJTbbrtNrVu31pNPPqnJkyerZ8+eatmypVauXKk9e/bolltuUatWrbRt2zbNmTNH27Zt04YNG2QYhq699lrt2LFDb7zxhp577jnFxcVJkhwOhxYtWqTx48frwgsv1IQJEyRJSUlJkqRt27apb9++ioiI0AMPPKDAwEC9/PLLGjBggD7//HP16tXLY38P4HR9+eWXevfddzVp0iSFh4frhRde0O9+9zvt27dPsbGxng4PfuRkfbQkffbZZ3rrrbd0xx13KC4ursaEruONHj1aCQkJmjlzpjZs2KAXXnhBR48erXZz0+nGNX78eC1cuFAjR47Ufffdp6+++kozZ87UTz/9pKVLl9bxrwA0PHfPbQL+xJ3xD20HAADUm+mGnJwcU5KZk5PjzuoA4NXe3Jltvrunen/2zTffmJLMlStXmqZpmk6n02zTpo151113udZJSUkxJZmxsbFmVlaWq/w///mPKclctmyZq6xfv35meHi4uXfv3iq/43Q6Xf+fNm2aKcm89dZbq6wzYsQIMzY21vU5NTXVtFqt5l//+tcq623dutUMCAioVm6aprkju9ic+V26mV9acbI/B9BgnE6nOfO7dPP7jKJqy+bPn29KMlNSUlxltbVF4GS+OVJoPr0pvVr5zTffbFosFnPjxo3VljmdTvPuu+82JZlr1qxxlefl5ZmJiYlmQkKCWVFxrK9ctWqVKck8++yzzdLSUte61113nWkYhnn55ZdX2Xbv3r3N9u3bVymTZEoyv/nmG1fZ3r17zeDgYHPEiBGussLCwmqxrl+/3pRkvvrqT3hnHgAAKFZJREFUq66yO++80zQMw9y0aZOrLDMz04yJianWrtq3b29KMr/44gtX2ZEjR0ybzWbed999rrLK/Vy1alW1v9XM79LNzTW0Y6DS05vSzY1Hqtff4cOHm8HBwVXGPj/++KNptVrN46ee7du3N8eOHVvlu0cKy8yZ36WbB/JLTaAp/VJwrO4dKiirUn782OXIkSNmUFCQOWTIENfxwjRN85///KcpyXzllVdcZVdeeWW144JpmubGjRtNSeb8+fOrLavpeDBz5kzTMIxqcwnTNM3/pOSayTuy67CX8Hd/35RufltDv+3ufLSmOjp06FCzQ4cOVcrcHYdUtq9BgwZVmR/fc889ptVqNbOzs13fdbftmaZprtyfZ879McsE3PH5wXzzxR8yq5VXjpPffvttV1lNbeCNN96oVt+ffvrpauPzSna7vdr4xzSPjZ+CgoLM3bt3u8rS0tLM8PBws1+/ftXWf3NntrmUeTSaQG3nFceOHVvjWKfymFJJkhkUFGTu2rXLVfb999+bksxZs2ZV+/77KbnmazuONlj88F/rDhWYz3+fUa28tj5akmmxWMxt27ZV+44kc9q0aa7PlfV82LBhVdabNGmSKcn8/vvvTdP83zWEmsb+J26ztrg2b95sSjLHjx9fpXzKlCmmJPOzzz6rUn741zn1QebUaCQf788z/33CWNvdc5uVFv581Pxgb26jxQiczDdHCs2nariuUDk/rem6gmm6P/YxTffHP3VtO8tSc81F24/WtmtAk5nzY5b5yf48t9d/fkuGue5QQSNGBADNQ13yci2Nkj0MAD4oOTlZLVu21MCBAyUde8XK73//e7355puqqKiosu7vf/97RUdHuz5XPpVrz549kqT09HR98cUXuvXWW9WuXbsq3z3xVS6S9Kc//anK5759+yozM1O5ubmSpHfffVdOp1OjR49WRkaG61+rVq101llnadWqVae59wDgu5xOp9577z1dffXVuuCCC6otNwxDH374oS688EJdcsklrvKwsDBNmDBBqamp+vHHH6t85+abb67yhLhevXrJNE3deuutVdbr1auX9u/fr/Ly8irlvXv3Vo8ePVyf27Vrp2uuuUYrVqxwHVNCQkJcy8vKypSZmakzzzxTUVFR+u6771zLPvroI/Xu3VvnnnuuqywmJkY33HBDjX+Prl27uo5L0rEnwXTq1Ml1jAIaQ0VFhVasWKHhw4dXGft06dJFQ4cO9WBkwOn75JNPVFpaqrvvvlsWy/9Oo/zxj39URESEPvjgg9Pa/vHHg4KCAmVkZKhPnz4yTVObNm06rW0D7jjVfPT4OpqTk6OMjAz1799fe/bsUU5OTpXv1mUcMmHChCrz4759+6qiokJ79+6V1PhtD3DX8W2guLhYGRkZuuiiiySpyri9rioqKvTxxx9r+PDh6tChg6v8jDPO0PXXX68vv/zS1Q4BXzRo0CDX06slqXv37oqIiGBuCq/Tv39/de3a1e31T3yjwZ133ilJ+vDDDxsspspt3XvvvVXK77vvPkliHASv4O65TcCfuDP+oe0AAID6ItEXAHTs4sqbb76pgQMHKiUlRbt27dKuXbvUq1cvHT58WJ9++mmV9U9M3q1M+j169Kik/yX8Vr6e/VROtb2dO3fKNE2dddZZcjgcVf799NNPOnLkSB33GACaj/T0dOXm5p60z927d686depUrbxLly6u5cc7sV+OjIyUJLVt27ZaudPprJbkctZZZ1X7rY4dO6qwsFDp6emSpKKiIk2dOlVt27aVzWZTXFycHA6HsrOzq2xv7969OvPMM6ttr6aymmKXjh1XKo8pQGNIT09XUVFRjXW/prYH+JLKY8SJdTkoKEgdOnSodgypq3379mncuHGKiYlRWFiYHA6H+vfvL0nVji9AYzjVfHTt2rUaNGiQ7Ha7oqKi5HA49Mgjj0iqXkfrMg451e82dtsD3JWVlaW77rpLLVu2VEhIiBwOhxITEyWdXj+dnp6uwsLCWucpTqdT+/fvr/f2AU9jbgpfUdmnu+vEeW9SUpIsFotSU1MbLKa9e/fKYrFUO/fTqlUrRUVFMQ6CV3D33CbgT9wZ/9B2AABAfQV4OgAA8AafffaZDh06pDfffFNvvvlmteXJyckaMmSI67PVaq1xO6Zp1uv3T7U9p9MpwzC0fPnyGtcNCwur1+8CAGpWW7/ckP3/nXfeqfnz5+vuu+9W7969FRkZKcMwNGbMGDmdzjpvrzFiBAA0roqKCg0ePFhZWVl68MEH1blzZ9ntdh08eFDjxo07reMB4K6TjR12796t3/72t+rcubOeffZZtW3bVkFBQfrwww/13HPPVaujdRmHMGaBrxg9erTWrVun+++/X+eee67CwsLkdDp12WWX0U/D79T0pjJJ1d6GJtHPw3cc/2TF+jixXdSlndR124A3aaxzm4A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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "src_mt_alignments = tagger_xlmr.align_source_mt([src_tokens], [mt_tokens], 'eng', lang_id)[0]\n", + "draw_aligned_qe(src_tokens, mt_tokens, src_tbd_qe, mt_tbd_qe, src_mt_alignments, title='SRC - MT (XLMR)')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "bert" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "ExecuteTime": { + "end_time": "2023-06-12T11:16:04.511296Z", + "start_time": "2023-06-12T11:16:03.682058Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Aligning mt-pe: 100%|██████████| 1/1 [00:00<00:00, 4.46it/s]\n" + ] + }, + { + "data": { + "image/png": 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e2KnHyhqV543V5AyPEmIOfSMMBKWtzV0qre/QCaMSNS/XO4iVAgPzelWrVu9qVXGGR/nJcYrWz5+bOoP6oK5d7QFLiyelK40vSDFAZQ2dery8UWOSYzU53SPP59hvR6OWrqA+qutQQ2dQiyam8QUoBiRoWXpwo08NnUHNyEpQVkKMojUbZVlSQ2dQH+xplyQtnpyupNgoPVjhsPL81iZ9sKdD07I8yvXGaSgOH02dQX1Y16E2f1BXTEonyI7PzbIsLf+0QTVtAU0fkaDsKN6/D4Ze19Q5iZo3mmtqDI2uoKUHNvrU5g+dO2V4BrZtWZbk6wxofW2H4mKkxZPSCX3BmEc2Nwyr7xV6zpNa/UEt5jwJUWB3m1/LNvmU4YnR1AyPvP18v2tZUk17QOtr25WdGKPLjkjjRiQAOICDyeXyyTcAxymt61BX0NLVkzMO24DhjKwEPVHRpLd3txH0RUS9vbtN45LjdPGE1P1euB2V6dEfS+v1ia9Dx2QnRrBCDHe17QFVNHVpQUGKpmR4TJdzWJmWJeUkxeq5rc3ydQT44BD79E5Nm3KSYnXpEWmD8ojQ6SMS5I1z692adp2QQ0dpRFbAsvTO7jbNzE7QqWOSTZdzQDNGJOju0np9VNehE3OSTJeDYeLtmjblemN1yYTB2W9Ho6NHJOrej+u1rrZdp42N/m0Z5m1t6lJ1W0CXHpGmccnDoxP0tCyP7i6t1yf1XEdj6LX5g/pgT4fm5yZp9qihPec4ekSC7uo+vzlpNOc3+Hx2tvq1vcWvCwtTVZQWb7qciJg+IkFJsS69W9OuE0clKYZragyB8sZO7WkP6MpJ6RqVdPDxguIMj+792KfNDZ2amsl3Voi8mjb/sPxe4egRCbp7Q70+rGvXXG7mgGHratuVEOPW5RPTFXeA84385Dg9Vt6oXa1+5fL0JQAYNIdnwg0A9mNHq19jvHGHbchXklwulyamx6u2PaCOQPQ/fgaHB8uytLPVr4lp8Qe8OzM1PkajvbHa0eKPUHU4XOzsXmeOcMiXNZE2sfv/dUdLl+FKEM12tvh1RFr8oIbFJqbFqyNoaU97YNCWCQyEryOgtoBl7/+iXWKsW2OTY9lP46AMxX472sTHuDQ+Jc4+VwQOZEerX4kxLo31Dp8+GKnxMRqdxHU0IqOq1S9L0sT0oQ/CJMS6NS4ljvMbDIqdLf7uR507K1AyKd2jjoClPR1cU2No7GzxKy3efUghX0nKSojViIQYzmNgzHD9XiEh1q1xyXFsO4gKO1q6VJgad8CQrxQ6F4t1SdtZdwFgUB2+KTcA2IdA0FJ8lDyqdOnSpXK5XKqtrR30Zcd3n2T7yfkigoKW+t2+HnjgAblcLlVWVtrT4t0u+S0rgtXhcOC3LMW6RMfPz2nlypVyuVxauXJlr+k926+fTRP74T/Ic6me850eBQUFWrx4ca959q57rHyIrJ5z5c8+OrS/c5doEe92cY6Pg+K3LPv6MNxQXo+aEB/D9QUGruezof5uUn377bd1wgknyOv1yuVyad26dZEvcB/iY1wKsJ4jAnr2p/0dP4aCh8+IMEj8QUtxbteg3ODkcrm0dOlS++doPneyr6mDbEcYGvu6pjgYfB8Ak/yWpZhh+r1C6HMgth2Y57cGfn3gdrkU53YpwLoLAIOKoC8AfMZdd90ll8ul2bNnmy7lczmMmzUBAIYIhw6YwrqHw8XDDz+s3//+932m79y5U0uXLh2UsNiBnpwAADh0XV1duvDCC1VXV6ff/e53WrZsmfLz8wf03jfeeENLly6Vz+fr89ptt92mJ5988nPXxxEAAA7N/vbRJkVrXQDgFD03er/zzjumS9knPgbCsMW6CwCDjqAvAHzG8uXLVVBQoLVr12rz5s2mywEOC5dffrna2toG/AUpgKE1b948tbW1ad68eaZLgQNt3LhR9957r+kygCGxv6DvrbfeGlVdIQEAfZWVlWnLli268cYbtWTJEi1atEgZGRkDeu8bb7yhW2+9dUiDvgCAQ7O/ffS+tLW16Sc/+cnQFaVDqwsAAAAA4EwEfQEgTEVFhd544w399re/VXZ2tpYvX266JOCwEBMTo4SEBDrQAVHC7XYrISFBbjeXA4g8j8ejuLg402UAAAD0sXv3bklSenq62UIAAEYEg0G1t7dLkhISEhQbG2u4IgAAAAAAQvhmHwDCLF++XBkZGTr77LN1wQUX9An6VlZWyuVy6Te/+Y3uueceFRUVyePx6LjjjtPbb7/dZ3mffPKJLrroImVnZysxMVGTJk3Sj3/84z7z+Xw+LV68WOnp6UpLS9OVV16p1tbWPvM99NBDmjlzphITE5WZmamLL75Y27ZtG7z/AGCI9Dz+qLKy0nQpOIzt2LFDX/3qV5WbmyuPx6Px48frmmuuUWdnpySpvLxcF154oTIzM5WUlKTjjz9ezz77bK9lrFy5Ui6XS48++qhuvfVW5eXlKSUlRRdccIEaGhrU0dGhG264QSNHjlRycrKuvPJKdXR09FqGy+XSN7/5TS1fvlyTJk1SQkKCZs6cqVdffbXXfFu2bNG1116rSZMmKTExUVlZWbrwwgv73U4++OADlZSUKDExUWPGjNHPf/5z3X///X22q4KCAp1zzjl67bXXNGvWLCUkJKiwsFB//etf+/07V65ceej/4UA/XnvtNR133HFKSEhQUVGR/vSnP/WZp6CgQIsXL458ccDndNddd2nq1KnyeDzKzc3VN77xjV6dt+bPn69nn31WW7ZskcvlksvlUkFBgVauXKnjjjtOknTllVfarz3wwAOSpNWrV+vCCy/UuHHj5PF4NHbsWH37299WW1ubgb8STnSg69H7779fX/jCFzRy5Eh5PB4VFxfr7rvv7rOcgZ6H9FwbvP766/rOd76j7Oxseb1enX/++aqpqemz3ANte8BgWbx4sUpKSiRJF154oVwul+bPn68PPvhAixcvVmFhoRISEpSTk6OrrrpKe/bssd+7dOlSfe9735MkjR8/3t7X93yO1NLSogcffNCeHn4u9P777+vMM89UamqqkpOT9cUvflFvvfVWRP92YKAWL16sgoKCPtOXLl3a6+bunuviJ598UkceeaQ8Ho+mTp2qF154IYLVAiEH2kf3fIbTc77Rs566XC4tXbq0z/Jqa2t10UUXKTU1VVlZWbr++uvtcLC09zuEnvP9cOHL3F9dkuT3+/X//t//s7+DKCgo0I9+9KM+n0MBphzMZ5vAcDXQcx9p4Oc/bDsAAOBQcSsqAIRZvny5vvSlLyk+Pl6XXHKJ7r77br399tv2F/M9Hn74YTU1NenrX/+6XC6XfvWrX+lLX/qSysvL7Q51H3zwgebOnau4uDgtWbJEBQUFKisr09NPP61f/OIXvZZ30UUXafz48br99tv13nvv6c9//rNGjhyp//mf/7Hn+cUvfqGf/vSnuuiii3T11VerpqZGd955p+bNm6f333+fbjMAHG3nzp2aNWuWfD6flixZosmTJ2vHjh16/PHH1draqvr6ep1wwglqbW3Vddddp6ysLD344IM677zz9Pjjj+v888/vtbzbb79diYmJuummm7R582bdeeediouLk9vtVn19vZYuXaq33npLDzzwgMaPH6+bb7651/tXrVqlRx55RNddd508Ho/uuusunXHGGVq7dq2OPPJISdLbb7+tN954QxdffLHGjBmjyspK3X333Zo/f742bNigpKQkSaEA88knnyyXy6Uf/vCH8nq9+vOf/yyPx9Pv/8XmzZt1wQUX6Ktf/aquuOIK/eUvf9HixYs1c+ZMTZ06dQj+94GQDz/8UKeddpqys7O1dOlS+f1+3XLLLRo1apTp0oDPbenSpbr11lt1yimn6JprrtHGjRvta4XXX39dcXFx+vGPf6yGhgZt375dv/vd7yRJycnJmjJlin72s5/p5ptv1pIlSzR37lxJ0gknnCBJeuyxx9Ta2qprrrlGWVlZWrt2re68805t375djz32mLG/Gc5xoOvRu+++W1OnTtV5552n2NhYPf3007r22msVDAb1jW98o9eyDuY85Fvf+pYyMjJ0yy23qLKyUr///e/1zW9+U4888og9z0C2PWCwfP3rX1deXp5uu+02XXfddTruuOM0atQovfzyyyovL9eVV16pnJwclZaW6p577lFpaaneeustuVwufelLX9KmTZv0t7/9Tb/73e80YsQISVJ2draWLVumq6++WrNmzdKSJUskSUVFRZKk0tJSzZ07V6mpqfr+97+vuLg4/elPf9L8+fO1atUqzZ4929j/B/B5vfbaa/rnP/+pa6+9VikpKbrjjjv05S9/WVu3blVWVpbp8uAg+9tHS9J//vMfPfroo/rmN7+pESNG9BvoCnfRRRepoKBAt99+u9566y3dcccdqq+v73Nz0+et6+qrr9aDDz6oCy64QN/97ne1Zs0a3X777fr444/1xBNPHOT/AjD4BvrZJuAkAzn/YdsBAACHzBqAhoYGS5LV0NAwkNkBIKr9/VOf9c/yvvuzd955x5Jkvfzyy5ZlWVYwGLTGjBljXX/99fY8FRUVliQrKyvLqqurs6evWLHCkmQ9/fTT9rR58+ZZKSkp1pYtW3r9nmAwaI9vueUWS5J11VVX9Zrn/PPPt7KysuyfKysrrZiYGOsXv/hFr/k+/PBDKzY2ts90y7KsTb526/b3aqzmzsD+/juAQRMMBq3b36ux1te29Xnt/vvvtyRZFRUV9rR9bYvA/ryzu9X69fs1faZ/5Stfsdxut/X222/3eS0YDFo33HCDJclavXq1Pb2pqckaP368VVBQYAUCoX3lK6+8YkmyjjzySKuzs9Oe95JLLrFcLpd15pln9lr2nDlzrPz8/F7TJFmSrHfeeceetmXLFishIcE6//zz7Wmtra19an3zzTctSdZf//pXe9q3vvUty+VyWe+//749bc+ePVZmZmaf7So/P9+SZL366qv2tN27d1sej8f67ne/a0/r+TtfeeWVPv9Xt79XY63rZzsGevz6/Rrr7d1919+FCxdaCQkJvc59NmzYYMXExFjhl575+fnWFVdc0eu9u1u7rNvfq7G2N3daQCRVtYTWvV0tXb2mh5+77N6924qPj7dOO+00+3hhWZb1f//3f5Yk6y9/+Ys97eyzz+5zXLAsy3r77bctSdb999/f57X+jge333675XK5+lxLWJZlrahotJZv8h3EXwmn+5/3a6x3+9lvD/R6tL919PTTT7cKCwt7TRvoeUjP9nXKKaf0uj7+9re/bcXExFg+n89+70C3PcuyrJe3NVn3bqizgIFYtaPZuuujPX2m95wnP/bYY/a0/raBv/3tb33W91//+td9zs97eL3ePuc/lhU6f4qPj7fKysrsaTt37rRSUlKsefPm9Zn/75/6rCe4jkYE7OtzxSuuuKLfc52eY0oPSVZ8fLy1efNme9r69estSdadd97Z5/1PVTRaD22qH7T64Vxv7Gqxfr++ts/0fe2jJVlut9sqLS3t8x5J1i233GL/3LOen3feeb3mu/baay1J1vr16y3L2vsdQn/n/p9d5r7qWrdunSXJuvrqq3tNv/HGGy1J1n/+859e06u7r6l3cE2NIfLStibrz5851x7oZ5s9Hvyk3np2S+OQ1Qjszzu7W61f9fO9Qs/1aX/fK1jWwM99LGvg5z8Hu+08XdloLdtYv68/DYiYezbUWf/a1jTg+X//Qa31xq6WIawIAA4PB5PLdQ9JehgAhqHly5dr1KhROvnkkyWFHrHyX//1X/r73/+uQCDQa97/+q//UkZGhv1zT1eu8vJySVJNTY1effVVXXXVVRo3blyv9372US6S9N///d+9fp47d6727NmjxsZGSdI///lPBYNBXXTRRaqtrbX/5eTk6IgjjtArr7zyOf96ABi+gsGgnnzySZ177rk69thj+7zucrn03HPPadasWTrppJPs6cnJyVqyZIkqKyu1YcOGXu/5yle+0qtD3OzZs2VZlq666qpe882ePVvbtm2T3+/vNX3OnDmaOXOm/fO4ceO0YMECvfjii/YxJTEx0X69q6tLe/bs0YQJE5Senq733nvPfu2FF17QnDlzNGPGDHtaZmamLrvssn7/P4qLi+3jkhTqBDNp0iT7GAUMhUAgoBdffFELFy7sde4zZcoUnX766QYrAz6/f/3rX+rs7NQNN9wgt3vvxyhf+9rXlJqaqmefffZzLT/8eNDS0qLa2lqdcMIJsixL77///udaNjAQB7oeDV9HGxoaVFtbq5KSEpWXl6uhoaHXew/mPGTJkiW9ro/nzp2rQCCgLVu2SBr6bQ8YqPBtoL29XbW1tTr++OMlqdd5+8EKBAJ66aWXtHDhQhUWFtrTR48erUsvvVSvvfaavR0Cw9Epp5xid6+WpGnTpik1NZVrU0SdkpISFRcXD3j+zz7R4Fvf+pYk6bnnnhu0mnqW9Z3vfKfX9O9+97uSxHkQosJAP9sEnGQg5z9sOwAA4FAR9AUAhb5c+fvf/66TTz5ZFRUV2rx5szZv3qzZs2erurpa//73v3vN/9nwbk/ot76+XtLewG/P49kP5EDL+/TTT2VZlo444ghlZ2f3+vfxxx9r9+7dB/kXA8Dho6amRo2Njfvd527ZskWTJk3qM33KlCn26+E+u19OS0uTJI0dO7bP9GAw2CfkcsQRR/T5XRMnTlRra6tqamokSW1tbbr55ps1duxYeTwejRgxQtnZ2fL5fL2Wt2XLFk2YMKHP8vqb1l/tUui40nNMAYZCTU2N2tra+l33+9v2gOGk5xjx2XU5Pj5ehYWFfY4hB2vr1q1avHixMjMzlZycrOzsbJWUlEhSn+MLMBQOdD36+uuv65RTTpHX61V6erqys7P1ox/9SFLfdfRgzkMO9HuHetsDBqqurk7XX3+9Ro0apcTERGVnZ2v8+PGSPt9+uqamRq2trfu8TgkGg9q2bdshLx8wjWtTDBc9+/SB+ux1b1FRkdxutyorKwetpi1btsjtdvf57CcnJ0fp6emcByEqDPSzTcBJBnL+w7YDAAAOVazpAgAgGvznP//Rrl279Pe//11///vf+7y+fPlynXbaafbPMTEx/S7HsqxD+v0HWl4wGJTL5dLzzz/f77zJycmH9HsBAP3b1355MPf/3/rWt3T//ffrhhtu0Jw5c5SWliaXy6WLL75YwWDwoJc3FDUCAIZWIBDQqaeeqrq6Ov3gBz/Q5MmT5fV6tWPHDi1evPhzHQ+AgdrfuUNZWZm++MUvavLkyfrtb3+rsWPHKj4+Xs8995x+97vf9VlHD+Y8hHMWDBcXXXSR3njjDX3ve9/TjBkzlJycrGAwqDPOOIP9NBynvyeVSerzNDSJ/TyGj/DOiofis9vFwWwnB7tsIJoM1WebQDQ52H36QM5/2HYAAMChIugLAAoFeUeOHKk//OEPfV775z//qSeeeEJ//OMfB7y8nkcufvTRR4NSX1FRkSzL0vjx4zVx4sRBWSYAHC6ys7OVmpq6331ufn6+Nm7c2Gf6J598Yr8+mD799NM+0zZt2qSkpCRlZ2dLkh5//HFdccUV+v/+v//Pnqe9vV0+n6/X+/Lz87V58+Y+y+tvGmBKdna2EhMT+133+9v2gOGk5xixcePGXo9W7+zsVEVFhU455RR72r6+ANrX9A8//FCbNm3Sgw8+qK985Sv29JdffnkwSgc+t6efflodHR166qmnenUmeuWVV4b8dx/MtgcMlfr6ev373//Wrbfeqptvvtme3t85z/7CWP29lp2draSkpH1ep7jd7j5PFAFMy8jI6HPNKvV9Sg4QbQYzMPvpp5/26gK8efNmBYNBFRQUSNr7lILPbiv9bSf7qis/P1/BYFCffvqp/TQqSaqurpbP5xv0z7GAQzHQzzaB4Wwozn3YdgAAwKFymy4AAExra2vTP//5T51zzjm64IIL+vz75je/qaamJj311FMDXmZ2drbmzZunv/zlL9q6dWuv1w6la8WXvvQlxcTE6NZbb+3zfsuytGfPnoNeJgAcLtxutxYuXKinn35a77zzTp/XLcvSWWedpbVr1+rNN9+0p7e0tOiee+5RQUGBiouLB7WmN998U++9957987Zt27RixQqddtpp9l39MTExffbpd955Z59uAKeffrrefPNNrVu3zp5WV1en5cuXD2rNwOcRExOj008/XU8++WSvc5+PP/5YL774osHKgM/vlFNOUXx8vO64445e++377rtPDQ0NOvvss+1pXq+338cser1eSX2/7O85JoQv17Is/e///u9g/gnAIetvHW1oaND9998/5L/7YLY9YKj0tw1I0u9///s+8+5rX9/zWn/HgNNOO00rVqzo9bj36upqPfzwwzrppJOUmpr6ueoHBltRUZEaGhr0wQcf2NN27dqlJ554wmBVwIHtbx99sD7bLOTOO++UJJ155pmSpNTUVI0YMUKvvvpqr/nuuuuuAdd11llnSep7vPntb38rSZwHISoM9LNNYDgbinMfth0cblpbW/XJJ5+otrbWdCkAcNijoy8Ax3vqqafU1NSk8847r9/Xjz/+eGVnZ2v58uWaPXv2gJd7xx136KSTTtIxxxyjJUuWaPz48aqsrNSzzz7bK6w1EEVFRfr5z3+uH/7wh6qsrNTChQuVkpKiiooKPfHEE1qyZIluvPHGg1omABxObrvtNr300ksqKSnRkiVLNGXKFO3atUuPPfaYXnvtNd10003629/+pjPPPFPXXXedMjMz9eCDD6qiokL/+Mc/5HYP7v1vRx55pE4//XRdd9118ng89pc5t956qz3POeeco2XLliktLU3FxcV688039a9//UtZWVm9lvX9739fDz30kE499VR961vfktfr1Z///GeNGzdOdXV1PMYRUePWW2/VCy+8oLlz5+raa6+V3+/XnXfeqalTp/b6MBwYbrKzs/XDH/5Qt956q8444wydd9552rhxo+666y4dd9xxWrRokT3vzJkz9cgjj+g73/mOjjvuOCUnJ+vcc89VUVGR0tPT9cc//lEpKSnyer2aPXu2Jk+erKKiIt14443asWOHUlNT9Y9//EP19fUG/2Jgr9NOO03x8fE699xz9fWvf13Nzc269957NXLkSO3atWtIf/fBbHvAUElNTdW8efP0q1/9Sl1dXcrLy9NLL72kioqKPvPOnDlTkvTjH/9YF198seLi4nTuuefK6/Vq5syZ+te//qXf/va3ys3N1fjx4zV79mz9/Oc/18svv6yTTjpJ1157rWJjY/WnP/1JHR0d+tWvfhXpPxc4oIsvvlg/+MEPdP755+u6665Ta2ur7r77bk2cOLHXza5AtNnXPvpQVFRU6LzzztMZZ5yhN998Uw899JAuvfRSTZ8+3Z7n6quv1i9/+UtdffXVOvbYY/Xqq69q06ZNA65r+vTpuuKKK3TPPffI5/OppKREa9eu1YMPPqiFCxfq5JNPPqTagcE00M82geHgL3/5i1544YU+0y+//PJBP/dh28HhZu3atTr55JN1yy23aOnSpabLAYDDGkFfAI63fPlyJSQk6NRTT+33dbfbrbPPPlvLly8/qM6506dP11tvvaWf/vSnuvvuu9Xe3q78/HxddNFFh1TnTTfdpIkTJ+p3v/udHRQbO3asTjvttH2GlAHAKfLy8rRmzRr99Kc/1fLly9XY2Ki8vDydeeaZSkpKUnp6ut544w394Ac/0J133qn29nZNmzZNTz/99JB0QSkpKdGcOXN06623auvWrSouLtYDDzygadOm2fP87//+r2JiYrR8+XK1t7frxBNP1L/+9S+dfvrpvZY1duxYvfLKK7ruuut02223KTs7W9/4xjfk9Xp13XXXKSEhYdDrBw7FtGnT9OKLL+o73/mObr75Zo0ZM0a33nqrdu3aRdAXw97SpUuVnZ2t//u//9O3v/1tZWZmasmSJbrtttsUFxdnz3fttddq3bp1uv/++/W73/1O+fn5OvfccxUXF6cHH3xQP/zhD/Xf//3f8vv9uv/++7V48WI9/fTTuu6663T77bcrISFB559/vr75zW/2CgoApkyaNEmPP/64fvKTn+jGG29UTk6OrrnmGmVnZ+uqq64a8t8/0G0PGEoPP/ywvvWtb+kPf/iDLMvSaaedpueff165ubm95jvuuOP0//7f/9Mf//hHvfDCCwoGg6qoqJDX69Vvf/tbLVmyRD/5yU/U1tamK664QrNnz9bUqVO1evVq/fCHP9Ttt9+uYDCo2bNn66GHHjqom82BSMnKytITTzyh73znO/r+97+v8ePH6/bbb9enn35K0BdRbV/76EPxyCOP6Oabb9ZNN92k2NhYffOb39Svf/3rXvPcfPPNqqmp0eOPP65HH31UZ555pp5//nmNHDlyQHX13ORdWFioBx54QE888YRycnL0wx/+ULfccssh/z8Ag2mgn20Cw8Hdd9/d7/TFixcP+rkP2w4AADhULmsAz5BvbGxUWlqaGhoaeFwYgGHvkc0Nio9x6fzxh/f+7NOGDv2jvEnfOjJT3rjB7VQJ9MeyLP3Puj06a1yypmUdOHjolG0Rg+vdmja9sqNFN84YYbqUfXK5XPrGN76h//u//xvS33PDDTfoT3/6k5qbm+1HCn9ePdvxmeOSNX0A2zGc6TfrajU/z6tjsxMHbZk1bX7d94lPl09MU56X4BYip7rVr/s3+rR4UrpykobHvdBPVTapuSuoS49IM10KholfravVKXleHTOI++1o9K/tzaps6tLVUzJMl4Jh4NWdLSqt79A1UzNNl3JQHtncIE+MSwu5jsYQi/Tnik9XNqmxK6DLjkgf8t+Fw9ubVa1au7tN109zVlfE3W1+/eUTn74yMU25XFNjCLy8vVlbm7r01c9xrv3XjT6NSIzRWeNSBrEyYGDerWnTf3a06HtR/L3CvjyzpUm+joAWTUw3XQoc7t6P61WYEqcvjkke0Pz/++EezcpO1JycpCGuDACGt4PJ5ZL8AgAAAKJcW1tbr5/37NmjZcuW6aSTThq0kC8AAAAAAAAAAAAAAIg+w6NdDQAAAOBgc+bM0fz58zVlyhRVV1frvvvuU2Njo37605+aLg0AAAAAAAAAAAAAAAwhgr4AcJiyLNMVAAAGy1lnnaXHH39c99xzj1wul4455hjdd999mjdv3qD+Hg4dMIV1Dxg4ixN9AHAsjgAAAACAs/AxEIYt1l0AGHQEfQE4Tozbpc7A4X9m2RkM/Y2xbsOFwFHcLg14++oMWkpiBcVBinW55Lckf9BSrNtlupx+DUUA67bbbtNtt9026Mv9rJ7tNzY6/2sRJWKH4Fxq77rHyofI6jkV6QgEzRZyEDqDFuf4OCixLpd9fXg46wxYHEcwYDFulzoClizLkmsYrTedAUteDgKIgJ79aWfQkjcCv68jyD4cgyPW7VJX0FLQsuR20DplX1NH6WdlGP4G45qik309DIp1uRSI8u8V9qVzGNaMw1OsSwM+FgQtS11BSzGsuwAwqPhUEIDj5CXFantLl1q7hs+X+QfLsixt8nVqREKMPDHs6hEZLpdLuUmx2tTQecCgY2NnQLta/Mrzcs8RDk5u9zrzaUOn4UoOT5u6/1/zvHGGK0E0y/XG6tOGTgUHMdS+qaFTHrdLWQkxg7ZMYCAyPDFKjHHZ+79o1+YPaluzn/00DspQ7LejTWfAUkVTl32uCBxIXlKs2gOWtrX4TZcyYI2dAe1q5ToakZGTFCuXpE2+jiH/Xe3+oLY2dXF+g0GR642V35IqGrtMlxJRG30d8sS4lOXhmhpDI9cbq4bOoKpbD+3caU+7X7XtAc5jYMxw/V6h3R/U1uYuth1EhTxvnMobu9Q1gLBvRWOX/JY0hnUXAAYVe1UAjjM106O3a9r0100+TctKUGr84RWE7Qpa2tzQqbLGLp05Ltl0OXCY40Ym6omKJv19c6MmpsfLE9P7Tk3Lkhq7glq/p13eOLcmp3sMVYrhakRCjManxOm5rU3a0dKlkYmx4obgzy9oSVWtfn2wp10T0+KVzhdD2I9jsxP1WFmjHv60QZMzPEqIOfSNMBCUtjZ3qbS+QyeMSqQ7BSLO7XLp2JGJWr2rVa1dQRWkxCta75Nr6gzqg7p2xbilIzM5h8LAHZedqMfLG/W3zQ2alP759tvRqKUrqI/qOtQRsDRjRILpcjBMjEuJ06jEGP2zvFEzshKUlRCjaG0wZ1lSQ2dQH+xpV0qcW5MzOAZg6CXGujUty6OVO1tV1xFQrjdOQ3H4aOoM6sO6DsW4OL/B4MhNitUYb6yeqmzS9BEJGpEQc1h/btTrmjonka55GDKFqfEakRCjR8oaND0rQZmegZ07WZbk6wxofW2HMjxuTUiLH/pigX5kJ8YOu+8Ves6TXJKOyuRaF+bNGJGgD+vatWyTT1MzPPLG9f0QNWhJte0Bra9t1xhvrEYnEUkDgMHksgbwbOHGxkalpaWpoaFBqampkagLAIZUbbtfq3e1qryxU4djY9+cpFjNHJGgo7K48EPkbfR1aO3uNu1s8au/k4x4t0tFqXGal+tVBmFCHIKuoKVVO1v0ia9TzYfjTtyQ1Di3pmR4NHd0EmFLHNDmhk6t2d2q7c397+sPRlZCjKZlejRrZOKwenQ2Dh+WZemdmnat39Ou2vaA6XL2KdYV+nJ17ugkZSfyITkOzqcNHVpT3aYd+zhHH85iXFJBSpxOyEmiGyQOSps/qJU7W7SpoVNt/ujeMnquo0tyvdyUh4gJWpZer2pVaV2HfJ1Dc+0d65LGd5/fjOT8BoOkIxDUqp2t2ujrUEuU798HA9fUiJSWrtC506cNnWoPDHzb8sS4NCE1XiW5SUqN5zwG5gy37xU4T0I02tHSpderWrWlqUv7OhR4Y12alO5RSW4STx4GgAE4mFwuQV8AjmZZ1j5PQgfTlPNv066aRo3OTtXHT/xoSH9XjEt8oIeo0LN9rftkh+Zffack6asLZ+v33zufdRSDJmBZivanUK//tEol3/iLJOkrZ87QHd8+y3BFfblcUgzbJQ7BQM6l1pfVqOS7j0qS/qtkov707VPt19yuUEdVIFoELUsDePrcQSnd0aDL/vS2JOmUqSP1m/+adkjL4TwfgyFS18Dh3v14u7547b2SpNNmH6FHf7loUJfPtoHBEAhaQxaCX7upRuf84mVJ0gmTR+rJH55yUO93KXTOxHoOk4biHEliH46hN9SfG33/kQ/0zPu7JEmPX3e8inPThu6X9YNrapjSc+70f6sr9a+NNZKk355frMIsb6/5XBKdphGVBuv48Oc3t+rljbWSpNvPnazCrKTPv9BunCchmoV/vvTVv63TnpYuZXnj9JdLZ/BdEwAcpIPJ5XLrDwBHc7lcio3AuWYwEFTAH1AwEKRLIhyjZ/vKSktUwB/qjudrbOODCQyqGJcr9IlxFCvMTVcgEOoQULmznuMADisDOZc6siBTLkn+QFDrynazDSCquV2uQX9045F5afLGx6ip3a+3y+rkluRmO4AhkboGDnfclDzlZCZrx+4G/Xvtp2pp7VBaMk+fQXQZygDKnEnZKhyZrE93NWp1aZW27G5SUQ7NNDC8DMU5EhAJQ/250YbtjQoGLcXFuDQ5J5XrXThGz7nT7sYOOyyZm5rANoBhY7COD62dAXsbSEuIZRuAY4R/vmRZe/8R8gWAoUWfdAAAMKTSkhPtcUNzm8FKADMyUhKV5vVIkiqrfGaLAQzwxMVqan6mJGnjtnq1dfgNVwREVozbpWPHZ0iS6lu79Gl1s+GKgMhyu906b16xJKmzK6AX3txouCIgslwulxbNn2D/vHxVmcFqAACDpa0zoPLdoXP7I3JSFB/LV65wnqqmDkmSNz5GyR76i8F5WjoD9tgbH2OwEgAA4ARcdQIAgCEV3q2rvpGgL5wpPyddkrStusHu7gs4yfSikZJCj3b8qLLWcDVA5B1fmGmP15bXGawEMGNhSbE9XrFqg8FKADMumVtod757+NUyBYJcEwDAcLdxV5OC3V0ci/Po1A7nCQQt1TSHgr6jUjyGqwHMaO7YG/RNjCPoCwAAhhZBXwAAMKRiYtxK9YbCvnT0hVPlj06XJPkDQW2vaTRbDGDA9MJse7yurMZgJYAZs4v2Bn3fIugLBzphWr5GpCdJkl58c6PaOroMVwRE1qj0RJ1+dJ4kaVd9m/69fpfhigAAn1fpjgZ7XJxL0BfOU9vSaYfdCfrCqVo7Q08uS4qPsW/sAwAAGCoEfQEAwJBLSwkFfX109IVDFXR39JWkrVUN+54ROExNL9ob9F1P0BcONGFksjKS4iRJ71TUK9DzbSjgELGxMTrnpCmSpNb2Lv1r7aeGKwIib1HJBHu8bNVmg5UAAAbDhh1N9ngqHX3hQNVNHfY4J5WgL5ypp6OvN55uvgAAYOgR9AUAAEMuIyXUvctHR184VEF3R19JqqzyGasDMGXa+BFydTe1WFdO0BfO43a7NKsw1NW3qd2vj3fS3R3Os6Bkqj1esXKDwUoAM06bkaeRaaGbYJ9/d7tqG9sNVwQA+Dw27Aid07tc0uTcFMPVAJFX3bg36EtHXziRZVlq6QwFfZMJ+gIAgAgg6AsAAIZcT0ffjk6/2nlMLxyoV9B3V725QgBDUpLiNSE3XZJUWlkrfyBotiDAgNndQV9JWlNeZ7ASwIyTjy1SSlIoAPDs6x+ryx8wXBEQWXGxbl08t1CS1BUI6pHXyg1XBAA4VF2BoDZWhTr6jh/hldcTa7giIPKqmgj6wtk6A5b83U9s8noI+gIAgKFH0BcAAAy5tOREe1zfRFdfOE9+Tro9rtzlM1YHYNL0omxJUntnQBu3EXiH88wuIugLZ/PEx+rMEydJknxN7Xr1vQrDFQGRd3nJBHu8bOVmWZZlsBoAwKEq392iTn/oBtbivFTD1QBmVPcK+sYbrAQwo6XDb4+T4rnhAwAADD2CvgAAYMhlpO4N+jYQ9IUD9Qr6VvmM1QGYNL0w2x6vK9ttsBLAjIIRScru7nL0bqXPDgYATrKgZKo9XrGq1GAlgBkT89I0e2LonOjj7Q16r3yP4YoAAIeidHuDPSboC6eqpqMvHK6lc+9TapLj6egLAACGHkFfAAAw5MI7+voI+sKBkhPjlZ2eJEnaUtVwgLmBw9OMovCgb43BSgAzXC6X3dW3rTOgj3ZwPIDznDb7CCV0dzp66tUNCgQIvMN5FpUU2eNlKzcbrAQAcKg27Gy0xwR94VQ9Qd/0xFglxBFyhPOEB329BH0BAEAEEPQFAABDLryjL0FfOFVPV9+dtY3q6PTvf2bgMDQ9LOi7vpygL5xpdmGmPV5bVm+wEsCM5CSPTpk1QZJUXdesNaXbDFcERN75xxfI6wkF3v/xRqVaO7g2AIDhpnQHQV84W1cgqLqWLkl084VzNXeEBX27z+8BAACGEkFfAAAw5NJSCPoCBaPTJUmWJW3bTRdHOE92WpLyRiRLkj4or5FlWYYrAiJvdmGGPV7D49rhUAtKptrjFatKDVYCmJGSGKfzj8+XJDW2demptVsNVwQAOBjBoKWPdzZJkkanJyjTG2+4IiDydjd1qOdTHYK+cKqWsGYedPQFAACRQNAXAAAMufRkgr5AT0dfSarc5TNWB2DSjMJQV9+Glk5VVjUeYG7g8DMmM0l5GQmSpPe3NqijK3CAdwCHn7NOmqyYmNBHkitWbeDGDzjSopIJ9vihVZsNVgIAOFjb6lrV3B4Kd9HNF05V1dRhjwn6wqlaOsM6+hL0BQAAEUDQFwAADLn01L1B3waCvnCogrCg75YqOvrCmaYXZdvj98t2G6wEMGdWYaYkqdMf1LqtHA/gPJmpSSo5erwkacuueq3/dJfhioDIO35StiaMDoXDVm+oVnl1k+GKAAADVbpj702rxbkEfeFMu5s67TFBXzhVS8feoG+yh6AvAAAYegR9AQDAkEsL7+jbTNAXzlQwOt0eV1bVmysEMCg86Lu+rMZgJYA5s7uDvpK0trzOYCWAOQvmT7XHK1aWGqwEMMPlcmlRSZH983K6+gLAsLEhPOhLR184VHhH35xUgr5wpt4dfWMNVgIAAJyCoC8AABhyGWEdfX2NBH3hTL2Cvrt8xuoATOoV9C0n6AtnCg/6riHoC4c6d+4UuVwuSdKKVRsMVwOYccncQsW4Q9vBw6+WKxAMGq4IADAQ4UHfqQR94VDVjXuDvnT0hVO1dPrtsZeOvgAAIAII+gIAgCFHR19AGjsyTd15FoK+cKxx2SnKTEmQREdfONeotAQVjEiSJH2wrUEtHf4DvAM4/IwekarZR46VJH1cuVsbt3BMgPPkZCTptBl5kqSdda165cNdhisCAAzEhp2hoG9GUpxGpycYrgYwo7q7o69LUnZyvNliAEN6d/Ql6AsAAIYeQV8AADDk0lPo6At44mM1OitFkrSlyme2GMAQl8ul6YWhrr5V9a2qqmsxXBFgRk9XX3/Q0vtbfGaLAQxZUDLVHj9FV1841KKSInv811c2G6wEADAQuxvbVdvUKUkqzku1n1AAOE1Vd9A3yxuvuBjiBnCm5g6CvgAAILI48wYAAEPOmxiv2O4P/Bro6AsHKxidLkmq8bWqpa3TbDGAIdOLsu3x+nI6OMKZZnUHfSVpTXmdwUoAcxbMK7bHT64qNVgJYM7pR49RdmqoG+Rz727XnsZ2wxUBAPZnw45Ge1ycl2qwEsCctq6AGttDT6YZleIxXA1gTktnaDuIdbvkiSV2AwAAhh5nHAAAYMi5XC67q6+viS8u4VwFOen2mK6+cKrwoO+6zQR94UyzCjPs8VqCvnCo8XmZmn7EaEnSe5/s0FbOjeBAcbFuXTy3UJLUFQjqkdcrDFcEANifUoK+gKq7u/lKUk5qvMFKALNaO0MdfZPiY+jwDgAAIoKgLwAAiIi9Qd9Ww5UA5vR09JWkSsIscKgZhWFB3/LdBisBzMlK9uiIUcmSQmGBxrYuwxUBZiwo2dvV96lXNxisBDDn8vkT7PFDKzfLsiyD1QAA9oeOvoBU3bg36EtHXzhZc0co6JvsiTFcCQAAcAqCvgAAICLSuoO+Dc3tCgaDhqsBzCgYvbeDY+Uun7lCAIOOyEtXkidWkrS+jI6+cK7ZhZmSpKAlvVNZb7gawIwFJVPt8YpVpQYrAcyZlJemWUeEboQq3ebT+xV0egeAaNUT9E2Mi1HBCK/hagAzwjv6EvSFUwUty+7o640n6AsAACKDoC8AAIiIno6+waClptaOA8wNHJ7yc9LsMUFfOFVMjFtHjR8hSaqoalRDC8cEONPsokx7vLacUBecacr4kTpibOiY8MYHW7S7vtlwRYAZi+YX2eNlr2w2WAkAYF8a27q0ra5NkjQlL0Uxbh7TDmeqIugLqK0zoJ7ncHjjY43WAgAAnIOgLwAAiIieoK8k+ZraDFYCmJOfk26Pt1T7jNUBmDajKNsef1BOV18407HjM+TqzgasKSPoC2dyuVxaUFIsKXRD4LOrPzZcEWDG+bPzldT9yN/H36hQa4ffcEUAgM/q6eYrScW5qQYrAcza3dRpjwn6wqlaurv5SnT0BQAAkUPQFwAARER40Lehqd1gJYA5Y7JTFRsTOgXfQkdfONj0sKDv+5sJ+sKZ0hLj7IDAxqpm1bd0HuAdwOFpwfyp9njFqg0GKwHMSU2K1/mzCySFOkY+/fZWswUBAPooDQ/65hH0hXP1dPSNdbuU5Y03XA1gRq+gr4egLwAAiAyCvgAAICLSkhPssa+p1WAlgDkxMW6NHZUmSaok6AsHm1440h6vp6MvHGxWYYY9XltOV18408zJecobGTo/+s87ZWpo5qZAONOi+UX2eNnKzQYrAQD0ZwNBX0CWZam6O+ibnRyvGLfLcEWAGc0ddPQFAACRR9AXAABERHpqkj320dEXDpbfHfRtaOlQfVOb4WoAM6YWZNrdrdeXEfSFc80uzLTHawj6wqFcLpcWlBRLkrr8AT3/xkbDFQFmzJk0UkU5KZKk1RuqVVHdZLgiAEC4nqBvrNulI3KSDVcDmNHcEVBrdyfTkSkew9UA5rR0+u2xNz7WYCUAAMBJCPoCAICISKejLyBJKhidbo/p6gun8sTFqnhcKOD4ybY6tXX4D/AO4PA0syBDsd0dkNaW1xuuBjBnYclUe7xiVanBSgBzXC6XFs2fYP+8fFWZwWoAAOHauwIqr2mRJB2RkyxPLN0b4UxV3d18JSmHoC8crKUzrKOvh2MCAACIDIK+AAAgIujoC4SEB323VPmM1QGYNr0oW5IUCFoq3bLHcDWAGV5PrI4cE3rsb3lNi3Y3co4EZzphWr6y072SpJfe2qTW9k7DFQFmXDK3UG5X6AaQh18tUyAYNFwRAECSNu5qUiBoSZKm5qUargYwZ3dY0HcUQV84WEvH3qBvcjxBXwAAEBkEfQEAQESkJyfa44bmNoOVAGbl56Tb40qCvnCwnqCvJK0r222wEsCs2YWZ9piuvnCqmBi3zpk7RZLU2t6lf63dbLgiwIzRGUk6bUauJGlHXate+bDKcEUAAEkq3d5oj4sJ+sLBwjv6jkol6Avn6tXRNz7WYCUAAMBJCPoCAICISEtJsMf1jQR94VwFozPsceUun7lCAMOmF+4N+q4vqzFYCWDWrLCg75ryOoOVAGYtKCm2xytWlRqsBDBr0fwJ9njZSkLvABANNuwk6AtIUnXj3qBvDh194WDNnX577PXQ0RcAAEQGQV8AABARGSlJ9rihiaAvnCs/J80ebyHoCwebVjjCHq8j6AsHOzo/XXExoce0ryXoCwebP7NIqd5QWOC51z9RZ5f/AO8ADk+nH52nEd0d8p59Z5v2NLYbrggAsGFHKOjrckmTRxP0hXNVh3X0HZkSb7ASwKzWjvCOvgR9AQBAZBD0BQAAERHe0dfX1GqwEsCsnMxkJXQ/zquyyme2GMCg1CSPJuSmS5I+qqyVPxA0WxBgSEJcjI4ely5J2lbXph313BAFZ/LEx+rMEyZLknxN7Xr1vQrDFQFmxMfG6OK5RZKkrkBQj77OtgAAJvkDQW3c1SRJKhjhVXICj2iHc1V1B309sW5lJMYZrgYwp7mToC8AAIg8gr4AACAi0lMS7bGviY5EcC6Xy2V39d1S5ZNlWYYrAsyZXpQtSWrvDGjT9nrD1QDmzCrMtMdr6OoLB1tQUmyPV6wqNVgJYNblJUX2eNnKzVwzAIBB5btb1OEP3ZhanEc3XziXZVna3R30HZkcL5fLZbgiwJyWzr1PoPF6uAEEAABEBkFfAAAQEfFxsUpKCN3l39BMpzo4W8HoDElSW4dfu+tbDFcDmDO9MNsery+rMVgJYNbsor1B37UEfeFgpx0/0X7ywdOrP1aAbu9wqMlj0nXchBGSpNJtPq2r4NgAAKaU7my0x8W5KQYrAcyqb+tSZyB089GoVI/hagCzWjpCHX0TYt2KdRN6BwAAkUHQFwAARExad1ff+iaCvnC2gpx0e1xZ5TNWB2DajKK9Qd/3y3YbrAQw66gxaUqMC31Es6asjs6NcCxvYrxOPf4ISVJ1XbPe+mir4YoAcxbNn2CPl63cbLASAHC20u1hQV86+sLBqhs77HFOCkFfOFtLZyjomxQfY7gSAADgJAR9AQBAxGR0B30bCPrC4cblpNnjyl0+c4UAhk0voqMvIEnxsW4dUxDq9l7d2KEte1oNVwSYs7Bkqj1esWqDwUoAs750fL6SPKHgwONvVKgt7PHAAIDI2bCDoC8gSdVNe4O+Iwn6wuF6gr7JHoK+AAAgcgj6AgCAiOnp6NvS1qkuf8BwNYA5BaPT7fEWgr5wsJHpScrN8kqSPiivpYspHG1WYaY9XlPGI9rhXGeeMFmxMaGPLFesKuXYAMdKTYrXwtn5kqSG1i498/Y2wxUBgPNYlqWPd4aCvjlpCcpKJtwI56pq6rTHdPSFk3UFgurwByVJ3vhYw9UAAAAnIegLAAAipqejryT56OoLByvIybDHW6p85goBosCM7q6+vpYObaluPMDcwOFrdnjQt5ygL5wrIzVR82cWSpK2Vvm0btNOwxUB5iwqmWCP/7pys8FKAMCZttW1qak91FGdbr5wut1hHX1HEfSFg/V085UkLx19AQBABBH0BQAAEZMWFvRtIOgLBwvv6FtJ0BcON71wpD1eV1ZjsBLArOLcFCV7Qp1g1pbX0cUUjragZKo9XrFqg8FKALNOmDxShaNSJEmvllapcneT4YoAwFk27Nh7MypBXzhdVXjQN5WgL5yrNTzoG0/QFwAARA5BXwAAEDFpyXuDvvUEfeFgGSkJSvWGPhCv3OUzWwxg2PTujr4SQV84W2yMW8eOT5ck1bV0aXN1s9mCAIPOmTtFLpdLkrRiVanhagBzXC6XFs0vsn9evqrMYDUA4DylYUHfqQR94XDV3UFfb3yMUrpvUgWcqLmDoC8AADCDoC8AAIiYjFQ6+gJS6Av7/FFpkqRtuxsUCAQNVwSYMyMs6LueoC8cbnZhlj1eU15vsBLArJysFM05apwk6ZPKGn1SudtwRYA5l8wtkrs7+P7wq2UKBLl2AIBI6dXRN5egL5wrELRU09wpSRqZQjdfOFtLp98eJxN6BwAAEUTQFwAARAwdfYG98kenS5K6/EHt3MMjeOFc40amKCM59CXRunKCvnC2WYUZ9nhNeZ3BSgDzFpRMtcdPrdpgsBLArNzMJJ06I1eStH1Pq1Z+VGW4IgBwjp6gb1pinHIzEgxXA5izp6VTgaAlScoh6AuHa6GjLwAAMISgLwAAiBg6+gJ7FYzeG+basstnrhDAMJfLpendXX2r6lpUXd9quCLAnEk5KUpLjJMkvV1RZ3+RCjjRgpJie7xiVanBSgDzFpVMsMfLVm42WAkAOEdNY4dqmjokSVPHpMrV3V0dcKLq7m1BkkalxBusBDCvpZOgLwAAMIOgLwAAiJjwjr4+gr5wuIKcdHtcSdAXDje9MNsery/j8exwLrfbpdndXX0b2/zauIuO73Cu/NEZOnpSqIvpext3aktVveGKAHPOOCZPI1JD3fOefWeb6sLCNgCAodHTzVeSinNTDVYCmFcVHvRNpaMvnK130DfWYCUAAMBpCPoCAICISU8h6Av0KBidbo8rq3zG6gCiQU9HX0laV1ZjsBLAvFmFmfb4rfI6g5UA5oV39X1q1QaDlQBmxcfG6OKTCiVJnf6gHnujwnBFAHD4K93RYI+L8wj6wtmqG/cGfXNSCPrC2Zo7/PbY66GjLwAAiByCvgAAIGLSU/cGfRsI+sLh8nPS7DEdfeF0Mwj6ArbZRXuDvmsJ+sLhFpRMtccrCPrC4S4rKbLHy1ZulmVZBqsBgMNfaXhHX4K+cLjqsI6+Iwn6wuF6d/Ql6AsAACKHoC8AAIiY9OS9Qd96gr5wuILRGfZ4Cx194XAT8zKU6Ak96m59OUFfOFthtlcjUuIlSe9U1KsrEDRcEWDO5IKRmpQfuhnkjQ+2qLqu2XBFgDnFYzM0syhLkvThlnqtr+RmEAAYSht2hoK+iXExGp/tNVwNYFZ1U6c9HkXQFw5H0BcAAJhC0BcAAEQMHX2BvZIT4zUiLUmSVEnQFw4XE+PWUeNHSJLKdzWooaXjAO8ADl8ul0uzx4e6+rZ2BrQhrJMY4EQLSoolSZZl6ZnVdPWFs10+f4I9XrZys8FKAODw1tTWpW17Qp9dTs5NUYzbZbgiwKyejr5pCbFKjCPYCGdr6fDb4+TuxgUAAACRQNAXAABETEqSRy5X6INxXzNBXyB/dLokaUdNozq7AvufGTjMzSjMtscfVtQarAQwb3ZRpj1+q5yOjXC2BSVT7fGKVQR94WxfmlOgxO6uYY+9Xqm2Tv8B3gEAOBQ93XwlqTgv1WAlgHldgaD2tIQ6+o5KpZsv0NPR1+2SEuKI2wAAgMjhzAMAAESM2+1WWnKCJMnXSNAXKMhJlyRZlrRtd4PZYgDDphftDfq+v3m3wUoA82YV7g36rikj6AtnO3pSrsaOSpckvfJOmXw8GQQOlpYUr4Wz8yVJDa2devadbYYrAoDDU+kOgr5Aj93NnbK6xzkpBH2BnqBvUlyM3C46vgMAgMgh6AsAACIqPSVRktRAR19A+Tlp9rhyl89cIUAUCA/6ri+vMVgJYN7YzESNTg/dHPX+Fp86/UHDFQHmuFwuLSgpliT5A0E9/8ZGwxUBZi2aP8Ee//WVzQYrAYDD14awoO9Ugr5wuOrGDns8kqAvYAd9vZ4Yw5UAAACnIegLAAAiqifoW9/YJsuyDjA3cHgrGJ1hj7dU+cwVAkSBqflZinGHumCsLyPoC2dzuVya3d3Vt8Mf1PqtPrMFAYYtKJlqj59cWWqwEsC8EyeP1PhRKZKkVaVV2lLTbLgiADj89AR9Y90uHZGTbLgawKzqpr1BXzr6wuksy1Jzh1+S5I2PNVwNAABwGoK+AAAgonqCvv5AUK3tnYarAcwqyEm3x3T0hdMlxMeqOD9LkvTx1jq1d/oNVwSY1RP0laQ15XUGKwHMm3PUOI3M8EqSXl7zqVrauI6Ac7lcLi0qKbJ/Xr6qzGA1AHD4ae8KqGx3iyTpiJxkeWLp2AhnCw/6jiLoC4dr9wcV7O5fk0xHXwAAEGEEfQEAQESldQd9JcnX1G6wEsC8gtHp9riSjr6AphdmS5ICQUulW/YYrgYwaxZBX8AWE+PWufOKJUltHV16ec2nhisCzLp0XpHcrtCTEJav2qxAMGi4IgA4fGza1aRAd4qrODfVcDWAeb2CvqkEfeFsLZ0Be5wUT9AXAABEFkFfAAAQUenJ4UHfVoOVAOaNHZVmj+noC0jTi7Lt8fqyGoOVAOaNTk/QuKwkSdL6bQ1qC/syCXCiBSVT7fGKVaUGKwHMy81M0inTcyVJ2/e0alVpleGKAODwsWFnoz0uziPoC1R1B31dkkYmx5stBjCspWPvZzNegr4AACDCCPoCAICISk+loy/QIyE+VqNHpEiSttDRF9CMsKDvOoK+gGZ3d/X1Byy9v8VnthjAsJJjxistOUGS9Nzrn6izy2+4IsCsRfOL7PGyVzYbrAQADi+l2wn6AuGqG0NB30xvnOJiiBbA2Vo6916HJntiDVYCAACciLNxAAAQUXT0BXoryEmXJO2ub1Fre5fZYgDDphWOsMfrynYbrASIDrMLM+zxW+V1BisBzIuPi9VZJ06WJDW2dGjlu+WGKwLMOvOYMcpKCT0++5l3tqmuueMA7wAADMSGHXuDvpNzUwxWApjX3hVQQ3so2JjTfd4BOFkzHX0BAIBBBH0BAEBEhXf0baCjL6CC0en2mK6+cLrUJI8KR6dJkj6q3KNAIGi4IsCsWd0dfSVpLUFfQAtKiu3xilWlBisBzIuPjdHFJxVKkjr9QT3+eoXhigBg+PMHgvpkV5MkKX9EklIS4gxXBJhV3bT3RqJRBH0BtXQS9AUAAOYQ9AUAABGVlhLe0bfNYCVAdOjp6CtJlbt8xuoAosWMomxJUluHX5t21BuuBjBrRIpHRSO9kqTSHY1qbvcf4B3A4e3U2Uco0RMK3Dz96sfcEALHWzS/yB7/deVmg5UAwOGhvKZFHf7Q+cXUvFTD1QDmEfQFemsND/p6Yg1WAgAAnIigLwAAiKj05AR77GtqNVgJEB3yw4O+dPQF7KCvJK0rqzFYCRAdZnd39Q0ELb1bSfgdzpaUEK/Tjp8oSarxtejND7cYrggwq3hsho4pzJIkfbilXusr6f4OAJ/Hhh2N9riYoC+gKoK+QC/NHXtvwKajLwAAiDSCvgAAIKLSU5Pssa+p3WAlQHQoGJ1uj+noC0jTCwn6AuGOL8q0x2+VE+ACFpQU2+MnV24wWAkQHS4/eYI9XvYKXX0B4PMoJegL9FLdGBb0TSXoC7SEdfRNJugLAAAijKAvAACIKDr6Ar2FB3230NEX0PSikfZ4PUFfQMeOz5TLFRqvKSPoC5x5wiTFxYa+UF3xaqksyzJcEWDWl+cUKLE7ZPDo6xVqDwsfAAAOTnhH36m5BH2B6qZOe5xDR1+gV9DX6yHoCwAAIougLwAAiCg6+gK95WWnKsYdSnAR9AWkURlJysn0SgoFfQlwwenSk+I0eXSKJGljVZN8rZ0HeAdweEtPSdTJxxZKkrZXN+i9T3YYrggwKy0pXgtm5UuSGlo79cw7Ww1XBADDk2VZ+nhnKOg7KtWjLEKNgKqbQh19Y9wuZXnjDVcDmNfS4bfH3vhYg5UAAAAnIugLAAAiKj050R43NLcZrASIDrExbo0dlSZJqtzlM1sMECWOLsqWJPlaOrR1d5PhagDzZhVmSpIsS3q7vN5wNYB5C0qm2uMVqzYYrASIDovmF9njZSs3G6wEAIav7XVtamwLBbiK8+jmC0h7g77ZyfF2owLAyXp19I2noy8AAIgsgr4AACCiEjyxio8LfQDiayToC0hSfk66JMnX3C5fM52ugendQV9JWldWY7ASIDoc3x30laQ15XUGKwGiwzlzp8jlCgUNnlxZSvd3ON6Jk0epYGSyJGlVaZW21DQbrggAhp8N3d18JWkqQV9ATR1+O9Q4KpkO14AkNXdvE3ExLsXHErUBAACRxdkHAACIKJfLpYyUJEmSj46+gCSpoDvoK0mVu+jUCEwvDA/67jZYCRAdZhZk2N2TCPoC0siMZJ04PV+S9Om2Wn1SyU0hcDa326VF8ydICnV//9urZYYrAoDhp3T73qAvHX0Bqbqxwx6PSiXoC0hSS0co6Es3XwAAYAJBXwAAEHFpKQmSpIYmgr6AJBWMTrfHW6oazBUCRIkZYR1919PRF1ByQqzdVaxsd4tqmjoO8A7g8LegZKo9XrGq1GAlQHS4dG6huhtda/mqMgWDdLoGgIOxYQdBXyBcddh156gUgr6AJLV2+iVJ3vhYw5UAAAAnIugLAAAiLr27o29Dc7sCgaDhagDz8nt19PUZqwOIFvmjUpXuDX2JtK6coC8gSbMLM+3x23T1BXTevGJ7TNAXkPKyvPritFxJ0tbaFq0qrTJcEQAMLxt2hoK+aYlxystINFwNYF540DcnJd5gJUB0CAQttXaFvs9K9tDRFwAARB5BXwAAEHHp3R19Jamxpd1gJUB0CO/oW1nlM1YHEC1cLpemd3f13bWnRbt9rYYrAswLD/quIegLaFxOuo6ZnCdJWrdplyp3sl0Al8+fYI+XrdxssBIAGF5qmzq0uzEUaizOS5Wrp0U64GB09AV6a+0K2GNvPEFfAAAQeQR9AQBAxKWl7O2K4WtqM1gJEB3CO/puoaMvIEl20FeS1pfR1Rc4Oj9dsTGhwMGa8nrD1QDRYUF4V99XNxisBIgOZx4zRpnJoSDOM+9sVV1zxwHeAQCQpNIdjfa4OC/VYCVA9Kgi6Av00tJB0BcAAJhF0BcAAERcBkFfoJeczGR54kIfDm6hoy8gSZpRuDfou65st8FKgOiQGB+j6WPTJElb97Rql4+nIgAL5k+1xytWEfQFPHExunjueElSR1dQ/3ij0mxBADBM9A76phisBIgeu5s6JUnxMS5lJMUZrgYwr6XTb4+9nliDlQAAAKci6AsAACIuvKNvA0FfQG63y+7qW1nlk2VZZgsCokB4R991dPQFJEmzCzPt8dryOoOVANFhUn62JheEjhdvfbhVu2obD/AO4PC3qGSCPf7rys0GKwGA4WNDeNA3l46+gGVZqu7u6DsyxSOXy2W4IsC8lk46+gIAALMI+gIAgIhLDwv61hP0BSRJBaPTJUmt7V2q8bWaLQaIAhPHZCih+0Pz9eUEfQFJOr5ob9B3DUFfQJK0oCTU1deyLD2z+mPD1QDmTR2XoaMLsyRJH1TWaX0lxwsAOJCeoG9CnFuFI5MNVwOY52vzq8MflCTlpHgMVwNEh+aOsKCvh6AvAACIPIK+AAAg4tLp6Av0UdDd0VeSKnf5jNUBRIvYGLeOGj9CklS2s0GNrR2GKwLMmzY2XZ7Y0Ec5b5XV0QEekLSgpNger1i1wWAlQPS4fP7err4P0dUXAParqa1LW/eEbrienJuqGDedS4Gebr6SNIqgLyDpsx19Yw1WAgAAnIqgLwAAiLi0ZDr6Ap81LjzoW1VvrhAgiswoGmmPPyivNVgJEB3iY906piBdklTV0K5tdZxHATMm5trnUaveK1ddI09GAL48p0AJcaEuY4++XqH2sFACAKC3j3c22ePi3FSDlQDRo4qgL9BHS4ffHnvj6egLAAAij6AvAACIuIxUOvoCn1UwOt0eb6lqMFcIEEWmF2bb4/XlNQYrAaLHrMJMe7ymnMexAy6XSwtKpkqS/IGgnn99o+GKAPPSvfFaMHucJMnX0qln391muCIAiF4bdjba4+I8gr6AJO0OD/qmEvQFpN4dfZM9BH0BAEDkEfQFAAARF97R19dM0BeQPhP03eUzVgcQTaYX7Q36rttM0BeQpOPDgr5rywj6ApK0oKTYHj+5qtRgJUD0WFQywR4vW7nZYCUAEN1Kt+8N+k4l6AtIkqoa6egLfFZ40JeOvgAAwASCvgAAIOLCO/r6Ggn6ApJU0P3IaUmqrPIZqwOIJkcWZCnG7ZJER1+gx9S8VHm7O8e8VV4ny7IMVwSYd/yR4zQqM1mS9K81n6q5teMA7wAOfydNGaX87NB2sfKjXdpa02y4IgCITj0dfWPcLk3MSTZcDRAdqsM6+uYQ9AUkSS0dfnvsjY81WAkAAHAqgr4AACDiwjv6NtDRF5AkZaYmKiUpXpJUSUdfQJKUEB+ryWND3Us/3lqnji7/Ad4BHP5iY9yaWZAhSdrT3Kmy3S2GKwLMi4lx69y5UyRJ7Z1+vbTmU8MVAea53S5dPr9IkmRZ0t9WlxuuCACiT0dXQJurQzdCTBiVLE8cHRoBaW/QNyk+RsketgtA+kxHX7YLAABgAEFfAAAQcWnJCfa4no6+gCTJ5XIpv7ur79ZqnwKBoNmCgCgxY0K2JMkfCKq0ss5wNUB0mF2YaY/XlrNdAJK0oGSqPV6xqtRgJUD0uGRekVyhhyPooVWbFQzSBR4Awm2qalage984NS/VcDVAdAgELdU0d0qSRiXHy9VzMgE4XHPH3qBvEjeGAAAAAwj6AgCAiIuNjVGKN/TILzr6AnsVdAd9u/xB7arj0bqAJE0vzLbH68t3G6wEiB7hQd81BH0BSdK8Y8YrPSV0Q+Hzr29URydd4IExWV594ahcSdLWmha9uqHKcEUAEF1KdzTa42KCvoAkqa61U/7uAPyoVI/haoDo0dPRNynOrRg3AXgAABB5BH0BAIARacmJkiRfU7vhSoDokT863R5X7vIZqwOIJjOK9gZ9122uMVgJED0mjU5RamKsJGlteT0dGgFJ8XGxOuvEyZKkptYOrXy3zHBFQHS4fP4Ee7xs5WaDlQBA9Nmwo8EeF+cS9AUkqbqxwx6PSiHoC/Ro6b6ZNCk+1nAlAADAqQj6AgAAIzJSeoK+rYYrAaJHT0dfSdqyq95cIUAUmRbW0XddOUFfQJJi3C4dNz7U1behrUsbq5oMVwREhwUlU+3xilUbDFYCRI+zZo5RZnIopPP021tV39xxgHcAgHOEd/SdkpdisBIgelQ17T1XyCHoC9h6Ovome2IMVwIAAJyKoC8AADAirTvo297hV3tHl+FqgOiQHxb0razyGasDiCZpXo/G54Q6K31UUatAIGi4IiA6zC7MtMdryusMVgJEj1NmTVBSQpwk6enVGzhmAJI8cTH6r5PGS5I6uoJ6/M1KswUBQJQIBC1t3BW6YW5cVpJSus8hAKerDgv6jiToC0iSOv1BdQVCT1PyxhP0BQAAZhD0BQAARqR3B30lydfUZrASIHoUjE63x5W7fMbqAKLNjKKRkqTWDr8+3eEzWwwQJWYX7Q36riXoC0iSkhLiddrxEyVJtb5WvfHBFsMVAdHhspIie7zslc0GKwGA6FG+u1ntXaGbgqbmpRquBoge1XT0Bfpo7u7mKxH0BQAA5hD0BQAARoQHfRua2w1WAkSP8KDvlqoGc4UAUWZGUbY9XldeY7ASIHpMGOlVljdekvR2Rb38dC4FJEkLSqba4ydXlRqsBIgeR+Vnasb40A0i6yvr9EElN4gAwIadjfa4mKAvYKtu6rTHo1IJ+gKS1Nrpt8deT6zBSgAAgJMR9AUAAEak0dEX6CMlyaOs1NC2UVnlM1sMEEWmhwd9N+82WAkQPVwul2YVhkJbLR0BbdjZZLgiIDqcecIkxcWGOiytWLVBlmUZrgiIDpfPn2CPl68qM1gJAESH0u0EfYH+9HT0TU2IVWIcnUsBKfS5Sw86+gIAAFMI+gIAACPSkxPssa+p1WAlQHTJ7+7qu6OmUV3+wP5nBhxieuHeoO96OvoCtlmFGfZ4TTndGQFJSktO0BeOLZIk7djdoHc/2WG4IiA6XHDCeCV0h3Ueeb1cHV1cawBwtg079gZ9pxL0BSRJXYGgaptDHX1zUujmC/Ro7iToCwAAzCPoCwAAjEhPTbLHvqZ2g5UA0SU/J12SFAxa2lbdYLYYIErkZHqVkxE6bqwvq6E7I9BtdlGmPV5L0BewLSgptscrVpYarASIHuneeJ173FhJUn1zp559d5vhigDAHMuytGFnKOg7MtWjEQQaAUlSTXOnej5xGcl2AdhawoK+yZ5Yg5UAAAAnI+gLAACMoKMv0L+C7qCvJFVW+YzVAUSbGUUjJUn1zR3aWtNkuBogOuRnJSknLfTl67uV9er0Bw1XBESHs+dOkdvtkiQ9uaqUG0SAbpefPMEeL3tls8FKAMCsHfVtamzzS6KbLxCuuqnDHo8i6AvYWjr89jiJjr4AAMAQgr4AAMCI8I6+DXT0BWwFo9Pt8ZYqOvoCPaYXZdvj9WU1BisBoofL5dKswlBX3/auoD7YxnEDkKSRGck6cXqBJGnztj36uGK32YKAKDF3So7ys5MlSa98tEvbalsMVwQAZmzY0WiPiwn6ArbwoG9OKkFfoEfvjr4EfQEAgBkEfQEAgBG9O/q2GawEiC7hQd/KXfXmCgGiTHjQd91mgr5Aj9ndQV9JWlteZ7ASILosKCm2xytWlRqsBIgebrdLi0qKJEmWJf3t1TLDFQGAGaUEfYF+VTeGd/SNN1gJEF3Cg75eOvoCAABDCPoCAAAj0lIS7TFBX2Cvgpx0e1y5y2esDiDazCgM6+hbTmdGoMessKDvGoK+gO28eeFB3w0GKwGiyyXziuRyhcbLVm1WMGiZLQgADOjV0TeXoC/Qoyq8o28KHX2BHs0dfnvsjY81WAkAAHAygr4AAMCI9NQke0zQF9hrXHjQt8pnrA4g2hTkpCrNG+oms66Mjr5Aj7yMRI3NDN1AtW6rT+1dgQO8A3CGsaPSNXNKniRp/ae7VLGDIDwgSWNHeHXykaMlSVtrWrT64yrDFQFA5PUEfVMTYzUmM/EAcwPOUR0W9M1OJugL9OjV0ddDR18AAGAGQV8AAGBEenKCPSboC+yVEB+rnKxkSdIWOvoCNpfLpendXX137mlRTUOr4YqA6NHT1bcrYOn9LT6zxQBRZEHJVHu84lW6+gI9Lj95gj1+aGWZwUoAIPL2NHWoujEUZizOTZWrp805ADvom5UUp/hYYgRAj5aOsKBvPEFfAABgBmfoAADAiOQkj2JiQqciDQR9gV4Kurv6Vte3qK2jy2wxQBSZXpRtj9fT1Rewze4O+krSmnK6lgI9FoYHfVeVGqwEiC5nzxyrjOTQkxKeWrtVvpZOwxUBQOSU7my0x8V5qQYrAaJLe1dAvja/JGlUKt18gXA9HX1jXFICIXgAAGAIZyEAAMAIl8tld/X1NRP0BcIVjE63x1uqfMbqAKLNjLCg7zqCvoAtPOi7lqAvYDti3AgVjx8pSXrrw63aVdt4gHcAzuCJi9FFJ46XFAr1PP5GheGKACByNuwg6Av0Z3fz3ht/RqUQ9AXCtXSGQvBeTyyd4AEAgDEEfQEAgDFpKYmSJF8jQV8gXHjQt3KXz1gdQLSZXjjSHhP0BfbKTvWoMNsrSfpwe6NaOvyGKwKix4Kwrr5Pr/7YYCVAdLl8/gR7vGzlZoOVAEBklW4n6Av0p6qxwx4T9AV6a+kIdfT1xscYrgQAADgZQV8AAGBMek/Qt7lNlmUZrgaIHvmj0u0xQV9gr0ljM5TQ/YH6eoK+QC89XX0DQUvvVtYbrgaIHgtKiu3xipWlBisBostR+ZmaXhA6dqyrqNOHW+gID8AZNuwMBX09sW77ZjkAUnUTQV+gP0HLUmsXQV8AAGAeQV8AAGBMT9A3GLTU1NJxgLkB5wjv6LulusFcIUCUiY1xa2rBCEnS5p0+NbV2HuAdgHPMKsywx2vKCWsBPaYdMVoFuaHtY9X7FaprbDVcERA9Lj95b1ff5avKDFYCAJHR1N6lLbWhc4HJo1MUG8PXpEAPgr5A/9q6ggp296kh6AsAAEziChYAABjTE/SVQl19AYSEB30rd9GVEQh3dFG2Pf6gotZgJUB0mdXd0VeS1pQR9AV6uFwuLSiZKkkKBIJ67rVPDFcERI8L5hTIExf6iuDvr5Wro7tTGQAcrj7Z2WSPp45JNVgJEH3Cg745qQR9gR4tHX577PXEGqwEAAA4HUFfAABgTHjQt6GJoC/QY8zINMW4XZKkLbt8ZosBosz0sKDv+rIag5UA0SXDG69Jo1MkSR/valJDW5fhioDosaCk2B6vWFVqsBIgumQke3TuceMkSfXNnXru3e2GKwKAobVhR6M9Ls4j6AuE6wn6ul3SCG+84WqA6NHSufdmODr6AgAAkwj6AgAAY9KSwzr6EvQFbLExbo0ZmSZJqqzymS0GiDLTC/cGfdeV7TZYCRB9ZhdmSJIsS3q7go7wQI/ZU8cqJytZkvTy2s1qbu04wDsA57h8/gR7vGzlZoOVAMDQKw0P+uYS9AXCVTWGzpGzkz12AwIABH0BAED0IOgLAACMyUgl6AvsS35OKOhb39SuhuZ2w9UA0ePIghH2F0509AV6m12YaY/XltUZrASILm63W+fNC3X17ej068W3NhmuCIge84pzNC7bK0n6z4c7tX1Pi+GKAGDo9HT0jXG77KdhAJCaO/x2mHFUCt18gXAtHXuDvsmeWIOVAAAApyPoCwAAjKGjL7BvBTnp9ngLXX0BW6InVpPGhrqWbthap44uv+GKgOhx7PgM9TReWlNO0BcIt6Bkqj1esWqDwUqA6OJ2u7SoJNTV17Kkv71aZrgiABgaHf6ANlc3S5KKRnrliaMrI9CjumnvEy9GpXgMVgJEn+bOvZ890tEXAACYRNAXAAAYk05HX2CfCkan2+NKgr5ALzOKRkqS/IGgNmwhzAj0SEmI09S80COIP61u1p7mjgO8A3COuUePV0ZK6Prj+dc/UXtHl+GKgOhxydxCubpvFFm2skzBoGW2IAAYAp9WNcvfvX+bmpdmuBoguoQHfXNSCfoC4Vo793b09XoI+gIAAHMI+gIAAGPSU/YGfRsI+gK99Ar67vIZqwOIRtMLs+3x+rIag5UA0Wd2YaY9Xlteb7ASILrExcbo7JMmS5Ka2zr1yrt0LQV6jMtO1vwjR0uSttQ067WPqw1XBACDr3RHoz0uzksxWAkQfejoC+xbS3jQl46+AADAIIK+AADAmLTkvUHfeoK+QC/5ORn2mKAv0NuMor1B33Vluw1WAkSfWWFB3zXldLwGwi0omWqPV6zaYLASIPpcPn+CPX5o1WaDlQDA0CjdHh70TTVYCRB9qhoJ+gL70tzht8fe+FiDlQAAAKcj6AsAAIzJSKWjL7Av+Tl7HyO5pcpnrhAgCk0rDA/60tEXCHdMQbpi3aHnr68l6Av08sVZE+RNjJckPbP6Y/n9gQO8A3COs2eOVbo3tH2sWLNVvpZOwxUBwODasDMs6JtL0BcIR0dfYN/CO/ome+joCwAAzCHoCwAAjAnv6Osj6Av0MjorRZ640AeHBH2B3tKTPSoYFfpi9sOKWgUCQcMVAdEjKT5W08aGbhaprG1VdUO74YqA6JHoidPpcyZKkvY0tOr19VsMVwREj4T4GF104nhJUntXQP94s9JsQQAwiAJBSxt3NkmSxmYlKiUxznBFQHTZ3RS6wScuxqWMJLYPIFxLx96gb1I8QV8AAGAOQV8AAGBMegpBX2Bf3G6XxnV39a3c5ZNlWYYrAqLLjAmhrr6tHX5t3ukzWwwQZWYXZtrjNXT1BXpZUDLVHj+5qtRgJUD0uXz+BHu8bOVmg5UAwOCqqGlRW1coqDU1L+0AcwPOYlmWqro7+o5K8cjtchmuCIgu4R19vQR9AQCAQQR9AQCAMZ74WCV6Qh0CGprpNgd8VkFOhiSppb1LtQ2thqsBosuMwpH2eF1ZjcFKgOgzi6AvsE9nzJmo+O6nJjz16gYFg3SFB3pMK8jUtILQMeT98j36aGu94YoAYHBs2NFoj4vzUg1WAkSfhna/Ovyhc+JRKR7D1QDRp6XTL0nyxLoVF0O8BgAAmMOZCAAAMCqtu6tvfSMhRuCz8nP2dpmp3OUzVwgQhaYXZdtjgr5AbzPGpSk+NvSRz5oygr5AuFRvgr54XKhr6c6aRr3z8Q7DFQHR5SthXX2Xr6KrL4DDQ2lY0HdqLkFfIFxVY4c9JugL9NXcEeroSzdfAABgGkFfAABgVEZ30JeOvkBf+aPT7TFBX6C38KDveoK+QC+euBgdPS5dkrTT167tddxQBYRbUFJsj1esKjVYCRB9vnxCgTxxoa8N/r66Qh1dgQO8AwCiHx19gX2rbiLoC+xPaydBXwAAEB0I+gIAAKN6Ovo2t3aoy88XiEC4gpx0e7yl2mesDiAajc70alR6kiRpfXmNLMsyXBEQXWYXZtrjNeV09QXCnX3SFLndLknSilUbOIYAYTKTPTrn2HGSpLrmDj3/3nbDFQHA52NZlh30zU7xKDuVICMQjqAvsG/+oKV2f1CS5PUQ9AUAAGYR9AUAAEb1dPSV6OoLfFbB6Ax7vIWOvkAfMyaEuvrWNbVrW02z4WqA6DK7KDzoW2+wEiD6jEj3au6M8ZKksu17VFpebbgiILpcPn+CPX5o5WaDlQDA57ezvl0NbV2SpKl08wX6CA/65qTGG6wEiD4tHX57nBz//7N35+FRlXcbx+9Zsm+TQCBhDUkA2QyKElAhUPcVrUvVilpt1brV1qW1r1btorWt3WzdFatgsVUQbVG0rcQVUFnUIFtCAoYkBLLvmZnz/jHJMJElCQLPGfL9XFevPkwmkxuvHM6ZM/f5HbfBJAAAABR9AQCAYUmhRd/6ZoNJAPvJSPcE18UUfYHd5GSmBtdrCrcbTALYz/ghiYrpuK3k8sIqJpYCXzErb2xwvSi/wGASwH7yxqVpaP84SdJ/Py1T6c5Gw4kAYP+t3VYXXI+l6Avshom+wN41tu26C2VcJBN9AQCAWRR9AQCAUUkJ0cF1dX2TwSSA/fRLjFFcdIQkqbi8xmwYwIZyskKKvkWVBpMA9hPhcmpShkeSVFnfquIdHGcBoc6eHlr0XWswCWA/TqdD356eJUnyW5ZeeLfIcCIA2H8FpRR9gX2pqAsUfWMinEqIYmIpECq06BsbRdEXAACYxdE6ABwC15x/nOobW5QQF939k4E+5oSJmfL7LSXFxyjVE286DmArDodDp+Zmy+e3lDU4WZZlyeFwmI4F2MbErFQ5HNKItCTFcPs8YDe5mSl6b8NORbgcKtreqBGpcaYjAbYxZECSjh07VB+t3arPNpVra0WNhg70mI4F2Ma387L04MJPZVnS3KWbdOs54+V08l4EQPhJiYvQ+CGJ2lDeQNEX+Aqf39L2hjZJgWm+nHcEukqMduubR6apsc2rsQP5/AoAAJjlsHpw78a6ujolJSWptrZWiYm8CQaA3vJ6fbIkOSS53VzxCXyV37Lk8/nldDrkcnLDAWBPOrcRTrgDu1iWpaZWb3DyNYCuKmpbtHlHo44a7lEU70OA3bywZJXKd9Tr/BMnaHhasuk4gO2ce/9b8votzZ6RrQuPG0HRF0BY6rxo2ue35HSI8ypAiB0Nbbpi3mpJUu5wj3522iizgQCb8VuW/JbkYv8B7NXlc1dpZ2O7+sVF6LnLjjIdBwDCTm96uYw8AoBDgHIvsG9Oh0NOthNgn1wuSvDAVzkcDkq+wD4MSIxSakIUxSxgLy499Si1e32K4L0IsEfzbp2puCi32r1+9iUAwlZnMcvFv2PAbsrrW4PrtIQog0kAe3I6HGL3AQAA7IKiLwAAAAAAwGHI4XCIgTPAvlHyBfYuNjKwfUS4uegQAIDDUUVI0XdgIkVfAAAAwM4o+gIAAAAAAADAHnTe7hvoi/jdBwDg8DZzZD9NSE/QtroWpSdGm44DAAhDf71ggvyy5BTvHwHgYKPoCwAAAIQRr9er1tZWxcXFmY4CAABw2PH7/WpqalJNTY2Kiop03HHHye3mFCoAAAAOP06HQwMSopQaH2k6CmArlmVJ4sI3oCcSojlnAgCHCv/iAsAhVFlZqa1bt0qShg4dqtTUVMOJAPsoLy/XJ598osLCQtXU1Ki1tVXDhw/XFVdcoagobhsGrFixQi+99JI2btyonTt3atOmTRo+fLh+/etfa9q0aXI6uZ0uIEnbtm1TcXGxtm/frtbWVp199tmKjY01HQuwBa/XqzfeeEMffvih3nvvPblcLt1xxx3Ky8tTTEyM6XiAcevWrdOzzz6r999/X59//rmOOOIIjRkzRt///vd17LHHmo4H2EJdXZ0+/fRTLV68WOXl5brqqqs0efJkRUZSEAJgXkVFheLi4hQfHy+fzyen00lJC+gBthMgoPOOLmwTAADAjhxW5+VI+1BXV6ekpCTV1tYqMTHxUOQCgMPKhg0bdN1112nlypVKT0+XJJWVlWnSpEl6/PHHlZ2dbTghYNb69et18803a/PmzRozZoxSUlLU1tam5uZmjRw5Uvfccw8lLfRpX3zxhX76058qIiJCeXl5GjVqlIYPH66ioiLdc889+tOf/qQpU6aYjgkY9corr+gPf/iDysvLFR0dLb/fr0GDBmnYsGG67bbbNHr0aNMRAaOeeOIJ3XvvvfJ4PJo6daqmTJmilJQUPfroo7rwwgt17bXXmo4IGPW73/1O999/v0455RRdeumlOvvss+VwOHT33XertLRUzzzzjOmIgHHPP/+85syZo8rKSo0aNUrHH3+8Fi9erDPOOEM/+tGPTMcDAM2ePVs7d+7UY489pmHDhpmOAwAII50l3/LycuXn56u1tVUJCQkaNGiQRo0apeTkZNMRAQDAYag3vVwm+gLAIXDllVfq2muv1X/+85/gxEW/36958+bpiiuu0Pvvv284IWDWTTfdpG9+85u67rrrujzu8/k0ceJEXX311Ro1apShdIB5Dz74oCZOnKh77rmny+OjRo3S/Pnz9fnnn1P0RZ9WUFCgF198UWeeeaYuvPBCjRgxIvi1K6+8Uq+++qpuv/12gwkBs7744gstXbpUTz75pM4888wuX2tvb9f8+fMp+qJP27hxo5YtW6a3335bOTk5Xb523nnn6aqrrjKUDLCP+++/X3PmzNFtt92myy67THFxcZKkKVOm6KabbqLoC8AWNmzYoH79+ik3N1cXXHCBbrvtNg0fPtx0LABAGHA4HFq5cqXmz5+vNWvWaPny5WpoaFBycrKOO+44/epXv9L48eNNxwQAAH0Y9/cFgENg586duuKKK7rcVt3pdGr27NnasWOHwWSAPTQ3N+voo4/e7XGXy6WoqChVV1cbSAXYR79+/fZ4u7CGhgY1Nzd32b8AfdG//vUvRUZG6o477uhS8pWk8ePH67PPPjOUDLCHoqIilZaW6swzz5Tf7+/ytaqqKg0YMMBQMsAeIiIiVFBQsFvJt6ioSA899JC+//3vG0oG2MOWLVv03nvvafHixbr22muDJV8pcCF7dna26uvrDSYEAKmtrU2WZWnx4sVasGCB6uvrddttt+mNN95Qa2ur6XiArYTe8LcHN/8F+ozHH39cLpdLCxcuVH5+vv7v//5PS5cu1QknnKC77rpLGzduNB0RsK3KykqtXLlSK1euVGVlpek4AHBYYqIvABwCSUlJeumll3TBBRd0efzll1+Wx+MxEwqwkQkTJujZZ59VU1OToqOjVVtbq/Lyci1dulRTpkxRRkaG6YiAUTNmzNBTTz2l22+/XVFRUaqrq9O6deu0Zs0affvb32bKHPq8CRMm6PXXX+/y2Jdffqk33nhD7733HhPm0OedcMIJuvHGG/X5559r7Nix+uSTT/TOO+/opZdeUlVVlV599VXTEQGjMjIyFB8fr4ceekjHHHOMPvzwQy1fvlwFBQXKzc3VueeeazoiYNSwYcNUVFTUpeBbUlKiOXPm6IknntADDzyghIQEgwkBQFqzZo1iYmIkSVOnTlVycrKeeOIJ3XHHHTrvvPN0xx13dPl3DOjLQgcK7Gm4ANBXFRUV6fvf/75iY2OVk5Ojn//855o8ebJuv/12/ec//9GmTZs0cuRI0zEBW9mwYYOuu+46rVy5Uunp6ZKksrIyTZo0SY8//riys7MNJwSAw4fD6sFlenV1dUpKSlJtba0SExMPRS4AOKysXbtWV155pbZu3aphw4ZJCkxDGTp0qP72t79pzJgxhhMCZvl8Pv3iF7/Q3//+d/Xr10/9+vWTz+dTdna2brnlFmVmZpqOCBhXUFCg3/72t4qMjFRaWpqGDh2q4447TuPGjTMdDTCupqZGt956q7788kvV1NTI7XbL6/UqIiJCl112ma699lo+uEKf98gjj+hf//qXPvroI40YMUJjxozRscceq8svv5xzPYCkjz76SAsXLtQ///lPHX300TrhhBM0bdo0TZw40XQ0wBZuvPFGbd++XUcccUTwNsZDhw7Vd7/7XZ100kmm4wGAXnnlFS1fvlwPPPCAfD6fXC6XpMA+/sYbb5TH49GSJUsMpwTM2rZtm377298qOztbN9xwg/Lz81VXV6cJEyYwbAOQdOutt8rv9+uiiy5SVVWVHn74YT344IPKycnRzJkz9etf/1q5ubmmYwK2ctxxx+naa6/V7Nmzg3ef9Pv9mjdvnh577DG9//77hhMCgL31ppdL0RcADqGioiJt2bJFUmAaCuVFIKC1tVVRUVGSArd2aW5u1oABAxQdHW04GWAPfr9fJSUlGjFihCSppaVFkZGRwZMmAAJefvlltbW1KTk5WQMHDtT48eMVERFhOhZgG1VVVcFbGrvdbvXv318Oh0N+v599CvAVXq9Xbrd7tzXQV9XW1mr58uV6/vnnNWnSJE2ePFlZWVkaOHBg8JbfXFgFwKSKigpFREQoJSVFfr9flmUFj3ulQMFx0KBBhlMC5tTV1emb3/ymJkyYoOrqao0aNUrvvPOOqqqqlJCQwNRFQIHPcX/2s5/J5/OpsLBQ1113nWbPni23262ZM2fqySefZKIv8BWjR4/W+vXre/01AEAARV8AsJnvfe97evLJJ03HAGzr2WefldPp1OWXX97lca/XK6fTSfEEfZ7P59M111yjp556ig/Pgb1YtWqVRo4cqfj4eFmWFdxWvF6vXC4X2w76vIqKCr355puaPXv2Hou9odsN0Ff96le/0qWXXhq8uGpP2FbQV331d39P2wLbBwDTampq5PF4TMcAbGn16tW65ZZbtHTpUi1YsEC33XabFixYoLFjx+p3v/udVq9erX/84x+mYwLGtbW16d1331VmZuY+3xsCCJg8ebLuuOMOXXDBBV0ef/nll/Wb3/xGy5cvN5QMAMJDb3q5tGYA4BCYNWuW6QiArU2bNk2TJ0+WFJhc2sntdsvpdKoH1yUBhzWXy6WbbropuC34fD7DiQD7WbVqld566y1JXafJud1uCieApOTkZJWUlMiyrGDJt3N/4vP52E4ASUcddZQaGxsl7XpfsnPnTr366qv68MMP1d7ezraCPsvhcOjNN9/Ur371q+CfQ9+rb9++ne0DgFEvvfSSrrzySv3oRz+SJP3zn//UM888o6KiIsPJAHsoKirSgAEDJElOp1NDhgzRxIkTFRkZqRNPPFGtra2GEwLmLViwQA0NDTrxxBMp+QI99Oyzz+o3v/mN0tPTlZubq9zcXKWnp+vBBx/Us88+azoeABxWmOgLAABsI3T6z7p161RZWakjjzxSSUlJTAYCOnROYdywYYNeeOEFud1uXXjhhRo9erTpaIBRNTU1am9vV2pqavCxHTt2qKCgQEOGDFFWVpbBdIA97NixQ/3791dTU5Oam5vVr18/bdmyRQsXLtSgQYN0/PHHcztj9Gk+n08ul6vLe4/77rtPRUVFKikpUXp6un77299qyJAhhpMCZrS1temDDz7QjBkzgo9t375dDz/8sLZu3ap+/frprLPO0syZM82FBNAnrV+/Xt/61rf0wx/+UMuWLVNjY6NqampUX1+viIgIzZkzR4MHDzYdEzBq2bJluvfee+V2u+VyudTW1qa7775b2dnZeuyxx1ReXq5HHnnEdEzAGJ/Pp4yMDE2fPl0zZszQzJkzlZ2dbToWEDaKioq0ZcsWSdKwYcOUmZlpOBEAhIfe9HIp+gLAIbB9+3YlJSUpKipKUuDKtk8++UQ5OTm6+uqrKS+iz2tsbNTWrVt1xBFHSJLeeustPfHEE4qIiFBSUpJuvPFGjRs3znBKwKzVq1fLsiwdddRR2rx5s2677TYlJibK5XJpy5Yt+vGPf6wTTzzRdEzAOK/XK7fbraamJt11113asWOHKisrdfrpp+vmm282HQ8wprO4uHnzZt1///2KiYlRZmamysvLtWTJEmVmZio5OVlPPfWU6aiAUU1NTWpqalL//v2D00vvuOMO5ebm6oEHHlBsbKx+8YtfmI4JGNd5zPXYY49pwYIFuvDCC1VVVaWFCxdq2bJlpuMB6GOeffZZvfvuu3r66af1t7/9TY8//rgWLVokj8ej//u//1NjY6P++te/mo4JGNP5fvD999/Xv//9b+Xl5Wnnzp168803VVNTI6/XqzvuuEPTp083HRUwpqamRjk5OfrOd76jJUuWKDs7WyeddJK+8Y1vKD09XW6323REwJa+973v6cknnzQdAwDCVm96uc5DlAkA+rQZM2aoublZkvTzn/9cTzzxhAYPHqz58+fr9ttvN5wOMG/16tWaPXu2JKmwsFB/+MMfNHnyZF133XXyeDz6zW9+YzghYN68efP0zDPPSJLefPNNpaSk6PHHH9dTTz2lM888U6+++qrhhIB5Dz30kCorKyVJf/nLX7Rx40bNnj1bd9xxh1588UWtW7fOcELAHIfDofr6ej3wwANqaGjQlClTVFVVpXnz5mnVqlV6+eWX9eGHH5qOCRj3y1/+Ug8++KAkacuWLRo9erTOPPNM9e/fX+eff75WrFhhOCFgVnt7uxYsWCC3263W1la9+eabuvvuu/W9731PP/7xjxUXF6e33nrLdEwAfcy2bduUkJAgSXrnnXc0depUpaamKiIiQtnZ2YqMjDScEDCrc9jM8ccfr5/97Gc69dRTdemll+pb3/qWzj77bD333HOUfNHn1dTUaNSoUbr33nu1cOFCjR49Wr/+9a81e/ZsPfnkk6qpqTEdEbClWbNmmY4AAH0GRV8AOARcLpc8Ho8k6ZVXXtGSJUv0k5/8REuWLNGSJUvMhgNsICUlJXjCva2tTXV1dbr99ts1ffp03X333VqzZo3hhIB548ePV0NDgyQpPj5elmUFt5v+/ftr586dJuMBtjB//vzgPmPZsmW69dZbdfLJJ2vmzJkaPXo05Sz0ebGxsVq+fLmeeeYZXXrppfr5z38uKXCr486vr1y50mREwLhTTz1VH3/8sSTp7LPP1qZNm7RmzRpt2LBBL774oo4//nj5fD7DKQFzIiIidMcdd2jZsmWKioqSZVkqLy+XJJWXl8vj8WjHjh2GUwLoa8444wwVFRVp1KhRam5uVnFxsZYuXari4mK9+eabOuqoo0xHBGwjOjpanTf8Pf3003X11VdzR19AUnV1dXBoU1pamu666y598cUXuuaaa/T444/r5JNPNpwQsKezzjrLdAQA6DO4vwAAHAJ+v187duxQ//79FRsbq+joaEmBAjAfEAJScnKyWltbVVhYqMTERI0YMUKfffaZ0tLStGDBAg0aNMh0RMC48ePH6/nnn9fLL7+ssWPHavXq1XrggQc0ZswYvfLKKzrjjDNMRwSMO+aYY/TFF1/otNNO06RJk/TBBx9ozJgx2rlzp6qqqoITbIC+yuVyKTU1VR9//LGmTZumnTt3Ki0tTXfffbcGDhyonJwcpaWlmY4JGJWXl6f6+no9+OCDmj59us477zxdeOGFioyM1MSJE3XXXXfJ5XKZjgkYddlll+mFF17QlClTdPvtt+vll1/WkiVLFBMTo+HDh+uSSy4xHRFAHzNx4kQ98MADKikp0dSpU/XKK6/oueeeU3FxsYYNG6ZTTjnFdETAFizLksPhCJ4f8fv9cjqdmj9/vi6++GK53VQH0Hc5nU6dcMIJkiSv1yun0ymn06lLL71Ul156qcrKygwnBOypra1Nf/3rX1VUVKTLL79cxxxzjF577TVZlqVzzjmHc/IAcAA5rM5L9vahrq5OSUlJqq2t5Yo+ANgPTz75pB555BH95Cc/0RdffKH169froosu0uuvvy7LsvTkk0+ajggYZVmWnn/+ef35z3/WWWedpU8//VTLli3TzJkzFRkZqWuuuUZTp041HRMwyufzafHixXriiSfk9/v18ccfa8eOHZo4caJ+8IMf6PLLLzcdETDurbfe0p/+9CeNHDlSM2bM0HPPPaeNGzcqOztbU6dO1e233x78UAvoq37/+9/rv//9r04//XQtX75cw4cP16WXXqpHHnlEp5xyis455xzTEQHj1qxZo6efflpffvmlPvnkEw0dOlSXXHKJLrnkEqWkpJiOBxi3bds23XDDDdq5c6dOPPFEvfrqq/r888915pln6uc//7nGjx8fLA4BwKHw1fd5bW1tWrx4sWJjYyn5Al/R0tKimpoaxcXFKSEhQZL0wx/+UH/4wx8MJwPMamxsVHV1tYYMGdLlcc4lAvt2zTXXqLi4WN/4xjf0v//9T+PGjVN+fr6ioqJ03HHH6aGHHjIdEQBsrTe9XIq+AHCILF68WA8++KAKCgrk9XqVkZGh2bNn6wc/+AFXSQMdVq1apb/85S+qqKjQ4MGDlZ2drdNOO00TJkwwHQ2wjfLycr377rtKSkrSqFGjNGjQIEVGRqq1tVVRUVGm4wFG+f1+rVq1SgsXLtTixYvV2Nioo446SjNnztQ3v/lNpaammo4IGNfY2Kh33nknWIq//fbbNWzYMNOxAFvauXOnYmJiFBsbK0mqr6/Xu+++qyFDhmjcuHFM9kWfVlFRoZUrV+rDDz9UTEyMvvOd7wSnwq9bt06bNm3iFq4ADhnLsmRZlpxO526FLC48AALTSd944w3deeedcjgcyszMVEpKikaPHq1vfetbysjIMB0RsDXKvsDejR07VmvWrFFERITq6+uVnp6u8vJyxcbGasKECSooKDAdEQBsjaIvAAA4LFRVVSk2NlbR0dGmowC24PP59loomTt3rk455RQNGDDgEKcC7Mfv98vr9SoyMrLL49XV1UpISOAiKyDE+vXr9eGHH8rv98vv9+vkk0/W8OHDTccCbGHnzp3697//rcTERA0bNky/+MUv9O6772ry5Mm64oor9K1vfct0RMBW/H6/JGnz5s361re+pccee0zHHHOM4VQADnf7Kl+tWLFCLS0tmj59+iFOBdjLSy+9pMcee0w//vGP5fF4tHXrVm3YsEH/+9//lJiYqD/+8Y+7TTEF+iK/3y+Hw7HbfmXlypXy+Xw69thjDSUD7GvixIlavXp18M+DBg1SaWmpHA6HcnJytGbNGnPhACAM9KaXy6ebAGDA0qVL9cknn+jII4/UySefbDoOYAtVVVWKiYlRVFSUqqur9dvf/lalpaWqq6vTNddco9NPP53pG+jzQku+7e3twW3C5XLpxRdfVFJSks4++2xT8QBb2Lp1q1wulwYNGiSv16tFixbpH//4hwoLC7Vy5Uq9/PLLOu+880zHBIyrrq7WQw89pJdeekkej0dDhgxRc3OzPvzwQ51++um64IILTEcEjPL7/XrwwQe1cuVK9e/fX16vV83NzdqxY4f++9//6ne/+x1FX/R5n332mYYMGaLk5GRJktPpVHt7u7KysnTppZfq8ccfp+gL4KBzOBxauXKlmpqaFBkZqfj4eGVmZio6OlpFRUVKSEgwHREw7rPPPtPUqVODn0d1lhV/8pOf6Prrr9cjjzyi+++/32REwLjOyfCd67a2NkVGRsrhcGj9+vVyu90UfYE9SEtL04MPPqizzz5bf/vb3zR48GD95Cc/UWJiovr37286HgAcVij6AsAhcOSRR+rNN99UWlqaHnvsMf3ud7/TSSedpDlz5uiKK67Q7bffbjoiYNypp56q+++/XyeffLKeffZZFRQU6MILL1Rtba3++Mc/asyYMcrMzDQdEzDqvffe02effaYTTzxRo0aNkhSY8itJEyZM0JYtW0zGA2zhH//4h/r3768rrrhCbrdbixcv1urVq/WPf/xDTz/9tFavXk3RF31eW1ub7rzzTu3YsUO33nqrtm3bJpfLpbvuuksvvfSSHnnkEYq+6PPWrl2rZcuWac6cOcrKytKXX36pyZMnSwpMqykpKVFDQ4Pi4+MNJwXMWbBggR544AHdeeedmj59umbOnKmIiAhJ0llnnaWFCxdym2MAB92DDz6ot956S8XFxUpKSlJ8fLwSEhJ07rnn6rvf/a7peIAtuN1ubdmyRX6/f7dhGl6vVwMHDjSUDLAPh8Oh/Px8vf322/L5fIqNjdWAAQN09NFH65JLLjEdD7Ctxx57TN///vf1l7/8RTNmzFB+fr5+9rOf6bPPPtOTTz5pOh4AHFYo+gLAIWBZltLS0iRJTz/9tD744AMNGDBATU1Nys3NpegLSEpOTg7eiuC1117Tn/70J+Xk5EiS3njjDZWUlFD0RZ/36aef6tlnn9XixYtVVVWlCRMm6JJLLlFeXp4SExNVVFRkOiJgXFRUlN5//31dccUVkqTzzz9fbrdbOTk5Ov/88/WHP/zBcELAvMLCQn322Wd6//33JUlNTU2aPn267rrrLl1wwQX68Y9/rJaWFkVHRxtOCpgzcuRIbd++XVlZWZKkwYMHy+l06qc//ani4uJ0wgknBC+4Avqqb37zm5ozZ46io6N177336qGHHtJFF12kuLg4LVmyRDNnzpTf7+9yZxIAOJAqKir05JNPBs+3t7S0aP369frvf/+rF154QQMGDNA555xjOiZg3I9//GPdeOONGjp0qHJycjR69GilpqaqsbFRpaWluvrqq01HBIybP3++FixYIElKSUmR3+/XW2+9pV/+8pe65ZZbdMMNN8jtpl4DfFVGRoZef/31Lo/97ne/M5QGAA5vHIkAwCHQ1tampqYmxcbGyu12a8CAAZKk2NhYWZZlOB1gD8nJyVq+fLlyc3N14oknau3atRo3bpxqa2vV0tIir9drOiJg3PXXX6/LL79c//vf/1ReXq7CwkLdeOONqq2tlcfjUUZGhumIgHFTpkwJnpSXpPr6etXW1kqSRo0apW3btpmKBtjGmDFj1NzcrLKyMqWnp6uwsFADBw5UeXm50tLSNHv2bJWVlWnEiBGmowLGREVFafz48frlL3+pH/zgB3ryySd10UUX6eijj9YjjzyiH/7wh0pKSjIdEzBqwoQJGjVqlGbNmqUf/vCHmjt3rv75z38qLi5OLpdL559/PiVfAAfVmjVrlJaWFjzfHh0drZycHOXk5Gjs2LG67777KPoCkiIjI/XYY4/po48+0vLly1VSUqKVK1cqISFBf/jDH4J3DgP6sieeeELXX3/9bnc4qqqq0umnn65jjz1Wxx13nKF0AAAAFH0B4JC4/PLLdd555+nnP/+5zjvvPN1666267LLLtHjxYo0ePdp0PMAWbrnlFv36179WSUmJ3G63fvWrX+m5556Tx