fixed python 3.11 compatibility

fixed torch dependency resolving

Author: stsouko

chytorch/utils/data/_abc.py

@@ -20,40 +20,10 @@
 # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 # SOFTWARE.
 #
-from typing import NamedTuple, NamedTupleMeta
-
 try:
     from torch.utils.data._utils.collate import default_collate_fn_map
 except ImportError:  # ad-hoc for pytorch<1.13
     default_collate_fn_map = {}
 
 
-# https://stackoverflow.com/a/50369521
-if hasattr(NamedTuple, '__mro_entries__'):
-    # Python 3.9 fixed and broke multiple inheritance in a different way
-    # see https://github.com/python/cpython/issues/88089
-    from typing import _NamedTuple
-
-    NamedTuple = _NamedTuple
-
-
-class MultipleInheritanceNamedTupleMeta(NamedTupleMeta):
-    def __new__(mcls, typename, bases, ns):
-        if NamedTuple in bases:
-            base = super().__new__(mcls, '_base_' + typename, bases, ns)
-            bases = (base, *(b for b in bases if not isinstance(b, NamedTuple)))
-        return super(NamedTupleMeta, mcls).__new__(mcls, typename, bases, ns)
-
-
-class DataTypeMixin(metaclass=MultipleInheritanceNamedTupleMeta):
-    def to(self, *args, **kwargs):
-        return type(self)(*(x.to(*args, **kwargs) for x in self))
-
-    def cpu(self, *args, **kwargs):
-        return type(self)(*(x.cpu(*args, **kwargs) for x in self))
-
-    def cuda(self, *args, **kwargs):
-        return type(self)(*(x.cuda(*args, **kwargs) for x in self))
-
-
-__all__ = ['DataTypeMixin', 'NamedTuple', 'default_collate_fn_map']
+__all__ = ['default_collate_fn_map']

chytorch/utils/data/molecule/conformer.py

@@ -28,8 +28,8 @@
 from torch.nn.utils.rnn import pad_sequence
 from torch.utils.data import Dataset
 from torchtyping import TensorType
-from typing import Sequence, Tuple, Union
-from .._abc import DataTypeMixin, NamedTuple, default_collate_fn_map
+from typing import Sequence, Tuple, Union, NamedTuple
+from .._abc import default_collate_fn_map
 
 
 class ConformerDataPoint(NamedTuple):
@@ -38,11 +38,20 @@ class ConformerDataPoint(NamedTuple):
     distances: TensorType['atoms', 'atoms', int]
 
 
-class ConformerDataBatch(NamedTuple, DataTypeMixin):
+class ConformerDataBatch(NamedTuple):
     atoms: TensorType['batch', 'atoms', int]
     hydrogens: TensorType['batch', 'atoms', int]
     distances: TensorType['batch', 'atoms', 'atoms', int]
 
+    def to(self, *args, **kwargs):
+        return ConformerDataBatch(*(x.to(*args, **kwargs) for x in self))
+
+    def cpu(self, *args, **kwargs):
+        return ConformerDataBatch(*(x.cpu(*args, **kwargs) for x in self))
+
+    def cuda(self, *args, **kwargs):
+        return ConformerDataBatch(*(x.cuda(*args, **kwargs) for x in self))
+
 
 def collate_conformers(batch, *, padding_left: bool = False, collate_fn_map=None) -> ConformerDataBatch:
     """

chytorch/utils/data/molecule/encoder.py

@@ -27,9 +27,9 @@
 from torch.nn.utils.rnn import pad_sequence
 from torch.utils.data import Dataset
 from torchtyping import TensorType
-from typing import Sequence, Union
+from typing import Sequence, Union, NamedTuple
 from zlib import decompress
-from .._abc import DataTypeMixin, NamedTuple, default_collate_fn_map
+from .._abc import default_collate_fn_map
 
 
 class MoleculeDataPoint(NamedTuple):
@@ -38,11 +38,20 @@ class MoleculeDataPoint(NamedTuple):
     distances: TensorType['atoms', 'atoms', int]
 
 
-class MoleculeDataBatch(NamedTuple, DataTypeMixin):
+class MoleculeDataBatch(NamedTuple):
     atoms: TensorType['batch', 'atoms', int]
     neighbors: TensorType['batch', 'atoms', int]
     distances: TensorType['batch', 'atoms', 'atoms', int]
 
+    def to(self, *args, **kwargs):
+        return MoleculeDataBatch(*(x.to(*args, **kwargs) for x in self))
+
+    def cpu(self, *args, **kwargs):
+        return MoleculeDataBatch(*(x.cpu(*args, **kwargs) for x in self))
+
+    def cuda(self, *args, **kwargs):
+        return MoleculeDataBatch(*(x.cuda(*args, **kwargs) for x in self))
+
 
 def collate_molecules(batch, *, padding_left: bool = False, collate_fn_map=None) -> MoleculeDataBatch:
     """

chytorch/utils/data/reaction/encoder.py

@@ -26,9 +26,9 @@
 from torch.nn.utils.rnn import pad_sequence
 from torch.utils.data import Dataset
 from torchtyping import TensorType
-from typing import Sequence, Union
+from typing import Sequence, Union, NamedTuple
 from ..molecule import MoleculeDataset
-from .._abc import DataTypeMixin, NamedTuple, default_collate_fn_map
+from .._abc import default_collate_fn_map
 
 
 class ReactionEncoderDataPoint(NamedTuple):
@@ -38,12 +38,21 @@ class ReactionEncoderDataPoint(NamedTuple):
     roles: TensorType['atoms', int]
 
 
-class ReactionEncoderDataBatch(NamedTuple, DataTypeMixin):
+class ReactionEncoderDataBatch(NamedTuple):
     atoms: TensorType['batch', 'atoms', int]
     neighbors: TensorType['batch', 'atoms', int]
     distances: TensorType['batch', 'atoms', 'atoms', int]
     roles: TensorType['batch', 'atoms', int]
 
+    def to(self, *args, **kwargs):
+        return ReactionEncoderDataBatch(*(x.to(*args, **kwargs) for x in self))
+
+    def cpu(self, *args, **kwargs):
+        return ReactionEncoderDataBatch(*(x.cpu(*args, **kwargs) for x in self))
+
+    def cuda(self, *args, **kwargs):
+        return ReactionEncoderDataBatch(*(x.cuda(*args, **kwargs) for x in self))
+
 
 def collate_encoded_reactions(batch, *, padding_left: bool = False, collate_fn_map=None) -> ReactionEncoderDataBatch:
     """

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = 'chytorch'
-version = '1.59'
+version = '1.60'
 description = 'Library for modeling molecules and reactions in torch way'
 authors = ['Ramil Nugmanov <nougmanoff@protonmail.com>']
 license = 'MIT'
@@ -32,7 +32,7 @@ python = '>=3.8,<3.12'
 torchtyping = '^0.1.4'
 chython = '^1.70'
 scipy = '^1.10'
-torch = '>=2.0,>=1.8'
+torch = '>=1.8'
 lmdb = {version='^1.4.1', optional = true}
 psycopg2-binary = {version='^2.9', optional = true}
 rdkit = {version = '^2023.9.1', optional = true}