+PRKaecokmTJpmOCNhCfHx88EOqlpYW3XXXXWpoaNDcuXODZUagLzvyyCMVHR2t6667Tscdd5yeeeYZ3XnnnZKk9PR0zZ8/33BCwB6OPfZY3XXXXXK73Xrrrbd0xx13KC0tTV6vlwIj0OHWW2/VCy+8oEGDBqlfv3564okndMopp+z2wS/Ql51zzjl68MEHNWfOHF111VW66qqrVF5ersTERMXGxpqOB+AwN3LkSI0cOVIPP/ywrrjiiuDdwiSpqKhIw4YNM5gOsBen06nc3Fzl5uaajgLYUktLi+Li4nZ7PCUlRW63m7seAQAA4xxWD0ZJ1tXVKSkpSbW1tV3eJAMAeu6RRx7Rgw8+qC+//FJSoKg1e/Zs3X///fzbij7Psiw5HA5t3bpVv/3tb7V8+XKlpKQoJSVFubm5uuyyy5SSkmI6JmAbnYfwDofDcBLAnkpKSvTss89q9erVOumkk3TDDTeYjgTYTmtrqxYsWKA33nhDZ5xxhs4//3xuQQnsQXNzs+rr64OTAjv5/X45nU5DqQD7aGxs1M9+9jP99re/ZZsAYMQbb7yhu+66S9u3b9egQYMUHx+v/v37y+/368ILL9SFF15oOiJgK37LkpNzisBunn/+ef3rX//ShAkTNG7cOCUnJysuLk4fffSRXnzxRc2fP1/p6emmYwIAgMNMb3q5FH0B4BBraGiQ1+uVx+MxHQUAcJihcALsunikra1NkZGRpuMAAA4TlmXJsiyOtQAAsKmioiIVFBRoy5Ytqqmp0YUXXqhRo0aZjgXYzmfb6hTldiotMUqJ0RGm4wC2MmfOHL3yyitqaGhQa2urysrKNHr0aP3pT3/SyJEjTccDbKm+xSu/LDnlUEI0QwQAoLco+gIAgLDl9/sl7ZpUyofpAICvo7P4C2DP/H6/HA4H2wkAAAAA9AHX//MzlVQ1K8Ll0IKrj2G6L7AHbW1tam9vV1xcnOkogO1dPneVdja2q19chJ677CjTcQAg7PSml8vlFAAAwFa+WuqldAIA+DrYjwD71nnsxe1bgX2zLEs+v19ul8t0FMC2fH5LDkfg/yNcXLALAIDdWJalirpWSdKA+CjeAwJ7ERkZyZ3CAACA7XC2DQAOAa/Xp3avT16vz3QUwLZ8Pr+8Xp96cLMBoE/z+vymIwC2F7ovqW9qM5gECA/FOxq1oqjKdAzA1jZt3anfPv+OXnunwHQUwLaKyut0+5wVyr7un1r3ZY3pOAD6mM7zJZZlcX4R2Iu6Fq9avIFtZWBClOE0gL0FLva05PezTwEAAPbARF8AOAQeen6p6htblBAXrR9/50TTcQBb+nxTmf7x5irV1DfrwpMnasaxI01HAmxpyfJNWpD/hUrKqvXQzacpJzvNdCTAdhwOh3785Lta8N5Gle5s0PZ/Xqf4GKZwAF/V5vXrtIfeVXltq4amxGjJbdNMRwJs6dONZcq98i+SpNOPG62zp48znAiwp/+s2aan/rNBkvR8fqF+9e1JhhMB6CsaW7065mf/UeaAeJ06YaBuPoXzisCelNe3BtcDEzhPAuzNp6V1en9zteIiXZqWlaIR/WJNRwIAAGCiLwAcCk+8/IEeeu5tPfHyB6ajALa1cUulfv/8Uj3zynKt3lBqOg5gW2uLK/XCm5/q/c+2auPWnabjALbV0NymL3c0yLKkzzazrQB7Eul2alhK4MOqrVXNKq1uNpwIsKfxWQM1KDVRkvTfjzaprrHFcCLAnr51QqYi3YGPHOa/W6g27mwF4BBZt61e7T5L68vqtb2utftvAPqoipDtY2AiE32BvSmqatJb63folc8qVMZ+BQAA2ARFXwAAYAvJibuuiK6t54NzYG8y0jzBdUl5rbkggM3lZKUG12uKthtMAtjb5MyU4Hp5UZXBJIB9OZ1OnTN9rCSprd2nNz7cYDgRYE8pCVE6Y9JQSdKOulYtWcVFvAAOjYLSuuB67KBEg0kAe6to2FVYTEug6AvsTVPrrgvW4iJdBpMAAADsQtEXAADYQlJCdHBdU880OWBvMtI9wXVJeY2xHIDddSn6FlYaTALYW27WrqLvCoq+wF6dmzcuuF6UX2AwCWBvs2dkB9dzl24ymARAX7J2W0jRdzBFX2Bvukz0pegL7FVjG0VfAABgPxR9AQCALXjiY4Jrir7A3oUWfYvLaozlAOxuQkZ/OZ0OSdJqir7AXk0YkqSYiMDpoeWFVbIsy3AiwJ6OzxmufkmBu5As+XCDmlvbDScC7GnmhDQNTglsK2+u3qay6ibDiQD0BQVfBu545HRIo9MTDKcB7KuinqIv0BMNoUXfKIq+AADAHij6AgAAW/AkxgbXtRR9gb3qnxSr2OgISVJxebXhNIB9xUZHaNRgjySpoGSn2tp9+/4GoI+KdDt1dEaypMB0p5KdFLKAPXG7XTpr2hhJUmNzm/67gkmlwJ64nE5dOj1LkuS3LP393SLDiQAc7tq8fm2qaJAkZQ6IVwyTF4G9Ku8o+ka7nUqMdhtOA9hXY6s3uI6PZFsBAAD2QNEXAADYQlJ8dHBdTdEX2CuHw6GMNI8kqaS8Vn4/kxeBvcnJGiBJavf69cWWKsNpAPuanJkSXC8vZFsB9mZW3tjgelF+gcEkgL19Oy8ruJ67dBPT4gEcVBsr6tXuC/w7M3ZwouE0gH35LUvb69skSQMTo+RwOAwnAuyrMWSibywXkAAAAJug6AsAAGwhwu1SfGzgdmFM9AX2LSPdI0lqa/epvKrBbBjAxiZmpQbXa4oqDSYB7C03tOhbRNEX2JuZk7KU0PGe5d/vrVO7l2nxwJ6MGJigaWMHSpIKy+v14frthhMBOJytLa0LrsdR9AX2qqqpXd6OgQEDE6IMpwHsrbPoGxPhlMtJKR4AANgDRV8AAGAbnVN9axoo+gL7Mrxjoq8kFZfVGMsB2F1o0Xd1IUVfYG/GDkpQfFTgVpQriqqYvAjsRXRUhE47brSkwF1I3l212XAiwL5mz8gOrucuLTSYBMDhbm1pfXDNRF9g7yrqWoPrNIq+wD51Fn3jmOYLAABshKIvAACwDU9CjCSppo6iL7AvnRN9JamkvNpcEMDmjswMLfoySQ7YG7fLqWNGeCRJVY3t2lTBtHhgb2bljQ2uF+UXGEwC2Ns5k4cpKTZCkrRwebHqmtoMJwJwuCoorQ2uxw6i6AvsTUX9rqIvE32BfWts9UqS4iLdhpMAAADsQtEXAADYRmfRt7m1Xa1tXsNpAPtioi/QMykJ0Ro2IEGS9NnmHfL7mVIK7E1uZr/genkRF5EAe3PqlFGK6viw99V31srv9xtOBNhTTKRb508dIUlqavVp4fISw4kAHI58fkvrtgUm+g5JjgleYABgd+UUfYEeafP61eYLnEOMi2KiLwAAsA+KvgAAwDY6i76SVFPPVF9gb7pO9K3d+xMBKCcrMNW3obldhRTjgb2anJkcXC8vqjKYBLC3+NgonTw5W5JUvrNBywu2Gk4E2NfsmdnB9dylhQaTADhclexoVFPH7dXHDmaaL7Av20OLvokUfYG9aezYr0hSXCRFXwAAYB8UfQEAgG2EFn1rGyj6AnuTETrRt7zGWA4gHByVNSC4Xl1YaTAJYG+j0xKUFBOYgPbR5ir5mIAN7NWsvHHB9aL8tQaTAPZ21IgUjRvqkSSt2Fip9aVcpAjgwFpbWhdcjxtC0RfYl9CJvmlM9AX2KrToGx/lNpgEAACgK4q+AADANpKY6Av0SFJ8tJIToiVJxUwoBfapc6KvJK2h6AvsldPpUG7HVN+6Zq/Wl9UbTgTY1xknHCGXK3BadVF+gSyLYjywJw6HQ5fN2DXV9/mlmwymAXA4Ci36jh1E0RfYl4q6QNE3IcqlWKaUAnvV2OYNrtlWAACAnVD0BQAAtuGh6Av0WOdU3y+318rr85sNA9hYTuauou/qwu0GkwD2NzkzJbheVlRlMAlgbymJsco7aoQkqXhbtT7dWGY4EWBf3zp+hCI6ivHz3y1Su5f3LgAOnILQou9gir7A3nh9fu1obJMkDWSaL7BPja27JvrGUfQFAAA2QtEXAADYBkVfoOeGdRR9fX5LX27nFrjA3gzqF6fUpMD+ZU1hJVMXgX3IzdpV9F1B0RfYp1kzxgXXi/LXGkwC2Fu/xGidMWmIJKmyrkVLVn1pOBGAw4VlWcGJvv3iIzUgkfIisDeVjW3yd5wOoegL7Ftj266ibzxFXwAAYCMUfQEAgG10KfrWUfQF9iUj3RNcl5RT9AX2xuFwKCcrMNV3R12LSnc2GE4E2Fdmapz6J0RKkj7eXK12JsYDe3X2tDFyOBySpEX5BYbTAPY2e0Z2cD03v9BgEgCHk7LaFlU3tUsKTPPt3C8D2N32+rbgmqIvsG8Nrd7gOi7KbTAJAABAVxR9AQCAbYQWfWsbKPoC+5LRMdFXkorLaozlAMLBxI6irxSY6gtgzxwOh3JHBKb6NrX5ghPSAOwuvX+icscPlSSt3bxdG0rYvwB7840j0zUoJVaS9ObqUpVXNxlOBOBwEHqsOm5wosEkgP2V17cG1wOZfg3sU+hE3zgm+gIAABuh6AsAAGyjy0Tfeoq+wL6ETvQtLq82FwQIAzkUfYEem5yVElwvK6oymASwv1l544LrRe+sNZgEsDeX06lLp2dKknx+S39/t8hwIgCHg9Ci71iKvsA+VdTtKvqmMdEX2Kcmir4AAMCmKPoCAADbSIqn6Av0VJeiLxN9gX2amDkguF5F0RfYp9zMXUXf5YUUfYF9mTV9bHC9KL/AYBLA/r6dlx1cz80vlGVZBtMAOBwUUPQFeqwidKIvRV9gnxpaQ4q+URR9AQCAfVD0BQAAtuFJpOgL9NSwgZ7gmqIvsG+Z6UlKiImQxERfoDtDU2KU7omWJK0qqVGb1284EWBfIwanKGdkuiTpky9KtaW8xmwgwMYyByZo2tiBkqRNZXVatp5jMgBfT+dE37gol4alxBpOA9hbeUjRdwBFX2CfGtu8wXV8pNtgEgAAgK4o+gIAANvwMNEX6LHY6AgNTI6TJJVU1BpOA9ib0+nQhMxUSdLWynrtrGMfA+yNw+EITvVt9fq1ZkuN2UCAzc3K2zXV97V31xpMAtjfZV2m+m4ymARAuKtubFNZTYskaeygRDmdDsOJAHvb3lH0TY6NUJSbegCwL41tIRN9I5noCwAA7IMjeQAAYBsJcVHBE/MUfYHuDU/3SJLKdtSrJWTSAIDdTcxKDa4/LdphMAlgf51FX0laXlRlMAlgf7PyxgXXi/Ip+gL7cs7kYUrsuMvCwmUlqm9uN5wIQLjqnOYrSeOGJBpMAthfq9evqqbAPncg03yBbnUWfZ0OKTqCOg0AALAPjkwAAIBtOByO4FTf2oYWw2kA+8tI8wTX3Coa2LeJmbuKvqsLuVU0sC+TKfoCPTZmxACNHNpfkvT+mmJtr24wnAiwr9got84/LkOS1Njq1cJlxUbzAAhfoUXfsYMo+gL70jnNV5LSKPoC3WpsDRR94yJdcjiYGA8AAOyDoi8AALCVpIRA0bemrslwEsD+hocUfYsp+gL7lBMy0XdNEUVfYF/SPdEa1i9WkrRma62aQ25bCaArh8OhWXljJUl+v6V/v/uF4USAvc2ekR1cz80vNJgEQDhbuy2k6DuYoi+wLxUhRd8BCZEGkwDhoaHjznlxkW7DSQAAALqi6AsAAGwlubPo29Aiy7IMpwHsLSPdE1wXl9UYywGEgzHDUhTpDrwFXr1pu+E0gP3ldkz19fosrSqpMRsGsLlZM8YF14vy1xpMAtjf0Zn9NHaoR5K0fEOl1pfWmg0EICwVfBko+ka6ncoaGG84DWBv5Uz0BXrMsiw1dVzsHBflMpwGAACgK4q+AADAVjon+vp8fjU0tXbzbKBvCy36ljDRF9inCLdL44b3kyRtKK1WY0u74USAveVmJgfXy4qqDCYB7G/SEYM1eECSJOl/HxeqtqHFcCLAvhwOhy7Lywr+eW7+JoNpAISjxlavNu9olCSNSotXhIuPOoF92R5S9B1I0RfYp+Z2v/wd82fiIin6AgAAe+HdLwAAsBVPR9FXCkz1BbB3GWme4JqJvkD3crJSJUmWJX22eYfhNIC9Te6Y6CtJKyj6AvvkcDg0K2+sJKnd69PrH6w3nAiwt2+dkBks5s1/t0jtXr/hRADCyfqyenXeBGzs4ESzYYAwEDrRd2AiRV9gXxo7pvlKUnyU22ASAACA3VH0BQAAthJa9K2tbzaYBLC/IQOS5HQ6JEnFTPQFujUxa0Bwvaaw0mASwP76J0Qpa0CcJKmgtE4NLV7DiQB7OzdvXHC9KL/AYBLA/vonRuv0SUMkSdtrW/Tm6lLDiQCEk4LSuuB6HEVfoFsVdYGir9MhpcZFGk4D2Ftj265zH0z0BQAAdkPRFwAA2EpSQnRwXVPXZDAJYH+RES4N6p8gSSphoi/Qrc6JvpK0poiiL9Cd3I6pvj6/pU+Kqw2nAeztuCOHK9UTKMe/uWyDmlraDCcC7G12XnZwPTd/k8EkAMLN2pCiLxN9ge5VdEz07RcXKbeLagCwL42tuyb6UvQFAAB2w9E8AACwleSE2OC6pqHFYBIgPGSkeSRJO+uaVd/Uuu8nA33chBH95QgMwdbqTdvNhgHCwJSslOB6WVGVwSSA/blcTp01bYwkqamlXf9ZQXER2JcTc9I1KCXw/n/JqlKVV3OhL4Ce6Zzo63RIR6RT9AX2panNp/qO4mJaQpThNID9NbaFFH2jKPoCAAB7oegLAABshYm+QO9kpHuC65LyGmM5gHAQFx2hUYOTJUkFJTvV7vV18x1A33bMiJRgOX55IUVfoDuz8sYG14vyCwwmAezP5XTqkmmZkgKT4+e/t9lwIgDhoM3r18byeknSiNQ4xTBtEdinzmm+kjSQoi/QrYZWb3AdF+k2mAQAAGB3FH0BAICteJjoC/RKaNG3uKzGWA4gXORkpUoKfED8xRaKi8C+eGIjdER6giRpfXm9apraDCcC7G3GpCwlxgUKFIvfX6e2dm833wH0bd/Oywqu5y7dJMuyDKYBEA42VTSo3Rf4t2LcYKb5At0pDy36JlL0BboTOtE3notJAACAzVD0BQAAtuIJmehbW99sMAkQHjLSPME1RV+gexOzBgTXaworDSYBwsPkzBRJkmVJHxVVG04D2FtUpFunH3eEJKmmvkXvrGRCKbAvWWmJOmHMQEnSxrI6Ld/AsRmAfVtbWhdcjx2cZDAJEB4q6nYVfdOY6At0K7ToGxtF0RcAANgLRV8AAGArSfExwXU1RV+gW8NDi77lNcZyAOFiYsdEX0laXUSZBOjOlI6iryQtL2IKNtCdWXljg+tF+QUGkwDh4bKQqb7P528ymARAOFi7bVfRl4m+QPcqQib6DqDoC3SrsXVX0TeOib4AAMBmKPoCAABbSU6MDa6Z6At0b3i6J7guYaIv0K2c0KIvE32Bbk3KSJbL6ZBE0RfoiVOmjFJ0pFuS9Nq7X8jn8xtOBNjbrNzhSoiJkCQt/LBEDS3thhMBsLOCL3cVfccMTjCYBAgP5fVM9AV6o7HNG1zHd7yvAwAAsAuKvgAAwFaS4qOD6xqKvkC3BvVLUIQ7cFhfwkRfoFspCdEamhr4QPjTokr5/ZbhRIC9xUe7g9PSCrc3qjLkg2IAu4uLidTJU0ZKkiqqGrTs8y2GEwH2Fhvl1vlTMyRJja1eLVxWYjYQANvy+y2tKwsUfQcnx8gTG2k4EWB/2zvev7mdDqXERRhOA9hfY1vIRN8oJvoCAAB7oegLAABsxZMQE1xT9AW653I5NWygR5JUXF4jy6K0CHSnc6pvQ3O7ispqDacB7C83MyW4/oipvkC3zs0bF1wvyl9rMAkQHmbPyAqu5y7dZDAJADsr2dkUvKX62I4L0QDsnWVZqugo+g5IiJTT4TCcCLC/zv2MJMVFUvQFAAD2QtEXAADYSnRUhKKjArdEqqXoC/RIRrpHklTf1KaqOrYboDtHdRR9JWl10XaDSYDwEFr0XU7RF+jW6ccdIbcrcNp1UX4BF2IB3ZiU1V9jhiRJkpZtqNSGUi7EArC7taV1wfU4ir5At+pavGpu90uS0hKiDKcBwkPnRN9Il0MRLqo0AADAXjg6AQAAtuOJD0z1raboC/TI8IFJwXVxeY25IECYyAkp+q4prDSYBAgPRw33yO0KTH9aXlRtOA1gf8mJMco7OlOStKW8Rqs3bDOcCLA3h8Ohy/Kyg3+e+06hwTQA7KogpOjLRF+ge53TfCVpAEVfoEca2rySpLiOYTQAAAB2QtEXAADYjicxUPStbaDoC/RE50RfSSqh6At0K7Tou5qiL9CtmEiXcoYGLirZsrNJZTUthhMB9jcrb2xwvSh/rcEkQHi4eFpm8KKSv79TqHav33AiAHZTEDLtm6Iv0L2K+rbgeiBFX6BHOif6xke6DCcBAADYHUVfAABgO0kdE33rG1vl9foMpwHsb3hI0be4rMZYDiBcDO4Xr/6J0ZICE325pTrQvdzMlOB6RVGVwSRAeDhr2hg5HIHS4qL8AsNpAPvrnxit048eKknaXtuit9aUGk4EwE4sy9Lajom+KXGRGphIaRHoTuhE3zSKvkC3fH5LLe2Bi81iKfoCAAAbougLAABsJ7ljoq8k1TYwMQ7oTkZacnBdQtEX6JbD4QhO9a2sbda2nY2GEwH2NyVrV9F3OUVfoFvp/RM1ZcIwSdK64kqtK95uOBFgf7NnZAXXc5duMpgEgN2U17aourFdUmCab+fFNAD2LrToSzke6F7nNF9Jio9yG0wCAACwZxR9AQCA7XRO9JWk2oZmg0mA8JAROtG3vMZYDiCcTMwaEFyvKao0mAQID0cO9SjKHTiNtKywiknYQA/MyhsbXL+av9ZgEiA8nHjkIKUnB84HvLGqVBU1nA8AENA5zVeSxg1JNJgECB/ldUz0BXqjsc0bXMcx0RcAANgQRV8AAGA7noRdRd/qOj7YA7qT6olVbHSEJKmYib5Aj3RO9JWkNYUUfYHuRLqdOjrDIykwUW1rFcdoQHdmTd9V9F2UX2AwCRAe3C6nLpkWmOrr81ua/26R4UQA7CK06Dt2EEVfoCc6J/pGuZ1KjGY6KdCdhtZdE30p+gIAADui6AsAAGwntOjLRF+gew6HQ8MHJkmStlTUMmUR6IGJIUXf1RR9gR6ZnJkSXC8vqjKYBAgPGYNSNHFUuiRp5fptKimvNpwIsL/L8rKC67n5m3hvA0CStHZbfXA9bjBFX6A7fsvS9oZA0TctIUoOh8NwIsD+GttCir5RFH0BAID9UPQFAAC2kxRS9K2pp+gL9ERGerIkqaXNq/KqBsNpAPvLSvcoPiYwCXt14XbDaYDwMCWk6LuikKIv0BOz8sYF16/mrzWYBAgPWemJOv6IAZKkDdvqtGIjF2QBkAq+rJUUKF4N6xdrOA1gf9VN7Wr3BS6WGZgQZTgNEB6aQou+kUzBBgAA9kPRFwAA2E4yRV+g14ane4Lr4rIaYzmAcOF0OjRhRH9J0pbt9aqqbzGcCLC/cYMTg1NtlhVVMWUR6IHQou8iir5Aj1w2Izu4fn5pocEkAOygurFN22oC79fGDEqU08lkUqA7FfWtwfXARIq+QE80tHqD63gm+gIAABui6AsAAGyHib5A72WkeYJrir5Az+RkpgbXnxYxLQ7ojtvl1KSMwAT5nQ1tKtzeaDgRYH9HZKRq1LDAhSUffFqiCu68AHRr1uRhSui488LCZcVqaGk3nAiASV9sqwuuxw5ONJgECB/ldSFFXyb6Aj3S2GWiL0VfAABgPxR9AQCA7XjiKfoCvTU8LSm4LimvMRcECCNHZQ8IrlcXUvQFeiI3MyW4XlFUZTAJEB4cDkdwqq9lWfrXu0z1BboTFx2hb04ZLklqaPHqlWUlhhMBMGlt6a6i7ziKvkCPdJnomxBpMAkQPhpbKfoCAAB7o+gLAABsx5O4q+hbS9EX6JGMdE9wTdEX6JnQib5rKPoCPRJa9F1O0RfokVl5Y4PrV9+h6Av0xOwZ2cH13PxCg0kAmBZa9GWiL9AzXYu+TPQFeqKxzRtcx0W6DSYBAADYM4q+AADAdjwJoRN9WwwmAcJHRnpycF1cVmMuCBBGxgxLUYQ78LZ4deF2w2mA8DA6PUGJMYEPvFYUVcvvtwwnAuzv6CMGa8jAwN0X3v64iLuWAD1wTHZ/HTE4sN18uH67Nm6rNZwIgCkFHUXfCJdD2QPjDacBwkNo0TeNoi/QI41tIRN9o5joCwAA7IeiLwAAsJ2uRd8mg0mA8OGJj5YnPlqSVMxEX6BHIiNcGje8nyRpQ2mNmlraDScC7M/ldOjYEYGpvrXN7VpfXm84EWB/DodDs6aPkyS1e316/YP1hhMB9udwOHQZU32BPq+p1auiykZJ0qi0BEW4+FgT6InOom98lEtxUUwmBXqioXVX0Tc+kqIvAACwH94RAwAA20mMiw6umegL9NzwtMDEq60VtfL6/IbTAOEhJzNVkuT3W/qseIfhNEB4yM1MCa6XF1UZTAKEj3NnjA2uF+WvNZgECB8XnzBCbpdDkvT3d4t4jwP0QevK6mV13EBi7OBEs2GAMOHzW6psaJMkDWSaL9BjnRN9HZJiKPoCAAAbougLAABsx+VyBsu+TPQFem54ukdS4IR+aWWd2TBAmJiYnRpcrymsNJgECB+5WbuKviso+gI9MnXCcKV64iRJby7boKaWNsOJAPtLTYrRaUcPkSRV1DTrrdWlhhMBONTWlu46tzGOoi/QI5UNbfJ3FOQp+gI919jmlRQo+TodDsNpAAAAdkfRFwAA2JInMUaSVMtEX6DHMtKSg+uS8hpzQYAwkpM5ILim6Av0TPaAOPWLi5QkfbS5mgmLQA+4XE6dPX2MJKm5tV1vLd9oOBEQHmbnZQfXc/MLDSYBYMLabbuKvkz0BXqmor41uKboC/RcY2tgom8803wBAIBNUfQFAAC25IkPFH1rGpoNJwHCR0bHRF9JKi6rMZYDCCcTRvRT55CO1RR9gR5xOByanBmY6tvY6tPabfWGEwHhYVbeuOB6Uf5ag0mA8HFSziCleQLnB95Y9aW213KOAOhLCr4MFH0dDumI9ATDaYDwEFr0TaPoC/SIZVlqbAsUfeMo+gIAAJui6AsAAGwpKSFaktTa5lVzS7vhNEB4yEjzBNcUfYGeiY+J1MjBgWnYnxfvULvXZzgREB4mZ+6aIr+8qMpgEiB8zJiUqcS4QNli8fvr1NbuNZwIsD+3y6lLpmdKkrw+S/PfLTKcCMCh0u7za0N54IKyzNQ4xUa5DScCwkOXib6JFH2BnmjzWfL6LUlSXBRFXwAAYE8UfQEAgC15EmKDa6b6Aj0zPC0puC4urzEXBAgzOZmpkqQ2r1/rtlYbTgOEh9yslOB6BUVfoEciI9w64/gjJEm1DS3KX7nZcCIgPFyWlx1cz80vlGVZBtMAOFQ2VTSo3RfY3scOTjScBggf5XUhRV8m+gI90ti66yLMuEguLAEAAPZE0RcAANiSp2OiryTV1DUZTAKEj+FM9AX2y8Ts1OB6TWGlwSRA+BjeL1ZpSYEPjT8prlab1284ERAeZuWNC64X5RcYTAKEj+z0RB13xABJ0vrSWn20cYfhRAAOhbWldcH1OIq+QI+FTvQdEB9pMAkQPhradt3hKy6Sib4AAMCeKPoCAABb6jrRt8VgEiB8xMVEakBynCSphIm+QI9NzNxV9F1dRNEX6AmHw6HJmYGpvi3tfn26tdZwIiA8nJw7UjFREZKk195ZK5+PkjzQE6FTfZ/P32QwCYBDJbToy0RfoOe2dxR9PTERio6gsAj0RGNo0TeK7QYAANgTRV8AAGBLoRN9a+ubDSYBwkvnVN+ynfVqbfPu+8kAJEk5WSFF303bDSYBwktuR9FXklYUVRlMAoSPuJhInZw7UpK0vbpRH362xXAiIDycmztM8dGB2ygv+LBYjS3thhMBONgKKPoCvdbm9WtnU2AfmZbANF+gpxpbQyf6ug0mAQAA2DuKvgAAwJaSEmKC62qKvkCPZaR7JEmWJW2pYLoi0BP9EmM0JDVekvRpUaX8fstwIiA8TA4p+i6n6Av02LkzxgXXi/ILDCYBwkdcdIS+OTVDktTQ4tUryynJA4czv9/SF9sCRd9Bnmh5YiksAj2xvaE1uB6YGGUwCRBeGkMGZsRFMtEXAADYE0VfAABgS8kJscE1E32BnhuelhRcF5fVmAsChJmJmYGpvvXN7dpcTkke6InByTEamhK4OGv1lhq1tPu6+Q4AknT6caPldgVOyy7KXyvL4gIToCdm52UH13PzNxlMAuBg27KzKThdkWm+QM+V14UUfeMp+gI91di263xGfBRFXwAAYE8UfQEAgC0lJUQH1zV1FH2BnspISw6ui8trzAUBwszE7AHB9erCSoNJgPDSOdW33WdpVUmN2TBAmPAkxGjmMVmSpK0VNVq1fpvhREB4OHZkf40eHLiw8YN127WprM5wIgAHS0Hpru17HEVfoMcq6pnoC+yPhtZdRV8m+gIAALui6AsAAGzJEzLRt6aBoi/QUxnpnuC6hKIv0GM5HRN9JWlNEUVfoKdyO4q+krS8qMpgEiC8zMobG1wvyi8wmAQIHw6HQ5flZQX/zFRf4PC1dtuuoi8TfYGeq6hvC64HJlD0BXqqsc0bXMdFuQ0mAQAA2DuKvgAAwJY8IRN9a+sp+gI9FVr0LS6rMZYDCDc5WSFFXyb6Aj0WWvRdQdEX6LGzpo2Vw+GQJC3KX2s4DRA+Lp6WKbcrsO38/Z0ieX1+w4kAHAxrSyn6AvsjdKJvGkVfoMea2pjoCwAA7I+iLwAAsKWk+JjgupqiL9BjQwckqaMzomIm+gI9NqR/vPolBi4yWV24XZZlGU4EhIfUxChlpsZJkj77sk6Nrd5uvgOAJA1MiddxRw6XJK0vqdS64u2GEwHhYUBSjE47aogkqbymWf9Zs81wIgAHmmVZKvgyUPRNjotQWlJ0N98BoFNn0dfpkPrHRxpOA4SPhlaKvgAAwP4o+gIAAFtKTtxV9GWiL9BzkREuDeofmHZTwkRfoMccDodyMgNTfbfXNKusqtFwIiB8dE719fktfVJcbTgNED5m5Y0NrhflFxhMAoSXy/Kyguvn8zcZTALgYKioa1VVY5ukwDTfzgn4ALrXWfTtFxepCBc1AKCnGkMm+sZHuQ0mAQAA2DuO8AEAgC3FRkfK3XEysoaiL9ArGekeSdKO2iY1NLeZDQOEkZys1OB6TWGlwSRAeJmcmRxcLy+qMpgECC/ndCn6rjWYBAgvJ08crIGewMXBb6z8UpW1nDMADidrS+uC63GDEg0mAcJLU5tPdS2BO6wMTIgynAYIL513J3I7HYp0cYEJAACwJ4q+AADAlhwOR3CqL0VfoHcy0jzBdUl5jbEcQLiZmDUguF5TRNEX6KnJHRN9JWlFERN9gZ4anpaso0cPkiStWr9NJWVsP0BPuF1OXTItU5Lk9Vma/95mw4kAHEhdir5DkgwmAcLL9o5pvhJFX6C3Oif6xkW6mCQPAABsi6IvAACwraT4QNG3toGiL9AbnRN9Jam4rMZYDiDcTAyZ6Luaib5AjyXHRWp0eoIkae22OtU2txtOBISPWXnjgmum+gI9d1leVnA9d+kmWZZlMA2AAym06Dt2MBN9gZ4qDyn6plH0BXolWPSNchlOAgAAsHcUfQEAgG0lJXRO9G2R3+83nAYIH6ETfSn6Aj2XPcijuOgISRR9gd7KzUyWJFmW9NFmppICPTVrRmjRt8BgEiC8jByUpKmjA3djWFdaq4837TCcCMCBUtBR9I2Lcml4v1jDaYDwURE60TeRoi/QU37LUlPIRF8AAAC7ougLAABsK7mj6GtZluqbWrt5NoBOw0OLvuU1xnIA4cbpdGjCiP6SpJKKOlXXtxhOBISP3MyU4HpFYZXBJEB4GT08VUdkBCbKf/jZFpXvrDecCAgfoVN9n1+6yWASAAdKTVObSqsDd/Y6Ij1RTie3Twd6qqIupOgbH2kwCRBemtt86rw3RFyk22gWAACAfaHoCwAAbKtzoq8k1dQ1G0wChJfh6Z7guoSiL9ArOVmpwfWnRUz1BXrqmBHJ6uxhLC+i6Av0xqy8wFRfy7L0r3e/MJwGCB/nThmu+OhAGWPBshI1trQbTgTg6/qidNcFL2MHJxpMAoSfigYm+gL7o6Fjmq/ERF8AAGBvFH0BAIBtJYcWfRso+gI9Nbh/giLcgUP9krIas2GAMHNUSNF3NUVfoMcSoiM0rqOMsbGiQTsbuBsD0FOz8sYG14vyCwwmAcJLfHSEzpuSIUmqb27XohVbzAYC8LUVlNYF1+Mo+gK90jnR1+10KCWWib5ATzW27ir6xkdR9AUAAPZF0RcAANhW6ETf2nqKvkBPuVxODR2QJEkqLq+RZVndfAeATqETfdcUUvQFeiM3MyW4XlFUbTAJEF4mjhqkYWkeSdLST4pUzd1MgB6bPSMruJ67dJPBJAAOhLXbdhV9megL9JxlWSqvDxR9B8RHytV5uxUA3WrsMtHXbTAJAADAvlH0BQAAthVa9OXDbqB3MtI9kqS6xlZV17eYDQOEkbHD+gUnYq+m6Av0yuSQou/yoiqDSYDw4nA4NCtvnCTJ6/Pr9Q/WGU4EhI/JI1M1alCgDPj+uu0qLKvr5jsA2FnBl7WSpAiXQ9kD4w2nAcJHfatXze1+SdLAxCjDaYDw0tjmDa5jI5noCwAA7IuiLwAAsK3k0Im+DRR9gd4Y3jEVTpKKy2qM5QDCTWSES2OHBcqK67+sVlNLu+FEQPg4OsMjd8fkqBUUfYFemZU3Nrh+Jb/AYBIgvDgcDl2Wlx3889z8QoNpAHwdzW0+ba5slCSNTEtQpJuPMIGequiY5itJA+Ip+gK9ETrRNz6Koi8AALAv3iUDAADbYqIvsP8yQoq+JeU1xnIA4Sgna4Akye+39HnxTsNpgPARG+nWkUOTJEnFO5pUUctEeaCnpowfpoEpgcmFby3bqMbmNsOJgPBx8bTM4C3K//5uobw+v+FEAPbHurI6+a3AeuzgRLNhgDBTUb/r2DGNib5ArzS07ir6xjHRFwAA2BhFXwAAYFtM9AX23/B0T3BdTNEX6JWJWanB9ZqiSoNJgPCTm5kSXC9nqi/QYy6XU2dPGyNJamnz6s1lGwwnAsLHQE+MTjtqsCSprLpZ//10m+FEAPbH2tK64HocRV+gVyrqdk30HZhA0RfojcY2b3AdF+U2mAQAAGDfKPoCAADbCp3oW1PPRDigNzJCir4lZTXGcgDhKCe06FtI0RfojckUfYH9NitvXHC9KH+twSRA+LlsRnZw/fzSQoNJAOyvtaX1wTVFX6B3yusp+gL7q7GNib4AACA8UPQFAAC25YkPLfo2GUwChJ/haZ7gmom+QO8cOaK/HIG7P2s1E32BXpk4LEmR7sDppuWFFH2B3ph+9Ah5EqIlSa9/sE6tIZOlAOzbKRMHa6AncA7h9ZVbtaOOi4WBcNM50dfhkEanJxhOA4SXCoq+wH5rbKXoCwAAwgNFXwAAYFuexF1F39r6ZoNJgPAzMDlOMR23GisuqzacBggv8TGRyh7kkSR9vnmH2r2+fX8DgKCoCJeOGuaRJG2radGXVVysBfRUZIRbZxx/hCSprrFVSz9hKinQU26XUxdPy5QkeX2W5r9bZDgRgN5o9/m1rixQ9B3RP45bpwO91Fn0jXI75Ylh+wF6I3Sibzz7HwAAYGMUfQEAgG15EnYVfasp+gK94nA4glN9S8prZVmW2UBAmMnJSpUktbb7tP5LyvJAb+RmpgTXy4uY6gv0xqy8ccH1ovy1BpMA4eey6VnB9fNLN/EeCAgjhRUNavcFttmxgxMNpwHCi2VZ2t5R9B2YECVH5y2KAPRIQ+uuO6nEMtEXAADYGEVfAABgWxFul+JiIiUx0RfYHxkdRd+WNq8qqhvNhgHCzMSsAcH1msJKg0mA8JObFVr0pSgP9MZJk7MVGx0hSXrt3bXy+fyGEwHhY9TgJE0ZFbhYa11prVYW7jScCEBPrS2tC67HUfQFeqW6qV1tHUX5gQmRhtMA4aepY6JvdIRTbidFeQAAYF8UfQEAgK0ldUz1rWloMZwECD/D0z3BdXFZjbEcQDia2DHRV5JWU/QFemX8kETFdEzBWV5YxURFoBdioyN1ypRRkqQdNU364NMSw4mA8HLZjOzg+rmlmwwmAdAba7ftKvoy0RfonfKOab5SYKIvgN5p7Cj6xjHNFwAA2BxFXwAAYGue+GhJUk1dk+EkQPgZ3jHRV5KKy5ioCPRGTuauou+aIoq+QG9EuJyalOGRJFXWt6p4B8dxQG/MyhsXXL+SX2AwCRB+zs0drrgotyTp5Q+L1RRyK2YA9lVQStEX2F8VFH2Br6Wh43iRoi8AALA7ir4AAMDWPAmxkqSmlna1tfMBHdAbGSFF35LyGmM5gHDUPylGg/vHS5LWFFbK72ciKdAbuZkpwfXywiqDSYDwc/pxoxXhDnzIvCh/LVOxgV5IiInQeVOHS5Lqm9u1aDlTsQG78/stre0o+qZ7opUcF2k4ERBeKPoC+6/d51ebL/B+Kz7SbTgNAADAvlH0BQAAtuZJiA6ua+qbDSYBwk9Guie4LimvNRcECFMTO6b61jW1qbiCbQjojdCi77Iiir5AbyTFR+sbx2RJkkq31+qTdaWGEwHhZXZednA9N7/QYBIAPbGlqkmNrYHbpjPNF+i9ivq24DotkaIv0BuNbb7gOjaKib4AAMDeKPoCAABb65zoK0m19S0GkwDhJ7ToW1xWYywHEK5yslKD69WFlQaTAOFnzKBEJUQHpuF8VFTFVGygl2bljQ2uFy0tMJgECD+5o1I1Mj1QFnzviwoVltcZTgRgXzqn+UrS2EEUfYHeqqhjoi+wvzovNJGk+EiKvgAAwN4o+gIAAFtLSogJrmsamOgL9EZyQoyS4gIn+IvLa8yGAcLQxJCi7xqKvkCvuJwOHTMiWZJU3dSujRUNhhMB4eXMaWPkdDokSa/kF8iyKMsDPeVwOHTZjF1Tfecx1RewtdCi77ghFH2B3iqvDxR94yJdio9yG04DhJfQib5xkWw/AADA3ij6AgAAW/MkRAfXNXUUfYHeGp7mkSRtraiVz+c3GwYIMzlZA4LrNUUUfYHempKZElyvKKoymAQIPwOS43V8ToYkadPWnfpi83azgYAwc8m0TLk6yvIvvFMon5/3QoBddZnoO5iiL9AbPr+lyoZA0ZdpvkDvNbR5g+u4KCb6AgAAe6PoCwAAbM2TEBtcM9EX6L3h6R5Jktfn15eV3LIW6I2hqfFK6bjgZPUmir5Ab00OKfouo+gL9NqsvLHB9aL8AoNJgPAz0BOjU48aLEkqq27W/z4tM5wIwJ5YlqWCjqJvcmyE0pOiu/kOAKF2NLbJ33HjB4q+QO81tYZO9KXoCwAA7I2iLwAAsLWuE32bDCYBwlNGx0RfSdpSXmsuCBCGHA6HcjJTJUkVNU0qq2o0nAgILyMHxis5NkKS9PHmavk6P4EG0CPnTA8t+q41mAQIT5flZQfXzy3dZDAJgL3ZXteqnQ1tkgLTfB0Oh+FEQHipqG8NrtMSKfoCvdXQRtEXAACED4q+AADA1rpO9G0xmAQITxkdE30lqbi8xlgOIFzlZKUG12sKmeoL9IbT6QhO9a1v8eqLbUyWB3pj6ECPJo0JTCRds7FMm0uZjA30xikTB2tAx3TQ1z/5UjvqOKcA2M3a0l3Hh2MHJxpMAoSnirpdRV8m+gK919jmDa7jo9wGkwAAAHSPoi8AALC10Im+tfXNBpMA4alL0bes2lwQIExNpOgLfC25HUVfSVpeREkR6K1ZeeOC60XvMNUX6I0It1MXT8uUJLX7/HrxvSLDiQB81dqQC8HGUfQFeq28nqIv8HU0tjLRFwAAhA+KvgAAwNaSEmKC6xqKvkCvDU/zBNfFZTXGcgDhKrTou7pou8EkQHjKzaLoC3wd54YWffMLDCYBwtPsvOzg+vmlm2RZlsE0AL6Kib7A11PRpegbaTAJEJ4a23YVfWMp+gIAAJuj6AsAAGzNQ9EX+FpCi74lFbXmggBhKnuQR7Edt+5bvYmJvkBvZfSPVWrHZKlPimvU5vUbTgSEl5HD+mtMxgBJ0rLPtqhsR1033wEg1KjBScodFbhw64sva7WyaKfhRABCFXwZ2K/FRrqU0T/OcBog/Gxnoi/wtYQWfeM7zv8BAADYFUVfAABga6FF31qKvkCvxcdEKtUTK4mJvsD+cLmcmjCivySpuKJONQ2t3XwHgFAOhyM41be5zafPS7noBOitWXljg+vX3v3CYBIgPF2WlxVcP790k8EkAELVNrXry+rAub4jBiXI6XQYTgSEn/KOoq8nxq3oCKaRAr3V2OoNruOY6AsAAGyOoi8AALC1hNio4In+aoq+wH7pnOq7bUedWtu8+34ygN1MzEoNrj8tYqov0Fu5mSnB9YrCaoNJgPA0a8a44HrR0gKDSYDwdN6UDMV1TGh7+YNiNbXyngiwg7Xbdk2pHzso0WASIDy1+/yqamyXxDRfYH81dEz0dTqkmAiqMwAAwN44WgEAALbmdDqVFB8tiYm+wP7KSPdIkixL2rqdSYpAb03MGhBcry6k6Av0Vm5mcnC9nFumA72WMzJdw9MD21H+qs2qqmsynAgILwkxETpvynBJUl1zu15dscVwIgCStLZ0V9F33BCKvkBvba9vldWxpugL7J+mjqJvbKRLDgeT5QEAgL1R9AUAALaXFB8jSaqh6Avsl86JvpJUXFZjLAcQrnJCJvquYaIv0GtDUmI1ODlw4daqLbVqbfcZTgSEF4fDoVl5YyVJPp9fi99bZzgREH4uy8sOrufmbzKYBECn0KLv2MEUfYHeKq9vDa4p+gL7p6HjTg9xkS7DSQAAALpH0RcAANhecuKuoq9lWd08G8BXZYQUfUvKmegL9NbY4SlyuwJvn9cw0RfYL5MzUyRJbV6/Vm9hXwT01qy8ccH1ovwCg0mA8DRldKqy0wNFwnfXVqioot5wIgCdRd8Il0MjByYYTgOEn+31bcE1RV+g9yzLUmPHRN/4KLfhNAAAAN2j6AsAAGyvc6Kv1+dXU0tbN88G8FUZ6Z7guri82lwQIExFRbg1dligpLhua5WaO6Z9AOi53I6iryStKKoymAQIT1PGD1Vav3hJ0lsrNqmhqbWb7wAQyuFw6LK8rOCf5zHVFzCquc2nwu0NkqTsgfGKdPNxJdBboRN90xIp+gK91eL1y98xV4aJvgAAIBzwzhkAANiep2OiryRV1zUbTAKEpy5F37IaYzmAcJaTlSpJ8vktfV68w3AaIPyEFn2XU/QFes3pdOrsaWMlSa1tXi1ZtsFwIiD8XDItUy6nQ5L0wjtF8vn9hhMBfdf6svpguWrc4ESzYYAwVVG3q+jLRF+g9xpbfcE1RV8AABAOKPoCAADb88TvKvrWNlD0BXpr6IAkOQKfZ1P0BfZTZ9FXktYUVRpMAoSngUnRyugfK0n6dGutGpmMDfTarLxxwfWi/LUGkwDhKS05VqdMHCxJ2lbVpLc/KzOcCOi71pbWBddjKfoC+6WiY6KvQ1JqfKTZMEAYamwLLfq6DSYBAADoGYq+AADA9pISmOgLfB1RkW6l90uQJJWU15gNA4SpiVkDgus1hRR9gf3ROdXX67e0qqTGbBggDE0/eoQ8CdGSpNffX6eW1nbDiYDwc1leVnD93NubDCYB+ra123YVfccNTjKYBAhf5R1F335xkYpw8ZE/0FsNIRcgx0Ux0RcAANgfR/0AAMD2khOY6At8XRnpHklSZU2TGpvbzIYBwtCRI/oHJ2OvpugL7JfJHUVfSVpeVGUwCRCeItwunXn8GElSQ3Ob3v6k0HAiIPycetQQpSYGCvOLP/lSO+taDCcC+qbOib4Oh3TEoATDaYDw09zuU11LoKQ4MCHKcBogPHWd6EvRFwAA2B9FXwAAYHuhE31r6/kQDtgfGWme4JqpvkDvJcRGKqujMP958Q55fX6zgYAwNDkzObheQdEX2C+zZowNrhflrzWYBAhPEW6nLp6WKUlq9/n14vubDScC+p52n1/ryuolSRn94xQXxe3Sgd6q6JjmK0lpiZEGkwDhi6IvAAAINxR9AQCA7XlCir7V9U0GkwDhq3OiryQVU/QF9ktOVqokqaXNp/Vbqw2nAcJPv/gojRwYL0kqKK1TXXO74URA+Dlp8kjFRkdIkv793hfyen3dfAeAr5o9Izu4nrt0kyzLMpgG6HuKtjeqzRu4cHLs4ETDaYDwVFG3q+jLRF9g/zS2eoPreC46AQAAYYCiLwAAsD1Pl4m+zQaTAOFreMhE3+KyGmM5gHCWk5kaXK8p2m4wCRC+cjNTJEl+S/q4mMI80FsxURE6depoSdKOmiZ98GmJ4URA+Bk9OEmTRwaO6wq21mjVZqbMA4fS2tK64HrsoASDSYDwFTrRdwBFX2C/MNEXAACEG4q+AADA9pJCJ/rWUfQF9keXib4UfYH9clT2rqLv6sJKg0mA8JWblRJcryiiWAXsj1l5Y4PrV/LXGkwChK/LZmQF18+/vclgEqDvKQgp+o4bkmQwCRC+ykOKvmkUfYH90kDRFwAAhBmKvgAAwPaSQyf6NlD0BfZH6ETfkooaYzmAcJaTFTLRl6IvsF+OGZEshyOwXl5I0RfYH6cfN1qREYEPohflF8jv9xtOBISf83KHKzYqsB299MFmNYXcuhnAwdVlou/gRINJgPC1vb4tuB5I0RfYL02hRd8ot8EkAAAAPUPRFwAA2F7oRN+a+haDSYDwNSQ1UW5X4PC/hIm+wH5JTYrVoH5xkqQ1RZWyLMtwIiD8JMVEaOygQKFjfXmDqhvbuvkOAF+VGBetbxwTmEa6rbJOn6wrNZwICD+JsZE6LzdDklTX3K5/fbTFbCCgj/D7La3dFij6piVFKyUu0nAiIDx1TvR1Ox3qx3YE7JfGkAu9mOgLAADCAUVfAABge5740KJvk8EkQPhyuZwaOjBwS8xiir7AfpvYMdW3trFNxeV13TwbwJ5MzkwOrlcUMdUX2B+z8sYF14vy1xpMAoSvy2ZkBdfPLd1kMAnQd2ytalJDS6BYxTRfYP9YlqWKjqJvanykXE6H4URAeGoInehL0RcAAIQBir4AAMD2YqIjFBUZuHVSLRN9gf02vKPoW9vYqur6ZsNpgPCUkzkguF5dVGkwCRC+cjNTgusVRdUGkwDh68wTxsjZUep4ZWkBU+aB/TB19ABlpSVIkt5dW6HNFfWGEwGHv7Wluy6WpOgL7J+GVp+aOgqKAxKiDKcBwldjx3YU6XIo0k1tBgAA2B9HLAAAICx0TvWtZqIvsN8y0j3BNVN9gf0zMTs1uF5TSNEX2B+TMpLl7igoLmeiL7BfUpPjdEJOhiSp8MudWrt5u9lAQBhyOBy6bEZ28M/z8gsNpgH6hrXbdhXqx1H0BfZLecc0X0lKo+gL7LfG1sCE+Vim+QIAgDBB0RcAAIQFT2Kg6MtEX2D/hRZ9S8prjOUAwllOFkVf4OuKi3Jr/JBAsaOoslHb6zi+A/bHrLxxwfWipQUGkwDh65JpmXI6AhefvPBOoXx+v+FEwOEtdKIvRV9g/2wPKfoOpOgL7LfOib7xUW7DSQAAAHqGoi8AAAgLSR0TfesaW+Tz8cEbsD+Gp3mC62KKvsB+GZaaoOT4wAdpqwuZngjsr9zMlOB6RVG1wSRA+Dp7+pjgelE+RV9gf6Qnx+qUiYMkSaVVTXr7s3LDiYDDW0FprSTJExuhdE+04TRAeAqd6DswkaIvsD98fkvN7YHPmeKY6AsAAMIElycB6NMsy5LPOvg/x+lyyuV2yelyyus/uD/Q5QjcehA4nFiWpeSkWLncgRMuVfXNSk6M5fcdtnWo9i+9NSwtWS5X4Fq/4vLag75POhDYzmEHfstS6OYyceQAvfNpqSrrWlS6s0EDk+P2+f38HqMvsDq2k57uWY4Zkawn3ymWJC0vqtJpR6YdkBxsb7CzA32MmJ6apMnjh+mTdaUqKK7Uxq07NGJwvwP3A3qAbQ525bMsWT3c3i6dka23Pi2TJM3NL9SMCekHPI/ToeDkYOBQ6M02cKhU1reoqrFdTqdD44YkBvaJBkOyD4Pd7e3YsbyuVZ2/uqnxkfL6LfYzOOz19pxDdxpavMHtKC7SdUDOkzskuZxsh+gbQvdRDseu//ksSy72RwBw0Dgsq/t30XV1dUpKSlJtba0SE7mVDoDwt6PFq3e2NWlzfZvaD8PBoGkxbk1KjdaEfkxFQHgrb/Lq3bJGFde37/GkplPSsIQITR0Yo+EJkYc8H/BVG2patWJ7s0obvQfspGNf53JIGQkROj4tVoPiIkzHQR/ityy9V96kgqpW1bZ9vQNGp0MaHh+h49JiNTSe32McXmrbfMrf1qTC2ja12uACEqekofGB48OMRI4PYQ/F9W36sLxZWxvadbidgghuc2kxyuA9GQxr9fm1dFuT1te0qslrfp/0Vf2iXcrpF61jU6MpF+KgaPdbyt/WqHU1bWo4HE96H2Cd5xWPGxirYQm8T4N9rO84v7itl+cX+0e7NLFftCaxn8FhxG7nHLoT7XJoVFKkZgyOU6ybm2vj8FPa2K73y5pU0rDnz20lKc7t0GhPlPIGxSrKxXYAAN3pTS+Xoi+APqeuzadn19co0unQkf2ilRh5eB1gtvstbaptU2Fdu04fFq8cyr4IUztbvHpuQ60SIpyakBKluIjdt9Vmr6WC6lbtaPbq0pFJlABh1MbaVi0oqtfQ+AiN9kQqysUJ9QOhsd2vzzuKlpePSlL/GG5KgkNjcUm9Pq9q1ZH9ojUozq2vM5CjyWupoKpFO1t8+vaoJKXHsr/C4aHF69ec9TXyW1JOv2glRTpl+vPkZq+lL6pbVdHs1cXZSZTrYdyXDe36+6ZaDYhxa2xylGLch9cxYrPX0trqVm1v9uqS7CQNYZuDIZZlae7GWu1o9imnf7RSo13G90mhfH6ppKFda6tbdXxajKal7/uOEEBvWZalfxTW6cvGduX0i9aAmK/3HqYvaOrYh3FeEXayoaZVCzbXa1gvzy96/VJJfZu+qGnT9PRYHZcWe5CTAgefHc857ItlSVWtPq3e2aJ4t1NXjvYw4ReHle3NXj2/oUbJUS6NS97z57aWJVW2+LRmR4tSY1z69sgkLj4BgG70ppfLp+QA+pyCqla1+y1994hkxe7hAPRwMLFftBZurtdH25sp+iJsrdnZKpdDmj0qaZ9XfB7VP1rPrKvRyh0tnJCHUR9tb9HgOLcuyU7kxMUBdlT/GD2+tkprdrboxCHxpuOgD2hq9+uzqlbNHBynyQNiDshrHt0/Wk9+Ua1VO1qUPoz9FQ4P62vaVNfm17Vjk+WJcpmOE3R0/2g9u75Gn1Q2U/SFcZ9UNssT5dJlI5MO2w95j+4frWfWB96TUfSFKaWNXpU2enVRVqIybTrRPad/tGLdDn1S2aLjBsYetv8mwIzKFp8217fr3IwEHZEcZTpO2Di6f7SeXlfNeUXYxseVLRoav3/nFyf2j1bM1gZ9XNmsKQNj5OT8JMLc+to21bb5dZ3Nzjl0JzspUs9vqFVxfbuykux5XArsj9U7WhTtcmr2KI8iunkvMzw+Qv8sqlNZk5djLAA4gA7PhhsA7ENpk1dD4iIO25KvJDkcDo3yRGpHi0+tPm7ThvC0rbFdGQmR3d7Wxe10KDMxQqWN7YcoGbBn2xrbNTIpkpLvQRDpcigjIVKljV7TUdBHlDUFbo856gCejHc7HcpKjNQ2fo9xGCltateAGJftPnBzsb3BRrY1eZWdGHlYF/pcToeyEyN5TwajtjV5FeGUMhLs/SHyaE+UWnyWqlp9pqPgMNN53JNNoahXeJ8GuyltbNfIpKj9Pr84yhOpJq+lmlY+F0L429bYroE2POfQnUGxbsW7nbw/wmGntLFdmYkR3ZZ8JWlEYoTcDulLjrEA4IA6fFtuALAXPr+lSJvcTv3ee++Vw+HQjh07DvhrR3YcZHs5n4Mw5bV2/R6HevbZZ+VwOFRcXBx8LNLl4HcdxnktdVtMR/eWLl0qh8OhpUuXdnk8yuWQ17LMhEKf0/m71tNbZEq7jus6ZWRk6Morr+zynEinQ14/v8c4fPj8vdv3fXU7OZgi2W/AJrx7OQdxMM8HmBB4T8Y2B3O8fksRToftpxcGz9exj8IB5rUsuR2B4mpfs6f3Xr3B+zTYic+Sor7Gdsx+BocTr1+2+Ty3NxwOR8c5CdNJgANrb5/b7onT4VCE0yEfx1gAcEDRRACAr3jkkUfkcDiUm5trOsrXYvPPNYADil93AEBYYIcFAAAAAPbC+zQcRvh1Rl/QOQzm448/Nh0FwL6wUwKAA85tOgAA2M28efOUkZGhFStWaNOmTcrOzjYdCUCI2bNn6+KLL1ZUVJTpKAAOgunTp6u5uVmRkdxuFOFt/fr1cjq5thYIddddd+knP/mJ6RgAAAA4jPDeCwAAAADQF/DOFwBCbN68WR988IF+//vfKzU1VfPmzTMdCcBXuFwuRUdHH7LbPgM4tJxOp6Kjo/mQDmEvKipKERERpmMAttHY2Ci3263o6GjTUQAAfVxjY6PpCAAOIN57AQAAAAD6Aj49B4AQ8+bNU3Jyss4880xdcMEFuxV9i4uL5XA49Lvf/U5PPPGEsrKyFBUVpWOPPVYfffTRbq+3bt06XXTRRUpNTVVMTIxGjx6t//u//9vteTU1Nbryyivl8XiUlJSk73znO2pqatrteXPnztWkSZMUExOjlJQUXXzxxdq6deuB+w8AhIHO2zIVFxebjgL0WGlpqa6++moNGjRIUVFRGjFihL7//e+rra1NklRUVKQLL7xQKSkpio2N1ZQpU/Tvf/+7y2ssXbpUDodD//jHP3Tfffdp8ODBSkhI0AUXXKDa2lq1trbqlltu0YABAxQfH6/vfOc7am1t7fIaDodDN954o+bNm6fRo0crOjpakyZN0jvvvNPleSUlJbr++us1evRoxcTEqF+/frrwwgv3uN19+umnysvLU0xMjIYMGaJf/vKXmjNnzm7baUZGhs466yy99957mjx5sqKjo5WZmannnntuj3/PpUuX7v9/cOAge++993TssccqOjpaWVlZevzxx3d7TkZGhq688spDHw6wgXvvvVcOh0Nr167VpZdequTkZJ1wwgnBx0N17pteeeUVjR8/XlFRURo3bpzeeOON3V536dKlOuaYY7pse3t6TSCcdHc+YM6cOfrGN76hAQMGKCoqSmPHjtWjjz662+v09Fir8/3U+++/rx/96EdKTU1VXFyczjvvPFVWVu72uo888ojGjRunqKgoDRo0SDfccINqamoO+H8H4GDZ2z5J6tl5to0bN+r8889XWlqaoqOjNWTIEF188cWqra0NPsfr9eoXv/hF8DxhRkaGfvrTn+72fgwwZdWqVTr99NOVmJio+Ph4nXjiiVq2bFnw6537hvfee08333yzUlNT5fF4dO2116qtrU01NTW6/PLLlZycrOTkZN1xxx2yLKvLz/jd736n4447Tv369VNMTIwmTZqkl156abcszc3Nuvnmm9W/f38lJCTonHPOUWlpqRwOh+69994uz+3psd+e3nv15DwLEC56c54OgHTllVcqIyNjt8e/zjkJtkMAAGAHbtMBAMBO5s2bp29+85uKjIzUJZdcokcffVQfffSRjj322C7Pe+GFF1RfX69rr71WDodDv/nNb/TNb35TRUVFwekBn376qaZNm6aIiAhdc801ysjIUGFhoV577TX96le/6vJ6F110kUaMGKEHHnhAK1eu1FNPPaUBAwbowQcfDD7nV7/6le6++25ddNFF+u53v6vKyko9/PDDmj59ulatWiWPx3PQ//sAAHpv27Ztmjx5smpqanTNNdfoiCOOUGlpqV566SU1NTWpurpaxx13nJqamnTzzTerX79++tvf/qZzzjlHL730ks4777wur/fAAw8oJiZGP/nJT7Rp0yY9/PDDioiIkNPpVHV1te69914tW7ZMzz77rEaMGKGf/exnXb4/Pz9fL774om6++WZFRUXpkUce0WmnnaYVK1Zo/PjxkqSPPvpIH3zwgS6++GINGTJExcXFevTRRzVjxgytXbtWsbGxkgIF5pkzZ8rhcOjOO+9UXFycnnrqKUVFRe3xv8WmTZt0wQUX6Oqrr9YVV1yhZ555RldeeaUmTZqkcePGHYT/+sCB99lnn+mUU05Ramqq7r33Xnm9Xt1zzz0aOHCg6WiA7Vx44YUaOXKk7r//flmWpe3bt+/xee+9954WLFig66+/XgkJCfrzn/+s888/X1u2bFG/fv0kBQoqp512mtLT03XffffJ5/Pp5z//uVJTUw/lXwk44Lo7H/Doo49q3LhxOuecc+R2u/Xaa6/p+uuvl9/v1w033NDltXpzrHXTTTcpOTlZ99xzj4qLi/XHP/5RN954o1588cXgc+69917dd999Oumkk/T9739f69evD54nef/995meiLDy1X1ST86ztbW16dRTT1Vra6tuuukmpaWlqbS0VP/6179UU1OjpKQkSdJ3v/td/e1vf9MFF1ygW2+9VcuXL9cDDzygL774QgsXLjT8N0dfV1BQoGnTpikxMVF33HGHIiIi9Pjjj2vGjBnKz89Xbm5u8Lmdv+f33Xefli1bpieeeEIej0cffPCBhg0bpvvvv1+LFy/Wb3/7W40fP16XX3558Hv/9Kc/6ZxzztG3v/1ttbW1af78+brwwgv1r3/9S2eeeWbweVdeeaX+8Y9/aPbs2ZoyZYry8/O7fL3T1zn2q6io6NV5FsDuenqeDsD+6ck5CbZDAABgC1YP1NbWWpKs2tranjwdAGxt/sYaa0HR7v+effzxx5Yk66233rIsy7L8fr81ZMgQ6wc/+EHwOZs3b7YkWf369bOqqqqCjy9atMiSZL322mvBx6ZPn24lJCRYJSUlXX6O3+8Pru+55x5LknXVVVd1ec55551n9evXL/jn4uJiy+VyWb/61a+6PO+zzz6z3G73bo9blmVtqGmxHlhZaTW0+fb1nwOwrTnrqq3XS+p3f3zOHEuStXnz5uBj72xrsP7y2c5DmA7Y3QMrK61Vlc27PX755ZdbTqfT+uijj3b7mt/vt2655RZLkvXuu+8GH6+vr7dGjBhhZWRkWD5f4N/xt99+25JkjR8/3mpraws+95JLLrEcDod1+umnd3ntqVOnWsOHD+/ymCRLkvXxxx8HHyspKbGio6Ot8847L/hYU1PTblk//PBDS5L13HPPBR+76aabLIfDYa1atSr42M6dO62UlJTdttPhw4dbkqx33nkn+Nj27dutqKgo69Zbbw0+1vn3fPvtt7v8/CVb6q2nv6iygENhXXXgOKqpfffjqHPPPdeKjo7ucoy3du1ay+VyWaFvsYcPH25dccUVXb737dIG69HP2V/h8PHq5jpr3oaa3R7vfJ9zySWX7PHxUJKsyMhIa9OmTcHH1qxZY0myHn744eBjZ599thUbG2uVlpYGH9u4caPldrt3e03Lsqz3yxqtP326Y7//bsCB8udPd1jvlTXu9nhPzwfs6bjs1FNPtTIzM7s81tNjrc73UyeddFKX8xM//OEPLZfLZdXU1AS/NzIy0jrllFOCx6OWZVl/+ctfLEnWM8880+Xnv1fWaP2ZbQ4G7e3f/T3tk3p6nm3VqlWWJOuf//znXn/u6tWrLUnWd7/73S6P33bbbZYk63//+1+Xx8sb260HVlZa2xrbLOBA+mh7k/XbVZW7PX7uuedakZGRVmFhYfCxbdu2WQkJCdb06dMty9q1bzj11FO77BumTp1qORwO67rrrgs+5vV6rSFDhlh5eXldfs5X91dtbW3W+PHjrW984xvBxz755BNLknXLLbd0ee6VV15pSbLuueee4GO9Ofb76nuvnp5n6ZS/rcH6K+cVYRMPrKy0Vn/l/GJPz9NZlmVta2izHlhZaVU0tR/UnMCh8OrmOmvuhurdHu/cb+3pfLtlWdYVV1yx23lxy/p65yR6sx1almU9XlBl/ffLhr391YCw9MTaKus/W3f/3HZv/vjpDuuDPZwPAQB01ZtervPgVYgBILzMmzdPAwcO1MyZMyUFbtfyrW99S/Pnz5fP5+vy3G9961tKTk4O/nnatGmSArcEk6TKykq98847uuqqqzRs2LAu37un28ped911Xf48bdo07dy5U3V1dZKkBQsWyO/366KLLtKOHTuC/0tLS9PIkSP19ttvf82/PQDgYPD7/XrllVd09tln65hjjtnt6w6HQ4sXL9bkyZODt4+VpPj4eF1zzTUqLi7W2rVru3zP5Zdf3mV6Wm5urizL0lVXXdXlebm5udq6dau8Xm+Xx6dOnapJkyYF/zxs2DDNmjVLS5YsCe7vYmJigl9vb2/Xzp07lZ2dLY/Ho5UrVwa/9sYbb2jq1KmaOHFi8LGUlBR9+9vf3uN/j7Fjxwb3mZKUmpqq0aNHB/efgN35fD4tWbJE5557bpdjvDFjxujUU081mAywp6++z9mbk046SVlZWcE/H3nkkUpMTAzuH3w+n/7zn//o3HPP1aBBg4LPy87O1umnn35gQwOHWHfnA0KPy2pra7Vjxw7l5eWpqKhItbW1Xb63N8da11xzTZfzE9OmTZPP51NJSYkk6T//+Y/a2tp0yy23yOncdQr5e9/7nhITE7n9OcJO6LbW0/NsnRN7lyxZoqampj2+7uLFiyVJP/rRj7o8fuutt0oS2wqM8vl8evPNN3XuuecqMzMz+Hh6erouvfRSvffee8H9jSRdffXVXfYNnecbrr766uBjLpdLxxxzzG77ltD9VXV1tWprazVt2rTdziFI0vXXX9/le2+66abdcn+dY7/enmcB7K6n5+kA7J/uzklIbIcAAMAeKPoCgAInD+fPn6+ZM2dq8+bN2rRpkzZt2qTc3FxVVFTov//9b5fnf7W821n6ra6ulrSr8Nt5C/TudPd6GzdulGVZGjlypFJTU7v874svvtjrLXABAGZVVlaqrq5un/uDkpISjR49erfHx4wZE/x6qK/uMzo/fB46dOhuj/v9/t0KICNHjtztZ40aNUpNTU2qrKyUJDU3N+tnP/uZhg4dqqioKPXv31+pqamqqanp8nolJSXKzs7e7fX29NieskuBfV7n/g6wu8rKSjU3N+9xO9rTdgz0dSNGjOjR87rbP2zfvl3Nzc292ucA4aK78wHvv/++TjrpJMXFxcnj8Sg1NVU//elPJWm347zeHGt193M7j0G/un+LjIxUZmbmbseogN2F7pN6ep5txIgR+tGPfqSnnnpK/fv316mnnqq//vWvu70ncjqdu+2P0tLS5PF42FZgVGVlpZqamvZ6zsHv92vr1q3Bx3pzvuGr+5Z//etfmjJliqKjo5WSkqLU1FQ9+uije9xevnqM+NXt5+se+/X2PAtgdz09Twdg//TkfRTbIQAAsAO36QAAYAf/+9//VFZWpvnz52v+/Pm7fX3evHk65ZRTgn92uVx7fB3Lsvbr53f3en6/Xw6HQ6+//voenxsfH79fPxcAEH72ts84kPumm266SXPmzNEtt9yiqVOnKikpSQ6HQxdffLH8fn+vX+9gZAQA2F/oxJt9Yf+Avmxfv/+FhYU68cQTdcQRR+j3v/+9hg4dqsjISC1evFh/+MMfdjsu6822xHaHviZ0n9Sb82wPPfSQrrzySi1atEhvvvmmbr75Zj3wwANatmyZhgwZEnzenu7gBYSb3pxvCN1fvPvuuzrnnHM0ffp0PfLII0pPT1dERITmzJmjF1544aDlBfqKg3WeDjhc7e247Kt3b+3Uk/dGbIcAAMAOKPoCgAJF3gEDBuivf/3rbl9bsGCBFi5cqMcee6zHr9d5K7TPP//8gOTLysqSZVkaMWKERo0adUBeEwBw8KWmpioxMXGf+4Phw4dr/fr1uz2+bt264NcPpI0bN+722IYNGxQbG6vU1FRJ0ksvvaQrrrhCDz30UPA5LS0tqqmp6fJ9w4cP16ZNm3Z7vT09BhwOUlNTFRMTs8ftaE/bMYADY8CAAYqOjmafgz7ntddeU2trq1599dUuU6befvvtg/6zO49B169f3+V2721tbdq8ebNOOumkg54BOFh6e55twoQJmjBhgu666y598MEHOv744/XYY4/pl7/8pYYPHy6/36+NGzcGp4VKUkVFhWpqag74+zmgN1JTUxUbG7vXcw5Op1NDhw7VRx999LV+zssvv6zo6GgtWbJEUVFRwcfnzJnT5Xmd28vmzZu73CXlq8dzX/fY71CfZwEOtp6epwMQkJycvMft4+tMdGc7BAAAduA0HQAATGtubtaCBQt01lln6YILLtjtfzfeeKPq6+v16quv9vg1U1NTNX36dD3zzDPasmVLl6/tz3Scb37zm3K5XLrvvvt2+37LsrRz585evyYA4OBzOp0699xz9dprr+njjz/e7euWZemMM87QihUr9OGHHwYfb2xs1BNPPKGMjAyNHTv2gGb68MMPtXLlyuCft27dqkWLFumUU04JTi9wuVy77W8efvjh3aYenHrqqfrwww+1evXq4GNVVVWaN2/eAc0M2IXL5dKpp56qV155pcsx3hdffKElS5YYTAYc3lwul0466SS98sor2rZtW/DxTZs26fXXXzeYDDi4Oo/NQo/LamtrdytOHQwnnXSSIiMj9ec//7nLz3/66adVW1urM88886BnAA6Wnp5nq6urk9fr7fL1CRMmyOl0qrW1VZJ0xhlnSJL++Mc/dnne73//e0liW4FRLpdLp5xyihYtWqTi4uLg4xUVFXrhhRd0wgknKDEx8YD8HIfD0eWcQXFxsV555ZUuzzv11FMlSY888kiXxx9++OHdXu/rHPsd6vMswMHW0/N0AAKysrJUW1urTz/9NPhYWVmZFi5cuN+vyXYI7F1TU5PWrVunHTt2mI4CAIc9JvoC6PNeffVV1dfX65xzztnj16dMmaLU1FTNmzdPubm5PX7dP//5zzrhhBN09NFH65prrtGIESNUXFysf//7310KUT2RlZWlX/7yl7rzzjtVXFysc889VwkJCdq8ebMWLlyoa665RrfddluvXhMAcGjcf//9evPNN5WXl6drrrlGY8aMUVlZmf75z3/qvffe009+8hP9/e9/1+mnn66bb75ZKSkp+tvf/qbNmzfr5ZdfltN5YK/NGz9+vE499VTdfPPNioqKCn7Adt999wWfc9ZZZ+n5559XUlKSxo4dqw8//FD/+c9/1K9fvy6vdccdd2ju3Lk6+eSTddNNNykuLk5PPfWUhg0bpqqqKm5fi8PSfffdpzfeeEPTpk3T9ddfL6/Xq4cffljjxo3r8gECgAPr3nvv1Ztvvqnjjz9e3//+9+Xz+fSXv/xF48eP7/X7KyBcnHLKKYqMjNTZZ5+ta6+9Vg0NDXryySc1YMAAlZWVHdSfnZqaqjvvvFP33XefTjvtNJ1zzjlav369HnnkER177LG67LLLDurPBw6mnp5n+9///qcbb7xRF154oUaNGiWv16vnn39eLpdL559/viQpJydHV1xxhZ544gnV1NQoLy9PK1as0N/+9jede+65mjlzpuG/Lfq6X/7yl3rrrbd0wgkn6Prrr5fb7dbjjz+u1tZW/eY3vzkgP+PMM8/U73//e5122mm69NJLtX37dv31r39VdnZ2l/dIkyZN0vnnn68//vGP2rlzp6ZMmaL8/Hxt2LBBUtdbrX+dY79DfZ4FONh6ep4O6GueeeYZvfHGG7s9Pnv2bP34xz/Weeedp5tvvllNTU169NFHNWrUqC4DMHqD7RDYuxUrVmjmzJm65557dO+995qOAwCHNYq+APq8efPmKTo6WieffPIev+50OnXmmWdq3rx5vZqcm5OTo2XLlunuu+/Wo48+qpaWFg0fPlwXXXTRfuX8yU9+olGjRukPf/hDsIw1dOhQnXLKKXstKQMAzBs8eLCWL1+uu+++W/PmzVNdXZ0GDx6s008/XbGxsfJ4PPrggw/04x//WA8//LBaWlp05JFH6rXXXjso05/y8vI0depU3XfffdqyZYvGjh2rZ599VkceeWTwOX/605/kcrk0b948tbS06Pjjj9d//vOf4PSdTkOHDtXbb7+tm2++Wffff79SU1N1ww03KC4uTjfffLOio6MPeH7AtCOPPFJLlizRj370I/3sZz/TkCFDdN9996msrIyiL3AQTZo0Sa+//rpuu+023X333Ro6dKh+/vOf64svvgjehhk43IwePVovvfSS7rrrLt12221KS0vT97//faWmpuqqq6466D//3nvvVWpqqv7yl7/ohz/8oVJSUnTNNdfo/vvvV0RExEH/+cDB1JPzbDk5OTr11FP12muvqbS0VLGxscrJydHrr7+uKVOmBF/rqaeeUmZmpp599lktXLhQaWlpuvPOO3XPPfcY+bsBocaNG6d3331Xd955px544AH5/X7l5uZq7ty5vRpqsS/f+MY39PTTT+vXv/61brnlFo0YMUIPPvigiouLd3uP9NxzzyktLU1///vftXDhQp100kl68cUXNXr06C7nEL7Osd/AgQMP6XkW4GDr6Xk6oK959NFH9/j4lVdeqYULF+pHP/qR7rjjDo0YMUIPPPCANm7cuN9FX7ZDAABgBw6rB/eQr6urU1JSkmpraw/IbXwAwKQXN9Uq0uXQeSMO73/PNta26uWiet00PkVxEUwpQPh5dn2N0mLcOm1YfLfPfbesUZ/ubNUN41MOQTJgz369aodOGxqvif3tW251OBy64YYb9Je//OWg/pxbbrlFjz/+uBoaGoK3nP663tzaoC8b23XVEckH5PWAfVlf06qFm+v1gwkpinEfuOOopdsata66VdeNY3+Fw8NrxfWqb/fr0pFJxjKce+65Kigo0MaNG7s8/kF5kz6ubNbNE5iuA7Me/mynjk6N0fFpsaajHFTvlzdpZWWzbmKbgyHh8u9+RZNXc9bX6IrRSUqPpTCPA+fjymYtLW3UbRP7m47Sa6tXr9ZRRx2luXPn6tvf/vY+n7u3Y7+v452yRn2+s1XXc14RNvDrVTt0+tB45ezn+cWyxnb9bUOtrjrCowExzPtCeHutuF517T59e6THdJRee2JttbKTIvWNwXGmowAHzJNfVCszIUInDun+c1tJ+tNnOzU5NUZTD/PzIQDwdfWml0vzCwAAAMB+a25u7vLnnTt36vnnn9cJJ5xwwEq+AABIu+9zNm7cqMWLF2vGjBlmAgEAAKBXvno8J0l//OMf5XQ6NX369H0+l2M/AAAAAEBfxqV8AAAAAPbb1KlTNWPGDI0ZM0YVFRV6+umnVVdXp7vvvtt0NADAYSYzM1NXXnmlMjMzVVJSokcffVSRkZG64447TEcDAABAD/zmN7/RJ598opkzZ8rtduv111/X66+/rmuuuUZDhw7t8lyO/QAAAAAA2IWiLwAcpizLdALg0OHXHTDnjDPO0EsvvaQnnnhCDodDRx99tJ5++undJvEAEDss4Gs67bTT9Pe//13l5eWKiorS1KlTdf/992vkyJGmowEAAKAHjjvuOL311lv6xS9+oYaGgZn1VAAACkhJREFUBg0bNkz33nuv/u///m+35x6yYz/ep+Ewwq8zAMA22CkBwAFH0RdAn+NyOtTmO/yPLNv8gb+j22k4CLCf3I5dv8fdafNZ/K7DOLdDavX5TcfYJ+sgXAVy//336/777z/gr/tVrT5LbofjoP8cQFLwd63VZynmAL5rbvNbcjv5Pcbhw+U8tPu+OXPm9Pi5bew3YBPuvnIOwsc+Dma5nQ61/397d/MbV3WAcfi9nrE9GcdxyAcEm5B0UUUqhbRFLApS200qIfWvLK1YtVIX3YNKqVQ2RS1QnBBCCyFQMPZM7Hg+bxeBStBgnNjxHI+fZz2yzuJe33PO/ObccZ1xXWem4P///9uvK3iMHE7NqsqwToaFrzmuXLmSK1eu7Oqz9zP32wvrNErSqJLeLvfE78VzhmnSnMmhXEvVdf3lnsSkRwL7636+tx3XdQbjOg1zLIB9JYkBjpyVdjMfbg6yNSg7xtqLuq6zut7PmVYj8w3/6jmclhdmc6Pb/854ZDiuc70zyMrC7AGNDO5teWE2Vzf6DyWmPer6ozo3uv2sLPidIgfj8XYzVZLVjf6+/c3huM57nX6WXcdMkZX2bD69M8p6bzTpoXzNyP1GQZbbzVzr9DPaQ7BRutG4zrVO35qMiVpuNzMYJze6g0kPZUfvrvfSalQ5Nd+Y9FCYMl/Ne67t4xrmKLBOozQrC7O5utF74P3F1fV+2s0qJ+d9L8Tht7wwm08K3HP4Lje3hrk9HFsfMXVWFmZzvTPIYBf7G+93BhnWyRPmWAD7yn9V4Mh56tR83vjPnfx6dT3PnG7lxNx0bXgMxnWubfTzXmeQF588PunhwAO7fHo+b36+nZdWN/LDU/M5Pvv/9+rWsM7bX/TS6Y/yqwuudybruUdb+f31bl6+1smlk3OZb/il8n7YHIzzj7VeBuPk8unWpIfDEdGencnTp+bzykebWdseZXmhmb0cPrA1rPPW2nY2B+P8+IzrmOlx6eRcXv9kJr+9upHLp1tZmpvJpA+OujOs884Xvaz1RvnlefNDJu/Zs8fy8rWN/ObqRn7wyHyOTdmxTne+XJOt90Z50T3HBK0sNLOy0Mwf3u/m8plWzrQae5q/7bfROPng9iBvf9HLC+eOOdmKfXe21cj3Fmfzx3918+HmII8e29sa5ijYGtZ5e62Xbn+cn1ywTqMMzz3ayu8eYH9xOE4+6Pbzzno/P3u8XfTp9rBbl5bm8vrc3T2HZ07P5+RcY+J7Djup62StN8rfPt/O2VYjFxeFvkyXH51p5e9r23lpdT1PPTKfhXt8bzuuk8+2R3nzs+08sdDM421JGsB+qupd/CSw0+lkaWkpGxsbOXHixEGMC+Ch+mx7mD99vJXrnX6m8WDfc+1mnj3TytOCKA65W1vDvPbxVt7v9nOvNzTNJHlycTY/fexYLizOHfj44JtW13v566d38tHmMNN7ZtvBalTJxcXZvHCunWWnIHCAxnWd125t5a21Xjb6e5swzlTJheOzef5cO+ePu46ZLhv9UV69uZX3Nvp7esXsfplJcv743fnhxRPmh5ThRrefv9y6k3/fHmTatiD+d8+dO5aL1mRMWG80zis3t7K63svmcPLPpG8602rkmdOtPHe2larkSoVDazCu8+rNzfxzvZ/b07jpvc9mquTJ47N5/rF2nhRjUZB313t54wH2F8+2Grl8upVnPWeYIqXtOXyXY40q31+ayy9WFtJuTtdBU5AkH20O8udbW/mgO7jn97ZJstCscunkfH6+3PbmYYBduJ8uV+gLHGl1XX/rJPSwalSxicPU+bZ71fVOqabx+TIp7nNKMK7r7OW7BNcxR8VoXE/8hy7uN0o2jXNE9xylGtV1HvCt5w/FTBWnK3KgSrsHSuQZRunuZ+7oOcNRUMKew06qxFsbODK+7RlVVUnD8wjgvtxPl+ucdOBIq6oqU/bWTJhK7lUOG9csTJeZqvLaW9gFX2jBzswR4eA0qupubQFHlHsADj9zR/g6ew5QDs8ogMlwTjoAAAAAAAAAAAAAFEjoCwAAAAAAAAAAAAAFEvoCAAAAAAAAAAAAQIGEvgAAAAAAAAAAAABQIKEvAAAAAAAAAAAAABRI6AsAAAAAAAAAAAAABRL6AgAAAAAAAAAAAECBhL4AAAAAAAAAAAAAUCChLwAAAAAAAAAAAAAUSOgLAAAAAAAAAAAAAAUS+gIAAAAAAAAAAABAgYS+AAAAAAAAAAAAAFAgoS8AAAAAAAAAAAAAFEjoCwAAAAAAAAAAAAAFEvoCAAAAAAAAAAAAQIGEvgAAAAAAAAAAAABQIKEvAAAAAAAAAAAAABRI6AsAAAAAAAAAAAAABRL6AgAAAAAAAAAAAECBhL4AAAAAAAAAAAAAUCChLwAAAAAAAAAAAAAUSOgLAAAAAAAAAAAAAAUS+gIAAAAAAAAAAAB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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mt_pe_alignments = tagger_bert.align_mt_pe([mt_tokens], [tgt_tokens], lang_id)[0]\n", + "draw_aligned_qe(mt_tokens, tgt_tokens, mt_tbd_qe, None, mt_pe_alignments, title='MT - PE (BERT)')" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "ExecuteTime": { + "end_time": "2023-06-12T11:16:05.279593Z", + "start_time": "2023-06-12T11:16:04.510135Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Aligning src-mt: 100%|██████████| 1/1 [00:00<00:00, 4.98it/s]\n" + ] + }, + { + "data": { + "image/png": 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7AMDpnOi861RO19Oqwtdffy2bzaYhQ4ZUOneMiIhQkyZNzvpaGwD8E+eAAFxZ7969HSFfqfyhhoCAAO3YscOx7FyvowEAAMA1MaIvAPxDx44d1b59+0rLXnzxRUlyBNX+KSAg4Kx+Y+fOnbr44ouPW968eXPH561atVLbtm3l4+Oj5ORkXX755UpOTtYzzzyjiIgIvfXWWyoqKnKE/44N/QFwTUlJSSorK9OSJUsUGRmpgwcPKikpSevXr68U9G3RooVCQkIc34uJiTluW1u3btWaNWscU6L+08GDByWVj2Qyc+ZMXXnllWrQoIEuu+wyDRkyRFdcccU57cPWrVu1cePG0/7uqWqv6poAuI7CwkK98MILmjp1qvbu3Su73e747FRBuJiYGD3wwAN69dVX9dlnnykpKUn9+/fXjTfe6AgB79y5U2azWY0bN6703YiICAUFBWnnzp0XZqcA1ApHZlA4Ii4uTmazWampqZKkRYsW6emnn9aSJUuOeyAhOzvb0av+aefOnWrcuLFMJlOl5c2aNau64gHUeCc67zqV0/W0qrB161bZ7fbjfusId3f3KvstALUT54AAXFlUVNRxy4KDg5WZmel4f67X0QAAAOCaCPoCwBmw2WySpE8++UQRERHHfe7mdmHaqbu7uy6++GL9/vvv2rZtmw4cOKCkpCTVrVtXpaWlWrZsmZKTkxUfH3/SUB0A19G+fXt5eXnp999/V1RUlOrUqaOmTZsqKSlJ77zzjoqLi5WcnKxrrrmm0veOfXL/CJvNpj59+uiRRx454W81bdpUklSnTh2tWrVKc+bM0c8//6yff/5ZU6dO1U033aRp06ad9T7YbDYlJCTo1VdfPeHnkZGRp629qmsC4DruueceTZ06Vffdd586deqkwMBAmUwmDR061HE8djKvvPKKRo4cqW+//Va//vqrxo0bpxdeeEFLly5Vw4YNHev9MywHABfCsb1m+/btuvTSSxUfH69XX31VkZGR8vDw0E8//aTXXnvttP0NAM7Xic67zsY/j59OdjxltVrPeJs2m00mk0k///yzLBbLcZ/7+fmdXZEAcBKcAwJwBmd7/HSi4yNJlcK853MdDQAAAK6HoC8AnIEj0+PUqVNHvXv3Pu/tNWrUSJs3bz5u+aZNmxyfH5GUlKRJkyZp3rx5CgsLU3x8vEwmk1q2bKnk5GQlJyerX79+510TAON5eHioY8eOSk5OVlRUlJKSkiSV94Hi4mJ99tlnSktLU7du3U67rbi4OOXl5Z1Rz/Lw8NDVV1+tq6++WjabTXfddZfee+89PfXUUyccQe50v7t69Wpdeuml53Uj5XQ1AaiZvvzyS91888165ZVXHMuKioqUlZV1Rt9PSEhQQkKCxo8fr8WLF6tLly6aPHmynn32WTVq1Eg2m01bt251zKIgSWlpacrKyqp0/AUAZ2vr1q2VRszctm2bbDaboqOj9f3336u4uFjfffddpVGZzmRa+kaNGmndunWy2+2Vjq1OdD4JoHaryiDbqXqaVD6anKTjjtFONDrmyeqKi4uT3W5XTEyM40FUADhXJ+o1nAMCcCbBwcEnvL51PqOLn+91NAAAALgWs9EFAIAruPzyyxUQEKDnn39epaWlx31+6NChs9reVVddpeXLl2vJkiWOZfn5+Xr//fcVHR2tFi1aOJYfCfi9/vrr6tq1q+OiZVJSkj755BPt27fPEQYE4PqSkpK0bNkyzZ8/3/HfdlhYmJo3b65JkyY51jmdIUOGaMmSJZozZ85xn2VlZamsrEySlJGRUekzs9ms1q1bS5KKi4slSb6+vo7vncnv7t27Vx988MFxnxUWFio/P/+02ziTmgDUTBaLpdLIJJL01ltvnXZ0uJycHEdfOyIhIUFms9nRN6666ipJ0uuvv15pvSMjkPft2/d8SgdQy/3nP/+p9P6tt96SJF155ZWOkZj+OY3q1KlTT7vdq666Svv27dOXX37pWFZQUKD333+/KsoGUIOczXnb6Zyqp0lSQECAwsLC9Pvvv1da75133jnjuq699lpZLBY988wzxx3/2e32484LAeBUTtRrOAcE4Ezi4uKUnZ2tNWvWOJbt379fs2fPPudtnut1NACoSgUFBdq0aZPS09ONLgUAajxG9AWAMxAQEKB3331XI0aMUNu2bTV06FCFh4dr165d+vHHH9WlSxe9/fbbZ7y9xx57TDNmzNCVV16pcePGKSQkRNOmTVNKSoq++uormc1Hn8Po1KmT3NzctHnzZo0ZM8axvFu3bnr33XclnVnoD4BrSEpK0nPPPafdu3dX+m+7W7dueu+99xQdHV1pCvqTefjhh/Xdd9+pX79+GjlypNq1a6f8/HytXbtWX375pVJTUxUWFqbRo0fr8OHD6tWrlxo2bKidO3fqrbfeUmJiomO0k8TERFksFk2aNEnZ2dny9PRUr169VKdOneN+d8SIEZo5c6buuOMOzZ8/X126dJHVatWmTZs0c+ZMzZkzR+3btz9l7WdSE4CaqV+/fvrkk08UGBioFi1aaMmSJZo3b55CQ0NP+b3ffvtNY8eO1eDBg9W0aVOVlZXpk08+kcVi0XXXXSdJatOmjW6++Wa9//77ysrKUvfu3bV8+XJNmzZNAwcOVM+ePatjFwHUUCkpKerfv7+uuOIKLVmyRJ9++qmGDx+uNm3ayMvLyzFbwe233668vDx98MEHqlOnjvbv33/K7d522216++23ddNNN2nlypWqV6+ePvnkE/n4+FTTngFwFe3atZMkPfnkkxo6dKjc3d119dVXn9O2TtXTjhg9erQmTpyo0aNHq3379vr999+1ZcuWM64rLi5Ozz77rB5//HGlpqZq4MCB8vf3V0pKimbPnq0xY8booYceOqf6AdQ+J+s1nAMCqG7//e9/9csvvxy3fMSIEXr00Ud1zTXXaNy4cSooKNC7776rpk2b6q+//jqn3zrX62gAUJWWL1+unj176umnn9aECROMLgcAajSCvgBwhoYPH6769etr4sSJeumll1RcXKwGDRooKSlJt9xyy1ltq27dulq8eLEeffRRvfXWWyoqKlLr1q31/fffHzeSgK+vry666CL9+eef6tq1q2P5kQBgZGQk04wBNUjnzp1lsVjk4+NT6SZqUlKS3nvvvTMO9vv4+GjhwoV6/vnnNWvWLH388ccKCAhQ06ZN9cwzzygwMFCSdOONN+r999/XO++8o6ysLEVEROj666/XhAkTHA8dREREaPLkyXrhhRd06623ymq1av78+ScM+prNZn3zzTd67bXX9PHHH2v27Nny8fFRbGys7r333jOakvVMagJQM73xxhuyWCz67LPPVFRUpC5dumjevHm6/PLLT/m9Nm3a6PLLL9f333+vvXv3Onrozz//rEsuucSx3ocffqjY2Fh99NFHmj17tiIiIvT444/r6aefvtC7BqCG++KLL/Svf/1Ljz32mNzc3DR27Fi99NJLkqRmzZrpyy+/1Pjx4/XQQw8pIiJCd955p8LDwzVq1KhTbtfHx0f/+9//dM899+itt96Sj4+PbrjhBl155ZW64oorqmPXALiIDh066N///rcmT56sX375RTabTSkpKee0rVP1tCP+9a9/6dChQ/ryyy81c+ZMXXnllfr555+PO088WV2+vr567LHH1LRpU7322mt65plnJJVf57rsssvUv3//c/uHAFArnazXcA4IoLodGaDnn0aOHKnZs2frgQce0COPPKKYmBi98MIL2rp16zkHfc/1OhoAAABck8n+z/kcTiAnJ0eBgYHKzs5WQEBAddQFABfMtM1ZquNt0ZVR/kaXckHtyivV9K3ZGtM8WCFeFqPLAXCMl1elq0d9X7Wv4210KS7tUGGZpmzK0oimgWrg6250OQDO0qdbshTkaVG/Rs55TLbkQIGWHyrUvQmMggJAemf9YbUK9lS3+r6Vlk+YMEHPPPOMDh06pLCwMIOqO2ppWoGWphXqvtb0LqCmen1Nhi6p661L6taukb1/35evdZnFuqtliNGlADDQykOF+m1vvh5OrPrjrqxiqyZvyNTQxgGK9veo8u0DqBn25pfqky3ZujU+SOHerjWm2oqDhVqwL18PXYAeCqDm+2BjpmL93XVpQ78zWv+NtRnqGO6tThG169wVAM7W2eRyGRINAAAAAAAAAAAAAAAAAAAAcEIEfQEAAAAAAAAAAAAAAAAAAAAnRNAXAGoqu9EFAAAAnJjNbldOUZnRZQAAAAAAAAC1ArcNAVQrmg4AVDmCvgBqHTezVGKt+UeWJbbyfbTQ6QGn42Y2Of4bxbk78m/oZjIZXAmAM2Wz25W8PUNjZ63TrsxCFTvxMVmJzU5/AeDgZjKp+ATHbxMmTJDdbldYWJgBVR2vxErvAmo6N5OpVlzX+qdijs0AqPyamtUulV2A62pcZwJwJo70CFe8vl9is8vNTI8DcG7cTGfe+2x2u0ptdlnoOQBQpYh/Aah1Gvi4KzW3VEVWm9GlXFBbsorl725WgDutHnA2DXzdtDW7RDa7610MdCabs0rkaTYp1MtidCkATsNqs2vhtgzdPWudJs7brp2ZhcrJLVJqTomKypzvmMxqt2tbdoka+LoZXQoAJ1Hf1007ckouSKikqljtdm3LKVEDP3oXUJPV93XTtpwSWWvR+WSZza4dORybAZDq+5T3ga3ZJVW+7c1ZxXIzSXW86TUATi7UyyJPs0lbsqq+D11INq51AThPDXzdtSOnVKVncG0sJadUZXapIT0HAKoUXRVArdM61Et/ZxTpky3ZSgjxlF8NC8KW2aSU3BJtzipRrwa+MjECAeB02od7a+b2HE3fmq34YE95Wfjv9GxYbdKuvFKtzyxW57rejEIAODGrrXwE38//2qfdWUWVPvNTecD34y1ZSgjxkr+HcxyTFZbZtTGzWBnFVvWJ9DO6HABOom2YlzZlFuvTLdlqEeIpHzfnOv4oLLNrU1ax0ousurSBr9HlALiA2oV76Ysj55NBnvJ2sn5U1QrK7NpwuFi5JTZd1MjL6HIAGCzc202x/u76aVeu9uSXqq63m873spDVLu3JK9Xaw8VqH+4lD67TATgFN7NJ7cK9tDitUHmlNkX5uTv9zJpFVrs2ZRbrQEGZhsQFGF0OABeVGOaltYeL9MmWLLUM9pTvCTIWNruUXmTV6vQiNfR1Uz0fImkAUJVMdvvpH/3PyclRYGCgsrOzFRDAwR8A13egoEx/HChQak6JymrgACh1vC26KMxLF4V5G10KgJPYll2iZQcLtCevTDWwDV1woZ4WtQ71VMc63jzQADghq82u3ysCvnv+EfBtGeGnYe0aKLFBgA4WWvXHgQKlONExmVlSpJ+7LqnrrZgAD6PLAeBEduWVasmBAu3MK5WzDexrUnnv6kTvAmqFlJwSLUkr1O680hp/Pmk2SY383NUpwkdRfu5GlwPACZTa7Fq4L1+bskqUV1o1M8SUlpSprKBY47tFyt3ZE3sADGe327X8YKHWZJQ/KO7sTJIa+rnp4jo+ahzI+SKAc7c3v1SLDhRoZ26prCc5GfV1M6lZkKe61/eRJ8dVAHBaZ5PLJegLoFaz2+0nPQh1VWaTZCb0BrgMo/vQX7uzNel/2yVJl0QH6/4eMVW6/TKbTZ8v36MpyTtVbD168+XKlnU17tI4Bfqc/Y1a+hzgvKw2uxZuKw/47s3+Z8DXXze0b6DW9f2PC+gb3QuPZTGJBwgAnFJ19iy73a43FqZqSWqmJKmOv4ee7xsvf6/KI6LQu4DayZmOoU5myY4M/fvnbSqtOB9sFuGr565urkDvMzsXpL8BOBWr3a7T3+U8uYISqx79bqPSckskSde2idCN7RtUUXUAagOb3X5BHwR9Z9EurdyTLUm6N6mRWtc/+6wGx1MAqtqx56K3zliljPxShfq667/DE2Wh3wDAWTmbXC7jpAOo1Uwmk2r4DIcAnJzRfej37YcdN0R6NA6R2/nOd/gPbmaLRnZupF7x4Xrux81auTNLkvTj2gNavD1DD13eRJe1qMOFRsDFWW12zd+ari/+3qd92cWVPkuo569h7U4c8D3C6F4IAGejenuWSfd0i1ZabrG2pxcoLadEry3YoX9d0bTKj9sAuB5XOIZKahyml65x1xPfbVResVUb9+fpvlnr9NK1LRQR4GV0eQBcnMVkKh+q8hwFeLnpgZ6xeuL7TbLapdmrDyihnr/aNGDQIwBnxmwy6UKemnWNCdKK3eVB3yWpWWrbMPDC/RgAnKFjz0Xt9qN/hHwB4MJinHQAAIBaKr+4TH/uypJUfmPjogt4kTAqxEeTb0zU+L7N5OdZ/qxZZkGpnpy9QQ/MXKsD/xj5E4BrsNrsmrv5kG7/Yo1eW5BSKeSbUN9fL1wdr4n9m6tNgwAC/QBwjjzdzHq0d5yCvMuPodbtz9OUJbsMrgoAzlybhoF6c0iCQn3LR/HdlVmosV+sVUp6gcGVAYDUJNxXN1SM4muX9MbCFGUVlhpbFABUaF3PX/6eFknSX3tyVFBiNbgiAAAAGIWgLwAAQC21ODVLpRVz6yTFVv1ovv9kMpk08KL6+vKOjuoZH+5Ynrw1Q9e/t1yzVuyV7XzmWwRQbcqsNv266ZDGfLFGry9I0f6cowHfNvUDNOnqeE28uvk5TScIADhemK+HHu0d5zhem7MpXb9sPGRwVQBw5uLCfPWf61urYVD5KL6H8kp0z8y1Wrcvx+DKAEDqn1BXiRWj+GYVlumt31O5RgXAKbhZzLqkUZAkqdRm158Vo/sCAACg9iHoCwAAUEst3JrheN29cUi1/W6Yv6deGtRKLw5qpVA/D0lSfolVk37Zotum/a3U9PxqqwXA2Smz2jRn40GN+WKt3liYogPHBHwTGwToxf7N9fzV8WpFwBcAqlyzOn66s2sjx/sPl+zS2n25BlYEAGenXqCX3r4+Qc3q+EqScovL9MBX67Vkx2GDKwNQ25lNJo3rHu2YQeHvPTn6bl2awVUBQLkuMcGO13+kZBpYCQAAAIxE0BcAAKAWSsst1oa0PElSwyAvxYX5VHsNveLD9eUdHTUwsZ5j2eo92Rr2wZ/6MDlVpVZbtdcE4MRKrTb9svGgbvt8jd78PVVpuUcDvhc1DNBLA5rruX7xalnP38AqAaDm69kkVAMS6kqSbHbppd+2V3roAgCcXbCPh14f3ErtogIlScVlNj353Ub9sv6gwZUBqO2CvN01rnuM4/1nf+7V1kM8jA7AeDEh3qoX4ClJ2nwwX4fySgyuCAAAAEYg6AsAAFALLdx2dMSkHo1DZTKZDKnD38td4/vFa/KNiYoM9pYklVrtmrwwRSOmrNC6vUzjChip1GrTzxvKA75v/Z6qg8fcSGjbMFAvD2iuZ/vGq0UEAV8AqC43tm+gtg3LR07PK7bqhbnbVFBiNbgqADhzPh5umjighXo1DZMkWe3SC79u1YwVewyuDEBtl9ggQNe2jpBU3ptenb+D4ywAhjOZTOp6zKi+i1MZ1RcAAKA2IugLAABQy9jtdi3cliFJMknqFhdibEGS2kcHa8aYDrq5c5QsFaHjbQfzdcvUlXrl160q5KYKUK1KrTb9VBHwfTs5tdJIIe0jA/XKwBb6d99mak7AFwCqncVs0v09Y9UwyEuStDurSK8vSJHVZje4MgA4cx5uZj11VVNdc8wML5OTd+rd31Nks9PPABhnaLv6ahruK0lKyy3Re4t2yk5fAmCwTtFBjtd/pGTSlwAAAGohgr4AAAC1zJaD+dpfMcVzq3r+CvPzMLiicl7uFt3TK07TRrVTswg/SZJd0ozle3T9e8u1dPvhU28AwHkrtdr0w/o0jZ6xRv/5Z8A3KlCvXtNCz1zVTPF1/QysEgDg62HR433i5OdhkSSt2J2t6Sv3GlwVAJwds8mke3vE6NbOUY5ln6/cp4lztqrMajOwMgC1mZvZpPt7xsjHvfwWavKOTP22NcPgqgDUdmG+Hmpe9+hDCNszCgyuCAAAANWNoC8AAEAts2Db0cBsjyahBlZyYvH1/DVtVDvd0ytWnm7lh6v7sos0dsZqPf3tRmUVlBpcIVDzlJTZ9P26NN06Y7Xe/WOn0vOPBnw7RgXptWta6Jkrm6lZHQK+AOAs6gV46cFesTKXT4ag2WvSHLM2AICrMJlMuuniSD14aZyjn83ZeEjjv9+kolJmdgFgjLr+nrorKdrx/sMlu7Unq9C4ggBAUteYYMfrRSlZxhUCAAAAQxD0BQAAqEVKrTYt2lEe9PWwmHRJoyBjCzoJN7NZN3dupBljOqjdMTX+uPaABk9epjnr05ieDKgCJWU2fbfugEbPWK3Ji3YqI/9okP7iRkF6/dqWevrKpmpKwBcAnFKbBgG65eJIx/t3/tiprYfyDawIAM5N/9YReqZvvDws5WnfJSmZeuCr9cop4kFPAMboHBOsPs3CJEnFZTa9Mj9FJWWMNg7AOO0jAx3HSkt3ZqmUGRAAAABqFYK+AAAAtcjK3dnKKykfFemS6GB5V0z37KyiQnw0+cZEje/bTH6ebpKkzIJSPTl7gx6YuVYHsosMrhBwTcVlNn279oBunbFa7y3apYxjRsq+JDpIb1zbUv+6oqmahPsaWCUA4Exc1SLcEUIptdo1cd52ZRwzMjsAuIpuTUL14jUt5Vtxnrp+f67u+WKtDuYWG1wZgNpq1CWRigzykiTtPFyoacv3GFwRgNrM292idg0DJUn5JVat3pdrcEUAAACoTgR9AQAAapEF2w47XvdoHGpgJWfOZDJp4EX19eUdHdUzPtyxPHlrhq5/b7lmrdgrG6P7AmekqNSq2WvKA77vL96lw8cEfDtFB+vN61rqqcubqjEBXwBwGSaTSaM7RapFRPno65kFpZo4b7uKGXEOgAu6KDJQbwxupRAfd0lS6uFC3f3FGqVmFBhcGYDayNPNrAd6xjpG0Px54yEtS80ytigAtVqXmGDH60UpmQZWAgAAgOpG0BcAAKCWyCkq01+7syVJwT7uSqjvb3BFZyfM31MvDWqlFwe1Uqifh6TykQsm/bJFt037W6npTFMNnEx5wHe/bp2xRh8u2aXMYwK+XWKC9dagVhp/eRPFhRHwBQBX5G4x6+FesQqvOEbanl6gd5JTZedhKAAuqEkdP719fYIaBJaPonkwt0T3zFyrDfsZtQ5A9WsU4q1bLo50vP9PcqrS85g9AYAxWkb4Kci7fOa7VftylVtcZnBFAAAAqC4EfQEAAGqJRTsOq8xWHvboFhcii9lkcEXnpld8uL68o6MGJtZzLFu9J1vDPvhTHyanqtTK6HXAEUWlVn21er9unbFaHy7ZrazCYwK+scF6e1ArPXFZE8WG+hhYJQCgKgR6u+vxPnHyciu/3Je8I1Oz16QZXBUAnJsGQd56+/oEx0wTOUVluv/LdVqWysh1AKrfZfFhuiQ6SJKUV2LVawtSZLXxQBWA6mcxm9SpUZAkyWqza9nOLEPrAQAAQPUh6AsAAFBLLNiW4Xjdo3GogZWcP38vd43vF6/JNyaqYbC3JKnUatfkhSkaMWWF1u3NMbhCwFiFpVZ9uWq/Rk1frf8u3a2swvLRPUySkmJD9J/BrfREnyaKIeALADVKdIiPxnWPdrz/bMVe/bkry7B6AOB8hPh66I3BrZTYMECSVFRm0+PfbtTcjQcNrgxAbWMymXRX10YK8y2fPWFjWp5mrdpvcFUAaqsuMcGO14tSsowrBAAAANWKoC8AAEAtsDerSFsPFUiSYkK81SjE2+CKqkb76GB9PqaDbu4cJYupfITibQfzNeqjlXp17lYVllgNrhCoXoWlVs1atU+jpq/W1GW7lV10NODbLa484PtYn8aKDiHgCwA11SXRwRrWtr4kyS7ptQUp2nm40NiiAOAc+Xm66cVrWqpbxcOqVptdz/6yVbP+2mdwZQBqGz9PNz3QM0ZHJsia9fd+rdufa2xRAGqlqGBvRQV5SZK2ZxRof06xwRUBAACgOhD0BQAAqAUWHjOab3cXH833n7zcLbqnV5ymjWqnZhF+kiSbXZq+bI+uf2+5lm4/bHCFwIVXUGLVzL/LA74fLdujnGMCvt0bh+idIQl6tHdjNSLgCwC1wqDECMcoT0WlNk2ct83x/xsAwNV4upk1oW8z9U+o61j29sIUvf9Hqux2u4GVAaht4uv6aegxD1S9viCFYywAhqg8qm+mgZUAAACguhD0BQAAqOFsdrsWbCsPu5pNUlJciMEVXRjx9fw1bVQ73dMrVp5u5Ye5+7KLNHbGaj397UZlFZQaXCFQ9QpKrPr8r30aNX2Vpi0/GvA1m6QejUP17pAEPXJpY0UF14xRvAEAZ8ZkMmlst2jFhZU/4JGWW6KXf9uuMhuBOACuyWI26YFL43TzxZGOZZ/9uVcvzt1GbwNQra5pHaGEev6SpMMFpXr7dx46AFD9OkUHqWKCOy1OzZSNPgQAAFDjEfQFAACo4TYeyFN6fokkKbFBgIJ93A2u6MJxM5t1c+dGmjGmg9o1CnIs/3HtAQ2evExz1qdx8wU1Qn5xmT7/a69umb5Kn/y5R7nFVknlAd9eTcoDvg9fGqdIAr4AUGt5upn1aO84BXm7SZLW7c/TlCW7DK4KAM6dyWTSqM5Ruq9XrCpyLfpp/UH96/tNKi6zGlobgNrDYjbp3u7RCvAqP8ZasTtbP64/aHBVAGqbIG93JUSUP3SQnl+qLYfyDa4IAAAAFxpBXwAAgBpuwbYMx+vujUMNrKT6RIX4aPKNiRrft5n8PMtvvGQWlOrJ2Rv0wMy1OpBdZHCFwLnJKy7T9JV7dcv01frkz73KOybge2nTUE0ekqAHe8WpYRABXwCAFObroUd7x8nNXB6Jm7MpXb9sPGRwVQBwfq5pU0//uqqZo7ct2nFYD329QbkVs1sAwIUW4uuhcd2iHe8//nOvdqQXGFcQgFqpc0yQ4/WilEzjCgEAAEC1IOgLAABQgxWX2bS44iKft7tZHY8Z5bamM5lMGnhRfc26o6N6xoc7lidvzdD17y3XrBV7mdIMLiOvuEyfrdijUdNX67MVe5VfcjTg27tpmN67vrUe6BmnBgR8AQD/0KyOn+7s2sjx/sMlu7R2X66BFQHA+evVLEwvXtNC3u7ltzjW7M3RuFlrlZ5XbHBlAGqLtpGBurpVHUlSmc2uV+bvUGEJo4sDqD7tGgbKy638WGj5rmyVlNkMrggAAAAXEkFfAACAGuzPnVkqLC2/wNc5JliebrXv8C/c31MvDWqlFwe1UqifhyQpv8SqSb9s0W3T/lZqOtOawXnlFpfp0z/LA77TV+6rFPDt0yxM71/fWvf3jFX9QC+DKwUAOLOeTUI1IKGuJMlml176bbsO5BCGA+Da2kUF6Y3BCQrydpck7Ugv0N1frNXuzEKDKwNQW9zYvoHiwnwkSftzivXBkl0GVwSgNvF0M6tDVKAkqbDUpr/25hhcEQAAAC6k2pf0AAAAqEUWbMtwvO7RONTASozXKz5cX97RUQMT6zmWrd6TrWEf/KkPk1NVamXEAziP3KIyfVIR8J3x19GAr8Vs0mXx4Xp/aGvd1yNW9Qj4AgDO0I3tG6htwwBJUl6xVS/M3aYCRp0D4OKa1fXTf65PUESApyTpQE6xxn6xVpvT8gyuDEBt4G4x64GeMfKqGF18wbbDWrA14zTfAoCq0zUm2PF6UcXMfgAAAKiZCPoCAADUUJkFpVpV8RR/mK+Hmkf4GVyR8fy93DW+X7wm35iohsHekqRSq12TF6ZoxJQVWseoBzBYTlGppi3frVumr9Lnf+1zBLAsZpMujw/X+9e31r3dY1QvgIAvAODsWMwm3d8zVg0qHhLZnVWk1xekyGqzG1wZAJyfhsHe+s/1CYqtGFUzq7BU985aqxU7s4wtDECtUC/AS3d0aeR4//7iXdqXXWRgRQBqk2Z1fBXqUz67wdr9ucouLDW4IgAAAFwoBH0BAABqqD92HNaR3EaPxiEym0zGFuRE2kcH6/MxHXRTpyhZKv5dth3M16iPVurVuVtVyOh2qGbZhaWatmy3Rk1frZl/71dhafkI025mk65sHq4Ph7bWuO4xjpHKAAA4F74eFj3eJ05+HhZJ0ord2Zqxcp/BVQHA+Qvz89SbgxPUukH5yOWFpTY9+s0G/bb5kMGVAagNusWFqGeT8pm0ispsenV+CjNHAagWZpNJnStG9bXZpSU86AQAAFBjEfQFAACooY6dKrB741ADK3FOXu4Wjbs0TtNGtVOzitGObXZp+rI9uv695Vq6/bDBFaI2yC4s1dQjAd9VlQO+V7Woow+GttbYbjGq40/AFwBQNeoHeunBXrEyVzwD9vWaA1q4jSmmAbg+fy83vXxtC3WNC5Ekldns+r+ftujrVfsNrgxAbTC6U6QaBJafu+/IKNCnf+41uCIAtUWX6CDH60UpmcYVAgAAgAuKoC8AAEANlHq4QCmHCyVJTcJ91CDIy+CKnFd8PX9NG9VO9/SKladb+eHxvuwijZ2xWk9/u1FZBUx3hqqXVViq/y7dpVHTV+vLVftVVHY04Nu3RR19OKy17k6KJuALALgg2jQI0C0XRzrev/PHTm09lG9gRQBQNTzdLHqmX7yuallHkmSX9Mb8HZqyeKfsdruxxQGo0bzdLXqgZ6zcKp6m+n79Qa3YlW1wVQBqg/qBXooN8ZYk7cws0u6sQoMrAgAAwIVA0BcAAKAGWrjt6Gi0PRjN97TczGbd3LmRZozpoHaNghzLf1x7QIMnL9Oc9WncFEaVyCos1ZQl5QHfr1YfqBTw7deyjqYMa6O7kqIV7kfAFwBwYV3VIlx9moVJkkqtdk2ct10Z+SUGVwUA58/NbNIjfRrrhg4NHcs+XrZHr/xvu6w2zusAXDgxoT66uePR3vN2cirHVwCqRZeYYMfrRSlZxhUCAACAC4agLwAAQA1jtdn1+/byoK+b2aQusSEGV+Q6okJ8NPnGRI3v20x+nm6SpMyCUj05e4MemLlWaTlFBlcIV3W4oEQfVgR8v15zQMUVAV93i0lXt6qrKcPb6M6u0Qrz8zC4UgBAbWEymTS6U6RaRPhJKj/mmThvu+P/RwGAKzOZTBrTtZHu6R7jWPb92jRN+HEzfQ7ABXVVi3B1iAqUJOUUlemNhSk8ZADggrukUZAs5QOKa0lqpmz0HQAAgBqHoC8AAEANs3ZfrjILSiVJbRsGKMDLzeCKXIvJZNLAi+pr1h0d1TM+3LE8eWuGhkxerlkr9srG6L44Q4fzS/T+4p26dfpqzT4m4OthMWlAq7qaMqyN7ujSSGG+BHwBANXP3WLWw71iFV7xoMn29AK9k5zKTAYAaoxBbetr/JVNZTGXJ19+35ahR2avV15xmcGVAaipTCaTxiZFK9TXXZK0bn+evl59wOCqANR0/l5ual0/QJKUWVimDWl5BlcEAACAqkbQFwAAoIZZsC3D8bpHk1ADK3Ft4f6eemlQK704qJVCK8Iv+SVWTfpli26b9rdS0/MNrhDO7HB+id5ftFO3zlitb9emqcRaHpjysJg0IKE84DumSyOFEvAFABgs0Ntdj/eJk5db+WXC5B2Zmr0mzeCqAKDq9IkP18QBzeXtXt7nVu3J0b2z1ikjv8TgygDUVP5ebrqve4wqnjHQF3/v08YDhO4AXFhdY4Idr/9IyTSwEgAAAFwIBH0BAABqkMISq5btzJIk+XlY1C4y0NiCaoBe8eH68o6OGphYz7Fs9Z5sDfvgT32YnKpSK9O+4qj0/BJNXrRTo2as1rfrjgZ8Pd3MuqZ1hKYMb6MxnRsphIAvAMCJRIf4aFz3aMf7z1bs1Z+7sgyrBwCqWsfoYL02qJUCK2a82XYoX2O/WKu9WYUGVwagpmpZz1+DK64l2ezSawt2KJfRxAFcQIkN/OXjbpEkrdidraJSq8EVAQAAoCoR9AUAAKhBlu7MUnFZefC0S2yw3C0c7lUFfy93je8Xr8k3JqphsLckqdRq1+SFKRoxZYXW7c0xuEIYLT2vRO/+karRM1br+3VpKj0m4Htt6whNGdZGoztFKcSHgC8AwDldEh2sYW3rS5Lskl5bkKKdhwnAAag5mkf4663rE1TX31OStC+7SGO/WKstBxllE8CFMSixnlpE+EmS0vNL9U7yTtntdoOrAlBTuVvMurhR+cAfJVa7VuzmmjUAAEBNQvIDAACgBlmwNcPxukfjUAMrqZnaRwfr8zEddFOnKFlM5fMvbjuYr1EfrdSrc7eqsIRREmqbQ3nFeic5VbfOWK0f1h+sFPC9rk2E/ju8jW7tFKVgH3eDKwUA4PQGJUaoS8V0r0WlNk2ct005RYw8B6DmaBTio7evT1BMqI8k6XBBqe6dtU5/7842uDIANZHFbNJ9PWLk51k+wuaynVn6ZeMhg6sCUJN1rTifk6Q/UjINrAQAAABVjaAvAABADZGeV6J1+3MlSfUCPNW0jq/BFdVMXu4Wjbs0TtNGtVOzilFZbHZp+rI9uv695Vq6/bDBFaI6HMwt1n+SUzV6xhr9uOGgymzlAV8vN7MGJdbT1OFtNOqSKAV5E/AFALgOk8mksd2iFVsRgEvLLdHLv213/P85AKgJ6vh76s0hrdSqnr8kqaDEqodnr9fCrekGVwagJgrz9dDYpGjH+4+W71Hq4QLjCgJQozUO81Edv/IZxTam5elwQYnBFQEAAKCqEPQFAACoIX7fflhHIhjdG4fKVDHiLC6M+Hr+mjaqne7pFStPt/LD6n3ZRRo7Y7We/najsgpKDa4QF8LB3GK9/XuKbvt8jX46JuDr7W7WkMR6+u/wNrrl4kgFEvAFALgoTzezHusdpyBvN0nSuv15mrJkt8FVAUDVCvBy1yvXtVSnilHvSq12Pf3DZn235oDBlQGoiTo2CtJVLcIllfebV+enqKiUWaEAVD2TyeQY1dcuaXFqlqH1AAAAoOoQ9AUAAKgB7Ha7FmzLcLzv3jjEwGpqDzezWTd3bqQZYzqoXaMgx/If1x7Q4MnLNGd9mux2RsCrCdJyi/XmwvKA788bD1UO+F5UHvC9mYAvAKCGCPPz0KO94+RmLn9wbM6mQ0wzDaDG8XK36Nmr43VFizqSysMwr/xvuz5auovzOABV7qYODRUT4i1J2pNVpClLeZAKwIXROSbI8XpRSibHNQAAADUEQV8AAIAaYHt6gfZkFUmSWtT1U11/T4Mrql2iQnw0+cZEje/bTH6e5aPfZRaU6snZG/TAzLVKyykyuEKcq/05RXqjIuA7Z1PlgO/QtvU1dXiibu4YqQAvAr4AgJqlWR0/3dm1keP9h0t2ae2+XAMrAoCq52Yx67HLGmtouwaOZVOX7NYb83fIaiMUA6DqeLiZ9UDPWHlVzAr1vy0Z+mPHYYOrAlAT1fHzVNNwH0nS3uxipWYWGlwRAAAAqgJBXwAAgBpg4bajNwa6Nwk1sJLay2QyaeBF9TXrjo7q2SzMsTx5a4aGTF6uWSv2ysboCS5jf06RXl+wQ2M+X6NfNx1y3OT38bBoWNv6mnpDokZ0aCh/LzeDKwUA4MLp2SRUAxLqSpJsduml37brQE6xwVUBQNUymUy6s1u07kyKdiybvfqA/v3zZpWU2YwrDECN0yDIS6M7RTreT/5jJ8dWAC6ILjHBjteLUrKMKwQAAABVhqAvAACAiyuz2ZVcMQKIu8WkztFBxhZUy4X7e+qlwQl6cVArhfp5SJLyS6ya9MsW3Tbtb6Wm5xtcIU5lX3aRXptfHvCduzldRwbx8vWwaHi7+vrv8Da6sUND+XsS8AUA1A43tm+gtg0DJEl5xVa9MHebCkqsBlcFAFVvaPsGevzyJrKYyt/P35Khx77ZoIKSMmMLA1Cj9GwSqm5xIZKkglKbXluwwzF7EABUlY5RQXI3lx/ULEnNpM8AAADUAAR9AQAAXNzfe7KVU1R+47FDVJB8CSA6hV7x4fryjo4amFjPsWz1nmwN++BPfZicqlIrI0M5k73ZRXp1/nbd/sUazdtSOeB7Q/sG+u/wNrqhPQFfAEDtYzGbdH/PWDUI9JIk7c4q0usLUpjSHkCNdEWLOnquf3N5upXfOlm5O1v3zVqnzIISgysDUFOYTCbd3jlKEf6ekqSthwo0fcVeg6sCUNP4elh0UcUDm7nFVq3dn2twRQAAADhfBH0BAABc3MJthx2vezQONbAS/JO/l7vG94vX5BsT1TDYW5JUarVr8sIUjZiyQuv25hhcIfZmFeqV37brji/W6H9bMhwBXz9Pi25s30BTh7fR8HYN5EfAFwBQi/l6WPR4nzj5eVgkSSt2Z2vGyn0GVwUAF0an2BC9el1Lx0N+mw/ma+wXa7U/u8jgygDUFN4eFj3QM0ZuFaNtfrM2TX/vyTa4KgA1TZeYYMfrRSmZBlYCAACAqkDQFwAAwIXlFZfpz11ZkqQALzclVjylD+fSPjpYn4/poJs6RcliKr+Js+1gvkZ9tFKvzt2qQqa/rna7Mwv10v+2646Za/Xb1qMBX39Pi0Z0aKipwxM1rF0DRsgGAKBC/UAvPdgrVhV5FH295oAWbsswtigAuEBa1Q/QW0MSFO7nIUnak1Wku79Yo+2H8g2uDEBN0TjcVze2b+B4/+bCVGUWlBpYEYCaJqGev/w9yx/W/HtPjvK5Bg0AAODSCPoCAAC4sMUpmSq1licUk+JCHCOBwPl4uVs07tI4TRvVTk3r+kmSbHZp+rI9uv695Vq6/fBptoCqsCuzUC/+b5vunLlWC7YdDfgGeLnp5o4N9d/hiRratr58KkYsBAAAR7VpEKBbLo50vH/nj53aSugNQA0VE+aj/1zfWlEh5bOzZOSXatystVrNqJsAqki/VnXUtuKh/eyiMr25MEU2u93gqgDUFG5mkzo1CpIkldrsWl4xYAgAAABcE0FfAAAAF7Zw29FwaI/GoQZWgjMVX89fH49qp3t6xcrTrfxwfF92kcbOWK0J321UFqO3XBC7Mgs1ad423TVzrRZuO6wjt82OBHynDGujIRcR8AUA4HSuahGuPs3CJEmlVrsmztuujPwSg6sCgAujboCn3hqSoBYR5Q9r5hVb9dDXG7RoOyOaAzh/ZpNJY7tFK9jHXZK0el+uvlmTZnBVAGqSLrHBjteLU7KMKwQAAADnjaAvAACAizqQU6yNaXmSpMggL8WGehtcEc6Um8Wsmzs30owxHdSuYlQFSfphzQENnrxMc9anyc4ILlUi9XCBJs4tD/j+vr1ywPeWiyP13+EEfAEAOBsmk0mjO0WqRcUMBZkFpZo4b7uKy2wGVwYAF0aQt7teHdRKHSvO3UqsNo3/fpN+XEcYD8D5C/J2133do3Vkjq4ZK/dq88E8Q2sCUHNEB3urfoCnJGnzoXwdzCs2uCIAAACcK4K+AAAALmrhtqMjCHVvHCqTyXSKteGMokJ8NPnGRI3v20x+nm6SysMyT87eoAdmrlVaTpHBFbqu1IwCPT93q+6etU7JO44GfIO83TTqkkhNHd5GgxLrydudgC8AAGfL3WLWw5fGKtzPQ5K0Pb1A7/yxkweVANRY3u4WPT+guXrHl49obrNLL87dpk+X76H3AThvCfUDdF2bCEmS1S69Nj9F+cVlBlcFoCYwmUzqEsOovgAAADUBQV8AAAAXZLfbtXDbYUmSSVK3xiHGFoRzZjKZNPCi+pp1R0f1rJgGW5KSt2ZoyOTlmrVir2zcOD5jKRkFev7Xrbr7y3VatCPTsTzI2023XhKpKcPa6Lo29eRFwBcAgPMS6O2ux/vEycut/PJi8vbDms1U0wBqMHeLWU9e0VSDL6rnWPbBop16e2EK52wAztv1beurWR1fSdLBvBK9u2gXDxIAqBKdo4Mco4YvSsmktwAAALgogr4AAAAuaPPBfB3ILZ9mK6G+v8J8PQyuCOcr3N9TLw1O0IuDWim0YnS8/BKrJv2yRWM+/lup6fkGV+jctqfn69k5WzX2y3ValHI04Bvs467RnaI0ZVgbXUvAFwCAKhUd4qNx3aMd7z9bsVd/7soyrB4AuNDMJpPu7h6jMV0aOZZ9+fd+PffLFpVabQZWBsDVWcwm3d8jRr4e5dctFqdkat6WjNN8CwBOL9TXQ83r+kmS0vJKtC29wOCKAAAAcC4I+gIAALigBduOXujv3jjUwEpQ1XrFh2vW7R01MPHoKFGrdmdr2Ad/6sPkVG4e/8P29Hz9e84WjftqvZakHg34hvi4a0zn8oDvNa0jCPgCAHCBXBIdrGFt60uS7JJeW5CinYcLjS0KAC4gk8mkGzo21CN9GstcMTzevE3peuLbjSoosRpbHACXVsffU3d1PfogwZQlu7Q7k+MqAOevS0yQ4/WxgyQAAADAdRD0BQAAcDElZTYt2lF+Mc7TzaxLooOMLQhVLsDbXeP7xWvyjYlqGOwtSSq12jV5YYpGTFmhdXtzDK7QeFsP5ev/fikP+C5NzXIsD/Fx1+2do/ThsDYakBAhTzdOeQAAuNAGJUaoS0ywJKmo1KaJ87Ypp6jM4KoA4MLq26qu/n11vDws5eccy3dm6YGv1imrsNTgygC4sk4xwbo8PkySVGK165X5O1RcxkPfAM5P+8hAeVjKn1BatiubwSQAAABcEHe9AQAAXMzK3dnKrxgl6JLoIHkzUmmN1T46WJ+P6aCbOkXJYiq/ELvtYL5GfbRSr87dqsJaOFrU1kN5eubnLbrv6/VatjPLsTzU1113dGmkKcPaqD8BXwAAqpXJZNLYbtGKDfWRJKXllujl37arzGY3uDIAuLC6xoXq5WtbyM+z/Lx844E83fPFWqXlFBtcGQBXNvLiSEUFe0mSdmUW6aNluw2uCICr83a3qH1koCQpv8SqVftyDa4IAAAAZ4u73wAAAC5mwbYMx+vujUMNrATVwcvdonGXxmnaqHZqWtdPkmSzS9OX7dH17y3X0u2HDa6wemw+mKenf96s+77eoOW7shzLw3w9dGfXRvpwaBtd3aquPAj4AgBgCE83sx7rHacgbzdJ0rr9eZqyhFAKgJqvTcNAvTk4QaG+7pKkXZmFuvuLNUpJLzC4MgCuytPNrAd7xjpG35yzKV1LUjINrgqAqzsyC4skLaKnAAAAuBzuggMAALiQ7MJS/bU7W5IU4uOuhHr+BleE6hJfz18fj2qnsb1iHaPV7ssu0tgZqzXhu43KKqiZ08NuSsvT0z9t1gOzN2jFrmzH8nA/D93VtZE+HNZa/VoS8AUAwBmE+Xno0d5xcjMfCaUc0i8bDxlcFQBceHHhvvrP9a3VMKh8BM5DeSW6Z+ZarduXY3BlAFxVZLC3br0k0vH+nT926mAuo4UDOHct6/o5HsxcvTdHuUVlBlcEAACAs8HdcAAAABeyKCVT1ooZkLvFhchSEaJA7eBmMWtk50aaMaaD2jUKciz/Yc0BDXlvmX5dnya7vWZMkb3xQK6e+nGzHvxmg1bsrhzwHZsUrQ+GtlbflnXlbuGUBgAAZ9Ksjp/u7NrI8f7DJbu0lmlhAdQC9QK99Pb1CWpWx1eSlFtcpge+Wq8lO2rHLCwAql7vZmHqXDECZ36JVa8tSJHVVjOu+wCofmazSZ2jy3uK1S4tO2bWNAAAADg/7ooDAAC4kAVbMxyvuzcONbASGCkqxEeTb0zU+L7N5OdZPgrD4fxSPTF7gx6YuVZpOUUGV3juNhzI1VM/btJD327UX3uOBnzr+Hnonm7lAd8rW9Qh4AsAgBPr2SRUAxLqSpJsduml37brQA4j0AGo+YJ9PPT64FZqFxUoSSous+nJ7zbqlw0HDa4MgCsymUy6s2sj1fHzkCRtPpivL/7aZ3BVAFxZl5ggx+s/UjKNKwQAAABnjbvjAAAALmJPVpG2pRdIkmJCvdUoxNvgimAkk8mkgRfV16w7OqpnszDH8uStGRoyeblmrdgrmwuN7rt+f66e/GGTHv52o/7ac3R627r+HhrXLVrvD22tK5oT8AUAwFXc2L6B2jYMkCTlFVv1wtxtKiixGlwVAFx4Ph5umjighXo1LT9Ps9qlF+Zs1ecr9hpcGQBX5Oth0f09Y3RkUq+vVh/Q2n05p/4SAJxEZJC3ooK9JEk7Mgq134UHjAAAAKhtuEsOAADgIhZuOzqabw9G80WFcH9PvTQ4QS8OaqXQihFe8kusmvTLFo35+G+lpucbXOGprduXoye+36RHvtuoVXuP3qiKCPDUvd1j9P71rXU5AV8AAFyOxWzS/T1j1SCw/Cby7qwivc500wBqCQ83s566qqmuSaznWPZucqre/T3FpR7IBOAcmtXx0/B2DSRJdkmvL0xVdmGpsUUBcFldY4IdrxelZBlXCAAAAM4Kd8sBAABcgM1udwR9zSYpKS7E4IrgbHrFh2vW7R018Jgbyat2Z2vYB3/qw+RUlVptBlZ3vLX7cvT49xv16PebtPqYkWjqBXjqvh4xem9Igi6LD5cbAV8AAFyWr4dFj/eJk5+HRZK0Yne2ZqxkumkAtYPZZNK9PWI0qlOUY9nnK/dp4pytKnOy8zMAzm9g67pqU99fkpRZUKq3fk/lwQEA5+SSRkEyVYwSviglk14CAADgIrhrDgAA4ALW789Ten75SB2JDQIU5O1ucEVwRgHe7hrfL17v3pCohsHekqRSq12TF6ZoxJQVWrfX2Kkd7Xa7Vu/N0WPfbdRj32/Smn25js/qB3jq/h4xeu/61urTjIAvAAA1Rf1ALz3YK9Yx3fTXaw5UmqkCAGoyk8mkmy+J1IOXxjn64JyNhzT++00qKrUaWxwAl2I2mTSue4wCvdwkSX/tydEP6w4aXBUAVxTk7a6EiPIHBzIKSrX5oHPPCAcAAIBy3D0HAABwAceGIXo0CTWwEriCDjHB+nxMB93UKUqWiuEZth3M16iPVurVuVtVWFK9N5SPBHwf/W6Tnvhhk9buPxrwbRDopQd7xmry9a3Vu1m4LEfufgMAgBqjTYMA3XJxpOP9O3/s1NZD3EwGUHv0bx2hZ/rGy91Sfr6zJCVTD3y1XjlFpQZXBsCVBPu4697uMY73n67Yq+3pHFMBOHtdY4IdrxelZBpYCQAAAM4UQV8AAAAnV1xm0+LU8ottPu5mdYgKMrYguAQvd4vGXRqnaaPaqWldP0mSzS5NX7ZH17+3XEu3H77gNdjtdv29J1uPfrdRT/ywSesPHA34Ngzy0kO9YvXukAT1ahpGwBcAgBruqhbh6tMsTFL5jAMT521XRn6JwVUBQPXp1iRUL17TQj4eFknS+v25uueLtTqYW2xwZQBcSWLDAA1MqCtJKrPZ9cpvKSqo5ge6Abi+tg0D5O1eHhVZvitbxWU2gysCAADA6RD0BQAAcHLLdmapqLT8QlunmGB5unEIhzMXX89fH49qp7G9Yh3/t7Mvu0hjZ6zWhO82Kqug6keQstvt+mt3th7+dqPG/7hZ6w/kOT5rGOSlh3vF6p3BCerZhIAvAAC1hclk0uhOkWpR8QBSZkGpJs3bzg1lALVK28ggvTG4lUJ83CVJqYcLdfcXa7TzcIHBlQFwJcPa1VeTcB9J0oHcYr2/eJfsdrvBVQFwJR5uZnWIDJQkFZXZ9NeebIMrAgAAwOmQEgEAAHByC7ZmOF73aBxqYCVwVW4Ws0Z2bqQZYzqoXaMgx/If1hzQkPeW6df1aVVyQ8hut2vl7iw99M0GPfXTZm1MOxrwjQr21iOXxumdwQnqQcAXAIBayd1i1sOXxircz0OStC29QO/8sZNgCoBapWkdP719fYLqB3pJkg7mluieL9Zqw/7c03wTAMq5W8y6v0esfCpG4/x9+2Et2HbhZ24CULN0jQl2vF6UkmVcIQAAADgjBH0BAACc2OGCUq3ZlyNJquPnoeYRfgZXBFcWFeKjyTcmanzfZvLzdJMkHc4v1ROzN+iBmWuVllN0Ttu12+1asStLD36zQf/6aYs2Hcx3fNYo2FuP9Y7Tfwa3UvfGoQR8AQCo5QK93fV4nzh5Vcw0kLz9sGavSTO4KgCoXg2CvPX29QlqHO4rScouKtP9X67T8tRMgysD4CoiAjx1R9dGjvfvL96lvVnndl0HQO3UtI6vwnzLZxlYeyBXWYVVP/MbAAAAqg5BXwAAACeWvP2wbBUDnHVrHCKziZAkzo/JZNLAi+pr1h0d1bNZmGN58tYMDZm8XLNW7JXtDEfVs9vtWr4zSw98s0FP/7xFm48N+IZ467HejfX24FZKigvl/3YBAIBDdIiPxnWPdrz/bMVe/bkry7B6AMAIob4eemNwKyU2DJBUPm32Y99u1NxNhwyuDICr6Bobokubls/+VVxm06vzd6jUajO4KgCuwmwyqXN0+ai+dru0JDXL2IIAAABwSgR9AQAAnNiCbRmO1z0ahxpYCWqacH9PvTQ4QS8OaqXQiumz80usmvTLFo35+G+lpuef9LvlAd9M3T97g575ZYu2HBPwjQnx1hN9GuvtQa2UFEc4HQAAnNgl0cEa1ra+JMku6bUFKdp5uNDYogCgmvl5uunFa1qqW8X5vtVm17M/b9GXf+0zuDIAruLWSyLVINBLkpRyuFAfL99rcEUAXEmXmCDH60UpzCwAAADgzAj6AgAAOKmUjAJH2KFpuK/qV1y0B6pSr/hwzbq9owYm1nMsW7U7W8M++FMfJqdWGgnGbrdraWqm7vt6vZ75Zau2Hjom4Bvqoycva6w3B7VSl1gCvgAA4PQGJUaoS0z5CFJFpTZNnLdNOUVlBlcFANXL082sCX2bqX9CXceytxam6P0/dsp+hrOtAKi9vNwterBXjNwt5ddhftxwUMt3ZhlbFACXUS/AS3GhPpKkXVlF2pXJw5cAAADOiqAvAACAk1q47bDjdffGIQZWgpouwNtd4/vF690bEtUw2FuSVGq1a/LCFI2YskLr9mZrSUqm7v16vf49Z6u2pRc4vhsb6qPxlzXRm9e1VOcYAr4AAODMmUwmje0WrdiKG8tpuSV6+bftKrMRbANQu1jMJj1waZxuvjjSseyzP/fopXnb6IkATis6xEcjOzZ0vH87OVXp+SUGVgTAlVQa1TeVUX0BAACcFUFfAAAAJ2S12fX79gxJkpvZpK6xBH1x4XWICdbnYzropk5RslQEdndnFenBbzbo2V+3avsxAd+4MB89dXl5wLdTTDABXwAAcE483cx6rHecgrzdJEnr9udpypLdBlcFANXPZDJpVOco3dczVkfOrn5cd1BP/7BJxWVWQ2sD4PyuaB6uixsFSZLyiq16fUGKrDwoAOAMXBwVJIu5/OhjSWqWbPQOAAAAp0TQFwAAwAmt2ZejrMLyaYvbRQbK38vN4IpQW3i5WzS2V6wevKKJ6oX7KijQS25uFsfn9fw99fQVTfTGtS11SXSwTAR8AQDAeQrz89CjvePkVnFzec6mQ/pl4yGDqwIAY1yTWE//uqqZoyf+sf2wHvp6g3KLygyuDIAzM5lMuiupkcJ83SVJGw7k6ctV+w2uCoAr8PdyU2J9f0lSVmGZ1qflGVwRAAAAToSgLwAAgBNasO2w43X3xozmi+phs9uVvP2w7vlynaat2CvbMZ+VllqVmV2kVTsO66fVB5RdyE1mAABQdZrV8dOdXRs53n+4ZJfW7ss1sCIAME6vZmGaNLCFvN3Lb+Gs2ZujcbPWKj2v2ODKADgzf0833d8jVhXPCWjWqv1av5/jKQCn1zkm2PF6UUqmgZUAAADgZAj6AgAAOJnCEquWpZZfTPPztKhdZKDBFaGmKw/4ZmjsrHWaOG+bUg8XOj5rVsdXd3dtpOgAT5WUlE8X+8OaAxry3jL9uj5NdjtTuQEAgKrRs0moBiTUlSTZ7NJLv23XgRxCbQBqp/aNgvT6oFYK8i4fnXNHeoHu/mKtdmcWnuabAGqz5hF+uv6i+pLKj6deW5DCiOAATiuxvr98PcpndVuxO1uFpVaDKwIAAMA/EfQFAABwMktSM1ViLQ9Pdo0NkbuFQzZcGFabXQu3ZejuWes0cd527TzmhnF8HV/931VN9crAFrqqZV1NHnGRxvdtJj9PN0nS4fxSPTF7gx6YuVZpOUVG7QIAAKhhbmzfQG0bBkiS8oqtemHuNhWUcJMZQO0UH+Gvt69PUESApyTpQE6xxn6xVpuZUhvAKVzbJkKt6vlJkg4XlOrt5FQe1AZwSu4Wsy5uFCRJKrHatWJ3trEFAQAA4DikRgAAAJzMgm2HHa97NA41sBLUVFabXQu2puvuWWv14v+2a9cxAd/mdf3076ua6eWBLdQuMkgmU/l8j2aTSQMvqq9Zd3RUz2ZhjvWTt2ZoyOTl+nLlXtm4aQQAAM6TxWzS/T1j1SDQS5K0O6tIry9MkdXGcQaA2iky2Fv/uT5BsWE+kqSswlLdO2utVuzMMrYwAE7LYjbp3u4x8vcsH53zz13Z+mnDIYOrAuDsusYEOV4vSskyrA4AAACcGEFfAAAAJ3Iwt1jr9udKkuoFeKpJuI/BFaEmsdrsmr81XXfNWquXftuh3VlHR+JtEeGnZ/s200sDmqttZKAj4PtP4f6eemlwgl4c1Eqhfh6SpPwSqyb+vEVjPv5bqen51bIvAACg5vL1sOjxPnHyOzJ17K5szVi5z+CqAMA4YX6eenNwglo3KB/xvLDUpke/2aDfNqcbXBkAZxXq66F7usU43k9bvkcpGQUGVgTA2cWF+qiuf/n13o1peUrPLzG4IgAAAByLoC8AAIAT+X175dF8Txa2BM6G1WbXb1vSdefMtXr5tx3ac0zAt2WEv57r20wv9m+uixqePOD7T73iwzXr9o4amFjPsWzV7mwN++BPfZicqlKrrcr3AwAA1B71A730YK9YmSsOTb5ec0ALt2UYWxQAGMjfy00vX9tCXeNCJEllNrv+76fNmr1qv8GVAXBW7aMC1a9lHUnlPePV+TtUWGo1uCoAzspkMqlLdLAkyS5pSWqWofUAAACgMoK+AAAATsJut2vhtqNB3+6NQwysBjWB1WbXvM2HdMfMNXpl/g7tzT4a8E2o56/n+8VrUv94JZ5FwPdYAd7uGt8vXu/ekKiGwd6SpFKrXZMXpmjElBVavy+nyvYFAADUPm0aBOiWiyMd79/5Y6e2HmL2AAC1l6ebRc/0i9dVFcE9u6TX5+/Qfxfvkt1uN7Y4AE5pRIcGig0tnzFsb3axPlyy2+CKADizLjHBjteLUjI5vgAAAHAiBH0BAACcxLb0AkcQs0WEn+r4expcEVyV1WbX3M2HdPsXa/TaghTtyy52fJZQ318vXB2vif2bq02DgCoZNbpDTLA+H9NBN3WKkqVie9sO5uuWqSv16tytKixhtBgAAHBurmoRrj7NwiSVP1A0cd52ZTCFLIBazM1s0iN9GuuGDg0dy6Yt261X/7ddVhthHACVuVvMeqBnjLzcy28Jz9+aUWlGMQA4Vrifh5qF+0qS9uUUK+VwocEVAQAA4AiCvgAAAE5iwTFTEfdoHGpgJXBVZVabft10SGO+WKPXF6Rof87RgG+b+gGadHW8Jl7dXK3rB1T5b3u5WzTu0jhNG9VOTev6SZJsdmn6sj26/r3lWspNJAAAcA5MJpNGd4pUi4rji8yCUk2at13FZTaDKwMA45hMJo3p2kj3dI9xLPtubZqe+XEz/RHAceoHemlMpyjH+/cW7dT+nKJTfANAbdYlJsjxelFKpnGFAAAAoBKCvgAAAE6g1GrToh3lF808LCZ1OmaKLOB0yqw2zdl4UGO+WKs3FqbowDEB38QGAXqxf3M9f3W8Wl2AgO8/xdfz18ej2mlsr1h5upWfbuzLLtLYGas14buNyiooveA1AACAmsXdYtbDl8Yq3M9DUvlMGO/8sZNpZAHUeoPa1tf4K5vKYi6fWWXhtgw9Mnu98orLDK4MgLPp0SRUPRqHSJIKS216dX6KSq08GADgeB2jguRecWyxdGeWypgxAAAAwCkQ9AUAAHACf+/JUU5R+Y24DlFB8vWwGFwRXEGp1aZfNh7UbZ+v0Zu/pyot92jA96KGAXppQHM91y9eLev5V2tdbhazRnZupBljOqhdoyDH8h/WHNCQ95bp1/VpBHMAAMBZCfR21+N94uRV8SBR8vbDmr0mzeCqAMB4feLD9cKA5o7+uGpPju6dtU4Z+SUGVwbA2dzWOUr1AjwlSdv/n737Do+qzNs4fk9J7w1IAqQQepUmTWm6rqhYkCKKIhZwBVHUVSy7+uoqdmyAKLCighR1bWsBMSAdIoZeEhISAgmQ3svMvH9MGIJrAYWclO/nuva6zpyQcOuaKefcz+85UaIPth4xOBGAusjb3aILmjsHRhSW27TjSKHBiQAAACBR9AUAAKgT4pOyXceDWocYmAT1QaXNrq92Owu+r69J1bGiUzdwuzcP0ItXt9fTV7RTh2a1W/D9uZbB3pp9Uzc9OqytfD2skqSc4ko98sluTVu6Q1lsEwkAAM5CdLC37hkY7Xr8wdYMbUnLMywPANQVF0YH6ZXrOynA0/m5K+l4sSYv2aGMvFKDkwGoS7zcLJo2OFbW6kmdn+3M0o/p+QanAlAXDaix4+DalFwDkwAAAOAkir4AAAAGKyqv0tY050X1AE+rukX6G5wIdVWlza4vd2Xpjg+3640fUnW8RsG3Z4sAvXRNBz11RVu1N7jgW5PZZNK13SO0bFJvDW4b6jr/w4FsjZqzWcsTMmRnui8AADhDfaKDdEP3CEmSQ9Ir8Sk6lEORDQA6hPvp9dGd1cTPXZJ0JL9Mk5fs0P5jRQYnA1CXtAr11s29Il2PX1uTqpySSgMTAaiLOoX7yb96AdFPGQUqrqgyOBEAAAAo+gIAABhsXUququzOouNFrYJlqZ6qAZxUabPri11Zun3xds1ae+j0gm/LAL18bQc9Oayt2jX1NTDlbwvz89ALIzvr+es7KcTXeeO5uMKmGV/t150Ltyn1RLHBCQEAQH1xfbdm6lc9Yaqs0q4ZK5NUWMaNZwCICvbWm6O7KDrYS5KUU1Kpqct2ahsTOwHUcEXHJurZIkCSVFBWpVfjU2SzswgbwClWs0l9ogIlSZV2hzan8V4CAADAaBR9AQAADBZ/INt1PCguxMAkqGsqquz6fGeWblucqNlrD+lE8amCb++WgXrl2g568vK2atuk7hZ8f25IuzAtm9hb13QLd537KT1fN7y9Re/8kKpKm93AdAAAoD4wmUyacnG0YkO8JUlZhRV6YVWya/EcADRmTfw89ProzuoY7tzppaTCpgc/2aU1Na49AGjcTCaTJl8crWBvN0nSjqOF+mR7psGpANQ1A2ICXcdrU3KNCwIAAABJFH0BAAAMlVlQrn3HnJNMWwZ5KibEy+BEqAvKq+z6bEembl+cqDnrDim7+NQWihdGBWrmdR31z8vbqE09KvjW5O/lpseubKfZN3ZT8yDnf/OVNofmrE7RuHlbtetIgcEJAQBAXedhNevhS1op0Mu5nezOo0WatyHd4FQAUDf4e7rp5REd1ad6+nmlzaF/frlXn1HkA1DN39OqewfF6OS+Yh/+eER7s4oMzQSgbokK8lJkgIck6cDxEmUVlhucCAAAoHGj6AsAAGCg+KTTp/maTKbf+NNo6Mqr7Pq0uuD71vo0ZZecKvj2iQ7Uq9d11D/+2katw3wMTHnu9IoJ0od39tLNfVvKUv3fftKxYt26IEEvrzig0gqbwQkBAEBdFurrrocuaSWr2fk+4pu9x/X1nuMGpwKAusHTzaJ/XdVOl7UPkyTZHdJL3yXr3Y3pcjiYgA5A6hTup5EXOHdcsjukl79PUVF5lcGpANQVJpNJA6oXDUnS+tQ848IAAACAoi8AAIBRHA6HVlcXfU2SLmoVbGwgGKas0qZPtmfqtsWJmrs+TTk1Cr59o4P02oiOevyyNoprIAXfmjzdLLpnaCu9O6GH2jR1Tii2O6RFmw5r9FubtTE5x+CEAACgLmvbxFd3DYhyPX5nQ5p2HCk0MBEA1B1Wi1nTL2utMT0iXefmb0jTq98flJ2yLwBJI7uFq0P19ZgTxRWatfYQiwEAuPSNDnRN/l6XksvzAwAAgIEo+gIAABhkb1axsgorJEldIvwU4uNucCLUtrJKmz5OPKrbFm/XOxvSlFuj4Ns/JkivX99Jj13WWq1CG17B9+fahftp4YQemjwkVh5W58eUI/llmrw4UU98tkd5Nf7dAAAA1DS4dYiu7txUknPB0AurkpVZwLayACA5p/HddXG07roo2nXuk8RMPfXf/aqoshsXDECdYDGbdO+gGPm6WyRJG1Pz9O2+EwanAlBXBHu7q0Mz52KAY0UVOnCixOBEAAAAjRdFXwAAAIPEV0/zlaRBrUMMTILaVlZp00eJR3Xb4kTN25iuvNIaBd/YIL1xfSc98pfWig3xNjBl7bNazBrfL0qL7+ylHlGBrvNfbM/UqLc26dtdWUyNAAAAv+imnpHq3txfklRUbtOzK5JUUmEzOBUA1B1jekZq+mWtZakey7dq/wk9/OlulVRUGRsMgOFCfd11d43FAAs2putQTqlxgQDUKQNiglzH61JyDUwCAADQuFH0BQAAMEBFld11UczTataFNUqNaLhKK21a/tNRTViUqPkb05VX6ryhapJ0UWyw3hzZSY9c2loxjazg+3Mtg701+6ZuenRYW/l6WCVJOcWVeuST3Zq2dIeyCsoMTggAAOoai9mk+wbHKjLAU5KUnlemmatTZLOzSAgATvprhyb61/D2rl1UEtLyde/yXeygAkAXRgfq8vZhkqQKm0Mvf39Q5Uz9BiCpR3N/uVevFNp0KF8VNp4bAAAAjEDRFwAAwABb0/NdE8b6RAfK081icCKcTyUVNi3ddkQTFiVqwaZ05ZedKvhe3MpZ8H340jhFBzfugm9NZpNJ13aP0LJJvTW4bajr/A8HsjVqzmYtT8iQnem+AACgBh93i6Zf2sq19fTWtHwtTjhicCoAqFv6xgbr5REd5Ve9qHJfVpHuXrJdR/NZUAk0drf0bq6oYC9JzkVT8zemG5wIQF3g6WZRrxYBkqSSSpt+yigwOBEAAEDjRNEXAADAAPFJ2a7jQa1DDEyC8+lkwfe2xYl6d/NhFdQo+A6MC9asUZ310CVxiqLg+6vC/Dz0wsjOev76TgrxdZckFVfYNOOr/bpz4Talnig2OCEAAKhLIgI8df+QWJmrt6b/eHumVtd47w0AkDpF+Ov1UZ0VVv0Z63Beme5esl3Jx/l8BTRm7laz7h8c65r6vWLfCa2v3pEMQOPWPzbIdbwuJc+4IAAAAI0YRV8AAIBalldaqW3p+ZKkYG83dWzmZ3AinGslFTZ9+OMRTVj002kFX7NJGhQXotmjOuvvQ+PUMsjL4KT1x5B2YVo2sbeu6RbuOvdTer5ueHuL3vkhVZVsGQcAAKp1jfTXrRe2cD2etfaQDlBeA4DTxIR6683RXVyfS7OLK3XPsh1KPJxvcDIARmoe6Knb+57+PiqrsNzARADqgg5NfBXk5dwNYPuRAtf1bgAAANQeir4AAAC1bN3BXNkczuOBccGynBw3hnqvuLxKixMydOuin/TelsMqLLdJchZ8h7R2FnwfHNpKLSj4/iH+Xm567Mp2mn1jNzWv/ndYaXNozuoUjZu3VbuOsG0cAABwGtYhTJe0CZXkfL8wY2WycoorDE4FAHVLU38PvT66s9o385UkFZXb9MDHu7UumUnoQGM2pHWILqqe3llSYdPL36eoyu4wOBUAI5nNJvWLdj4v2BzSxkN5xgYCAABohCj6AgAA1LL4GlsHD4wLMTAJzpWi8iotSsjQrYsS9f7WDBXVKPgObROiOaM66/4hrdQ8kILvudArJkgf3tlLN/dtKYvJWZRPOlasWxck6OUVB1RaYTM4IQAAMJrJZNId/VqoQ1NneS23pFIzViarvIpdAACgpkAvN708opN6RwVKkipsdj32+V59uTPL2GAADGMymTSxf5Sa+rlLkg4cL9aHCUcMTgXAaP1jglzH61NyDUwCAADQOFH0BQAAqEXpuaVKPlEiSWoV6u3aIhP1U1F5lT7YelgTFiXqg60ZKq44VfC9pE2o3hrdRdMGt1IkBd9zztPNonuGttK7E3qoTXWBx+6QFm06rDFzN2vjwRyDEwIAAKO5Wcx6cGiswnydJZWkEyWatfaQHA4m0gFATd7uFj1zdXsNbeuchG53SM+vSNIHmw/znAk0Ut7uFk0bHCtL9UZkH2/P1E8Z7KQENGbNAz0VXX0/42BOqTLyywxOBAAA0LhQ9AUAAKhFq5NOlQ8Htgo2MAn+jMLyKr2/xVnwXZRw5LSC76VtQzV3dBfdNzhWEQGeBidt+NqF+2nhhB6aPCRWHlbnx5uMvDJNXpSoJz7bo7ySSoMTAgAAIwV4uWn6pa3kWf0+4YfkHH2ynSmVAPBzbhazHru8ja6/INx1bu66Q3pzdYrslH2BRql1mI9u7BXpevza6hTllXKdBWjM+sUEuo6Z6gsAAFC7KPoCAADUErvDodXJ2ZKchdABFH3rncKyKr1XXfBd/OOpgq/FbNJf2oVp7pguundQrMIp+NYqq8Ws8f2itPjOXupRvd2sJH2xPVOj3tqkb3dlMYUKAIBGLDrYW/cMjHY9/mBrhrak5RmWBwDqKrPJpMkDY3Rn/yjXuWXbjuqZrw+o0mY3MBkAowzv1FTdIv0lSXmlVXptdSrlf6AR6xsVKHP1pO91qXk8HwAAANQiir4AAAC1ZNfRQmUXO6dedG8eoEAvN4MT4UwVlFXq3c3punXRT/rwxyMqqVHwvaxdmOaO7qKpA2MU7k/B10gtg701+6ZuenRYW/l6WCVJOcWVeuST3Zq2dIeyCthODgCAxqpPdJDGdI+QJDkkvRKfokM5pcaGAoA6yGQy6cbezfX3S+NcRZ4Ve4/rkU/3qLTSZmw4ALXObDLpnoHRCvRyXmf5KaNAn+1gdwSgsQrwclPncD9JUk5JpfYeKzY4EQAAQONB0RcAAKCWxCfluI4HxjHNtz7IL63Uu5vSNWFRopZuO6rSSucEI6vZpMvbh+mdMV10z8AYNfP3MDgpTjKbTLq2e4SWTeqtwW1DXed/OJCtUXM2a3lCBpMmAABopEZ2a6Z+MUGSpLJKu2asTFJhWZXBqQCgbrqiU1M9dVU7uVuct5E2H8rTtOU7lV9aaXAyALUt0MtNUwfGqLr7rw+2ZujAccp9QGPVv/ozlSStS8k1MAkAAEDjQtEXAACgFpRV2rSh+qKXt7tFPVsGGhsIvym/tFILThZ8fzq94DusQxO9PaaLJl8coyZ+FHzrqjA/D70wsrOev76TQnzdJUnFFTbN+Gq/7ly4TaknuCEFAEBjYzKZNOXiaMWGeEuSsgor9MKqZFXZWQQEAL9kQKsQvXhdB/l6WCRJuzOLNHnpDmUVlBucDEBt6xrpr2u7NJMk2RzSy98fVHEFU76Bxqh7pL+83Jw1ky1p+SqvshucCAAAoHGg6AsAAFALNh3KU1n1Ba/+MUHysPI2rC7KK63U/I1pmrAoUct/Our6/8xqNumKDk30zg1ddPdF0RR865Eh7cK0bGJvXdMt3HXup/R83fD2Fs1bm6pKGxeiAQBoTDysZj18SSvX9tM7jxZp3oZ0g1MBQN3VtXmAXhvZWSE+bpKktJxS3b1ku1KzSwxOBqC2jekRoTZhPpKcC6beWndIDnZNAhodd6tZvasHmZRV2ZVwON/YQAAAAI0EDRMAAIBasDopx3U8MC7YwCT4JbkllZq3wVnw/Sgx87SC75Udm2jeDV31t4uiFeZLwbc+8vdy02NXttPsG7upeZCXJKnS5tDs+BSNm7dVu44UGJwQAADUplBfdz10SStZzc4NqL/Ze1xf7zlucCoAqLtahfnozdFd1DzQU5J0vKhCk5fs0E4+SwGNitVs0rTBMfJ2d075XnswV6sOZBucCoAR+scEuo7XVe9kCAAAgPOLoi8AAMB5llNcoe3VN7+a+rmrXVNfgxPhpJySCr2zIU23LU7Ux9szXduMuVlMuqpTU80b21V3DYhWqK+7wUlxLvSKCdKHd/bSzX1bymJyFnuSjhXr1gUJennFAZWy5SQAAI1G2ya+umtAlOvxOxvStONIoYGJAKBuCw/w1BujO6ttE+c0z8LyKk37aJc2HMz5ne8E0JA08fPQ32q8h3p7fZoO55UamAiAEdqE+Si0etr/zswi5ZVWGpwIAACg4aPoCwAAcJ6tOZgje/UudgPjQmSuLhjCODnFFZq7/pBuW5SoT2oUfN0tJg3v1FTzbuiqSf2jFOpDwbeh8XSz6J6hrfTuhB5qU126tzukRZsOa8zczdrITWoAABqNwa1DNLxTU0nO9wMvrEpWZkG5wakAoO4K8nbXzJGd1KNFgCSpvMquRz/bo693HzM4GYDa1C8mSJe2DZUkVdgceun7FFVUX1sD0DiYTSb1jwmSJDkc0obUPGMDAQAANAIUfQEAAM4jh8Oh+Bpb2F3cKtjANMgprtDcdYd02+JEfbojSxU2ZwPb3WLS1Z2dBd+J/aMUQsG3wWsX7qeFE3po8pBYeVidH4sy8so0eVGinvhsj/KZQgEAQKMwrlekujf3lyQVldv07IoklTDlHwB+lbe7VTOu6aDBbUIkSTaH9Ow3B/Th1gyDkwGoTRP6tFCLQE9J0qGcUr27+bDBiQDUtv7RQa7jtSm5BiYBAABoHCj6AgAAnEepOaVKyy2TJLVt4qOIAE+DEzVOJ4orNGfdIU1YnKhPd54q+HpYzbq2SzPNG9tVd/aLUjAF30bFajFrfL8oLb6zl3pEBbrOf7E9UyPnbNK3u7LkcDiMCwgAAM47i9mk+wbHKrL6fXp6Xplmrk6Rzc57AAD4Ne5Wsx6/vK2u7drMdW72D6mavSaVz1BAI+FhNWva4Fi5W5w7l32157g2plL0AxqTZv4eahXiLcn5OSott9TgRAAAAA0bRV8AAIDzKD7p1DTfQXEhBiZpnE4UVWj22lTdvjhRn+/MUuXPC743dNXtfVsq2JuCb2PWMthbs2/qpkeHtZWvh1WSlFNcqUc+2a1pS3coq6DM4IQAAOB88nG3aPqlreTrbpEkbU3L1+KEIwanAoC6zWI2aergWN3at4Xr3IcJGZrxbZKqbHYDkwGoLVHBXrq1z6nngFk/HNLxogoDEwGobQNiT031XcdUXwAAgPOKoi8AAMB5YrM79ENyjiTJajapX42LXji/jheVa9YPqbptcaK+2HXstILviK7NNH+ss+Ab5O1mcFLUFWaTSdd2j9CySb01uG2o6/wPB7I1as5mLU/IkJ3JVAAANFgRAZ66f0iszM6hdPp4e6ZW11i0BwD4XyaTSeP7tNT9Q1u5nj+/3n1Mj32+V2WVNmPDAagVf2kbqr7RgZKkogqbXok/yM4IQCNyYcsAWarfBKxPzeP3HwAA4Dyi6AsAAHCeJGYUKK+0SpLUs2WA/KonheL8OVZYrjd/SNXti7fry93HVFV9YdHTatb13cK1YGxXTejTUoFeFHzxy8L8PPTCyM56bkRHhfg6Jz0XV9g046v9unPhNqWeKDY4IQAAOF+6Rvrr1gtrTKVbe0gHjvPaDwC/Z3iXZnryinZysziLPhtScnX/R7tUUFZpcDIA55vJZNLfBkQprPoayt6sYi3ddtTgVABqi6+HVd0i/CRJ+WVV2pVZZHAiAACAhouiLwAAwHkSX2MC2KC4EAOTNHzHCsv1xpoU3fHhdv23RsHXy82skd3CNX9sV916YQsFUPDFGRravomWTeytq7uFu879lJ6vG97eonlrU9mKFgCABmpYhzBd0sY53b/S5tCMlcnKKWYLagD4PRe3DtHz13aQt7tFkrTzaKHuWbpTxwrLDU4G4Hzz8bBq2qAY12Tv5T8d1Y4jhcaGAlBrBsSc2slwbUqugUkAAAAaNoq+AAAA50FJhU2bD+VJkvw8LLqgub+xgRqorMJyvbbaWfD9as/x0wq+oy5wFnzHU/DFH+Tv5abHr2yn2Td2U/MgL0nOws/s+BTdNG+rdh0pMDghAAA410wmk+7o10IdmvpKknJLKjVjZbLKq1jkAwC/p3uLQL06spOCvZ2fwVOySzR5yQ4dyikxOBmA861tU1/d0CNCkuSQ9OrqFBWUVRkbCkCt6BrhJ5/qhT4/Hs5XaaXN4EQAAAANE0VfAACA82BDaq4qbM7S6YDYYLlZeNt1Lh0tKNOr1QXfb/aeXvAd0z1CC8Z20y29W8jfk4Iv/rxeMUH68M5eurlvS1lMzvE0SceKdeuCBL284oBKK7h4DQBAQ+JmMevBobGuLaiTTpRo1tpDcjgcBicDgLqvTRNfvTG6syICPCU5F+hOWbJDezKZ7gk0dNd2aaYuEX6SpJySSr2+JpX3T0AjYLWY1ScqUJJUYXNoS1q+sYEAAAAaKBonAAAA50H8gWzX8aDWIQYmaViOFpRpZvxB3fnhdn2797hs1QVfb3eLq+A7rldz+XlaDU6KhsbTzaJ7hrbSuxN6qE31hD+7Q1q06bDGzN2sjQdzDE4IAADOpQAvN02/tJU8rc7Lpz8k5+iT7VkGpwKA+iEy0EtvjO6suDAfSVJ+WZXuW75Tm1PZzhtoyMwmk+4ZGCP/6utyCen5+mLXMYNTAagNA2KCXMfrUni9BwAAOB8o+gIAAJxjxwrLtSuzSJIUGeChuFBvgxPVf0fyy/TK986C74p9J1Td75WPu0Vje0Ro/tiuFHxRK9qF+2nhhB6aPCRWHtXFn4y8Mk1elKgnPtuj/NJKgxMCAIBzJTrYW/cMjHY9/mBrhrak5RmWBwDqkxAfd706spO6NfeXJJVW2vXwp3u0Yu9xg5MBOJ+Cvd10z8XRrsfvbclQ8okS4wIBqBWxIV5q5ufcEWXPsWKdKK4wOBEAAEDDQ9EXAADgHFuTfGqy58C4EJlMJgPT1G8Z+WV6+ftkTVyyXSv3n17wvbFnpOaP7aobezaXnwcFX9Qeq8Ws8f2itPjOXureMtB1/ovtmRo5Z5NW7D7G1pQAADQQfaKDNKZ7hCTJIemV+BSl5ZYaGwoA6glfD6uev7ajLo5z7nRkszv09Ff7tfzHIwYnA3A+dW8RoOGdmkqSquwOvfz9QZVW2AxOBeB8MplM6l9jqu/61DzjwgAAADRQFH0BAADOIYfDofikbNfji1sFG5im/srIK9VLq5I1acl2fbc/21Xw9fWw6KaekVowtqvG9oiULwVfGKhlsLfmjOumR4e1df23mFNcqekf79K0pTuUVVBmcEIAAHAujOzWTP2qb1qXVdr17IokFZZVGZwKAOoHD6tZT1zRVld1buo69/rqFM1de4gFkkADdmPPCLWq3uXsaEG55m5IMzgRgPOtX/Spou+6lFxe5wEAAM4xir4AAADnUPKJEh3JL5ckdQr3VRM/D4MT1S/puaV64btkTVq6Q6sOnCr4+nlYNK5Xcy0Y20039IiUDwVf1BFmk0nXdo/Qskm9NbhtqOv8DweyNWrOZi1PyJCdi9oAANRrJpNJUy6OVmyIs6ySVVihF1Ylq8rOazwAnAmL2aT7h7bSzRc2d537YMthvbAyiedSoIFys5h1/+BYebk5b0WvTspR/IHs3/kuAPVZmK+72jXxkeQs+KfmsBMKAADAuUTRFwAA4ByKCvbSXQNaqn1TXw2s3poSvy8tt1TPf5eku5buUHzSqYKvv6dVt/Rurvlju2lM9wh5u1uMDQr8ijA/D70wsrOeG9FRIb7ukqTiCptmfLVfdy7cptQTxQYnBAAAf4aH1ayHL2mlQC/ngrOdR4s0b0O6wakAoP4wmUy6rV+Upg6Olan63Jc7j+mfX+xVeZXN0GwAzo9m/h6a2D/K9Xju+jQdyWf3I6Ah6x8TpDZh3prQO1LNAz2NjgMAANCgmBxnsGdCQUGBAgIClJ+fL39//9rIBQAAUG9V2e2yms2yOxwym0y//w2NWFpuqRYnZOiH5BzVfFPq72nVtV2a6cqOTSn3ot4pKK3Uq98l69OfjrrOuVlMuv2iaN3St6WsFtZbAgBQX+3LKtLj/93vmkB5Z7+W+mv7MINTAUD9smrfcf3r6wOu59Iukf56Znh7+Xmyew/QEL2xJlWrqqf5xoR46bnh7WQ1c20EaIhO3hOx2R2ymLk3AjQGN7+/TdnFlQrxcdPCmy4wOg4A1Dtn08vlUxQAAMA5dvJCNSXfX5eaU6IZK5L0t6U7tKZGydff06pbL2yh+WO7atQFTPBF/eTv5abHr2yn2Td2U/MgL0lSpc2h2fEpumneVu06UmBwQgAA8Ee1beqruwacmkz3+c5MlVUyiRIAzsaQtmF67poO8nJzXj/ZnlGgqct2KLuowuBkAM6H2/u2UGSAh7zdzLquSzNKvkADdvKeCCVfAACAc4+JvgAAAKg1qdklWvRjhtYdzD3tfKCXVdd1DdcVHZrI041yLxqOskqb5q5J1Qcb02Wr/uhlNkk39G6hSQNj5EWZHQCAeunfmw7rYHaxHrqklTytFm5kA8AfsDezUA/9Z4/ySislSc38PfTGqM4K8/MwOBmAc+1IfpncrWYFebnxvgkAgAaksKxKdjlklokdOgDgDzibXi5FXwAAgHPAbrfLzDSKX5WSXaLFCRlal/K/Bd8RXcM1jIIvGri9Rwv1f1/s1f6sIte5yEBPTR/WVn1igw1MBgAA/gi73SGZJIeDaVUA8Gek55bqgY93KbOgXHFhPnpzdGd5WM0ysUsS0ODYHQ52QAMAAACAGij6AgAA1JK8vDwFBgaedo7S7ynJJ4q1OOGINqSeXvAN8nbTiK7hurx9GAVfNBpVNrve35Sut9ekqrzK7jp/ZZdm+vtfW8vbndXuAAA0JIWFhfL09JTJZJLVyus8APyaE0Xlen5Fkh75axv5eVhk+ZVrKiUlJSooKFCzZs1qOSGA8628vFwvvviiQkJCFBAQoBtuuMHoSADOkdLSUqWkpGjt2rVKS0vTzTffrLi4OO6hAAAA6Ox6uVxhBgAA+INmz56txMREff311+rfv79uvfVWXXLJJVygkrPguyghQxtT8047H+ztpuu7heuv7ZvIw8q/JzQuVotZ4/tFaUi7MD39xT79mJYnSVqfnC2Ho7UcDscvTq0qKSnRvn37dMEFF9RyYgAA8EdUVlbq/fff18svv6xu3bqprKxMy5YtMzoWANRZob4emnF1Bzn061PSp0yZooyMDKWnp2v06NF64IEHajckgPPC4XDoP//5j5544gn5+Pho1KhRev311yWJsi/QAHz22WeaN2+ekpOT5evrqz59+uiuu+7STTfdpFtvvdXoeAAAAPUKE30BAAD+gKVLl2r27Nl67LHHlJmZqfvuu095eXnq1KmTFi5cqE6dOhkd0RAHjhdrcUKGNh3KO+18sLebRnYL12UUfAFJzu0qP912VK9+l6xHr2irwe1CZf2FRQIff/yx5s6dq4qKChUWFurFF1/UwIEDDUgMAADORH5+vv75z3/qiy++0IwZM9SvXz9NmzZNwcHBmjVrltHxAKDeyc3N1cMPP6y1a9dq2bJlslgsGjVqlO655x7ddtttRscD8CfNmDFD//73v/W3v/1N99xzjyRpzZo1uvfee/XDDz/Ix8fH4IQA/qgZM2Zo1qxZmjRpkm655RZFRkZKkv773/9qxowZWrNmjcEJAQAAjMdEXwAAgPNswYIFevTRRzV06FBJ0tGjR9W5c2fFx8fr/fff14wZMwxOWLsOHC/Soq1HtLl6QulJIT5uGtktQpe1C5M7BV/AxWwy6druERrSLkwB3m6/+GcSExN19913a8aMGbrlllu0cOFCvfDCC+rfvz/bfwMAUEeNGzdO+fn5+u677xQVFSVJmj59ul5++WXZ7XZ2/wCAszRjxgzt2rVLq1evVmhoqCRpwoQJstvtBicD8GfNnz9fb775pj755BP17NnTdb6qqkrdu3en5AvUY6mpqVq3bp1WrFihtm3bnva1pk2bytvbW0VFRfL19TUoIYBz7fjx40pPT5cktWjRQmFhYQYnAoCGhyvLAAAAZ6mqqkpRUVHas2ePKisrJTmnbvr6+urRRx/Vjz/+qO3btxuc8txZtWqVFi9erMzMTEnOLfV+7sOE00u+oT7uumtAlN4Z01VXdWpKyRf4Fb9W8i0rK9Ozzz6rESNG6JZbbpEkXX/99crOztbhw4drMyIAADhDM2fO1IkTJ7R69WpXyVeSXnvtNdntdplMv7wdPQDgl3300UeaN2+eFi5c6Cr5StKcOXNc12MA1F9Hjx7VU089dVrJ99tvv9XNN9+sNm3aGJgMwJ8VHh6uvXv3KiIiwnUuPz9fc+fO1dixYzV69GhKvkADsX//fg0ZMkStW7fWjTfeqBtvvFGtW7fW0KFDlZSUZHQ8AGhQGAMFAABwFhwOh6xWq0aNGqWnnnpK69ev14kTJxQTE6P+/ftLklJSUtSiRQuDk/5527Zt07Rp01RZWam4uDi99957euutt/7nn81md+imXs218VCewnzdNbJbuP7SLkxuFsq9wB/13//+V5s2bdKuXbtc595++21VVVUpOjradc7hcFAaAgCgjsjPzz9tG/mqqipNnTpV27dv13/+8x9eswHgLO3bt08PPfSQYmNjXeduvfVWdejQQX/7298knVqMzHMsUP8cPnxYhw8f1vjx4yVJ//d//6cVK1booYce0pQpUySJHRGAesrDw0O9evXS1KlT1bVrV23cuFGHDx+Wt7e3HnvsMY0bN87oiADOkfHjx2vixIlauXKl6zXbbrfrgw8+0C233KJ169YZnBAAGg6KvgAAAGfh5I2jIUOGqKKiQocPH1ZQUJAuvvhiSdKLL76odu3aKSgoyMiY58T8+fM1bNgwPfjgg0pKStLjjz8um832P8VCi9mkmBBv/d/lbdQl0p+CL3AO/Pvf/9Zdd90lb29vSVJ6erqWLVumBx54QJJks9lksVhkMplkt9u1f/9+tWvXzsjIAAA0ev7+/nrvvfc0YsQIZWRkaOrUqQoNDdWsWbMUGRlJUQUAzlJFRYX279/venzbbbcpNzdX//jHPyT9bwGQ51mgfnn11VfVq1cvTZgwQevXr1d4eLimT5+uiy66yPVnTv5Os9AZqH9efvllxcfHa+HCherVq5fGjx+vjh07qnnz5pL4vQYaiuzsbNeuhCeZzWaNGzdOTz/9tEGpAKBhougLAABwFpKSkvTkk0/q1Vdf1V//+tf/uYkUERGhqVOnGpjw3CgqKlJubq7r8XvvvadDhw4pNzdXQUFBCggIOO1CnMPhUI+WgQalBRqevn37nrZ93b333qvOnTu7bnZZLBZJ0saNGzVr1iylpaXJx8dHb7755mkTfwEAQO257777tHPnTo0fP167du3S1VdfrcmTJ6tly5aSThVV8vLytGbNGg0fPtzIuABQ5z3wwAMaPny4hg0bJpvNpvz8fM2fP19t27aVdOp5dfPmzVq1apVrkUVcXJyRsQGcIXd3d33++efKzMzUNddco+HDh/9iYT8pKUlfffWVa8ovgPqhWbNmGjNmjMaMGSPpf4u9lHyBhiEgIEDLly/X9ddff9r5jz76SIGBgcaEAoAGyuQ4ua/RbygoKFBAQIDy8/Pl7+9fG7kAAADqpEceeUQzZ87U6NGj9eSTT6ply5b1fmJMVlaWmjZtKunUlFC73a4vv/xSH374oVavXq0OHTpo8ODBWr9+vUJCQvTvf/+73v9zA3XZp59+qgcffFB/+ctfVF5erg0bNuibb75RRESETCaT63f15Zdf1kcffaR169bp9ddf19tvv633339fXbp0MfofAQCARqu4uFgVFRW/ucvHbbfdppCQED3//PO1mAwA6qePPvpIzZo1U//+/SWdPrm3qqpKEyZM0LFjxzR06FC99957mj9/vnr27GlkZABnadeuXerYseOvfn3YsGEaOnSo7r///lpMBeDPWrRokUpKSnT77bf/4td3796tDh061HIqAOfS7t27NX78eKWnp7sWOqelpalFixZ699131b59e4MTAkDddja9XJoZAAAAZ+H777/XF198oaqqKk2cOFH79u07bQs5u91ucMIz98UXX6hfv3666aabdN9996miosI1JdRsNuuqq67SHXfcoYEDB+rbb7/V9OnT9fTTT2v16tXKyMig5AucR1dffbXi4+Pl6empyy+/XCtXrlRkZKRr0oXZbFZxcbEcDodiY2MlSVOmTFGfPn2UmZlpZHQAABo9Hx8frV+/XqtXr/6fr538vDBz5kwlJCTo3//+dy2nA4D6Z8SIEerfv7+rLHTyekRubq4SExNVVVWll156SQ8++KDuuOMO/fOf/1RZWZnBqQGcjaVLl+r777//n/NVVVWSpLfeeksfffSRtmzZUtvRAPwJl1xyiVq3bv2rX1+4cKG+/PLLWkwE4Fzr0KGDNm/erHXr1um5557Tc889p3Xr1mnz5s2UfAHgHKOdAQAAcIZWrlwpX19fDRkyRDNnzlRUVJSuvfZaff75564/U1/Kr8ePH9dzzz2nadOmaeHChdq5c6emTJniuhF2ctOHgoIChYSEKD8/X5LzJtqAAQPY5QGoBREREXrxxRd13XXXKSsrS2+++abra2+//bamTJmiPXv2aN++ferWrZsmT56slJQUVVRUSDr1ewwAAGpfq1atlJ6e7iqnSKdPoPTz89M777yjRx99lMIKAJyhkpISJSQkSHJO+X3ooYf0+OOPKycnRzfccIN27twpSQoLC5PVajUyKoCz9Oijj6pz586SdNogBavVqoqKCrVo0UJ/+ctfNGnSJNlsNqNiAjhLTZo00cCBAyWd+t222+2uz0nt2rXTggULDMsH4M+74447JEmxsbEaNGiQBg0a5BpOAgA4t+pHEwUAAKAO+O677zRy5EhJUkhIiObMmaNbbrlFb7zxhlasWOGatFkfrFmzRk2bNtX111+v8PBwvfPOO9q6davi4+PlcDhc/yxlZWVKSUnRM888o1mzZmnatGlq3bq1/Pz8DP4nABqXtm3bqmnTpsrKylJBQYHWrVundu3a6Z133tHmzZvVvXt3RUREaMGCBbryyislyfV7/NNPP2nr1q1GxgcAoNFp166dRo0adVrR7GTJ97vvvtPChQs1adIkeXh46PXXX1dlZaVRUQGg3rj99tvVu3dvSVJiYqKsVqvefvttff311+rTp49uvvlm7d27V126dJHValVKSopWrVplcGoAZ8Ld3V2hoaGSnO+ZSktLVVpaqk8++UT/+te/dNVVV2nZsmVyOBz65JNPDE4L4I84+XnIbDa7PieNHz9ehw8fVl5enoHJAPwZV199tdERAKDRYEkzAADAGRo+fLh69OghSbLZbLJYLLrvvvskSVdccYWWLl2qa665xsCEZ65du3ZatWqVqqqqZLVaFRUVpUGDBumjjz7S0KFD5ebmJkm69tprFRERoffff18JCQlauHChOnXqZHB6oPHx9PTU9ddf73pcVVWl5s2bux53795du3fvdp3Lz8/XunXrtHPnTq1cuVIrV65UQkKCLrjgglrPDgBAY+Xu7u46Xrt2rbZv366PP/5YR48eVefOnXXhhRfq3XffldVqdb3/BgD8tpPPrcePH1fr1q0VGRkpSRo6dKiaNWum//u//5MkVVZWKikpSQ8//LCuvPJKPfnkk4ZlBnDmysrK9NRTT2nXrl0qLy/Xtm3bdOutt6pbt25666235O3trcDAQKNjAjgLDodD8+bN0+HDh9WqVSu5ubnp8OHDat++vfz9/ZWVlaVly5a5poICqF9ODh4BAJx/JscZ7OdaUFCggIAA5efns00zAADAL/jyyy/Vq1cvNWnSxOgoZ8ThcOiKK67QwIED9dBDD0mSkpOT1b9/f+3fv1/+/v764Ycf1LlzZwUGBqqyspLyAVCHzJ49W7NmzdKjjz6qoKAgPfLII7rtttt000036auvvtLChQsVGRmpv//977rzzjsVEhKiZcuWGR0bAIBG6dVXX9UDDzygCRMmKDQ0VPfee6/c3NxcJZXMzEwdPXpUFotFXbp0MTYsANQTX3/9taZNm6YXX3xR0dHReuKJJ+Tn56d58+ad9ufy8vI0ZswY9enTR0888YQxYQGclREjRuiyyy5TWFiYLr/8cnl6erq+dvToURUWFqqwsFBxcXEKCAgwMCmAM3XTTTfJZrPJZrNp79696tq1q9LS0lRSUiI/Pz/FxcVp7ty5RscE8AdUVFTozTff1MGDB3XzzTerZ8+e+vzzz+VwODR8+PB6tRsqABjhbHq5FH0BAAD+BLvd7tpyqq5zOBynfaD+6KOP9PDDD2vv3r2yWCySpMGDB+vNN99UVlaWEhISdPfdd8vLy8uoyAB+w8aNG/XEE08oPDxcTZs21YwZM/Tggw/q66+/1uuvv65BgwZp9erVGjp0qNLS0hQREfE/zwMAAOD8czgcOnr0qJo2bep6333yNXnjxo1auHChVq9ercjISPXr148iGgCcodWrV+uhhx5SVFSUcnNz9dlnn8nT01Pl5eXy8PBQRUWF3N3d9d1332nu3LlavHhxvbmGA+CUkzur5eXlaebMmVq1apU8PT1ltVr13//+1+h4AM5CYWGh/Pz85HA4VFhYKH9/f+Xl5cnd3V3e3t5GxwPwB9x5551KTU3VkCFDtGrVKnXs2FGrV6+Wh4eH+vXrp5deesnoiABQp1H0BQAAgIvD4dD2IwXq0MxPbpbTb2j99a9/VWhoqP7+979r2bJl2rp1q7788ktufAH1yMmb2JJz+9opU6YoJSVFM2fO1HXXXadHHnlEU6ZMqVcLEwAAaIhOvhbXXHjzwAMPKDU11TWRskePHnr66ad1+eWXG5wWAOqHEydOyNfXVyaTyfW5qKbk5GQtWbJEBw8e1BtvvCEPDw8WPwL1SM33Td9++63uuecevfLKK7r88st1++23KyQkRM8995zBKQGciV/6PHTy3NGjR5WcnKwBAwYYnBLA2erQoYMSExPl5uamwsJChYeHKzMzU97e3urcubN27dpldEQAqNPOppdrraVMAAAA9dqhQ4fk6+srT09PmUwm1+ry8vJyrVixQldccUWdu1HkcDiUkJ6vRQkZ2nesWLf1aaGrOzeTxXwq54IFC7RgwQLdddddatWqlV5//XWKgEA9c/Jmtt1uV1hYmD788EOtWLFCV199tcrKyjRlyhRJ4ncbAACDmc1m10Q6SSorK1NhYaGmTp2q6OhoSdJll12mlJQUA1MCQP0SGhoqSUpNTdXSpUuVmZmp4uJi5eXlqaioSAcOHFC/fv30l7/8RZ6enganBXA2fr4rUUZGhq6++mrXgqgbbrhBb731lkpKSuTl5VXnrs0CON3Ja5M1f1dPnispKdE999yjH3/80ZBsAP44d3d3ubm5SZL8/Pzk7+8vHx8fmUwmWa1U0gDgXOJZFQAA4Hc88sgj2rlzpzZs2KAuXbpo8ODBuuqqq9S1a1ft27dPP/74o6688kqjY7o4HA5tScvX4oQM7T9e7Dr/UeJRXdWpqSw6dSEtPDzcNe3Tz8/PiLgAzhGz2azKykq5ubkpNjZWOTk5uuyyy7R48WKNHj2aoi8AAHWAxWJRQUGBiouLFR4ersjISL3wwgtq3769jh07pr1792rgwIFGxwSAeqdZs2ZaunSpDh48qLfeekvu7u7y9PRUaGio4uLiFBAQYHREAGfJZDLp8OHD+u6773TLLbfoggsu0LPPPqtbbrlF3t7eWr16tXr06OEayACg7qu549jJRZB2u12tWrVSaGio1q5dy1RfoJ5p1qyZnnvuOV111VV69913FRkZqYcfflj+/v6uRXkAgHPD5HA4HL/3h85mRDAAAEBDkpCQoPHjx2vFihXy9/fXZ599pk8//VTr1q3TNddco9dee00VFRVyd3c3Omp1wTdPixKO6ECNgq8kRQd7aWyPSPWLCWK6BdAITJo0Sb6+vnrxxRdVXl5+2ha2DodDxeU2+Xqy7hMAACM8/fTTWrVqlVatWiVJevPNN7Vjxw7Nnz9fAwYM0IoVK1xTfwEAZ85ut2vixIny9vbWq6++anQcAOfArl27dPnll2vNmjWKjo7WV199pbVr12r27Nlyc3PTV199pe7duxsdE8BZKisrc03aP3ntcvbs2UpMTNScOXMMTgfgbKSmpuquu+7Szp07NWjQIL311lv6xz/+ocOHD+uZZ55RbGys0REBoE47m14uRV8AAIDf8I9//ENZWVl66623Tju/e/duPfbYY7r//vvVv39/g9I5ORwObTqUp8UJGUo6UXLa12JCvHVD9wj1jQmSmYIv0Kj8fItLSbLZHVqy5bD+vf6QHrysjS5pH0b5HwAAAwwfPlytWrXSJZdcotzcXN15550aPXq0FixYIOmXX8cBAGdmwoQJstls+ve//81zKdAAvP7661q8eLFGjhwpPz8/TZ8+XZ07d9YHH3yg8PBw3jcB9ciSJUv097//Xdddd53S0tJUVFSkCy64QIcOHVJ5ebnWr1+vI0eOsDMZAABoNCj6AgAAnCOJiYl6+umn9fDDD6tHjx6nfW3KlCny9vbWc889Z0g2h8Ohjal5WvxjhpJ/VvCNDfHW2B6RujA6kIIvAEmS3eHQiaIKjZi1SaWVNknSRa1D9PDlbdTU39PgdAAANC7FxcV6/PHHlZOTo6qqKvXt21d33323JEq+AHAufPbZZ7riiitOm5B+8nYYz7FA/bNgwQIlJyeroKBAMTExuu+++yQ5J3lTCATqj8zMTK1atUrp6ekqKChQdHS0kpOTVV5erjZt2mj16tWaMWOGoqOjjY4KAABQKyj6AgAAnCOVlZV64oknNGfOHPXu3VujRo3SiBEjlJWVpeuuu04LFixQz549azWT3eHQxtRcLUo4opTs0wu+rUKrC75Rgdy4AvA/8koq9PSX+xS/74TrnI+7RVOGttJ13SNYGAAAQC2y2WyyWCynbVsLADi/XlyZpL4xQerfKsToKAD+gKqqKlmtVkksjgIAAABQ/1H0BQAAOMcOHz6sDz/8UJ9//rm2b9+uIUOGqFOnTnryySdrLYPd4dCGlFwtTshQSk7paV+Lqy749qbgC+AMfLfnmJ7/5oCyiypc5y5oEaBHr2yn6BBvA5MBANB4/FI5xe5wyGwyqcpml9XCdDoAOJfeWpuqRVsyZDFJD1wap2EdmxodCcCfRNkXqL9qTuT+tWMAAICGjqIvAADAeXDywnFBQYEKCgrUvHnzWvl77Q6H1h3M1eIfM3ToZwXfNmE+uqFHpHq1DOCiNoCzUlBaqVe/S9anPx11nXOzmHT7RdG6pW9LykUAANQym92hKrtDM+NT5G41696B0bzHB4BzxGZ36F9f79d3NXY3mTggSjf0jOS5FqiHbHaHzCZp2+ECdW8RYHQcAAAarcKyKtnlkFkm+XlajY4DAPUORV8AAIAGwFnwzdHihCM6lPuzgm8TH93YI1I9WlDwBfDnbEnJ1dNf7lVGXpnrXFwTHz1+ZTt1jODzHwAAtaXKbtdjX+zX/uPFkqSbekbquq7NDE4FAA2H3eHQm6tTtHzbqcWOI7tH6G8XR8vMtRWg3nA4HCooq9JL36do59FCPTgkVn1jgoyOBQBAo3Tz+9uUXVypEB83LbzpAqPjAEC9cza9XEY0AQAA/A67w+H6X22w2R1anZStu5ft1IyVyaeVfNs18dGTl7fRy9d0UM+WgZR8AfxpvWKCtGRib43r20Lm6qeUpGPFunVBgl5ZkaTSCpuxAQEAaCSsZrOu6XJqG/kPtmZoS1qecYEAoIExm0yaPDBGd/SPcp1b9uMRPfP1AVXa7AYmA3A2TCaTtmUUaOfRQknSm2sP6VhhucGpAPwRJ++7nMFsOgAAgEaPoi8AAMBvsNkduvej3Vqw6bDSfjZV93z8XfEHTuhvy3bo+e+ST/v72jf11VPD2upFCr4AzgNPN4umDo3Twgk91aapryTJ7pA+2JSuMXM3a9PBHIMTAgDQOPSJDtKY7hGSJIekV+JTzvvnEABoTEwmk27q3VwPXtLKtdBxxd7jeuTTPSqtZJEjUF8MbBWs/tVTfEsqbHolPkVVdoqCQH1zOK9Mi348okf+u182focBAAB+E0VfAACA3/BTRoEO55fpy13HtKzG1o7nks3u0PfVBd8XVh3U4bwy19c6NPPV01e01QtXt1f3FgEUfAGcV+3C/bRwQg9NHhIrD6vz42JGXpnuXpSoJz/bo/zSSoMTAgDQ8I3s1kz9qosrZZV2PbsiSYVlVQanAoCG5crOzfR/V7aTu8V5nWXzoTxNW76TzzxAPWEymTRpQJSa+rlLkvYdK9aSH48YnArA2fp05zF9uy9bGfnl2plZaHQcAACAOo2iLwAAwG+IP5DtOh4YF3JOf7bN7tCq/Sd019IdevFnBd+Ozfz0ryva6vnh7XVBcwq+AGqP1WLW+H5RWnxnL3VvGeg6//n2TI2cs0krdh9jOz0AAM4jk8mkKRdHKzbEW5KUVVihF1YlM6UOAM6xi+JC9OJ1HeXrYZEk7c4s0uSlO5RVUG5wMgBnwsfdovsGxaq6r6+PEzO1/UiBsaEAnJV+MYGu43UpucYFAQAAqAco+gIAAPyK4gqbNqflSZL8Pa26oLn/Ofm5NrtDK/cd16Ql2/XS9weVkX+q4Ns53E/PXNlOzw1vp24UfAEYqGWwt+aM66ZHh7WVT/WN75ziSk3/eJemLd2hrIKy3/kJAADgj/KwmvXwJa0U6GWVJO08WqR5G9INTgUADU/X5gF6bWRnBXu7SZLSckp195LtSs0uMTgZgDPRpomPxvaMlCQ5JM2MT1Eek7mBeqNruJ9rwU3C4QKVVNgMTgQAAFB3UfQFAAD4FRtSclVpc07NGhAbJDfLn3vrZLM7tGLfcU1csl2vxKfoSI0JMZ0j/PTsVe00Y3h7dY30p+ALoE4wm0y6tnuElk+6UIPahrrO/3AgW6PmbNbyhAzZme4LAMB5EerrroeGtpLV7Pxs8M3e4/p6z3GDUwFAw9MqzEezxnRRZKCnJOl4UYUmL9mhnUwGBeqFqzs3VbdI54CGvNIqvbEmlWsVQD1htZjVJypQklRpc2hLer6xgQAAAOowir4AAAC/Ij4p23U8MC7kD/+cKptd3+49rjuXbNfM+BQdrVHw7Rrhr+euaqcZV7VXl4hzMzEYAM61MD8PvTiys54b0VEhvu6SnFPPZ3y1XxMXbmPaFQAA50nbpr66a0CU6/G8DWnaebTQwEQA0DCFB3jqzdGd1baJjySpsLxK0z7apY0pOQYnA/B7zCaTplwcrQBP504IPx4u0Oc7jxmcCsCZ6h8d5Dpel5JrYBIAAIC6jaIvAADALzhWWK7dmUWSpMgAT8WFep/1z6iy2fXNnmO6c8kOvbo6RZk1Cr7dIv31/PD2euaqdupEwRdAPTG0fRMtm9hbV3cLd53blp6vsXO3aN7aVFXZ7AamAwCgYRrcOkTDOzWVJNkc0vPfJZ/22QIAcG4Eebtr5shO6tEiQJJUXmXXI5/u0Te7KQwCdV2Qt5umDoxxPf5ga4aSjhcbmAjAmYoN8VK4v4ckae+xYh0vqjA4EQAAQN1E0RcAAOAXrE46NbFlUOtgmUymM/7eSptdX+85pjs+3K7X1qQqq/DUTfgLmvvrhavb619XtlPHcL9zmhkAaoO/l5sev7KdZt/YzbW1bYXNrtnxKbpp3lbtYntbAADOuXG9ItW9uXOBYFG5Tc+uSFJJhc3gVADQ8Hi7WzXjmg4a3Ma5s5PNIT3zzQEtScgwOBmA39Otub+u6excHFVld+jl71N4vwTUAyaTSf1jTk313ZDKVF8AAIBfQtEXAADgZxwOh+KTsl2PL24VckbfV2mz66vdzoLv62tSdazGyvPuzQP04tXt9fQV7dShGQVfAPVfr5ggLZnYW+P6tpC5ei1E0rFi3bogQa+sSFIpN9MAADhnLGaT7hscq8gA56Sr9LwyzVydIpvdYXAyAGh43K1mPX55W13btZnr3Kw1qZq9JlUOB8+7QF02tmekWof5SJIyC8v11vo0fm+BeqBfdKDreG1KLr+3AAAAv4CiLwAAwM/sP16so9Vb4XYK91OYr/tv/vlKm11f7srSHR9u1xs/pJ62tVTPFgF66ZoOeuqKtmpPwRdAA+PpZtHUoXF6d0JPtWnqK0myO6QPNqVrzNzN2nQw53d+AgAAOFM+7hZNvzROPu4WSdLWtHwtTjhicCoAaJgsZpOmDo7VrX1buM59mJChGd8mqYpFFkCdZTWbNG1wjLzdnLfAf0jO0fcHsn/nuwAYLdTHXe2bnCzpV+hgdqnBiQAAAOoeir4AAAA/szrpVDFtUFzwr/65SptdX+zK0u2Lt2vW2kOnF3xbBujlazvoyWFt1a66/AYADVX7cD8tnNBDk4fEysPq/JiZkVemuxcl6snP9ii/tNLghAAANAwRAZ66f0isa5r+x9sztSaZhTUAcD6YTCaN79NS04bEqvppV1/vPqbHPtujskp2MAHqqqZ+Hpo0IMr1+O0N6crIKzMwEYAz0T8myHW8LiXXwCQAAAB1E0VfAACAGiptdq2tnkDpbjGpb3TQ//yZiiq7Pt+ZpdsWJ2r22kM6UXyq4Nu7ZaBeubaDnry8rdo2oeALoPGwWswa3y9Ki+/spe4tA13nP9+eqZFzNmnF7mNsuwcAwDnQLdJft154asLkrB9SdeB4sYGJAKBhu7pruJ68sq3cLM6674aUXN3/0S4VlLGgEairBsQG65I2oZKk8iq7Xvr+oCqq7AanAvBberUMkPvJ19pDeaqy8TsLAABQE0VfAACAGhLS81VU7pzKcmF0kLyqt8WVnBeFP9uRqdsXJ2rOukPKLj51Q+fCqEDNvK6j/nl5G7Wh4AugEWsZ7K0547rp0WFt5ePhfA7NKa7U9I93adrSHcoqYIoOAAB/1rAOYa7ySoXNoRkrk5VTYwEiAODcGtg6VM9f20He1deJdh4t1D1Ld+pYYbnByQD8mtv6tlDzQE9JUmpOqRZuOWxwIgC/xcvNou7NAyRJxRU2JR4pNDgRAABA3ULRFwAAoIb4pFPb3g6KC5bkLPh+Wl3wfWt9mrJLThV8+0QH6tXrOuoff22j1mE+tZ4XAOois8mka7tHaPmkCzWobajr/A8HsjVqzmYtT8iQnem+AAD8YSaTSXf0a6EOTZ2LDHNLKjVjZbLKmVQHAOdN9xaBenVkJwV5u0mSUrJLNHnJDh3KKTE4GYBf4mE16/7Bsa5p3P/dfVybD+UZGwrAbxoQc2qHxbUpuQYmAQAAqHso+gIAAFQrLKvSj+n5kqQgbze1beKjT7Zn6rbFiZq7Pk05NQq+faOD9NqIjnr8sjaKo+ALAL8ozM9DL1zfSc+N6KgQX3dJzokcM77ar4kLtyk1mxviAAD8UW4Wsx4cGquw6tfYpBMlmrX2kBwspgGA86ZNE1+9MbqzIgKcU0KzCss1ZckO7clk6iBQF0UFe+nWC1u4Hr/xQ6pOsAsCUGd1bOarAE+rJOmnI4UqKq8yOBEAAEDdQdEXAACg2tqDOaqyO+RwONTE1113fLhD72xIU26Ngm//mCC9fn0nPXZZa7UKpeALAL/HZDJpaPsmWjaxt67uFu46vy09X2PnbtH8tamqsjF9EACAPyLAy00PX9JKnlbnZd4fknP0yfYsg1MBQMPWPNBLb4zu7Fr4nV9WpfuW79SWQ0weBOqiy9qF6sKoQElSUblNM79Pkc3OwiigLrKYTeobHShJstkd2pSWb2wgAACAOoSiLwAAQLVV+7NVZberwmbX9iMFyiutUfCNDdIb13fSI39prdgQbwNTAkD95O/lpsevbKfZN3ZTZKBz+lWFza5Z8SkaN2+rdh0pMDghAAD1U0yIt+4ZGO16/MHWDG1JyzMsDwA0BiE+7np1ZCd1a+4vSSqttOuh/+zRyr3HDU4G4OdMJpPuvihKoT7OXRB2ZxVp2U9HDU4F4NcMiAlyHa89yCIaAACAkyj6AgCARq+00qb5G9O0O6vwtGkOJkkXxQbrzZGd9MilrRVDwRcA/rReMUFaMrG3xvVtIbPJee7AsWLduiBBr6xIUmmFzdiAAADUQ32igzSme4QkySHplfgUpeWWGhsKABo4Xw+rnr+2oy6OC5HknDz41Ff7tXzbEYOTAfg5Xw+r7hsc47oOsfyno9p1tNDYUAB+UcsgL7WoHhKQnF2izIJygxMBAADUDRR9AQBAo1VSYdPSbUc0YVGiPkrMPO1rF7dyFnwfvjRO0cEUfAHgXPJ0s2jq0Di9O6Gn2jT1lSTZHdIHm9I1Zu5mbTqYY3BCAADqn5Hdmqlf9fSrskq7nl2RpMKyKoNTAUDD5mE164kr2uqqzk1d516PT9Hb6w7J4XD8xncCqG3tm/q6FkbZHc6FUbxXAuqm/jWm+q5LZaovAACARNEXAAA0QicLvrctTtS7mw+roMYFXYvJpBlXtdNDl8QpioIvAJxX7cP9tHBCD00eHCt3i/PjaUZeme5elKgnP9uj/NJKgxMCAFB/mEwmTbk4WjEhXpKkrMIKvbAqWVV2imYAcD5ZzCbdP7SVbr6wuevc+5sP64WVPAcDdc21XZqpU7ifJCmnpFKv/5BKKR+og/pGBcpUPYF7XUqu7PyeAgAAUPQFAACNR0mFTR/+eEQTFv10WsHXJMlsMsnNYlavqEB1jvA3NigANCJWi1nj+0fpw4m91L1loOv859szNXLOJq3YfYybbgAAnCEPq1nTL4lToJdVkrTzaJHmbUg3OBUANHwmk0m39YvS1MGxqu4l6cudWfrnF3tVXmUzNBuAUyxmk+4dGC1/T+d7pa1p+frv7uMGpwLwc0HeburUzLkL2IniSh04XmJwIgAAAONR9AUAAA1ecXmVFidk6NZFP+m9LYdVWO68wWI2SUNah+iiVsFys5hlNpk0KC7E4LQA0Di1DPbWnHHd9OiwtvLxsEiScoorNf3jXbp/2U5lFZQZnBAAgPoh1NddDw1tJavZWTX7Zu9xfb2HAgsA1IbruoXrH8PauJ6D1ybn6MGPd6uwxm5SAIwV7OOuyRdFux6/u/mwDp6gRAjUNf1jglzHa1NyDUwCAABQN1D0BQAADVZReZUWJWTo1kWJen9rhopqFHyHtgnRnFGdNfniGO04UihJ8nIzq3dUoIGJAaBxM5tMurZ7hJZPulCD2oa6zq/Zf0Kj5mzW8oQMtuoDAOAMtG3qq7sGRLkez9uQpp1HCw1MBACNx5C2YZpxTQd5uTlvwSVmFGjqsh3KLqowOBmAk3q2DNBVHZtIkqrsDr0cf1CllUzfBuqSHs0D5Gl1vpZuTstTRZXd4EQAAADGougLAAAanKLyKn2w9bAmLErUB1szVFxxquB7SZtQvTW6i6YNbqXIQC9tSctTSaXzAlHf6CB5WHl7BABGC/Pz0AvXd9JzIzoqxMddklRcYdOMr/Zr4sJtSs1m0g4AAL9ncOsQDe/UVJJkc0jPf5eszIJyg1MBQOPQKypQM6/vpAAvqyQp+USJ/rZkuw7nlhqcDMBJN/WKVGyItyTpSH653tmQbnAiADV5WM3q1TJAklRaade2jAKDEwEAABiLJgsAAGgwCsur9P4WZ8F3UcKR0wq+l7YN1dzRXXTf4FhFBHi6vif+QLbreFDrkFrPDAD4ZSaTSUPbN9GySb11dbdw1/lt6fkaO3eL5q9NVZWNSR4AAPyWcb0i1b25vySpqNymZ1ckqaSCaXUAUBvaNfPTm6O7qJm/hyQps6Bcdy/ZoX1ZRQYnAyBJbhazpg2OkWf19O3vD2RrdVL273wXgNrUPybIdbw2JdfAJAAAAMaj6AsAAOq9wrIqvVdd8F3846mCr8Vs0l/ahWnumC66d1CswmsUfCUpt6RSP1WvAg/1cVeHZr61nh0A8Nv8vdz0+JXtNPvGbooMdD6PV9jsmhWfonHztmr3EaZ5AADwayxmk+4bHKvIAGfJLD2vTDNXp8hmdxicDAAahxZBXnpzdGfFVE8NzSut1NRlO5SQlmdsMACSpIgAT03s19L1+K31aTpaUGZgIgA1tWvio2BvN0nSjqOFyi+tNDgRAACAcSj6AgCAequgrFLvbk7XrYt+0oc/HnFNprKYTbqsXZjmju6iqQNjFO7v+Yvfv/Zgjk7e3x4YFyyzyVRb0QEAZ6lXTJCWTOytcX1byFz9dH3gWLHGL0jQzJVJKmU6IQAAv8jH3aLpl8bJx90iSdqalq/FPx4xOBUANB6hvh56fVRndY5wTlgvrbTrof/s1vf7TxicDIAkDYwL0eDqnd7KKu16+fsUVbKDEFAnmE0m9Y8OlCTZHdKGQ3mG5gEAADASRV8AAFDv5JdW6t1N6ZqwKFFLtx1VaaXzwqvVbNJf24fp7TFddM/AGNfWiL8mvsZWbAPjQs5rZgDAn+fpZtHUoXF6d0JPtWnqnMJud0jvb0zXmLmbtelgjsEJAQComyICPHX/kFjXYpmPEzO1JpnXTQCoLX6eVr00ooP6xTq3IK+0OfTkl/v0SeJRg5MBkKTb+7ZQRPUOCMknSvT+1gyDEwE4qV9MkOt4fUqecUEAAAAMRtEXAADUG/mllVpwsuD70+kF32EdmujtMV005eIYNfX77YKvJB3KKVVKdqkkKS7UW80Df3nqLwCg7mkf7qeFE3po8uBYuVucH2sz8sp096JEPfnZHrbxAwDgF3SL9NetF7ZwPZ71Q6oOHC82MBEANC4eVoueuqq9hnVsIklySJq56qAWbEiTw+EwNhzQyHm5WTRtUKys1auiPt95TAnp+QanAiBJkQGeign2kiSl5pbqcF6ZwYkAAACMQdEXAADUeXmllZq/MU0TFiVq+U9HVVZ1quB7RYcmeueGLrr7omg1OYOC70mra0zzHdSaab4AUN9YLWaN7x+lDyf2UveWga7zn2/P1Mg5m7Ri9zFulgMA8DPDOoTpkjahkqQKm0MzViYrp7jC4FQA0HhYzSb9/dI43dgr0nXu3xvT9fKqg7LZ+fwCGCk21Fu39G7uevz6mlTeJwF1RP8aU33XpeQamAQAAMA4FH0BAECdlVtSqXkbnAXfjxIzTyv4Xtmxiebd0FV/uyhaYb5nXvCVJJvd4dqm1mKSBsQGn/PsAIDa0TLYW3PGddOjw9rKx8MiScoprtT0j3fp/mU7dayg3OCEAADUHSaTSXf0a6H2TX0lOT9zzViZrPLqz1oAgPPPZDLpzgHRmjwwxnXus+2ZevK/+1TB8zFgqGEdwtSzZYAkqaCsSq+uTqWED9QBfaICZXEO3Nb61DzZ+b0EAACNEEVfAABQ5+SUVOidDWm6bXGiPt6e6brp7GYx6apOTTVvbFfdNSBaob7uf+jn7zhaqJwS57bu3VsEyN/Tes6yAwBqn9lk0rXdI7R80oUa1DbUdX7N/hMa+dYmLU/IkJ3pvgAASJLcLGY9ODRWoT7Oz1NJJ0o0e+0hJuEDQC0b2T1Cj/21tSxmZ3Np9YFs/f0/u1VcXmVwMqDxMplMmnxRtIK93SQ5ryN/sj3T4FQA/D2t6hLhJ0nKLa3U7mNFBicCAACofRR9AQBAnZFTXKG56w/ptkWJ+qRGwdfdYtLwTk0174aumtQ/ynVD+o9afSDbdTwoLuRP/SwAQN0R5uehF67vpOdGdFRI9WtFcblNM77ar4kLtyk1u8TghAAA1A2BXm6afmkreVqdl4fXJOfok+1ZBqcCgMbn0vZN9OzV7V3Px9vS8zV1+U7lFFcYnAxovPw9rbpvUIyqO/j68Mcj2ptFqRAwWv+YINfxuoO5BiYBAAAwBkVfAABguOziCs1dd0i3LU7UpzuyVGFzTpJyt5h0dWdnwXdi/yhXaevPKK20aeOhPEmSj7vFtRUbAKBhMJlMGtq+iZZN6q2ru4W7zm9Lz9fYuVs0f22qqmxshwsAQEyIt+4ZGO16/MHWDG1JyzMsDwA0VhdGB+mV6zu5dpw6cKxYk5fs0JG8MoOTAY1Xx3A/XV99TcHukF7+PkVFTNsGDNUt0l/ebhZJ0tbDBSqrtBmcCAAAoHZR9AUAAIY5UVyhOScLvjtPFXw9rGZd26WZ5o3tqjv7RSn4HBR8T9qYmueaFDwgNkhuFt4OAUBD5O/lpsevbKfZN3ZTZKCnJKnCZtes+BSNm7dVu48UGJwQAADj9YkO0pjuEZIkh6RX4lOUlltqbCgAaIQ6hPvp9VGd1cTPeQ0sI79Mdy/ZrgNsTQ4YZmS3cHVo6ivJeR171tpDcjgcBqcCGi93i1kXRjkHt5RX2ZVwmGt7AACgcaHZAgAAat2JogrNXpuq2xcn6vOdWar8ecH3hq66vW9LBXufu4LvSauTsl3HA+NCzvnPBwDULb1igrRkYm+N69vCte3mgWPFGr8gQTNXJqm0gukfAIDGbWS3ZupXvQ1uWaVdz65IUmEZE+sAoLZFh3jrzdFdFBXsJUnKKanU1GU7tS093+BkQONkMZt076AY+bo7J4huTM3Tt3tPGJwKaNz6V39ukaS1KbkGJgEAAKh9FH0BAECtOV5Urlk/pOq2xYn6Ytex0wq+I7o20/yxzoJvkLfbefn7TxRXaMeRQklSMz8PtW3ic17+HgBA3eLpZtHUoXF6d0JPtamexmN3SO9vTNeYuZu16WCOwQkBADCOyWTSlIujFRPiLJZlFVbohVXJqrIzsQ4AalsTPw+9PqqzOoT7SZKKK2z6+ye7tOZA9u98J4DzIdTXXZMvjnY9nr8pXYdy2P0AMErrUG818XUOiNmdWaSckgqDEwEAANQeir4AAOC8O1ZYrjd/SNXti7fry93HXDeMPa1mXd8tXAvGdtWEPi0V6HV+Cr4nrUnK0clb1YNaB8tkMp3Xvw8AULe0D/fTwgk9NHlwrNwtzo/DGXlluntRop78bI/ySysNTggAgDE8rGZNvyROgV5WSdLOo0WavzHd4FQA0DgFeLnp5REddWG0c2phhc2hf365V5/vyDQ4GdA49Y4K1OUdwiRJlTaHXvr+oMqr7AanAhonk8nkmurrkLQhNc/QPAAAALWJoi8AADhvjhWW6401Kbrjw+36b42Cr5ebWSO7hWv+2K669cIWCjjPBV9JcjgcWp10avrJxa1CzvvfCQCoe6wWs8b3j9LiO3upe8tA1/nPt2dq5JxNWrH7mBwOJhgCABqfUF93PTS0laxm54LIr/cc1zd7jhucCgAaJy83i54Z3k5/ae8sF9od0osrk7VwUzqfVwAD3NKruaKDnbsfHM4rY0EUYKB+0YGu47UpubwuAgCARoOiLwAAOOeyCsv12mpnwferPcdPK/iOusBZ8B1fSwXfkw5mlyg9r0yS1L6pr5r5e9Ta3w0AqHuiQrw1Z1w3PTqsrXw8LJKknOJKTf94l+5ftlPHCsoNTggAQO1r29RXdw2Icj1+Z0Oadh4tNDARADReVotZ0y9rrdE9Ilzn5q1P06vxKbJTagJqlbvVrGmDY+Vhdd5aX7HvhNYezDE4FdA4NfXzUOswb0lSRn65DuWWGpwIAACgdlD0BQAA58zRgjK9Wl3w/Wbv6QXfMd0jtGBsN93Su4X8PWuv4HtSfNKpC6+D4oJr/e8HANQ9ZpNJ13aP0PJJF2pQ21DX+TX7T2jkW5u0PCGDG+gAgEZncOsQDe/UVJJkc0jPf5esTBbAAIAhzCaT/nZxjCZddGoRxic/HdVTX+1Xpc1uYDKg8Wke6Knb+7ZwPZ6z9pCyCnmPBBhhQEyQ63hdSp5xQQAAAGoRRV8AAPCnHS0o08z4g7rzw+36du9x2aoLvt7uFlfBd1yv5vLztBqSr8ru0A/JzqKvm8WkfjUuAgEAEObnoReu76TnRnRUiI+7JKm43KYZX+3XxIXblJpdYnBCAABq17hekbqgub8kqajcpmdXJKmkwmZwKgBovG7o2VzT/9JaFpPz8ap9J/Twf/aopKLK2GBAIzOkdYguauUcIlFSadfL36e4hl0AqD29WwbIana+KG44lMfvIQAAaBQo+gIAgD/sSH6ZXvneWfBdse+ETl5L8XG3aGyPCM0f29XQgu9JPx3OV0GZ88ZHr5aB8vEwNg8AoO4xmUwa2r6Jlk3qreFdw13nt6Xna+zcLZq/NlVVTMwCADQSFrNJ0wbFKDLAQ5KUnlemmavZKh4AjPTXjk309PD28rA6b+1tTcvTvct3Ka+k0uBkQONhMpk0sV9LNfNzvkc6cLxYixMyDE4FND4+7lZdEOlcmFhQVqWdRwsNTgQAAHD+UfQFAABnLSO/TC9/n6yJS7Zr5f7TC7439ozU/LFddWPP5vKrI4Xa+KQc1/GguGADkwAA6jp/Lzf946p2mnVjV0UGekqSKmx2zYpP0bh5W7X7SIHBCQEAqB0+HlZNvzROPu4WSdLWtHwtSjhicCoAaNz6xQbr5REd5evhfG7el1WkyUu362h+mcHJgMbD292i+wbHuCZsf7I9Sz8d5loBUNsG1Ni5cV1KroFJAAAAagdFXwAAcMYy8kr10qpkTVqyXd/tz3YVfH09LLqpZ6QWjO2qsT0i5VtHCr6SVFxepS1peZIkf0+rujUPMDYQAKBe6B0TrCUTe2tc3xaq3glQB44Va/yCBM1cmaRSti8HADQCEQGeun9IrOu18OPETK1JzvntbwIAnFedIvz1+qjOCvVxlySl55Zp8pIdSj5RbHAyoPFoHeajm3o1dz1+bU2K8kqZrg3Ups4RfvKrXvjy4+ECFXOtDgAANHAUfQEAwO9Kzy3VC98la9LSHVp14FTB18/DonG9mmvB2G66oUekfOpQwfek9Sm5qrQ5A18UGyzryTvUAAD8Dk83i6YOjdO7E3qqTVNfSZLdIb2/MV1j5m7WpoMUnQAADV+3SH/demEL1+NZP6TqwHHKZABgpNhQH705prNaBnlJkk4UV+iepTu0PYOpokBtuapTE13Q3F+SlFdapddWp8rucBicCmg8rGaT+kQFSpIq7Q7XwBcAAICGiqIvAAD4VWm5pXr+uyTdtXSH4pNOFXz9Pa26pXdzzR/bTWO6R8i7eivXumh10qkS1qDWwQYmAQDUV+3D/bRwQg9NHhwrd4vzY3RGXpnuXpSoJz/fo3ym9gAAGrhhHcJ0SZtQSVKFzaEZK5OVU1xhcCoAaNya+Xvq9dGd1a56UWJRuU33f7RL65KzDU4GNA5mk0lTLo5WoJdz+MVPGQX6dEeWwamAxqV/TJDreF1qnnFBAAAAagFFXwAA8D/Sckv13Mok/W3pDq1OytHJOQQnC77zbuiqURfU7YKvJGUWlGt3VpEkqUWgp2JDvA1OBACor6wWs8b3j9LiO3upe8sA1/nPEzM1cs4mrdh9TA4m9wAAGiiTyaQ7+rVQ++oyWW5JpWasTFZ5ld3gZADQuAV6uemV6zupV/VEwwqbXY9/vlf/3UXZEKgNgV5umjowRif3kFu0NUP7j7HzAVBbYoK9FOHvIUnad6xYx4tYjAgAABouir4AAMAlNadEM1Y4C75rkk8v+N56YQvNH1s/Cr4nrakxwWRgXIhMJtNv/GkAAH5fVIi35oy7QI8MaysfD+frYU5xpaZ/vEv3L9upYwXlBicEAOD8cLOY9eDQWIX6uEuSkk6UaPbaQyx0AQCDebtb9OzV7TW0rXPyus0hPfdtkhZtOcxzNFALukb669quzSQ5f/9eiT+o4gqbwamAxsFkMp0+1Tcl18A0AAAA5xdFXwAAoNTsEj2z4oDuXrZTPxw8VfAN9LJqQp8WWjC2q67vFi4vt/pR8JUkh8Oh+AM5kiSTpItbBRsbCADQYJhNJl3XPULLJ12oQdU30yVpzf4TGvnWJi1PyJCdG+oAgAYo0MtN0y9tJU+r87LymuQcfbKdqZEAYDQ3i1mPXd5GI7qFu869tfaQ3lyTymcToBaM6R6htk18JElZhRWaw2IooNb0jQ50TdVel5LL7x4AAGiwKPoCANCIpWSX6JlvD+ju5Tu17uCplc6BXlbd1qeF5t3QVSO6hsuzHhV8T9p/rFiZhc6pip0j/BTq625wIgBAQxPm56EXru+k50Z0VEj1dMPicptmfLVfExduU2p2icEJAQA492JCvHXPwGjX4w+2ZmhLWp5heQAATmaTSVMGxej2/i1d55b9eETPfH1AVTa7gcmAhs9qNum+QTGunfDWpeTqu/3Zv/NdAM6FUB93tW/qK0nKKqpQMtfjAABAA0XRFwCARij5RLGe/uaAJi/fedpWRkHebrq9b0vNu6GrrqunBd+T4pNOXUgdGBdiYBIAQENmMpk0tH0TLZvUW8O7npqetS09X2PnbtH8tancVAcANDh9ooM0pnuEJMkh6ZX4FKXllhobCgAgk8mkcb1b6MFLWslcPd5wxd7jmv7ZHpVW2owNBzRwTfw89LcBUa7H72xI0+E83h8BtaF/TKDreG1KnmE5AAAAzieKvgAANCLJJ4r11Df7dc9Hu7Qh9VTBN9jbTXf2cxZ8r+3SrF4XfCWp0mZ3TSj2sJrVJyrQ2EAAgAbP38tN/7iqnWbd2FWRgZ6SpAqbXbPiUzRu3lbtPlJgcEIAAM6tkd2aqV9MkCSprNKuZ1ckqbCsyuBUAABJurJzM/3fle3kbnG2fTen5mna8p3KL600OBnQsPWLCdJf2oVKkipsDr20KkXlVSz+Bc63ni0CXK95mw7lqZJF9wAAoAGi6AsAQCNw4Hix/u9rZ8F3Y2qe63ywt5sm9mupd27oqqs7N5OHtWG8NUhIz1dRhXNKSZ+oQHm51+/iMgCg/ugdE6wlE3trXN8WrglaB44Va/yCBM1cmaTSCqZoAQAaBpPJpCkXRysmxEuSlFVYoRdWHVSV3WFwMgCAJF0UF6IXr+soXw/ndbHdmUWasnSHjhWWG5wMaNhuvbCFWlQvAD6UW6p3Nx82OBHQ8Hm5WdSzRYAkqbjCpsQjhQYnAgAAOPcaRpsHAAD8ogPHi/TkV/t178e7tOlQnut8iI+bJvWP0rwbump4Ayr4nhR/INt1PDAuxMAkAIDGyNPNoqlD4/TuhJ5q09RXkmR3SO9vTNeYuZu16WCOwQkBADg3PKxmTb8kToFeVknSzqOFmr8x3eBUAICTujYP0GsjOyvY202SdCinVH/7cLtSs0sMTgY0XB5Ws+4fEuuaLvr1nuPaWGN3PQDnR7/oINfxuhR+5wAAQMPTsFo9AABAkrTvWJH++dU+3fvxbm1Oy3OdD/Vx110DovTOmK66qlNTuTewgq8kFZRVKSE9X5IU5O2mzhF+BicCADRW7cP9tHBCD00eHCt3i/M1NyOvTHcvStSTn+9h21wAQIMQ6uuuh4a2ktV8qszyzZ7jBqcCAJzUKsxHs8Z0UWT1hNHjRRWasnSHdh4pMDgZ0HC1DPLShD4tXI/f/OGQjhdVGJgIaPg6NvN1LUD86UihCsurDE4EAABwbjW8dg8AAI3Y3qwi/fO/+zTtk93ampbvOh/m666/DYjSOzd00ZUdG2bB96R1B3Nkq94pdmCrYFlO7psOAIABrBazxveP0uI7e6l7ywDX+c8TMzVyziat2H1MDgdbnAMA6re2TX1114Ao1+N3NqRp51G2ywWAuiI8wFNvju6sNk18JDkXyk/7aJc2prDbCHC+XNo2VP1inBNGiytseiX+oGx2Pv8D54vFbFLf6EBJks3uOG2XSwAAgIag4bZ8AABoRPZkFurxL/fp/v/s1tb00wu+ky+K1ttjuuiKjk3lZmn4L/3xSdmu44FxIQYmAQDglKgQb80Zd4EeGdZWPh4WSVJOcaWmf7xL9y/bqWMF5QYnBADgzxncOkTDOzWVJNkc0vPfJSuT1zcAqDOCvN018/pO6t7CuQCxvMquRz7do292HzM4GdAwmUwm3dW/pcJ83SVJe7OKtWTbEYNTAQ1b/+gg1/G6lDzjggAAAJwHDb/tAwBAA7Y7s1CPfblXD3y6Rz8ePlXwbVKj4Ht5hyaNouArObdDP3C8RJIUE+ylqGAvgxMBAHCK2WTSdd0jtGzihRrYJtR1fs3+Exr51iYtT8iQnem+AIB6bFyvSF3Q3F+SVFRu07MrklRSYTM4FQDgJB8Pq567poMGtXYujrc5pGe+OaAlCRkGJwMaJh8Pq6YNitHJTec++ilTO46w6wFwvrQM8lLLQE9JUnJ2iY4WlBmcCAAA4NxpHK0fAAAamF1HC/XoF3v14Kd7tO1wget8Uz933XNxtOY2soLvSatrTPM9ecMCAIC6pom/h14c2UnPjeioEB/nZJ/icptmfLVfk97bptTsEoMTAgDwx1jMJk0bFKPIAA9JUnpemWauTmEhCwDUIe5Ws/4xrK2u6drMdW7WmlTNXpMqB8/XwDnXtqmvxvaIlCQ5JM1cnaL80kpjQwENWP8YpvoCAICGqXG1fwAAqOd2HinQI5/v1d8/26OfMmoWfD00dWCM5o7uosvaN76CryTZHQ7FJ+VIkswmaUBssMGJAAD4dSaTSUPbN9GySb01vGu46/yPafkaO3eL5q9NVZXNbmBCAAD+GB8Pq6ZfGicfd4skaWtavhYlsE01ANQlFrNJ9w6O1a19WrjOfZiQoRnfJqnKTtkXONeu6dJUXSL8JEm5JZV644dDFOuB86RvdKBM1VO016fmsugQAAA0GI2vBQQAQD20/UiBHv58jx76fK8Sj5wq+Ib7e+jegTGaO7qz/tIuTNZGWPA9aXdmkU4UV0iSukX6K8jbzeBEAAD8Pn8vN/3jqnaadWNXRVZvLVhhs2tWfIrGzduq3TVe9wEAqC8iAjx1/5BY1zbVHydmak1yjrGhAACnMZlMGt+3paYNiVX107W+3n1Mj322R2WVNkOzAQ2N2WTSPQNj5O9plSQlpOfri13HDE4FNEyBXm7q3MxZrD9RXKn9x4oNTgQAAHBuNN42EAAAdZzD4VBiRoEe/myPpn++VzuOFLq+FuHvofsGxeit0V10aSMv+J4UfyDbdTwoLsTAJAAAnL3eMcFaMrG3xvVt4SpFHThWrPELEjRzZZJKK7jRDgCoX7pF+uvWC09Nipz1Q6oOHOcmOwDUNVd3DdcTV7SVm8X5QWRDSq4e+HiXCsuqDE4GNCzB3m66Z2C06/F7WzKUfIL3RsD50D8m0HW8NiXXuCAAAADnEK0gAADqmJMF34c+26tHvtirHUdrFHwDPHT/4FjNGd1Fl7QNk+VkE6iRK6+ya0Oq82KNt5tZvaICjQ0EAMAf4Olm0dShcXp3Qk+1aeorSbI7pPc3pmvM3M3adJBJiACA+mVYhzBd0iZUklRhc2jGymTlVO/EAgCoOwa1CdXz13SQt7tFkrTjSKGmLN2h40XlBicDGpbuzQN0deemkqQqu0Mvf5/Cwl7gPOjePECeVmcVZnNaviqq7AYnAgAA+PMo+gIAUEc4HA5tO5yvhz7bo0e+2KtdmacKvs0DPZ0F31FdNKRNKAXfn9l8KE+llc4LNX1jguRh5S0OAKD+ah/up4UTemjy4Fi5V0/tz8gr092LEvXk53uUX1ppcEIAAM6MyWTSHf1aqH31ApbckkrNWJmscm60A0Cd071loF4d2UlB3m6SpJTsEt394Q6l5ZQYnAxoWMb2iFBcqLck6WhBueauTzM4EdDweFjN6tUyQJJUVmXXj4cLDE4EAADw59GCAQDAYA6HQz+m5+vBT/fosS/3aVdmketrzQM99eCQWM0a2ZmC72+IT8p2HQ+KCzEwCQAA54bVYtb4/lFafGcvda++MSFJnydmatSczVq5+5gcDoeBCQEAODNuFrMeHBqrUB93SVLSiRLNXnuI1zEAqIPaNPHVG6M7KyLAU5KUVViuyUt3aE+NgQQA/hw3i1nTBsfKy815m351co6+P5D9O98F4GwNiAlyHa9NyTUwCQAAwLlB0RcAAIM4HA4lpOfpgf/s1uP/3ac9WacKvi0CPfX3oa00a2RnDWpNwfe35JZUKjHDuRo7zNdd7Zv5GpwIAIBzJyrEW3PGXaBHhrWVj4dzG93s4go9/PEu3b9sp44VsJUuAKDuC/Ry0/RLW7m2z12TnKNPtmcZnAoA8EuaB3rpjdGdFRfmI0nKL63Sfct3asshSlLAudLM30OT+ke5Hr+9Pk1H8ssMTAQ0PG2b+Cikekr9zsxC5bFDFgAAqOco+gIAUMscDoe2puXp/v/s1j/+u197jxW7vhYV5KWHhrbSmyM7a2BcCAXfM/BDco7s1YOgBrYKltnEvzMAQMNiNpl0XfcILZt4oQa2CXWdX7P/hEa+tUnLEzJkZyoiAKCOiwnx1j0Do12PP9iaoS1peYblAQD8uhAfd706spO6NfeXJJVW2vXwf/Zo5d7jBicDGo6LWgVraBvn7nRlVXa99P1BVdrsBqcCGg6zyaR+1VN97Q5p46E8YwMBAAD8SRR9AQCoJQ6HQ5sP5WnaJ7v1z6/2a1/Ngm+wlx6+JE5vjOykiyn4npX4pFPbmg2MCzEwCQAA51cTfw+9OLKTnhvRUSHV258Xl9s046v9mvTeNqVmlxicEACA39YnOkhjukdIkhySXolPUVpuqbGhAAC/yNfDquev7aiL4oIlSVV2h576ar+WbzticDKg4bitTwtFBnhKklKyS7VwS4bBiYCGpX90oOt4bQqT6QEAQP1G0RcAgPPMWfDN1X2f7NaTX+/X/uOnCr7RwV565NI4vXF9J13ENNqzlppTotQc503h1mE+igz0NDgRAADnl8lk0tD2TbRsUm8N7xruOv9jWr7Gzt2i+WtTVcUEIABAHTayWzPXZK2ySrueXZGkwrIqg1MBAH6Jh9WsJ69opys7NXWdez0+RW+vOyQHu4oAf5qnm0XTBsfIzeK8L/DlrmPseACcQxEBnooN8ZIkpeWWKT2PRYYAAKD+ougLAMB54nA4tDE1V/d+vEtPfn1AB2oUfGNCvPXIpXF6/fpO6h9LwfePWp2U4zoeVD1dBACAxsDfy03/uKqdZt3Y1bXQpcJm16z4FI2bt1W7jxQYnBAAgF9mMpk05eJoxVTfcM8qrNALqw6qyk5hDADqIovZpAcuaaWbL2zuOvf+5sN6YWUyz93AORAT4q3xvU/9fr2xJlXZxRUGJgIalv7RQa7jdSl5xgUBAAD4kyj6AgBwjjkcDm1IydXUj3fpqW8OKOnEqW20Y0O89dhfWuu1ER0p+P5JNrtDa5KdRV+r2aT+sRR9AQCNT++YYC2Z2Fvj+rSQufptxYFjxRq/IEEzVyaptMJmbEAAAH6Bh9Ws6ZfEKdDLKknaebRQ8zemG5wKAPBrTCaTbusXpXsGxejk1cwvd2bpn1/sVXkVnzmAP+uv7cPUOypQklRYbtPM+BTZKNID50SfqEBVD83WhtRc2fndAgAA9RRFXwAAzhG7w6H1KTma8tEuPf3tASXXKPi2CvXW45c5C759Y4Io+J4DO44UKrekUpLUvUWA/D2tBicCAMAYnm4WTb0kTu9O6Kk2TX0lSXaH9P7GdI2Zu1mbDub8zk8AAKD2hfq666GhrWStXqny9Z7j+mbPcYNTAQB+y4gLIvT4sDau5+61yTl68OPdKiyrMjgZUL+ZTCbdfVGUQn3cJEm7Mov0UeJRg1MBDYOfp1VdI/0lSbmlVdqdVWRwIgAAgD+Goi8AAH+S3eHQuoM5umf5Tv3r2ySlZJ8q+MaFeusfl7XWq9d1VJ/oIJko+J4z8UnZruNBcUzzBQCgfbifFk7oocmDY+VucX7cz8gr092LEvXk53uUX1ppcEIAAE7Xtqmv7hoQ5Xr8zoY07TxaaGAiAMDvGdo2TDOu6SAvN+dnjsSMAk1dtkPZRRUGJwPqNz8Pq+4dFOParWfptqPancn7IuBc6B8T5Dpem5JrYBIAAIA/jqIvAAB/kN3h0A/JOZq8fKeeWZGklJxS19fahPnon39to5nXddSFFHzPudIKmzamOi/G+Lpb1KNFgMGJAACoG6wWs8b3j9LiO3upe8tTr4+fJ2Zq1JzNWrn7mBwOtigEANQdg1uHaHinppIkm0N6/rtkZRaUG5wKAPBbekUF6pXrOynAy7nDVvKJEt29ZLsO55b+zncC+C0dmvlp1AXhkpw79bwSn6LCciZmA39Wtwg/+bhbJElb0/NVWmkzOBEAAMDZo+gLAMBZchZ8szV52U7NWJmkQzULvk189OTlbfTytR3UOyqQgu95siE1VxU2Z0mpf2yw3Cy8pQEAoKaoEG/NGXeBHhnWVj4ezhsZ2cUVevjjXXpg2U4do0AFAKhDxvWK1AXNndvpFpXb9OyKJJVWcPMdAOqy9s389OboLmrm7yFJOlpQrruX7NB+tkQH/pQRXcPVsZmvJCm7uFJvrkllwS7wJ7lZzLqwekF8hc2hren5BicCAAA4e7RiAAA4Qza7Q6uTsnX3sp2asTJZh2pMqGh3suB7TQf1bEnB93xbnZTjOh7UOtjAJAAA1F1mk0nXdY/QsokXamCbUNf51ftPaORbm7Q8IUN2bhYCAOoAi9mkaYNiFBngLIul55Vp5uoUXqcAoI5rEeSlN0d3VkyItyQpr7RSU5fv1I9pecYGA+oxi9mkewfFyK960e7mtHx9vee4wamA+q9/TJDreF1KnnFBAAAA/iCKvgAA/A6b3aH4Ayf0t2U79Px3yUqrUfBt39RXTw1rqxcp+Naa40UV2nm0UJIU7u+hNmE+BicCAKBua+LvoRdHdtKMER0V4uMuSSout2nGV/s16b1tSs0uMTghAACSj4dV0y+Nc22puyUtX4sSjhicCgDwe0J9PfT6qM7qHOGczF5SYdPf/7Nb8ftPGJwMqL9CfNw15eJo1+N/bz6sFD67A39KXKi3mvo6r4vtySpSdnGFwYkAAADODkVfAAB+hc3u0PfVBd8XVh3U4bwy19c6NPPV01e01QtXt1f3FgEUfGvRmuRsnZzpNDAuhH/3AACcAZPJpEvaN9GySb01vGu46/yPafkaO3eL5q9NVZXNbmBCAACkiABP3T8kVubqj3kfJ2ZqTXLOb38TAMBwfp5WvTSig/rFOqclVtoceuLLffpP4lGDkwH1V8+WgbqiYxNJzt+pl78/qLJKm8GpgPrLZDK5pvo6JK1PzTM0DwAAwNmi6AsAwM/Y7A6t2n9Cdy3doRd/VvDt2MxP/7qirZ4f3l4XNKfgW9scDodWJ526yTswLtjANAAA1D/+Xm76x1XtNOvGrooM9JQkVdjsmhWfopvnJ2j3kQKDEwIAGrtukf669cIWrsezfkjVgePFBiYCAJwJD6tFT13VXpdXFxMdkl5ZdVALNqTJ4XD89jcD+EU394pUTIiXJCkjv1zzNqYbnAio3/rFBLqO16fm8voEAADqFYq+AABUs9kdWrnvuCYt2a6Xvj+ojPxTBd/O4X565sp2em54O3Wj4GuY5BMlruJ1h6a+aurnYXAiAADqp94xwVoysbfG9Wnhmpq4P6tI4xckaObKJJVWMCUIAGCcYR3CNLRNiCSpwubQjJXJymFrXQCo86xmkx66NE5je0a6zv17Y7peXnVQNjtlKuBsuVnMun9wrDytzlv63+3P1g/sdgD8YU18PdQmzFuSszyfmltqcCIAAIAzR9EXANDo2ewOrdh3XBOXbNcr8Sk6UlDu+lrnCD89e1U7zRjeXl0j/Sn4Giw+Kdt1PLB1iIFJAACo/zzdLJp6SZzendBTbZr6SpLsDun9jekaM3ezNh3k5iEAwBgmk0l39mup9tWvT7kllZqxMlnlVXaDkwEAfo/JZNLEi6J198Bo17nPtmfqyf/uUwXP48BZiwjw1B39Wroez1l3SJk17mEAODv9Y4Jcx+sO5hqYBAAA4OxQ9AUANFpVNru+3Xtcdy7ZrpnxKTpa4+JY1wh/PXdVO824qr26RPgbmBInVdkdWlt90cXNYlK/GhdjAADAH9c+3E8LJ/TQ5MGxcrc4LxNk5JXp7kWJevLzPcovrTQ4IQCgMXKzmPXg0FiF+rhLkpJOlGj22kNsrwsA9cSo7pF69K+tZaneQmT1gWz9/T+7VVxeZXAyoP4Z3DpEA+OCJUmllXa99P1BVdoozgN/RO+WgXKrfm3acChPVUycBwAA9QRFXwBAo1Nls+ubPcd055IdenV1ymmr37tF+uv54e31zFXt1ImCb52y7XC+CsqcNwJ6tQyUj7vF4EQAADQcVotZ4/tHafGdvdS9ZYDr/OeJmRo1Z7NW7j5GsQoAUOsCvdw0/dJWru2q1yTn6JPtWQanAgCcqb+0b6Jnhrd3PY9vS8/X1OU7lVNcYXAyoP65s29Lhft7SJKST5RoUcIRgxMB9ZOPu0UXNHfe/ysst2nH0UKDEwEAAJwZir4AgEaj0mbX13uO6Y4Pt+u1NanKKjxV8L2gub9euLq9/nVlO3UM9zMwJX5N/IFs1/GguBADkwAA0HBFhXhrzrgL9MiwtvLxcC6qyS6u0MMf79IDy3bqGNuDAgBqWUyIt+6psf37B1sztCUtz7A8AICz0ycmSC9f30n+nlZJ0oFjxZq8ZIeO5JUZnAyoX7zcLZo2OEbW6kmkn+7I0o+H8w1OBdRP/WvsGLkuJdfAJAAAAGeOoi8AoMGrtNn11W5nwff1Nak6VnRqYkT35gF68er2evqKdurQjIJvXVVUXqWt6c6LlgGeVnVrzrRlAADOF7PJpOu6R2jZxAs1sE2o6/zq/Sc08q1N+ighQ3am+wIAalGf6CCN6R4hSXJIeiU+RWm5pcaGAgCcsY7hfnp9VGeF+bpLkjLyy3T3ku1KOl5scDKgfmkV6qNxvSJdj19bnaqckkoDEwH1U+dwP/lVL3DfdrhAxRU2gxMBAAD8Poq+AIAGq9Jm15e7snTHh9v1xg+pOl6j4NuzRYBeuqaDnrqirdpT8K3z1qfkqtLmLBRd1CrYNbUAAACcP038PfTiyE6aMaKjQnycN+SLy2169qv9mvTeNqVmlxicEADQmIzs1kz9qidvlVXa9eyKJBWWVRmcCgBwpqJDvDVrTBdFBXtJknJKKnXP0h36iYmkwFm5smMT9WgRIEkqKKvSa6tTWIwLnCWr2aS+0c7PFpV2hzazYwgAAKgHKPoCABqcSptdX+zK0u2Lt2vW2kOnF3xbBujlazvoyWFt1a6pr4EpcTbik7JdxwPjQgxMAgBA42IymXRJ+yZaOqm3hncNd53/MS1fY+du0fy1qaqy2Q1MCABoLEwmk6ZcHK2YEGdBLKuwQi+sOqgqO8UWAKgvmvh56PVRndUh3Dl4objCpgc/3qU1Na79AfhtJpNJky+KUpC3myRp+5FC/Wd7lsGpgPqnf0yg63hdSq5xQQAAAM4QRV8AQINRUWXX5zuzdNviRM1ee0gnik8VfHu3DNQr13bQk5e3VdsmFHzrk8yCcu3Ncm7j1yLQU7HVN3UBAEDtCfBy0z+uaqdZN3ZVZKCnJKnCZtes+BTdPD9Bu48UGJwQANAYeFjNmn5JnAK9rJKknUcLNX9jusGpAABnI8DLTS+P6KgLqycpVtgc+ucXe/XFjkyDkwH1R4CXm+4dGKOT+94tSsjQvqwiQzMB9U10kJciAzwkSfuPl+hYUbnBiQAAAH4bRV8AQL1XXmXXZzsydfviRM1Zd0jZxZWur/WOCtTM6zron5e3URsKvvXS6hoTPQa1DpHJZPqNPw0AAM6n3jHBWjKxt8b1aSFz9Uvy/qwijV+QoJkrk1RWaTM2IACgwQv1dddDQ1vJWv1C9PWe4/pmz3GDUwEAzoaXm0XPDG+nv7QPkyTZHdILK5P13qZ0ORxMagfOROcIP43o1kyS83fo5fgUFZdXGZwKqD9MJpP6xwS5Hq9PyTMuDAAAwBmg6AsAqLfKq+z6tLrg+9b6NGWXnCr49okO1KvXddQ//9pGrcMo+NZXDodD8dVFX5Oki1oFGxsIAADI082iqZfE6d0JPdWmqfN9lt0hvb8xXaPf2qxNB3MMTggAaOjaNvXVXQOiXI/f2ZCmnUcLDUwEADhbVotZ0y9rrdE9Ilzn3lmfptfiU2Sn7AuckdEXRKhdUx9J0vGiCs1ae4iyPHAW+kUHuiZjr03J5fcHAADUaRR9AQD1TlmlTZ9sz9RtixM1d32acmoUfPtGB+m1ER31+GVtFBfmY2BKnAt7jxUrq7BCknNCQaiPu8GJAADASe3D/bRwQg9NHhwrd4vz8kJGXpnuXpSoJz/fo/zSyt/5CQAA/HGDW4foqk5NJEk2h/TCd8nKLGC7XQCoT8wmk/52cYwmXXRq8cbHPx3VU1/tV6XNbmAyoH6wmE26b1CsfN0tkqQNqXlase+EwamA+iPY213tqxexHyuqUNKJEoMTAf/f3n2HR1Xlfxz/3Jn0CemhhJYQOggIAkpHAdcOKogoishix4ZrWVdhi6zlp664oKiURVAQxZVV7DQRBJEmIDWhJAgppPeZ+/sjMBKTQAJJZibzfj1Pnidz5+bOd/Kcc+45937vOQAAVI5EXwCAxygotuujrUd153vb9Pa6QzpxWoJvn7hwTb+xs56+vI3io0jwrS9WnZzNV5IGtY50YSQAAKAiPlaLxvVtqfcm9lT3FqHO7cu2/qpRb2zQ1zuPMxsKAKDW3NazmS5sFiJJyi60659f71N+kd3FUQEAquvmi5rpiWGtZT05reK3u1P1xMe7lFdU4trAAA8QHeyne09Llp+9/rAOnch3YUSAZ+kXF+78fW3CCRdGAgAAcGYk+gIA3F5BsV0fbj2qO9/bqnfWH1bGabPD9W0Vrtdv7Kw/D2ujVpFBLowSNa2oxKG1B0ovqvj7WNQ7Nsy1AQEAgEq1jAzSG2Mv1FNXtpPNv3QmobTcIj3x0Q5N/uBnHWeGRQBALbBaDD0yKE5NQ/0lSYdOFOjVVSz5DgCe6IpOjfS3azo4Vwv58VCGHlqyQxl5rBQCnM3FseH6Q4doSVKR3dT/fXtAhSXMig1UxUXNQ+R38kmT9QczmVEeAAC4LRJ9AQBuK7/YriVbjmr8wq2avf6wMvJLZ3AwJPVvFaF/j+ysp4a2URwJvvXSj4czlXtyJqaLY8MU6Gt1cUQAAOBMLIah67vH6IO7emtg2yjn9lV7UjXyzR/04aYkEq8AADXO5u+jJ4e2lu3kktUbD2Vq4aZkF0cFADgXfeMj9PINnRR88uHB3cdydP/ibTqaWeDiyAD3d3uvZmoZHihJOpxRoDk/HHZxRIBnCPC16qLmpatU5RXbtSUp28URAQAAVIxEXwCA28krsmvx5mSNX7hVc344rMyC3xJ8B8SXJvg+MbS1YiNI8K3PVu1Lc/4+qHWkCyMBAADV0TDEXy+N7Kx/3tBJkTY/SVJuoV3Tlu/R3fM3KzEtz8URAgDqm5jQAD16aStZTi75/tHWX7V6f7prgwIAnJMLmoZo+qgLFHVyLHH4RIHuX7Rd+1NzXRwZ4N78fSx69NI4+fuU3v7/8pdUfZ9wwsVRAZ6hX1y48/e1idQbAADgnkj0BQC4jVMJvne+t1XzNhxR1mkJvgNbR2jGqAv0+JDWakmCb72XmV+snw5nSpIignzVuUkDF0cEAACqwzAMDenQUIvv7qVruzZxbv/pUKbGzNqo2d8lqoSlEAEANahb0xDd0bu58/WMNYnam0JSGAB4olZRNv179AVqHh4gSUrNLdKkxdu1LSnLxZEB7q1ZWKDuvPi0/tB3B3U8u9CFEQGeoWOjYIUH+kiStiZlKfvk/UkAAAB3QqIvAMDl8orsev+nZI1fuKVMgq/FKJ3JdeaoC/Sny1qrxcllp1D/fXfghOwnV/YeEB8h66lpmQAAgEcJDfTVM9e014xbuqppWOlN+iK7QzNWJui22Zu0M5kb9QCAmnNlx2hd1rZ0RZgiu6nnv96v9NwiF0cFADgXjUMC9PqoLmrfKFiSlFNo16Mf7tBaZmwHzuiytpHqe3J20rwiu15ZmaASh+niqAD3ZrEYuiS2tN7YTWn9wQzXBgQAAFABEn0BAC6TW1ii9zYl6Y6FWzR/4xFlF9ollSb4Dm5TmuD72GXxak6Cr9dZtS/N+fvA1pEujAQAANSEXnERWnRXL429uLlzWfU9x3I0bs4mvfr1PhUU210bIACgXjAMQxP7tFCHk0lh6XnFev6b/SosYRZ5APBEYUG+euXGzurZMkxS6UODf1m2S5/tOObawAA3ZhiG7u7XUo0a+EmSdh/P1fs/Jbs4KsD99TuZIC9JaxNPuDASAACAipHoCwCoczmFJVq4KUl3LNyqd39MUs5pCb6XtY3UG6Mu0ORL49UsjARfb3QkI1/7UvMkSa0ig9QygnIAAEB9EOBr1YNDWmve+IvU9mQClsOU3l1/WDe9uUEbEpiZCwBw/nytFj12WStF2UqTW/am5GnmdwdlmsxkBwCeKMjPqmnXddBl7aIklc60+PyX+7Rw4xHadqASNj+rHhncStaTD9ou3fqrtiaxog5wJs3CAtQyvHQ1qgNp+UrOLHBxRAAAAGWR6AsAqDM5hSVa8OMRjV+4VQt+TFJu0W8JvkPaRunNm7rokcHxakqCr1dbue+3JJ+BrSNcGAkAAKgNHZo00H/G99B9g1vJz1p6WSIpo0D3Ltiqqct2KTO/2MURAgA8XVigr54cGq8An9LzzOr96Vq6jdkfAcBT+VotevqKtrqhWxPntje/O6gZqxPlINkXqFCbaJtuuaipJMmU9K9VCcpgvA2cUd8ys/pmuC4QAACACpDoCwCoddmFJZq/8YjuWLhVCzcll0nwHdouSrNu6qKHB7dSTGiAiyOFqzlMU6v3pUkqLR/940n0BQCgPvKxWnRH35Z6b2JPdW8R6ty+bOuvGvXGBn298zizcwEAzktcZJAmDYx1vl7wY5I2HspwWTwAgPNjMQw9MChOE/q2cG5b/FOynvt8r0rsDhdGBrivay9opG5NQyRJGfklmk5yPHBGF7cMk+XkTNjfJ5ygvgAAALdCoi8AoNZkF5Qm+I5fuFXv/5SsvJMJvlaLoWHtozVrdBc9NKiVmpDgi5N2HM1Ram7prAIXNgtVWKCviyMCAAC1qWVkkN4Ye6GeurKdbP5WSVJabpGe+GiHJn/ws45nFbo4QgCAJ7s4Nlyju5fO/mhKemVlgg6dyHdtUACAc2YYhsb2aq7HhsQ7E7G++iVFT33yi/KL7a4NDnBDFsPQpIGxCgv0kSRtPpKlZT8fd3FUgPsKC/TVBU0aSJLS8oq1+3iuiyMCAAD4DYm+AIAal1VQrHkbDuuOhVvKJfhe3j5as27qogcHxqlJCAm+KGvlydl8JWlga2bzBQDAG1gMQ9d3j9EHd/XWwLZRzu2r9qRq5Js/6MNNScygAgA4ZyO7NVGfk0vwFhQ7NO2rfcouKHFxVACA83H1BY3116vby89amu37Q+IJPfLhDmXmF7s4MsD9hAX6atLAOOfrdzce0d4UkheByvSNDXf+/l3CCRdGAgAAUBaJvgCAGpOZX6y5PxzW+IVbtXjzUeUXly6Z5mMx9IcO0XprdBdNGhinxiH+Lo4U7qiwxKF1iaUXTYJ8LerZIsy1AQEAgDrVMMRfL43srH/e0EmRNj9JUm6hXdOW79Hd8zfrYFqeiyMEAHgiwzD0wIBYxUUGSpKOZRfpxW8PqMTBQyQA4Mn6t47Ui9d3ks2vdGWQnUez9cDi7TqezaogwO91axqiEV0aSZLspvTyigPOCVoAlNW9WYgCfUvTaDYeylRhicPFEQEAAJQi0RcAcN4y84s152SC7wdbyib4Xtmxod4a3UUPDIhTowYk+KJyPxzMUMHJstMnLkL+PnRTAADwNoZhaEiHhlp8dy9d27WJc/tPhzJ186yNmrP2oErs3GABAFSPv49FTw5p7Vy2+uej2Zq9/rCLowIAnK9uzUL12qgLFBHkK0k6mJ6ve9/fpkQeEgTKublHU7WJtkkqffDpzbUHZbJ6DlCOn49FvVqESpIKShz66UimiyMCAAAoRQYNAOCcZeQXa/b6Qxq/cKuWbDmqgpLfEnyv6thQb9/cRff1j1VDEnxRBSv3pjl/H9g6woWRAAAAVwsN9NUz17TXjFu6qmlYgCSpyO7Qv1cc0G2zN2lncpaLIwQAeJqoYD89flm8fCyly7x/vitFX+xKcXFUAIDz1Trapn/f1MU5bkjJKdIDi7frZ8YMQBk+FkOPDI5T0MmZStccOKFvT7smD+A3fWPDnb+vTchwXSAAAACnIdEXAFBtJ/KK9c660gTfD7f+WibB9+pODfXOzV11b/9YRQeT4IuqSc8r1raTF98bBvupQ+NgF0cEAADcQa+4CC26q5fGXtxcJ/OytOdYjsbN2aRXv96ngmKWGgUAVF27RsG6p19L5+u31x3Sz0ezXRgRAKAmxIQF6PVRF6hNw9LZSrMKSvTIhzu0PuGEiyMD3EujBv6/6wsd1pGMAhdGBLintg1tirKVzha//ddsZeQXuzgiAAAAEn0BANWQnlekt9cd0p3vbdVH235V4ckEX1+roWs6N9I7Y7rqnn6xigr2c3Gk8DRr9qfLcXKVsIGtI2UxDNcGBAAA3EaAr1UPDmmteeMvUttGpQ8DOUzp3fWHddObG7QhId3FEQIAPMngNpG6pnNDSZLdlF78Zr9+zSp0cVQAgPMVYfPTv27srO7NS5dbLyxx6KlPdunLXcddHBngXvq2itDQdlGSSuvJ/604oKKT93oAlLIYhvqcnNXXNKV1iRmuDQgAAEAk+gIAqiA9t0izvj+oOxdu1dLTEnz9rIau7dxI79zcVXf3bakoGwm+ODcr9/22RNjA1hEujAQAALirDk0a6D/je+i+wa3kZy29nJGUUaB7F2zV1GW7lMnsKgCAKrqtZzNd2CxEkpRdDDvKKgAAQhxJREFUaNc/v96n/CJmiQcAT2fz99HzwztqYJtISZLdYeofn+/Vok1JLo4McC/jL26u5mEBkqSD6fmat/GIiyMC3E/fuHDn72uZIR4AALgBEn0BAJVKyy3SrLUHded7W/Xf7cdUZC+dctXPaui6C0oTfO/q21KRJPjiPCSk5elger4kqW20TTGhAS6OCAAAuCsfq0V39G2p9yb2VPcWoc7ty7b+qlFvbNDXO4/LNE0XRggA8ARWi6FHBsWpaai/JOnQiQK9uipBDs4hAODx/HwsevbKdhretbFz24zViXpjTSJjBeAkfx+LHhncSn7W0pX1lu9M0YaDGa4NCnAzTUL8FR8ZJEk6lFGgQyfyXRwRAADwdiT6AgDKSc0t0hunEnx//i3B19/HohFdGuudMV01sU9LRZDgixpw+my+g07OtgEAAHAmLSOD9MbYC/XUle1k87dKKn1I7YmPdmjyBz/rOEuwAwDOwubvoyeHtpbNr/Q8svFQphZuSnZxVACAmmC1GHpocCvdcXFz57b3fkzS81/tU4mDZF9AklpGBOqO3r/VkddXJyo1p8iFEQHup29cmPN3ZvUFAACuRqIvAMApNadIM79L1IT3tmrZz8dU/PsE35u7asIlLRQRRIIvaobdYWrN/nRJko/FUN9W4Wf5CwAAgFIWw9D13WP0wV29NbBtlHP7qj2pGvnmD/pwUxIzMwIAzigmNECPXtpKltLJ7PTR1l+1+uQYFQDg2QzD0LhLWuiRS1vpZDOv5TuO6y/Ldqmg2O7S2AB3Max9lC6ODZMk5RTZ9erKBNlJhgecercMk/XkYOH7xAzqBwAAcCkSfQEASskp1Iw1ibrzva36347jZRJ8b+jaWLPHlCb4hgf5ujhS1Ddbk7OUkV8iSerRPFQN/H1cHBEAAPA0DUP89dLIzvrnDZ0UeXLFidxCu6Yt36O752/WwbQ8F0cIAHBn3ZqGaFyvZs7XM9Ykam9KrgsjAgDUpOu6NtGUq9rJ13oyUevACU3+aIeyC0pcHBngeoZh6N5+LRV1ciy981iOPthy1MVRAe6jgb+PusU0kCRlFpRox685Lo4IAAB4MxJ9AcCLHc8u1L/XJGrCe9v06c7jzmXLAnwsurFbE80Z01XjL26hsEASfFE7Vu1Nc/4+qHWkCyMBAACezDAMDenQUIvv7qVruzZxbv/pUKZunrVRc9YeVInd4cIIAQDu7KpODXVZ29IxaZHd1PNf71d6LktXA0B9MahtlF4Y3lFBflZJ0vbkbD2weLtScgpdHBngesH+PnpkcJxzhYMlW45qx9Fs1wYFuJG+cb+tRLk24YQLIwEAAN6ORF8A8ELHswv1+uoE/fH9bfrstATfQF+LRnZrotljuuqO3s0VSoIvalFekV0/HMyQJAX7W9W9eYhrAwIAAB4vNNBXz1zTXjNu6aqmYQGSpCK7Q/9ecUC3zd6knclZLo4QAOCODMPQxD4t1KFRsCQpPa9Yz3+zX4UlPCQCAPVF9xZh+teNnZ2r1iWk5em+97frUDorgADtGwVrdPcYSZLDlF5ZmaAsZr0GJEldYxrIdvJBkU1HMpVfbHdxRAAAwFuR6AsAXuRYdqFeW1Wa4Lt8V0qZBN9RF5Ym+I4jwRd1ZF3iCRXZS8tgv1YR8rXSLQEAADWjV1yEFt3VS2Mvbu6clWjPsRyNm7NJr369TwXclAEA/I6v1aLHLmvlXLp6b0qeZn53UKZpujgyAEBNadsoWK/fdIGahPhLKr1efv/i7dr1K7OXAiO6NNYFTRpIKn3o6fU1ifSDAJWOE3q3DJNUuvrHj4czXRsQAADwWmTUAIAXOJpVoH+dTPD94peyCb6ju8dozphuur1Xc4UEkOCLurNqX5rz90GtI10YCQAAqI8CfK16cEhrzRt/kdqenKHRYUrvrj+sm97coA0J6S6OEADgbsICffXk0HgF+JReNl+9P11Ltx1zcVQAgJrULCxQ/76pi1pH2yRJmfklenjJz9p4kOXY4d2sFkMPDoxVSICPJOnHQ5n6bGeKi6MC3EO/uDDn798lcL4AAACuQaIvANRjR7MK9OrKA5r4/jZ9+UuK7CcTfIP8rM4E37E9m6nByQs3QF05nl2on4/mSJJiQv3VJjrIxREBAID6qkOTBvrP+B66b3Ar+Z1cQSApo0D3Ltiqqct2KSu/2MURAgDcSVxkkCYNjHW+XvBjkjYeynBZPACAmhcZ7Kd/jeysrk1DJEn5xQ498fEuff0LSY3wbhE2Pz0wINb5et6GIzqQmue6gAA3ER8ZpEYNSlf++OVYrlJzi1wcEQAA8EYk+gJAPZScWaBXVpQm+H61O1Un83tl87NqTI8YzR7TlQRfuNTq/b/NoDcwPlKGYbgwGgAAUN/5WC26o29LvTexp7q3CHVuX7b1V418Y4O+3nmcJUkBAE4Xx4ZrdPcmkiRT0isrE3ToRL5rgwIA1Khgfx+9eH0n9YuPkCSVOEz9bfkeLdmc7OLIANfq0TxU13RuKKm0XvzfigPKL7a7OCrAtQzDUN/YcEml44N1iRkujQcAAHgnEn0BoB5JyizQyyv2665F2/T1nrIJvrdc1FSzx3TVLRc1UwN/EnzhOqZpatW+NOfrga0jXBgNAADwJi0jg/TG2Av11JXtZPO3SpLScov0xEc7NPmDn3U8q9DFEQIA3MXIbk3UJ670Zn5BsUPTvtqn7IISF0cFAKhJ/j4WTb26va7u3Mi5bfrKBL299iAPAsKr3XpRU8VHla7CdzSrUG+vO+ziiADX63tybCBJ3yWc4DwBAADqHJleALyaaZqy14NxWHJmvhZvPqrV+9Nkd/y2PdjfquEXNNa1nRvJRnIv6lhl9etAaq6SMkuTaDo1DlbDBv51HBnqE7tpiutp588wJCsza+McnG9fymJIFsoe6pjFMHR99xj1ax2p5z/fo1V7UiVJq/ak6seDJ3T/pfG6rlsTtyubVkOsgoDzVl/GwKejbqAm2B2mKqoa9/RrqaNZBUpMz9fxnCK9+O0BPTWstXwsdVvmKOdwNYdpOicUqEmUbdS2ql43eujSVgq3+WrBhiRJ0oKNSUrPK9akwa3qvM2vDsbUqC2+VoseGRynRz/epYJih1bsTVOXmAYa2DpSUuV9p98zVFpOaevhbs7lvkJ4kK/aNbRpT0qufs0u1L60PMVFBNVOgKKfBPd2+vUlw/jtx26a3GsCgFpkmFV41CgrK0uhoaHKzMxUSEhIXcQFALUqtaBEq5PzlJBdpGLH2ff3JKZpqrCgWM0CLBrTOVpBflZXhwQvszujUBuO5ys5t6TCi30WmUo9kafDSRma0LupLmsbVecxwrMVO0ytSs7VLxlFyqlvjbgLNfC1qEO4vwY0CXLrm1hwD/syi/TD8Twdyam4ra+OSH+rukT6q1fDQC5eo86Zpqmvd6Xo/Z9TFBMTJpvNfR9A8jGkuBA/DWgSpOhAHuJD9ezNLNQPx/KVVEkf3ZNZDallA1/1bRykpjZfV4cDD5Jf4tDK5FztySxSfol71ww/i6H4EF8NjLEpzJ/rPKgbDtPU2l/ztCO9UBlFtTP2PtW/6d8kSA3p36CGFNodWpWcp90Zhcp18/a9JkQGWNUlgjE1aseqfWn616pESVJwoI+u6dlSSfl2FVTj6UF/q6HWIX4aGBOkEO5XwYU87b4C/SS4o6TcYq09mqeDOcWVPkhu8zHULsxfA2OC5G9lkXkAOJvq5OWS6AvA62QV2TV3d4b8LIa6RAYoxK9+dTCLHab2ZRZpf1axrmgRrK6RAa4OCV5kd0ahliZkq2Wwr9qF+cnPWvbismlKWcUObU7JV2ZBie7qFKGoIG7Go+pM09Ti/Vk6klusrpEBahjoI3JSz5/DlH7NK9G2tAK1CvHT9a3o86NyB7KK9MH+LDW1+ah9uL8CrOdeCe0O6VBOsXacKFSfRoEaEGOrwUiBqln7a57WHM1ThzA/xTbwk7tef84ucmhbeoEK7KbGtQtTKDdIUUX7M4u05ECWmgX7qH2Yv/zPo912R7nFDv2cXqjMIodubRvKDVBUicM0NW93hjKLHOoWGaDIAKvcNTfKNKXMIoe2pRVIksa1D1OQj5uerFCvLD+UrW1pheoS6a8Ym69q4/SRXeTQ9vRC5Zc4dHu7MBLZcd5M09SCvZlKybera1SAot24fa8JZcbUjQM1oAljatS86asTtXJfurp0bKLQID91jwpQuH/V6pZpShlFdm1NLZSvVRrXLoykL7jMon2ZHnVf4VQ/Ka/EoXH0k+AGjueXaP6eDIX7W9Up3F823/LtuWlKKQV2bU0tUHSgVbe0CeVBJAA4i+rk5XLlG4DX2ZFeqGKHqQntwxVUQQe0PugWGaClCdnaeDyfRF/UqY3H89Ui2FejW4ecceB2QYS/Zu44oUO5JST6olpSC+xKyC7WdbEN1CHcfWdd9ERdIqXGQT767FCOMgrtXDhEpX5MyVfjIB+NaRNaI0uEdo0KkM3Xok0pBerTmBmlUbfspqkfj+erR3SAhjYLdnU4Z9UtKkAzd5zQz+mF6tu49paHRP2yMSVfMTYf3dy6Ztptd3RhVKDe2nVCW1ILNKy5+9dluN6h7GIdy7drTJtQtQj2jDFpl8jScfQvJwrVPTrQ1eGgnssvcWhbWqEGxQSpd6Pa7XNcGBWgGSf7N/2a0L/B+UnOK9GR3BKNbBWi+FA/V4dTJ7pGBSjIx9CmlAL1bRQkK2Nq1LAJlzTXwdxi+fv76ubWoWoUVP30go7h/nprV4b2ZRapUwT3rFD3UvJLPPK+woVRAZq584S2pxeoPw9zwMW2pBYowGrR2LZh8j1Lf6NlsK8+OJClo3klimH1JQCoMfUzww0AziApr0TNbL71NslXkgzDUNswP6UW2FVod//lZ1A/mKap5LwStQ31O+vTmSF+VjUJ8lFSbkkdRYf6IvlkmWnjJTdr6lrbk//XpNxiF0cCd5acW6I2oX41mizWNtRPhQ5TaQX2GjsmUBUZhXbl201n++fuAn0sah7sQzuNaqmNdtvd+FkNxTXwdfYVgbNJyitRoNVQc5vnzIPBOBp16de8EpmS2obVfiJMgI9FLRr40r9BjUjOLTm51Ll3JZS0C/NXod1UWiFjatS8QF+r+rWJls3HOKckX0mKDPBRVICVfgxcxlPvKwT4WNQi2Je6A7eQlFusViG+Z03ylUr7Yj6GdISyCwA1qv5muQFAJewOU35uslTplClTZBiGUlNTa/zYfic72SXk+aIOOUxVWL/mzp0rwzCUmJjo3OZnNVRimnUYHeqDEtOUjyFm/DxPK1eulGEYWrlyZZntp+pvCVUTZ1BSzb7Uqf7OKbGxsRo3blyZfX4rexQ+1K1TfeXfLx1aUd/FXfhZDPr4qJYS03SOD09Xm+NRV2B8geo4dW2ooodUN27cqD59+shms8kwDG3ZsqXuA6yEn9WQnXKOOnCqPa3o/FEb/C204agZJQ5TvhajRh5wMgxDU6ZMcb52576Tc0ztoB6hdtgCfBRgPb+0Aj/aerhQiWnK6qH3FUqvA1F34HolZtXHBxbDkK/FkJ2yCwA1ikRfAPidGTNmyDAM9e7d29WhnJd6PFkT6gmKKOB+qJdwFcoe6ouFCxfq1VdfLbc9OTlZU6ZMqZFksbOtnAAAOHfFxcUaOXKk0tPT9corr2j+/Plq2bJllf72+++/15QpU5SRkVHuveeee04ff/zxecfHGQAAzs2Z2mhXcte4gIp4YH4kcFanHvT+8ccfXR1KpbgMBI9F2QWAGkeiLwD8zoIFCxQbG6sNGzZo3759rg4HqBfGjh2r/Pz8Kt8gBVC7BgwYoPz8fA0YMMDVocAL7d69W2+99ZarwwBqxZkSfadOnepWs0ICAMrbv3+/Dh48qMmTJ2vixIm69dZbFR4eXqW//f777zV16tRaTfQFAJybM7XRlcnPz9fTTz9de0Hp3OICAAAAAHgnEn0B4DQJCQn6/vvv9fLLLys6OloLFixwdUhAvWC1WhUQEMAMdICbsFgsCggIkMXCcAB1z9/fX76+vq4OAwAAoJzjx49LksLCwlwbCADAJRwOhwoKCiRJAQEB8vHxcXFEAAAAAACU4s4+AJxmwYIFCg8P11VXXaUbb7yxXKJvYmKiDMPQSy+9pFmzZik+Pl7+/v7q2bOnNm7cWO54v/zyi0aNGqXo6GgFBgaqXbt2+vOf/1xuv4yMDI0bN05hYWEKDQ3VHXfcoby8vHL7vfvuu+rRo4cCAwMVERGh0aNH6/DhwzX3DwBqyanljxITE10dCuqxpKQk3XnnnYqJiZG/v7/i4uJ0zz33qKioSJJ04MABjRw5UhEREQoKCtLFF1+sTz/9tMwxVq5cKcMwtHjxYk2dOlVNmzZVgwYNdOONNyozM1OFhYV66KGH1LBhQwUHB+uOO+5QYWFhmWMYhqH7779fCxYsULt27RQQEKAePXpo9erVZfY7ePCg7r33XrVr106BgYGKjIzUyJEjK6wn27Zt08CBAxUYGKhmzZrp73//u+bMmVOuXsXGxurqq6/Wd999p169eikgIECtWrXSf/7znwq/58qVK8/9Hw5U4LvvvlPPnj0VEBCg+Ph4vfnmm+X2iY2N1bhx4+o+OOA8zZgxQ506dZK/v79iYmJ03333lZl5a9CgQfr000918OBBGYYhwzAUGxurlStXqmfPnpKkO+64w/ne3LlzJUlr1qzRyJEj1aJFC/n7+6t58+Z6+OGHlZ+f74JvCW90tvHonDlzdOmll6phw4by9/dXx44dNXPmzHLHqWo/5NTYYO3atXrkkUcUHR0tm82mESNGKCUlpdxxz1b3gJoybtw4DRw4UJI0cuRIGYahQYMGadu2bRo3bpxatWqlgIAANW7cWOPHj1daWprzb6dMmaLHHntMkhQXF+ds609dR8rNzdW8efOc20/vC23evFlXXHGFQkJCFBwcrMsuu0zr16+v0+8OVNW4ceMUGxtbbvuUKVPKPNx9alz88ccfq3PnzvL391enTp30+eef12G0QKmztdGnruGc6m+cKqeGYWjKlCnljpeamqpRo0YpJCREkZGRevDBB53JwdJv9xBO9fdPd/oxzxSXJJWUlOhvf/ub8x5EbGysnnrqqXLXoQBXqc61TcBTVbXvI1W9/0PdAQAA54pHUQHgNAsWLND1118vPz8/3XzzzZo5c6Y2btzovDF/ysKFC5Wdna277rpLhmHohRde0PXXX68DBw44Z6jbtm2b+vfvL19fX02cOFGxsbHav3+/li1bpn/84x9ljjdq1CjFxcVp2rRp+umnn/T222+rYcOGev755537/OMf/9Bf/vIXjRo1ShMmTFBKSoqmT5+uAQMGaPPmzcw2A8CrJScnq1evXsrIyNDEiRPVvn17JSUlacmSJcrLy9OJEyfUp08f5eXladKkSYqMjNS8efN07bXXasmSJRoxYkSZ402bNk2BgYF64okntG/fPk2fPl2+vr6yWCw6ceKEpkyZovXr12vu3LmKi4vTM888U+bvV61apUWLFmnSpEny9/fXjBkz9Ic//EEbNmxQ586dJUkbN27U999/r9GjR6tZs2ZKTEzUzJkzNWjQIO3cuVNBQUGSShOYBw8eLMMw9OSTT8pms+ntt9+Wv79/hf+Lffv26cYbb9Sdd96p22+/XbNnz9a4cePUo0cPderUqRb++0Cp7du3a9iwYYqOjtaUKVNUUlKiZ599Vo0aNXJ1aMB5mzJliqZOnaohQ4bonnvu0e7du51jhbVr18rX11d//vOflZmZqSNHjuiVV16RJAUHB6tDhw7661//qmeeeUYTJ05U//79JUl9+vSRJH3wwQfKy8vTPffco8jISG3YsEHTp0/XkSNH9MEHH7jsO8N7nG08OnPmTHXq1EnXXnutfHx8tGzZMt17771yOBy67777yhyrOv2QBx54QOHh4Xr22WeVmJioV199Vffff78WLVrk3KcqdQ+oKXfddZeaNm2q5557TpMmTVLPnj3VqFEjffXVVzpw4IDuuOMONW7cWDt27NCsWbO0Y8cOrV+/XoZh6Prrr9eePXv03nvv6ZVXXlFUVJQkKTo6WvPnz9eECRPUq1cvTZw4UZIUHx8vSdqxY4f69++vkJAQ/elPf5Kvr6/efPNNDRo0SKtWrVLv3r1d9v8Aztd3332njz76SPfee68aNGig1157TTfccIMOHTqkyMhIV4cHL3KmNlqSvv32Wy1evFj333+/oqKiKkzoOt2oUaMUGxuradOmaf369Xrttdd04sSJcg83nW9cEyZM0Lx583TjjTfq0Ucf1Q8//KBp06Zp165dWrp0aTX/C0DNq+q1TcCbVKX/Q90BAADnzKyCzMxMU5KZmZlZld0BwK29vzfD/OhA+fbsxx9/NCWZX331lWmapulwOMxmzZqZDz74oHOfhIQEU5IZGRlppqenO7f/97//NSWZy5Ytc24bMGCA2aBBA/PgwYNlPsfhcDh/f/bZZ01J5vjx48vsM2LECDMyMtL5OjEx0bRareY//vGPMvtt377d9PHxKbfdNE1zT0aBOe2nFDOnyH6mfwdQYxwOhzntpxRza2p+uffmzJljSjITEhKc2yqri8CZ/Hg8z3xxc0q57bfddptpsVjMjRs3lnvP4XCYDz30kCnJXLNmjXN7dna2GRcXZ8bGxpp2e2lbuWLFClOS2blzZ7OoqMi5780332wahmFeccUVZY59ySWXmC1btiyzTZIpyfzxxx+d2w4ePGgGBASYI0aMcG7Ly8srF+u6detMSeZ//vMf57YHHnjANAzD3Lx5s3NbWlqaGRERUa5etWzZ0pRkrl692rnt+PHjpr+/v/noo486t536nitWrCj3v5r2U4q5pYJ6DJzy4uYUc+Px8uV3+PDhZkBAQJm+z86dO02r1WqePvRs2bKlefvtt5f52+N5xea0n1LMIzlFJlCXfs0tLXtHc4vLbD+973L8+HHTz8/PHDZsmPN8YZqm+frrr5uSzNmzZzu3XXXVVeXOC6Zpmhs3bjQlmXPmzCn3XkXng2nTppmGYZQbS5imaf43IctcsCejGt8S3u75zSnmpgra7aqORysqo5dffrnZqlWrMtuq2g85Vb+GDBlSZnz88MMPm1ar1czIyHD+bVXrnmma5leHs823dqabQFWsSsoxZ/ycVm77qX7yBx984NxWUR147733ypX3F198sVz//BSbzVau/2Oapf0nPz8/c//+/c5tycnJZoMGDcwBAwaU2//9vRnmUsbRqAOVXVe8/fbbK+zrnDqnnCLJ9PPzM/ft2+fctnXrVlOSOX369HJ//0lClvnunhM1Fj+81/dHc81Xt6aW215ZGy3JtFgs5o4dO8r9jSTz2Wefdb4+Vc6vvfbaMvvde++9piRz69atpmn+dg+hor7/749ZWVxbtmwxJZkTJkwos33y5MmmJPPbb78ts/3YyTF1EmNq1JIvD2ebb/+ur13Va5unzPvlhPnpwaxaixE4kx+P55kvVHBf4dT4tKL7CqZZ9b6PaVa9/1PdurMsMcucv/tEZV8NqDOzdqabXx/OrvL+r25LNb8/mluLEQFA/VCdvFxLrWQPA4AHWrBggRo1aqTBgwdLKl1i5aabbtL7778vu91eZt+bbrpJ4eHhztenZuU6cOCAJCklJUWrV6/W+PHj1aJFizJ/+/ulXCTp7rvvLvO6f//+SktLU1ZWliTpo48+ksPh0KhRo5Samur8ady4sdq0aaMVK1ac57cHAM/lcDj08ccf65prrtFFF11U7n3DMPTZZ5+pV69e6tevn3N7cHCwJk6cqMTERO3cubPM39x2221lZojr3bu3TNPU+PHjy+zXu3dvHT58WCUlJWW2X3LJJerRo4fzdYsWLXTdddfpiy++cJ5TAgMDne8XFxcrLS1NrVu3VlhYmH766Sfne59//rkuueQSdevWzbktIiJCt9xyS4X/j44dOzrPS1LpTDDt2rVznqOA2mC32/XFF19o+PDhZfo+HTp00OWXX+7CyIDz9/XXX6uoqEgPPfSQLJbfLqP88Y9/VEhIiD799NPzOv7p54Pc3FylpqaqT58+Mk1TmzdvPq9jA1VxtvHo6WU0MzNTqampGjhwoA4cOKDMzMwyf1udfsjEiRPLjI/79+8vu92ugwcPSqr9ugdU1el1oKCgQKmpqbr44oslqUy/vbrsdru+/PJLDR8+XK1atXJub9KkicaMGaPvvvvOWQ8BTzRkyBDn7NWS1KVLF4WEhDA2hdsZOHCgOnbsWOX9f7+iwQMPPCBJ+uyzz2osplPHeuSRR8psf/TRRyWJfhDcQlWvbQLepCr9H+oOAAA4VyT6AoBKb668//77Gjx4sBISErRv3z7t27dPvXv31rFjx/TNN9+U2f/3ybunkn5PnDgh6beE31PLs5/N2Y63d+9emaapNm3aKDo6uszPrl27dPz48Wp+YwCoP1JSUpSVlXXGNvfgwYNq165due0dOnRwvn+637fLoaGhkqTmzZuX2+5wOMolubRp06bcZ7Vt21Z5eXlKSUmRJOXn5+uZZ55R8+bN5e/vr6ioKEVHRysjI6PM8Q4ePKjWrVuXO15F2yqKXSo9r5w6pwC1ISUlRfn5+RWW/YrqHuBJTp0jfl+W/fz81KpVq3LnkOo6dOiQxo0bp4iICAUHBys6OloDBw6UpHLnF6A2nG08unbtWg0ZMkQ2m01hYWGKjo7WU089Jal8Ga1OP+Rsn1vbdQ+oqvT0dD344INq1KiRAgMDFR0drbi4OEnn106npKQoLy+v0nGKw+HQ4cOHz/n4gKsxNoWnONWmV9Xvx73x8fGyWCxKTEyssZgOHjwoi8VS7tpP48aNFRYWRj8IbqGq1zYBb1KV/g91BwAAnCsfVwcAAO7g22+/1dGjR/X+++/r/fffL/f+ggULNGzYMOdrq9Va4XFM0zynzz/b8RwOhwzD0PLlyyvcNzg4+Jw+FwBQscra5Zps/x944AHNmTNHDz30kC655BKFhobKMAyNHj1aDoej2serjRgBALXLbrdr6NChSk9P1+OPP6727dvLZrMpKSlJ48aNO6/zAVBVZ+o77N+/X5dddpnat2+vl19+Wc2bN5efn58+++wzvfLKK+XKaHX6IfRZ4ClGjRql77//Xo899pi6deum4OBgORwO/eEPf6CdhtepaKUySeVWQ5No5+E5Tp9Z8Vz8vl5Up55U99iAO6mta5uAO6lum16V/g91BwAAnCsSfQFApYm8DRs21L///e9y73300UdaunSp3njjjSof79SSiz///HONxBcfHy/TNBUXF6e2bdvWyDEBoL6Ijo5WSEjIGdvcli1bavfu3eW2//LLL873a9LevXvLbduzZ4+CgoIUHR0tSVqyZIluv/12/d///Z9zn4KCAmVkZJT5u5YtW2rfvn3ljlfRNsBVoqOjFRgYWGHZr6juAZ7k1Dli9+7dZZZWLyoqUkJCgoYMGeLcVtkNoMq2b9++XXv27NG8efN02223Obd/9dVXNRE6cN6WLVumwsJCffLJJ2VmJlqxYkWtf3Z16h5QW06cOKFvvvlGU6dO1TPPPOPcXlGf50zJWBW9Fx0draCgoErHKRaLpdyKIoCrhYeHlxuzSuVXyQHcTU0mzO7du7fMLMD79u2Tw+FQbGyspN9WKfh9XamonlQWV8uWLeVwOLR3717nalSSdOzYMWVkZNT4dSzgXFT12ibgyWqj70PdAQAA58ri6gAAwNXy8/P10Ucf6eqrr9aNN95Y7uf+++9Xdna2PvnkkyofMzo6WgMGDNDs2bN16NChMu+dy6wV119/vaxWq6ZOnVru703TVFpaWrWPCQD1hcVi0fDhw7Vs2TL9+OOP5d43TVNXXnmlNmzYoHXr1jm35+bmatasWYqNjVXHjh1rNKZ169bpp59+cr4+fPiw/vvf/2rYsGHOp/qtVmu5Nn369OnlZgO4/PLLtW7dOm3ZssW5LT09XQsWLKjRmIHzYbVadfnll+vjjz8u0/fZtWuXvvjiCxdGBpy/IUOGyM/PT6+99lqZdvudd95RZmamrrrqKuc2m81W4TKLNptNUvmb/afOCacf1zRN/etf/6rJrwCcs4rKaGZmpubMmVPrn12dugfUlorqgCS9+uqr5fatrK0/9V5F54Bhw4bpv//9b5nl3o8dO6aFCxeqX79+CgkJOa/4gZoWHx+vzMxMbdu2zbnt6NGjWrp0qQujAs7uTG10df1+spDp06dLkq644gpJUkhIiKKiorR69eoy+82YMaPKcV155ZWSyp9vXn75ZUmiHwS3UNVrm4Anq42+D3UH9U1eXp5++eUXpaamujoUAKj3mNEXgNf75JNPlJ2drWuvvbbC9y+++GJFR0drwYIF6t27d5WP+9prr6lfv37q3r27Jk6cqLi4OCUmJurTTz8tk6xVFfHx8fr73/+uJ598UomJiRo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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "src_mt_alignments = tagger_bert.align_source_mt([src_tokens], [mt_tokens], 'eng', lang_id)[0]\n", + "draw_aligned_qe(src_tokens, mt_tokens, src_tbd_qe, mt_tbd_qe, src_mt_alignments, title='SRC - MT (BERT)')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} From 6448e1f0605986b88cfabca0d8369d42ada424bc Mon Sep 17 00:00:00 2001 From: Konstantin Chernyshev Date: Sun, 17 Sep 2023 23:17:00 +0200 Subject: [PATCH 23/23] fix: refactor analysis notebook with faster data loading and rate plots --- notebooks/fine-grained-analysis.ipynb | 4132 +++++++++++++++++++++---- 1 file changed, 3609 insertions(+), 523 deletions(-) diff --git a/notebooks/fine-grained-analysis.ipynb b/notebooks/fine-grained-analysis.ipynb index 6236796..d31c693 100644 --- a/notebooks/fine-grained-analysis.ipynb +++ b/notebooks/fine-grained-analysis.ipynb @@ -2,45 +2,63 @@ "cells": [ { "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "source": [ "## Imports" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2023-09-08T13:32:41.028504Z", + "start_time": "2023-09-08T13:32:33.050723Z" + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\r\n", - "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip available: \u001B[0m\u001B[31;49m22.3.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m23.2.1\u001B[0m\r\n", - "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpip install --upgrade pip\u001B[0m\r\n" + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.2.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n" ] } ], "source": [ "!pip install -q seaborn pandas " - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "start_time": "2023-08-27T13:14:02.233295Z" - } - } + ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 40, + "metadata": { + "ExecuteTime": { + "end_time": "2023-09-08T13:32:59.390736Z", + "start_time": "2023-09-08T13:32:41.036979Z" + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "outputs": [], "source": [ "from pathlib import Path\n", "import ast\n", "from collections import defaultdict\n", + "import math\n", "\n", "from tqdm import tqdm\n", "import matplotlib.pyplot as plt\n", @@ -49,45 +67,60 @@ "import seaborn as sns\n", "from stanza.models.common.doc import Sentence as StanzaSentence, Word as StanzaWord, Token as StanzaToken\n", "from astred import Sentence, AlignedSentences" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-08-27T13:14:08.022332Z", - "start_time": "2023-08-27T13:14:07.457666Z" - } - } + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, + "metadata": { + "ExecuteTime": { + "end_time": "2023-09-08T13:32:59.427131Z", + "start_time": "2023-09-08T13:32:59.389984Z" + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "outputs": [], "source": [ "import warnings\n", "warnings.filterwarnings(\"error\")" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "source": [ "## Load data" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, + "metadata": { + "ExecuteTime": { + "end_time": "2023-09-08T13:32:59.476502Z", + "start_time": "2023-09-08T13:32:59.416277Z" + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "outputs": [ { "data": { - "text/plain": "True" + "text/plain": [ + "True" + ] }, - "execution_count": 3, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -96,25 +129,264 @@ "DATASET_FOLDER = Path() / '..' / 'data' / 'processed'\n", "MERGED_FOLDER = DATASET_FOLDER / 'merged'\n", "MERGED_FOLDER.exists()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-08-27T13:14:08.053094Z", - "start_time": "2023-08-27T13:14:08.034709Z" - } - } + ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 79, + "metadata": { + "ExecuteTime": { + "end_time": "2023-09-08T13:33:08.861627Z", + "start_time": "2023-09-08T13:33:05.556743Z" + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5160\n" + ] + }, { "data": { - "text/plain": " src_text \\\nunit_id \nflores101-main-ukr-66-pe2-3 The qualities that determine a subculture as d... \nflores101-main-nld-9-pe2-4 A course will normally be from 2-5 days and wi... \nflores101-main-ara-41-pe2-3 For example, one might say that the motor car ... \nflores101-main-ita-3-pe1-3 Most modern research telescopes are enormous f... \nflores101-main-ukr-98-pe1-4 Searches at security checkpoints have also bec... \nflores101-main-vie-19-pe1-1 South Africa have defeated the All Blacks (New... \nflores101-main-ara-102-ht-4 Some cruises feature Berlin, Germany in the br... \nflores101-main-ukr-31-ht-2 It is still produced today, but more important... \nflores101-main-ukr-9-pe2-5 Books and magazines dealing with wilderness su... \nflores101-main-tur-54-ht-1 Murray lost the first set in a tie break after... \nflores101-main-tur-53-ht-5 They all ran back from where the accident had ... \nflores101-main-ukr-16-ht-3 An up-bow usually generates a softer sound, wh... \nflores101-main-tur-68-pe1-4 He was initially hospitalised in the James Pag... \nflores101-main-tur-42-pe1-4 The presence of a true “invisible team” (Larso... \nflores101-main-nld-39-pe2-1 After its adoption by Congress on July 4, a ha... \nflores101-main-vie-80-ht-2 Accepted were Aristotle's views on all matters... \nflores101-main-vie-29-ht-1 The satellites, both of which weighed in exces... \nflores101-main-nld-91-ht-1 The use of video recording has led to importan... \nflores101-main-vie-42-pe2-2 Virtual team members often function as the poi... \nflores101-main-ukr-44-ht-2 The definition has geographic variations, wher... \n\n mt_text \\\nunit_id \nflores101-main-ukr-66-pe2-3 Якістю, яка визначає субкультуру як індивідуал... \nflores101-main-nld-9-pe2-4 Een cursus zal normaal van 2-5 dagen zijn en r... \nflores101-main-ara-41-pe2-3 على سبيل المثال، يمكن القول أن السيارة النارية... \nflores101-main-ita-3-pe1-3 I telescopi di ricerca più moderni sono enormi... \nflores101-main-ukr-98-pe1-4 Обшуки на блокпостах безпеки також стали набаг... \nflores101-main-vie-19-pe1-1 Nam Phi đã đánh bại All Blacks (New Zealand) t... \nflores101-main-ara-102-ht-4 NaN \nflores101-main-ukr-31-ht-2 NaN \nflores101-main-ukr-9-pe2-5 Бібліотеки та журнали про виживання в дикій пр... \nflores101-main-tur-54-ht-1 NaN \nflores101-main-tur-53-ht-5 NaN \nflores101-main-ukr-16-ht-3 NaN \nflores101-main-tur-68-pe1-4 Başlangıçta Great Yarmouth'daki James Paget Ha... \nflores101-main-tur-42-pe1-4 Gerçek bir “görünmez ekibin” varlığı (Larson v... \nflores101-main-nld-39-pe2-1 Nadat het 4 juli door het Congres werd aangeno... \nflores101-main-vie-80-ht-2 NaN \nflores101-main-vie-29-ht-1 NaN \nflores101-main-nld-91-ht-1 NaN \nflores101-main-vie-42-pe2-2 Các thành viên trong nhóm ảo thường là điểm ti... \nflores101-main-ukr-44-ht-2 NaN \n\n tgt_text \\\nunit_id \nflores101-main-ukr-66-pe2-3 Рисами, які визначають субкультуру як відмінну... \nflores101-main-nld-9-pe2-4 Een cursus duurt normaal gesproken 2-5 dagen z... \nflores101-main-ara-41-pe2-3 على سبيل المثال، قد يقول الفرد أن السيارات تؤد... \nflores101-main-ita-3-pe1-3 I telescopi di ricerca più avanzati sono costi... \nflores101-main-ukr-98-pe1-4 Обшуки на безпекових блокпостах також стали на... \nflores101-main-vie-19-pe1-1 Đội tuyển Nam Phi đã đánh bại đội All Blacks (... \nflores101-main-ara-102-ht-4 تعرض بعض الرحلات البحرية زيارة برلين في ألماني... \nflores101-main-ukr-31-ht-2 Їх виготовляють і зараз, але, що важливіше, ві... \nflores101-main-ukr-9-pe2-5 Книги та журнали про виживання в дикій природі... \nflores101-main-tur-54-ht-1 Her iki adamın setteki her bir servisi kazanma... \nflores101-main-tur-53-ht-5 Hepsi kaza yerinden koşarak gelmişti. \nflores101-main-ukr-16-ht-3 Рух смичком угору зазвичай дає м’якший звук, т... \nflores101-main-tur-68-pe1-4 Sürücü önce Great Yarmouth'daki James Paget Ha... \nflores101-main-tur-42-pe1-4 Gerçek bir \"görünmez ekibin\" varlığı (Larson v... \nflores101-main-nld-39-pe2-1 Nadat het op 4 juli door het Congres werd aang... \nflores101-main-vie-80-ht-2 Các quan điểm của Aristotle về tất cả các lĩnh... \nflores101-main-vie-29-ht-1 Hai vệ tinh đã va chạm trong vũ trụ cách Trái ... \nflores101-main-nld-91-ht-1 Het gebruik van video-opnamen heeft geleid tot... \nflores101-main-vie-42-pe2-2 Các thành viên trong đội ngũ ảo thường là đầu ... \nflores101-main-ukr-44-ht-2 Визначення залежить від регіону: для деяких мі... \n\n aligned_edit \\\nunit_id \nflores101-main-ukr-66-pe2-3 REF: якістю , яка визначає субкультуру як *... \nflores101-main-nld-9-pe2-4 REF: een cursus zal normaal van 2-5 d... \nflores101-main-ara-41-pe2-3 REF: على سبيل المثال ، يمكن القول أن السي... \nflores101-main-ita-3-pe1-3 REF: i telescopi di ricerca più moderni sono... \nflores101-main-ukr-98-pe1-4 REF: обшуки на блокпостах безпеки та... \nflores101-main-vie-19-pe1-1 REF: *** ***** nam phi đã đánh bại *** all_bl... \nflores101-main-ara-102-ht-4 NaN \nflores101-main-ukr-31-ht-2 NaN \nflores101-main-ukr-9-pe2-5 REF: бібліотеки та журнали про виживання в ди... \nflores101-main-tur-54-ht-1 NaN \nflores101-main-tur-53-ht-5 NaN \nflores101-main-ukr-16-ht-3 NaN \nflores101-main-tur-68-pe1-4 REF: ****** başlangıçta great yarmouth'daki j... \nflores101-main-tur-42-pe1-4 REF: gerçek bir “görünmez ekibin” varlığı (... \nflores101-main-nld-39-pe2-1 REF: nadat het ** 4 juli door het congres wer... \nflores101-main-vie-80-ht-2 NaN \nflores101-main-vie-29-ht-1 NaN \nflores101-main-nld-91-ht-1 NaN \nflores101-main-vie-42-pe2-2 REF: các thành_viên trong nhóm ảo_thường l... \nflores101-main-ukr-44-ht-2 NaN \n\n lang_id \\\nunit_id \nflores101-main-ukr-66-pe2-3 ukr \nflores101-main-nld-9-pe2-4 nld \nflores101-main-ara-41-pe2-3 ara \nflores101-main-ita-3-pe1-3 ita \nflores101-main-ukr-98-pe1-4 ukr \nflores101-main-vie-19-pe1-1 vie \nflores101-main-ara-102-ht-4 ara \nflores101-main-ukr-31-ht-2 ukr \nflores101-main-ukr-9-pe2-5 ukr \nflores101-main-tur-54-ht-1 tur \nflores101-main-tur-53-ht-5 tur \nflores101-main-ukr-16-ht-3 ukr \nflores101-main-tur-68-pe1-4 tur \nflores101-main-tur-42-pe1-4 tur \nflores101-main-nld-39-pe2-1 nld \nflores101-main-vie-80-ht-2 vie \nflores101-main-vie-29-ht-1 vie \nflores101-main-nld-91-ht-1 nld \nflores101-main-vie-42-pe2-2 vie \nflores101-main-ukr-44-ht-2 ukr \n\n src_tokens \\\nunit_id \nflores101-main-ukr-66-pe2-3 ['The', 'qualities', 'that', 'determine', 'a',... \nflores101-main-nld-9-pe2-4 ['A', 'course', 'will', 'normally', 'be', 'fro... \nflores101-main-ara-41-pe2-3 ['For', 'example', ',', 'one', 'might', 'say',... \nflores101-main-ita-3-pe1-3 ['Most', 'modern', 'research', 'telescopes', '... \nflores101-main-ukr-98-pe1-4 ['Searches', 'at', 'security', 'checkpoints', ... \nflores101-main-vie-19-pe1-1 ['South', 'Africa', 'have', 'defeated', 'the',... \nflores101-main-ara-102-ht-4 ['Some', 'cruises', 'feature', 'Berlin', ',', ... \nflores101-main-ukr-31-ht-2 ['It', 'is', 'still', 'produced', 'today', ','... \nflores101-main-ukr-9-pe2-5 ['Books', 'and', 'magazines', 'dealing', 'with... \nflores101-main-tur-54-ht-1 ['Murray', 'lost', 'the', 'first', 'set', 'in'... \nflores101-main-tur-53-ht-5 ['They', 'all', 'ran', 'back', 'from', 'where'... \nflores101-main-ukr-16-ht-3 ['An', 'up', '-', 'bow', 'usually', 'generates... \nflores101-main-tur-68-pe1-4 ['He', 'was', 'initially', 'hospitalised', 'in... \nflores101-main-tur-42-pe1-4 ['The', 'presence', 'of', 'a', 'true', '“', 'i... \nflores101-main-nld-39-pe2-1 ['After', 'its', 'adoption', 'by', 'Congress',... \nflores101-main-vie-80-ht-2 ['Accepted', 'were', 'Aristotle', \"'s\", 'views... \nflores101-main-vie-29-ht-1 ['The', 'satellites', ',', 'both', 'of', 'whic... \nflores101-main-nld-91-ht-1 ['The', 'use', 'of', 'video', 'recording', 'ha... \nflores101-main-vie-42-pe2-2 ['Virtual', 'team', 'members', 'often', 'funct... \nflores101-main-ukr-44-ht-2 ['The', 'definition', 'has', 'geographic', 'va... \n\n src_annotations \\\nunit_id \nflores101-main-ukr-66-pe2-3 [{'lemma': 'the', 'upos': 'DET', 'feats': 'Def... \nflores101-main-nld-9-pe2-4 [{'lemma': 'a', 'upos': 'DET', 'feats': 'Defin... \nflores101-main-ara-41-pe2-3 [{'lemma': 'for', 'upos': 'ADP', 'feats': '', ... \nflores101-main-ita-3-pe1-3 [{'lemma': 'most', 'upos': 'ADJ', 'feats': 'De... \nflores101-main-ukr-98-pe1-4 [{'lemma': 'search', 'upos': 'NOUN', 'feats': ... \nflores101-main-vie-19-pe1-1 [{'lemma': 'South', 'upos': 'PROPN', 'feats': ... \nflores101-main-ara-102-ht-4 [{'lemma': 'some', 'upos': 'DET', 'feats': '',... \nflores101-main-ukr-31-ht-2 [{'lemma': 'it', 'upos': 'PRON', 'feats': 'Cas... \nflores101-main-ukr-9-pe2-5 [{'lemma': 'book', 'upos': 'NOUN', 'feats': 'N... \nflores101-main-tur-54-ht-1 [{'lemma': 'Murray', 'upos': 'PROPN', 'feats':... \nflores101-main-tur-53-ht-5 [{'lemma': 'they', 'upos': 'PRON', 'feats': 'C... \nflores101-main-ukr-16-ht-3 [{'lemma': 'a', 'upos': 'DET', 'feats': 'Defin... \nflores101-main-tur-68-pe1-4 [{'lemma': 'he', 'upos': 'PRON', 'feats': 'Cas... \nflores101-main-tur-42-pe1-4 [{'lemma': 'the', 'upos': 'DET', 'feats': 'Def... \nflores101-main-nld-39-pe2-1 [{'lemma': 'after', 'upos': 'ADP', 'feats': ''... \nflores101-main-vie-80-ht-2 [{'lemma': 'accept', 'upos': 'VERB', 'feats': ... \nflores101-main-vie-29-ht-1 [{'lemma': 'the', 'upos': 'DET', 'feats': 'Def... \nflores101-main-nld-91-ht-1 [{'lemma': 'the', 'upos': 'DET', 'feats': 'Def... \nflores101-main-vie-42-pe2-2 [{'lemma': 'virtual', 'upos': 'ADJ', 'feats': ... \nflores101-main-ukr-44-ht-2 [{'lemma': 'the', 'upos': 'DET', 'feats': 'Def... \n\n mt_tokens \\\nunit_id \nflores101-main-ukr-66-pe2-3 ['Якістю', ',', 'яка', 'визначає', 'субкультур... \nflores101-main-nld-9-pe2-4 ['Een', 'cursus', 'zal', 'normaal', 'van', '2-... \nflores101-main-ara-41-pe2-3 ['على', 'سبيل', 'المثال', '،', 'يمكن', 'القول'... \nflores101-main-ita-3-pe1-3 ['I', 'telescopi', 'di', 'ricerca', 'più', 'mo... \nflores101-main-ukr-98-pe1-4 ['Обшуки', 'на', 'блокпостах', 'безпеки', 'так... \nflores101-main-vie-19-pe1-1 ['Nam', 'Phi', 'đã', 'đánh', 'bại', 'All Black... \nflores101-main-ara-102-ht-4 NaN \nflores101-main-ukr-31-ht-2 NaN \nflores101-main-ukr-9-pe2-5 ['Бібліотеки', 'та', 'журнали', 'про', 'вижива... \nflores101-main-tur-54-ht-1 NaN \nflores101-main-tur-53-ht-5 NaN \nflores101-main-ukr-16-ht-3 NaN \nflores101-main-tur-68-pe1-4 ['Başlangıçta', 'Great', \"Yarmouth'daki\", 'Jam... \nflores101-main-tur-42-pe1-4 ['Gerçek', 'bir', '“görünmez', 'ekibin”', 'var... \nflores101-main-nld-39-pe2-1 ['Nadat', 'het', '4', 'juli', 'door', 'het', '... \nflores101-main-vie-80-ht-2 NaN \nflores101-main-vie-29-ht-1 NaN \nflores101-main-nld-91-ht-1 NaN \nflores101-main-vie-42-pe2-2 ['Các', 'thành viên', 'trong', 'nhóm', 'ảo thư... \nflores101-main-ukr-44-ht-2 NaN \n\n mt_annotations \\\nunit_id \nflores101-main-ukr-66-pe2-3 [{'lemma': 'якість', 'upos': 'NOUN', 'feats': ... \nflores101-main-nld-9-pe2-4 [{'lemma': 'een', 'upos': 'DET', 'feats': 'Def... \nflores101-main-ara-41-pe2-3 [{'lemma': 'عَلَى', 'upos': 'ADP', 'feats': 'A... \nflores101-main-ita-3-pe1-3 [{'lemma': 'il', 'upos': 'DET', 'feats': 'Defi... \nflores101-main-ukr-98-pe1-4 [{'lemma': 'обшук', 'upos': 'NOUN', 'feats': '... \nflores101-main-vie-19-pe1-1 [{'lemma': 'Nam', 'upos': 'NOUN', 'feats': '',... \nflores101-main-ara-102-ht-4 NaN \nflores101-main-ukr-31-ht-2 NaN \nflores101-main-ukr-9-pe2-5 [{'lemma': 'бібліотека', 'upos': 'NOUN', 'feat... \nflores101-main-tur-54-ht-1 NaN \nflores101-main-tur-53-ht-5 NaN \nflores101-main-ukr-16-ht-3 NaN \nflores101-main-tur-68-pe1-4 [{'lemma': 'başlangıç', 'upos': 'NOUN', 'feats... \nflores101-main-tur-42-pe1-4 [{'lemma': 'gerçek', 'upos': 'ADJ', 'feats': '... \nflores101-main-nld-39-pe2-1 [{'lemma': 'nadat', 'upos': 'SCONJ', 'feats': ... \nflores101-main-vie-80-ht-2 NaN \nflores101-main-vie-29-ht-1 NaN \nflores101-main-nld-91-ht-1 NaN \nflores101-main-vie-42-pe2-2 [{'lemma': 'Các', 'upos': 'DET', 'feats': '', ... \nflores101-main-ukr-44-ht-2 NaN \n\n tgt_tokens \\\nunit_id \nflores101-main-ukr-66-pe2-3 ['Рисами', ',', 'які', 'визначають', 'субкульт... \nflores101-main-nld-9-pe2-4 ['Een', 'cursus', 'duurt', 'normaal', 'gesprok... \nflores101-main-ara-41-pe2-3 ['على', 'سبيل', 'المثال', '،', 'قد', 'يقول', '... \nflores101-main-ita-3-pe1-3 ['I', 'telescopi', 'di', 'ricerca', 'più', 'av... \nflores101-main-ukr-98-pe1-4 ['Обшуки', 'на', 'безпекових', 'блокпостах', '... \nflores101-main-vie-19-pe1-1 ['Đội', 'tuyển', 'Nam', 'Phi', 'đã', 'đánh', '... \nflores101-main-ara-102-ht-4 ['تعرض', 'بعض', 'الرحلات', 'البحرية', 'زيارة',... \nflores101-main-ukr-31-ht-2 ['Їх', 'виготовляють', 'і', 'зараз', ',', 'але... \nflores101-main-ukr-9-pe2-5 ['Книги', 'та', 'журнали', 'про', 'виживання',... \nflores101-main-tur-54-ht-1 ['Her', 'iki', 'adamın', 'setteki', 'her', 'bi... \nflores101-main-tur-53-ht-5 ['Hepsi', 'kaza', 'yerinden', 'koşarak', 'gelm... \nflores101-main-ukr-16-ht-3 ['Рух', 'смичком', 'угору', 'зазвичай', 'дає',... \nflores101-main-tur-68-pe1-4 ['Sürücü', 'önce', 'Great', \"Yarmouth'daki\", '... \nflores101-main-tur-42-pe1-4 ['Gerçek', 'bir', '\"görünmez', 'ekibin\"', 'var... \nflores101-main-nld-39-pe2-1 ['Nadat', 'het', 'op', '4', 'juli', 'door', 'h... \nflores101-main-vie-80-ht-2 ['Các', 'quan điểm', 'của', 'Aristotle', 'về',... \nflores101-main-vie-29-ht-1 ['Hai', 'vệ', 'tinh', 'đã', 'va', 'chạm', 'tro... \nflores101-main-nld-91-ht-1 ['Het', 'gebruik', 'van', 'video-opnamen', 'he... \nflores101-main-vie-42-pe2-2 ['Các', 'thành viên', 'trong', 'đội ngũ', 'ảo ... \nflores101-main-ukr-44-ht-2 ['Визначення', 'залежить', 'від', 'регіону', '... \n\n ... doc_id time_s time_m time_h \\\nunit_id ... \nflores101-main-ukr-66-pe2-3 ... 66 86.563 1.4427 0.0240 \nflores101-main-nld-9-pe2-4 ... 9 19.836 0.3306 0.0055 \nflores101-main-ara-41-pe2-3 ... 41 52.346 0.8724 0.0145 \nflores101-main-ita-3-pe1-3 ... 3 137.284 2.2881 0.0381 \nflores101-main-ukr-98-pe1-4 ... 98 28.684 0.4781 0.0080 \nflores101-main-vie-19-pe1-1 ... 19 182.751 3.0458 0.0508 \nflores101-main-ara-102-ht-4 ... 102 74.626 1.2438 0.0207 \nflores101-main-ukr-31-ht-2 ... 31 91.587 1.5264 0.0254 \nflores101-main-ukr-9-pe2-5 ... 9 45.923 0.7654 0.0128 \nflores101-main-tur-54-ht-1 ... 54 142.789 2.3798 0.0397 \nflores101-main-tur-53-ht-5 ... 53 26.832 0.4472 0.0075 \nflores101-main-ukr-16-ht-3 ... 16 97.448 1.6241 0.0271 \nflores101-main-tur-68-pe1-4 ... 68 19.456 0.3243 0.0054 \nflores101-main-tur-42-pe1-4 ... 42 30.930 0.5155 0.0086 \nflores101-main-nld-39-pe2-1 ... 39 73.994 1.2332 0.0206 \nflores101-main-vie-80-ht-2 ... 80 42.489 0.7081 0.0118 \nflores101-main-vie-29-ht-1 ... 29 178.737 2.9789 0.0496 \nflores101-main-nld-91-ht-1 ... 91 35.700 0.5950 0.0099 \nflores101-main-vie-42-pe2-2 ... 42 101.728 1.6955 0.0283 \nflores101-main-ukr-44-ht-2 ... 44 226.385 3.7731 0.0629 \n\n time_per_char time_per_word key_per_char \\\nunit_id \nflores101-main-ukr-66-pe2-3 0.5549 4.1220 1.9872 \nflores101-main-nld-9-pe2-4 0.1681 0.9016 0.6102 \nflores101-main-ara-41-pe2-3 0.5690 3.2716 0.4022 \nflores101-main-ita-3-pe1-3 1.2480 9.8060 1.3182 \nflores101-main-ukr-98-pe1-4 0.2758 1.7928 1.0096 \nflores101-main-vie-19-pe1-1 1.2265 7.3100 1.6174 \nflores101-main-ara-102-ht-4 0.3969 1.9638 1.1330 \nflores101-main-ukr-31-ht-2 0.7386 4.5794 1.5161 \nflores101-main-ukr-9-pe2-5 0.4064 2.7014 2.2478 \nflores101-main-tur-54-ht-1 1.5354 7.1394 2.3226 \nflores101-main-tur-53-ht-5 0.4879 2.6832 0.7273 \nflores101-main-ukr-16-ht-3 1.0592 6.4965 1.3913 \nflores101-main-tur-68-pe1-4 0.2560 1.6213 0.7500 \nflores101-main-tur-42-pe1-4 0.2621 1.4729 0.5339 \nflores101-main-nld-39-pe2-1 0.3474 1.9472 0.0329 \nflores101-main-vie-80-ht-2 0.5311 3.8626 2.6375 \nflores101-main-vie-29-ht-1 1.1606 7.1495 2.6299 \nflores101-main-nld-91-ht-1 0.2364 1.6227 1.2914 \nflores101-main-vie-42-pe2-2 1.0708 6.7819 1.8421 \nflores101-main-ukr-44-ht-2 1.7967 10.7802 2.4762 \n\n words_per_hour words_per_minute \\\nunit_id \nflores101-main-ukr-66-pe2-3 873.3524 14.5559 \nflores101-main-nld-9-pe2-4 3992.7405 66.5457 \nflores101-main-ara-41-pe2-3 1100.3706 18.3395 \nflores101-main-ita-3-pe1-3 367.1222 6.1187 \nflores101-main-ukr-98-pe1-4 2008.0881 33.4681 \nflores101-main-vie-19-pe1-1 492.4734 8.2079 \nflores101-main-ara-102-ht-4 1833.1413 30.5524 \nflores101-main-ukr-31-ht-2 786.1378 13.1023 \nflores101-main-ukr-9-pe2-5 1332.6655 22.2111 \nflores101-main-tur-54-ht-1 504.2405 8.4040 \nflores101-main-tur-53-ht-5 1341.6816 22.3614 \nflores101-main-ukr-16-ht-3 554.1417 9.2357 \nflores101-main-tur-68-pe1-4 2220.3947 37.0066 \nflores101-main-tur-42-pe1-4 2444.2289 40.7371 \nflores101-main-nld-39-pe2-1 1848.7986 30.8133 \nflores101-main-vie-80-ht-2 932.0059 15.5334 \nflores101-main-vie-29-ht-1 503.5331 8.3922 \nflores101-main-nld-91-ht-1 2218.4874 36.9748 \nflores101-main-vie-42-pe2-2 530.8273 8.8471 \nflores101-main-ukr-44-ht-2 333.9444 5.5657 \n\n per_subject_visit_order \nunit_id \nflores101-main-ukr-66-pe2-3 284 \nflores101-main-nld-9-pe2-4 388 \nflores101-main-ara-41-pe2-3 181 \nflores101-main-ita-3-pe1-3 424 \nflores101-main-ukr-98-pe1-4 421 \nflores101-main-vie-19-pe1-1 74 \nflores101-main-ara-102-ht-4 20 \nflores101-main-ukr-31-ht-2 132 \nflores101-main-ukr-9-pe2-5 385 \nflores101-main-tur-54-ht-1 235 \nflores101-main-tur-53-ht-5 234 \nflores101-main-ukr-16-ht-3 67 \nflores101-main-tur-68-pe1-4 294 \nflores101-main-tur-42-pe1-4 187 \nflores101-main-nld-39-pe2-1 93 \nflores101-main-vie-80-ht-2 429 \nflores101-main-vie-29-ht-1 119 \nflores101-main-nld-91-ht-1 394 \nflores101-main-vie-42-pe2-2 178 \nflores101-main-ukr-44-ht-2 194 \n\n[20 rows x 66 columns]", - "text/html": "
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src_textmt_texttgt_textaligned_editlang_idsrc_tokenssrc_annotationsmt_tokensmt_annotationstgt_tokens...doc_idtime_stime_mtime_htime_per_chartime_per_wordkey_per_charwords_per_hourwords_per_minuteper_subject_visit_order
unit_id
flores101-main-ukr-66-pe2-3The qualities that determine a subculture as d...Якістю, яка визначає субкультуру як індивідуал...Рисами, які визначають субкультуру як відмінну...REF: якістю , яка визначає субкультуру як *...ukr['The', 'qualities', 'that', 'determine', 'a',...[{'lemma': 'the', 'upos': 'DET', 'feats': 'Def...['Якістю', ',', 'яка', 'визначає', 'субкультур...[{'lemma': 'якість', 'upos': 'NOUN', 'feats': ...['Рисами', ',', 'які', 'визначають', 'субкульт......6686.5631.44270.02400.55494.12201.9872873.352414.5559284
flores101-main-nld-9-pe2-4A course will normally be from 2-5 days and wi...Een cursus zal normaal van 2-5 dagen zijn en r...Een cursus duurt normaal gesproken 2-5 dagen z...REF: een cursus zal normaal van 2-5 d...nld['A', 'course', 'will', 'normally', 'be', 'fro...[{'lemma': 'a', 'upos': 'DET', 'feats': 'Defin...['Een', 'cursus', 'zal', 'normaal', 'van', '2-...[{'lemma': 'een', 'upos': 'DET', 'feats': 'Def...['Een', 'cursus', 'duurt', 'normaal', 'gesprok......919.8360.33060.00550.16810.90160.61023992.740566.5457388
flores101-main-ara-41-pe2-3For example, one might say that the motor car ...على سبيل المثال، يمكن القول أن السيارة النارية...على سبيل المثال، قد يقول الفرد أن السيارات تؤد...REF: على سبيل المثال ، يمكن القول أن السي...ara['For', 'example', ',', 'one', 'might', 'say',...[{'lemma': 'for', 'upos': 'ADP', 'feats': '', ...['على', 'سبيل', 'المثال', '،', 'يمكن', 'القول'...[{'lemma': 'عَلَى', 'upos': 'ADP', 'feats': 'A...['على', 'سبيل', 'المثال', '،', 'قد', 'يقول', '......4152.3460.87240.01450.56903.27160.40221100.370618.3395181
flores101-main-ita-3-pe1-3Most modern research telescopes are enormous f...I telescopi di ricerca più moderni sono enormi...I telescopi di ricerca più avanzati sono costi...REF: i telescopi di ricerca più moderni sono...ita['Most', 'modern', 'research', 'telescopes', '...[{'lemma': 'most', 'upos': 'ADJ', 'feats': 'De...['I', 'telescopi', 'di', 'ricerca', 'più', 'mo...[{'lemma': 'il', 'upos': 'DET', 'feats': 'Defi...['I', 'telescopi', 'di', 'ricerca', 'più', 'av......3137.2842.28810.03811.24809.80601.3182367.12226.1187424
flores101-main-ukr-98-pe1-4Searches at security checkpoints have also bec...Обшуки на блокпостах безпеки також стали набаг...Обшуки на безпекових блокпостах також стали на...REF: обшуки на блокпостах безпеки та...ukr['Searches', 'at', 'security', 'checkpoints', ...[{'lemma': 'search', 'upos': 'NOUN', 'feats': ...['Обшуки', 'на', 'блокпостах', 'безпеки', 'так...[{'lemma': 'обшук', 'upos': 'NOUN', 'feats': '...['Обшуки', 'на', 'безпекових', 'блокпостах', '......9828.6840.47810.00800.27581.79281.00962008.088133.4681421
flores101-main-vie-19-pe1-1South Africa have defeated the All Blacks (New...Nam Phi đã đánh bại All Blacks (New Zealand) t...Đội tuyển Nam Phi đã đánh bại đội All Blacks (...REF: *** ***** nam phi đã đánh bại *** all_bl...vie['South', 'Africa', 'have', 'defeated', 'the',...[{'lemma': 'South', 'upos': 'PROPN', 'feats': ...['Nam', 'Phi', 'đã', 'đánh', 'bại', 'All Black...[{'lemma': 'Nam', 'upos': 'NOUN', 'feats': '',...['Đội', 'tuyển', 'Nam', 'Phi', 'đã', 'đánh', '......19182.7513.04580.05081.22657.31001.6174492.47348.207974
flores101-main-ara-102-ht-4Some cruises feature Berlin, Germany in the br...NaNتعرض بعض الرحلات البحرية زيارة برلين في ألماني...NaNara['Some', 'cruises', 'feature', 'Berlin', ',', ...[{'lemma': 'some', 'upos': 'DET', 'feats': '',...NaNNaN['تعرض', 'بعض', 'الرحلات', 'البحرية', 'زيارة',......10274.6261.24380.02070.39691.96381.13301833.141330.552420
flores101-main-ukr-31-ht-2It is still produced today, but more important...NaNЇх виготовляють і зараз, але, що важливіше, ві...NaNukr['It', 'is', 'still', 'produced', 'today', ','...[{'lemma': 'it', 'upos': 'PRON', 'feats': 'Cas...NaNNaN['Їх', 'виготовляють', 'і', 'зараз', ',', 'але......3191.5871.52640.02540.73864.57941.5161786.137813.1023132
flores101-main-ukr-9-pe2-5Books and magazines dealing with wilderness su...Бібліотеки та журнали про виживання в дикій пр...Книги та журнали про виживання в дикій природі...REF: бібліотеки та журнали про виживання в ди...ukr['Books', 'and', 'magazines', 'dealing', 'with...[{'lemma': 'book', 'upos': 'NOUN', 'feats': 'N...['Бібліотеки', 'та', 'журнали', 'про', 'вижива...[{'lemma': 'бібліотека', 'upos': 'NOUN', 'feat...['Книги', 'та', 'журнали', 'про', 'виживання',......945.9230.76540.01280.40642.70142.24781332.665522.2111385
flores101-main-tur-54-ht-1Murray lost the first set in a tie break after...NaNHer iki adamın setteki her bir servisi kazanma...NaNtur['Murray', 'lost', 'the', 'first', 'set', 'in'...[{'lemma': 'Murray', 'upos': 'PROPN', 'feats':...NaNNaN['Her', 'iki', 'adamın', 'setteki', 'her', 'bi......54142.7892.37980.03971.53547.13942.3226504.24058.4040235
flores101-main-tur-53-ht-5They all ran back from where the accident had ...NaNHepsi kaza yerinden koşarak gelmişti.NaNtur['They', 'all', 'ran', 'back', 'from', 'where'...[{'lemma': 'they', 'upos': 'PRON', 'feats': 'C...NaNNaN['Hepsi', 'kaza', 'yerinden', 'koşarak', 'gelm......5326.8320.44720.00750.48792.68320.72731341.681622.3614234
flores101-main-ukr-16-ht-3An up-bow usually generates a softer sound, wh...NaNРух смичком угору зазвичай дає м’якший звук, т...NaNukr['An', 'up', '-', 'bow', 'usually', 'generates...[{'lemma': 'a', 'upos': 'DET', 'feats': 'Defin...NaNNaN['Рух', 'смичком', 'угору', 'зазвичай', 'дає',......1697.4481.62410.02711.05926.49651.3913554.14179.235767
flores101-main-tur-68-pe1-4He was initially hospitalised in the James Pag...Başlangıçta Great Yarmouth'daki James Paget Ha...Sürücü önce Great Yarmouth'daki James Paget Ha...REF: ****** başlangıçta great yarmouth'daki j...tur['He', 'was', 'initially', 'hospitalised', 'in...[{'lemma': 'he', 'upos': 'PRON', 'feats': 'Cas...['Başlangıçta', 'Great', \"Yarmouth'daki\", 'Jam...[{'lemma': 'başlangıç', 'upos': 'NOUN', 'feats...['Sürücü', 'önce', 'Great', \"Yarmouth'daki\", '......6819.4560.32430.00540.25601.62130.75002220.394737.0066294
flores101-main-tur-42-pe1-4The presence of a true “invisible team” (Larso...Gerçek bir “görünmez ekibin” varlığı (Larson v...Gerçek bir \"görünmez ekibin\" varlığı (Larson v...REF: gerçek bir “görünmez ekibin” varlığı (...tur['The', 'presence', 'of', 'a', 'true', '“', 'i...[{'lemma': 'the', 'upos': 'DET', 'feats': 'Def...['Gerçek', 'bir', '“görünmez', 'ekibin”', 'var...[{'lemma': 'gerçek', 'upos': 'ADJ', 'feats': '...['Gerçek', 'bir', '\"görünmez', 'ekibin\"', 'var......4230.9300.51550.00860.26211.47290.53392444.228940.7371187
flores101-main-nld-39-pe2-1After its adoption by Congress on July 4, a ha...Nadat het 4 juli door het Congres werd aangeno...Nadat het op 4 juli door het Congres werd aang...REF: nadat het ** 4 juli door het congres wer...nld['After', 'its', 'adoption', 'by', 'Congress',...[{'lemma': 'after', 'upos': 'ADP', 'feats': ''...['Nadat', 'het', '4', 'juli', 'door', 'het', '...[{'lemma': 'nadat', 'upos': 'SCONJ', 'feats': ...['Nadat', 'het', 'op', '4', 'juli', 'door', 'h......3973.9941.23320.02060.34741.94720.03291848.798630.813393
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"execution_count": 5, + "execution_count": 79, "metadata": {}, "output_type": "execute_result" } @@ -133,55 +405,542 @@ "\n", " df = pd.concat([df, lang_df], ignore_index=False)\n", "\n", - "df.sample(20)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-08-27T13:17:13.399083Z", - "start_time": "2023-08-27T13:17:11.128478Z" + " \n", + "# filter out not MT samples \n", + "df = df[df['mt_tokens'].notna()]\n", + "\n", + "print(len(df))\n", + "df.sample(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 17.3 s, sys: 414 ms, total: 17.7 s\n", + "Wall time: 18.6 s\n" + ] } - } + ], + "source": [ + "%%time\n", + "# process all saved strings to actual lists\n", + "df['tgt_tokens'] = df['tgt_tokens'].apply(ast.literal_eval)\n", + "df['tgt_annotations'] = df['tgt_annotations'].apply(ast.literal_eval)\n", + "df['mt_tokens'] = df['mt_tokens'].apply(ast.literal_eval)\n", + "df['mt_annotations'] = df['mt_annotations'].apply(ast.literal_eval)\n", + "df['mt_tbd_qe'] = df['mt_tbd_qe'].apply(ast.literal_eval)\n", + "df['mt_wmt22_qe'] = df['mt_wmt22_qe'].apply(ast.literal_eval)\n", + "\n", + "\n", + "def process_alignments(mt_pe_tbd_qe_alignments):\n", + " mt_pe_alignments_dict = defaultdict(list)\n", + " for k, v, score in mt_pe_tbd_qe_alignments:\n", + " if k is not None:\n", + " mt_pe_alignments_dict[k].append(v)\n", + " return mt_pe_alignments_dict\n", + "df['mt_pe_tbd_qe_alignments'] = df['mt_pe_tbd_qe_alignments'].apply(ast.literal_eval)\n", + "df['mt_pe_tbd_qe_alignments_dict'] = df['mt_pe_tbd_qe_alignments'].apply(process_alignments)" + ] }, { "cell_type": "markdown", - "source": [], - "metadata": { - "collapsed": false - } + "metadata": {}, + "source": [ + "## Check errors distribution " + ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 248, + "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - " 79%|███████▉ | 4098/5160 [23:06<07:44, 2.29it/s] " + "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5160/5160 [00:01<00:00, 2670.68it/s]\n" ] + }, + { + "data": { + "text/html": [ + "
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108483 rows × 11 columns

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" + ], + "text/plain": [ + " unit_id lang_id mt_tbd_qe_tags OK \\\n", + "0 flores101-main-ukr-100-pe1-1 ukr {OK} True \n", + "1 flores101-main-ukr-100-pe1-1 ukr {BAD-SUB} False \n", + "2 flores101-main-ukr-100-pe1-1 ukr {BAD-SUB, BAD-DEL-R} False \n", + "3 flores101-main-ukr-100-pe1-1 ukr {OK, BAD-DEL-L} True \n", + "4 flores101-main-ukr-100-pe1-1 ukr {OK} True \n", + "... ... ... ... ... \n", + "108478 flores101-main-vie-48-pe1-4 vie {OK} True \n", + "108479 flores101-main-vie-48-pe1-4 vie {OK} True \n", + "108480 flores101-main-vie-48-pe1-4 vie {OK} True \n", + "108481 flores101-main-vie-48-pe1-4 vie {OK} True \n", + "108482 flores101-main-vie-48-pe1-4 vie {OK} True \n", + "\n", + " BAD-SUB BAD-INS BAD-CON BAD-EXP BAD-SHF BAD-DEL-R BAD-DEL-L \n", + "0 False False False False False False False \n", + "1 True False False False False False False \n", + "2 True False False False False True False \n", + "3 False False False False False False True \n", + "4 False False False False False False False \n", + "... ... ... ... ... ... ... ... \n", + "108478 False False False False False False False \n", + "108479 False False False False False False False \n", + "108480 False False False False False False False \n", + "108481 False False False False False False False \n", + "108482 False False False False False False False \n", + "\n", + "[108483 rows x 11 columns]" + ] + }, + "execution_count": 248, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ "# process data: read files, read lists to python lists, read alignments \n", "\n", - "df_overlap = pd.DataFrame()\n", - "\n", + "error_types_list = []\n", "for _id, x in tqdm(df.iterrows(), total=len(df)):\n", - " pe_tokens = ast.literal_eval(x['tgt_tokens'])\n", - " mt_tokens = ast.literal_eval(x['mt_tokens'])\n", - " mt_tbd_qe = ast.literal_eval(x['mt_tbd_qe'])\n", - " mt_wmt22_qe = ast.literal_eval(x['mt_wmt22_qe'])[:-1] # as omission rule right\n", + " pe_tokens = x['tgt_tokens']\n", + " mt_tokens = x['mt_tokens']\n", + " mt_tbd_qe = x['mt_tbd_qe']\n", + " mt_wmt22_qe = x['mt_wmt22_qe'][:-1] # as omission rule right\n", + " mt_pe_alignments_dict = x['mt_pe_tbd_qe_alignments_dict']\n", "\n", - " mt_pe_alignments_raw = ast.literal_eval(x['mt_pe_tbd_qe_alignments'])\n", - " mt_pe_alignments_dict = defaultdict(list)\n", + " for i, mt_tok in enumerate(mt_tokens):\n", + " paired_pe_tok_i = mt_pe_alignments_dict[i][0] if mt_pe_alignments_dict[i] else None # SUB have to be paired with one PE token\n", + " if paired_pe_tok_i is None:\n", + " continue\n", "\n", - " for k, v, score in mt_pe_alignments_raw:\n", - " if k is not None:\n", - " mt_pe_alignments_dict[k].append(v)\n", + " tbd_qe_tags = mt_tbd_qe[i]\n", "\n", - " for i, mt_tok in enumerate(mt_tokens):\n", + " error_types_list.append({\n", + " 'unit_id': _id,\n", + " 'lang_id': x['lang_id'],\n", + " 'mt_tbd_qe_tags': tbd_qe_tags,\n", + " **{\n", + " error_name: error_name in tbd_qe_tags\n", + " for error_name in ['OK', 'BAD-SUB', 'BAD-INS', 'BAD-CON', 'BAD-EXP', 'BAD-SHF', 'BAD-DEL-R', 'BAD-DEL-L']\n", + " },\n", + " })\n", + "\n", + "df_error_types = pd.DataFrame(error_types_list)\n", "\n", + "df_error_types" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "## Check overlap with wmt22" + ] + }, + { + "cell_type": "code", + "execution_count": 249, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5160/5160 [00:01<00:00, 3504.74it/s]\n" + ] + }, + { + "data": { + "text/html": [ + "
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unit_idlang_idmt_tokpe_tokmt_tbd_qemt_wmt22_qe
0flores101-main-ukr-100-pe1-1ukrШокШокOKOK
1flores101-main-ukr-100-pe1-1ukrпривідBAD-SUBBAD
2flores101-main-ukr-100-pe1-1ukrвступіповерненняBAD-SUBBAD
3flores101-main-ukr-100-pe1-1ukrвступіповерненняBAD-DEL-RBAD
4flores101-main-ukr-100-pe1-1ukrнастаєнастаєOKOK
.....................
141186flores101-main-vie-48-pe1-4vienốinốiOKOK
141187flores101-main-vie-48-pe1-4vievớivớiOKOK
141188flores101-main-vie-48-pe1-4viedịch vụdịch vụOKOK
141189flores101-main-vie-48-pe1-4vienàynàyOKOK
141190flores101-main-vie-48-pe1-4vie..OKOK
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141191 rows × 6 columns

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" + ], + "text/plain": [ + " unit_id lang_id mt_tok pe_tok mt_tbd_qe \\\n", + "0 flores101-main-ukr-100-pe1-1 ukr Шок Шок OK \n", + "1 flores101-main-ukr-100-pe1-1 ukr при від BAD-SUB \n", + "2 flores101-main-ukr-100-pe1-1 ukr вступі повернення BAD-SUB \n", + "3 flores101-main-ukr-100-pe1-1 ukr вступі повернення BAD-DEL-R \n", + "4 flores101-main-ukr-100-pe1-1 ukr настає настає OK \n", + "... ... ... ... ... ... \n", + "141186 flores101-main-vie-48-pe1-4 vie nối nối OK \n", + "141187 flores101-main-vie-48-pe1-4 vie với với OK \n", + "141188 flores101-main-vie-48-pe1-4 vie dịch vụ dịch vụ OK \n", + "141189 flores101-main-vie-48-pe1-4 vie này này OK \n", + "141190 flores101-main-vie-48-pe1-4 vie . . OK \n", + "\n", + " mt_wmt22_qe \n", + "0 OK \n", + "1 BAD \n", + "2 BAD \n", + "3 BAD \n", + "4 OK \n", + "... ... \n", + "141186 OK \n", + "141187 OK \n", + "141188 OK \n", + "141189 OK \n", + "141190 OK \n", + "\n", + "[141191 rows x 6 columns]" + ] + }, + "execution_count": 249, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# process data: read files, read lists to python lists, read alignments \n", + "\n", + "overlap_list = []\n", + "for _id, x in tqdm(df.iterrows(), total=len(df)):\n", + " pe_tokens = x['tgt_tokens']\n", + " mt_tokens = x['mt_tokens']\n", + " mt_tbd_qe = x['mt_tbd_qe']\n", + " mt_wmt22_qe = x['mt_wmt22_qe'][:-1] # as omission rule right\n", + " mt_pe_alignments_dict = x['mt_pe_tbd_qe_alignments_dict']\n", + "\n", + " for i, mt_tok in enumerate(mt_tokens):\n", " paired_pe_tok_i = mt_pe_alignments_dict[i][0] if mt_pe_alignments_dict[i] else None # SUB have to be paired with one PE token\n", " if paired_pe_tok_i is None:\n", " continue\n", @@ -189,106 +948,677 @@ " tbd_qe_tags = mt_tbd_qe[i]\n", "\n", " for tbd_qe_tag in tbd_qe_tags:\n", - " _df_tok_stats = pd.DataFrame([{\n", + " overlap_list.append({\n", " 'unit_id': _id,\n", " 'lang_id': x['lang_id'],\n", " 'mt_tok': mt_tok,\n", " 'pe_tok': pe_tokens[paired_pe_tok_i],\n", " 'mt_tbd_qe': tbd_qe_tag,\n", " 'mt_wmt22_qe': mt_wmt22_qe[i],\n", - " }])\n", - " df_overlap = pd.concat([df_overlap, _df_tok_stats], ignore_index=True)\n", + " })\n", + "\n", + "df_overlap = pd.DataFrame(overlap_list)\n", "\n", "df_overlap" - ], + ] + }, + { + "cell_type": "code", + "execution_count": 250, "metadata": { - "collapsed": false, "ExecuteTime": { - "end_time": "2023-08-27T13:52:55.913558Z", - "start_time": "2023-08-27T13:17:51.561633Z" + "end_time": "2023-09-08T13:33:27.754556Z", + "start_time": "2023-09-08T13:33:24.589114Z" + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: BAD-DEL and BAD-SHF is overlapping with other cats\n" + ] + }, + { + "data": { + "text/html": [ + "
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lang_idaraitanldturukrvie
mt_wmt22_qeBADOKBADOKBADOKBADOKBADOKBADOK
mt_tbd_qe
BAD-CON99368584214151491018923
BAD-DEL-L57618866018662418845617911653191265498
BAD-DEL-R504515566692478635459387116075711331282
BAD-EXP36381771723616387175992868261
BAD-INS0010100031220
BAD-SHF1237109010829551411119513538662593121724862351
BAD-SUB205502366217262221704294121442
OK69014073753173149921589573512030110610296184214551
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" + ], + "text/plain": [ + "lang_id ara ita nld tur ukr \\\n", + "mt_wmt22_qe BAD OK BAD OK BAD OK BAD OK BAD OK \n", + "mt_tbd_qe \n", + "BAD-CON 99 3 68 5 84 2 141 5 149 10 \n", + "BAD-DEL-L 576 188 660 186 624 188 456 179 1165 319 \n", + "BAD-DEL-R 504 515 566 692 478 635 459 387 1160 757 \n", + "BAD-EXP 363 8 177 17 236 16 387 17 599 28 \n", + "BAD-INS 0 0 1 0 1 0 0 0 3 1 \n", + "BAD-SHF 1237 1090 1082 955 1411 1195 1353 866 2593 1217 \n", + "BAD-SUB 2055 0 2366 2 1726 2 2217 0 4294 1 \n", + "OK 690 14073 753 17314 992 15895 735 12030 1106 10296 \n", + "\n", + "lang_id vie \n", + "mt_wmt22_qe BAD OK \n", + "mt_tbd_qe \n", + "BAD-CON 189 23 \n", + "BAD-DEL-L 1265 498 \n", + "BAD-DEL-R 1133 1282 \n", + "BAD-EXP 682 61 \n", + "BAD-INS 22 0 \n", + "BAD-SHF 2486 2351 \n", + "BAD-SUB 2144 2 \n", + "OK 1842 14551 " + ] + }, + "execution_count": 250, + "metadata": {}, + "output_type": "execute_result" } - } - }, - { - "cell_type": "markdown", - "source": [ - "## Check overlap with wmt22" ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 9, - "outputs": [], "source": [ "print('Note: BAD-DEL and BAD-SHF is overlapping with other cats')\n", - "pd.crosstab(df_overlap['mt_tbd_qe'], [df_overlap['lang_id'], df_overlap['mt_wmt22_qe']], rownames=['mt_tbd_qe'], colnames=['lang_id', 'mt_wmt22_qe'])" - ], - "metadata": { - "collapsed": false - } + "pd.crosstab(\n", + " df_overlap['mt_tbd_qe'], \n", + " [df_overlap['lang_id'], df_overlap['mt_wmt22_qe']], \n", + " rownames=['mt_tbd_qe'], \n", + " colnames=['lang_id', 'mt_wmt22_qe']\n", + ")" + ] }, { "cell_type": "code", - "execution_count": 10, - "outputs": [], + "execution_count": 289, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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unit_idlang_idmt_tokpe_tokmt_tbd_qemt_wmt22_qe
133931flores101-main-vie-25-pe1-2vievàvàOKBAD
121804flores101-main-vie-93-pe2-1viecủacủaOKBAD
60478flores101-main-ita-91-pe2-3itanonnonOKBAD
14086flores101-main-ukr-75-pe2-4ukr18611861OKBAD
10197flores101-main-ukr-28-pe1-3ukrІнтернетуІнтернетуOKBAD
127553flores101-main-vie-50-pe2-3vieđạiđạiOKBAD
38197flores101-main-ara-95-pe1-3araالفناءالفناءOKBAD
89619flores101-main-nld-62-pe2-3nldininOKBAD
100122flores101-main-tur-107-pe2-4turAvrupaAvrupaOKBAD
10221flores101-main-ukr-28-pe1-4ukrстосункистосункиOKBAD
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" + ], + "text/plain": [ + " unit_id lang_id mt_tok pe_tok mt_tbd_qe \\\n", + "133931 flores101-main-vie-25-pe1-2 vie và và OK \n", + "121804 flores101-main-vie-93-pe2-1 vie của của OK \n", + "60478 flores101-main-ita-91-pe2-3 ita non non OK \n", + "14086 flores101-main-ukr-75-pe2-4 ukr 1861 1861 OK \n", + "10197 flores101-main-ukr-28-pe1-3 ukr Інтернету Інтернету OK \n", + "127553 flores101-main-vie-50-pe2-3 vie đại đại OK \n", + "38197 flores101-main-ara-95-pe1-3 ara الفناء الفناء OK \n", + "89619 flores101-main-nld-62-pe2-3 nld in in OK \n", + "100122 flores101-main-tur-107-pe2-4 tur Avrupa Avrupa OK \n", + "10221 flores101-main-ukr-28-pe1-4 ukr стосунки стосунки OK \n", + "\n", + " mt_wmt22_qe \n", + "133931 BAD \n", + "121804 BAD \n", + "60478 BAD \n", + "14086 BAD \n", + "10197 BAD \n", + "127553 BAD \n", + "38197 BAD \n", + "89619 BAD \n", + "100122 BAD \n", + "10221 BAD " + ] + }, + "execution_count": 289, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df_overlap[(df_overlap['mt_tbd_qe'] == 'OK') & (df_overlap['mt_wmt22_qe'] == 'BAD')].sample(10)" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", - "execution_count": 11, - "outputs": [], + "execution_count": 87, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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unit_idlang_idmt_tokpe_tokmt_tbd_qemt_wmt22_qe
116969flores101-main-vie-46-pe1-2viechính phủchính phủBAD-EXPOK
105196flores101-main-tur-89-pe2-1turbüyürkenbüyürkenBAD-EXPOK
126690flores101-main-vie-41-pe1-1viekhoa họccơ bảnBAD-EXPOK
119938flores101-main-vie-75-pe2-2vieđịa điểmkỳ nghỉBAD-EXPOK
111608flores101-main-tur-85-pe1-2tur..BAD-EXPOK
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" + ], + "text/plain": [ + " unit_id lang_id mt_tok pe_tok mt_tbd_qe \\\n", + "116969 flores101-main-vie-46-pe1-2 vie chính phủ chính phủ BAD-EXP \n", + "105196 flores101-main-tur-89-pe2-1 tur büyürken büyürken BAD-EXP \n", + "126690 flores101-main-vie-41-pe1-1 vie khoa học cơ bản BAD-EXP \n", + "119938 flores101-main-vie-75-pe2-2 vie địa điểm kỳ nghỉ BAD-EXP \n", + "111608 flores101-main-tur-85-pe1-2 tur . . BAD-EXP \n", + "\n", + " mt_wmt22_qe \n", + "116969 OK \n", + "105196 OK \n", + "126690 OK \n", + "119938 OK \n", + "111608 OK " + ] + }, + "execution_count": 87, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df_overlap[(df_overlap['mt_tbd_qe'] == 'BAD-EXP') & (df_overlap['mt_wmt22_qe'] == 'OK')].sample(5)" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", - "execution_count": 12, - "outputs": [], - "source": [ - "df_overlap" - ], + "execution_count": 259, "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 13, - "outputs": [], - "source": [ - "pd.crosstab(df_overlap['mt_tbd_qe'], df_overlap['mt_wmt22_qe']).T" + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "mt_tbd_qe OK BAD-SUB BAD-INS BAD-CON BAD-EXP BAD-SHF BAD-DEL-R \\\n", + "mt_wmt22_qe \n", + "BAD 6118 14802 27 730 2444 10162 4300 \n", + "OK 84159 7 1 48 147 7674 4268 \n", + "\n", + "mt_tbd_qe BAD-DEL-L \n", + "mt_wmt22_qe \n", + "BAD 4746 \n", + "OK 1558 " + ] + }, + "execution_count": 259, + "metadata": {}, + "output_type": "execute_result" + } ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 14, - "outputs": [], "source": [ - "df_overlap" - ], - "metadata": { - "collapsed": false - } + "pd.crosstab(\n", + " df_overlap['mt_tbd_qe'], \n", + " df_overlap['mt_wmt22_qe'],\n", + ").T[['OK', 'BAD-SUB', 'BAD-INS', 'BAD-CON', 'BAD-EXP', 'BAD-SHF', 'BAD-DEL-R', 'BAD-DEL-L']]" + ] }, { "cell_type": "code", - "execution_count": 15, - "outputs": [], + "execution_count": 91, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plt.figure(figsize=(12, 4))\n", "sns.countplot(\n", @@ -297,65 +1627,281 @@ " hue='mt_wmt22_qe',\n", ")\n", "plt.show()" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "source": [ "## Analyse BAD-SUB" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", - "execution_count": 162, + "execution_count": 95, + "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 5160/5160 [00:58<00:00, 88.93it/s] \n" + "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5160/5160 [00:00<00:00, 8042.61it/s]\n" ] }, { "data": { - "text/plain": " unit_id lang_id mt_tok pe_tok mt_pos \\\n0 flores101-main-ukr-100-pe1-1 ukr при від ADP \n1 flores101-main-ukr-100-pe1-1 ukr вступі повернення NOUN \n2 flores101-main-ukr-100-pe1-1 ukr фази фаза NOUN \n3 flores101-main-ukr-100-pe1-1 ukr бути проходити AUX \n4 flores101-main-ukr-100-pe1-3 ukr Повернувшись Проживши VERB \n... ... ... ... ... ... \n14804 flores101-main-vie-48-pe1-3 vie bằng trên ADP \n14805 flores101-main-vie-48-pe1-3 vie vận chuyển tàu thuyền VERB \n14806 flores101-main-vie-48-pe1-3 vie cuộc đoàn NOUN \n14807 flores101-main-vie-48-pe1-3 vie thoại truyền NOUN \n14808 flores101-main-vie-48-pe1-4 vie điện thoại thông NOUN \n\n pe_pos same_word same_pos same_lemma same_morf same_deprel \n0 ADP False True False False True \n1 NOUN False True False False False \n2 NOUN False True True False False \n3 VERB False False False True False \n4 VERB False True False True True \n... ... ... ... ... ... ... \n14804 ADP False True False True True \n14805 NOUN False False False True False \n14806 NOUN False True False True True \n14807 VERB False False False True True \n14808 ADJ False False False True False \n\n[14809 rows x 11 columns]", - "text/html": "
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unit_idlang_idmt_tokpe_tokmt_pospe_possame_wordsame_possame_lemmasame_morfsame_deprel
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"execution_count": 162, + "execution_count": 95, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# collect all BAD-SUB: read files, filter sents with BAD-SUB token, process to python lists, read alignments\n", - "df_stats = pd.DataFrame()\n", "\n", + "stats_list = []\n", "for _id, x in tqdm(df.iterrows(), total=len(df)):\n", - " pe_tokens = ast.literal_eval(x['tgt_tokens'])\n", - " pe_annotations = ast.literal_eval(x['tgt_annotations'])\n", - " mt_annotations = ast.literal_eval(x['mt_annotations'])\n", - " mt_tokens = ast.literal_eval(x['mt_tokens'])\n", - " mt_tbd_qe = ast.literal_eval(x['mt_tbd_qe'])\n", - " mt_pe_alignments_raw = ast.literal_eval(x['mt_pe_tbd_qe_alignments'])\n", - " mt_pe_alignments_dict = defaultdict(list)\n", - "\n", - " for k, v, score in mt_pe_alignments_raw:\n", - " if k is not None:\n", - " mt_pe_alignments_dict[k].append(v)\n", - "\n", + " pe_tokens = x['tgt_tokens']\n", + " pe_annotations = x['tgt_annotations']\n", + " mt_tokens = x['mt_tokens']\n", + " mt_annotations = x['mt_annotations']\n", + " mt_tbd_qe = x['mt_tbd_qe']\n", + " mt_wmt22_qe = x['mt_wmt22_qe'][:-1] # as omission rule right\n", + " mt_pe_alignments_dict = x['mt_pe_tbd_qe_alignments_dict']\n", + " \n", " for i, mt_tok in enumerate(mt_tokens):\n", " if 'BAD-SUB' in mt_tbd_qe[i]:\n", " paired_pe_tok_i = mt_pe_alignments_dict[i][0] if mt_pe_alignments_dict[i] else None # SUB have to be paired with one PE token\n", " if paired_pe_tok_i is None:\n", " continue\n", "\n", - " _df_tok_stats = pd.DataFrame([{\n", + " stats_list.append({\n", " 'unit_id': _id,\n", " 'lang_id': x['lang_id'],\n", " 'mt_tok': mt_tok,\n", @@ -367,41 +1913,194 @@ " 'same_lemma': mt_annotations[i]['lemma'] == pe_annotations[paired_pe_tok_i]['lemma'],\n", " 'same_morf': mt_annotations[i]['feats'] == pe_annotations[paired_pe_tok_i]['feats'],\n", " 'same_deprel': mt_annotations[i]['deprel'] == pe_annotations[paired_pe_tok_i]['deprel'],\n", - " }])\n", - " df_stats = pd.concat([df_stats, _df_tok_stats], ignore_index=True)\n", - "\n", + " })\n", "\n", + "df_stats = pd.DataFrame(stats_list)\n", "df_stats = df_stats.astype({'same_word': bool, 'same_pos': bool, 'same_lemma': bool, 'same_morf': bool, 'same_deprel': bool})\n", "\n", "df_stats" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-07-18T21:01:51.313256Z", - "start_time": "2023-07-18T21:00:53.213021Z" - } - } + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "source": [ "---" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", - "execution_count": 413, + "execution_count": 97, + "metadata": {}, "outputs": [ { "data": { - "text/plain": " total_sub diff_pos same_pos \\\nunit_id lang_id \nflores101-main-ara-1-pe1-1 ara 3 1 2 \nflores101-main-ara-1-pe1-4 ara 2 2 0 \nflores101-main-ara-1-pe2-2 ara 5 3 2 \nflores101-main-ara-1-pe2-3 ara 1 0 1 \nflores101-main-ara-1-pe2-4 ara 3 2 1 \n... ... ... ... \nflores101-main-vie-99-pe1-4 vie 1 1 0 \nflores101-main-vie-99-pe2-1 vie 3 0 3 \nflores101-main-vie-99-pe2-2 vie 3 1 2 \nflores101-main-vie-99-pe2-3 vie 7 1 6 \nflores101-main-vie-99-pe2-4 vie 4 0 4 \n\n diff_pos_percent \nunit_id lang_id \nflores101-main-ara-1-pe1-1 ara 0.333333 \nflores101-main-ara-1-pe1-4 ara 1.000000 \nflores101-main-ara-1-pe2-2 ara 0.600000 \nflores101-main-ara-1-pe2-3 ara 0.000000 \nflores101-main-ara-1-pe2-4 ara 0.666667 \n... ... \nflores101-main-vie-99-pe1-4 vie 1.000000 \nflores101-main-vie-99-pe2-1 vie 0.000000 \nflores101-main-vie-99-pe2-2 vie 0.333333 \nflores101-main-vie-99-pe2-3 vie 0.142857 \nflores101-main-vie-99-pe2-4 vie 0.000000 \n\n[4088 rows x 4 columns]", - 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total_subdiff_possame_posdiff_pos_percent
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" + ], + "text/plain": [ + " total_sub diff_pos same_pos \\\n", + "unit_id lang_id \n", + "flores101-main-ara-1-pe1-1 ara 3 1 2 \n", + "flores101-main-ara-1-pe1-4 ara 2 2 0 \n", + "flores101-main-ara-1-pe2-2 ara 5 3 2 \n", + "flores101-main-ara-1-pe2-3 ara 1 0 1 \n", + "flores101-main-ara-1-pe2-4 ara 3 2 1 \n", + "... ... ... ... \n", + "flores101-main-vie-99-pe1-4 vie 1 1 0 \n", + "flores101-main-vie-99-pe2-1 vie 3 0 3 \n", + "flores101-main-vie-99-pe2-2 vie 3 1 2 \n", + "flores101-main-vie-99-pe2-3 vie 7 1 6 \n", + "flores101-main-vie-99-pe2-4 vie 4 0 4 \n", + "\n", + " diff_pos_percent \n", + "unit_id lang_id \n", + "flores101-main-ara-1-pe1-1 ara 0.333333 \n", + "flores101-main-ara-1-pe1-4 ara 1.000000 \n", + "flores101-main-ara-1-pe2-2 ara 0.600000 \n", + "flores101-main-ara-1-pe2-3 ara 0.000000 \n", + "flores101-main-ara-1-pe2-4 ara 0.666667 \n", + "... ... \n", + "flores101-main-vie-99-pe1-4 vie 1.000000 \n", + "flores101-main-vie-99-pe2-1 vie 0.000000 \n", + "flores101-main-vie-99-pe2-2 vie 0.333333 \n", + "flores101-main-vie-99-pe2-3 vie 0.142857 \n", + "flores101-main-vie-99-pe2-4 vie 0.000000 \n", + "\n", + "[4088 rows x 4 columns]" + ] }, - "execution_count": 413, + "execution_count": 97, "metadata": {}, "output_type": "execute_result" } @@ -415,23 +2114,28 @@ "df_stats_ext_sum = df_stats_ext_sum[(df_stats_ext_sum['total_sub'] < 10) & (df_stats_ext_sum['diff_pos'] < 6)]\n", "df_stats_ext_sum['diff_pos_percent'] = df_stats_ext_sum['diff_pos'] / df_stats_ext_sum['total_sub']\n", "df_stats_ext_sum" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-07-19T10:23:35.738184Z", - "start_time": "2023-07-19T10:23:35.680165Z" - } - } + ] }, { "cell_type": "code", - "execution_count": 414, + "execution_count": 98, + "metadata": { + "ExecuteTime": { + "end_time": "2023-07-19T10:23:59.005619Z", + "start_time": "2023-07-19T10:23:54.861742Z" + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "outputs": [ { "data": { - "text/plain": "
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Bgwc3PeLi4njqqadOeKZ5yJAhBAQE8NlnnzV7/qWXXuKOO+7AbrczcOBAdu3a1Wzbffv2bRqhqCVO1r2kMfwc+aH4gw8+wOl0kpaW5vF7jR49mg0bNjS7QTozM5OsrKwWVNw2n376abOv//Of/2AwGBg9ejSpqalYLJamIUgbrV27lpycHIYPH05NTQ0XXnhh06RoiYmJXHPNNVx88cXNRto6XM+ePYmJiTlqu1lZWWRkZDB8+HCP61+3bh2hoaH8+te/bgoOlZWVrFu37pijEbWHzZs3ExYWRkREBPn5+Xz55ZdcfPHFjBo1qtlj9OjRXHTRRaxcufK4N8Ef7vPPP2fz5s388pe/BBrC5eH79ODBg7FYLPTp04fi4mJee+21o7bhdDrJysqiX79+Tc+NHDmSL7/88phXYv797397dAIAGgLytm3b6N69+0nXFRH/pCsPInJKmEwmfve73/HAAw9gMpkYP348ZWVlzJ8/n7y8vKPumThcZGQkU6dO5eWXX8ZisTBy5Eg2btzIm2++yT333IPRaOSOO+7g5ptv5s477+RnP/sZTqeTl156iY0bNzZN8uWp0NBQ1q9fz5o1axgxYsRRfcH79OnD5ZdfzvPPP091dTVnnHEG33//PfPmzWPUqFGMHTvW4/e67rrrWLp0KTfeeCO33XYbTqeTZ555pk2Te7VURkYGd911F5deeik//PADc+fO5ec//3nTVYWbb76Z9PR0AgICGD9+PNnZ2Tz33HNN3webzcagQYOYN28eAQEBJCcns3v3bt59910uvPDCY75n489s1qxZTT+z4uJi5s2bR1hYGNdff73H9Q8ZMoQ333yTxx9/nPHjx3Pw4EEWLlxIQUFBm28aLioqIiMjo+nr6upq3nvvPb799lvuuOMOTCYT7733HvX19ce86gUNoy4tWbKExYsXc9tttwENXYUat+t2uykrK2Pt2rW8+uqrjBo1il/96lcnrGvMmDFMmjSJp59+mu3bt3PhhRcSGRlJbm5u070nzz77bNP6t9xyCx9//DFXX301119/Pd26daO8vJxPP/2UpUuX8uCDDx7VvS83N7dZ20tLS3njjTfYvXt3s0EPROT0ovAgIqfMlVdeSVBQEC+++CJvv/02drud4cOH8+STT550TPq7776bqKgo3nrrLV588UWSkpK4//77+cUvfgHA2WefzcKFC5k3bx633347AQEBDBo0iH/84x8tHoHolltuYf78+dx00018+OGHx7x5+U9/+hPdu3dn2bJl/P3vfyc2NpapU6cyffp0j2+MhYYzy2+++SZ/+tOf+P3vf09QUBC//vWv+fDDD1tUc1tcd9115OXlMXPmTCIiIrjlllv4zW9+07S8cdSlRYsW8fbbbxMeHs5FF13E//3f/zXdE/HII4/w7LPP8tJLL5Gfn09UVBRTpkzht7/97XHfd/LkyQQFBfG3v/2NGTNmEBwczNixY7njjjtadM/H5ZdfTnZ2NsuWLeONN94gLi6OcePGcfXVV3P//fezc+dOevfu3arvzcqVK1m5cmXT13a7nZ49e/Lggw9y9dVXAw3zUfTt27fZmf7DpaWlkZSUxJIlS5qCbH5+PlddddVR27399tu59tprPQqPc+bMYeTIkSxfvpw//vGPVFVVERkZyZgxY5g9e3azY6pr164sW7aMF154geeff56CggKCg4Pp378/f/3rX4/Zperw+zcMBgNBQUH069ePZ599tk2Tz4lI52Zwt+QuQRER8SvJycnMnDmz6Yy4iIjIiejKg4iIn3G5XB719T98eFYRERFP6C+HiIifue+++3j33XdPut727dtPQTUiIuJP1G1JRMTPZGdnHzX86rF4MrqOiIjI4RQeRERERETEI5rnQUREREREPKLwICIiIiIiHlF4EBERERERjyg8iIiIiIiIRxQeRERERETEIwoPIiIiIiLiEYUHERERERHxiMKDiIiIiIh4ROFBREREREQ8ovAgIiIiIiIeUXgQERERERGPKDyIiIiIiIhHFB5ERERERMQjCg8iIiIiIuIRhQcREREREfGIwoOIiIiIiHhE4UFERERERDyi8CAiIiIiIh5ReBAREREREY8oPIiIiIiIiEcUHkRERERExCMKDyIiIiIi4hGFBxERERHxiNvt9nYJ4mUKD+Izfv/73zNhwgRvlyEiIiKHTJgwgd///vcAzJ8/n4ULF3q5IvE2hQcREREROaZ58+Yxffp0AJ577jmqq6u9XJF4m9nbBYiIiIiIbxo4cKC3SxAfoysPckokJyczd+7cZs/NnTuX5OTk475m27ZtjBgxgptuugmHw8GqVatITk7mrbfeYvz48QwfPpyvv/66o0sXkROoqanhqaee4oILLiAlJYXhw4dz/fXX8/333wMN3RGvu+46HnzwQYYPH87EiRNxOp0UFRXx8MMPM378eFJSUhg5ciQzZswgOzvbyy0SkcM1dltq/Hs9b968Zn+7V6xYwdVXX82wYcNISUnhoosu4vXXX/dWuXIK6MqD+KSdO3dy4403kpqaSnp6OhaLpWnZvHnz+OMf/0hNTQ3Dhg3zYpUics8997B27VruuOMOunXrxt69e3nuuee48847+eCDDwBYu3YtVquV9PR0qqqqMBqN/OY3v6G0tJS77rqL6Ohotm/fzrPPPsuDDz6oPtUiPujtt9/mqquuYsqUKVx55ZUAfP7558yYMYOpU6dy2223UVNTwxtvvMEjjzxCSkoKqampXq5aOoLCg/icrKwspk2bRv/+/Zk/f36z4ABw9dVXc9FFF3mpOhFp5HA4qKys5I9//CMTJ04EYOTIkVRUVPD4449TUFAAQH19PY888gjx8fEA5OXlERgYyL333suIESMAGDVqFPv27ePtt9/2TmNE5ISGDh0KQHx8fNP/MzMzufzyy/nDH/7QtN6wYcMYNWoUq1atUnjwUwoP4lMqKyuZNm0a+fn5vP7661it1qPWGTBggBcqE5EjWSyWpqsEeXl57N69mz179vDZZ58BDeECIDw8vCk4AMTFxfHqq6/idrvJzs5m79697Nq1i/Xr1ze9RkR8369//Wug4W/37t272bdvH5s3bwbQsezHFB7Ep5SUlNCrVy/KysqYM2fOUfdJANjtdi9UJiLH8uWXX/LnP/+ZXbt2ERQURP/+/ZuO0cbx4IOCgo563fLly3n66ac5cOAA4eHhDBgwAJvNdkprF5G2KSoq4sEHH2TFihUYDAa6d+/edDVR80H4L90wLaeM0+ls9nVVVdVR64SHh/Piiy9yxx138PHHH7NixYpTVZ6ItNC+ffuYMWMGAwYM4L///S/r1q3jjTfeYPz48Sd83dq1a7n33nu54IIL+OKLL1i1ahUvv/xyU1cIEekc7rrrLjZv3szLL79MRkYGH330Effdd5+3y5IOpvAgp0RwcDB5eXnNnlu/fv1R6wUFBREUFMRVV13F0KFDefjhhykvLz9VZYpIC2zZsoXa2lpuvvlmunXrhsFgABquRsDxzzxu2LABl8vFbbfdRlxcHNBwcuGbb74BwOVynYLqRaSljMbmHxvXrVvHBRdcwKhRo5ruT/ziiy8AHcf+TN2W5JQ499xz+eCDD0hNTaV79+6888477N2797jrG41GHn74Ya644grmzJnDI488cgqrFRFPDBo0CLPZzJw5c7jhhhtwOBy88847fP7558Cxry4CDBkyBIBHHnmEK664gtLSUl5//XV++OGHptcFBwefkjaIiOdCQ0NZv349a9asYcSIEQwZMoT333+fQYMGER8fz/r161mwYAEGg0GTyfkxXXmQU2LWrFmMHz+ev/zlL9x+++3Y7XbuvPPOE76mf//+TJ06lcWLF7NmzZpTVKmIeKp79+489dRT5OXlceutt/LAAw8A8Nprr2EwGFi7du0xXzdq1CgeeOABNmzYwE033cTjjz9OYmIi8+bNAxrOZoqI77nlllvYsmULN910EwcOHODxxx8nNTWVRx99lBkzZvDJJ5/w8MMPc/bZZx/3+JfOz+DWHS0iIiIiIuIBXXkQERERERGPKDyIiIiIiIhHFB5ERERERMQjCg8iIiIiIuIRhQcREREREfGIwoOIiIiIiHhE4UFERERERDyi8CAiIiIiIh4xe7sAbysqKsfl8nYVbWMwQFRUCIWF5XT2Kf/UFt9lNEJkZIi3y/CIjmvf4k9tAf9qj47rU8uf9h1/agv4V3s6+rg+7cOD202n30kaqS2+yV/a0pna4C/fc1BbfJk/tKcz1e8P3+9Gaovv8of2dHT96rYkIiIiIiIeUXgQERERERGPKDyIiIiIiIhHFB5ERERERMQjCg8iIiIiIuIRhQcREREREfGIwoOIiIiIiHhE4UFERERERDyi8CAiIiIiIh5ReBAREREREY8oPIiIiIiIiEcUHkRERERExCMKDyIiIiIi4hGFBxERERER8YjCg4iIiIiIeEThQUREREREPKLwICIiIiIiHlF4EBERERERjyg8iIiIiIiIRxQeRERERETEIwoPIiIiIiLiEYUHERERERHxiMKDiIiIiIh4xKvhYe/evdx4440MGzaMc889lxdffLFpWVZWFtOmTWPo0KFMnDiRr776qtlrv/nmGyZNmkRqaipTp04lKyvrVJcvIiIiInJa8Vp4cLlc3HzzzURERPDuu+/y8MMP88ILL/D+++/jdruZMWMG0dHRLFu2jEsvvZSZM2eSk5MDQE5ODjNmzGDy5MksXbqUyMhIpk+fjtvt9lZzRERERET8ntlbb1xQUMCAAQN46KGHCA4OpkePHpx55pmsW7eO6OhosrKyeOutt7Db7fTu3Ztvv/2WZcuWcdttt7FkyRJSUlK44YYbAJg9ezZjxoxh9erVjBo1qkV1GI1GDIaW1e52u3G5FFRERERE5PTitfAQGxvLs88+CzR8GF+/fj1r1qzhwQcfZOPGjQwcOBC73d60flpaGhkZGQBs3LiRESNGNC0LDAxk0KBBZGRktDg8REQEtbh2p8tFSXGVAoSIiIiInFa8Fh4ON2HCBHJychg/fjwXXnghf/7zn4mNjW22TlRUFLm5uQDk5+efcHlLvLt2LwfLaj1ePzLIwpWjemAwGACFBxERERE5ffhEeHj++ecpKCjgoYceYvbs2VRXV2OxWJqtY7FYcDgcACdd3hIlVXUUlHseHhoZDLS4u1NHaazDV+ppC7XFd3WmdvjS8dla/rT/+FNbwL/a05naoOPat/hTW8C/2tPRbfCJ8DB48GAAamtrueuuu7jiiiuorq5uto7D4cBmswFgtVqPCgoOh4PQ0NAWv7fNGoDd7vJ4/UBbQ2hpTXenjhYVFeLtEtqN2iJtERnpP99zf9p//Kkt4H/t8XU6rn2TP7UF/K89HcGrN0xnZGRw3nnnNT3Xp08f6urqiImJYdeuXUet39hVKS4ujoKCgqOWDxgwoMV11NTWUVXl+ZUHu6nh3+LiSpxOz0NHRzIYGnb2wsJyOvuAU2qL7zIaO88f76Kicly+cXi2mj/tP/7UFvCv9ui4PrX8ad/xp7aAf7Wno49rr4WH7OxsZs6cycqVK4mLiwNgy5YtREZGkpaWxksvvURNTU3T1YZ169aRlpYGQGpqKuvWrWvaVnV1Ndu2bWPmzJktrqO1O4jb3frXdhRfrKm11Bbf05na4C/fc1BbfJk/tKcz1e8P3+9Gaovv8of2dHT9XpvnYfDgwQwaNIj77ruPzMxMVq5cyZw5c7jlllsYOXIkCQkJzJo1ix07drBgwQI2bdrElClTALjiiitYv349CxYsYMeOHcyaNYukpKQWj7QkIiIinYPRaMRsbvvDaPSDTu0iXuS1Kw8mk4n58+fz6KOPctVVVxEYGMi1117L1KlTMRgMzJ8/nz/84Q9MnjyZ7t27k56eTmJiIgBJSUnMnTuXP//5z6SnpzNs2DDS09MPjYAkIiIi/qa97jV0utyUFFdquHWRVvLqDdNxcXHMmzfvmMu6d+/OokWLjvvacePGMW7cuI4qTURERHzIvzZkk1daffIVTyAqxMZlaV0xGg0KDyKt5BOjLYmIiIicSFFFLbmlNd4uQ+S057V7HkREREREpHNReBAREREREY8oPIiIiIiIiEcUHkRERERExCMKDyIiIiIi4hGFBxERERER8YjCg4iIiIiIeEThQUREREREPKLwICIiIiIiHlF4EBERERERjyg8iIiIiIiIRxQeRERERETEIwoPIiIiIiLiEYUHERERERHxiMKDiIiIiIh4ROFBREREREQ8ovAgIiIiIiIeUXgQERERERGPKDyIiIiIiIhHFB5ERERERMQjCg8iIiIiIuIRhQcREREREfGIwoOIiIiIiHhE4UFERERERDyi8CAiIiIiIh5ReBAREREREY8oPIiIiIiIiEcUHkRERERExCMKDyIiIiIi4hGFBxERERER8YjCg4iIiIiIeEThQUREREREPKLwICIiIiIiHlF4EBERERERjyg8iIiIiIiIRxQeRERERETEIwoPIiIiIiLiEYUHERERERHxiMKDiIiIiIh4ROFBREREREQ8ovAgIiIiIiIeUXgQERERERGPKDyIiIiIiIhHFB5ERERERMQjCg8iIiIiIuIRhQcREREREfGIwoOIiIiIiHjEq+EhLy+P22+/nZEjRzJ27Fhmz55NbW0tAFlZWUybNo2hQ4cyceJEvvrqq2av/eabb5g0aRKpqalMnTqVrKwsbzRBRHyQ0WjEbG7bw2g0eLsZIiIiPsfsrTd2u93cfvvthIaG8vrrr1NaWsp9992H0WjknnvuYcaMGfTr149ly5axYsUKZs6cyYcffkhiYiI5OTnMmDGD2267jbFjx5Kens706dNZvnw5BoP+4Iuc7iIigtq8DafLTUlxJS6Xux0qEhER8Q/tHh6KioqIjIw86Xq7du0iIyODr7/+mujoaABuv/12/vKXv3DOOeeQlZXFW2+9hd1up3fv3nz77bcsW7aM2267jSVLlpCSksINN9wAwOzZsxkzZgyrV69m1KhR7d0kEelk/rUhm7zS6la/PirExmVpXTEaDQoPIiIih2lVeBgwYABff/31USFh//79TJo0iQ0bNpx0GzExMbz44otNwaFRRUUFGzduZODAgdjt9qbn09LSyMjIAGDjxo2MGDGiaVlgYCCDBg0iIyND4UFEKKqoJbe0xttliIiI+B2Pw8N7773HO++8AzR0OZoxYwYBAQHN1jl48CAxMTEebS80NJSxY8c2fe1yuVi0aBGjR48mPz+f2NjYZutHRUWRm5sLcNLlLdHaXk4GQ+tf294a6/CVetpCbfFdnaod7Virt9rtT/uPP7UF/Ks9naoN7VyrN9ruj/uOP7QF/Ks9Hd0Gj8PD+eefT3Z2NgCrV69m6NChBAU171dst9s5//zzW1XInDlz2LZtG0uXLuXll1/GYrE0W26xWHA4HABUV1efcHlL2KwB2O0uj9cPtDW8b3v0qW5vUVEh3i6h3agt0hZWawB2u7XVr7f50HHuT/uPP7UF/K89vq6txzX4zrHtT/uOP7UF/K89HcHj8BAUFMTMmTMB6NKlCxMnTsRqbdtB3GjOnDm88sorPPPMM/Tr1w+r1UpJSUmzdRwOBzabDQCr1XpUUHA4HISGhrb4vWtq66iqqvV4fbup4d/i4kqcTs9DR0cyGBp29sLCctydvHu22uK7jEaIjOwcv1RrW3hcH6kmoOG0jTePc3/af/ypLeBf7Tmdjmvw/rHtT/uOP7UF/Ks9HX1ct+qeh8svv5y9e/eyZcsW6urqjlp+2WWXebytRx99lDfffJM5c+Zw4YUXAhAXF0dmZmaz9QoKCpq6KsXFxVFQUHDU8gEDBrSwJbR6B3G7W//ajuKLNbWW2uJ7OlUb2rFWb7fbX/Yf8K+2gH+0p1PV3861erPt/rDvNPKntoB/tKej629VeHjxxRd58sknCQsLO6rrksFg8Dg8zJs3j7feeounn36aiy66qOn51NRUFixYQE1NTdPVhnXr1pGWlta0fN26dU3rV1dXs23btqYrIyIiIiIi0v5aFR5eeukl7r77bm688cZWv/HOnTuZP38+N998M2lpaeTn5zctGzlyJAkJCcyaNYvp06fz2WefsWnTJmbPng3AFVdcwcKFC1mwYAHjx48nPT2dpKQkjbQkIiIiItKBWjXDdG1tLRdccEGb3viTTz7B6XTywgsvcPbZZzd7mEwm5s+fT35+PpMnT2b58uWkp6eTmJgIQFJSEnPnzmXZsmVMmTKFkpIS0tPTNUGciIiIiEgHatWVh0suuYQ33niDe+65p9Uf2G+++WZuvvnm4y7v3r07ixYtOu7ycePGMW7cuFa9t4iIiIiItFyrwkNFRQVLly7lX//6F0lJSUfN9/Dqq6+2S3EiIiIiIuI7WhUeevTowS233NLetYiIiIiIiA9rVXjQqEYiIiIiIqefVoWHWbNmnXB546hIIiIiIiLiP1o12tKR6uvr2b17Nx9++CGRkZHtsUkREREREfExrbrycLwrCy+++CI//vhjmwoSERERERHf1C5XHhpddNFF/Pe//23PTYqIiIiIiI9ot/BQVVXF4sWLiYiIaK9NioiIiIiID2lVt6X+/fsfc3I4q9XKY4891uaiRERERETE97QqPBw5CZzBYCAgIIA+ffoQHBzcLoWJiIiIiIhvaVV4GDlyJAB79uxh586duFwuevbsqeAgIiIiIuLHWhUeysrKmDVrFp988glhYWE4nU4qKys544wzSE9PJyQkpL3rFBERERERL2vVDdOPPfYYubm5fPjhh6xatYq1a9fy/vvvU1VVpQniRERERET8VKvCw6effspDDz1Er169mp7r06cPDzzwAJ988km7FSciIiIiIr6jVeHBarViNB79UoPBgNPpbHNRIiIiIiLie1oVHiZMmMDDDz/Mvn37mp7bs2cPjz32GOPGjWu34kRERERExHe06obpu+++mxkzZnDhhRcSGhoKQGlpKeeccw73339/uxYoIiIiIiK+ocXhYe/evSQmJvLaa6+xfft2du7cidVqpUePHvTu3bsjahQRERERER/gcbclt9vNY489xk9/+lM2bNgAQHJyMhMnTmTZsmVMmjSJxx9/HLfb3WHFioiIiIiI93gcHl599VU+/PBD0tPTmyaJazR//nzS09N59913efPNN9u9SBERERER8T6Pw8PixYu5//77GT9+/DGXT5gwgbvuukvhQURERETET3kcHvbv38+QIUNOuM7o0aPJyspqc1EiIiIiIuJ7PA4PUVFR7N+//4Tr5ObmEh4e3taaRERERETEB3kcHs4//3zmzp1LXV3dMZfX19czb948zj777HYrTkREREREfIfHQ7VOnz6dKVOmMHnyZK699lpSUlIICQmhtLSUrVu3smjRIiorK3niiSc6sl4REREREfESj8NDaGgoixcv5sknn+Txxx+nuroaaBjCNSQkhIkTJ3LbbbcRHR3dYcV2hOWbD9AvOoiwwABvlyIiIiIi4tNaNElceHg4jz32GA888ABZWVmUlZURHh5Ot27dMJlMHVVjh8rMr+KH3AomDowlKsji7XJERERERHxWi2eYBrBYLH4zm3RsiIVdBVV8vD2fyUMSsJo9vg1EREREROS0ctp/Ur5yaCIhVhMVtU6+21Ps7XJERERERHzWaR8erAEmxvdtuE/jx/xK8itqvVyRiIiIiIhvOu3DA0BciJU+0XYAVu8r8W4xIiIiIiI+SuHhkBHdwjEYIKe0VlcfRERERESOQeHhkBCrmT7RQQBs3F/m5WpERERERHyPwsNhhiSEALCnuJoqh9PL1YiIiIiI+BaFh8NEBlmIDbbgdjfcPC0iIiIiIv+j8HCE5NhgALYfrMDtdnu5GhERERER36HwcITe0XYCjAbKauo5UKYbp0VEREREGik8HCHAZKT3oRunfzhY4eVqREREBOBfW3PJ2F9KSXWdt0sROa2ZvV2AL0qODeKHgxXsLaqmzukiwKSMJSIi4k3bDlRQUVvPmn2l9Iqyc2aPCOwWk7fLEjnt6FPxMcQEWwixmql3udlXXO3tckRERE575/SJIincBsCuwiqWbTxArroXi5xyCg/HYDAY6H1oxuldhVVerkZERERG94jgpwNiuXxIPJH2AGrqXXy4LY+c0hpvlyZyWlF4OI5eUQ3hIau4Gke9y8vViIiICEB0kIWfpcTRNdyG0w0fb8+noMLh7bJEThsKD8cRaQ8gPNCM0w171XVJRETEZwSYjJyXHENCqJU6p5uPvj9IRW29t8sSOS0oPByHwWCgV1TDqEs7CzRhnIiIiC8xGw1ckBxD1KEuTJ/uKMSl+ZlEOpzCwwk03veQXVpDTZ3Ty9WIiIjI4SxmI+clRxNgMpBXXsv6rFJvlyTi9xQeTiA8MIBIewBuN+wpUtclERERXxNqC2Bsr0gANuwv0whMIh1M4eEk2nvUJaPRgMlkbPHDaDS0y/uLiIj4m97RQfSNaehq/OWuQpwudV8S6Sg+ER4cDgeTJk1i1apVTc9lZWUxbdo0hg4dysSJE/nqq6+aveabb75h0qRJpKamMnXqVLKysjqktsb7HnJKa6hytK3rktFoIDzCTmRkUIsf4RF2BQgREZHjGN09nMAAIyXV9WTsL/N2OSJ+y+szTNfW1nLnnXeyY8eOpufcbjczZsygX79+LFu2jBUrVjBz5kw+/PBDEhMTycnJYcaMGdx2222MHTuW9PR0pk+fzvLlyzEY2vcDdqjNTEywhfwKB7uLquh2aAjX1jAYDJiMRpas2kNRpefDykUGWbhyVI9DbdPZFBERkSPZAkyc2SOCT3cUkrG/lN7RdsIDA7xdlojf8eqVh8zMTH7+85+zb9++Zs9/9913ZGVl8cgjj9C7d29+85vfMHToUJYtWwbAkiVLSElJ4YYbbqBv377Mnj2b/fv3s3r16g6ps/ehwLCzoH26LhVVOigor/X40ZKgISIicrrqFWWna7gNlxu+21Ps7XJE/JJXw8Pq1asZNWoUb7/9drPnN27cyMCBA7Hb/3eWPy0tjYyMjKblI0aMaFoWGBjIoEGDmpa3t8YJ4/LKaymrqeuQ9xAREZG2MRgMjO4RgcEAWSU1ZGmeJpF259VuS1dfffUxn8/Pzyc2NrbZc1FRUeTm5nq0vCU86eUUZDWTEGrlQFkt3+dWNL2upT2k2tqj6njv2fhcO/fY8gq1xXd1qna0Y63earc/7T/+1BbwjfYYjYZ26abbqX4mHtYaHhjAoPgQthwo57u9xXQJsx3znkFvtN0X9p324k9tAf9qT0e3wev3PBxLdXU1Foul2XMWiwWHw+HR8pawWQOw210nXa9/QigHyvL5Ia8hPEREBLX4vRoF2izYW3DvdaDN4tF7RkWFtLomX6O2SFtYrQHY7dZWv97m4TF3KvjT/uNPbQHvtsfldmP0h085LdCS4/rsvjHsLKiipLqencU1pHYNB3zn2PanY8Gf2gL+156O4JPhwWq1UlJS0uw5h8OBzWZrWn5kUHA4HISGhrb4vWpq66iqOvmY0F1CLBhoGHVpd0El4SZwOk8eOg5nMhmJiAiiusbh0Xs2spsa/i0urjzmexoMDTt7YWE5nX1yTbXFdxmNEBnZOX6p1np4XB9PTUDDh7LjHXOngj/tP/7UFvB+exr/lry3LovC8po2bSs+LJCLhyW1U2Udq6XH9fCkUL7eXcy3uwrpGmrBFmDy+rHt7X2nPflTW8C/2tPRf699MjzExcWRmZnZ7LmCgoKmrkpxcXEUFBQctXzAgAEtfi9Pd5DAABNdwmxkl9bwr405XDMsscU7V1t3Rrf7xNs42fLORG3xPZ2qDe1Yq7fb3Rn2H6PR4NFQ0kbjyW+zc7ncuDrJGP3e/tkUlteQW9q28BBg8okR2z3Twu91/7hgvs+roKiqjg3ZZZzZM6L55rz4s/P2vtOe/Kkt4B/t6ej6ffK3RmpqKlu3bqWm5n+/FNetW0dqamrT8nXr1jUtq66uZtu2bU3LO0rjhHHLN+bg7ux7lohIO2iYvyaIiJM8gJOuExERRHhEkOa0kXZhNBgY1T0cgK155ZRWa8ATkfbgk1ceRo4cSUJCArNmzWL69Ol89tlnbNq0idmzZwNwxRVXsHDhQhYsWMD48eNJT08nKSmJUaNGdWhdPSLtfL27mB0HK9iaW86A2OAOfT8REV9nNBowGQ0n7UJjs1moqTnxfWlRITYuS+uK0WjoNFcfxLclhQfSNdxGVkkNq/eVkBzfObpeivgyn7zyYDKZmD9/Pvn5+UyePJnly5eTnp5OYmIiAElJScydO5dly5YxZcoUSkpKSE9Pb/cJ4o5kMRtJjmsIDO9uPNCh7yUi0pk0dqE53uNgee0Jl+eW1rS5/77IsYzsHo4B2FNUzb6i9pmvSeR05jNXHrZv397s6+7du7No0aLjrj9u3DjGjRvX0WUdZWiXMLYeKOff3+fx23E9CbL4zLdQREREjhBpt5AcG8wPByv47/Z8HtRVLZE28ckrD76sa4SNXjFBVNe5+PiHfG+XIyIiIieR1jWMAKOBnNJa3t+U4+1yRDo1hYcWMhgM/OKMrgC8t7nlk9KJiIjIqWW3mEjt0jCc+xP/3k5tfQsmWzpNGY0GzGbjcR+mQyN1mUzHX6fxoUEQ/Iv63LTCFcOTeOLf29mWW87G/aWkdgnzdkkiIiJyAoMTQth+sJL9JdW8uW4/v0rrHPNbeEPjKGomDz70ezLhntPlpqS4UgMh+AmFh1aICrZySUo87246wEur9vHc5MHeLklEREROwGwyMr5fNMs357Lwm71cPCCWCLvF22X5JI2iJiei8NBK00Z145+bD/DN7mK+zytnQFz7D/9W53RRUl3PwQoHS9dlU1vtIMoeQO/oIMIDA9r9/UT8xWurs6hzuoi0W0gMsxIfYu3w0dhExPcNSQxhZ2EVW3PKePHbfdz9kz7eLsmnnWwiQnudu0Wzfot/UHhopa4RgVzQP5Z/f3+Qf6zK4omfDWyX7dY5XewsqGJXYRU5ZTVNswT+84j7K7qG2xjbO4qf9IthSKLGrRY53IGyWipq69lTVM36bAixmhkYH8zA+BDM6nsrctoyGAz84eIBXP33VSzbdIArhyXSI9Lu7bJEOhXdMN0G00Y23Dj92Y4CtuaWt2lbdU4X67NLeXN9Dl/uKmJ/aUNwCAwwkhRuY1y/GEZ0CycxzAZAVkkNb6zbz41vZvDLV9bz+qq91Na72twmEX9w2ZA4RncPp0+0nQCjgfLaelbtLWHJhhx2FlRqhniR09hZvaM5p08UTpebJz/N1O8DkRbSlYc26B0dxE8HxPLR9weZ80kmC3851KObi460t6iKb3YXU+FoGP0hxGqmf1wwPSMDCQsMIDrEym8m9KOoqBKn00VFbT2r9hbzeWYhn+0oILOgkj+8u4XoIAvXnpHE5UMSCAwwtXdzRTqNfrEhBJobjoHGq3nrs0upcDj5dEche4qqObtXJFazzp+InI7unNCb73YXsWpvCR//kM+FA2K9XZJIp6Hw0Ea3n9OTL3YWsjW3nNfXZjP10NUIT1TU1vPN7mL2FlcDEGw1cUa3cHpF2TGeoH92sNXMT/rF8JN+MZTV1PHBtjzeWp9DTmkNz3y+i5dXZXHtGUlMGZpIYIAJo9HQ6v7ebrdbNzhJpxZgMtI/Lpg+0XY25pSxIbuMXYVV5JXXckH/GKKDdMOkyOmmW4SdG0Z3469f7+Xpz3dyZs8IQm26l1DEEzrt1kbRwVZ+d24vAF74eg9r95Wc9DUul5tNOWUsyTjA3uJqDAZITQzlytQE+kQHnTA4HCnUFsDVaUl8fvd4/nhhX5LCbRRX1/H8F7u57MXVvL4uG2uQlcjIoFY9wiPsGp9Z/ILZZCStazg/S4kj1Gam0uFk+ZY8dhVWebs0EfGCa0d0pWeknaKqOuZ9udvb5Yh0Grry0A5+lhLP6r0lfLw9n7v+uZWnLhtEWtfwY677xY/5LPx2HwWVDUObxYVYObtXBJFtHC7OYjZy2eAELh4Yz0fb8lj43T72l9bw7Oe7WLR2P2f2DCc5NpgAk+d5MTLIwpWjehy6aqGrD+IfYkOsXDY4nk9/LCC7tIZPfiygqkc4KQmh3i5NRE4hi9nI78/vw2/e3sS7m3KZOCCOoUmat0nkZBQe2oHBYOCBi5IpqHSwPruUGUs3c/XwLlw+JIGkcBul1fV8tbuQdzflsimnDACr2cjIbuEkxwa16xCSZqOBS1Li+emAWD7cdpCFq/aRU1rD+5vzWBGQz9AuofSPDcbcghAh4m+sZiMXDojhuz3FbM2t4Ns9JdTWuxieFKYhXUVOI8OTwrk0JZ5/bsnlkf9sZ9G1adgtumdQvMNoNHSK3h4KD+3Eajby3OQUHv3Pj3y8PZ/X1mbz2tpsjjxnbzYaGJYUxoC44BbfrGkyGThWT7PGzzomk7FpaFeTycjlQxO5dGgCKzKLmP3h95TW1PPtnhI27i8ntUso/eOCNWylnLaMBgNn9ojAFmBiXVYp67PLqKlzcVbPCG+XJiKn0O3jevLd3mKySmp46rNM7r8w2dslyWmoJbN6e5vCQzuyBZj406QBXDQgljfWZbMhuxTnoQ/zPSPtXJwSx9SxvXh3zT4Kyj2fVMVuMeFyuwkLO/FY1MebIv4XI4MpLq/h652FbMgupdLh5Ns9xWTsL2VwQigD4oOx6EqEnIYMBgPDk8KwmY18vbuYbXkV1Lnc/HxYordLO672OjPlcmkwBBFouHfw4Z8mc+viTSzfkseZPSI5LznG22XJacbTWb09ERcWyKRhSe1U2dEUHjrA2N5RjO0dRZ3TRWl1HUFWM4EBJkwmI5EhthZvzxpgwmgwsGT1Xooqjh06Am0Wqo8xRXyP6CAuHNIFk9HAgLhg+sUEsf1gBRv3l1HhcLJ6Xwkb95cxKCGEQfHB2HxkiNfDr6K0hEaHktYYGB+CxWTk88xCduRX8u6mA0wb53szz7bnmSmny01JcaWOFxEgrWs400Z15R+rsnj0Pz/SI8pOn+hjn5DrCDopII1ONqu3J1pyf2trKDx0oACTkehga7ttr7jScdwrFnYnx5wiPuKIYShNRgMD40NIjg0ms6CSjP1llNXUsz67lM05ZQyID2ZwQqjX+nwajQZcbvdxr6KcjNPloqS4Sr88pcX6xDR8KP90RwHbciu4ddF6HpvYDxO+cwm5vc5MRYXYuCyta8PxpmNFBICbz+zO5pwy1maVcud7W3nlmmGEB3b88K06KSCdjcLDacpkNJAcG0zfmCB2F1aRsb+Moqo6NuWUs/VAOcmxwZzbL/qU12UwGBqusqzaQ1Hl0VdSTkSjQ0lb9Yyyc4ExhhU/FrDi+zwqqh3M+dlAn7ki16g9zkydiMvt5mB5LdklNWSVVJNbVkNhVR3FVXUUVzkoramntt6Fo96F0+3GGmCivt6FwQCBASaCLCaCrSYi7BaigwIIsepPjfg+s8nI7EsGct3rG8gpreGu97by/BWDO/xkmk4KSGej3+inOaPBQO/oIHpF2dlXUkNGdikHKxxsy6vgh4MVHKyq55phiSSFtby7VVsUneAqi0hH6hoRyC/SuvDOxgN8t6eY29/ZwjOXDyLI4n+/Lt1uNwfKavjxYAU/5leyI7+SPUVV7C+pxuFswYePmvrDvqg7arHFZCAxPJDEECtJ4TZCbWaNaiU+KTwwgKcuG8RNb2WwMaeMO9/bwjOXp5ySEwgdfVJApL34319DaRWDwUD3iEC6hdvIKaslY38pOaW1LF2XzTvrs/npwDh+PbobSeGB3i5VpMP1jLLz2o0juW7hajZklzJz6Waem5zS6WegraitJ6+8lk05ZfxnewHf55RRXlt/zHVNRgNdwmx0DQ8kIdRKVJCFSHsAEXYLYYFmbGYTFpMRq8WIzW5jyao95JbVUOVwUuFwUl5TT1FVHUVVDhxON3sKq9hzaEK+8EAzfaKD6BMTpKsS4nP6RAcx94rBzFy6mbVZpfzfu1v4yyUDCTsFXZhEOgP91pZmDIaGDwxdwmzUOl1klzn49IeDfLA1j39vy2PSoHhuGN2NxFN8JULkVEvrHsnffjmU6W9vZMuBcm5dvInnrxhMVFDbJnQ8VdxuN8XVdeSU1pBb7uBgeS2VDudR65mMBnpF2ekbE0TfmGB6R9vpGh5IfKjNo6GczWYjERFBJEUEHnN9l8tNUVUdB6vr2HWwgrzyWkqq61mbVcrarFISQ60MSgihW4ROTIjvSEkI5bnJKdy+bAvrskq5/o0NPHVZCj2jTjzqYUdzu93U1ruaHnVON/VuN06Xm4MVDt5YtY/ismqqHU4cThf1roZlTpf7iP+7mj1Xf+jhcruxmhu6HUaG2MjMK8flchNiNRFsNRNmM2ueKFF4kOPrEh7II5NT+WLrAV74cjff7inmn1ty+WBbHpcOjmfayK7EhypEiP8aGB/CX3+eyoylm/gxv5Jpr2/gqcsG0S822NulHVNlbT37S2uaHtV1rmbLDUBkUAA9o+z8YlQPkoID6BZm69CROYxGA9HBFrrFhjAwJghHvYvdRVVk5leSU1bb9Ai2mjizZyS/dBz7SojIqZbaJYyFvxzKne9tIaukhmsXreeWMT345fCGEQzbMkKS6bBj7vD/O11uSqrrKK6uo7ymnoraesprnVQ66qmuawgMJ/LPzbmtqsdTBiDCHkBMsIX4ECt9E3xpSAk5VRQe5KRSEkJ5/orBbNxfyoJv9rJ6XwnLNh5g+ZZcLh+cwHUjuxIb0n6jSon4kj4xQfz9F0P53btb2Fdcza/fyuDRif0Z1+fUDyhwLKXVdewuqmJ3YTUFRwwyYDIaSAixEh9qJS7ESkywhQCTkfgwG1PSkigurqT+JB9G2pvFbCQ5Npjk2GAqauvZlttwf1VFrZP//pDP2L98xq/OSGLy4ATN9Cte1ycmiJevGcYfP/iB1ftKeG7lLj7clsf1o7pxxajuWFt5L0R4uJ2c0hq255bx/YFytueW8+3OAgoqHR4NUx5gMmA1G7GYjJiMBkwGA0FWE71iQzC63QQYDVhMRsyHbsY+8t/mzxkxG8FsNGIwQE29i5p6F26Tke8yC8ivqKWi1kl5bcNACQ3dEevYfrCSlTuLiA6y0CMykL4xQQSrG+JpQT9l8VhqlzDSrxzC+uwS/vb1XtZnl7I4I4dlmw7wk77R/HxYIkMSQ3UjpPidbhGB/OPqocx6/3tW7yvh7n9u4/pRXbnpzO6n/BK++9BISOuyStlTVEVRVfMblGOCLHQJb+h6GBdi9enZSoOtZkZ2D2d4UiiZBVVsOVBOYaWD5z7fxcvf7eOaEUn8YngXAn1stCs5vUTYLcybMpjlW3J5buVuduRXct+/vuf5L3bTNzaILqFW4kKthFiPHgig3umivLaekup68strya9wUFBVR15ZzXGvIgSYDETaLYTazE3dhYKtZuwBJqxmI1az8ZjHdXyYjRvP7dsuJwUauyMutBibbuJ2u91UOpwUVDrIr3CQXVJDQaWj6bEuq5SkcBvJscF0jwzEqM8CfkvhQVpseFI4f7sqnLX7Sljw7V42ZJfy8fZ8Pt6eT5/oIC7oH8N5/WLoqj7M4kdCbQE8NzmFpz/fxZKMHF5alcXqfSU8eGEyPTq4H7Tb7eaHgxV8nlnI55mF7CqobFpmMEBiqI2eUYF0j7B3yrP1ZpOR/nHBjO0TRWSYnedW/EhWcTXzv9rD4g053HRWd36WEu/RPRgiHcFgMHDp4ATO7RPN4owcFm/YT25ZDbll/xsdyWxsuBpgPjRfkcPpPmE3I4MBwm0B9Iq285NB8ewrqMDgchNkMfnkSTiDwdAUZHpE2jmjG7hNJrbnlLIjv5Lc8lqySmrIKqkh1GYmNTGUvjGnbqI9OXUUHqTVRnQLZ0S3cH7IK2dJRg7/+SGfzIJKMr+qZP5Xe+gbE8So7hGc0S2coV3COuWHGpHDmU1G7vlJH4YnhfHn/+5gy4FyfvnqOq4ZkcT1o7q263CuTpebNXuKeG/NPj7dUcCBsv8NXdw4ElLPyEC6RQT63DwUrWUyGpiSlsS4HmF8uCWPv32zl5zSGmb/dwdvrM1mxtienNsnyic/WMnpISwwgJvO7M4NZ3Zja0E1cz/5kT2F1ZRW1zXcdHysQQkOdSmKCAwgwh5AfLidIFPDtkxGA4O6hHHZiG689EUmB4qrvdCq1guymukfF0z/uGBKqxu6Mv2QV0FZTT1f7ipiXVYp4/tFcZ3z1HaPlI6l8CBt1j8uhPsvTOb2c3rxeWYBK7YXsGZfMTsOjRu/aG12w8zWccEMSwpjWFIYqYlhhNi0+0nndF5yDCkJIfzlk0y+2lXEK6uzeG/TAa4a3oUpqQlE2JuPyOTpjZXVDier9hbz+Y4CvthZSPFhXZJsAUbG9o7ikmFJZOeXU9zCSRQ7E7PRyMSBcZzXL4Zlmw6w8Nu97C2u5p7l2xicEMrt5/RkaFKYt8uU05jVbOInA+LYk1dGbmkN9S43VY6GewLqXW5MBgNmk6Gpq9Hhgddut1JV5X/zGIUFBjCyezjDkkL5Ia+CzQfKqXQ4+WDrQb5/9gtmnN2DsT0jFf79gD69SbsJCwzg0sEJXDo4gZKqOr7bW8zafSWs2VdMTlktmw+Us/lAOa+uycYA9IsNZnhSGOP6RDG0S5hP980WOVJ8qI1nLk/hi52FPLdyF/uKq1nwzV4WfrePM3tEcE7vKIYlhdEjyk54RNBR+7fT5Sa7uIodeRVsyCrmu11FbMwqof6wmWFDbWYm9I/lopQExvWLIfDQ1buFKzNPaVu9xWI28svhXbhkUByvrcni9XX72XygjJve3sg5vaOYfnYPekerW4R4n9lo6PTzwLSXAJORwYmhDIwP4fu8cjL2l7Erv5I7393K4IRQ/u/cXgxJDPV2mdIGCg9yUiaTAWjZTaGRwRYuGhDLRQNiAcgprWFDdmnDY38p+4qr2X6wgu0HK3hz/X4i7QFckhLPVWldiIjQhwHpPM7pHcWYnpF88mM+i9Zm831eBV/tKuKrXUUAWM1GkiICcR0aQ73e6aam3klpdX2zoNAozGYmOS6YfrHB9EsIo85Rx/6Cct4oKKdXbAjjB8Zzup24C7aaufXsnkwZmsjfv93L8s25fLGzkC93FjJxUBy/Oas7CRo2WsSnmIwGUhJCObt3FDVuA3//chebD5Rx45sZXJAcw8xzeuq47aQUHuS47BYTLrebsLCW3wzqdLkoKa7CdejDUWKYjcQwGxcPigMgv6KWDdmlfLenmJU7CymqquOV1Vm8tiaLy4Z1IS5IZ3Ck8zAZDVzQP5YL+seyu7CK/24/yLqsUrYcKKO23sXO/Mpjv87QcMUuKshCQqiVhFAbIdb/3SxZWOlo1r0hKvj0HhI5JtjKfef34+rhSbzw9R4+3VHAB1vz+PiHg1yRmsj1o7oSae8ck/h5i9vtprzWSXWdE0e9C4dLfdGlY9kCTMw4ty+T+seQ/sUu3t+Sx8fb81m5s5Br0rpw3chuuieyk1F4kOOyBpgwGgwsWb2XogrP+2dGBlm4clSPQx+Ajj1gdUywtenDVp3TxZe7iliyYT9rs0p5Z/1+jAZISQhheFJYh05gJdLeekbZufmsHkDDMI0F1XWUOeG9dfsorHBgMhqwmozYLSZCbGYNZ9gKPaLs/OVnA9l6oIx5X+1h7b4S3lq/n+Wbc7k6rQvXjEjSePOHOF1uDpQ1TBp4oLSWouo6nIdd8Qq2mnn48lQvViini9gQK/dfmMzPh3bhmZU7WZdVykursvjnljymj+nBxYPi1H25k9BvVzmp4koHBeUdd3NXgMnIhL7RTOgbzfb8Cv727T6+3FHAppxydhVWcU7vKLqE6dKmdD5mk5Gk8EAiIoL4IbuYXFvNyV8kHhuUEMoLVw5h1d5i0r/czfd5Fbz43T7e2rCfq4Z14RfDuxAeeHpexcyvcPBDXgW7i6qOGi7UZIBAiwmryUiErvLKKZYcF8wLVw5hZWYhz32xi+ySGh79+Efe3rCfO8b3Jq1ruLdLlJNQeBCfMighlNduHMXdb2/go615VNQ6+XDbQVITQxnRNcyjEWtExDOmdriq1x7baKtR3SMY2S2cT3cU8Lev97K7qIqF3+3jzXX7mTI0gavTkogK8v/uTC63m92FDZPtHaz432hcgQFGuoYH0iXMRnRww+RjjVe8ukZ27BwlIsdiMBg4t280Z/WMZHFGDgu/28uP+ZXcsngT5/aJ4vZzemmuKB+m8CA+qU9MEFOGJrBqbwnf51WwMaeMA2U1jO8bTaiGeBVpkyCrGZfbTWhoO/5x9nL3K4PBwE/6xTC+bzSf7Sjgpe/28WN+Ja+uyebtDTn8bHA8v0zrQs+olg/I4HK5m+7f8kUut5vM/Eo2ZJdRVlsPgNHQ0IUuOTaYhFCruseJT7KYjfxqRBIXD4xlwTd7eXfTAT7PLOSrXUVcNawLN47upmHdfZB+IuKzAkxGzu4VSZcwG1/sLORghYN3Nx1gfN9ouumMhEir2Q7dz7R8fRb5ZW3rSuVrI0AZDQbO7x/LlNE9WPljPs9/msnGrBKWbMhhyYYcxvWL4foxPTinb4zHVzKdLjclxZU+FyBcLjc78itZn11KWU1DaLCZjQyMD2ZAXIhuQpVOI8Ju4d7z+nLlsESe/XwX3+4p5vV12XywLY+bz+rO5UMSNMO8D1F4EJ/XM8pOdLCFT38s4GCFg//8kM+wLqEM7xqms2kibVBQUUtuadvCgy+OAGU0GjCbjJRXObhkYAyD44NZvbeYHw9WsvLHfFb+mE9UUAAjuoUzOCG0af6MY4kKsXFZWleMRoPPhAeny817G/bzwld7KDo0kaDNbGRIYigD44M1yMRprjN3R+wVFcTzVwzmm91FPPv5LnYXVfHEJ5ksychpmGSud5T+7p+E2+3G7e7Y31UKD9IphFjNTBoUx3d7itmWV8GG/WXkVzgY3zcKW4DOronI0QrLa8grqyXQbGRc7yiGdQlla27D/DKFlXX85/t8/vtDPj0i7STHBtElzObTs9+63G5WbM/nxW/3sbuoCmiYR2RIYgiD4kMUGk5z/tQd8ayekYzsHsG7mw7wt6/3sLuwirv+uY1eUXauG9mVC/rH6koEUFFbT3ZJDQWVDoqq6qisrae6zkWgxcR15/TpsPdVeJBOw2Q0MKZXJLEhVr7cVUR2aQ3vbs7lvH7RxPjg2U8R8S2htgDO7BFBWtcwdhys5IeDFRRV1bGrsIpdhVUEW0z0jQmiV7Tdp+aLcLndfL6jgAXf7mVnQUNoCAsMYFhSKN0jArEoNAj+1x3RbDRw5dBELuofy6trsliSkcOuwioe/Gg7f/t6D79MS2LSoLjTbljmwkoHmfmV7CupoaS6zis1nF7f8XbUmlmXG14jbdU3JohIewArfiygrKae5VvyGNMzkrNDFCBE5OQsJiODEkIYlBBCQYWD7QcryCyopMLhZMP+MjbsLyM8MIBeUXZGevH3dm29i4+25fHm+v3sKmwIDcFWE9ee0ZVbz+vHku/2tLnb2emqrd1yfGGUsePxt+6IITYzM8b25LqRXVm68QBvrMsmp6yWpz7byfyvdjNxUBxXDe9C35jgFm3X1wdCOJzD6WJHfiU/HqygoPJ/gcEAxIZYiAuxEmVvGEnNFmCiZ3THjqKm8NBCbZl1uZEvXxbvLKKCLFw2OJ6VmYXsLa7my11FlNbWc93Y3t4uTUQ6kehgC9HBkYzqHs6e4mp2FlSRXVJNSXUd67NLWZ9dyic/FnJWjwjO7BFBamIoAeaO/eC4p6iKD7fl8d6mXIoPnVkMspj45fAuXJ2WRESwhVCb5mdojXbv2qO/56dMaGAAd/x0ALddkMySddm8+s0edhysYFnGAZZlHCCtewRXDE/i4sEJhNlPfnz46kAIh6t01LP1QAXf55XjcDbUaTRA94hAekU3dLW0HuP3kd3SsR/vFR5aqLWzLgP0iA7iwiFdOqiy04/VbOT85Gg25pSxdl8pm/aXcfn8b3jggr70iW75cIwicvoym4z0iQ6iT3QQtfUu9hZXsaugiv2lNew4WMGOgxW8sjqLIIuJM7qFc3ZyLH3CrfSLafsNym63mz1F1Xy7p4iPf8hna25507KEUCtXDevCpYPjT7vuGR2hvbr2+EK3ntON0WjAZDTw/oYcHDUOrhqWwN6iatbuK+GHgxWs21vMur3F3P/eFvrFBjGkSyi9o4OOOWu1Lw6EcLiiSgebDpSzs6CSxvLCbGYGxofQJ9ru9Xs99ZuolVoz63LEaTBJ0almMBgY2iWMmGArn+0o5PsDZfzq1XVcM6Ir14/qqj+2Ip2Qt7uUWM1G+sUE0y8mmDB7AN1iQ/nvlgN8tbOQ4qo6Ps8s5PPMwqZ1B8aH0CcmiF5RdnpGBREXYiU62EKQxdTsSrPb7aaq1klhpYO9xVVk5leyI7+SdVklzSZ1MxngzJ6RXDIojnP6ROvG0A7Q1q49vtSt53RTWF7T9LOzmRuGdB+WFMrOgip+PFhJcXUd3+dV8H1eBVazke4RgfSMstMlzHbMIOEr3G43X2cW8Mba7KZ7mwDiQ6wMSQyhW0Sgz/Rc0Scr8Qtdwmz8+qxu7Cqq4YPNB3h1TRbvb8nlxtHduHRwvNdTuoicnC92KYkOtjJpSAI/S03E5XKzJaeUrzMLD53lLKK4qo4N2aVsyC496rVWsxGr2YjZZMRogPKaemrrXcd8H4vJwLCkMMb0iuLC/jE+dcO2iK8LspgZkhjK4IQQiqrq2JFfSWZBJdV1Ln7Mr+TH/EoCTAa6hTcEiUgfOplb73Tx3x/zeX3tfrYfrAAa7mXoEWVnSEIIsT54P6fCg/iNYKuZ9GuGc97qvTzz2U72FVfz5Gc7+fu3e5mcmsDEgXH0iOzYm4g6A6PR0KqzFz5ywkP8mC92KTlWTRZgfHIMo7uFUlhZR05pDfkVtRRUOiisdFBe48ThdFFb7zpmWAgwGegSZqNPdBC9o4MYlBDCsC5hOskh0kYGg4GoIAtRQRZGdg8nt6yW3YVV7CmqpqrOyc7CKnYWVvF5ZiHf7StlZNcwRnUL98pZ/YKKWt7fmseyjQfIO9STJTDAREpCCL2i7IT68MzavluZSCuN6xPN6G7hvLc5l9fWZJFTVss/VmXxj1VZ9I0JYnT3CEZ0Cyc5NpioQ2cfWvuBGhouNfpin8ljMRoNhEfYMRl9d6QQX9KZJ1vqzHyxS8mRNdnr3FRVNfzBjw22EBvc/ExmndNFdZ0Ll9uNy+3G7YYu4YHc8pN+1FbW4HR2jt8ZIu3lVHdHNBoMJIbZSAyzcVZPNwcrHOwprGJ3URXltc6mCSOhoffCWT0jGD8ogd6hlg678lflcPLN7iL+/f1BvtpVSOOvgUh7AL9MS+LX5/bhnTV7fX4UNYUH6TCtGc7W2E79Ec0mI1OGJnL5kARWZhawfEse3+0tZsehPsavrc0GGg7Y3tFB9IgJJjE8kMRwG4nhgSSE2YgLtRHiwYgmTpeLkuKqThEgDAYDJqORJav2UFTpOPkLDhMbauXyEd07qDLf4k+TLYl3BJiMR91IHW4PIMhqxlFlAHz/94VIe/CF7ogGg4G4ECtxIVZGdg/HZDISFRbIiq25rM8qZX9pDUsyDrAk4wAA3SICGdYljAHxwfSNCaZPdBD2E8xEfzz1Ljc78yvZmFPGt3uKWL23uGnUJIDUxFAuGxLP+cmxBNnMnebeWIUHaXe+NJytyWhgQr8YJvSLoaSqjm/2FLFmXwmbcsrIKq6mqKqOon0lrNlXcszXW0wGQmxmQqzmZv+GBwYQGWShR5Sdq0b3PFRv5/kwUNSKG/5Pp9lr/W2yJRERb/G17ogGg4HYECs3ntObXwxNpLzaweq9JXy3p5iNOWVszy1nX3E1+4qr+eeWQ68BkiIC6RoeSEKolbhQK2GBAdjMJgIDjLjcUO2op6bORUGlg/2lNewvqWFnQSVVdc5m758UbmN8n2guSYmnZ1Tn7Eqt8CDtrj2Gs+2Iz1nh9gAmDoxj4sA4AKrrnOwqrGJvcTWldS4+3ZZLQaWDylonlY56HE43Dqebwso6CiuPPYuj2WjgH99l0TXMRteIQHpF2ukXG0y3iEDMmhSw0/O3yZZERLzFl7ojHn41JDQ0kMvjwrh8ZMOV9dKqOtbuLWLt3mK25ZTx/YEyDpbXklVcTVZxdcvfy2JicEIoQ5NCGdc7mt7Rdp8ZNam1OnV4qK2t5eGHH+bjjz/GZrNxww03cMMNN3i7LDnEW8PZetpdKthkZEiXMIZ1CycszI7V4G5Wb53TRaXDSaXDSZWjIVA0BAsnpTX1lNXUUe9y80NuOT8cNi47NIyy0icmiMFJ4fQIt9E3Joi+McFHDR3rS/dL1DtdDVdiquqoqK2nwuGksraeOqebOpebEKuJG8b18XaZIiIibXK8qyE2m4WamoYuvdE2E+f0iuCcXhFUOuo5WO6gpLqOsuo6Sg+NnFbndOEGooNtmA0Nf/vDA80khQXSJdxG9wg7PaPsPj1EbGt06vDwxBNPsGXLFl555RVycnK49957SUxM5KKLLvJ2aeIFbe0udeSZgACTkfBAI+GBx77vweV2ExIYQEq3SPYUVLIzv4Lth4JElcPJ1gPlbD3QPFR0i7QzICGEAQmhJMeF0DsmiDCToYV3hrRddZ2T/aU1FFY6mkaIKa2uP2HHq3ofCTkiIiLt4UQDIRwp0GwkMMRKwhFDp8aH2bjx3L4UF1dSf5yhmP1Npw0PVVVVLFmyhL///e8MGjSIQYMGsWPHDl5//XWFh9NUa7tLtXbmb6PBQGJ4IOcNiGPJ6r30CLfRI9zGBcnRFFfVkVdeS1F1PTnF1eSV11JeW8++oir2FVXxn615TdsJMBnoHmGnd7SdXlENE00lhQeSEGYlqI1TzNc5XWSVVJOZX8nOgkoyCxqGqdtfcuxLr4EBRiLtFkJtZoKtJoItZgLMRsxGA10j2/HmYREREemUOm14+OGHH6ivr2fYsGFNz6WlpfHXv/4Vl8uFUUNRnrZa2l2qPbpKHes9Y4IsdI8JITm64UpITZ2Toqo6CisdFFXVUV5bT2lNPVUOJ5kFDRPaQH6zbYTZzCSE2oiwBxAWGECYzUxYYABWkxGDoSHAGI0GHPUuKh0N2yqsrCOvvIbc8loKKhzHvZoQYjU1jYcdHWQhKigAe4DpuH0x40Jsbf02iYiISCfXacNDfn4+ERERWCz/++AXHR1NbW0tJSUlREZGerE6kaPZAkwkhplIDGv4EB4dYuWmc/vyfVYRO/Iq2FVYRWZ+JbsLK8kpraG0pv7Qo6JN72sPMNE72k7v6CD6xQYzrFc0qzMPUlFT3x7NEpEWOtXj3YuItKdOGx6qq6ubBQeg6WuHw/Px62NCWnbWuXFK89gQK+YW3gDT2tee7HUGA9isAdRYjbjdLXutN+o94WsPTbQUE2Jt8Q1GXqn3BK890c8FICHMBgYY1D2KQd2jjlpeXlPHgdJqckpqKKp0UFrtoKSqntLqOmrrndTV1eNyuXG6G4aUtVvMBFlMhNkCiA+zEhdiIzbYQnhgQNPVBKPRQFiYndziCoqPM4LU8bT0WPGmuNC2XSWJPDSqR3yoDXMbR8Vo9bYMYLUGUBtoahoFuL3qOuXtO0ZbOrKuDm+fh+05UmKEvV3Hu48PC2zzEMptPVZOpfao1ev7mI7rjq2pLdtq5XHduK32OikQ1wmOa4PbfayPNb7vo48+4rHHHuPrr79uem7nzp1MnDiRVatWER4e7r3iRERERET8UKe99hkXF0dxcTH19f/repGfn4/NZiM0NNSLlYmIiIiI+KdOGx4GDBiA2WwmIyOj6bl169YxePBg3SwtIiIiItIBOu2n7MDAQC677DIeeughNm3axIoVK3jppZeYOnWqt0sTEREREfFLnfaeB2i4afqhhx7i448/Jjg4mBtvvJFp06Z5uywREREREb/UqcODiIiIiIicOp2225KIiIiIiJxaCg8iIiIiIuIRhQcREREREfGIwoOIiIiIiHhE4UFERERERDyi8CAiIiIiIh5ReBAREREREY8oPIiIiIiIiEcUHkRERERExCMKDyIiIiIi4hGFBxERERER8YjCg4iIiIiIeEThQUREREREPKLwICIiIiIiHlF4EBERERERjyg8iIhIu3C73d4uQUREOpjCg/iMa6+9lmuvvfaE68ydO5fk5ORTVJGIeOqTTz7h3nvv9XYZInIK6G/x6c3s7QJERKTze/nll71dgoicIldeeSVjx471dhniJQoPIiIiIuKx+Ph44uPjvV2GeIm6LckpM2HCBJ5//nn+8pe/cNZZZzFkyBBuvPFG9uzZc8z1a2trmT17NmPGjGHYsGHMmjWL2traU1u0iJzUtddey+rVq1m9ejXJycm88847JCcnk52d3Wy9CRMm8Pvf/77p6+TkZObNm8fkyZMZMmQI8+bNO9Wli8gx3H///YwZMwan09ns+T/96U+MGjWKZ5555qhuSytWrGDy5MkMHjyYMWPG8Nhjj1FVVXUqy5ZTROFBTqlXX32VXbt2MXv2bB577DG2bNly3H7Sd999N4sXL+Y3v/kNzz77LKWlpeoaIeKDHnzwQQYOHMjAgQN5++23qaio8Pi1f/3rX7nkkkt4/vnnufDCCzuwShHx1KWXXkpBQQGrVq1qes7lcvHRRx9x8cUXYzY377jy/vvvM2PGDHr16kV6ejozZ85k+fLlTJ8+XQMp+CF1W5JTKjQ0lPnz52MymQDYt28fc+fOpbi4uNl6O3bs4D//+Q8PPfQQv/zlLwEYO3Ysl1xyCZmZmae8bhE5vj59+hAcHAzA0KFD2bVrl8evHTFiBNdff31HlSYirZCWlkaXLl3417/+xVlnnQXAqlWryM/P59JLL+WLL75oWtftdvPkk08yduxYnnzyyabne/TowbRp01i5ciXnnnvuqW6CdCBdeZBTavDgwU3BAWjqM1ldXd1svbVr1wIN3RwaGY1GnZkU8TMDBgzwdgkicgSDwcDPfvYzVqxYgcPhAOCDDz6gR48epKamNlt3165d5ObmMmHCBOrr65seZ5xxBsHBwXz99dfeaIJ0IIUHOaUCAwObfW00NuyCLper2fOlpaUARERENHs+JiamA6sTkVPNbrd7uwQROYZLL72U0tJSvvzySxwOBx9//DE/+9nPjlqvpKQEgIcffphBgwY1e1RUVHDw4MFTXLl0NHVbEp/UGBoKCgpITExser7xl5SI+C6DwQAcfVKgsrLSG+WISCv07NmTIUOG8NFHH2E0GikrKztmeAgNDQXgnnvuYeTIkUctDwsL6/Ba5dTSlQfxSaNHjwbg3//+d7PnP/vsM2+UIyIn0XgVEWi6/yE3N7fpuZ07dyr8i3Qyl156KV9++SUffPABw4cPp2vXrket06tXL6KiosjOzmbw4MFNj7i4OJ566im2bdvmhcqlI+nKg/ik7t27c9VVV/HMM89QX1/PgAED+Oc//8n27du9XZqIHENoaCgbNmzg22+/ZeTIkdhsNh5//HF++9vfUllZyfPPP094eLi3yxSRFpg4cSKPP/44H374IQ8++OAx1zGZTPzud7/jgQcewGQyMX78eMrKypg/fz55eXkMGjToFFctHU1XHsRnPfjgg9x0000sWrSImTNnUlNTwy233OLtskTkGK655hoCAgK46aab+OKLL5g7dy5Op5MZM2bw3HPPMWPGDFJSUrxdpoi0QGRkJGeffTYmk4mLLrrouOtdeeWVPPXUU6xfv55bbrmFhx56iKSkJF577bVjXq2Qzs3g1gC8IiIiIiLiAV15EBERERERjyg8iIiIiIiIRxQeRERERETEIwoPIiIiIiLiEYUHERERERHxiMKDiIiIiIh4ROFBREREREQ8ovAgIiIiIiIeMXu7AG8rKirH5fJ2FW1jMEBUVAiFheV09in/1BbfZTRCZGSIt8vwiI5r3+JPbQH/ao+O61PLn/Ydf2oL+Fd7Ovq4Pu3Dg9tNp99JGqktvslf2tKZ2uAv33NQW3yZP7SnM9XvD9/vRmqL7/KH9nR0/eq2JCIiIiIiHlF4EBERERERjyg8iIiIiIiIRxQeRERERETEIwoPIiIiIiLiEYUHERERERHxyGk/VKuI+B+j0YixjadGXC43LlcnH69PRESknSk8iIjfiYgIavM2nC43JcWVChAiIiKHUXgQEb/zrw3Z5JVWt/r1USE2LkvritFoUHgQERE5zGkfHtS9QcT/FFXUklta4+0yRERE/M5pHx7UvUFERERExDOnfXhQ9wYREREREc+c9uFB3RtERER8X3t0MwZ1NRZpq9M+PIiIiIjva49uxqCuxiJtpfAgIiIiPq+t3YxBXY1F2oPCg4iIiPg8dTMW8Q3t0HtQREREREROBwoPIiIiIiLiEYUHERERERHxiMKDiIiIiIh4ROFBREREREQ8ovAgIiIiIiIeUXgQERERERGPeDU87N27lxtvvJFhw4Zx7rnn8uKLLzYty8rKYtq0aQwdOpSJEyfy1VdfNXvtN998w6RJk0hNTWXq1KlkZWWd6vJFRERERE4rXgsPLpeLm2++mYiICN59910efvhhXnjhBd5//33cbjczZswgOjqaZcuWcemllzJz5kxycnIAyMnJYcaMGUyePJmlS5cSGRnJ9OnTcbs1W6SIiIiISEfx2gzTBQUFDBgwgIceeojg4GB69OjBmWeeybp164iOjiYrK4u33noLu91O7969+fbbb1m2bBm33XYbS5YsISUlhRtuuAGA2bNnM2bMGFavXs2oUaO81SQREREREb/mtSsPsbGxPPvsswQHB+N2u1m3bh1r1qxh5MiRbNy4kYEDB2K325vWT0tLIyMjA4CNGzcyYsSIpmWBgYEMGjSoabmIiIiIiLQ/n7hhesKECVx99dUMGzaMCy+8kPz8fGJjY5utExUVRW5uLsBJl4uIiIiISPvzWrelwz3//PMUFBTw0EMPMXv2bKqrq7FYLM3WsVgsOBwOgJMubxFDq8s+elPtuK3WvK+33r89qS2+q1O1Q8e1T/GntoB/tadTtaGda/VG2/1x3/GHtoB/taej2+AT4WHw4MEA1NbWctddd3HFFVdQXV3dbB2Hw4HNZgPAarUeFRQcDgehoaEtfm+rNQC73drKysFmawgxERFBrd5Ge4mKCvF2Ce1GbZG20HHtm/ypLeB/7fF1bT2uwXeObX/ad/ypLeB/7ekIXr1hOiMjg/POO6/puT59+lBXV0dMTAy7du06av3GrkpxcXEUFBQctXzAgAEtrqO2to6qqtpWtKBBTUBDvCsursTpdLV6O21hMDTs7IWF5XT2AafUFt9lNEJkZOf4parj2rf4U1vAv9pzOh3X4P1j25/2HX9qC/hXezr6uPZaeMjOzmbmzJmsXLmSuLg4ALZs2UJkZCRpaWm89NJL1NTUNF1tWLduHWlpaQCkpqaybt26pm1VV1ezbds2Zs6c2fJC2nEH8fbO5nZ7v4b2orb4nk7VBh3XPsmf2gL+0Z5OVX871+rNtvvDvtPIn9oC/tGejq7fazdMDx48mEGDBnHfffeRmZnJypUrmTNnDrfccgsjR44kISGBWbNmsWPHDhYsWMCmTZuYMmUKAFdccQXr169nwYIF7Nixg1mzZpGUlKRhWkVEREREOpDXwoPJZGL+/PkEBgZy1VVX8Yc//IFrr72WqVOnNi3Lz89n8uTJLF++nPT0dBITEwFISkpi7ty5LFu2jClTplBSUkJ6ejoGf7jLRURERETER3n1hum4uDjmzZt3zGXdu3dn0aJFx33tuHHjGDduXEeVJiIiIiIiR/CJeR5ERERERMT3KTyIiIiIiIhHFB5ERERERMQjCg8iIiIiIuIRhQcREREREfGIwoOIiIiIiHhE4UFERERERDyi8CAiIiIiIh5ReBAREREREY8oPIiIiIiIiEcUHkRERERExCMKDyIiIiIi4hGFBxERERER8YjCg4iIiIiIeEThQUREREREPKLwICIiIiIiHlF4EBERERERjyg8iIiIiIiIRxQeRERERETEIwoPIiIiIiLiEYUHERERERHxiMKDiIiIiIh4ROFBREREREQ8ovAgIiIiIiIeUXgQERERERGPKDyIiIiIiIhHFB5ERERERMQjCg8iIiIiIuIRhQcREREREfGIwoOIiIiIiHhE4UFERERERDyi8CAiIiIiIh5ReBAREREREY8oPIiIiIiIiEcUHkRERERExCMKDyIiIiIi4hGFBxERERER8YjCg4iIiIiIeEThQUREREREPKLwICIiIiIiHlF4EBERERERjyg8iIiIiIiIRxQeRERERETEIwoPIiIiIiLiEYUHERERERHxiMKDiIiIiIh4ROFBREREREQ8ovAgIiIiIiIe8Wp4yMvL4/bbb2fkyJGMHTuW2bNnU1tbC0BWVhbTpk1j6NChTJw4ka+++qrZa7/55hsmTZpEamoqU6dOJSsryxtNEBERERE5bXgtPLjdbm6//Xaqq6t5/fXXeeaZZ/jss8949tlncbvdzJgxg+joaJYtW8all17KzJkzycnJASAnJ4cZM2YwefJkli5dSmRkJNOnT8ftdnurOSIiIiIifs/c3hssKioiMjLypOvt2rWLjIwMvv76a6KjowG4/fbb+ctf/sI555xDVlYWb731Fna7nd69e/Ptt9+ybNkybrvtNpYsWUJKSgo33HADALNnz2bMmDGsXr2aUaNGtXeTRERERESEVl55GDBgAEVFRUc9v3//fn7yk594tI2YmBhefPHFpuDQqKKigo0bNzJw4EDsdnvT82lpaWRkZACwceNGRowY0bQsMDCQQYMGNS0XEREREZH25/GVh/fee4933nkHoKlbUUBAQLN1Dh48SExMjEfbCw0NZezYsU1fu1wuFi1axOjRo8nPzyc2NrbZ+lFRUeTm5gKcdHmLGFr+kuNuqh231Zr39db7tye1xXd1qnbouPYp/tQW8K/2dKo2tHOt3mi7P+47/tAW8K/2dHQbPA4P559/PtnZ2QCsXr2aoUOHEhQU1Gwdu93O+eef36pC5syZw7Zt21i6dCkvv/wyFoul2XKLxYLD4QCgurr6hMtbwmoNwG63tqpmAJutoY6IiKCTrNnxoqJCvF1Cu1FbpC10XPsmf2oL+F97fF1bj2vwnWPbn/Ydf2oL+F97OoLH4SEoKIiZM2cC0KVLFyZOnIjV2raDuNGcOXN45ZVXeOaZZ+jXrx9Wq5WSkpJm6zgcDmw2GwBWq/WooOBwOAgNDW3xe9fW1lFVVdvq2msCGuJdcXElTqer1dtpC4OhYWcvLCyns98zrrb4LqMRIiM7xy9VHde+xZ/aAv7VntPpuAbvH9v+tO/4U1vAv9rT0cd1q26Yvvzyy9m7dy9btmyhrq7uqOWXXXaZx9t69NFHefPNN5kzZw4XXnghAHFxcWRmZjZbr6CgoKmrUlxcHAUFBUctHzBgQAtbArTjDuLtnc3t9n4N7UVt8T2dqg06rn2SP7UF/KM9nar+dq7Vm233h32nkT+1BfyjPR1df6vCw4svvsiTTz5JWFjYUV2XDAaDx+Fh3rx5vPXWWzz99NNcdNFFTc+npqayYMECampqmq42rFu3jrS0tKbl69ata1q/urqabdu2NV0ZERERERGR9teq8PDSSy9x9913c+ONN7b6jXfu3Mn8+fO5+eabSUtLIz8/v2nZyJEjSUhIYNasWUyfPp3PPvuMTZs2MXv2bACuuOIKFi5cyIIFCxg/fjzp6ekkJSVpmFYRERERkQ7UqqFaa2trueCCC9r0xp988glOp5MXXniBs88+u9nDZDIxf/588vPzmTx5MsuXLyc9PZ3ExEQAkpKSmDt3LsuWLWPKlCmUlJSQnp6OwR9ukRcRERER8VGtuvJwySWX8MYbb3DPPfe0+gP7zTffzM0333zc5d27d2fRokXHXT5u3DjGjRvXqvcWEREREZGWa1V4qKioYOnSpfzrX/8iKSnpqPkeXn311XYpTkREREREfEerwkOPHj245ZZb2rsWERERERHxYa0KDxrVSERERETk9NOq8DBr1qwTLm8cFUlERERERPxHq0ZbOlJ9fT27d+/mww8/JDIysj02KSIiIiIiPqZVVx6Od2XhxRdf5Mcff2xTQSIiIiIi4pva5cpDo4suuoj//ve/7blJERERERHxEe0WHqqqqli8eDERERHttUkREREREfEhreq21L9//2NODme1WnnsscfaXJSIiIiIiPieVoWHIyeBMxgMBAQE0KdPH4KDg9ulMBERERER8S2tCg8jR44EYM+ePezcuROXy0XPnj0VHERERERE/FirwkNZWRmzZs3ik08+ISwsDKfTSWVlJWeccQbp6emEhIS0d50iIiIiIuJlrbph+rHHHiM3N5cPP/yQVatWsXbtWt5//32qqqo0QZyIiIiIiJ9qVXj49NNPeeihh+jVq1fTc3369OGBBx7gk08+abfiRERERETEd7QqPFitVozGo19qMBhwOp1tLkpERERERHxPq8LDhAkTePjhh9m3b1/Tc3v27OGxxx5j3Lhx7VaciIiIiIj4jlbdMH333XczY8YMLrzwQkJDQwEoLS3lnHPO4f7772/XAkVERERExDe0ODzs3buXxMREXnvtNbZv387OnTuxWq306NGD3r17d0SNIiIiIiLiAzzutuR2u3nsscf46U9/yoYNGwBITk5m4sSJLFu2jEmTJvH444/jdrs7rFgREU98sDWP3LJab5chIiLidzwOD6+++ioffvgh6enpTZPENZo/fz7p6em8++67vPnmm+1epIhIS2w9UM77W/P4elcRLp3QEBERaTceh4fFixdz//33M378+GMunzBhAnfddZfCg4h4XUpCw0SV2/Iq+GxHoa6IioiItBOPw8P+/fsZMmTICdcZPXo0WVlZbS5KRKQtJg6K47x+0RgNsKuwinVZpd4uSURExC94HB6ioqLYv3//CdfJzc0lPDy8rTWJiLRZzyg7Y3tHArBhfxm5ZTVerkhERKTz8zg8nH/++cydO5e6urpjLq+vr2fevHmcffbZ7VaciEhb9IsJpl9MEAArdxbhdKn7koiISFt4HB6mT59OXl4ekydPZvHixWzbto2srCy2bNnC22+/zeWXX05WVha33XZbR9YrItIiZ/aIwB5goqymni0Hyr1djoiISKfm8TwPoaGhLF68mCeffJLHH3+c6upqoGEI15CQECZOnMhtt91GdHR0hxUrItJSFrORM7qFsXJnERv2l9I/Lhir2ePzJiIiInKYFk0SFx4ezmOPPcYDDzxAVlYWZWVlhIeH061bN0wmU0fVKCLSJn1jgth8oJyiqjq2HihneNcwb5ckIiLSKbV4hmkAi8XiN7NJv71+P9FBAfSJDsJoMHi7HBHpAAaDgaFdQvl0RyFbDpSTkhiCxaSrDyIiIi112v/13FtUzcrMIpZvzqOspt7b5YhIB+kZZSfMZqbW6WJbboW3yxEREemUTvvwMLZ3JBaTgfxKB8u35FJU5fB2SSLSAYyHrj4AbM4po14jL4mIiLTYaR8ezuwZyRWpCUTaA6iuc/HRtnwqanUFQsQf9YkOIshioqbexe7CKm+XIyIi0umc9uEBINhqZtKgOMIDA6iqc/Lf7QUaD17EDxmNBgbEBQOwLVfDtoqIiLSUwsMhVrORi/rHYDUZKah0sC6r1NsliUgHSI4NxmiAgxUOCirVTVGks3hzXTZr9pVQWn3syWpF5NRQeDhMiM3MOX0iAdiUU0ahPliI+B27xUTPSDsA3+vqg0inkVVcQ8b+MpZkHOCb3UXUO13eLknktKTwcIQekXZ6RgbiBr7eXYTbre5LIv5mQHxD16XMgioc9foAItIZ/HRgDF3DbbiBrbkV/HNLHlUOp7fLEjntKDwcw+geEZiNBvLKHfyYX+ntckSkncWHWAkPNFPvcrNLN06LdAqDE8O4aEAsPx0QQ2CAkaKqOt7fkkd1nQKEyKmk8HAMwVYzaYdmoF2zr4Q6XRoV8SsGg4F+MQ1XH3SCQKRzSQoP5Gcp8YRYTZTV1vPxD/kaelnkFFJ4OI6U+BBCrCaq6zShlIg/6hNjxwDkldfqBkyRTibUZuaiAbFYTAYOVjj4IrNQ3YxFThGFh+MwGg0MT2q4+rAxp0z9okX8TJDFTJdwGwA7dPVBpNMJDwzgvOQYDAbYWVjF9oM6jkVOBYWHE+gTE0SYzUxtvYstGpVFxO/0iwkCGsKDzlqKdD5dwmyM7BYOwHd7iimv0SSvIh1N4eEEjAZD070Pm3PKdO+DiJ/pHhGIxWSgwuHkQFmtt8sRkVZISQghLsRKncvNyp3qviTS0RQeTqJXlJ0wmxmH061LoiJ+xmwy0iuq4eqDbpwW6ZyMBgPn9onEbDRwoKxWf6tFOpjCw0kYDAYGJ4YAsPlAGS6d0RDxK/1iG8LD7sIqXV0U6aRCbQHNRkms1X2KIh1G4cEDfaODsJmNVNQ62a0x4UX8SmywhTBbw5wPu4t0fIt0VoPiQwgPNFNT72J9Vqm3yxHxWwoPHjCbjAyMP3T1Iadc/SlF/IjBYKDPYTdOi0jnZDIaOLNHBABbc8sprtIQzCIdQeHBQwPjgzEZDORXOsgt142VIv6kb3RDeMgpraWiVqO1iHRWSeGBdI8IxE1D9yURaX8KDx4KDDDR91Df6E05ZV6uRkTaU4jNTHyIFYDMAnVdEunMzugWjgHYW1xNnk72ibQ7nwgPDoeDSZMmsWrVqqbnsrKymDZtGkOHDmXixIl89dVXzV7zzTffMGnSJFJTU5k6dSpZWVkdXufghIauS/uKayjRjLQifqWv5nwQ8QsR9oCm43nNvhIdzyLtzOvhoba2ljvuuIMdO3Y0Ped2u5kxYwbR0dEsW7aMSy+9lJkzZ5KTkwNATk4OM2bMYPLkySxdupTIyEimT5/e4b8gwgMD6BbRMCPtVk0aJ+JXekbZMRmgpLqOXM35INKppXUNw2SAA2W17C+t8XY5nZLRaMBsNh73YTI1fIQ0mY6/TuPDaDR4uTXSnszefPPMzEzuvPPOoz70f/fdd2RlZfHWW29ht9vp3bs33377LcuWLeO2225jyZIlpKSkcMMNNwAwe/ZsxowZw+rVqxk1alSH1pwSH8q+4hp+PFjJiK7hHfpeInLqWM1Gukfa2VVYxWZ1TRTp1IKtZgbGh7D5QDlr9pXQJcyGwaAPsJ4yGg2ERwRh8uBDf0RE0EnXcbrclBRX4nLpKpA/8Gp4aPyw/7vf/Y6hQ4c2Pb9x40YGDhyI3W5vei4tLY2MjIym5SNGjGhaFhgYyKBBg8jIyOjw8JAYZiUiMIDi6jq2H6yge5T95C8SkU6hT0wQuwqr2HKgnHrN+SDSqQ3tEsoPBysoqKxjV2EVvaNP/iFXGhiNBkxGA++ty6Kw/PhXbmw2CzU1jhNuKyrExmVpXTEaDQoPfsKr4eHqq68+5vP5+fnExsY2ey4qKorc3FyPlrdIC09EGAwGUhJC+HJXEVtzyzmvf8xhy1r+9u2h8X394aSK2uK7OlU7Wllr1zAbNrORSoeTL3cUMDQuSMd1O/CntoB/tadTtaGFtdoCTAxJCGVddinrs0vpecTJPm+0vbPtO4XlNeQep9uXwQCBdW6qq2vxtNe4L7e7s/1sTqSj2+DV8HA81dXVWCyWZs9ZLBYcDodHy1vCag3Abre26DWDuwWwZl9Jw6RxxQ19oz25bNfRoqJCvF1Cu1FbpC1ac1w36h8fSkZ2Ce9s2M/4Xw5r58pazp/2H39qC/hfe3xda47rM3pHsTW3nJLqerLLHPSKb5iF2tt/szvLvmOzWbDXnTgZBAae+GdiszV8XvP299xTneVn400+GR6sVislJSXNnnM4HNhstqblRwYFh8NBaGhoi9+rtraOqqqW3xyZHBvMxpwyvt6RD0BxcSVOL3VzMBgadvbCwnKP07+vUlt8l9EIkZGd45dqa49rgB4RVjKy4eOtuezLLcVu9s64Ev60//hTW8C/2nM6HNeDE0NZs6+Eb3cWkpbU8DnBW3+zO8u+YzIZiYgIoqbGcdzvucHQEBxOduWhJqDhNLg3Pyd5whd+NkajoV3uzTEYIDy848KaT4aHuLg4MjMzmz1XUFDQ1FUpLi6OgoKCo5YPGDCg5W/Wyh1kYHwwm3LK2FtczdacUhIDzV7/ReB24/Ua2ova4ns6VRvaUGt0kIXoIAsFlQ4+2Z7PpIFx7VdXK/jL/gP+1Rbwj/Z0qvpbWeug+GC2HCijrLa+2TxN3my7P+07LWlHZ2izt342RqOBsHDPblL3Np8MD6mpqSxYsICampqmqw3r1q0jLS2tafm6deua1q+urmbbtm3MnDnzlNUYbDXTM6phZJZ/fL2HP5zX55S9t4h0HIPBwODEUD7bUcC/tuR6PTyISNsEmIykJoby3d4SvswspLbe6e2SRI7i6U3qnogLC2TSsKR2quxoXp/n4VhGjhxJQkICs2bNYseOHSxYsIBNmzYxZcoUAK644grWr1/PggUL2LFjB7NmzSIpKanDR1o6UsqhSeOWZ+RQVNny+y1ExDcNTgzBYIB1WaXkaIx4kU5vQFww9gATpTX1LF7T8ZPKirRW403qbXkUVXTsXEU+GR5MJhPz588nPz+fyZMns3z5ctLT00lMTAQgKSmJuXPnsmzZMqZMmUJJSQnp6emnfAzn2GALiWE2HE4XSzNyTul7i0jHCQsM4MxeUQD8a2srRnETEZ9iNhkZduh+h7mfZlJTp6sPIq3lM92Wtm/f3uzr7t27s2jRouOuP27cOMaNG9fRZZ2QwWBgZPdw3tuUy5INOVw7IokAk0/mMRFpoavO6Mo3Owv55+ZcbhjdHXMn6IcqIseXHBvMlgPlHCyvZWlGDr8Y1sXbJYl0Svqk20YD40OIDbFSUOngv9vzvV2OiLSTCwfFEx5o5mCFg293F3m7HBFpI5PRwNg+DVcUX/puH1UOXX0QaQ2FhzYyGQ1cO7o7AG+t34+7MwwlICInZQswMSklHoB3Nx3wcjUi0h6GJIbSPcpOcVUdizfs93Y5Ip2SwkM7uHpUNywmA9/nVTQbBk5EOrfJqQkAfL27iLzyjr0BrTMzGg2YzcbjPkyHunOaTMdfp/FhVPcw6UAmo4H/O68vAK+tzaaitt7LFYl0PgoP7SAq2MpPDw3n+NZ6nckQ8Rc9o4IYlhSGyw3v6OrDMRmNBsIjgog4yQM46ToREUGERwQpQEiH+llqF3pG2SmrqWfR2mxvlyPS6fjMDdOd3dUjkvjn5lw+21FAblkN8aE2b5ckIu3gqmGJbMgu5Z2NB7h+ZFdsASZvl+RTPB2b3GazUFNz4iGto0JsXJbWFaPRgMulLqDSMUxGA9PH9uTu97ayaG02lw2O9/rfbKPR0C6h2eVy69iRDqfw0E76xQYzomsYa7NKWZKRw23n9PJ2SSLSDsb1iSYh1MqBslr+/f1BLhuS4O2SfFLj2OTHY69zU1Wlrl/iG37SL5phXULZsL+M9K/28OjE/l6rpfHqXXvMLOx0uSkprlSAkA6l8NCOfjG8C2uzSnlvcy6/PrM7gTpDKdLpmY0GrhrWhWdX7uLN9fu5dHD8KZ9TRkTal8Fg4I7xvZm6aAP//v4gPx+ayODEUK/U0l4zC+vKnZwquuehHZ3dK4ouYTbKauo1OouIH7l0cDz2ABO7Cqv4bm+xt8sRkXbQPy6ES1Ia7ld8+vOduLw8WmJbZxZuS/AQaQmFh3ZkMhq4bmRXAF5dk60ZLEX8RLDVzKWDG4ZtXfjtPg3JLOInbh3TA3uAiS0Hyvn39we9XY5Ip6Dw0M4mDYojPsRKYaWDdzfnerscEWkn156RhNVsZGNOGav3lni7HBFpB9HBVqaNajjp99zKXZRU13m5IhHfp/DQzgJMRq4f3Q2AV1Zn6eqDiJ+ICbZy+aGbpRd8u1dXH0T8xDVpSfSMslNUVcezn+/0djkiPk/hoQNcctjVh8Ubcrxdjoi0k+sOXX3YlFOmex9E/ITFbOT+C/phAD7YdpBvdhd5uyQRn6bw0AECTEZ+M6Y7AP9YvY+SKl0GFfEH0cFWrjg06/Szn++i3unyckUi0h4GJ4byi+FdAHj0Pz9SXHXiOUlETmcKDx1k4sA4+sUEUVHr5MXv9nq7HBFpJzeM6kZ4YAC7CqtYnKEriyL+YvrZPegZaaeg0sGDH233+uhLIr5K4aGDGA0G/u/chonilm48wJ7CKi9X5BuMRgNms/G4D5OpYZc0mY6/jtlsbJeZOEVaIywwgBln9wBgwTd7Kag8tWcoT3YMefrQMSTSnC3AxJ8vGYDVbOTbPcW8sjrL2yWJ+CRNEteBzugWwdm9IvlqVxGzV+zghZ8PwXgaTy7Vklk0IyKCTrhcs2iKN/1scDzvbDrA93kV/GXFDp742cBTMnGcZqIV6Vh9ooO4a3xv/vTfHcz/ag9dwwM5LznG22W1SJ3TRWl1HZWOeqocThz1LmrqXdTWu3Ac+rf20HMOp4vaeicOZ8PvAQNgNDScwIsOC2RjdinVDidBFhMhNjM2s1GTZIrCQ0e7a0Jv1u4rYX12Kcs353LZodFaTkeezqJps1moqTn+2VzNoineZjQYuO/8vlz/RgafZxby7qYDTE5N7Pj31Uy0Ih3u0sHx7MivZHFGDg9+9ANRQRaGJYV5uyxq6xtCQUl1HRUOJ1WHHtV1TmrqXThdbp5YkUltfcfdi2U2GoiwBxAbbCE22ErPOCOmDns38VUKDx2sS1ggt57dg2c+38VzX+xiTK9IYoKtHf6+RqOh3boluFzudv2A0TiL5vHY69xUVdW22/uJdIT+cSHMHNuTZ1fu4unPd5HaJYze0Se+YtZeTnYMiUjrGQwG7hjfm7zyWlbuLOT/3tnCnEsHMrJ7xCmroabOSV55LQcrHBysqKW4qo7qupaFgsAAI4EBJmxmIxazEavZhPXQ/21mY8P/Tf/712AAl5umez3qDQa27i+lrLqOSoeTSoeTepeb/AoH+RUOtlLBZ5mFRAUF0D0ikJ6RdiKDLB3x7RAfo/BwClw1rAv/+SGfbbnlPPDhD8ybMqRduh0cT3t2bQB1bxA5nl+mdeG7vcV8t6eY3727hRd/MZTYkI4/OSAiHctkNPDYxf353XtbWbuvhP97dwsP/7Q/53dQF6Zqh5Md+RVs2l/G/pIaSmvqj7lekMVEmM1MiM2MPcCE3WIiMMBEYICRhPBApp7dC2eNA4vRiLkNnwHMZiMREUEs/HxH04kKp8tNeW09BYfCw/+3d9/hUVXpH8C/d3ov6b0BCRBCEoIJSBQUbAj2ruCKBRXrrrrWXRXL7uoPV3Rd29pWd1cQcFVUQCkKS1AQAgnphfQ6vbfz+2OSgdBMSCZleD/Pk0dz783cc2Z4Z+a995z3tFuc6LS40G11o9vqxi9NJkQpRJgYrUBauAxCPk2rDVWUPAwDPo/DMxdm4OZP9mJ3oxFv7zyEu2alBO18QzW0AaDhDYScDI/j8OxFGbjtP8Vo0Ntxz5oDePvabGikwpFuGiFkkCRCPl69fAqe/LocW6q68PhXZdjTaMADs9MgEfIHdYefz+ehQW/DjloddtTqsLvBcMxwI41UgCiFGNFKMcLlQqglQogEJ/5CHq0UI0Erg17P4AnC0CU+j4NGKoRGKsT4yJ67rAI+KlqMOKSzo8Fg77lTokNRvR6ZMUpMiVUOeTvIyKPkYZikhMvw+HkT8OTX5XivqAGToxWYPT4iqOekoQ2EBJ9WJsJrV2bh9v/sQ123Dbf/Zx9evjQTyWGykW4aIWSQRAIeXlwwCa//WIePdzdhTXErdtbpcPusFNxwZirEwv6P+Dc73Ciq1eHHH+rwQ2XnMVUYVRIB4lQSJGoliFVJID5JojBayEQCZEQpkBGlgM3lRWWnFRXtFpicHuxtNuFAqxnTkzS44ozkkW4qGUKUPAyjCyZFYV+zEZ8Vt+Lxr8qw8sos5CVqRrpZJMQN5fwXcnxxagleuyoL9352APU6O27+ZC8eP28CzsuIBMdxg75CCQB6mwv1bXY0l3diQ1kHui2ungoqXnh9/nHKPgbweYCgZ8iCgM8FxjQf+WNweFBU2w3O7YFcyIfqV6qouL0+WJweWF3eI/7rhdXlgd3jg5fHw47KThjsbgh5PIgEHBRiAdQSAbRSIQQ0fIGMYXweh/tnp2FGihbPfluBFpMTz3xTgbf/dwjpUQokqMWIVIihEPMDMeT2+mCwu9FmcqLd7ESTwYFmgx1H3sDncUCSVor81DDcdvY4/FjehjbD2L3gJxPxkROvQnacEvU6O/Y2G9FtdaOoXo+zX9qCJTOScHV23JhIisjJUfIwzH537nh0WlzYVtON364rxcorpyA7fuSrOJDQNNTzX8iJpYXL8eFN0/DoFwdR3GLCE+vLsWpvC24/MxnnZcf3+wql0eZGZYcZle1mVLbVobLdgqoOM7osQ7uexOq9fRe4E/I5KMX+JMLjY3B7Gdw+f2nH3jKOp4LjgEi5CCkRCiSoRAiTCanUIxmTCpK1WLPkDKze14KPfm5Cq9GB1iPu7vM4f6LBGOA5wTBflUSAlHA5ohVCxKklEPF5yIxXIz1aie0V7cPVlaDiOA6p4TKkhEnRaHBgX7MJ7WYnXt1ai0/3NOPus1JwwcSo07p0/VhHycMwE/A4PL9gEh5YV4LdDQbctXo/njw/HfMnRwMY3FXiIxdYIwQY2vkv0WopFuQmDFHLQlOEXIS/XzMVH+xqxEc/N6K4xYR7PjuAiG8rkRIuhVoigFoihJDv/4Lh8Piv6OttLuhtbnRZXbA4vSd+fIUIUxM0sDvd8PkYpEJ/9RQ+jwOP88/B8PoYPD0/bq+/jntvnffeGu8+BohFfOitLpgcHnh7kgWdzX3S/smEfMjFfMhFfCjEgsB/w1USHOq0wOnxweNjcHp8MDs8MDr8FWJ6x0H/BEArFSIzVoHxEXKaUEnGHImQj0VnJOKGMxJwoNOOld9VoEFnh8nh8VcqOiLRFvA4hMuFCJeJEC4XIU4tgUoigEwmPi0qCnIchyStFNOTNdCqZHjp23K0mZ34w9cVWLW3Bb87ZxymxKpGupnkFFDyMALEAh5WXJaJP3xdjq3V3fjjNxXY22TEfbPTkBynOeWrxMcsrEZZPekxFPNf6Ite/wj5PNx+ZjIuzYrBBz81YmN5B7osTnRZ+v9lQSHiQysTIlIlgVLIg1bmn6SYk6TFZdOT8N4P1WjV20+5jTFqCW6dMwF6vRVutxc2txdmhwcmhwcurw9CHg98Pgchj4NIwINCJIBMxD/ue9PxqrL0YozB4vSi1eRAk8mJ+i4r9HY3ttfqsbvBiOx4FSZHK2hYExlzxAI+zpscjYYOE9qMDnh8DA63F14fA8cBEgEfQj5Hd9ngv6hxVV4CZiWq8M+fGvHBrkaUtJpxy7/24eLJUVh2VuqwlLAnQ4eShxEiFfLx50sm480d9Xh/VyM+P9CGbTXdWHbOeMgEHOzO45dpO5HehdWcHi8UUhESwuXY02BAi8EOu9u/eqS756og4M8rOOCIes/+K5gKMR9KsSCwkiQh5NREKcV4ZO54PDxvPMp0Drz/Yw0adP549Hj9XzCEfB6kQh5UEgFUEiFUPXMEeiuqDMcVSo7jIBcJIBcJEDPEFwE5joNSIoBSokBOSjj0JjsqOywobTPD7PRi1yEDSlrNODNVi2StdGhPTsgwEvD883zIiUmEfNxSkISFmdF4Y3s9vixtx/qDHdhc1YVbCpJwQ14CzYcYI+hf+gjicRzuLkzFjBQt/vRdNeq6bXhufRmEfA7xagniVBKEyYVQigWBBVz8Vzd8cHi8sDq9MDo8MDncsLp90Fv9EyiHSu9KkkUNRsTIhYhXS5CgkSJRI0W0Ukzj6AnpByGfh9npkahuMaAtYuxOhhwKYgEPWXEqZMYoUdlpxd4mIywuLzZVdCFRI8GlU2NHuomEkCCLUIjxhwszcGVOHP5vczUOtJrxxvZ6fH6gDQ/MTsOc8eF0x2aUo+RhFJiWoMEni6bhm7IOrC5uRXmbGfU6O+p1pzYsQS7iIyVCDrfHCwHHQSriQ8TnQcTnAsMDGGNgDHB6/WOgnT0Jidl5uJpK70qSmw4eO4lLyOd6SspJexIKf2IRr5YgZoyUmCOEjAwej8PEaAXGR8iwt9mE/S0mNBoceGtHPWLD5bhoQvhIN3HUoypqZKzLjFHiH9fn4NvyDrz2Qx1ajA488sVBTE/S4Hdzxh1eS4KMOpQ8jBJCPg9X5MRhyZzxWP7fA9jfbEKryQmTwwOry9OnvJu4Z6iRTMTvGe4gQKRKCjEPUIkFyE0e/Ljo3pUkBXweJiVoUNFsxCGdDU0GO5qNDri9DIf0dhw6weOHyYSIVUkQoxIjRilBrEqMOK0UGQke2F1eMMboygIhpzkBn4czkjSYECnH9lodWk1OPLGuBBvGhePx8yYgXC4a6SaOSlRFjYQKjuNw0aRozB4XgQ9/bsTHPzdid4MBN/5zD66YGouls1Jo0c1+8DEGvc0Nk8MDm9uLZpMTi88K3vkoeRhlOI5DvEYKPscht2cb66nf7mOsp6rKsR8YQz02unclyRi1BItnpkCvtwZWrPT6GDosTjTq7Wgy2NFocKDJYEeD3o4WowMOjw86mxs6mxulbebjPr6wZ3yoQsxHuNxfiSJCLoJS3P8FdwghoUEjFeLiyVE4ZHBgW3U3fqjpxoEWE544Px2zx9NdiKNRFTUSamQiPu6alYJLp8Rg5Q+1+L6yC58Vt2JjRSfumJmMK7NjqbDCUWwuL+p1NtTr7Gg3O/uUBw72/BtKHsYAjuPA5wA+RsdVJj6PQ6zKvwJmfrK2zz7GGIwOD9pMDrSanGgzO4/4fwc6zC50W11w+xj0djf0djcaj1gUR8TnEK+VIaanBrZWSjXhCTkdcByHGSla/O7Cibj3k19Q1WnFQ/8txRVTY/HgnDRIBrCS7+mCqqiRUBOnluBPCydjT6MB/7elBlWdVry8pQZr9rfid3PGoSBF++sPEuLaTE6UtplRp7OBHTEqRcjnoJUKIRPxEa2SBLUNlDyQIcVx/jsWGqkQE6OVffb1lnR88/sK1HRaYXH6S0N2W13osrqhs7ng8jLUdVlR1+X/G6mQh0SNFGnhMsSpJXSbnpAQNzFGhY8X52Hl1lp8sqcJa/e34pcmA567eBIyohQj3TxCyDDIS9TgnzdNw38PtOKN7fWo67bhnjUHcPa4cNx3diqSw2Qj3cRh1252YnejAS3Gw6NMIuUipIbLkKiVQCMVBkamJAb5+aHkgQw7IZ8XSDCO5PMxdNvc6LJ7UNdpQZvZCbvbh8pOKyo7rRDxOSSHyTA9SQPvCVbvJISMfSIBDw/MScPMFC2e/rYC9To7bvnXXtxzViqumxZPK9MSchrg8zhckR2HeRmReHdnA1bta8EPNd3YXtuN8zIisWRGEtLCQ39StcHuxq5DejTo/XcZeRwwIVKOzBjliM0Lo+SBjBo8HodIhQjJUUpMipTB62NoMztR321Dnc4Gu9uHqk4rqjqt2FrdjfmTo3DxpGgkUn14QkJSQYoW/16ch+UbK/FDTTde2VqLnXV6/PGiDETQZGpCTgsqiRC/PWccLp8ai9d+rMWPNTpsKO/ExvJOzMuIxK0zk5Bx1EiH/vD5GHyj+EKk2+vD3iYTDrSa4GP+tbnSo+TITVBDOcJrilDyQEYtPs+/3kW8WoKZqVq0m52o7fInEq1GB/6xswH/2NmA3HgVFkyJwbz0SMhENC6akFCikQnx8qWTsXZ/K17ZWouiQ3rc8OEe/OHCdBSm0WRqQk4X4yLl+ODWGShrNeG1zVXYUNqOTRWd2FTRiZlp4VhSmIq5E6P6XcLY62Mw6K2jLoFgjKG224aiej2sLi8AIFEjwYwU7aipPEXJAxkTeNzhSdqXZscgIUKFfxXVY2edDnubTdjbbMLLm6sxLz0SC6fEICdeRROtyaDxh2Ay6VA8xumO4zhcmR2H3AQ1nlxfjqpOKx5cV4qFmdG4f3Ya1KPkA5UQEjy9Vcaq20zIT1QjWSPBjhodDrabsbO2GztruxEmE+KMZA1y4tUQnWS9qXClBJflJYLH40ZV8lDdYcEnu5tR120DACjEfJyZoh11czwoeSBjjoDHw8VTY3FmogotejvWH2zHV6XtaNDb8WVpO74sbUeiRoKFU2Iwf3I0opXikW4yGWPkYgF8jEGlGsIhcZTMDlpauBwf3JCLv22vw7/3NOPL0nbsqNPh4XPHY256BF0wIOQ0cGSVsTNTtZgap8TBNgvK2y3Q2dzYUNaJzZVdGBchx8QoOSLkolH/3mBxevDBj434154muL0MfA7IjlchO041KkvUUvJAxrQopRi3FCThN/mJ2N9iwpcl/tuYjQYH3thejzd31CM/WYuFmdE4a1w4pMcp9ziUK7WO9jGUpH8kQj54HIcvfmlEp2lwpTDTopQ4Z3IM5Q5DRCTg4cE543DuhAg8v7EKdTobHvuqDPlJGjxIq9ISctpRiAXIT9YgN0GFqk4rSlrNMDo8KG/3JxThMiEyohUYHyGH+CR3I0aCjzF8VdqOv/1YB53NDcA/GXpaggoqyei9o0rJAwkJHMchO16N7Hg1fnvOOGyu6sQXJe3Y22REUb0eRfV6SAQ8FKaFYV5GJGalhvm/IA7xSq2jdQzlqXB6fLA4PbC4vPB4fTA5PSPdpGHXZXEOuo5+uILufAVDdrwaHy+ahg9+asAHPzXip55VaS/NisHtM5MRSc87IacVIZ+HyTFKTIpWoNXkRHmHBfXdNnTb3PhfnR676vVI0koxLkKOCMXIF1z4pcmAV7fV4WDPYrrJYVI8fckU1LYZB/25E2yUPJCQIxPxsSAzBgsyY9BksOPLkjZ8W96JFqMD31V24bvKrp5EIhxz0sNxUU4CfqzoGPRKraN1DGV/MMbQbXWjyehAl8WJTosLlp6JWr0UYgH+PELtI8Ex1ud0iAQ83HFmCi7OjMbrP9Thu8ourNvfhvWl7bg0KxaLz0hATJAXSyJktBnrcT1YHMchTi1BnFoCR4oX1V02lLdboLe7Uaezo05nx4+1OlTrHDh3XBhyhnlo0L4mI97aeQi7GwwAALmIj1tnJOGm/ERERShR22YctracKkoeSEhL0EhxV2Eq7pyVgrJ2C76v7MR3FZ1oMTnxXWUnvqvsxFPryxGnkiBGJUaiRooI+emxqjVjDC0mJ2q7rGjQO2Bze485RirkQS7iQ8jnIYrmjoSMUJvTEa+W4sWFk3FtkxGv/1iH4hYTVu9rwbr9rbhgUhSuzolDZszASzmGCsYYzE4PdDY39DY3DHY3bG4vnB4flGIBFp81bqSbSIZAqMX1UJAI+ZgSq0RmjAI6mxvVXVbUdNlgdXmxek8TVu9pglIswJmpWsweH44F04NzscHp8WFzVSfWFrdiX7MJACDgcbg0Kwa3zUxGhFw0Kuc2nAglD+S0wHEcJscoMTlGiXvOSsXBdgu2VHVhZ70OlR1WNBsdaDY6sKfRCKmQh/ieqxZxasmI11MeamanB5UdVlR2WmBxHk4YBD2lcaOVYkQqRIiQi/pUqwj2ipVk+ITqnI6cBDXeuS4bexqNeLfoEPY0GrG+tB3rS9sxJVaJy7NjcV5GJJQDHEs8FucyuTw+NOjtgfc2q+vYiwMA4PSMrX6REwvVuB4KHMchXC5CuFyE/CQNXD4GD8fD1/tbYbC7saG8ExvKO/GHryswOUaJaQlq5CWqMTVOfcol4D1eH35pMmJrdTc2lnfA6PAP/eXzOFwyJRq3FCQhdozeGQ2tb0Uj6HS/TTiWcByHzBglMmOUeOCccXDweHj+i1KUtJrQbHDA7vahusuG6i5/qTSVWIA4tTiQTBxv0vVo5/b6UN1pRUWnpc/S9kI+h3HhcqSESxGrkkAwRHM/yNgQinM6OI5DfooW5+cmYH+TAR/tPIT1+1tR0mpGSasZf95UjXMmRuKynHicMzEKkn7E81iZy+T1MTTo7ajpsqJBb4f3iObyOCBMJoRGKoRWJoRCJIBIwKNFNkNQKMb1UOI4DslhUtw6ZwIePmcc9jUasK26C9uqulGns2F/iwn7W0z44KdG8DkgJVyG8ZEKTIiUI04t6bm4JoZUyAuMUrA4PdBZXGg2OlCns+FgmxkHWkxweHyB80YrxbgsKwaXZsWM+TlZlDwM0ulymzCUk6NYtRS5iWrEqsTw+hjazU40Gx1oMTrQaXHB5PTA1OFBeYcVAKCVCnvuTIgRo5KMuuoNvRhjKG014dtttVi9uwnOI97E4tRiZEQqkBImHVO3Sgnpj9568Ic6LZgSLUfq2SnY12zCgRYTOi0ubChtx4bSdgj5HFLDZUiPVGB8pBxKybEfiaN9LpOPMbSZnKjusqKu2wbXERmDRipAklaKeLUEMUrxcWP9dL2jONjPo9H6eTaajabnvPe7m1YjwzkaGc7JigMANHTbUFTXjV21OhTVdqPZYEdNlw01XTZsKBv4eTRSIc4eF4ZzJ0RiRop2yIqzjDRKHgYp1G8Tni7JUS8+7/BEK8B/67/tiGRCZ3NDb/f/lPRUSAiXCRGjEmNSjBJdFidG+r5Em8mBb8s68E1ZB2p7FpoBAIWIj/QoOSZEKqA6zpckQkLNkfXgx4XLkBYmPWbcc2WHFZU9FwYi5CIkaiSIVUsQrRi9Y5AZYyhpNmJTeScOtJj6DEmSi/gYFy7D+Eg5wmSnx/ytgRjyzzR6fn/VaHzOT/TdTSIRweFwYXKUDJOjZDA53Gg3OdFhcaHD7ITZ4YHZ6YHF6YXH578gx5i/oIhGKkCUUoyUMBkmRMqRHa9GWrgMvBD8N0LfIIZIqN4mDPXk6NeIBDwkaaVI6rm1b3d70Wo6nEyYHB5029zotrlR2mbBZ/takRomQ06CCtMS1Jg7VYDhKAjXZnJge60OGys6sbfpcKUGEZ/DhVNioRRykAp49EWCnNaOHvfcbXOjQW9Hg96OTosLXVb/z95mE3gcEKUQY0KUHJMStUhTicDHyMZPXbcNG8o7sKmiEw16e2C7qOcOyvgIOWJU4pD8sjJUhuozbSx+no2U0fycH/3dTeZmsNmcfY5RigVQigUYF378u3QxaglunTMBer0VniPu8IcySh5Iv4RqcjRQUiEfaeEypPW8iVhdHrSZnGgzOdFldaHD4kKdzoY6nQ3r9rfhqa8rEK0UY1K0AhOjFZgYrURGpBzhg1zxstvqwoGecZk76/Wo7rL22Z+XqMaFE6NwweQoJMVq8I+tVaO+bjQhw4njOETI/YUBpiWoYXN50WjwTzBuNTphc3vRZnaizezEjzW7IOBxmBApR2aMElNilZg1CVDzGLggJhRurw/7mo3YUavH/+p0qNMdvpMoFvCQFi5DvFqCJK00ZIZDDJfBfqaFwufZcKPnPHSM6eTB6XTimWeewcaNGyGRSLBkyRIsWbJkpJtFTiNykQDjIgQYFyFHjFqCK/OTsaWkFbsPGbC32YiKDgvazU60m53YWt19xN/xkaCRIlEjRbxGEpjIqJIIIOBx4Dj/VxKby+ufc+HwoNlgR6PBjkM6O9rMfa+M8DhgSqwKs8eF4/yJkYHa9oJROh+DkJMZibHRMhEfGVEKZEQpwBiDyeFBq8kJnd2Nzp4hC2XtFpS1W/BZcSvwbSVkQj5Sey4mpPb8xKr8FcsUA6zS5nB70aC3o15nQ1m7BQdaTChrN/eZwyDgcZiRosVFk6Nx6RlJ+HRnHV0UIIQMuzGdPPzlL39BSUkJPvzwQ7S0tOD3v/894uLicOGFF45008hpSiMTYc6ECBSmhoHjAIlSih0HW1HWZkF5hwXl7WYc0tlhdXlR0WFBRYfllM7DAUgNlyErToW8RDVmpoRBIx29S9kT0h+jZWw0x3FQS4VQS4WIUUuwZPZ4lNZ3Y3+zCaWtZhxsN6G83Qqb24vSNjNKe+Y/HUku4iNaKYZSLIBMxIdMxA9UavP4GDxe5q/QYnNBb3ejy+LC8aZjh8mEmJkahsLUMBQka6GUCCAQ8AacnBBCyFAZs+8+NpsNq1evxjvvvIPMzExkZmaiqqoKn3zyCSUPp4nRWgHqyMdUiAXITwlDfkpYYJvL40NTz12EBp1/mITB7obR7obR4YbXx+D1+auoKER8KCX+8ZYxKv/whCSNFOMj5fTlgYSc0To2muM4JIXLEa+R4qLMaACAUiXFvtou1HZZUdttQ21PtaM2kxNmpwdWl7dPwYL+UEkESNbKMD5ShqlxKmTFqpCkldJcJULIqDJmv32Ul5fD4/EgNzc3sC0vLw9vvvkmfD4feDwarhGqRmsFqBO1S6uVH3NsdKQSeSd5rLFSV56QYBhNY6NP9n4zfUIUpk849m+sTg9ajXa0GZ0wO9ywOD2wubywOP2LRHldHvB5HJQSAcJkQmhlIsSoxNBK+1cdicqEEkJG0phNHjo7O6HVaiESHa5lExERAafTCYPBgLCwsJP8NRnLRmsFqOO1q7fs20CM9rryhJxOfq2k40CMD5fjvKzYoauGRHckCBk1RtM6FsE2ZpMHu93eJ3EAEPjd5er/G3r0IJcGD+u5whWjkkAwyDfyU34sDhCLhXBK+QAbJW061cc6qi8nexwBj4NwsMHaU6FkKPsXaBcHCPgchALeCftyPL2rPA/lsKxotXTQz9VgY2U4hWJcD2W7RmNcD2W7gtG/Pu83pxjbUpE/ESmq7oTROrDE40gxGimyk8MQq5ZAOMgqS6dTXAOj4N8YxXVw2zSYx+pnf44Wp5UN6WiImDHwec0xxsbkpc1vvvkGzz33HHbs2BHYVlNTg/nz52PXrl3QaDQj1zhCCCGEEEJC0Ni5R3KU6Oho6PV6eDyewLbOzk5IJBKoVKoRbBkhhBBCCCGhacwmD5MmTYJAIMC+ffsC2/bs2YOsrCyaLE0IIYQQQkgQjNlv2VKpFJdddhmefvpp7N+/H9999x3ee+89LF68eKSbRgghhBBCSEgas3MeAP+k6aeffhobN26EQqHArbfeit/85jcj3SxCCCGEEEJC0phOHgghhBBCCCHDZ8wOWyKEEEIIIYQML0oeCCGEEEIIIf1CyQMhhBBCCCGkXyh5IIQQQgghhPRLSCUPTqcTjz/+OKZPn47CwkK89957Jzz24MGDuPrqq5GdnY0rr7wSJSUlffZ/9dVXmDdvHrKzs7Fs2TLodLpgN7+PgfRl69atuPTSS5Gbm4uFCxfi+++/77N/+vTpyMjI6PNjtVqD3YWAgfTlrrvuOqatW7ZsCez/4IMPcNZZZyE3NxePP/447Hb7cHShj/72Z9GiRcf0JSMjA4899hgAwGg0HrOvoKBgOLsS4HK5sGDBAuzateuEx4xUzFBcU1wPB4priutTFUpxDYRWbFNcBylmWAh59tln2cKFC1lJSQnbuHEjy83NZd98880xx1mtVjZr1iz2pz/9iVVXV7Ply5ezM888k1mtVsYYY8XFxWzq1Kls3bp1rKysjN10003sjjvuGJV9KSsrY5mZmezDDz9k9fX17OOPP2aZmZmsrKyMMcZYW1sbS09PZw0NDayjoyPw4/P5Rl1fGGPsvPPOY//973/7tNXpdDLGGPv2229ZXl4e27x5MysuLmbz589nzzzzzLD1o1d/+6PX6/v0Y9OmTSwzM5Pt37+fMcbY7t27WX5+fp9jurq6hrs7zOFwsGXLlrH09HRWVFR03GNGMmYorimuhwPFNcV1sPsyFuJ6IP1hbPTHNsV1cGImZJIHq9XKsrKy+jyZf/vb39hNN910zLGrV69m5557biAgfT4fO++889iaNWsYY4w9/PDD7Pe//33g+JaWFpaRkcEaGhqC3Au/gfTlpZdeYrfeemufbUuWLGErVqxgjDG2Y8cONmvWrOA2+CQG0hen08kmTZrEamtrj/tYN9xwA1u5cmXg959//plNnTqV2Wy2oW/4CQykP0fyeDxs/vz57JVXXglsW7VqFbv22muD1dR+qaqqYpdccglbuHDhSd+MRipmKK4Po7gOHopriutTFUpxzVhoxTbFdfBiJmSGLZWXl8Pj8SA3NzewLS8vD8XFxfD5fH2OLS4uRl5eHjiOAwBwHIdp06Zh3759gf3Tp08PHB8bG4u4uDgUFxcHvyMYWF8uv/xyPPTQQ8c8htlsBgBUV1cjNTU1uA0+iYH0pba2FhzHITEx8ZjH8Xq9OHDgQJ/XJScnB263G+Xl5cHrwFEG0p8jrV27FkajEbfffntgW3V1NVJSUoLZ3F/1008/oaCgAJ9++ulJjxupmKG47oviOjgorimuT1UoxTUQWrFNcR28mAmZ5KGzsxNarRYikSiwLSIiAk6nEwaD4Zhjo6Ki+mwLDw9HW1sbAKCjo+Ok+4NtIH0ZN24cJk6cGPi9qqoKO3fuxMyZMwEANTU1sNvtWLRoEQoLC3H77bejrq5uWPoBDKwvtbW1UCgUeOSRR1BYWIirrroK27ZtAwCYTCY4nc4+r4tAIIBGoxm21wUYWH96Mcbw7rvvYvHixZDL5YHtNTU1aGtrw1VXXYWzzjoLDz74IDo6OoLdhT5uuOEGPP7445BKpSc9bqRihuLaj+I6uCiuD6O4HphQimsgtGKb4vqwoY6ZkEke7HZ7n38gAAK/u1yufh3be5zD4Tjp/mAbSF+OpNPpcO+992LatGmYO3cuAH9wG41G3HXXXXjjjTcgkUjwm9/8BhaLJXgdOMJA+lJbWwuHw4HCwkK8++67mD17Nu666y4cOHAADoejz98e+VjD9boAp/ba7Nq1C21tbbjmmmv6bK+trYXFYsFjjz2GV155BR0dHbjzzjvh9XqD0/hBGKmYobimuB4OFNeHUVwPTCjFNRBasU1xfdhQx4xg8M0cHcRi8TEd7/1dIpH069je4060/9eyvaEykL706urqwi233ALGGFauXAkez58X/uMf/4Db7Q5k0C+//DJmz56NLVu2YOHChUHshd9A+nL33Xdj0aJFUKvVAICJEyeitLQUq1atwoMPPtjnb498rOF6XYBTe202bNiAs88+GxqNps/29evXg+O4wN+tXLkShYWFKC4uxrRp04a+8YMwUjFDcU1xPRworg+juB6YUIprILRim+L6sKGOmZC58xAdHQ29Xg+PxxPY1tnZCYlEApVKdcyxXV1dfbZ1dXUFbuOcaH9kZGSQWt/XQPoCAO3t7bjxxhvhcrnw0UcfISwsLLBPJBL1ufUmFouRkJCA9vb24Haix0D6wuPxAm9CvdLS0tDe3g6NRgOxWNzndfF4PDAYDMP2ugADf20A4McffwxcWTqSVCrt8wYWHh4OjUYzbK/NQIxUzFBcU1wPB4rrwyiuByaU4hoIrdimuD5sqGMmZJKHSZMmQSAQBCaEAMCePXuQlZUVyOp7ZWdnY+/evWCMAfCPcfvll1+QnZ0d2L9nz57A8a2trWhtbQ3sD7aB9MVms+G2224Dj8fDxx9/jOjo6MA+xhjmzZuHtWvX9jn+0KFDSEtLC3o/gIH15dFHHw3UVO5VXl6OtLQ08Hg8ZGVl9Xld9u3bB4FA0GcMabANpD+A/9Z0Y2Mj8vLy+my3WCw444wzUFRUFNjW3t4OvV4/bK/NQIxUzFBcU1wPB4priutTFUpxDYRWbFNcBzFm+l2XaQx46qmn2MUXX8yKi4vZpk2b2LRp09iGDRsYY4x1dHQwu93OGGPMbDazGTNmsOXLl7Oqqiq2fPlyNmvWrEAN3F9++YVlZmayVatWBWrgLl26dFT2ZcWKFWzq1KmsuLi4T/1hk8nEGGNs+fLlbM6cOayoqIhVVlayZcuWsQULFjCPxzPq+rJhwwaWmZnJ1q1bx+rr69lrr73Gpk6dyhobGxljjH311Vds2rRpbNOmTay4uJhdfPHFbPny5cPWj4H2hzHGioqKWFZW1nHrdC9dupRdcsklrLi4mJWUlLDrr7+e3XbbbcPWj6MdXfpttMQMxTXF9WjqD2MU10OB4np0xvVA+jMWYpviOjgxE1LJg81mY4888gjLyclhhYWF7P333w/sS09PD9S4Zcy/SMZll13GsrKy2FVXXcVKS0v7PNaaNWvY7NmzWU5ODlu2bBnT6XTD1Q3GWP/7csEFF7D09PRjfnpr+DocDvbiiy+yWbNmsezsbLZ06VLW0tIyKvvCmL+W8vnnn8+mTJnCLr/8cvbTTz/1eay33nqLzZw5k+Xl5bHHHnuMORyO4epGwED6s379+hPW7TYYDOzRRx9lBQUFLDc3lz300EPMYDAEu/kndPSb0WiJGYpriuvhQHFNcX2qQimuB9IfxkZ/bFNcBydmOMZ67msQQgghhBBCyEmEzJwHQgghhBBCSHBR8kAIIYQQQgjpF0oeCCGEEEIIIf1CyQMhhBBCCCGkXyh5IIQQQgghhPQLJQ+EEEIIIYSQfqHkgRBCCCGEENIvlDyEsNdeew2LFi3C2rVrce655wa2f/rpp5gxYwZyc3NRVVWFp556Cjk5OZg7d+4ItjZ0fPPNN+ju7h7pZpAQRXE9MiiuSbBRbI8Miu2BE4x0A0jwzZ8/H3PmzAn8/tJLL2Hx4sW48sorodfrsWrVKrz99tvIyMgYuUaGiObmZjzwwAP4/vvvR7opJMRRXA8fimsynCi2hw/F9qmh5OE0IJFIIJFIAr+bzWbk5+cjPj4ezc3NAICzzz4bHMeNVBNDBi3YToYLxfXwobgmw4lie/hQbJ8aGrYUQqqrq3H99dcjOzsbixcvhl6vB4A+t0B7r1TcfPPNWLRoERYtWgQAmDhxIl577bVfPceiRYvw+uuvB85zww03oKamJrC/ra0N999/P/Lz81FQUIDnnnsOLpcLAOB2u/Hkk0+ioKAAubm5uPPOO9He3t6vvv3aeVtbW3HnnXciOzsb5557Ll5//XV4vd5A/6+77josW7YMeXl5+OKLL+DxeLBixQoUFhYiLy8P9913X+D5crlceO6551BQUICCggI89NBDMBgMAICmpiZkZGRg48aNmDdvHrKysrB06dLA/t7byHPnzsXatWv71TdCTobimuKahCaKbYrtsYqShxDhcrlwxx13IDExEWvXrsUFF1yATz/99Jjjtm/fDsA/tvKvf/1r4M1n+/btWLJkSb/O9dZbb+GCCy7A2rVrER0djTvuuAMulwsulws333wz7HY7/vnPf+Kvf/0rtm7dir/85S8AgE8++QQ///wz3nvvPXz22WewWq144YUX+t3HE52XMYZ77rkH4eHhWLduHV588UV8+eWXePPNNwN/u3fvXowfPx6rVq1CYWEhXn31Vaxbtw4vvPACPv30U3R3d+OPf/wjAGDFihUoKSnBO++8g48++ggWiwX3339/n7a8+eabWLFiBT7++GMcOHAA77//PgBg9erVgf/Onz+/330j5HgorimuSWii2KbYHtMYCQlbtmxhubm5zGq1Brbdd9997KabbmJr1qxh55xzTmB7eno6KyoqYowxVlRUxNLT0/t9nptuuondfffdgd/NZjPLyclhmzdvZt999x3Lzs5mBoMhsH/btm1s8uTJzGKxsOXLl7OFCxcyvV7PGGOsqamJlZSUDPq8//vf/9iMGTOY1+sN7P/+++9Zfn4+Y4yxNWvWsIyMDGa32xljjPl8Ppafn8/WrFkTOL6qqoqtXLmS2Ww2lpmZycrLywP7jEYjmzhxIisvL2eNjY0sPT2dbdmyJbD/hRdeYLfccgtjjAX2NzY29qtfhJwMxTXFNQlNFNsU22MZzXkIEdXV1UhJSYFMJgtsy8rKwrZt24b8XNOmTQv8v0KhQGpqKmpqauDz+ZCSkgK1Wt3nWI/Hg4aGBlx77bVYv349CgsLkZ+fj3nz5uGKK64Y9HklEgkMBgPy8vIC+30+HxwOR+C2Znh4eGAMqV6vh8FgQGZmZuD48ePH495770VlZSXcbjeuu+66Puf2+Xyor68P/E1ycnKftrjd7n73g5D+orimuCahiWKbYnsso+QhhLCjJv4IhcKgnEcg6PvPxuv1gsfjHfd8vWMYvV4vJk2ahM2bN2Pr1q3YunUrVqxYga+++gqffPJJvyZ+nei8Ho8HaWlpeOONN475G6VSCQAQi8UnfJzjtfdf//pXnzd1wP9m1jtOMljPLSFHo7imuCahiWKbYnusojkPIWLChAmor6+H2WwObCsrKwvKucrLywP/bzab0dDQgIyMDKSmpqK+vj4QrACwb98+CAQCJCUl4fPPP8eWLVtw0UUX4c9//jPeffdd7Nmzp9/1lU923paWFoSFhSE5ORnJycloamrCypUrj/sGp1KpoNVq+zxeWVkZzj77bCQmJoLP58NgMAQeS6FQ4MUXX+xXO6n6BRlKFNcU1yQ0UWxTbI9llDyEiDPPPBOxsbF44oknUFNTg7Vr1+Lrr78Oyrm+/PJLfP7556ipqcETTzyBuLg4FBQUYNasWUhMTMQjjzyCiooKFBUVYfny5ViwYAFUKhXMZjOef/557Ny5E42Njfjyyy8RExMDrVY7qPMWFhYiPj4eDz/8MCoqKrB792489dRTkEql4PP5x32sRYsW4dVXX0VRURGqqqrw/PPPIycnBwqFAldffTWefvpp7Nq1C9XV1XjkkUdw6NAhJCQk/GobpVIpAP+bptVq7f+TSshxUFxTXJPQRLFNsT2WUfIQIoRCId566y0YjUZcfvnl+Pe//40bb7wxKOdauHAh/vOf/+CKK66A1WrFO++8A4FAAD6fH7gNec011+C3v/0t5s6di2effRYAcOONN+Kyyy7Dww8/jPnz5+PgwYP4+9//fsI3i4Gc9+9//zt8Ph+uueYa3HvvvZg9ezaefPLJEz7WHXfcgfPPPx8PPPAArr/+esTExGD58uUAgEcffRQzZ87Efffdh2uuuQYCgQBvv/12v9oZFhaGSy65BA888ECgigMhp4rimuKahCaKbYrtsYxjRw+6I+QkFi1ahPz8fNx7772nxXkJOR1QXBMSmii2STDQnQdCCCGEEEJIv1C1JRL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GNvi63n//fTp06MAHH3xQ7//9Qw891OBjOaK2thY4eifB19eXDz74gKqqKubPn1/vbgYc/dnOmjWL77//nnPOOed3j11XV8fixYvx8fGxb//MM8/UuyP3e3NLBAcHk5SUVG9Er/j4eGpra9m/f789qByzb98+6urqTnvSxl9fv4i0LXqGQkQaZcKECVgsFh5++OHjvv03DIP8/PzfPcZll11GXl4eL7300nHrjh1z4sSJ1NXV8eijjx63TW1t7UlnFz6VY7/0ns6+7dq1o0+fPrz55pv1tt+2bRtffPEFF110UYPPD3DRRRfxww8/sH79evuy3Nxcl84q/OGHH3LkyBH7+/Xr17Nu3Tp7ADjdn0VdXd1xQ5hGREQQHR19yn76FouFUaNG8dFHH9WbxTw7O5vFixdzzjnnODS06bG7Vr/+e7pu3TrWrl3b4GM1VEFBAWvWrCEqKsrebe+dd96hQ4cO3HrrrVx++eX1Xn/5y1/w8/M7rb8HdXV13H777ezcuZPbb7/d/rPp378/I0eOtL+6desGwObNm+0jMP3aoUOH2LFjR72uZsf+n5+obc6bN6/eNr/nk08+AaB3796ntb2ItB66QyEijZKUlMRjjz3GzJkzSU1NZfz48fj7+3Pw4EGWLl3Kn/70p3qzOJ/I9ddfz1tvvcVdd93F+vXrOffccykvL2flypXcdtttXHLJJQwbNoxbbrmFOXPmsGnTJkaNGoW7uzt79+5lyZIlzJ07t95EWqejT58+WCwWnnzySYqLi/H09GTEiBEnfU7j6aefZsyYMaSkpHDjjTfah0oNDAxk9uzZDTr3Mffccw9vv/02F154IX/+85/tw8bGx8ezZcsWh47ZWB07duScc85h6tSpWK1Wnn/+eUJDQ7nnnnvs25zOz6K0tJSYmBguv/xyevfujZ+fHytXrmTDhg0888wzp6zhscceY8WKFZxzzjncdtttuLm58Y9//AOr1cpTTz3l0HWNGzeODz74gEsvvZSxY8dy8OBBXnnlFbp16+b0b83ff/99/Pz8MAyDjIwMFixYQGFhIa+88gomk4mMjAy++uorbr/99hPu7+npyejRo1myZAkvvPCC/c5dcXEx77zzDnB0UshjM2Xv37+fSZMmnTBw/9aKFSt46KGH+MMf/sDgwYPx8/PjwIEDvP7661it1np/l/v06cNNN93E3Llz2bt3LxdccIH9GMuWLeOmm246YUD49ttvqaqqAo6Gqf/+97+sXr2aSZMm0aVLlwb9LEWkFXDV8FIi0vIdGypzw4YNv7vtf/7zH+Occ84xfH19DV9fX6NLly7GtGnTjN27d9u3OdWwlxUVFcb9999vJCYmGu7u7kZUVJRx+eWX1xs21DAM49VXXzX69+9veHt7G/7+/kbPnj2Ne+65x8jIyLBvEx8fb4wdO/a4c5xo2NXXXnvN6NChg2GxWE5rCNmVK1caQ4YMMby9vY2AgADj4osvNnbs2FFvm4YMG2sYhrFlyxZj2LBhhpeXl9G+fXvj0UcfNRYsWOCyYWOffvpp45lnnjFiY2MNT09P49xzzzU2b9583Pa/97OwWq3G3XffbfTu3dvw9/c3fH19jd69exsvv/zyadXz008/GaNHjzb8/PwMHx8f47zzzjPWrFnj8LXabDbjiSeeMOLj4w1PT0+jb9++xieffGJMnjzZiI+Pr7ctThw21tfX10hJSTH+/e9/27d75plnDMBYtWrVSY+1cOFCAzA++ugjwzD+N+zysZefn5/RqVMn49prrzW++OKL373+Yw4cOGA8+OCDxuDBg42IiAjDzc3NCA8PN8aOHWt8+eWXx21fV1dnzJ071+jdu7fh5eVleHl5Gb179zZeeOEFo66urt62Jxo21sPDw+jSpYvx+OOPG9XV1addp4i0HibD0BOKIiJydKbsxMREnn766d+9qyQiInKMnqEQERERERGH6RkKEZE2rq6ujtzc3FNu89vhfUVERE6XAoWISBuXnp7+uxO3PfTQQ0yZMqV5ChIRkTZFz1CIiLRxVVVVfPfdd6fcpkOHDqecvVpERORkFChERERERMRheihbREREREQcpkAhIiIiIiIOU6AQERERERGHKVCIiIiIiIjDFChERERERMRhChQiIiIiIuIwBQoREREREXGYAoWIiIiIiDhMgUJERERERBymQCEiIiIiIg5ToBAREREREYcpUIiIiIiIiMMUKERERERExGEKFCIiIiIi4jAFChERERERcZgChYiIiIiIOEyBQkREREREHKZAISIiIiIiDlOgkBbJZDIxffp0V5chIiIiwMKFCzGZTKSmprq6FGmBFChEREREpEFefvllFi5c6OoypIVQoBARERGRU7ruuuuorKwkPj4eUKCQ+hQopE2pqqrCZrO5ugwROU3l5eWuLkFEToPFYsHLywuTyeTqUqQFUqCQZjVlyhQSEhKOWz579uzf/ZB67LHHMJvNvPjiiwB8/fXXmEwm3n33XWbNmkX79u3x8fGhpKSkKUoXkZM4dOgQt912G8nJyXh7exMaGsoVV1xxXF/rY32wV69ezW233UZERAQxMTENOoaIuMavn6FISEhg+/btrF69GpPJhMlkYvjw4QAUFBTwl7/8hZ49e+Ln50dAQABjxoxh8+bNrr0AaVJuri5A5HTMmjWLJ554gn/84x/cfPPN9dY9+uijeHh48Je//AWr1YqHh4eLqhQ5M23YsIE1a9YwadIkYmJiSE1NZf78+QwfPpwdO3bg4+NTb/vbbruN8PBwHnzwQfsdioYeQ0Rc5/nnn2fGjBn4+flx//33AxAZGQnAgQMH+PDDD7niiitITEwkOzubf/zjHwwbNowdO3YQHR3tytKliShQSIv3l7/8heeee4433niDyZMnH7e+qqqKH3/8EW9vbxdUJyJjx47l8ssvr7fs4osvJiUlhf/85z9cd9119daFhISwatUqLBaLw8cQEdcZP348s2bNIiwsjGuvvbbeup49e7Jnzx7M5v91grnuuuvo0qULCxYs4IEHHmjucqUZqMuTtFiGYTB9+nTmzp3LO++8c8IwATB58mSFCREX+nX7q6mpIT8/n44dOxIUFMRPP/103PY333xzvTDhyDFEpGXy9PS0h4m6ujry8/Px8/MjOTlZbbkN0x0KabHeeustysrKmD9/PlddddVJt0tMTGzGqkTktyorK5kzZw5vvPEGR44cwTAM+7ri4uLjtj9Rm23oMUSkZbLZbMydO5eXX36ZgwcPUldXZ18XGhrqwsqkKSlQSLM62YPXv/7AOWbIkCFs2rSJl156iYkTJxISEnLCfXV3QsS1ZsyYwRtvvMEdd9xBSkoKgYGBmEwmJk2adMJR107UZht6DBFpmZ544gkeeOAB/vjHP/Loo48SEhKC2WzmjjvuUFtuwxQopFkFBwdTVFR03PJDhw4dt6xjx4489dRTDB8+nAsvvJBVq1bh7+/fDFWKSEO8//77TJ48mWeeeca+rKqq6oRtvSmPISLN52RfEL7//vucd955LFiwoN7yoqIiwsLCmqM0cQE9QyHNKikpieLiYrZs2WJflpmZydKlS0+4fa9evVi2bBk7d+7k4osvprKysrlKFZHTZLFY6nVRAnjxxRdPeOexKY8hIs3H19f3hIH/RG15yZIlHDlypJkqE1fQHQppVpMmTeLee+/l0ksv5fbbb6eiooL58+fTuXPnkz6sNXjwYD766CMuuugiLr/8cj788EPc3d2buXIROZlx48bx9ttvExgYSLdu3Vi7di0rV65sUH9pZxxDRJpP//79mT9/Po899hgdO3YkIiKCESNGMG7cOB555BFuuOEGzj77bLZu3cqiRYvo0KGDq0uWJqRAIc0qNDSUpUuXctddd3HPPfeQmJjInDlz2Lt37ylHfxgxYgT//ve/ueyyy7juuutYvHhxM1YtIqcyd+5cLBYLixYtoqqqiiFDhrBy5UpGjx7drMcQkebz4IMPcujQIZ566ilKS0sZNmwYI0aM4K9//Svl5eUsXryY9957j379+vHpp59y3333ubpkaUIm47f3pURERERERE6TnqEQERERERGHKVCIiIiIiIjDFChERERERMRhChQiIiIiIuIwBQoREREREXGYAoWIiIiIiDhM81AANpuNjIwM/P39TzqVvIj8j2EYlJaWEh0djdncMr+XULsWaRi1a5G2p7natQIFkJGRQWxsrKvLEGl10tPTiYmJcXUZJ6R2LeIYtWuRtqep27UCBeDv7w8c/WEHBAS4uBqRlq+kpITY2Fh722mJ1K5FGkbtWqTtaa52rUAB9tumAQEB+oASaYCW3OVA7VrEMWrXIm1PU7frltlJUkREREREWgUFChERERERcZgChYiIiIiIOEyBQkREREREHKZAISIiIiIiDlOgEBERERERhylQiIiIiIiIwxQoRERERETEYQoUIiIiIiLiMAUKERERERFxmAKFiIiIiIg4TIFCREREREQcpkAhIiIiIiIOU6AQERERERGHKVCIiIiIiIjDFChERERERMRhChQiIiIiIuIwBQoREREREXGYAoWIiIiIiDhMgUJERERERBymQCEiIiIiIg5ToBAREREREYcpUIiIiIiIiMMUKERERERExGEuDRTz58+nV69eBAQEEBAQQEpKCp999pl9fVVVFdOmTSM0NBQ/Pz8uu+wysrOz6x0jLS2NsWPH4uPjQ0REBHfffTe1tbXNfSkiIiIiImckN1eePCYmhr/97W906tQJwzB48803ueSSS/j555/p3r07d955J59++ilLliwhMDCQ6dOnM2HCBL7//nsA6urqGDt2LFFRUaxZs4bMzEyuv/563N3deeKJJxpcz86dO/Hz82vQPsHBwURHRzf4XCIiIiIibYHJMAzD1UX8WkhICE8//TSXX3454eHhLF68mMsvvxyAXbt20bVrV9auXcvgwYP57LPPGDduHBkZGURGRgLwyiuvcO+995Kbm4uHh8dpnbOkpITAwECH6vXz82f37l0KFXJGOdZmiouLCQgIcHU5J9QaahRpSVpDm2kNNYq0JM3VZlx6h+LX6urqWLJkCeXl5aSkpLBx40ZqamoYOXKkfZsuXboQFxdnDxRr166lZ8+e9jABMHr0aKZOncr27dvp27dvg2q49r6/E92hy2lvn5dxiNdnT6OwsFCBQkRERETOSC4PFFu3biUlJYWqqir8/PxYunQp3bp1Y9OmTXh4eBAUFFRv+8jISLKysgDIysqqFyaOrT+27mSsVitWq9X+vqSkBIDQdnFEJXRyxmWJSDM7WbsWkdZL7VqkdXD5KE/Jycls2rSJdevWMXXqVCZPnsyOHTua9Jxz5swhMDDQ/oqNjW3S84lI01O7Fml71K5FWgeXBwoPDw86duxI//79mTNnDr1792bu3LlERUVRXV1NUVFRve2zs7OJiooCICoq6rhRn469P7bNicycOZPi4mL7Kz093bkXJSLNTu1apO1RuxZpHVweKH7LZrNhtVrp378/7u7urFq1yr5u9+7dpKWlkZKSAkBKSgpbt24lJyfHvs2KFSsICAigW7duJz2Hp6enfajaYy8Rad3UrkXaHrVrkdbBpc9QzJw5kzFjxhAXF0dpaSmLFy/m66+/5vPPPycwMJAbb7yRu+66i5CQEAICApgxYwYpKSkMHjwYgFGjRtGtWzeuu+46nnrqKbKyspg1axbTpk3D09PTlZcmIiIiTWTTpk0NHub9RMLCwoiLi3NCRSJnNpcGipycHK6//noyMzMJDAykV69efP7551xwwQUAPPfcc5jNZi677DKsViujR4/m5Zdftu9vsVj45JNPmDp1KikpKfj6+jJ58mQeeeQRV12SiIiINLFhw4Y55TjePj7s2rlToUKkkVwaKBYsWHDK9V5eXsybN4958+addJv4+HiWLVvm7NJERESkhZp4x6PEdOreqGNkp+1n0ZN3k5eXp0Ah0kguHzZWREREpCHCYxIbHShExHla3EPZIiIiIiLSeihQiIiIiIiIwxQoRERERETEYQoUIiIiIiLiMAUKERERERFxmAKFiIiIiIg4TIFCREREREQcpkAhIiIiIiIOU6AQERERERGHKVCIiIiIiIjDFChERERERMRhChQiIiIiIuIwBQoREREREXGYAoWIiIiIiDhMgUJERERERBymQCEiIiIiIg5ToBAREREREYcpUIiIiIiIiMMUKERERERExGEKFCIiIiIi4jAFChERERERcZgChYiIiIiIOEyBQkREREREHKZAISIiIiIiDlOgEBERERERhylQiIiIiIiIwxQoRERERETEYQoUIiIiIiLiMAUKERERERFxmAKFiIiIiIg4TIFCREREREQcpkAhIiIiIiIOU6AQERERERGHKVCIiIiIiIjDFChERERERMRhChQiIiIiIuIwBQoREREREXGYAoWIiIiIiDhMgUJERERERBymQCEiIiIiIg5ToBAREREREYcpUIiIiIiIiMMUKERERERExGEKFCIiIiIi4jAFChERERERcZgChYiIiIiIOEyBQkREREREHObm6gJERJrSpk2b8PPza9QxwsLCiIuLc1JFIiIibYtDgaJDhw5s2LCB0NDQesuLioro168fBw4cOK3jzJkzhw8++IBdu3bh7e3N2WefzZNPPklycrJ9m6qqKv7v//6Pd999F6vVyujRo3n55ZeJjIy0b5OWlsbUqVP56quv8PPzY/LkycyZMwc3N+UlkTPdsGHDGn0Mbx8fdu3cqVAhIiJyAg79xp2amkpdXd1xy61WK0eOHDnt46xevZpp06YxYMAAamtr+etf/8qoUaPYsWMHvr6+ANx55518+umnLFmyhMDAQKZPn86ECRP4/vvvAairq2Ps2LFERUWxZs0aMjMzuf7663F3d+eJJ55w5PJEpA2ZeMejxHTq7vD+2Wn7WfTk3eTl5SlQiIiInECDAsV///tf+39//vnnBAYG2t/X1dWxatUqEhISTvt4y5cvr/d+4cKFREREsHHjRoYOHUpxcTELFixg8eLFjBgxAoA33niDrl278sMPPzB48GC++OILduzYwcqVK4mMjKRPnz48+uij3HvvvcyePRsPD4+GXKKItDHhMYmNChQiIiJyag0KFOPHjwfAZDIxefLkeuvc3d1JSEjgmWeecbiY4uJiAEJCQgDYuHEjNTU1jBw50r5Nly5diIuLY+3atQwePJi1a9fSs2fPel2gRo8ezdSpU9m+fTt9+/Y97jxWqxWr1Wp/X1JS4nDNItIyqF2LtD1q1yKtQ4NGebLZbNhsNuLi4sjJybG/t9lsWK1Wdu/ezbhx4xwqxGazcccddzBkyBB69OgBQFZWFh4eHgQFBdXbNjIykqysLPs2vw4Tx9YfW3cic+bMITAw0P6KjY11qGYRaTnUrkXaHrVrkdbBoWFjDx48SFhYmFMLmTZtGtu2bePdd9916nFPZObMmRQXF9tf6enpTX5OEWlaatcibY/atUjr4PAwSKtWrWLVqlX2OxW/9vrrrzfoWNOnT+eTTz7hm2++ISYmxr48KiqK6upqioqK6t2lyM7OJioqyr7N+vXr6x0vOzvbvu5EPD098fT0bFCNItKyqV2LtD1q1yKtg0N3KB5++GFGjRrFqlWryMvLo7CwsN7rdBmGwfTp01m6dClffvkliYmJ9db3798fd3d3Vq1aZV+2e/du0tLSSElJASAlJYWtW7eSk5Nj32bFihUEBATQrVs3Ry5PREREREROk0N3KF555RUWLlzIdddd16iTT5s2jcWLF/PRRx/h7+9vf+YhMDAQb29vAgMDufHGG7nrrrsICQkhICCAGTNmkJKSwuDBgwEYNWoU3bp147rrruOpp54iKyuLWbNmMW3aNH2rISIiIiLSxBwKFNXV1Zx99tmNPvn8+fMBGD58eL3lb7zxBlOmTAHgueeew2w2c9lll9Wb2O4Yi8XCJ598wtSpU0lJScHX15fJkyfzyCOPNLo+ERERERE5NYcCxU033cTixYt54IEHGnVywzB+dxsvLy/mzZvHvHnzTrpNfHw8y5Yta1QtIiIiIiLScA4FiqqqKl599VVWrlxJr169cHd3r7f+2WefdUpxIiIiIiLSsjkUKLZs2UKfPn0A2LZtW711JpOp0UWJiIiIiEjr4FCg+Oqrr5xdh4iIiIiItEIODRsrIiIiIiICDt6hOO+8807ZtenLL790uCAREREREWk9HAoUx56fOKampoZNmzaxbds2Jk+e7Iy6RERERESkFXAoUDz33HMnXD579mzKysoaVZCIiIiIiLQeTn2G4tprr+X111935iFFRERERKQFc2qgWLt2LV5eXs48pIiIiIiItGAOdXmaMGFCvfeGYZCZmcmPP/7Y6NmzRURERESk9XAoUAQGBtZ7bzabSU5O5pFHHmHUqFFOKUxERERERFo+hwLFG2+84ew6RERERESkFXIoUByzceNGdu7cCUD37t3p27evU4oSEREREZHWwaFAkZOTw6RJk/j6668JCgoCoKioiPPOO493332X8PBwZ9YoIiIiIiItlEOjPM2YMYPS0lK2b99OQUEBBQUFbNu2jZKSEm6//XZn1ygiIiIiIi2UQ3coli9fzsqVK+natat9Wbdu3Zg3b54eyhYREREROYM4dIfCZrPh7u5+3HJ3d3dsNlujixIRERERkdbBoUAxYsQI/vznP5ORkWFfduTIEe68807OP/98pxUnIiIiIiItm0OB4qWXXqKkpISEhASSkpJISkoiMTGRkpISXnzxRWfXKCIiIiIiLZRDz1DExsby008/sXLlSnbt2gVA165dGTlypFOLExERERGRlq1Bdyi+/PJLunXrRklJCSaTiQsuuIAZM2YwY8YMBgwYQPfu3fn222+bqlYREREREWlhGhQonn/+eW6++WYCAgKOWxcYGMgtt9zCs88+67TiRERERESkZWtQoNi8eTMXXnjhSdePGjWKjRs3NrooERERERFpHRoUKLKzs084XOwxbm5u5ObmNrooERERERFpHRoUKNq3b8+2bdtOun7Lli20a9eu0UWJiIiIiEjr0KBAcdFFF/HAAw9QVVV13LrKykoeeughxo0b57Timtt3mZBdcvy1iYiIiIjIiTVo2NhZs2bxwQcf0LlzZ6ZPn05ycjIAu3btYt68edTV1XH//fc3SaHNIacK3t94mIt7RxMX4uPqckREREREWrwGBYrIyEjWrFnD1KlTmTlzJoZhAGAymRg9ejTz5s0jMjKySQptDhFekGczWLY1k6sHxhHgffLnRURERERExIGJ7eLj41m2bBmFhYXs27cPwzDo1KkTwcHBTVFfs0qJgg3lXmSVVLFiRzYT+rXHZDK5uiwRERERkRarQc9Q/FpwcDADBgxg4MCBbSJMAFhMcGGPKNzMJg4XVbI/t9zVJYmIiIiItGgOB4q2KtDbnX5xRwPS9/vysP3SrUtERERERI6nQHEC/eOD8XQzU1RZw/6cMleXIyIiIiLSYilQnICHm5nesUEA/Hio0LXFiIiIiIi0YAoUJ9EnJgiLyUROqZUczU0hIiIiInJCChQn4e1hISnCF4BtGSUurkZEREREpGVSoDiF7tGBAOzOKqWmzubiakREREREWh4FilOIDfYm0Nud6jobe7P1cLaIiIiIyG8pUJyCyWSie3QAANsyil1cjYiIiABszLfww4F8PeMo0kIoUPyObu0CMAGZxVUUV9a4uhwREZEz3pFKC+sOFvCvDeks/fkIRRXVri5J5IymQPE7fD3daB/sDcDe7FIXVyMiIiLdAmvpGOGH2QRpBRUsWpfGPs0bJeIyChSnoXOkPwB79GElIiLich39bYzt2Y7rUxKICfKm1mbw6dZMdmfpiz8RV1CgOA0dw49+C5JbaqVQt1VFRERahEBvdy7t254evzzv+MWOLFLzyl1clciZR4HiNHh7WIgN8QFgj7o9iYiItBhms4kRXSJIjvTHZsCnWzPJKdXD2iLNSYHiNNm7PWn4WBERkRbFZDJxQbdI4kN9qLUZfLY1i+pazR8l0lwUKE5TUrgvFpOJgvJq8sqsri5HREREfsViNjG6exR+nm4UVdbw1e4cDMNwdVkiZwQFitPk6WYhPvRotydNciciItLyeLtbuLBHFCYT7MoqVa8CkWbi5uoCWpPOkf4cyCtnd3YpgzuENPp4GRkZFBYWOrRvcHAw0dHRja5BRESkLWkf5M3AhBDWHSxg9Z5c4kJ98Ha3uLoskTZNgaIBEsN8cTObKK6sIae0cd2eMjIySE7uQlmZYw95+/n5s3v3LoUKERGR3xiQEMLenDIKyqv5dm8uo7pFubokkTbNpYHim2++4emnn2bjxo1kZmaydOlSxo8fb19vGAYPPfQQr732GkVFRQwZMoT58+fTqVMn+zYFBQXMmDGDjz/+GLPZzGWXXcbcuXPx8/Nzer0ebmYSw3zZm1PGnuxSOrk7fqzCwkLKykr54+x5hEXHN2jfvIxDvD57GoWFhQoUIiIiv2Exmzi/SwRLNh5mZ2YpXaMC7KM1iojzuTRQlJeX07t3b/74xz8yYcKE49Y/9dRTvPDCC7z55pskJibywAMPMHr0aHbs2IGXlxcA11xzDZmZmaxYsYKamhpuuOEG/vSnP7F48eImqblzpP8vgaKMju0bf7yw6HiiEjr9/oYiIiJy2qKDvOnZPpCtR4pZvTeXqwfEYTabXF2WSJvk0kAxZswYxowZc8J1hmHw/PPPM2vWLC655BIA3nrrLSIjI/nwww+ZNGkSO3fuZPny5WzYsIGzzjoLgBdffJGLLrqIv//9703y7X1CqA8eFjNl1lryNMy1iIhIi5WSFMqe7FLyy6rZnlFCz5hAV5ck0ia12GcoDh48SFZWFiNHjrQvCwwMZNCgQaxdu5ZJkyaxdu1agoKC7GECYOTIkZjNZtatW8ell156wmNbrVas1v89A1FSUnLadblZzCRF+LIzs5Q0DR4h0mI0pl2LtGZpaWnk5eU1+jhlZS3vH7XGtmtvdwuDO4Syek8uaw/k0znSD089oC3idC02UGRlZQEQGRlZb3lkZKR9XVZWFhEREfXWu7m5ERISYt/mRObMmcPDDz/scG2dI/3ZmVlKejlg0si7Ii1BY9u1SGuUlpZGl65dqayocHUpTcIZ7bpn+0C2Hi6moKKa9akFnNsp3EnVicgxLTZQNKWZM2dy11132d+XlJQQGxt72vvHBvvg5W6mqsaGV1zPpihRRBqose1apDXKy8ujsqKCa+59msi4pEYd6/De7fz7+QecVJlzOKNdW8wmzu0cxkebMtiUXkSP9oEE+3g4u1SRM1qLDRRRUUeHeMvOzqZdu3b25dnZ2fTp08e+TU5OTr39amtrKSgosO9/Ip6ennh6ejpcm8VsomOEH9uOlODTdajDxxER52lsuxbncFb3G4CwsDDi4uKccqy2LjIuiZhO3Rt1DGtly7vL4ax2nRDqS3yoD4fyK/hubx4X99YIiSLO1GIDRWJiIlFRUaxatcoeIEpKSli3bh1Tp04FICUlhaKiIjZu3Ej//v0B+PLLL7HZbAwaNKhJ60uO9D8aKJKHUFNnNOm5RERaA2d3v/H28WHXzp0KFeIUQzuF807BIQ7klXO4sOWFJ5HWzKWBoqysjH379tnfHzx4kE2bNhESEkJcXBx33HEHjz32GJ06dbIPGxsdHW2fq6Jr165ceOGF3HzzzbzyyivU1NQwffp0Jk2a1OTzM0QHeeNtgUovP35Ir6BPryY9nYhIi+fM7jfZaftZ9OTd5OXlKVCIU4T4etAj+ugwst/uzeOcIFdXJNJ2uDRQ/Pjjj5x33nn298f6SU6ePJmFCxdyzz33UF5ezp/+9CeKioo455xzWL58uX0OCoBFixYxffp0zj//fPvEdi+88EKT1242mUgMgB2FsHxvKbc2+RlFRFoHZ3S/EWkKgzuEsDurlJxSK+keGlRFxFlcGiiGDx+OYZy8u5DJZOKRRx7hkUceOek2ISEhTTaJ3e/p4A/bC2xsyqoiLb+CuFDNwikiItJS+Xi4cVZCMGv257OtyILJTQ9niziD4nkj+LpDVeomAN77Mc21xYiIiMjv6hsbhL+XG5V1JvzPusTV5Yi0CS32oezWomzz53gn9mPJj4e5Y2Rn3C3KaCIiIi2Vm8XM2UmhfL49m8DBV1BUVefqklo8jd4mv0eBopEq9q4jyMtMTqmVT7dkMr5ve1eXJCIiIqeQHOnP+r2ZFOLDe9vLGHG2qytquTR6m5wOBYrGstVycZcA3t5UxLyv9vGH3tGYzSZXVyUiIiInYTKZ6BlUxzc5ZlYcqGBfTikdI/xdXVaLpNHb5HQoUDjBxckBfLirjL05ZXyxI4sLe7T7/Z0aqLbORmFFDUUV1eSVgG+34fycUUl4bBXh/p6YTAoxIifybY4b2zdnEObnQXSQN3HBPgr9IkK4l0HFnrX4dE5hzrJdLJgywNUltWgavU1ORYHCCXw9zExOSeClr/bx0lf7GN09yim/4NfZDPbllLE3p5TU/ArqbP8bESvs4r8wa1U2s1ZlExXgxfldI7iwRxRDksL0y5LIrxRWmynOK+dgXjlQiI+HhR7RgfSLD8LTzeLq8kTEhQq/fgP/5BRW7cphzb48zu4Y5uqSRFolPUHsJH88JxFvdwvbjpSwfFtWo45VZzPYlF7EwjWpLN+exf7ccupsBp5uZqICvIjyhsrUTbQPcMNsgqySKhatS+O6BesZ/vev+cfq/ZRW1TjpykRatwGhNQzvHE736AC83S1UVNexPrWAhWtS2ZxedMqhq0WkbastzGB00tEh32d/vJ3qWpuLKxJpnXSHwklCfD246dxEXvxyH499upNhyeH4eDT8x5tRVMmXu3LIL68GsH+b2jHCjzA/D0wmE1mpe1k3exZfPrCNDp268MOBfL7YkcUnWzJJK6hgzme7mPfVPm4YksgNQxII8tE423LmaudtkBQbBEBdssGB3DLWHsinsKKGr/fksi+3jFHdIvH3cndtoSLiEld292ddZi17sst47dsDTDuvo6tLEml1FCic6LbhHfngpyMcKarkiWU7eWx8z9Pet6qmju/25bE9owQAb3cLgzuE0K1dAG6nGIrW28PCeV0iOK9LBA+O687HmzP4xzf72Z9bztxVe/nntweYMiSBm8/tQJCPBxkZGRQWFjp0fcHBwURHRzu0r0hLYDGb6BTpT1K4H1uPFPPdvjwOF1ayaF0aY3pEER/q6+oSRaSZ+XuamTWuK3e+t5kXVu1lXK92+iwQaSAFCify9rDwt8t6ct2C9bzzQxpnxYf87jCyhgE7M0v4dm8elTVHx8LuHh3AOR3D8HJvWP9ubw8LEwfEcnn/GJZvz+LFL/exM7OEeV/t5801h7i8VyjP3nIxpfmOdcny8/Nn9+5dChXS6pnNJnrHBhEX6sPybVnklFr5aFMG53YKo09skAY5EDnDjO/Tnvc3Hub7ffnM+nAbb/1xoD4HRBpAgcLJzu0UztThScz/ej93v78Zd4uZsb1OPOqTR1QnVmVAflU2AKG+HozoEkF0kHejajCbTVzUsx1jekTxxY5snl+5l52ZJSzckE3QdXPp71tDn5ggPBqQV/IyDvH67GkUFhYqUEibEezjwRX9Y/hyVw47s0r5Zm8eZdZazukYpl8mRM4gJpOJx8b3ZPTz3/Dt3jz+uzmDS/poXimR06VA0QTuHpXMkcJK/rs5g2mLf+KbPbHccE4CnSL8Ka+uZc2+PF5dlUW7yc+RXwVuZhODEkPoGxeMxYkjNJlMJkZ3j+KCrpF8sSOLv32yldQiXw7WwpHDZvrGBtE3TiPdyJnNzWLmgm6RhPh58P2+fH5KK6Kqxsb5XSI0YprIGSQxzJcZ53XkmRV7eOi/2xncIZTIAC9XlyVnMGfMUF5WVuakak5NgaIJmM0mnruyD+H+niz47iDv/ZjOez+mYzId7eJ0jGHYSPA3M7J3An5eDf9fsW/fvtPeNtYEf+5SybX3PU3SFTMpqbGx7mABm9KL6BsXRJ9YBQs5c5lMJs6KD8Hb3cKqnTnsyCzBWlvHhd2jXF2aiDSjW4Yl8fmOLLYdKeGuf2/i7T8O0hcL4hLOnqG8qSlQNBGL2cQD47oxpkcUr6zezzd78qiuOzocXUywN2e39+C56Zcz6ekFDQ4TpUX5gInx48c7VNu5IeXYwpNYd7CAgvJqfjhQwE9pRfSOCaRvbDDeDekLJdKGdI8OxMvdwmdbjw7X/PGWTPr6uLqqk3PGt1cAYWFhmrVWBPBwMzN3Ul/GvfAd3+/L57VvD3DLsMbNDi3iCGfNUH5473b+/fwDTqzsxBQomthZCSH8MyGE2jobBeXVeHtY8PdyZ/v27TxdnO3QMa0VZYDBZXc+QVLXXqe9395Na1n68uNU11jpFulPxwg/9maXsf5gAQUV1WxILeTntCJ6tA+kf1ywQ3dNnE2jUklzSwr345I+0Xy8JYO0ggoqyt0weTTuuaam4Mxvr7x9fNi1c6dChQhHPwMevLgbMz/YylOf76ZH+0CGNOOEd876ogD0ZUFb0NgZyq2VzXOHw/W/MZ4h3CxmIpzcFzM4KoaohE6nvX1exqF6780mE8lR/nSO9GN/bjkbUgvIKbWyKb2IrYeL6drOn/7xwS6bxyIjI4Pk5C6UlZU6tL9GpRJHxYb4ML5Pez7alEGeFSKvfJSy6pY14ZWzvr3KTtvPoifvJi8vT794iPxi0oBYNhws4IOfj3Dbop/47/QhzTKUrLO7uejLAmkuChSCyWSiY4QfSeG+pBVUsD61gIyiKrZllLA9o4TOUf4kumDOr8LCQsrKSvnj7HmERcc3aF+NSiWNFR3kzYR+7flgYxpEd+Ghr/N5v4eVUD9PV5dWT2O/vfo9hmGQW2olNb+C1PxyDhdWkldmJb/MSn5ZNQUV1VhrbFhr66i01hAz7W0+O+KOd/6ho3dkPd3w83IjxNeDCH8vgnzcMWsELWnhTCYTT0zoyf68cjanF3HDGxt475YUwv2btv0764sC0JcF0rwUKMTOZDIRH+pLfKgvRwor2XCogEP5FezOKmU3ED7+r+zNt9J0v7qcWFh0fIPuxIg4S2SAF0Mjavl8fxkHCWbSqz+w6KZBTr/b2BIYhkFOSRXbM0vYmVnCzsxS9maXcii/wj5Hzumw+AVTUQcV5dVQfvx6N7OJqEAvEkJ9iQvxIczPQ0P0Sovk5W7htev6M37e9xzIK+faf67jX38aTIhv09+1b+ovCkScTYFCTqh9sDftg9uTXVLFhtQC9ueW45N8Nncsy+TT1B+5Y2QnukcHurpMkSYX6GGQtfg+et7+Gntzypj4j7Usunkw7Rs5X4yrVVTXkllcxZ4iCxFXPsoN/82hxHriSS/NpqOfCQmhvsSG+BDu50mYnwdhfp4E+3rg7W7Bw83M3t27uHzilVw98zmCoxOosNZSaq2lpKqG/LJqckut1NoMDhdWcriwEgB/LzeSI/3p2i6gWX5RE2mIiAAvFt88mCtfXcvu7FKuevUHFkw5i5jgFjxag4gLKFDIKUUGeDGuVzS79+zl/eVf4d/9PFbsyGbFjmzG9IjijpGdSY7yd3WZIk2qtuAIj50XyhNry0jNr2DiK2t556ZBJIY1fZ9qZzAMg+LKGtILKskoriSzuIriyppf1lrwTuhLidWG2XR0LP6u7QLo2i6ALlH+JIb5EhPsg4eb+XfPU5npRk1uKqGeBjEhx//CZTMMCsurSS+s5NAv3adKq2r58VAhPx4qJDLAk94xQXSK9HPyT0DEcQlhviy+eTCTXv2B3dmljJ/3Pa9c25+zEkJcWpdhGFTX2qiqtVFZU0d1rY06m0Ft3dE/c8vM+Pcbx9JdZXxbsJc6m41am3H0VWdQ+8v7ujqDGtsv+/5q/1qbgaebmaqyEkIuuJWtRRZy0goJ8HLH38uNYB+P0/pckDODAoWclkAPyP/kGd6eeQ3LDsEnWzL4bFsWy7dnMbZnO+4Y2YmOEQoW0nZF+bnx71tSuPaf6ziQV874ed8z/5p+nN2Mo780RGV1HemFFaQVVJBeUEFJVe1x24T4ehBAJRv/8zL//Pts/jB0QJMOG202mQj18yTUz5M+sUHU1tk4kFfOrqxSDuWXk11i5Ysd2Xy7N48EbwtmLwULaRmSwv34cNoQbn7zR3ZklnDlqz/wp6Ed+PP5nfBytzhtZKadO3fa/9tmMyiqrCG/zEpxZQ0lVbWUVtVQWlVLRXUdVbV19ea2Op4bIRfcyttbSgHHBjc5xr/fOPaUwJ6S+tcY7ONOZIAX7X7pxhjg7YIHLqVFUKCQBokL9ODFq7oz/byOzF21h2Vbs/hkSyafbs3kkt7R3H5+JzqE65cAaZuig7x575YUbn7rRzalF3H96+uZ/YfuXDu4YYMGNJXSqhr255azL6eMjKJKfv27htkE7QK9aR/sTXSgF1EBXni6Wzi8dzurN39Op5Anmn0OGjeLmc6R/nSO9KeiupbtGSVsOVxMmbWWnTUW2t/6Ou9sKSEhuVrdocTl2gd58/7UFO77z1b+uzmD+V/v55MtGUzsGcy9V55HZWmxw8e2+AbjHp6Ae3gCoRfdwYojZsoP76fOdsrEABx9LsnL3YKnmxk3iwmL2YSb2UxNZRn7fvqOcWNGExURjrvl6HI3iwk389GX5dfvLeZf/jz63mwyYa21sefAIV6Y/ypnXTgRk3cgpVVHuzFWVNdRWFFDYUUNu7JKgVxCfT3oEH70Lmewi0aIFNdQoBCHJEf58/I1/dmRUcLzK/fwxY5sPtyUwUebMzgvOYLrU+IZ2ilcM4xKmxPu78m7fxrMff/ZwoebMpj14Ta2Hi7moT90w8ej+T9Sy2ph46FC9uWUkVVSVW9dqK8HcSE+xIX4EB3k3aK7J/h4uDEgIYR+ccHszy1jze4MivHhg13lLH/yS65LiefWoUkEK1iIC/l4uPHCVX25uHc0sz7cSnpBJc+sriR0ystE+xgkhvkS5GHgY4HfjjVgM6CyDiprTZTUmCiuMVH6y5/Vtvobl9QBGLiZTYT6eRDk40GAl5u9u5Gvpxte7ha83My4WU7crg/v3c4P/32KGQ9dSb9+PR2+5p888nn0u0X0vuoyYjq1sy8vt9aSU2olu6SK9IIKMouryC+vJr/86LxW0UFe9IgOpFOkH27mlvvZI86hQCGN0i06gFevP4uth4uZu2oPK3fm8OWuo6/4UB8u7hXN2F7t6BLlr5FcpM3wcrfw3JV96BTpz9+/2M17P6azIbWAZyb2pm9ccJOff19OKUt2lNJuylw+z/AA/tcNoV2gFx0j/OgY7tcqux9YzCY6R/rjXVzLK88/yeCbHuZAYS3/WH2AxevSuG14R24YkoCXe/PeTRH5tQu6RTKkYyjvbUjnpZW7yCeQbCD7l6ZoMZvwcjfjbjFjsxnU1BmnHC3NBEeHVK7IZ/+3/2XI+RdxVv++BHq7t9h/O3093Uj0dCMxzJfBHUKpqqkjNb+c3VlHR4fLKKoio6iK7/fn0S82mB7tNZBLW6ZAIU7RMyaQf04ewMG8ct5ee4glP6ZzKL+Cl77ax0tf7aNDmC/ndgojJSmMlA6hBPq0vl90RH7NZDIx7byO9I0L4q73NnMgr5wJ89dw5Vmx3D062anzVRiGwfaMEj7fnsVn27LYl1MGgEdkEiYM2gf72EOEr2fb+Fg3maBy3zqeHhlGkU8MT3++m11ZpTy5fBdvrknlrgs6c1n/GCy6Cyou4uPhxg1DEunhWcDQy/7I2Tc/Sgk+FJRXU2czKLfWAfVDhNkEfp5H52UJ9fMk1Nfj6H/7euBmMbNx1RZ+WvMuYaNHuGxSWUd5uVvoEhVAl6gAyqpq2ZFVwpbDRZRb6/h2Xx7rUwvo5GfG5Na6rktOT9v4l0dajMQwXx68uBv/N6ozK3dm8+mWTL7ek8uBvHIO5JXz5tpDmEzQNSqAgYkhDEoMYWBiSIubLEzkdJ2dFMbyO87l0U928p+fDvPuhnQ+2pTBNYPiuOGcxOOGlz3dhzdr6gx25FWz/kgVGzKqyKv430zdbmbo4FvDmvdeZsrNU0nqEuP062opTCYT53eNZHhyBB9tOsIzX+zhSFEl9/xnC699e4B7L+zC+V0jWuy3uNL2WcwmqtK20CekjphOcdTZDMqstVhr6qipM7CYjz7X4Otpwdvdckb8XfXzcmNgQgj94oLYnVXKxkOFFFbUsK3Ijeg/vcqqAxX07mPoC4E2RIFCmoSvpxuX9GnPJX3aU1pVw/f78lizP5/v9+WxP7ecHZkl7MgsYeGaVAA6RvgxMDGEkV0jOKdjeIvu6y3yW0E+HjwzsTeTBsby6Cc72HK4mH9+d5AF3x9kUGIIF3aPYmBiKN7VhXTv3o3KiorjjmHxD8c9LBbPdp3xjO2JZ/sumN3/F7Rt1VVUHtxI5Z61VOxbz/7qo8eoqbyu2a7TlSxmExP6xXBRz3a8vfYQL321j705Zdz01o8MTAjh3jHJ9I937TCeInD072qgtzu0wi6HzuZmNtM9OpCu7QLYnVXKd7uzqPAPY96PxXyR9g1/vagrw5PDz4iQ1dYpUEiD7Nu3z6H9eoUEc2GPHgDklFSxPrWA9QcLWHeggN3ZpezLKWNfThmL16UR4OXG+L7tOTu85neOKtKyDEgI4aNpQ1i9J5d/rD7A2gP5/HCggB8OFABH7ywEX/M8SSFBeHp6YRhQbTNRWQe1xvH/oHqaDdp524j2sRHhacbScQBcMACAnetX89mbc6mqqjpuv7bMy93CzUM7MHFALPO/3s8b3x9kfWoBl81fy8iukdw9Ollz44i0MGaTia7tAvApTef1N98i7sKb2ZtTxg0LN3BupzAeGNeNzpFqt62ZAoWcltKifMDE+PHjHdrfz8+f3bt3ER0dTcQvk+WN6xUNQGF5NRtSC/huXx6fbcsit9TKW2sP8RYQPmEWRVaIctqViDQtk8nE8OQIhidHcLiwgk+2ZLJmfz4bUwsor67DPSSaUqDUWn8/s+nonY4wXw/aB3sTE+xDsM/JH8jMTtvf9BfTggV6u3PfmC5MPjue51fsZcnGdFbuzGbVrmwu7dueO0d2JvYEk+vJ/xjG0a455dY6rLV1ZFfpW2JpWhYTlG74kPkv3M+3Bb688f1Bvt2bx4XPf8NVA+O464LO6gLdSilQyGmxVpQBBpfd+QRJXXs1aN+8jEO8PnsahYWFREdHH7c+2NeDUd2jGNU9iocu7s7a/fm8uTaVlTuy8ek0mM8PQ3pdNud0CsNbI7tIKxIT7MOtw5K4dVgSdTaDld//yCXX/JHxtz9KcFQsZpMJTzczvp5uBHq7qz+xA9oFevPk5b24eWgHnl2xm2Vbs/jgpyN8vDmDawbFc9t5SUT4e7m6zBbBZhhkFleRll/B4cIK8sqqqa7737M5Nqu66Ejz8PUw89eLunLNoDjmLNvF8u1ZLFqXxn83ZTDj/I5MPjsBTzf9e9+aKFBIgwRHxRCV0KnJjm8xmzinUxjndArj8zWbuPapd/Htci47Mks4mFfOsM7h6s4grZLFbCLc14I1fRvtfQxidHvfqTpG+PHyNf3ZnF7E05/v5rt9eSxck8q/1qdx1cA4bhnWgXaB3r9/oDaosKKarUeK2ZNd+svIQ/9jNh0drcjL3Uy1UUa6i2qUM1N8qC+vXNefHw7k8+gnO9ieUcITy3axaF0aM8d0YXT3KD1f0UooUEiLFRPoTt5HT/KH889lU5EH+eXVLN+exaGCcoZ3jtCD2yJOtHPnzhZxjMbqHRvEOzcN4ru9efz9i91sSi9i4ZpUFq07xOX9Y7lteNIZ0RXKMAxS8yvYnF7EoYL/DQLg4WYmIdSH2BAf2gV4EeTjYb8ztn9LDltdVbCc0QZ3COXj6efwn58O89TnuzmUX8Gt7/zEoMQQHhjXTXNYtAIKFNLihXnBVQPj2PDLg9w7M0vJKq5iTI92hPurr6VIY5QU5AJw7bXXOu2YZWVlTjuWo87pFMaQjqF8vy+fpz/bxuaMcv61Po33NqRxTqwX4zr70jGk4ePhh4WFERcX1wQVO4dhGBzIK2fdgQJyy/73oE5imC89ogOIC/XRrMXSIpnNJq44K5aLerbjldX7efWbA6w7WMDFL33H5f1iuHt0MhEB6r7YUilQSKtgMZsY3CGU2GAflm/PorCihvd+TOe85HC6R+ubCxFHVZaVADD2lvtJ7tW/UcdqaSNPmUwm4jwrWDFzLLaQRALPvhLvxH58k1bFN2lVVB3eQenG/1Kxew0Ytt8/IODt48OunTtbXKgwDDiQW8a6gwXk/PLEv7vFRI/oQHrFBLa6SdLkzOXr6cb/jUpm0sA4nlq+i482ZbBk42E+3ZrJ1GFJ3Dy0A156nrLFUaCQVqV9sDdXD4pjxY5sDuaVs3JnDpnFVQzvHI6bRd+6iTgqNDqemE7dG3WMljjyVF5eHpUVFVwzYwqRcUkUWmvYV2omvcKMV0w3vGK64W0x6OBXR7yvDe9T/KuYnbafRU/eTV5eXosJFIZh4N3hLL7KdqMwPRM4GiR6xwTRLz5YA1lIo7siuqorY/sgb+ZO6svksxN49JMd/JxWxDMr9vCv9WncNSqZS/pE465/93+XYTTPeRQopNXxdrdwca92bEgtZO2BfLZnlJBbamVsz3YEaCIhETmByLgkYjp1JwboCZRba9lypJith4uprKlje7EbO4ohPtSH7tGBJIb5tuhRtwzD4Ju9eTy2Kp+IK2ZTWH00SPSKCaJ/XDDeHgoSZzpnd2d0VVfGfnHBfDD1bD7eksmTn+3iSFElf1mymedW7OGWYR2YeFas7lgAFdW1pOVXkF1iJa/cSkllDZU1dVgrmufupAKFtEomk4mBiSFEBniyfHsWOaVW/rU+jQt7RBEf6uvq8kSkhfP1dCOlQygD4oPZm1PGtiPFZBRXkZpfQWp+Bd7uFrpE+dM50p/IAM8WM9KMYRis2Z/Psyv2sPFQIQC2miqSQ9wZ3qcjPh76Z12OclZ3xpbQldFkMvGH3tGM6hbJwjWp/PPbAxwpquTBj7bzwqq9TE5JYNLAuDPuucqiimp2ZpWSmldu7+roKvrkcQJHZo92dMZpqS8+1JerBsTx6dZMckqtfLgpg8EdQmgZnRFEpKVzs5jp2i6Aru0CKCyvZntmCTszS6ioruPn9CJ+Ti/C38uNzhH+BFpdFypq62x8vj2bf353gJ/TigDwdDMzqoM3L8+4liuefl1hwkGttUvQ6Wpsd8aW1JXRy93CrcOSmHJ2Akt+TGfel3vIKq3mmRV7mLtqDykxXozp6Ety6MknBT2Rlj7Ywq/V2mzszS5je0YJR4oq660L9/ckJsibMH9Pgrzd8fGwkLl7M681Q1369GmExs4eDVBeXu60es5UAd7uXHFWDKv35LLtSAk/HCjgkA+YPXWnQkROX7CvB+d0DOPsDqGk5pezO7uUg3nllFbVsjGtEHAn+pZ/8tpPxUz0yWFwh9Am71qUUVTJ0p+PsHhdmv2XBw83M1cPjOO24Ukc3reDlyqKmrSGtqqtdAk6E3m5WxjW3swtf7scc1x//PuPg+gufJtWxbdpVVTnHKRs2yrKd3yNrbzod4/XUgdb+LWqmjq2HilmU3oRFdX/m08mPsSH5Ch/4kJ88PU8/tf6/GbqDaZA0QiNmT1676a1LH35cazVrr1F1Va4mc2c3yWSdgHefLk7h8wKg3ZT5rI5q5LujXvOVETOMGaziQ7hfnQI96OmzkZqfjl7s8s4kFsKQVF8tq+Cz/ZtwNPNzOAOoQxMDKF/fDC9Y4KcEjDSCyr4Zm8un27JZO2BfPtDlaG+Hlw7OJ5rB8fbu3YcbvTZzlxtqUvQmSgvL4/KslKuuXgskXEdKKyu4UCpmbQKMx4RiYSMuImQETcS6WUQ72ujnbeNE01f1RIHW/i18lpYvTuX7ZnF1NQd/TDw83SjZ/tAurbzx9+rZTw7qkDhBI7MHp2XcaiJqjmzdYsOINzfk49+SqM8KIq/rshmc+EW7r4wmTC/M6tvpUhb4OruKO4WM50i/OkU4U/q7gJem/skY2++h4NVPuRV1LF6Ty6r9xz9pttigsRgdxIC3YgNcKN9gBvhPhaCvCz4eZiO64LhHxSCb0gkqXnl7MoqZXdWKetTCziYV//O9eAOIVzWL4aLe0fr4dMm0Ja6BJ2JfjvgQlVNHXuyS9mVVUpmcRXZVSayq8y4W0wkhvrSMcKP+FDfFj857t6CasL+cA/LM9yBIgDC/DzoHxdMp0j/FjdohAKFtDnh/p6MjoU3//Mp/v3G8t6P6XyyJYNbhiUx+ewEAjUSlEiL1xK7o1QU5VK5bx3v33sZAO5hcXjF98Ezpiue7buBfyj7CmrYV1Bz3L5GbQ22WivY6sBmw+ThhdkjE9h+3LYWs4l+cUEMT47gkj7RxAS3/Zm9RZzFy91Cr5ggesUEUVhRza7MUnZllVBSVcuenDL25JRhMZuID/GhY4Qfnqc3BU2zsNkMvtqdwz++OcD6gwX4dh0KQFyID/3igogL8WkxA0T8lgKFtEnuZihYMZ/5M2/kne2VbDlczLMr9vDK6v1MPCuWy/rF0KN9QIttmM0lIyODwsLCBu+nvsLS1Fpid5RT1WQYUFFXTYHVTEmNidIaE6W1UFlrosYwYXJzx+J2/JcZFhO0Dz7aB7pLlD892weSkhTaYroxiLRmwT4epCSFMrhDCNklVvbllrEvp4ziyhoO5JVzIK8cE+5EXjWHD3aW4RVVQtd2/s3+u0FxRQ1Lfz7MO+vS2Jdz9N9XiwmKt6xi/AVD6d69fbPW4wgFCmnTukd48eGwfnyyNZOXv9rHrqxSFq5JZeGaVBLDfBnWOZyUpFB6xQQSFeBl/xBx9Bft4OBgoqOjnX0ZTSIjI4Pk5C6UlZW6upRWwRkjubT00WBaopbYHaWhNdXW2aiorqPOZmAzDGwG5Kfv47V7rmXDmm/o379xM5SLtDbN/XlqMpmICvQiKtCLIUmh5JVVsy+3jP05ZeSXV+MV15N3tpbyztZvifD3ZFjncM7uGMrAxFDaB3k3utYTsdbWsWZfPh9tOsKybVlU1x69VeLv6cbVg+LoH1DG6L89R+DYc5vk/M6mQCHNpqFD5TpraF2z+ej41Rf3asd3+/J4d306K3cenWn7YF45C9ekAhDg5UaXqADCvOH9t/5JZf4RakvzqCvJo640D5v190fk8vPzZ/fuXa0iVBQWFlJWVsofZ88jLDq+QftmHNjFO3/7SxNV1rI4u+sN6A7PmcbNYibAu35/bas7GNUVZ/xdUjmztITPU5PJRLi/J+H+nqR0CGXXzu0sXriA0ZPvYHteLTmlVpZsPMySjUeHPGgf5M3AxBB6xQTah5h2pOu0zWawP7eMjYcK+XZfHl/vyqH8V6M1dYny56qBcUzo1x5/L3d++umnBp/DlRQopMk1dnhdZw2tazKZOLdTOOd2CqfMWsu3e3L5fn8e6w8WsD+3nJKqWtanFgDgM2ACv+21bDGBtxv4WI7+eezl5wb+7lCZe4iFD0+jsLCwVQSKY8Ki4xs8qIC1sqKJqml5nNX1BjQajIic2Vri56mfG5T9/CmX/t9V/OXsLuzIrWZztpUdudXsL6zhyC9DNy/9+Yh9nzAfC+39LYT5HH0FeJjxdDPh5WYiICAA34BgKmvqKCiv5lB+BekFFezOLqW4sv7zVZEBnozqFsXl/WPoFRPYqr9gUKCQJufo8LpNObSun6cbY3q2Y0zPdkdrrK1jf045e7JL+Xl3Ki+9vojkc8ZSbfagzFpLVY2NOgPKao6+TsRMPO1ufJnHvs6hT7qFjuF+dG0XQMcIvxY/moT8vsZ2vQGNBiMiAi3r8/RUd01M7l54RifjGdsdj4gOeEQk4hYYSV5FHXkVdcdtf1QRkHbCNV7uZnrHBHFWQjAju0bSOyYIcwsbrclRChTSbBo6vK4zhtZtaLepTh5g8s6jYMV8zr1iLFEJR7sC1dbZKLPWUm6to9RaQ7m1jrKqWkqtNRRX1lBUUUOtzcAjLI616RWsTf/fB52bGWID3ekQ7EFisMfRP0M8CPCsP/xjS3r+os5mkF9uJa+smpLKGkp/udbqWhu1dQblJa6uUEREpPEaetek2lZNcbWJiloTFXVQUWuixmai1oDKqipy0g8yeEA/IkODCPR2Jz7Ul9gQHxJDfenSzh93S9v8grHNBIp58+bx9NNPk5WVRe/evXnxxRcZOHCgq8sSF3H2LOZuFjNBPh4E+QAc/4CWYRhs/WkD7/7jOdxDonEPjcE9LB6PiERqvfw4WFjDwcIa4H/HrC3JpTrnIDU5B6nOO4R7RT6bvllOQlyMwzU7osYGRworyS2zklNaRV5pNfnlVmzGyfex1TZffSIiIk3NGXdNDu/dzrNP3suD/7eRfv36Oamy1qFNBIr33nuPu+66i1deeYVBgwbx/PPPM3r0aHbv3k1ERISryxMXaO5ZzE0mE2ZrCVWpPzH20svt5zQMqKiFomooskLhL3+W14JbQDhuAeHQ8X/Bd8T8zSSG7aNzpL/9FR/qQ2ywDwHebo3qX1lbZ+NwYSW7skrYlVXKhj05RN/8Kh8cBA4eP9+up5uZcD9PAn3cCfByx8/LDS83M24WM3mH9vKOw5WIiIhIW9ImAsWzzz7LzTffzA033ADAK6+8wqeffsrrr7/Offfd5+LqxJVcMYv56ZzTWltHXlk1eaVW8sqsZBaUkFtUBl5+7M8tZ39uOZ9ty6q3j7+nG+2DvQn39yTYx4NgH3eCfDzw9rBgNoHZdHQm3mPds0qraikoryajqJKMokqySqqOu+vgHnK0i5Wfp9vRUS/8jo58EeHvib/XyQOMke34z0dERETallYfKKqrq9m4cSMzZ860LzObzYwcOZK1a9e6sDKRk/N0s9A+yNs+vnVWaglPPTyJhe99iCm4PWlF1RwqqiGtuJrsslqKqmyUWmvZlVXKrizH543wdDPTKdKP5MgAgkzlPHHPdP5076MkJCU669JEpAEaOx6/5jYRkZag1QeKvLw86urqiIyMrLc8MjKSXbt2nXAfq9WK1fq/7izFxcUAZKbuadC5cw4fPPrnoX34enm1+H1Vb9Pu25hzHt6zFYApV44/8QYWDywBYbgFRmDxDsDs5YfFyw+zdwBuHt4MO28Y7h5eGAZYzODjbsbb3Yyfh5kwXwth3m6E+1gI9LZgNpmAKg4cOERV6s8UHNqFqbphw8AeayuGcYoHLZrZydp1xv7G/cJ1bCSRrNQ97Pf97WDCrf9YLbEmZx6rJdYEkLrzZ8B54/Hv2fJjo4dzPtZWzoR2DW3/75iur/lryv3l94CNGzc2er6h3bt3A0efy2hM2262dm20ckeOHDEAY82aNfWW33333cbAgQNPuM9DDz1kAHrppVcjX/v372+OZn5a1K710ss5L7VrvfRqe6+mbtcmw2hBX0U4oLq6Gh8fH95///16I/pMnjyZoqIiPvroo+P2+e03HkVFRcTHx5OWlkZgYGBzlN1kSkpKiI2NJT09nYCAAFeX0yht6VqgbV1PcXExcXFxFBYWEhQU5OpyALXr1qItXQu0retRu25ebenvDrSt62lL19Jc7brVd3ny8PCgf//+rFq1yh4obDYbq1atYvr06Sfcx9PTE09Pz+OWBwYGtvq/OMcEBAToWlqotnQ9ZnPLGU9b7bp1aUvXAm3retSum1db+rsDbet62tK1NHW7bvWBAuCuu+5i8uTJnHXWWQwcOJDnn3+e8vJy+6hPIiIiIiLSNNpEoLjyyivJzc3lwQcfJCsriz59+rB8+fLjHtQWERERERHnahOBAmD69Okn7eL0ezw9PXnooYdOeFu1tdG1tFxt6Xpaw7W0hhpPl66l5WpL19MarqU11Hi62tK1QNu6Hl1Lw7X6h7JFRERERMR1Ws6TVyIiIiIi0uooUIiIiIiIiMMUKERERERExGEKFCIiIiIi4jAFChERERERcZgChYiIiIiIOEyBQkREREREHKZAISIiIiIiDlOgEBERERERhylQiIiIiIiIwxQoRERERETEYQoUIiIiIiLiMAUKERERERFxmAKFtFgJCQlMmTLld7dbuHAhJpOJ1NTUJq9JRERE6ps9ezYmk8nVZYgLKVCIiIhTrFmzhtmzZ1NUVOTqUkREpBkpUIiIiFOsWbOGhx9+WIFC5Awza9YsKisrXV2GuJAChYiItFjl5eWuLkFEfoebmxteXl6uLkNcSIFCmt2xvpb79u1jypQpBAUFERgYyA033EBFRcUp992+fTsjRozA29ubmJgYHnvsMWw2WzNVLiInM3v2bO6++24AEhMTMZlM9mebTCYTCxcuPG4fk8nE7Nmz6x3DZDKxY8cOrr76aoKDgznnnHOa6QpE5Lfef/99TCYTq1evPm7dP/7xD0wmE9u2bTvpMxTvvPMO/fv3x9vbm5CQECZNmkR6enpzlC7NzM3VBciZa+LEiSQmJjJnzhx++ukn/vnPfxIREcGTTz55wu2zsrI477zzqK2t5b777sPX15dXX30Vb2/vZq5cRH5rwoQJ7Nmzh3/9618899xzhIWFAY7dYbjiiivo1KkTTzzxBIZhOLtUETlNY8eOxc/Pj3//+98MGzas3rr33nuP7t2706NHD95///3j9n388cd54IEHmDhxIjfddBO5ubm8+OKLDB06lJ9//pmgoKBmugppDgoU4jJ9+/ZlwYIF9vf5+fksWLDgpIHiySefJDc3l3Xr1jFw4EAAJk+eTKdOnZqlXhE5uV69etGvXz/+9a9/MX78eBISEgAcGn2td+/eLF682LkFikiDeXt7c/HFF/P+++/zwgsvYLFYgKNf8K1evbreHcZfO3ToEA899BCPPfYYf/3rX+3LJ0yYQN++fXn55ZfrLZfWT12exGVuvfXWeu/PPfdc8vPzKSkpOeH2y5YtY/DgwfYwARAeHs4111zTpHWKSPP67WeDiLjOlVdeSU5ODl9//bV92fvvv4/NZuPKK6884T4ffPABNpuNiRMnkpeXZ39FRUXRqVMnvvrqq2aqXpqL7lCIy8TFxdV7HxwcDEBhYSEBAQHHbX/o0CEGDRp03PLk5OSmKVBEXCIxMdHVJYjILy688EICAwN57733OP/884Gj3Z369OlD586dT7jP3r17MQzjpD0I3N3dm6xecQ0FCnGZY7dOf0t9pkXajpNNdlVXV3fSffRclEjL4enpyfjx41m6dCkvv/wy2dnZfP/99zzxxBMn3cdms2Eymfjss89O+G+9n59fU5YsLqBAIa1GfHw8e/fuPW757t27XVCNiPzWicLDsTuPv52b4tChQ81Rkog4wZVXXsmbb77JqlWr2LlzJ4ZhnLS7E0BSUhKGYZCYmHjSuxjStugZCmk1LrroIn744QfWr19vX5abm8uiRYtcWJWIHOPr6wvUDw8BAQGEhYXxzTff1Nv25Zdfbs7SRKQRRo4cSUhICO+99x7vvfceAwcOPGXXxAkTJmCxWHj44YeP63VgGAb5+flNXbI0M92hkFbjnnvu4e233+bCCy/kz3/+s33Y2Pj4eLZs2eLq8kTOeP379wfg/vvvZ9KkSbi7u3PxxRdz00038be//Y2bbrqJs846i2+++YY9e/a4uFoROV3u7u5MmDCBd999l/Lycv7+97+fcvukpCQee+wxZs6cSWpqKuPHj8ff35+DBw+ydOlS/vSnP/GXv/ylmaqX5qBAIa1Gu3bt+Oqrr5gxYwZ/+9vfCA0N5dZbbyU6Opobb7zR1eWJnPEGDBjAo48+yiuvvMLy5cux2WwcPHiQBx98kNzcXN5//33+/e9/M2bMGD777DMiIiJcXbKInKYrr7ySf/7zn5hMJiZOnPi7299333107tyZ5557jocffhiA2NhYRo0axR/+8IemLleamcnQE7AiIiIiIuIgPUMhIiIiIiIOU6AQERERERGHKVCIiIiIiIjDFChERERERMRhChQiIiIiIuIwBQoREREREXGY5qEAbDYbGRkZ+Pv7YzKZXF2OSItnGAalpaVER0djNrfM7yXUrkUaRu1apO1prnatQAFkZGQQGxvr6jJEWp309HRiYmJcXcYJqV2LOEbtWqTtaep2rUAB+Pv7A0d/2AEBAS6uRqTlKykpITY21t52WiK1a5GGUbsWaXuaq10rUID9tmlAQIA+oEQaoCV3OVC7FnGM2rVI29PU7bpldpIUEREREZFWQYFCREREREQcpkAhIiIiIiIOU6AQERERERGHKVCIiIiIiIjDFChERERERMRhGjZWRNq0TZs24efn16hjhIWFERcX56SKRERE2hYFChFp04YNG9boY3j7+LBr506FChERkRNQoBCRNm3iHY8S06m7w/tnp+1n0ZN3k5eXp0AhIiJyAgoUv6KuESJtT3hMYqMChYiIiJyaAsWvqGuEiIiIiEjDKFD8irpGiIiIiIg0jALFr6hrhIiISMvnjC7KoG7KIs6iQCEiIiKtijO6KIO6KYs4iwKFiIiItCqN7aIM6qYs4kwKFCIiItKqqIuySMtidnUBIiIiIiLSeilQiIiIiIiIwxQoRERERETEYQoUIiIiIiLiMAUKERERERFxmAKFiIiIiIg4TIFCREREREQcpkAhIiIiIiIOc2mgmD9/Pr169SIgIICAgABSUlL47LPP7OurqqqYNm0aoaGh+Pn5cdlll5GdnV3vGGlpaYwdOxYfHx8iIiK4++67qa2tbe5LERERERE5I7k0UMTExPC3v/2NjRs38uOPPzJixAguueQStm/fDsCdd97Jxx9/zJIlS1i9ejUZGRlMmDDBvn9dXR1jx46lurqaNWvW8Oabb7Jw4UIefPBBV12SiIiIiMgZxc2VJ7/44ovrvX/88ceZP38+P/zwAzExMSxYsIDFixczYsQIAN544w26du3KDz/8wODBg/niiy/YsWMHK1euJDIykj59+vDoo49y7733Mnv2bDw8PFxxWSIiIiIiZ4wW8wxFXV0d7777LuXl5aSkpLBx40ZqamoYOXKkfZsuXboQFxfH2rVrAVi7di09e/YkMjLSvs3o0aMpKSmx3+UQEREREZGm49I7FABbt24lJSWFqqoq/Pz8WLp0Kd26dWPTpk14eHgQFBRUb/vIyEiysrIAyMrKqhcmjq0/tu5krFYrVqvV/r6kpMRJVyMirqJ2LdL2qF2LtA4uv0ORnJzMpk2bWLduHVOnTmXy5Mns2LGjSc85Z84cAgMD7a/Y2NgmPZ+IND21a5G2R+1apHVweaDw8PCgY8eO9O/fnzlz5tC7d2/mzp1LVFQU1dXVFBUV1ds+OzubqKgoAKKioo4b9enY+2PbnMjMmTMpLi62v9LT0517USLS7NSuRdoetWuR1sHlgeK3bDYbVquV/v374+7uzqpVq+zrdu/eTVpaGikpKQCkpKSwdetWcnJy7NusWLGCgIAAunXrdtJzeHp62oeqPfYSkdZN7Vqk7VG7FmkdXPoMxcyZMxkzZgxxcXGUlpayePFivv76az7//HMCAwO58cYbueuuuwgJCSEgIIAZM2aQkpLC4MGDARg1ahTdunXjuuuu46mnniIrK4tZs2Yxbdo0PD09XXlpIiIiIiJnBJcGipycHK6//noyMzMJDAykV69efP7551xwwQUAPPfcc5jNZi677DKsViujR4/m5Zdftu9vsVj45JNPmDp1KikpKfj6+jJ58mQeeeQRV12SiIiIiMgZxaWBYsGCBadc7+Xlxbx585g3b95Jt4mPj2fZsmXOLk1ERERERE5Di3uGQkREREREWg8FChERERERcZgChYiIiIiIOEyBQkREREREHKZAISIiIiIiDlOgEBERERERhylQiIiIiIiIwxQoRERERETEYQoUIiIiIiLiMAUKERERERFxmAKFiIiIiIg4TIFCREREREQcpkAhIiIiIiIOU6AQERERERGHKVCIiIiIiIjDFChERERERMRhChQiIiIiIuIwBQoREREREXGYAoWIiIiIiDhMgUJERERERBymQCEiIiIiIg5ToBAREREREYcpUIiIiIiIiMMUKERERERExGEKFCIiIiIi4jAFChERERERcZgChYiIiIiIOEyBQkREREREHKZAISIiIiIiDlOgEBERERERhylQiIiIiIiIwxQoRERERETEYQoUIiIiIiLiMAUKERERERFxmAKFiIiIiIg4TIFCREREREQcpkAhIiIiIiIOU6AQERERERGHKVCIiIiIiIjDFChERERERMRhChQiIiIiIuIwBQoREREREXGYAoWIiIiIiDhMgUJERERERBymQCEiIiIiIg5ToBAREREREYcpUIiIiIiIiMMUKERERERExGEKFCIiIiIi4jCHAkWHDh3Iz88/bnlRUREdOnQ47ePMmTOHAQMG4O/vT0REBOPHj2f37t31tqmqqmLatGmEhobi5+fHZZddRnZ2dr1t0tLSGDt2LD4+PkRERHD33XdTW1vryKWJiIiIiEgDOBQoUlNTqaurO2651WrlyJEjp32c1atXM23aNH744QdWrFhBTU0No0aNory83L7NnXfeyccff8ySJUtYvXo1GRkZTJgwwb6+rq6OsWPHUl1dzZo1a3jzzTdZuHAhDz74oCOXJiIiIiIiDeDWkI3/+9//2v/7888/JzAw0P6+rq6OVatWkZCQcNrHW758eb33CxcuJCIigo0bNzJ06FCKi4tZsGABixcvZsSIEQC88cYbdO3alR9++IHBgwfzxRdfsGPHDlauXElkZCR9+vTh0Ucf5d5772X27Nl4eHg05BJFRERERKQBGhQoxo8fD4DJZGLy5Mn11rm7u5OQkMAzzzzjcDHFxcUAhISEALBx40ZqamoYOXKkfZsuXboQFxfH2rVrGTx4MGvXrqVnz55ERkbatxk9ejRTp05l+/bt9O3b97jzWK1WrFar/X1JSYnDNYtIy6B2LdL2qF2LtA4N6vJks9mw2WzExcWRk5Njf2+z2bBarezevZtx48Y5VIjNZuOOO+5gyJAh9OjRA4CsrCw8PDwICgqqt21kZCRZWVn2bX4dJo6tP7buRObMmUNgYKD9FRsb61DNItJyqF2LtD1q1yKtg0PPUBw8eJCwsDCnFjJt2jS2bdvGu+++69TjnsjMmTMpLi62v9LT05v8nCLStNSuRdoetWuR1qFBXZ5+bdWqVaxatcp+p+LXXn/99QYda/r06XzyySd88803xMTE2JdHRUVRXV1NUVFRvbsU2dnZREVF2bdZv359veMdGwXq2Da/5enpiaenZ4NqFJGWTe1apO1RuxZpHRy6Q/Hwww8zatQoVq1aRV5eHoWFhfVep8swDKZPn87SpUv58ssvSUxMrLe+f//+uLu7s2rVKvuy3bt3k5aWRkpKCgApKSls3bqVnJwc+zYrVqwgICCAbt26OXJ5IiIiIiJymhy6Q/HKK6+wcOFCrrvuukadfNq0aSxevJiPPvoIf39/+zMPgYGBeHt7ExgYyI033shdd91FSEgIAQEBzJgxg5SUFAYPHgzAqFGj6NatG9dddx1PPfUUWVlZzJo1i2nTpulbDRERERGRJuZQoKiurubss89u9Mnnz58PwPDhw+stf+ONN5gyZQoAzz33HGazmcsuuwyr1cro0aN5+eWX7dtaLBY++eQTpk6dSkpKCr6+vkyePJlHHnmk0fWJiIiIiMipORQobrrpJhYvXswDDzzQqJMbhvG723h5eTFv3jzmzZt30m3i4+NZtmxZo2oREREREZGGcyhQVFVV8eqrr7Jy5Up69eqFu7t7vfXPPvusU4oTEREREZGWzaFAsWXLFvr06QPAtm3b6q0zmUyNLkpERERERFoHhwLFV1995ew6RERERESkFXJo2FgRERERERFw8A7Feeedd8quTV9++aXDBYmIiIiISOvhUKA49vzEMTU1NWzatIlt27YxefJkZ9QlIiIiIiKtgEOB4rnnnjvh8tmzZ1NWVtaogkREREREpPVw6jMU1157La+//rozDykiIiIiIi2YUwPF2rVr8fLycuYhRURERESkBXOoy9OECRPqvTcMg8zMTH788cdGz54tIiIiIiKth0OBIjAwsN57s9lMcnIyjzzyCKNGjXJKYSIiIiIi0vI5FCjeeOMNZ9chIiIiIiKtkEOB4piNGzeyc+dOALp3707fvn2dUpSIiIiIiLQODgWKnJwcJk2axNdff01QUBAARUVFnHfeebz77ruEh4c7s0YREREREWmhHBrlacaMGZSWlrJ9+3YKCgooKChg27ZtlJSUcPvttzu7RhERERERaaEcukOxfPlyVq5cSdeuXe3LunXrxrx58/RQtoiIiIjIGcShOxQ2mw13d/fjlru7u2Oz2RpdlIiIiIiItA4OBYoRI0bw5z//mYyMDPuyI0eOcOedd3L++ec7rTgREREREWnZHAoUL730EiUlJSQkJJCUlERSUhKJiYmUlJTw4osvOrtGERGH/Zhv4WBeOYZhuLoUERGRNsmhZyhiY2P56aefWLlyJbt27QKga9eujBw50qnFiYg0Vkalhf9uzqBjuB+jukfibnHoexQRERE5iQb9y/rll1/SrVs3SkpKMJlMXHDBBcyYMYMZM2YwYMAAunfvzrfffttUtYqINFgHvzrMJtiXW8aHm45QW6fnvERERJypQYHi+eef5+abbyYgIOC4dYGBgdxyyy08++yzTitORKSxegTVcVm/GDzczGQUVbFqV466P4mIiDhRgwLF5s2bufDCC0+6ftSoUWzcuLHRRYmIOFN0kDdje7bDZIJdWaXsyS5zdUkiIiJtRoMCRXZ29gmHiz3Gzc2N3NzcRhclIuJscSE+DEoIAeDr3TlUVte5uCIREZG2oUGBon379mzbtu2k67ds2UK7du0aXZSISFM4KyGEMD8Pqmpt/HAg39XliIiItAkNChQXXXQRDzzwAFVVVcetq6ys5KGHHmLcuHFOK05ExJksZhPDOocDsPVIMYUV1S6uSEREpPVr0LCxs2bN4oMPPqBz585Mnz6d5ORkAHbt2sW8efOoq6vj/vvvb5JCRUScISbYh4RQH1LzK/gxtZALukW6uiQREZFWrUGBIjIykjVr1jB16lRmzpxpHynFZDIxevRo5s2bR2Rk6/3H+btcN0rTi+gRHYCbxqoXabMGJoaQml/BrqwSBiWGEOB98mfDRERE5NQaPLFdfHw8y5Yto7CwkH379mEYBp06dSI4OLgp6mtWBVYzq/fksjm9iDE9oogI8HJ1SSLSBNoFehMb4k16QSU/HipkRJcIV5ckIiLSajn8NXxwcDADBgxg4MCBbSJMAPQIqsXX00JRZQ3v/3SYw4UVri5JRJrIwF9GfNqRUUK5tdbF1YiIiLRe6tfzKx38bFw7KJ7YYG9q6gz+uzmD3FKrq8sSkSYQE+xDu0Av6gyD7Rklri5HRESk1VKg+A0vdwt/6B1NzC+h4uMtGVhrNF69SFvUKyYQODrik82m2bNFREQcoUBxAm4WM2N7tiPQ253Sqlq+3JXj6pJEpAl0jPDD291CmbWWg/nlri5HRE7Td7lurN6TS07p8cPYi0jzU6A4CS93Cxf2iMJsgj05ZRzM0y8bIm2Nm9lM9+gAALYcLnZxNSJyugqsZjalF/Gv9el8ti2TKvUkEHEpBYpTiArwok9sEACr9+RSW2dzbUEi4nQ92x/t9pRWUKGJ7kRaiX4htXSO8ANgT3YZi9enUVCu9iviKgoUv2NQYih+nm4UV9aw4VChq8sREScL8HYnPtQHgJ2ZejhbpDWI8bExpmc7Jg2ItXdPXrIxnUKFChGXUKD4HR5uZoZ2CgNg46FCyqo0vKRIW9Ot3dFuTzszS+0TdopIyxcZ4MWVZ8US4e9JVY2NjzZnUKnuTyLNToHiNHSM8CM60Is6m8H61AJXlyMiTtYhzBdPNzNl1lrSCytdXY6INIC3h4VL+kQT4HW0N8GnWzKp06htIs1KgeI0mEwmUpJCAdieUUxJZY2LKxIRZ3KzmOkc6Q+o25NIa+Tj4cYfekfjYTFzpKiSdQfzXV2SyBlFgeI0xQT7EBvijc2AdQd1l0Kkrena7mig2JdTRnWtBmAQaW1C/TwZ2TUCgB9TC8kq1pCyIs1FgaIBUjocvUuxM6tEdylE2pioAC+CfdyptRnszSl1dTki4oBOkf4kR/pjAF/syKJGozOKNAsFigZoF+hNbIg3hgGb0otcXY6IOJHJZKLrrx7OFpHWaXhyOL4eFgoravgxVaMzijQHBYoG6h8XDMC2jGKsGklCpE3pEnW029ORokqKdRdSpFXycrcwLDkcgI1phWrLIs1AgaKB4kJ8CPX1oKbOYGuGZtYVaUv8vdyJCzk6J8UuPZwt0mp1DPcjNtibOpvBN3tyXV2OSJunQNFAJpOJfvFH71JsSi/S0HQibUzXX+5S7MzSnBQirZXJZGJY53DMJjiQV86h/HJXlyTSpilQOCA50h9fDwvl1jr2ZKuvtUhbkhThh7vFRHFlDVklGiVGpLUK9fOkV0wQAN/vz9cXBCJNSIHCARazid6xQcDR/pn6kBJpO9wtZjqG+wF6OFuktRuYEIKHxUxuqZW9OWWuLkekzVKgcFDP9oG4W0zkl1VrZl2RNqbLL6M97ckupU7fF4i0Wt4eFvrFBwGwdn++uimLNBE3V578m2++4emnn2bjxo1kZmaydOlSxo8fb19vGAYPPfQQr732GkVFRQwZMoT58+fTqVMn+zYFBQXMmDGDjz/+GLPZzGWXXcbcuXPx8/Nr0tq93C10axfA5sPFbEovsj/IKSKtX0ywN76eR7s1ZlWaXF2OiDRC39hgNqcXU1RZw47MEnq2D3R1Sa1OWloaeXl5TjlWWFgYcXFxTjmWtBwuDRTl5eX07t2bP/7xj0yYMOG49U899RQvvPACb775JomJiTzwwAOMHj2aHTt24OXlBcA111xDZmYmK1asoKamhhtuuIE//elPLF68uMnr7x0bxObDxRzMK6eworrJzycizcNsMtElKoCNhwpJK7e4uhwRaQQPNzMDE0NYvSeXdQfz6RLlj7tFHTROV1paGl26dqWyosIpx/P28WHXzp0KFW2MSwPFmDFjGDNmzAnXGYbB888/z6xZs7jkkksAeOutt4iMjOTDDz9k0qRJ7Ny5k+XLl7NhwwbOOussAF588UUuuugi/v73vxMdHd2k9Qf7eJAQ6kNqfgWb04voqM8nkTajS5Q/Gw8Vkllpwuzl7+pyRKQRerQP4Oe0Qkqqatl8uIiz4kNcXVKrkZeXR2VFBdfc+zSRcUmNOlZ22n4WPXk3eXl5ChRtjEsDxakcPHiQrKwsRo4caV8WGBjIoEGDWLt2LZMmTWLt2rUEBQXZwwTAyJEjMZvNrFu3jksvvfSEx7ZarVitVvv7khLHx5vvExtEan4FOzJLiG/n8GFEpJGc2a4Bwvw8Cff3JLfUik/Xcxtbnog4wFnt2s1sZnCHUL7Ykc3GQ4X0ah/kpArPHJFxScR06u7qMqSFarHfqWdlZQEQGRlZb3lkZKR9XVZWFhEREfXWu7m5ERISYt/mRObMmUNgYKD9FRsb63CdcSE+hPwy0V1qWYv9cYq0ec5s18ccmznbr/uIRh9LRBrOme06OcqfYB93qmpsbEovcl6RItJy71A0pZkzZ3LXXXfZ35eUlDj8IWUymegTE8SXu3PYV2oBk0KFiCs4s10fkxzpz7d7c/Fs34WM0lr6NbZIEWkQZ7Zrs8nE4A6hfLYti41phYRFOatKkabhjIfhy8qaZ7jkFhsooqKOtvTs7GzatftfX6Ls7Gz69Olj3yYnJ6fefrW1tRQUFNj3PxFPT088PT2dVmuXdv58vz+Pilob3kkDnHZcETl9zm7XAL6ebkR6GWRXmVh9qJJxTj26iPweZ7frThF+rPfzIL+smr0lGnBBWi5nPwzf1FpsoEhMTCQqKopVq1bZA0RJSQnr1q1j6tSpAKSkpFBUVMTGjRvp378/AF9++SU2m41BgwY1W63uFjM92gey8VAhAWdd0mznFZGmF+9rI7vKzOpDldhsBmazhpEVaa1MJhODE0P5dGsm+0rNmL0DXF2SyAk562H4w3u38+/nH3BiZSfm0kBRVlbGvn377O8PHjzIpk2bCAkJIS4ujjvuuIPHHnuMTp062YeNjY6Ots9V0bVrVy688EJuvvlmXnnlFWpqapg+fTqTJk1q8hGefqt3TCA/HSrAK74XqUU16hoh0ka087Zhs1aQgw8bUgsY1CHU1SWJSCMkhfsS4e9JTqmVgEGXubockVNq7MPw1srmucPh0g7/P/74I3379qVv374A3HXXXfTt25cHH3wQgHvuuYcZM2bwpz/9iQEDBlBWVsby5cvtc1AALFq0iC5dunD++edz0UUXcc455/Dqq682+7X4e7nT3scGwKd7y5v9/CLSNNzMULH7OwD+/eNhF1cjIo1l+uVZCgD/fmMprKxzcUUirZ9L71AMHz4cwzBOut5kMvHII4/wyCOPnHSbkJCQZpnE7nR09LdxuMLCN4cqyS+zEurn3P7cIuIapZu/wK/XKD7ZksGD47oR6OPu6pJEpBESQn0I8bBRgBcf7Crj/CGurkikddOQRE4U4mFgzdxDjQ0Wr0tzdTki4iTVGbuIC3TDWmvjw01HXF2OiDSSyWSie9DROxOf768go6jSxRWJtG4KFE5kMkHpj/8F4O0fDlFda3NxRSLiLBd08AHgX+vTTnlnVURah3BPg6q0rdTa4KWv9v3+DiJyUi12lKfWqnzXd3S84h5ySq0s25rJ+L7tXV2SiDjBsHhv3tlaxq6sUn5OL6JfXLCrS2qRnDFu+jFhYWHExcU55Vgiv2UyQdG37xB1zZP8e0M6tw5NIi7Ux9VlibRKChTOZqvlwiQf3t1exhvfH+SSPtGYTBpmUqS18/MwM7ZXOz746Qhvrz2kQHECzh433dvHh107dypUSJOxHt5On0gPNmVX8/zKPTx7ZR9XlyTSKilQNIFRST58sKuCzYeL+SmtiP7x+sVDpC2YcnYCH/x0hI83Z3DfmC5EBnj9/k5nEGeNmw6QnbafRU/eTV5engKFNKmre/qzKTufD34+wpQhCfSKCXJpPbrLJ62RAkUTCPKycEmfaJZsPMzr3x9UoBBpI3rFBDEgIZgNqYW8vfYQfxmd7OqSWqTGjpsu0pw6hngwoW97Pvj5CI98vIMlt6a4rGeB7vJJa6VA0URuGJLIko2HWb4ti4yiSqKDvF1dkog4wY3nJLIhtZBF6w4xfURHvNwtri5JRBrpngu78Nm2LH48VMinWzMZ16t5J8c9Rnf5pLVSoGgi3aIDGNwhhB8OFPDatwd46GJ9WyfSFlzQLYrYEG/SCyr5z0+HuWZQvKtLEpFGigr0YurwJJ5dsYc5y3YxsmukS78s0F0+aW00bGwTmnZeR+DonBQ5JVUurkZEnMFiNnHD2YkAzP96v4aHFmkjbj63A9GBXhwpquS1bw64uhyRVkWBogmd0zGMfnFBWGttvLJaH04ibcVVA+MI8/PkcOHRuxQi0vp5e1i4d0wX4Oi8FAfzyl1ckUjroUDRhEwmE3eM7AzAonWHdJdCpI3w9rAwdfjR/s0vfblPdylE2og/9I7mnI5hWGtt3PefLdhsmsRS5HQoUDSxczv97y7FC1/udXU5IuIk1wyKI9zfkyNFlSzZmO7qckTECUwmE3Mm9MTb3cK6gwX8a0Oaq0sSaRUUKJqYyWTi7tFHb6H+a306+3JKXVyRiDiDl7uF2365S/Hcij0UV9a4uCIRcYbYEB/u/mVI6Mc/3cn+3DIXVyTS8ilQNIOUpFBGdo2kzmbwt892ubocEXGSawbFkxTuS17Z0Vl2RaRtmHx2AmcnhVJRXce0RT9RVVPn6pJEWjQNG9tMZl7Uha9257ByZw7f78tjSMcwV5fkcpoNVFo7Dzczs//QnesWrOettYe4ckAsXaICmu38zmpDaj8i9VnMJp6/sg9j5n7LrqxSHv54B09c2sNlE96JtHQKFM0kKdyPawfF8ebaQzzw4TaW/fncM3pCLM0GKm3FuZ3CubB7FMu3Z3Hvf7by/q0puFua/uavM9uQ2o/I8SICvHjuyj5c//p6/rU+jQ5hvtw8tIOry2oYk5myahuHCysot9ZRVfPLq9aG9Zc/q2rqsB57/8t/GwaYTYDJRHZWKQEDL+VgmZnK7FJ8Pd0I8HbH18OigCV2ChTN6K5RyXy2LYsDeeW89OU+/vJLH80zkWYDlbbkwYu7sWZ/HpvTi3h2xR7uvbBLk5/TWW1I7Ufk5IZ2Duf+i7ry+LKdPL5sJ1GBXlzc2zWzaP9ada2NoopqCiqqKfn/9u48PKoqXfT/d9dclVQq80RGCCAzAoIoigo2ONu2U7dD7KO02rbDcfZoi1zv7XZ6hP559Id6FfVoizN2i+JAi9iIoMwQCBBCIGQeK1VJVWpY949ASSRICBkqlffzPPuB2rVr7bVq11vZ76611vb4afb6cXsDuFv9tPgCtPqDeH1Gsu//B9cvqQQqT2h/cWffyPo6oK4itE6v04i3mUiJMZPqsJAeayXOZjqxhol+SxKKXuSwGvlfl4zmljfXsfCbIs4fk8bI9N7pHhGuXSPkbqAiEqTHWnnyN2O59a31LPymiNOHJDJtaO90a5QYEqJn3XRGLqX1zby+uoT/fGcjgaDi0pMH9dr+vf4AFY0eKho9lDs91LpacXn9nXjlT78eWIw6okwGLEY9ZqMOi0GPxajDfPBfi1GP2fDTv5qmEVQKpaCyqpoP//EJeZPOQjPbcHn9uDx+AkFFtctLtcvL1jInAHE2I4OTohmSFEVqjEV+wRhAJKHoZbNHp4a6R9yxeAMf33Y6UeaePQzSNUKInnfemDR+OzmLt9fu47a/r+fdm6cyPNXe19USQpwgTdN49KJRNLb4WLKxjLve2Uitu5X/OD2nZ06YdXqqPRp7d9VQUuemxtXa4WZWo564KCMOq5Eok4Eos4Eokx6byYDJoKOudDcvP5DP6m+/ZvKkiV2uzvr163n55mfJP38aGUMzAQgEFU0eH9UuL5WNXiqcHsobW6hv9rGupJ51JfXER5kYnR7DiLSYAd3Fe6CQhKIP/O9fj2bD/np2V7l4+KMtzL9qfI9m8dI1Qoje8eiFIymscLJ+XwPXvbKG9285jawEW19XSwhxgvQ6jWevHE+szcRr3+3l8U8KWFtcyxOXjSUuynTCvQBqmwOsr/Dyzc5qMu94m5VVRqA+9HyMxUCaw0qaw0KS3Ux8lOmYJ+leAwQ9TRh03X9+oddpxNpMxNpMDE1uu3Di9QcoqW2mqNrFnmo3de5WVu6q4buiWkalxzAxO67b6yHChyQUfSAx2sxzv53Ab1/+niUbyxifGcsNp+f2+H6la4QQPctq0rPohslc9dJqdlQ0cfnC71h43UQmZMkfUiH6O51OY+5FI8mIs/Lksh18vq2SH/d+w2/HJ/DoNefQ0tTY6bI0gwlzxkgsOSdjzZ2AKfnQOYAJndmESQsyOMVBTkIUGXHWHu/J0B3MBj3DUuwMS7Hj9QXYUdnE1gON1Lha2VTayJYDjWTZ9OjtSX1dVdEDwv8TGqEm58Zz/6zh/PWzHcz7pIC4KBOXjO+9PpliYOqusTQul9zo6WgcNiNv/Mdkrvm/a9hV5eLqF7/n4QtGcM2ULAx6XbccA3drkG837yZ63Cw21+vZtq2CltYALb4AgaAicLDvs04Do16HQa9h1OkwGXSH9Z9u6yvtbtYwZ42huN5HSkPLwe4TR5+9xRcI4vL4cXn9OD0+XB4/TQcfF+x2EzPlcrY26NldWIVBp8Nk1GE3G3BYjSREmzAbpOuD6L80TeOmMwZz6uAE7npnI7urXPz3qnISbniBQbYgg5OicRgVVj0cCqGAgmY/NPo0Glt11LVq1Hg0ghweY4p4k0JfW8zmD5/jqlvu5+RR/XfiFrNRz7iMWMYOcrC/voUfiusobWhhr1vPoD+8yJubneSN9BFjMfZ1VUU3kYSiD/3hzMGUNbTw+uoS7nl3E2aDjtmj0/q6WiJCdfdUveLokmMsfHTb6dz9zka+KKhk7j+28daaEq4dH88tF0+jxeXsVDmayYoxIRNjYjamxCyMBxdDTBJgJ2H27exqApqaTqC2RlJ/+1fu+bIGvvwXAAadhsNqxGrS4w8o/MFg26wxB5dfEnfWDRQ6AeeRV2s1ICHaRFa8jbzkaBm0Kfqt0YMcfHbnGXy4vpRnPiugGgcVQEV12/M6DQw6HQqFL6A6LCPabCAr3ta2JNiwGvWsW17IjxW7iZSw0DQt1Mayhha+3rqPGkx8uMPNiqdXcMc5eVxzanavTLUtepYkFH1I0zTmXjQKp8fPRxsOcOtb67l/1kncMn0wmqZ129Xk7du3d0NtRX/XnVP1lu7axrsL/txNNYtM0WYDC6+dyP98X8L8r3ays9LFo5+7SLzxJZItQVIcNqIMoNfaTjb8SsMTAJdfw+3XcPk0mgNHP6swBDw07Ssgb+gwcnNysBr1WEw6DDodek1Dp2sbOOkPKHyBIL6govXQnPO+IB5/23zzTmcT5WWlJGfk0OyD1kAQf1BR624F99HbZzXqibYYsJsN2C0Goi0G/C0uli/7hHFTzyE+MZFAUOHxBXF6fDQ0+3B5/dS4WqlxtbJ+XwMxFgNjM2IZlS6DNkX/Y9TruOqULHK1aqZffiOn3vS/cCor9c2tBFVbLB2i12kkRJlIsptJijaTGW8jzmYcUAl1eqyVM5P9LJz/BCf/fh4Hmlp57J8F/M/3Jfz5wpGcNTy5r6soToAkFH1Mp9N4+vKxRJsN/M/3JTy5bAcb9tXzh0lxnHnK2G69mizdVAR0z1gab4v8ytEZOp1G/mk5XDp+EC+uLGLxmmLqiKYGqOlkd2ubSU9ClImEKDMJ0Sbio0wkRJnYunIpb737KLPnvcT4ExjsWLprG88+/kc+WbeOk08+GY8vSENLK40tPlpaAxj1ulC3KZNeh93SNptMR1cU169fz7t3/I3xF0wnY8iR0+a6vX4ONLSwp9pNcY0bp8fPv3fXsKa4lpMz45iQFYtZEgvRzxh0Gp6STUyID5AxNJtAUNHc2jatqgJsRj2mg1OxDnSaBi1Fa1kwK4mdgUSe/WInRdVublj0A+eclMwjF4xgcFJ0X1dTdIEkFGHAoNfx+KWjGZYSzbx/FvBFQSUrd1ZhmnApF55+CplZ2V0q1x+EZr9GYcEWNqz5NzucRg5sr8TrD9IaCOI7ePVEQ0PTwGxom5PaenCeavvBu2E6rEZsJvkjL0RXOWxG7p99EuckuZl2aT4zb/s/+CxxuL3+UByaDDqsRj2OgzEXazWREH3smVy6k6ZpWE16rCYraQ5rt5cfZTaEBm36AkEKK5rYWNpArauVtXvr2FzawOl5iYzqpfvzCNET9DoNu4wN+EV6ncY1k7K5aFw6zy3fxaJVe/nXjiq+3VXNDaflcPuMoTK+op+RhCKMXDc1h0k58TyyZCvrSuqJPf13/ICi0h1FZpyNZLuZGKsRi0GHTqfhDyg8vraBmC6vn4YWH43NvtDVRbc30FZw/AQSzptAiR8o61zf7Z/T6zRsOiNJv3mUVzY0ssWzl+wEGzkJUQyKs0r/RyE6QadpeEu3MTwmSMbQ1L6uTp8y6nWMHuRgVHoMRdVuVu+ppc7dyvIdVRSUOxlplau5QkS6GIuRhy8YydWTs/g/S7fzrx1VvPxtMR9tOMB9s4Zz+cRM9D0w7a3ofpJQhJkRaTG8d/NU/vsf3/GX91dhyRhFSW0zJbVd62JiMugw+JupKdrCkOEjyc7MxGzUYzzYfQFAAUF1qH91W99qT2uAJo+fxoOzuASCiqaghi1vMkt3NbN017bQPvQ6jYw4K9kJUeQk2EL/ZsXbGBRnxWaSj5kQomOappGXHM3gxCg2ljbw/Z5ayhs9VDQacJx2NYFgxwNaxU9k9jbR3w1JiubVG07h68IqHv+kgD3Vbh74YAv/830Jcy8axSk58X1dRXEMcqYXhnQ6jWlZVirfeoAb53+E155GaX0L9c2tNB08uT/EbNBhMeqxmfTE2tq6STisRhw2I7FWIxajnnXL/8Fb78/jvHkvMX7w+OOuTyCocHn9FO3exZI3FnLjnQ/SYoimpLaZvbVuPL5gKOlZ2cHr42xGBsVZSXdYGRRnZVCsFW99C6bUPLwBUEpJ31IhBjidTmNCVhzDku18s7Oa3dUuYs+4loe/ruXlXDfZCVF9XcWwJLO3iUhy9vBkpuUl8sbqEhZ8tZOtB5xcsXA1F41L58HzTmJQbPd3xYw0Sinqm33UN7fi9vopa+qdHiSSUIQ5h0mRkRUXujGWUoqgavtFQadpvfJToP7gFJLJFoVr0+dcP+4vTJgwIVSfqiYve2vcoQSjpLaZ4ho3++uaafL6D36wfWw90L67VVr+Aj45AIbyIuwWA3aLkcTotlkwku0WYm1GdJJoCDGgRFsMnD8mldWbdrDmgIedtdGc97dv+fOFI7n6lEy5+PAzMnubiDRGvY4bp+Vy6fh0nvliJ4t/2Mc/N5XxZUEFt0wfws1nDsEq4zrb8foC7Klxs7vKxYGGlnbTewe9vXOqLwlFP6NpGnoN9ITHH1VN00iJsZASY2HK4IQjnnd6fByob2lbGlooa2ihtKGFnaU1bC+pwGBPwB9UoaRjX91PV9mMeo1Uh4XMuLbuU0l2syQYQgwAmqaRFRXko1dvZ9bct9hW3cpDH25hRWEVT1w2lrgoU19XMezI7G0i0iREm/nrZWO49tQs5v2zgLXFdSz4ahfv/rCfh84fwYVj0wb8BYbqJi+bShvYUdHUrveKQacRH2Ui2mzAW9/E/l6oiyQUokfFWIzEpBkZkdZ+1pb169czceIs7vzvD3FkDG0br9Hio9rlpbqpbfEFFPvrWthf18J3RbWYDTqyE9puiJWTECUDwYWIcIGmah6bHs96dyzPfFHI59sq2bh/JfOvHM9peUdOSyuEiDyj0h2884dT+XRLBX/5dDsHGlq4/e0NvLF6L3++cCRjM2L7uoq9rtbl5fs9deyu/mncU7zNxNCUaHITo0iMNod6sBRtPsB3vVAnSShEn9JrEGszEWszkXnY+qBS1LlbKa1vYX9dM6X1bT/h7ax0sbPShUGnkZsYxdCUaEwyZlOIiKXXadw8fQin5yVyx9sb2FPj5ppX1nDzmUO4+9xhmAxyYUGISKdpGheMTWPGiGReWrmHF1bs5oe99Vz836s456Rkbj8nj5Ozun4/nv7C5fGzqqiGHRVNoXVDk6MZlxlLusPSp7/YSEIhwpJO00iMNpMYbWZ8ZizBoKKyycPuKhe7q1w4PX52VbnYVeXCpDMSN+MPFNf7mNDXFRdC9IjRgxx8csc0Hv+kgLfX7mfhN0Ws2l3D364eLzfCEmKAsBj13DFjKJdPzOCZzwtZsvEA/9pRxb92VDEuxcTlI6MZmWg67hPrxMREsrKyeqjWJy4QVGzc38Ca4lp8gbarqEOSojh1cAKJ0eY+rl0bSShEv6DTaaQ52m62NS0vkaomL7uqXOyocOL2BoiZdDH3fFnDq9u+5YqJGVwyfpD0sxYiwthMBv562VimD0vigQ+2sOVAIxc+928eu2gUV0zKGPD9qYUYKNJjrdw1NZ6X//M3mMdfRNToc9hU2cqmyjq85bto+vFj3Dv+DUF/p8qz2mzs2L49LJOKKo/G12v2UdfcCkCaw8L0YUmkxFj6uGbtSUIh+p3DB4KfNiSB9Vu288W33+MYeQbbypxsKyvgL5/u4NyRKVw+KYMzhybJjXHECdu+fXtYlCFg9ug0xmXGcvc7m1i9p5b7P9jMFwUVPH7p6B65w7cQIvzU1NTgKt/DJddnEJ0eoNAJJW4d5rShmC+6l4xL72FIdIDc6CDmX5gUqnJfEW89eR81NTVhlVDUNgdIvPh+vq0yAq1YjXqm5SUyIs0elhdPJKEQ/ZpO00i1Kmo+fpL3H7icvSqRd3/cz7YyJ0u3lLN0SzkpMWZ+MyGDyydmSNcIcdycddUAXHvttd1WptxA7MSlOay8edMUXlxZxPwvd/LV9iq+37OSB2YP55op2ejkIoIQA8KhGc6GAy2tAbaUNbJ5fwPu1gDbGg3saNLIS4pm9KAYBsVaw/Jk/HBef4BX/l3M//dVNVEjzgQU4zJiOXVwAhZj+E6XKwmFiBh2s478CTnkn5ZDQZmT99btZ8mGA1Q6vbywoogXVhQxKTuOKyZlMHtUGg6b8YgyuuuOsxD+fTJF57S42u6fcsHNDzN87MQTKmv72m/47PW/4fF4uqNqA55ep/HHs/KYOSKFBz/YzPp9Dfz54228t66URy4YyeRcubuuEAOJ1aRnck48E7Pi2FXVxIZ9DVQ1eSmsbKKwsgmH1cjo9BhGpMUQZQ6vU2ClFF9tr+J/Ly2gpLZtCmdPaQEXTBrK6OHJfVy7Ywuvd1OIbjIyPYa56aN48LyT+Nf2Kt5bV8qKwip+LKnnx5J6HlmylTOGJnH+mDTOHZmCw2rs9jvOhnOfzOPV6g/i9Phwefy0BoKUuQfezDoJ6dknPM9/5b6ibqqNONywFDvv33Iab64p4allhWwubeTKF1dz3uhU7p99ErmJcpdtIQYSvU7jpNQYhqfYqWrysrWskZ0VLhpbfKwqquW7PbVkxdsYnmLHEjx2eT2toMzJXz/bzre72i5oJtvNXD3Cwj1PPkDsaR/0ce06RxIKEdHMBj3njUnjvDFpVDo9fLj+AB9tKGVnpSs0M4RRr3HG0CSGR3lp1Vu55oF5J3zH2XDtk9kZSrXdaLCk1k2F00OV00tDi6/dNr11503Re/r7GBGdTuP6qTmcPyaNZ7/cyeK1+/hsawWfb6vg4nHp/OmcPPKS7X1WPyH6yonGZX8e+3X4mMsz8pLYVdXEtjIn5Y0eSmqbKaltRq8ZSbz4ftYe8DByTKBXuxVtL3fyt692sWxbBQAmvY6bzsjltrPzKNy2Geg/8+LLWYEYMFJiLNx61hBuPWsIuyqb2sZYbC5nV9XB5ALI+OPrbDEG8WgJ5CRGkRZjGRB9sZVSVDq9FFY0UVzrpvFnCQSAxaAj2mLAbNDjbXT1yp03Rc+LtDEiidFm/vLrMVw/NZunlhXyrx1VLNlYxsebyvjVyBTyp+YwdUhC2Pej7klNHh81rlZq3V7q3K24vQE8vgCuRjkliCTdHdv9feyXyaBjVLqDUekO6ptbKaxo6wbV0OwjasSZPLGqnr+t/ZIzhiZy7sgUzjkpmYQemJLVHwjydWE1b60pYUVh2zHSNLhwbDr3/moY2Qn98xdV+fYQA9LQFDt3pdi5a+YwdlY2sWxrBZ+sL6awxkujTxfqGmU26MiMs5ERbyUrzkaszRhRJyJur5/Ciia2lTupc7eG1us1jUFxVgbFWkmJMZNst2A1/XTVpmhzOev6osKi20XqGJGTUmN49YZT2FLayFNLN/NtsZPPt1Xy+bZKMmIMnDvYxrRMC3HW47sa2R/HRvkDQfbWNrOvrm3p6IIBQNA38LoyRrLuiu1wiuvuEmczcergBKbkxrO1YDsf/eOfDJ5+ObUtAb4oqOSLgko0DUalxzAlN4FTBycwOSe+w7GXnREMKjbsb+CLggr+sbGM8sa291LT4Pwxadw5YyjDUvr3L6iSUPSA/t51YKAZlmJnWIqdaXFNnDLtbC6b9wZOQxwltW48/iC7q12h29tHmw1kxFnJjLORGW/Fbunal0tfCirYU+2ioNxJcY2b4MFfVPU6jbzkaIYmR5MZZ5M7EA8wkTpGxBFs5IO7Z+O3JmCfcAFRo2dQ6rSyaKOTV9fX4ynZhLtgBc07V6NaW45ZXn8ZGxUMKvbXN1NY0URRtZvWwE8dxTUN4qNMJESZiI8yEWMxYjbqqCreIb88RqATje1wjOvuomkacSZFw9ev8p//cQ6WtDx+KPPwQ5mHPfV+th5wsvWAk1f+XYwGpNn1ZDmMZDsMpEbribPoibfqsBh0aLTFli0mFnNMAqX1LRRVu9lS2sCPJfU0eX66J0Z8lIkrJmbw28lZ5ETIGC9JKLpRpHUdOJpITpiCLU6yooJkDE0lqBSVTg/761rYX99MeaMHl9fPjoqm0G3vHVYjmXFWMuNtZMRZsZnCN6RKnT5iz/o9nx4w4t1fHlqfEmNmVJqDYanRmA3hOyWdEF1RU1NDS3Mz19zeNjbKF4R9bj/73DrqWvVYcydgzZ2AdoEiyaJIswZJtQaJ7iCUw31s1OFdF3dWNdHcGgg9Z7cYGJwYRVa8jUFx1g5jXZX1n/7a3WkgjzHoK+F0HnHo3O2669qfu+mj4zFnjsaSOQZL1miMCZmUNQUoawrwfekvlVjV4Vq72cBZJyUze1QqM0cmR9zf2/A9++mHIrXrwCEDJWE6RKf9dHfuybnx+ANByho9lNY3s7+uhUqnh8YWH40tPraWtR37OJuRQbFWrF4dentiH7cAalxePtlUxkcby9i0vwHHlN/gDYLVqGdEmp2RaTE90kdUiHBzaK56gNyD6xoO9qPecbAfdZVHo8qjY1N92xXEnAQbGXE20mMtYf3H3xA/iIIGPV+tLmnXncli1DE02c5JqXbSHJaI6q7ZHWSMQe8Lx/OIzp67eQKtNLZqNPo0nD6NZr+GJ6DhCYD/YC6ugGCrh7S4aDITY8hLjmZYip1TcuIZkWbHoI/cX/4loegBkdp1INITpmMx6HVkxdvIirfBkLabzxyob2F/fdsvGLWuVuqbfdQ3+wADGX98jVuWVjFt90am5MYzOTeBnARbj/9Rr3F5WVFYzSeby/h2Vw2Bg32adBq4dq7hnKkTmDQmT+4eLga8WJuJKYMTmJwbT0Ozjz01bopr3JQ1tlDnbqXO3cr6fQ1oWts0jg6lxzJ4Ii2+vp9nsrS+mX9uKufd76sZNOdFtjsBfBh0GoOTohieaic7Pkri/BfIGIPeF87nEd1x7la6axvP3nYFS9atY8KECd1Sr/5CEgpx3CI1YTpeZoOewUnRobtvt/gClDW0cKChhb0VddR5FFVu+HD9AT5cfwBom4FmzKAYxgxyMGqQg9GDHCc8k1Rjs48N++tZX1LPyl01bCptQB3Wc2FshoNLxw8iW6th5rTHGTTjQznJEOIwmqYRF2ViYpSJidlxeHwBSg4OYj7Q0EJji49Kp5dK9KRcMY/rllQybPVKxmXEMi4zlnGZDoal2DH24NXHQFCxqbSBFTuq+FdhFVsPOEPPqYCf1Cgd4wenMTgpWsY/HScZY9D75Dwi8khCIUQ3sRr1DEmKZkhSNIOpYv5/XsP//Wg5dfp41hbXsXF/AzUuL18XVvP1wanioK1bQnZ8FDmJNnISokiMNhNrMxJnM2E06NBrGjoN3K2BUBer/XXN7K1tu5p66I6ahxuVHsOMESlcMj6dIQcTnvXr63vtvRCiu/RF/3aLUc/wVDvDU9tmXXF6fByob6GwpIzdB6oxxqaGxlK982PbMGazQRea1GBoip2hydFkxNkYFGslxmo4rl8mW/1BSmrdFFW72FbmZF1JPZv2N+A+bEyEpsGpuQmMi/PzcP4FXP7Ma2SkxRx3W4UQojtETELx/PPP8/TTT1NRUcG4ceN47rnnmDx5cl9XSwxgqrWF8almJkwYDoDHF2BbmZNtZY1sKW1ky4FGdle58PiCFFa2zYfdVTkJNiZkxXFKbjxnD08m1WHprmYI0SfCqX97jMVITJoRu2s/Kx+9iS++XUswLpPNpQ1s2t/IptIGmjz+g/HtPOL1NpOeNIeFWJsJm0lPtNmAzWRAp4E/qPAFgjR5/NS5W6l1eals8oa6Kh7ObjFw5rAkzh6ezPRhSSTZzaxfv56HWo7cpxBC9KaISCjeeecd7r77bhYuXMiUKVNYsGABs2bNorCwkOTk5L6unugl4ThTx8/L1IDRZhg9BH47JBp/MIpqd4Byl59yV4BKVwCnN4CzVdHkDRJQiqACnU6PI9pKjMWAw2okPdZKTkLbrxrDU+wysFpEnHDu315VspMRNj0zkmBGkpHgyYlUugLsd/rZ7/RTenCpbg7g9AZpbg1QVO0G3J3eR7TZwOCkKIYm25mQHcvE7DiGJtulu6IQIixFRELx7LPPMmfOHH7/+98DsHDhQpYuXcqrr77Kgw8+2Me1Ez0tnK5kHtLddeovc98L0d3CqX97V+JaM5jQ2xMx2BPRzFHoTFY0kxWdyYrBaOT6a68hzhGD1aDhsOhwWHQkWPXEWXQHu0kFgFqay2rZVHZk+TJlqRAiHPT7hKK1tZV169bx0EMPhdbpdDpmzpzJ6tWr+7BmoreE45XM7pzJItznvhdioOjOuN6z9UeW/P9/YcG/3+6OqsmUpUKEkXC6z0Zv6fcJRU1NDYFAgJSUlHbrU1JS2LFjR4ev8Xq9eL3e0OPGxkYAyopO7OAduhJWsXcnRVG2iCsrHOt0eFm+Vi/eliMHKHeWr9UbdnUC8HnbEpx169ad0ElDYWEh0Dat3YnW6VCsKBU+N8KSuI6cOnVnWeH4XQPgaqgD4JTzriIjd2iXy9m3cwvrvvqYvTs2n/AsUwMpriG8P2PhVFY41qk7y+rOOu3dvgHo3vts7Nz84wl93/RaXKt+7sCBAwpQ3333Xbv19913n5o8eXKHr5k7d66i7f4jssgiywksRUVFvRHmnSJxLYss3bNIXMsiS+QtPR3XmlJhdCmiC1pbW7HZbLz//vtceumlofX5+fk0NDTw8ccfH/Gan1/xaGhoIDs7m3379uFwOHqj2j3G6XSSmZnJ/v37iYnp31MIRlJbILLa09jYSFZWFvX19cTGxvZ1dQCJ6/4iktoCkdUeieveFUmfHYis9kRSW3orrvt9lyeTycTEiRNZvnx5KKEIBoMsX76cP/3pTx2+xmw2YzYfOSuOw+Ho9x+cQ2JiYqQtYSqS2qPThc8NtCSu+5dIagtEVnskrntXJH12ILLaE0lt6em47vcJBcDdd99Nfn4+kyZNYvLkySxYsAC32x2a9UkIIYQQQgjRMyIiobjqqquorq7m0UcfpaKigvHjx7Ns2bIjBmoLIYQQQgghuldEJBQAf/rTn47axelYzGYzc+fO7fBn1f5G2hK+Iqk9/aEt/aGOnSVtCV+R1J7+0Jb+UMfOiqS2QGS1R9py/Pr9oGwhhBBCCCFE3wmfkVdCCCGEEEKIfkcSCiGEEEIIIUSXSUIhhBBCCCGE6DJJKIQQQgghhBBdFpEJxfPPP09OTg4Wi4UpU6awdu3aX9z+vffe46STTsJisTBmzBg+/fTTds8rpXj00UdJS0vDarUyc+ZMdu3a1ZNNaOd42vPyyy9zxhlnEBcXR1xcHDNnzjxi+xtuuAFN09ots2fP7ulmAMfXltdee+2Ieloslnbb9OWxOZ62nHXWWUe0RdM0LrjggtA2fXVcVq5cyUUXXUR6ejqaprFkyZJjvmbFihVMmDABs9lMXl4er7322hHbHG8cHovEtcR1b5C47t247kqZ4RzbEtcS1z0prONaRZjFixcrk8mkXn31VbVt2zY1Z84cFRsbqyorKzvcftWqVUqv16unnnpKFRQUqEceeUQZjUa1ZcuW0DZPPPGEcjgcasmSJWrTpk3q4osvVrm5uaqlpSXs2vO73/1OPf/882rDhg1q+/bt6oYbblAOh0OVlpaGtsnPz1ezZ89W5eXloaWuri7s2rJo0SIVExPTrp4VFRXttumrY3O8bamtrW3Xjq1btyq9Xq8WLVoU2qavjsunn36qHn74YfXhhx8qQH300Ue/uP2ePXuUzWZTd999tyooKFDPPfec0uv1atmyZaFtjvf9ORaJa4lrievj0x/iuitlhnNsS1xLXPe0cI7riEsoJk+erG677bbQ40AgoNLT09Vf//rXDre/8sor1QUXXNBu3ZQpU9TNN9+slFIqGAyq1NRU9fTTT4eeb2hoUGazWb399ts90IL2jrc9P+f3+5Xdblevv/56aF1+fr665JJLuruqx3S8bVm0aJFyOBxHLa8vj82JHpf58+cru92uXC5XaF1fHZfDdeYL6v7771ejRo1qt+6qq65Ss2bNCj0+0ffn5ySu25O47hkS170b110pM5xjW+LacdTy+vNxkbjunIjq8tTa2sq6deuYOXNmaJ1Op2PmzJmsXr26w9esXr263fYAs2bNCm1fXFxMRUVFu20cDgdTpkw5apndpSvt+bnm5mZ8Ph/x8fHt1q9YsYLk5GSGDx/OrbfeSm1tbbfW/ee62haXy0V2djaZmZlccsklbNu2LfRcXx2b7jgur7zyCldffTVRUVHt1vf2cemKY8VMd7w/h5O4PpLEdfeTuO7duO5qmeEa2xLXEtcDPa4jKqGoqakhEAiQkpLSbn1KSgoVFRUdvqaiouIXtz/07/GU2V260p6fe+CBB0hPT2/3YZk9ezZvvPEGy5cv58knn+Sbb77hvPPOIxAIdGv9D9eVtgwfPpxXX32Vjz/+mDfffJNgMMhpp51GaWkp0HfH5kSPy9q1a9m6dSs33XRTu/V9cVy64mgx43Q6aWlp6ZbP7eEkro8kcd39JK57N64hsmJb4lrieqDHteGEayvC1hNPPMHixYtZsWJFu8FRV199dej/Y8aMYezYsQwZMoQVK1YwY8aMvqhqh6ZOncrUqVNDj0877TRGjBjBiy++yOOPP96HNTsxr7zyCmPGjGHy5Mnt1veX4yL6lsR1eJK4FidC4jo8SVx3XkT9QpGYmIher6eysrLd+srKSlJTUzt8TWpq6i9uf+jf4ymzu3SlPYc888wzPPHEE3zxxReMHTv2F7cdPHgwiYmJ7N69+4TrfDQn0pZDjEYjJ598cqiefXVsTqQtbrebxYsXc+ONNx5zP71xXLriaDETExOD1WrtlmN9OInrn0hcS1z3lN6Oa4is2Ja4bk/iOjz0ZlxHVEJhMpmYOHEiy5cvD60LBoMsX768XeZ8uKlTp7bbHuDLL78MbZ+bm0tqamq7bZxOJ2vWrDlqmd2lK+0BeOqpp3j88cdZtmwZkyZNOuZ+SktLqa2tJS0trVvq3ZGutuVwgUCALVu2hOrZV8fmRNry3nvv4fV6ufbaa4+5n944Ll1xrJjpjmN9OInrNhLXEtc9qbfjuqtlhmtsS1y3J3EdHno1ro9rCHc/sHjxYmU2m9Vrr72mCgoK1B/+8AcVGxsbmr7suuuuUw8++GBo+1WrVimDwaCeeeYZtX37djV37twOp6CLjY1VH3/8sdq8ebO65JJLenV6yeNpzxNPPKFMJpN6//33201n1tTUpJRSqqmpSd17771q9erVqri4WH311VdqwoQJaujQocrj8YRVW+bNm6c+//xzVVRUpNatW6euvvpqZbFY1LZt29q1ty+OzfG25ZBp06apq6666oj1fXlcmpqa1IYNG9SGDRsUoJ599lm1YcMGVVJSopRS6sEHH1TXXXddaPtD09Ddd999avv27er555/vcBq6X3p/jpfEtcS1xPXx6Q9x3Zky+1NsS1xLXA/kuI64hEIppZ577jmVlZWlTCaTmjx5svr+++9Dz02fPl3l5+e32/7dd99Vw4YNUyaTSY0aNUotXbq03fPBYFD9+c9/VikpKcpsNqsZM2aowsLC3miKUur42pOdna2AI5a5c+cqpZRqbm5Wv/rVr1RSUpIyGo0qOztbzZkz54T+IPRUW+66667QtikpKer8889X69evb1deXx6b4/2c7dixQwHqiy++OKKsvjwuX3/9dYefmUP1z8/PV9OnTz/iNePHj1cmk0kNHjy43fzch/zS+9MVEtcS1+HWFqUkrk80ro9VZn+LbYlrieueFM5xrSml1PH9piGEEEIIIYQQbSJqDIUQQgghhBCid0lCIYQQQgghhOgySSiEEEIIIYQQXSYJhRBCCCGEEKLLJKEQQgghhBBCdJkkFEIIIYQQQoguk4RCCCGEEEII0WWSUESos846i7vuuguAnJwcFixYEHquoqKCc889l6ioKGJjY4+6TggRXiSuhYg8EtciEhj6ugKi5/3www9ERUWFHs+fP5/y8nI2btyIw+E46jrRM8466yzGjx/f7o+GEMdL4jq8SFyL7iBxHV4krjtPEooBICkpqd3joqIiJk6cyNChQ39x3UDj8/kwGo19XQ0hOkXiunMkrkV/InHdORLXYUiJfs/lcqnrrrtORUVFqdTUVPXMM8+o6dOnqzvvvFMppVR2draaP39+6P9AaMnPz+9w3bEA6oUXXlCzZ89WFotF5ebmqvfee6/dNps3b1Znn322slgsKj4+Xs2ZM0c1NTWFnv/666/VKaecomw2m3I4HOq0005Te/fuPea+586dq8aNG6cWLlyoMjIylNVqVVdccYVqaGhot93LL7+sTjrpJGU2m9Xw4cPV888/H3quuLhYAWrx4sXqzDPPVGazWS1atEgppdQrr7yiRo4cqUwmk0pNTVW33XZb6HX19fXqxhtvVImJicput6uzzz5bbdy48Yi6vfHGGyo7O1vFxMSoq666SjmdTqWUUvn5+e3ea0AVFxcfs81i4JG4lrgWkUfiWuI6UklCEQFuvfVWlZWVpb766iu1efNmdeGFFyq73d7hF1RVVZWaPXu2uvLKK1V5eblqaGjocN2xACohIUG9/PLLqrCwUD3yyCNKr9ergoICpVTbl2ZaWpq67LLL1JYtW9Ty5ctVbm5u6MvP5/Mph8Oh7r33XrV7925VUFCgXnvtNVVSUnLMfc+dO1dFRUWpc845R23YsEF98803Ki8vT/3ud78LbfPmm2+qtLQ09cEHH6g9e/aoDz74QMXHx6vXXntNKfXTF1ROTk5om7KyMvXCCy8oi8WiFixYoAoLC9XatWtD751SSs2cOVNddNFF6ocfflA7d+5U99xzj0pISFC1tbWhukVHR4favXLlSpWamqr+67/+SymlVENDg5o6daqaM2eOKi8vV+Xl5crv9x+zzWLgkbiWuBaRR+Ja4jpSSULRzzU1NSmTyaTefffd0Lra2lpltVo7/IJSSqlLLrnkiKsaHa37JYC65ZZb2q2bMmWKuvXWW5VSSr300ksqLi5OuVyu0PNLly5VOp1OVVRUqNraWgWoFStWdHqfh8ydO1fp9XpVWloaWvfZZ58pnU6nysvLlVJKDRkyRP39739v97rHH39cTZ06VSn10xfUggUL2m2Tnp6uHn744Q73++2336qYmBjl8XjarR8yZIh68cUXQ3Wz2WyhKxxKKXXfffepKVOmhB4ffjVKiI5IXLeRuBaRROK6jcR1ZJIxFP1cUVERra2tTJkyJbQuPj6e4cOH9/i+p06desTjjRs3ArB9+3bGjRvXbnDZ6aefTjAYpLCwkDPPPJMbbriBWbNmce655zJz5kyuvPJK0tLSOrXvrKwsBg0a1G7fh8q22+0UFRVx4403MmfOnNA2fr//iAFskyZNCv2/qqqKsrIyZsyY0eE+N23ahMvlIiEhod36lpYWioqKQo9zcnKw2+2hx2lpaVRVVXWqXUKAxPXh+5a4FpFC4vqnfUtcRx5JKESfWbRoEXfccQfLli3jnXfe4ZFHHuHLL7/k1FNPPaFyXS4XAC+//HK7L24AvV7f7vHhX6BWq/WY5aalpbFixYojnjt86r6fDxTTNI1gMNiZqgvR70lcCxF5JK7Fsch9KPq5IUOGYDQaWbNmTWhdfX09O3fu7PF9f//990c8HjFiBAAjRoxg06ZNuN3u0POrVq1Cp9O1uxpz8skn89BDD/Hdd98xevRo/v73v3dq3/v27aOsrKzdvg+VnZKSQnp6Onv27CEvL6/dkpube9Qy7XY7OTk5LF++vMPnJ0yYQEVFBQaD4YhyExMTO1VvAJPJRCAQ6PT2YuCRuP5p3xLXIlJIXP+0b4nryCO/UPRz0dHR3Hjjjdx3330kJCSQnJzMww8/jE7X87nie++9x6RJk5g2bRpvvfUWa9eu5ZVXXgHgmmuuYe7cueTn5/PYY49RXV3N7bffznXXXUdKSgrFxcW89NJLXHzxxaSnp1NYWMiuXbu4/vrrO7Vvi8VCfn4+zzzzDE6nkzvuuIMrr7yS1NRUAObNm8cdd9yBw+Fg9uzZeL1efvzxR+rr67n77ruPWu5jjz3GLbfcQnJyMueddx5NTU2sWrWK22+/nZkzZzJ16lQuvfRSnnrqKYYNG0ZZWRlLly7l17/+dbufY39JTk4Oa9asYe/evURHRxMfH98rx0v0HxLXEtci8khcS1xHMkkoIsDTTz+Ny+Xioosuwm63c88999DY2Njj+503bx6LFy/mj3/8I2lpabz99tuMHDkSAJvNxueff86dd97JKaecgs1m4ze/+Q3PPvts6PkdO3bw+uuvU1tbS1paGrfddhs333xzp/adl5fHZZddxvnnn09dXR0XXnghL7zwQuj5m266CZvNxtNPP819991HVFQUY8aMCd2N9Gjy8/PxeDzMnz+fe++9l8TERC6//HKg7afQTz/9lIcffpjf//73VFdXk5qayplnnklKSkqn37d7772X/Px8Ro4cSUtLC8XFxeTk5HT69WJgkLiWuBaRR+Ja4jpSaUop1deVEP2Ppml89NFHXHrppb2+78cee4wlS5aEBpQJIbqHxLUQkUfiWvQG+d1GCCGEEEII0WWSUIgjvPXWW0RHR3e4jBo1qsf3P2rUqKPu/6233urx/QsRiSSuhYg8EtciXEiXJ3GEpqYmKisrO3zOaDSSnZ3do/svKSnB5/N1+FxKSkq7OaOFEJ0jcS1E5JG4FuFCEgohhBBCCCFEl0mXJyGEEEIIIUSXSUIhhBBCCCGE6DJJKIQQQgghhBBdJgmFEEIIIYQQosskoRBCCCGEEEJ0mSQUQgghhBBCiC6ThEIIIYQQQgjRZZJQCCGEEEIIIbrs/wGh/0/ja0aengAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] }, "metadata": {}, "output_type": "display_data" @@ -454,31 +2158,38 @@ "ax.set(xlim=(0, 1))\n", "f.suptitle(\"Percent of diff_pos of all BAD-SUB\", fontsize=12)\n", "plt.show()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-07-19T10:23:59.005619Z", - "start_time": "2023-07-19T10:23:54.861742Z" - } - } + ] }, { "cell_type": "code", - "execution_count": 416, + "execution_count": 99, + "metadata": { + "ExecuteTime": { + "end_time": "2023-07-19T10:28:23.548919Z", + "start_time": "2023-07-19T10:28:22.673989Z" + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "outputs": [ { "data": { - "text/plain": "" + "text/plain": [ + "" + ] }, - "execution_count": 416, + "execution_count": 99, "metadata": {}, "output_type": "execute_result" }, { "data": { - "text/plain": "
", - "image/png": 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" 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", 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" 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", + "text/plain": [ + "
" + ] }, "metadata": {}, "output_type": "display_data" @@ -520,27 +2236,29 @@ " fmt=\".0f\",\n", ").set(title='#total_sub vs #diff_pos in sentences')\n", "plt.show()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-07-19T10:24:00.116236Z", - "start_time": "2023-07-19T10:23:59.016911Z" - } - } + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "source": [ "## Analyse Syntax Trees (using Stanza & Tree Edit Distance)" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 101, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "outputs": [], "source": [ "# process saved stanza sentences to original format\n", @@ -565,77 +2283,194 @@ "\n", " sentence = StanzaSentence(tokens=tokens)\n", " return sentence" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", - "execution_count": 304, + "execution_count": 102, + "metadata": { + "ExecuteTime": { + "end_time": "2023-07-19T09:36:35.619460Z", + "start_time": "2023-07-19T09:30:26.771815Z" + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - " 0%| | 22/5160 [00:01<06:32, 13.08it/s]19-Jul 11:30:28 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", - " 2%|▏ | 109/5160 [00:06<06:06, 13.76it/s]19-Jul 11:30:33 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", - " 7%|▋ | 386/5160 [00:33<05:47, 13.72it/s]19-Jul 11:31:00 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", - "19-Jul 11:31:00 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", - " 9%|▉ | 462/5160 [00:42<04:36, 17.01it/s]19-Jul 11:31:09 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", - " 14%|█▍ | 724/5160 [01:06<14:20, 5.15it/s]19-Jul 11:31:32 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", - "19-Jul 11:31:32 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", - " 14%|█▍ | 738/5160 [01:06<04:37, 15.93it/s]19-Jul 11:31:33 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", - "19-Jul 11:31:33 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", - " 19%|█▉ | 974/5160 [01:21<02:10, 31.98it/s]19-Jul 11:31:48 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", - " 20%|█▉ | 1015/5160 [01:23<02:57, 23.34it/s]19-Jul 11:31:49 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", - " 22%|██▏ | 1123/5160 [01:26<02:24, 27.99it/s]19-Jul 11:31:53 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", - " 23%|██▎ | 1175/5160 [01:29<02:29, 26.71it/s]19-Jul 11:31:55 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", - " 24%|██▍ | 1242/5160 [01:31<02:34, 25.42it/s]19-Jul 11:31:58 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", - " 24%|██▍ | 1260/5160 [01:32<03:43, 17.42it/s]19-Jul 11:31:59 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", - " 27%|██▋ | 1394/5160 [01:39<03:37, 17.31it/s]19-Jul 11:32:06 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", - "19-Jul 11:32:06 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", - " 30%|██▉ | 1538/5160 [01:46<02:30, 24.05it/s]19-Jul 11:32:13 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", - "19-Jul 11:32:13 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", - " 38%|███▊ | 1935/5160 [02:11<04:04, 13.19it/s]19-Jul 11:32:37 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", - " 41%|████ | 2090/5160 [02:20<02:30, 20.35it/s]19-Jul 11:32:47 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", - "19-Jul 11:32:47 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", - " 43%|████▎ | 2228/5160 [02:27<02:17, 21.36it/s]19-Jul 11:32:54 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", - "19-Jul 11:32:54 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", - " 49%|████▊ | 2512/5160 [02:46<04:09, 10.62it/s]19-Jul 11:33:12 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", - "19-Jul 11:33:12 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", - " 60%|█████▉ | 3092/5160 [03:29<02:34, 13.40it/s]19-Jul 11:33:56 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", - " 65%|██████▌ | 3373/5160 [03:52<02:09, 13.85it/s]19-Jul 11:34:19 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", - "19-Jul 11:34:19 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", - " 67%|██████▋ | 3443/5160 [03:57<01:31, 18.77it/s]19-Jul 11:34:24 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", - " 84%|████████▎ | 4309/5160 [04:38<00:50, 16.71it/s]19-Jul 11:35:05 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", - " 84%|████████▍ | 4339/5160 [04:41<01:39, 8.23it/s]19-Jul 11:35:07 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", - " 86%|████████▌ | 4427/5160 [04:50<01:11, 10.23it/s]19-Jul 11:35:17 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", - "19-Jul 11:35:17 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", - " 87%|████████▋ | 4473/5160 [04:54<00:52, 13.00it/s]19-Jul 11:35:20 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", - "19-Jul 11:35:20 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", - " 88%|████████▊ | 4520/5160 [04:58<01:05, 9.77it/s]19-Jul 11:35:25 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", - " 89%|████████▉ | 4613/5160 [05:08<00:47, 11.50it/s]19-Jul 11:35:35 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", - " 91%|█████████ | 4693/5160 [05:16<00:50, 9.33it/s]19-Jul 11:35:42 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", - "19-Jul 11:35:42 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", - " 91%|█████████ | 4702/5160 [05:16<00:42, 10.67it/s]19-Jul 11:35:43 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", - " 91%|█████████▏| 4721/5160 [05:19<00:46, 9.50it/s]19-Jul 11:35:46 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", - "19-Jul 11:35:46 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", - " 92%|█████████▏| 4749/5160 [05:22<00:34, 11.84it/s]19-Jul 11:35:49 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", - "19-Jul 11:35:49 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", - " 93%|█████████▎| 4815/5160 [05:28<00:38, 8.94it/s]19-Jul 11:35:55 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", - " 97%|█████████▋| 4985/5160 [05:42<00:14, 11.82it/s]19-Jul 11:36:09 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", - " 97%|█████████▋| 5030/5160 [05:47<00:18, 7.12it/s]19-Jul 11:36:14 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", - " 99%|█████████▉| 5133/5160 [05:56<00:02, 9.15it/s]19-Jul 11:36:22 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", - "100%|██████████| 5160/5160 [05:58<00:00, 14.41it/s]\n" + " 0%|▌ | 23/5160 [00:01<05:18, 16.14it/s]17-Sep 19:55:44 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 2%|██▋ | 109/5160 [00:09<04:46, 17.65it/s]17-Sep 19:55:52 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 7%|█████████▌ | 386/5160 [00:28<05:09, 15.44it/s]17-Sep 19:56:12 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + "17-Sep 19:56:12 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 9%|███████████▎ | 462/5160 [00:34<04:54, 15.98it/s]17-Sep 19:56:17 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 14%|█████████████████▊ | 722/5160 [00:54<09:39, 7.66it/s]17-Sep 19:56:38 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + "17-Sep 19:56:38 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 14%|██████████████████▏ | 739/5160 [00:55<04:19, 17.03it/s]17-Sep 19:56:39 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + "17-Sep 19:56:39 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 19%|████████████████████████ | 977/5160 [01:07<01:51, 37.50it/s]17-Sep 19:56:51 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 20%|████████████████████████▋ | 1013/5160 [01:09<02:30, 27.48it/s]17-Sep 19:56:52 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 22%|███████████████████████████▍ | 1122/5160 [01:12<02:01, 33.31it/s]17-Sep 19:56:55 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 23%|████████████████████████████▋ | 1175/5160 [01:14<03:24, 19.48it/s]17-Sep 19:56:57 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 24%|██████████████████████████████▎ | 1239/5160 [01:16<02:17, 28.43it/s]17-Sep 19:57:00 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 24%|██████████████████████████████▋ | 1258/5160 [01:17<03:02, 21.43it/s]17-Sep 19:57:00 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 27%|██████████████████████████████████ | 1395/5160 [01:23<02:59, 20.96it/s]17-Sep 19:57:07 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + "17-Sep 19:57:07 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 30%|█████████████████████████████████████▌ | 1538/5160 [01:30<04:02, 14.91it/s]17-Sep 19:57:13 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + "17-Sep 19:57:13 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 38%|███████████████████████████████████████████████▎ | 1935/5160 [01:54<04:00, 13.43it/s]17-Sep 19:57:38 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 41%|███████████████████████████████████████████████████ | 2090/5160 [02:03<02:29, 20.52it/s]17-Sep 19:57:47 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + "17-Sep 19:57:47 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 43%|██████████████████████████████████████████████████████▍ | 2229/5160 [02:11<02:07, 22.99it/s]17-Sep 19:57:54 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + "17-Sep 19:57:54 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 49%|█████████████████████████████████████████████████████████████▎ | 2512/5160 [02:30<04:24, 10.02it/s]17-Sep 19:58:13 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + "17-Sep 19:58:13 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 60%|███████████████████████████████████████████████████████████████████████████▌ | 3092/5160 [03:15<02:47, 12.33it/s]17-Sep 19:58:58 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 65%|██████████████████████████████████████████████████████████████████████████████████▎ | 3373/5160 [03:37<02:06, 14.13it/s]17-Sep 19:59:21 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + "17-Sep 19:59:21 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 67%|████████████████████████████████████████████████████████████████████████████████████ | 3443/5160 [03:43<01:33, 18.32it/s]17-Sep 19:59:26 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 84%|█████████████████████████████████████████████████████████████████████████████████████████████████████████▏ | 4309/5160 [04:25<00:49, 17.03it/s]17-Sep 20:00:08 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 84%|█████████████████████████████████████████████████████████████████████████████████████████████████████████▉ | 4339/5160 [04:27<01:04, 12.75it/s]17-Sep 20:00:10 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 86%|████████████████████████████████████████████████████████████████████████████████████████████████████████████▏ | 4428/5160 [04:35<01:04, 11.40it/s]17-Sep 20:00:18 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + "17-Sep 20:00:18 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 87%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████▏ | 4473/5160 [04:39<00:59, 11.54it/s]17-Sep 20:00:22 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + "17-Sep 20:00:22 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 88%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████▍ | 4521/5160 [04:44<01:20, 7.89it/s]17-Sep 20:00:27 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 89%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋ | 4613/5160 [04:55<00:52, 10.44it/s]17-Sep 20:00:38 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 91%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌ | 4693/5160 [05:02<00:41, 11.34it/s]17-Sep 20:00:45 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + "17-Sep 20:00:45 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 91%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊ | 4701/5160 [05:02<00:44, 10.37it/s]17-Sep 20:00:46 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 91%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎ | 4721/5160 [05:05<00:48, 9.06it/s]17-Sep 20:00:48 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + "17-Sep 20:00:48 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 92%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉ | 4749/5160 [05:08<00:35, 11.45it/s]17-Sep 20:00:52 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + "17-Sep 20:00:52 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 93%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌ | 4815/5160 [05:16<00:38, 8.89it/s]17-Sep 20:00:59 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 97%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋ | 4985/5160 [05:31<00:17, 9.88it/s]17-Sep 20:01:15 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 97%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊ | 5030/5160 [05:35<00:15, 8.56it/s]17-Sep 20:01:19 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + " 99%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎| 5133/5160 [05:44<00:03, 7.01it/s]17-Sep 20:01:27 - [WARNING]: Can only create a tree for a sentence if it has one (and only one) root (Word.is_root). A tree for this sentence was not created.\n", + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5160/5160 [05:47<00:00, 14.87it/s]\n" ] }, { "data": { - "text/plain": " unit_id lang_id ted\n0 flores101-main-ukr-100-pe1-1 ukr 14\n1 flores101-main-ukr-100-pe1-2 ukr 4\n2 flores101-main-ukr-100-pe1-3 ukr 9\n3 flores101-main-ukr-100-pe1-4 ukr 12\n4 flores101-main-ukr-100-pe1-5 ukr 14\n... ... ... ...\n5155 flores101-main-vie-106-pe2-4 vie 16\n5156 flores101-main-vie-48-pe1-1 vie 11\n5157 flores101-main-vie-48-pe1-2 vie 27\n5158 flores101-main-vie-48-pe1-3 vie 16\n5159 flores101-main-vie-48-pe1-4 vie 10\n\n[5160 rows x 3 columns]", - "text/html": "
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5160 rows × 3 columns

\n", + "
" + ], + "text/plain": [ + " unit_id lang_id ted\n", + "0 flores101-main-ukr-100-pe1-1 ukr 14\n", + "1 flores101-main-ukr-100-pe1-2 ukr 4\n", + "2 flores101-main-ukr-100-pe1-3 ukr 9\n", + "3 flores101-main-ukr-100-pe1-4 ukr 12\n", + "4 flores101-main-ukr-100-pe1-5 ukr 14\n", + "... ... ... ...\n", + "5155 flores101-main-vie-106-pe2-4 vie 16\n", + "5156 flores101-main-vie-48-pe1-1 vie 11\n", + "5157 flores101-main-vie-48-pe1-2 vie 27\n", + "5158 flores101-main-vie-48-pe1-3 vie 16\n", + "5159 flores101-main-vie-48-pe1-4 vie 10\n", + "\n", + "[5160 rows x 3 columns]" + ] }, - "execution_count": 304, + "execution_count": 102, "metadata": {}, "output_type": "execute_result" } @@ -643,8 +2478,6 @@ "source": [ "# use astred to calculate tree edit distance\n", "\n", - "df_synt_scores = pd.DataFrame()\n", - "\n", "langs = {\n", " 'vie': 'vi',\n", " 'tur': 'tr',\n", @@ -654,19 +2487,15 @@ " 'nld': 'nl',\n", "}\n", "\n", - "for _id, x in tqdm(df.iterrows(), total=len(df)):\n", - " pe_tokens = eval(x['tgt_tokens'])\n", - " pe_annotations = eval(x['tgt_annotations'])\n", - " mt_tokens = eval(x['mt_tokens'])\n", - " mt_annotations = eval(x['mt_annotations'])\n", - " mt_tbd_qe = eval(x['mt_tbd_qe'])\n", - " mt_pe_alignments_raw = eval(x['mt_pe_tbd_qe_alignments'])\n", - " mt_pe_alignments_dict = defaultdict(list)\n", - "\n", - " for k, v, score in mt_pe_alignments_raw:\n", - " if k is not None:\n", - " mt_pe_alignments_dict[k].append(v)\n", + "synt_scores_list = []\n", "\n", + "for _id, x in tqdm(df.iterrows(), total=len(df)):\n", + " pe_tokens = x['tgt_tokens']\n", + " pe_annotations = x['tgt_annotations']\n", + " mt_tokens = x['mt_tokens']\n", + " mt_annotations = x['mt_annotations']\n", + " mt_tbd_qe = x['mt_tbd_qe']\n", + " mt_pe_alignments_dict = x['mt_pe_tbd_qe_alignments_dict']\n", " mt_pe_alignments_pairs = [(k, v[0]) for k, v in mt_pe_alignments_dict.items() if len(v) > 0 and v[0] is not None]\n", "\n", " # fix 2 sentences examples with 2 heads to be 1 headed (match to first head\n", @@ -720,33 +2549,29 @@ " print('pe_annotations', [i['head'] for i in pe_annotations])\n", " ted = None\n", "\n", - " _df_synt_scores = pd.DataFrame([{\n", + " synt_scores_list.append({\n", " 'unit_id': _id,\n", " 'lang_id': x['lang_id'],\n", " 'ted': int(ted),\n", - " }])\n", - " df_synt_scores = pd.concat([df_synt_scores, _df_synt_scores], ignore_index=True)\n", + " })\n", "\n", + "df_synt_scores = pd.DataFrame(synt_scores_list)\n", "df_synt_scores['ted'] = df_synt_scores['ted'].astype(int)\n", "\n", "df_synt_scores" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-07-19T09:36:35.619460Z", - "start_time": "2023-07-19T09:30:26.771815Z" - } - } + ] }, { "cell_type": "code", - "execution_count": 419, + "execution_count": 116, + "metadata": {}, "outputs": [ { "data": { - "text/plain": "
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" 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", + "text/plain": [ + "
" + ] }, "metadata": {}, "output_type": "display_data" @@ -757,32 +2582,39 @@ " df_synt_scores,\n", " x=\"ted\",\n", " hue=\"lang_id\",\n", - " kde=True,\n", + " # kde=True,\n", + " kind='kde',\n", " # log_scale=(False, 2),\n", " multiple=\"layer\",\n", - " alpha=0.15,\n", + " # alpha=0.1,\n", " # facet_kws={'hist_kws':dict(alpha=0.1)}\n", ")\n", "plt.xlim(-1, 30)\n", "plt.ylim(0, None)\n", + "plt.title('Distribution of Tree Edit Distance (TED)')\n", "plt.show()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-07-19T10:31:42.618669Z", - "start_time": "2023-07-19T10:31:39.680391Z" - } - } + ] }, { "cell_type": "code", - "execution_count": 372, + "execution_count": 290, + "metadata": { + "ExecuteTime": { + "end_time": "2023-07-19T10:06:31.926535Z", + "start_time": "2023-07-19T10:06:27.991091Z" + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "outputs": [ { "data": { - "text/plain": "
", - "image/png": 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" + "image/png": 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" + ] }, "metadata": {}, "output_type": "display_data" @@ -792,7 +2624,9 @@ "\n", "f, axes = plt.subplots(2, 3, figsize=(9, 6), sharex=True, sharey=True)\n", "\n", - "for ax, lang in zip(axes.flat, df['lang_id'].unique()):\n", + "# langs = df['lang_id'].unique()\n", + "langs = ['ukr', 'ara', 'nld', 'vie', 'ita', 'tur']\n", + "for ax, lang in zip(axes.flat, langs):\n", " #\n", " # # Create a cubehelix colormap to use with kdeplot\n", " # cmap = sns.cubehelix_palette(start=s, light=1, as_cmap=True)\n", @@ -811,47 +2645,305 @@ " ax.set_title(lang)\n", " # ax.set_axis_off()\n", "\n", - "ax.set(xlim=(-1, 30))\n", - "f.suptitle(\"Distribution of Tree Edit Distance (TED)\", fontsize=12)\n", + "ax.set(xlim=(-1, 30))\n", + "f.suptitle(\"Distribution of Tree Edit Distance (TED)\", fontsize=12)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 286, + "metadata": { + "ExecuteTime": { + "end_time": "2023-07-19T09:36:35.620924Z", + "start_time": "2023-07-19T09:36:30.127487Z" + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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unit_idlang_idtedmt_tbd_bad_count
3812flores101-main-tur-16-pe2-3tur119
2849flores101-main-nld-101-pe1-3nld5718
4357flores101-main-vie-19-pe1-4vie87
3288flores101-main-nld-53-pe1-1nld108
3750flores101-main-tur-15-pe1-3tur43
\n", + "
" + ], + "text/plain": [ + " unit_id lang_id ted mt_tbd_bad_count\n", + "3812 flores101-main-tur-16-pe2-3 tur 11 9\n", + "2849 flores101-main-nld-101-pe1-3 nld 57 18\n", + "4357 flores101-main-vie-19-pe1-4 vie 8 7\n", + "3288 flores101-main-nld-53-pe1-1 nld 10 8\n", + "3750 flores101-main-tur-15-pe1-3 tur 4 3" + ] + }, + "execution_count": 286, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_synt_scores['mt_tbd_bad_count'] = df['mt_tbd_qe'].apply(lambda x: sum(len(i - {'OK', 'BAD-DEL-R'}) for i in x)).values\n", + "df_synt_scores.sample(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 287, + "metadata": { + "ExecuteTime": { + "end_time": "2023-07-19T09:43:18.169352Z", + "start_time": "2023-07-19T09:43:18.104678Z" + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pearson correlation\n" + ] + }, + { + "data": { + "text/plain": [ + "lang_id \n", + "ara ted 0.539678\n", + "ita ted 0.442620\n", + "nld ted 0.734874\n", + "tur ted 0.726095\n", + "ukr ted 0.776998\n", + "vie ted 0.707215\n", + "Name: mt_tbd_bad_count, dtype: float64" + ] + }, + "execution_count": 287, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print('Pearson correlation')\n", + "df_synt_scores.groupby('lang_id')[['ted', 'mt_tbd_bad_count']].corr(method='pearson').loc[(slice(None),'ted'), 'mt_tbd_bad_count']" + ] + }, + { + "cell_type": "code", + "execution_count": 288, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: BAD count\n" + ] + }, + { + "data": { + "image/png": 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unit_idlang_idtedmt_tbd_bad_count
2864flores101-main-nld-100-pe2-1nld114
433flores101-main-ukr-5-pe2-2ukr26
4835flores101-main-vie-93-pe1-5vie72
4776flores101-main-vie-69-pe1-4vie182
2975flores101-main-nld-39-pe1-5nld00
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1855flores101-main-ita-56-pe2-2ita02
\n", + "
" + ], + "text/plain": [ + " unit_id lang_id ted mt_tbd_bad_count\n", + "3702 flores101-main-tur-98-pe1-1 tur 0 0\n", + "77 flores101-main-ukr-28-pe2-4 ukr 36 23\n", + "2904 flores101-main-nld-15-pe1-3 nld 3 1\n", + "2644 flores101-main-nld-28-pe1-1 nld 0 0\n", + "1855 flores101-main-ita-56-pe2-2 ita 0 2" + ] }, - "execution_count": 309, + "execution_count": 280, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df_synt_scores['mt_tbd_bad_count'] = df['mt_tbd_qe'].apply(eval).apply(lambda x: sum(len(i - {'OK', 'BAD-DEL-L', 'BAD-DEL-R', 'BAD-SHF'}) for i in x)).values\n", + "df_synt_scores['mt_tbd_bad_count'] = df['mt_tbd_qe'].apply(lambda x: sum(len(i - {'OK', 'BAD-DEL-L', 'BAD-DEL-R', 'BAD-SHF'}) for i in x)).values\n", "df_synt_scores.sample(5)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-07-19T09:36:35.620924Z", - "start_time": "2023-07-19T09:36:30.127487Z" - } - } + ] }, { "cell_type": "code", - "execution_count": 327, + "execution_count": 281, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -862,9 +2954,18 @@ }, { "data": { - "text/plain": "lang_id \nara ted 0.556922\nita ted 0.434015\nnld ted 0.669098\ntur ted 0.689409\nukr ted 0.710757\nvie ted 0.642947\nName: mt_tbd_bad_count, dtype: float64" + "text/plain": [ + "lang_id \n", + "ara ted 0.556922\n", + "ita ted 0.434015\n", + "nld ted 0.669098\n", + "tur ted 0.689409\n", + "ukr ted 0.710757\n", + "vie ted 0.642947\n", + "Name: mt_tbd_bad_count, dtype: float64" + ] }, - "execution_count": 327, + "execution_count": 281, "metadata": {}, "output_type": "execute_result" } @@ -872,18 +2973,21 @@ "source": [ "print('Pearson correlation')\n", "df_synt_scores.groupby('lang_id')[['ted', 'mt_tbd_bad_count']].corr(method='pearson').loc[(slice(None),'ted'), 'mt_tbd_bad_count']" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-07-19T09:43:18.169352Z", - "start_time": "2023-07-19T09:43:18.104678Z" - } - } + ] }, { "cell_type": "code", - "execution_count": 355, + "execution_count": 282, + "metadata": { + "ExecuteTime": { + "end_time": "2023-07-19T10:07:10.961389Z", + "start_time": "2023-07-19T10:07:04.374076Z" + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "outputs": [ { "name": "stdout", @@ -894,8 +2998,10 @@ }, { "data": { - "text/plain": "
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" 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", + "text/plain": [ + "
" + ] }, "metadata": {}, "output_type": "display_data" @@ -903,45 +3009,12 @@ ], "source": [ "print('Note: BAD count except for BAD-DEL and BAD-SHF')\n", - "sns.jointplot(df_synt_scores, x=\"ted\", y=\"mt_tbd_bad_count\", hue=\"lang_id\", kind=\"kde\", fill=Fl, marginal_kws={'hist_kws':dict(alpha=0.1)})\n", - "plt.xlim(-5, 60)\n", - "plt.ylim(-5, 30)\n", - "plt.show()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-07-19T09:59:48.479409Z", - "start_time": "2023-07-19T09:59:42.344089Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 374, - "outputs": [ - { - "data": { - "text/plain": "Text(0.5, 0.98, 'Correlation of Tree Edit Distance (TED) vs #BAD-X tags')" - }, - "execution_count": 374, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": "
", - "image/png": 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- }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ "\n", "f, axes = plt.subplots(2, 3, figsize=(9, 6), sharex=True, sharey=True)\n", "\n", - "for ax, lang in zip(axes.flat, df['lang_id'].unique()):\n", + "# langs = df['lang_id'].unique()\n", + "langs = ['ukr', 'ara', 'nld', 'vie', 'ita', 'tur']\n", + "for ax, lang in zip(axes.flat, langs):\n", " #\n", " # # Create a cubehelix colormap to use with kdeplot\n", " # cmap = sns.cubehelix_palette(start=s, light=1, as_cmap=True)\n", @@ -961,30 +3034,36 @@ " ax.set_title(lang)\n", " # ax.set_axis_off()\n", "\n", - "ax.set(xlim=(-7, 40), ylim=(-5, 25))\n", - "f.suptitle(\"Correlation of Tree Edit Distance (TED) vs #BAD-X tags\", fontsize=12)\n", + "ax.set(xlim=(-5, 40), ylim=(-5, 25))\n", + "f.suptitle(\"Correlation of Tree Edit Distance (TED) vs #BAD-X tags (\\\\wo BAD-DEL/BAD-SHF)\", fontsize=12)\n", "plt.show()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-07-19T10:07:10.961389Z", - "start_time": "2023-07-19T10:07:04.374076Z" - } - } + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "source": [ "---" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 227, + "metadata": { + "ExecuteTime": { + "end_time": "2023-07-18T17:00:42.468181Z", + "start_time": "2023-07-18T17:00:42.176945Z" + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "outputs": [], "source": [ "import matplotlib.ticker as ticker\n", @@ -1012,11 +3091,11 @@ " # Draw a dot plot using the stripplot function\n", " g.map(\n", " sns.stripplot,\n", - " size=15,\n", + " size=10,\n", " orient=\"h\",\n", " jitter=False,\n", " palette=\"flare_r\",\n", - " linewidth=2,\n", + " linewidth=1,\n", " edgecolor=\"w\",\n", " )\n", "\n", @@ -1028,8 +3107,28 @@ " for ax, col in zip(g.axes.flat, summary_df.columns):\n", " avg = summary_df[col].mean()\n", " ax.axvline(avg, color='r', linestyle='--')\n", + " \n", + " if 'total' not in title:\n", + " continue\n", "\n", - " step = 5000 if summary_df[col].max() > 10000 else 2500 if summary_df[col].max() > 5000 else 1000 if summary_df[col].max() > 2000 else 500 if summary_df[col].max() > 800 else 100 if summary_df[col].max() > 500 else 50\n", + " step = 1\n", + " if summary_df[col].max() > 15000:\n", + " step = 10000\n", + " elif summary_df[col].max() > 10000:\n", + " step = 5000\n", + " elif summary_df[col].max() > 5000:\n", + " step = 2500\n", + " elif summary_df[col].max() > 2000:\n", + " step = 1000\n", + " elif summary_df[col].max() > 800:\n", + " step = 500\n", + " elif summary_df[col].max() > 500:\n", + " step = 100\n", + " elif summary_df[col].max() > 1:\n", + " step = 50\n", + " else:\n", + " step = 1\n", + " \n", " ax.set_xticks(np.arange(0, summary_df[col].max(), step=step))\n", " ax.xaxis.set_major_formatter(ticker.EngFormatter())\n", "\n", @@ -1040,103 +3139,926 @@ " ax.set(title=title)\n", " ax.xaxis.grid(True)\n", " ax.yaxis.grid(True)\n", + " if 'total' not in title:\n", + " # ax.set_xlim(0, 1)\n", + " ax.set_xlabel('Rate')\n", "\n", " sns.despine(left=True, bottom=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 228, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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unit_idlang_idmt_tokpe_tokmt_pospe_possame_wordsame_possame_lemmasame_morfsame_deprel
0flores101-main-ukr-100-pe1-1ukrпривідADPADPFalseTrueFalseFalseTrue
1flores101-main-ukr-100-pe1-1ukrвступіповерненняNOUNNOUNFalseTrueFalseFalseFalse
2flores101-main-ukr-100-pe1-1ukrфазифазаNOUNNOUNFalseTrueTrueFalseFalse
3flores101-main-ukr-100-pe1-1ukrбутипроходитиAUXVERBFalseFalseFalseTrueFalse
4flores101-main-ukr-100-pe1-3ukrПовернувшисьПрожившиVERBVERBFalseTrueFalseTrueTrue
....................................
14804flores101-main-vie-48-pe1-3viebằngtrênADPADPFalseTrueFalseTrueTrue
14805flores101-main-vie-48-pe1-3vievận chuyểntàu thuyềnVERBNOUNFalseFalseFalseTrueFalse
14806flores101-main-vie-48-pe1-3viecuộcđoànNOUNNOUNFalseTrueFalseTrueTrue
14807flores101-main-vie-48-pe1-3viethoạitruyềnNOUNVERBFalseFalseFalseTrueTrue
14808flores101-main-vie-48-pe1-4vieđiện thoạithôngNOUNADJFalseFalseFalseTrueFalse
\n", + "

14809 rows × 11 columns

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" + ], + "text/plain": [ + " unit_id lang_id mt_tok pe_tok mt_pos \\\n", + "0 flores101-main-ukr-100-pe1-1 ukr при від ADP \n", + "1 flores101-main-ukr-100-pe1-1 ukr вступі повернення NOUN \n", + "2 flores101-main-ukr-100-pe1-1 ukr фази фаза NOUN \n", + "3 flores101-main-ukr-100-pe1-1 ukr бути проходити AUX \n", + "4 flores101-main-ukr-100-pe1-3 ukr Повернувшись Проживши VERB \n", + "... ... ... ... ... ... \n", + "14804 flores101-main-vie-48-pe1-3 vie bằng trên ADP \n", + "14805 flores101-main-vie-48-pe1-3 vie vận chuyển tàu thuyền VERB \n", + "14806 flores101-main-vie-48-pe1-3 vie cuộc đoàn NOUN \n", + "14807 flores101-main-vie-48-pe1-3 vie thoại truyền NOUN \n", + "14808 flores101-main-vie-48-pe1-4 vie điện thoại thông NOUN \n", + "\n", + " pe_pos same_word same_pos same_lemma same_morf same_deprel \n", + "0 ADP False True False False True \n", + "1 NOUN False True False False False \n", + "2 NOUN False True True False False \n", + "3 VERB False False False True False \n", + "4 VERB False True False True True \n", + "... ... ... ... ... ... ... \n", + "14804 ADP False True False True True \n", + "14805 NOUN False False False True False \n", + "14806 NOUN False True False True True \n", + "14807 VERB False False False True True \n", + "14808 ADJ False False False True False \n", + "\n", + "[14809 rows x 11 columns]" + ] + }, + "execution_count": 228, + "metadata": {}, + "output_type": "execute_result" + } ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-07-18T17:00:42.468181Z", - "start_time": "2023-07-18T17:00:42.176945Z" + "source": [ + "df_stats" + ] + }, + { + "cell_type": "code", + "execution_count": 229, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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unit_idlang_idmt_tbd_qe_tagsOKBAD-SUBBAD-DEL-RBAD-DEL-LBAD-SHFBAD-CONBAD-EXPBAD-INS
0flores101-main-ukr-100-pe1-1ukr{OK}TrueFalseFalseFalseFalseFalseFalseFalse
1flores101-main-ukr-100-pe1-1ukr{BAD-SUB}FalseTrueFalseFalseFalseFalseFalseFalse
2flores101-main-ukr-100-pe1-1ukr{BAD-SUB, BAD-DEL-R}FalseTrueTrueFalseFalseFalseFalseFalse
3flores101-main-ukr-100-pe1-1ukr{OK, BAD-DEL-L}TrueFalseFalseTrueFalseFalseFalseFalse
4flores101-main-ukr-100-pe1-1ukr{OK}TrueFalseFalseFalseFalseFalseFalseFalse
....................................
108478flores101-main-vie-48-pe1-4vie{OK}TrueFalseFalseFalseFalseFalseFalseFalse
108479flores101-main-vie-48-pe1-4vie{OK}TrueFalseFalseFalseFalseFalseFalseFalse
108480flores101-main-vie-48-pe1-4vie{OK}TrueFalseFalseFalseFalseFalseFalseFalse
108481flores101-main-vie-48-pe1-4vie{OK}TrueFalseFalseFalseFalseFalseFalseFalse
108482flores101-main-vie-48-pe1-4vie{OK}TrueFalseFalseFalseFalseFalseFalseFalse
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108483 rows × 11 columns

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" + ], + "text/plain": [ + " unit_id lang_id mt_tbd_qe_tags OK \\\n", + "0 flores101-main-ukr-100-pe1-1 ukr {OK} True \n", + "1 flores101-main-ukr-100-pe1-1 ukr {BAD-SUB} False \n", + "2 flores101-main-ukr-100-pe1-1 ukr {BAD-SUB, BAD-DEL-R} False \n", + "3 flores101-main-ukr-100-pe1-1 ukr {OK, BAD-DEL-L} True \n", + "4 flores101-main-ukr-100-pe1-1 ukr {OK} True \n", + "... ... ... ... ... \n", + "108478 flores101-main-vie-48-pe1-4 vie {OK} True \n", + "108479 flores101-main-vie-48-pe1-4 vie {OK} True \n", + "108480 flores101-main-vie-48-pe1-4 vie {OK} True \n", + "108481 flores101-main-vie-48-pe1-4 vie {OK} True \n", + "108482 flores101-main-vie-48-pe1-4 vie {OK} True \n", + "\n", + " BAD-SUB BAD-DEL-R BAD-DEL-L BAD-SHF BAD-CON BAD-EXP BAD-INS \n", + "0 False False False False False False False \n", + "1 True False False False False False False \n", + "2 True True False False False False False \n", + "3 False False True False False False False \n", + "4 False False False False False False False \n", + "... ... ... ... ... ... ... ... \n", + "108478 False False False False False False False \n", + "108479 False False False False False False False \n", + "108480 False False False False False False False \n", + "108481 False False False False False False False \n", + "108482 False False False False False False False \n", + "\n", + "[108483 rows x 11 columns]" + ] + }, + "execution_count": 229, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_error_types" + ] + }, + { + "cell_type": "code", + "execution_count": 230, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "lang_id\n", + "ara 2055\n", + "ita 2368\n", + "nld 1728\n", + "tur 2217\n", + "ukr 4295\n", + "vie 2146\n", + "dtype: int64" + ] + }, + "execution_count": 230, + "metadata": {}, + "output_type": "execute_result" } - } + ], + "source": [ + "_df_stats[_df_stats['BAD-SUB']].groupby(['lang_id']).size()" + ] }, { "cell_type": "code", - "execution_count": 190, + "execution_count": 246, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "TOTAL:\t 14809\n", - "SAME POS:\t 10820\n", - "DIFF POS:\t 3989\n" + "TOTAL:\t 18206\n" ] }, { "data": { - "text/plain": " total same_pos diff_pos diff_deprel\nlang_id \nara 2055 1230 825 1027\nita 2368 1702 666 861\nnld 1728 1301 427 600\ntur 2217 1685 532 1079\nukr 4295 3425 870 1641\nvie 2146 1477 669 1217\nTOTAL 14809 10820 3989 6425\nAVG 2468 1803 664 1070", - "text/html": "
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totalsame_posdiff_posdiff_deprel
lang_id
ara205512308251027
ita23681702666861
nld17281301427600
tur221716855321079
ukr429534258701641
vie214614776691217
TOTAL148091082039896425
AVG246818036641070
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totalBAD-SUBBAD-INSBAD-CONBAD-EXPBAD-SHFBAD-DEL-RBAD-DEL-L
lang_id
ara25280.8129000.000000.04030.1468000.3089000.1642000.13410
ita26360.8983000.000400.02770.0736000.2329000.1756000.13510
nld20670.8360000.000500.04160.1219000.3527000.1592000.14510
tur27670.8012000.000000.05280.1460000.2895000.1395000.10010
ukr50850.8446000.000800.03130.1233000.3650000.1851000.16180
vie31230.6872000.007000.06790.2379000.4143000.2520000.18570
TOTAL182064.8802000.008700.26160.8495001.9633001.0756000.86190
AVG30340.8133670.001450.04360.1415830.3272170.1792670.14365
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" + ], + "text/plain": [ + " total BAD-SUB BAD-INS BAD-CON BAD-EXP BAD-SHF BAD-DEL-R \\\n", + "lang_id \n", + "ara 2528 0.812900 0.00000 0.0403 0.146800 0.308900 0.164200 \n", + "ita 2636 0.898300 0.00040 0.0277 0.073600 0.232900 0.175600 \n", + "nld 2067 0.836000 0.00050 0.0416 0.121900 0.352700 0.159200 \n", + "tur 2767 0.801200 0.00000 0.0528 0.146000 0.289500 0.139500 \n", + "ukr 5085 0.844600 0.00080 0.0313 0.123300 0.365000 0.185100 \n", + "vie 3123 0.687200 0.00700 0.0679 0.237900 0.414300 0.252000 \n", + "TOTAL 18206 4.880200 0.00870 0.2616 0.849500 1.963300 1.075600 \n", + "AVG 3034 0.813367 0.00145 0.0436 0.141583 0.327217 0.179267 \n", + "\n", + " BAD-DEL-L \n", + "lang_id \n", + "ara 0.13410 \n", + "ita 0.13510 \n", + "nld 0.14510 \n", + "tur 0.10010 \n", + "ukr 0.16180 \n", + "vie 0.18570 \n", + "TOTAL 0.86190 \n", + "AVG 0.14365 " + ] }, - "execution_count": 190, + "execution_count": 246, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "print('TOTAL:\\t', len(df_stats))\n", - "print('SAME POS:\\t', len(df_stats[df_stats['same_pos']]))\n", - "print('DIFF POS:\\t', len(df_stats[~df_stats['same_pos']]))\n", + "_df_stats = df_error_types[~df_error_types['OK']]\n", "\n", + "print('TOTAL:\\t', len(_df_stats))\n", + "\n", + "_total = _df_stats.groupby(['lang_id']).size()\n", "tmp = pd.DataFrame([\n", - " df_stats.groupby(['lang_id']).size(),\n", - " df_stats[df_stats['same_pos']].groupby(['lang_id']).size(),\n", - " df_stats[~df_stats['same_pos']].groupby(['lang_id']).size(),\n", - " df_stats[~df_stats['same_deprel']].groupby(['lang_id']).size(),\n", + " _total,\n", + " *[ \n", + " _df_stats[_df_stats[i]].groupby(['lang_id']).size() / _total\n", + " for i in ['BAD-SUB', 'BAD-INS', 'BAD-CON', 'BAD-EXP', 'BAD-SHF', 'BAD-DEL-R', 'BAD-DEL-L']\n", + " ],\n", "], index=[\n", " 'total',\n", - " 'same_pos',\n", - " 'diff_pos',\n", - " 'diff_deprel',\n", - "]).fillna(0).astype('int').T\n", - "tmp.loc['TOTAL'] = tmp.sum(numeric_only=True)\n", - "tmp.loc['AVG'] = (tmp.loc['TOTAL'] / len(set(df_stats['lang_id']) - {'TOTAL'})).astype('int')\n", + " *['BAD-SUB', 'BAD-INS', 'BAD-CON', 'BAD-EXP', 'BAD-SHF', 'BAD-DEL-R', 'BAD-DEL-L']\n", + "]).fillna(0).T.apply(lambda x: round(x, 4)) #.astype('int').T\n", + "tmp.loc['TOTAL'] = tmp.sum(numeric_only=True).astype('float')\n", + "tmp.loc['AVG'] = (tmp.loc['TOTAL'] / len(set(_df_stats['lang_id']) - {'TOTAL'})).astype('float')\n", + "tmp['total'] = tmp['total'].astype('int')\n", "\n", "# print(tmp)\n", "tmp" + ] + }, + { + "cell_type": "code", + "execution_count": 247, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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uDTJn6pJ91KKtmn9uv/32JMn8+fMr3v/kJz85SbJ06dJJG1MrOvfcc3PFFVfk8Y9//Eb3DQ0NjX5hPG/evNx5550ZGhrKTjvtVPGPLFtvvXW22WabrF69OnfffXeSv87vpuqw++67b/C4pHrtKj2nSK699tp88YtfzAtf+MK8+MUvrviYv/zlL3nooYfS09Mz2oC1vs7OztEDnpF5qGXeSqVS/vCHP2Tt2rU1P2f995iqGpkn5rz4Grk93HfffXn44YfzxCc+MRdffHGOPPLILFy4MIsWLcpxxx23wR/GaW3Nzok77rhjTO8/WWv6WBVtHvfee+98/etfz6c//emKj1+yZEmSZM6cOZk+fXqSZGBgIHfccUe22mqrXH/99XnVq16V/fffP/vtt1/+6Z/+Kddee23V362aos1j8tcmhc7Ozpx00kk59NBDs2DBgrzwhS/M+eefP3rsUs+4poKi1X48WdQszZzb6dOn56KLLsoVV1yRadOmbfT4VatW5c4770ySDZozR/ar1atX5y1veUsOOeSQ7L333jnyyCPz1a9+tekNVUnzt9lf/epXSZLtt98+Z555Zg4//PAsWLAgz3rWs/Kv//qvG325PJ7PXGyo2TVnnUZ/ZzZnzpy87W1vyw9/+MO87GUva8wgp4BG1uFf//Vfc/nll2f//feveP8tt9ySJJv8B2jtTO4Un32lPUzE32vuuOOOnHnmmdl3333z5je/uf5BNoDMmXyOa5pDNrcGmTN1yT5q0VbNPyP/4nH77beveP+2226bRKfo5nz1q1/Nvffem7lz5+bAAw+sOqfJX+f1z3/+c5K/zu92221X8fEjPx85e01Se+1G3qNIHnroobz73e/ODjvskH/913/d5ONG5mDbbbfd5L9Afew8jDxnU3Pd09OT2bNnZ2hoKA8++OAGz2nHuW6kRuaJOS++Rm4PI3+Uu+OOO/KRj3wkW265ZQ488MBsscUW+dGPfpRXv/rV+d73vtegkTORmpkTy5cvz5o1a9LR0bHJNeCx7z9Za/pYFW0eN2doaCif/OQnkyTPf/7zR39+xx13pL+/P4888khOPvnklMvlLFq0KNtss02uvfbavPGNb8znP//5qq+/OUWcx5E8/NSnPpUbb7wxe+yxR572tKfl7rvvzic+8Ym87nWvy+rVq8c9rqmiaLUfTxY1SysfD/7Hf/xH1qxZk9133330TILlcnm0seWMM87I7373uyxcuDC77bZbfvOb3+QDH/hATjjhhAwNDdX0HhOl2fM6cia2yy+/PJdcckme+MQnZr/99ktfX1++9rWv5Ygjjshvf/vbjd5jLJ+52FCza846jf7O7DnPeU5OPPHEzJ49uzEDnCIm67vLq666KjfeeGO6u7tz2GGH1fVaRSR3is++0h4aXce1a9fmn//5n9Pd3Z1///d/T2dnZ2MGWieZM/kc1zSHbG4NMmfqkn3UoqvZA2ikvr6+JBn9V9aPNfLz9f+IwF9dd911+djHPpYkOemkkzJjxozRuZoxY8Ymn9fT05Pkr/Na7Tkjjx8eHk5fX19mzJjR1rU75ZRT8uCDD+ZLX/rSZgN0ZA5qmetHH300SfW5Xv85I49t57lupEbOkzkvvkbWcOSP3bvuumsWL16cJz7xiUnWZeJnPvOZfPKTn8x73/veLFiwoOIZ2mgdzcyJao+v9JzJWtPHqmjzuCnlcjkf+MAHsmTJkmy99dYbnCZ1ZL/fdtttc955521wCZ9vf/vbed/73pd///d/zz777FPxsqy1KNo8rl27dvSsJe985ztz7LHHjp5e+K677soJJ5yQm2++OR/96EdzxhlnNPx3bCdFq/14sqhZWvV48Dvf+U4uuOCCdHR05JRTThn9+T333JOVK1ems7MzH/rQh3LkkUeO3nfLLbfk+OOPz49+9KN87nOfa+q/km72vI40SP3t3/5tPvrRj45ewnHlypV53/vel+9///s58cQTc9lll6Wzs7NQ22yranbNWcfctYbJqMPSpUvz3ve+N0lyzDHHZIcddhj3axWV3Ck++0p7aHQdP/axj+X222/PmWeeOXoJ3FYgcyafeWoO2dwaZM7UpV7Uoq3O/FNrp3e5XJ7gkRTP1Vdfnbe85S3p7+/Pa17zmrz85S9PUvucJhk9hfxEPqcVTlM/FiPXHz766KPz9Kc/fbOPfez1NDdnZBs21xOnkXlizouvkdvDyB/evvKVr4w2/iTrMuAtb3lLDj300KxduzZf//rXxztcJkkzc6LV1oz1nzNWRZvHSgYHB3PKKafk4osvzvTp03P22WdvcIaIl770pbn66qvzrW99a4PGnyQ54ogj8prXvCblcjkXXXRRzeN5rKLNY09PT6677rp873vfy1ve8pYNXmPXXXfNmWeemST51re+lVWrVo1rXFNF0Wo/GbnSKK14PHjJJZeMnkHspJNOysEHHzx63xOe8IRcd911ueKKKzZo/EnWXbLwtNNOS5JceOGFTf1M3Ox5/cIXvpDvfe97+cQnPjHa+JMks2bNyr/9279lu+22y29/+9v89Kc/HdN7PPZ9+Ktm15x1fGfWGia6DkuWLMnrX//6LF++PIceemhOOOGEcb1O0cmd4rOvtIdG1vHHP/5xvvzlL+cFL3hBXvKSl9Q5ssaSOZPPcU1zyObWIHOmLtlHLdqq+WfLLbdMsu5fEleyZs2aJMkWW2wxaWMqgosuuijHHXdc1qxZk6OOOiqnn3766H0jczoyd5WMzPfIvFZ7zsjjOzo6Rv/1ZK21G3lcEYxcf3iPPfbIiSeeWPXxEzHX43lOEed6IjQyT9px+55qGrk9dHV15fGPf3y23nrrivc/97nPTZLceuut4xkqk6iZOVHt8ZXef7LW9LEq2jw+1ooVK/LmN785//Vf/5Utt9wy559/fg444IANHtPR0ZEdd9xxk6dkbcR+X8R5nD179ui1wx9rzz33zA477JDBwcH8+te/3uB9HMNsqGi1H08WNUsrHQ8ODw/nrLPOymmnnZbh4eH8y7/8S4455piNHrf11ltv0Fy8vmc/+9np7OzMX/7ylyxbtqzqmCdKs+d1+vTp2X333TNt2rSNHj9z5swceOCBSf6ayUXaZltVs2vOOr4zaw0TWYcrr7wyr3vd6/Lwww/nec97Xs4+++yWuSTOZJM7xWdfaQ+NquOf//znvPe97828efPygQ98oLGDbACZM/kc1zSHbG4NMmfqkn3Uoq2af0b+hfWmrj04co279f8l9lQ2ODiY008/PR/60IcyPDyck046KaeeempKpdLoY0b+SLW56zk+dl6rPWfkmoSPe9zjRv+18MhzN3UdwiLW7qyzzsratWszffr0vPe978273vWu0f9GLv9xySWX5F3velcuvvji0Xn7y1/+ssnXHOtcr1mzJo888kg6OjpGr/VY7TlFnOuJ0Mg8acfte6qZzPVl3rx5Sf56CkdaVzNzYubMmZk5c2aGhoby4IMP1vScyVrTx6po87i+P/zhD3nFK16Rn/70p9luu+3y5S9/efSPxmMxcgrjevb7Is/jpozk4cipah3DVFa02o8ni5qlVY4HV69eneOOOy6f/exn093dnTPPPLNi408106ZNG20+buZxRqvM66Y89lhsPJ+52FCr13yq8J1Za5ioOpx77rl5xzvekb6+vvzjP/5jPv3pT1dscpwq5E7x2VfaQ6PquHjx4jz00EOZM2dOzjjjjA2+Zx85W+MPfvCDvOtd78rixYsb+BvURuZMPsc1zSGbW4PMmbpkH7Voq+af+fPnJ1l3xpVKfvvb327wuKlszZo1efOb3zx6eYpPfepTOfbYYzd63O67756urq7cc889FTsJH3rooTz44IOZMWNGnvCEJyT56/yOzPdjVarDeJ7T6kb+UHXDDTfk8ssv3+C/P/3pT0mSm266KZdffnluuummzJkzJ9tvv336+vpyzz33bPR6Q0NDueuuu5Ikvb29SWqft1122SU9PT1jes7Ie0xVjcyTdty+p5pGbg9nnnlmTjjhhCxdurTi/SP/En/kD0+0rmbnxEhO1/r+k7Wmj1XR5nHEkiVL8opXvCK/+93v8tSnPjXf+MY38rSnPa3ia3zmM5/JiSeemGuvvbbi/SPHBfVcx7xo8/iLX/wi73nPe/Lxj398k+N4bB46hqmsaLUfTxY1S7PnNlk3H6997Wtz1VVXZc6cOfniF7+4ycsc/OhHP8q73vWuXHDBBRXvX7t2bZYvX56Ojo5NnolsMjRzXv/85z/n1FNPzQknnJDBwcGKzxlv9qz/mYsNtcK+hO/MWkWj6zA8PJyTTz45Z599djo6OvK+970vp5122rgb89uF3Ck++0p7aFQdR75n//Wvf73R9+wj35fffvvtufzyyzf5uXsiyZzJ57imOWRza5A5U5fsoxZtlaDPfvazk6zr8n6shx9+OD/72c/S09OTgw46aJJH1lqGhoZy3HHH5ac//Wm23nrrXHTRRTn88MMrPranpycHHnhg+vv7c/XVV290//e///2Uy+X8zd/8zegp+Pbbb7/MnDkzN9988+gZAdZ35ZVXJkkOPfTQ0Z+N1O6HP/zhRtciHBgYyH//939v8LgiuOiii7J06dKK/41c3uMjH/lIli5dmo9+9KNJNr8NX3PNNVm5cmX22GOP0a7NXXbZJU960pNy3333VbxUSKW5/pu/+Zt0dHTkf/7nfyqeuv773//+Rs+ZihqZJ+24fU81jdwebrvttvzgBz/IFVdcUfH+yy67LMm6fZXW1uyc2Nz733nnnbn99tuzzTbbZM8990wyeWv6WBVtHpNk6dKlOfroo7N8+fI861nPyle/+tXNNu787ne/y5VXXplvfetbFe+/9NJLkyTPetazqv2Km1S0eRweHs6ll16aiy66KCtXrtzoOf/7v/+bP/3pT9lxxx3z5Cc/OYljmE0pWu3Hk0XN0uy5ffTRR/NP//RP+dWvfpUnPOEJufjiize6rOD6Vq5cmcsvvzw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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } ], + "source": [ + "plot_summary_df(tmp, title='BAD-X types')" + ] + }, + { + "cell_type": "code", + "execution_count": 240, "metadata": { - "collapsed": false, "ExecuteTime": { "end_time": "2023-07-13T11:05:51.373876Z", "start_time": "2023-07-13T11:05:51.325488Z" + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false } - } - }, - { - "cell_type": "code", - "execution_count": 193, + }, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TOTAL:\t 14809\n", + "SAME POS:\t 10820\n", + "DIFF POS:\t 3989\n" + ] + }, { "data": { - "text/plain": " total same_pos diff_pos diff_deprel\nlang_id \nara 2055 1230 825 1027\nita 2368 1702 666 861\nnld 1728 1301 427 600\ntur 2217 1685 532 1079\nukr 4295 3425 870 1641\nvie 2146 1477 669 1217\nTOTAL 14809 10820 3989 6425\nAVG 2468 1803 664 1070", - "text/html": "
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totalsame_posdiff_posdiff_deprel
lang_id
ara205512308251027
ita23681702666861
nld17281301427600
tur221716855321079
ukr429534258701641
vie214614776691217
TOTAL148091082039896425
AVG246818036641070
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totalsame_posdiff_posdiff_deprel
lang_id
ara20550.5985000.4015000.499800
ita23680.7188000.2812000.363600
nld17280.7529000.2471000.347200
tur22170.7600000.2400000.486700
ukr42950.7974000.2026000.382100
vie21460.6883000.3117000.567100
TOTAL148094.3159001.6841002.646500
AVG24680.7193170.2806830.441083
\n", + "
" + ], + "text/plain": [ + " total same_pos diff_pos diff_deprel\n", + "lang_id \n", + "ara 2055 0.598500 0.401500 0.499800\n", + "ita 2368 0.718800 0.281200 0.363600\n", + "nld 1728 0.752900 0.247100 0.347200\n", + "tur 2217 0.760000 0.240000 0.486700\n", + "ukr 4295 0.797400 0.202600 0.382100\n", + "vie 2146 0.688300 0.311700 0.567100\n", + "TOTAL 14809 4.315900 1.684100 2.646500\n", + "AVG 2468 0.719317 0.280683 0.441083" + ] }, - "execution_count": 193, + "execution_count": 240, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "_df_stats = df_stats\n", + "\n", + "print('TOTAL:\\t', len(_df_stats))\n", + "print('SAME POS:\\t', len(_df_stats[_df_stats['same_pos']]))\n", + "print('DIFF POS:\\t', len(_df_stats[~_df_stats['same_pos']]))\n", + "\n", + "_total_sub = _df_stats.groupby(['lang_id']).size()\n", + "tmp = pd.DataFrame([\n", + " _df_stats.groupby(['lang_id']).size(),\n", + " _df_stats[_df_stats['same_pos']].groupby(['lang_id']).size() / _total_sub,\n", + " _df_stats[~_df_stats['same_pos']].groupby(['lang_id']).size() / _total_sub,\n", + " _df_stats[~_df_stats['same_deprel']].groupby(['lang_id']).size() / _total_sub,\n", + "], index=[\n", + " 'total',\n", + " 'same_pos',\n", + " 'diff_pos',\n", + " 'diff_deprel',\n", + "]).fillna(0).T.apply(lambda x: round(x, 4)) #.astype('int').T\n", + "tmp.loc['TOTAL'] = tmp.sum(numeric_only=True).astype('float')\n", + "tmp.loc['AVG'] = (tmp.loc['TOTAL'] / len(set(_df_stats['lang_id']) - {'TOTAL'})).astype('float')\n", + "tmp['total'] = tmp['total'].astype('int')\n", + "\n", + "# print(tmp)\n", "tmp" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-07-13T11:06:01.710994Z", - "start_time": "2023-07-13T11:06:01.681673Z" - } - } + ] }, { "cell_type": "code", - "execution_count": 191, + "execution_count": 241, + "metadata": { + "ExecuteTime": { + "end_time": "2023-07-13T11:05:54.004076Z", + "start_time": "2023-07-13T11:05:52.391641Z" + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "outputs": [ { "data": { - "text/plain": "
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" + ] }, "metadata": {}, "output_type": "display_data" @@ -1144,18 +4066,21 @@ ], "source": [ "plot_summary_df(tmp, title='BAD-SUB types')" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-07-13T11:05:54.004076Z", - "start_time": "2023-07-13T11:05:52.391641Z" - } - } + ] }, { "cell_type": "code", - "execution_count": 182, + "execution_count": 238, + "metadata": { + "ExecuteTime": { + "end_time": "2023-07-13T10:37:02.689878Z", + "start_time": "2023-07-13T10:37:02.633850Z" + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "outputs": [ { "name": "stdout", @@ -1166,10 +4091,144 @@ }, { "data": { - "text/plain": " total (same pos) same_lemma same_morf same_lemma diff_morf \\\nlang_id \nara 1230 93 374 \nita 1702 100 500 \nnld 1301 153 100 \ntur 1685 115 623 \nukr 3425 128 1164 \nvie 1477 0 0 \nTOTAL 10820 589 2761 \nAVG 1803 98 460 \n\n diff_lemma same_morf diff_lemma diff_morf same_word (diff case) \nlang_id \nara 498 265 0 \nita 705 397 68 \nnld 867 181 114 \ntur 604 343 79 \nukr 1067 1066 124 \nvie 1468 9 160 \nTOTAL 5209 2261 545 \nAVG 868 376 90 ", - "text/html": "
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total (same pos)same_lemma same_morfsame_lemma diff_morfdiff_lemma same_morfdiff_lemma diff_morfsame_word (diff case)
lang_id
ara1230933744982650
ita170210050070539768
nld1301153100867181114
tur168511562360434379
ukr3425128116410671066124
vie14770014689160
TOTAL10820589276152092261545
AVG18039846086837690
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total (same pos)same_lemma same_morfsame_lemma diff_morfdiff_lemma same_morfdiff_lemma diff_morfsame_word (diff case)
lang_id
ara20550.07560.3041000.40490.2154000.000000
ita23680.05880.2938000.41420.2333000.040000
nld17280.11760.0769000.66640.1391000.087600
tur22170.06820.3697000.35850.2036000.046900
ukr42950.03740.3399000.31150.3112000.036200
vie21460.00000.0000000.99390.0061000.108300
TOTAL148090.35761.3844003.14941.1087000.319000
AVG24680.05960.2307330.52490.1847830.053167
\n", + "
" + ], + "text/plain": [ + " total (same pos) same_lemma same_morf same_lemma diff_morf \\\n", + "lang_id \n", + "ara 2055 0.0756 0.304100 \n", + "ita 2368 0.0588 0.293800 \n", + "nld 1728 0.1176 0.076900 \n", + "tur 2217 0.0682 0.369700 \n", + "ukr 4295 0.0374 0.339900 \n", + "vie 2146 0.0000 0.000000 \n", + "TOTAL 14809 0.3576 1.384400 \n", + "AVG 2468 0.0596 0.230733 \n", + "\n", + " diff_lemma same_morf diff_lemma diff_morf same_word (diff case) \n", + "lang_id \n", + "ara 0.4049 0.215400 0.000000 \n", + "ita 0.4142 0.233300 0.040000 \n", + "nld 0.6664 0.139100 0.087600 \n", + "tur 0.3585 0.203600 0.046900 \n", + "ukr 0.3115 0.311200 0.036200 \n", + "vie 0.9939 0.006100 0.108300 \n", + "TOTAL 3.1494 1.108700 0.319000 \n", + "AVG 0.5249 0.184783 0.053167 " + ] }, - "execution_count": 182, + "execution_count": 238, "metadata": {}, "output_type": "execute_result" } @@ -1178,13 +4237,14 @@ "_df_stats = df_stats[df_stats['same_pos']]\n", "print('TOTAL:\\t', len(_df_stats))\n", "\n", + "_total = _df_stats.groupby(['lang_id']).size()\n", "tmp = pd.DataFrame([\n", - " _df_stats.groupby(['lang_id']).size(),\n", - " _df_stats[_df_stats['same_lemma'] & _df_stats['same_morf']].groupby(['lang_id']).size(),\n", - " _df_stats[_df_stats['same_lemma'] & ~_df_stats['same_morf']].groupby(['lang_id']).size(),\n", - " _df_stats[~_df_stats['same_lemma'] & _df_stats['same_morf']].groupby(['lang_id']).size(),\n", - " _df_stats[~_df_stats['same_lemma'] & ~_df_stats['same_morf']].groupby(['lang_id']).size(),\n", - " _df_stats[_df_stats['same_word']].groupby(['lang_id']).size(),\n", + " _total_sub,\n", + " _df_stats[_df_stats['same_lemma'] & _df_stats['same_morf']].groupby(['lang_id']).size() / _total,\n", + " _df_stats[_df_stats['same_lemma'] & ~_df_stats['same_morf']].groupby(['lang_id']).size() / _total,\n", + " _df_stats[~_df_stats['same_lemma'] & _df_stats['same_morf']].groupby(['lang_id']).size() / _total,\n", + " _df_stats[~_df_stats['same_lemma'] & ~_df_stats['same_morf']].groupby(['lang_id']).size() / _total,\n", + " _df_stats[_df_stats['same_word']].groupby(['lang_id']).size() / _total,\n", "], index=[\n", " 'total (same pos)',\n", " 'same_lemma same_morf',\n", @@ -1192,29 +4252,35 @@ " 'diff_lemma same_morf',\n", " 'diff_lemma diff_morf',\n", " 'same_word (diff case)',\n", - "]).fillna(0).astype('int').T\n", - "tmp.loc['TOTAL'] = tmp.sum(numeric_only=True)\n", - "tmp.loc['AVG'] = (tmp.loc['TOTAL'] / len(set(df_stats['lang_id']) - {'TOTAL'})).astype('int')\n", + "]).fillna(0).T.apply(lambda x: round(x, 4)) #.astype('int').T\n", + "tmp.loc['TOTAL'] = tmp.sum(numeric_only=True).astype('float')\n", + "tmp.loc['AVG'] = (tmp.loc['TOTAL'] / len(set(_df_stats['lang_id']) - {'TOTAL'})).astype('float')\n", + "tmp['total (same pos)'] = tmp['total (same pos)'].astype('int')\n", "\n", "# print(tmp)\n", "tmp" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-07-13T10:37:02.689878Z", - "start_time": "2023-07-13T10:37:02.633850Z" - } - } + ] }, { "cell_type": "code", - "execution_count": 183, + "execution_count": 239, + "metadata": { + "ExecuteTime": { + "end_time": "2023-07-13T10:37:11.466933Z", + "start_time": "2023-07-13T10:37:08.060140Z" + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "outputs": [ { "data": { - "text/plain": "
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