




'''simple docstring'''





from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
   import timm


if is_torch_available():
   from torch import Tensor

class   _a							(		__lowerCAmelCase   ,   __lowerCAmelCase				):
     SCREAMING_SNAKE_CASE_				:       str       							=		"""pixel_values"""
     SCREAMING_SNAKE_CASE_				:       int       							=		False
     SCREAMING_SNAKE_CASE_				:       Any       							=		TimmBackboneConfig






     def __init__(					self   ,_SCREAMING_SNAKE_CASE   ,**_SCREAMING_SNAKE_CASE       )				->     Any:
          requires_backends(self   ,"timm"       )
          super().__init__(_SCREAMING_SNAKE_CASE       )
          _snake_case				= config

          if config.backbone is None:
               raise ValueError("backbone is not set in the config. Please set it to a timm model name."       )

          if config.backbone not in timm.list_models():
               raise ValueError(f"""backbone {config.backbone} is not supported by timm."""       )

          if hasattr(_SCREAMING_SNAKE_CASE   ,"out_features"       ) and config.out_features is not None:
               raise ValueError("out_features is not supported by TimmBackbone. Please use out_indices instead."       )

          _snake_case				= getattr(_SCREAMING_SNAKE_CASE   ,"use_pretrained_backbone"   ,_SCREAMING_SNAKE_CASE       )
          if pretrained is None:
               raise ValueError("use_pretrained_backbone is not set in the config. Please set it to True or False."       )

          # We just take the final layer by default. This matches the default for the transformers models.
          _snake_case				= config.out_indices if getattr(_SCREAMING_SNAKE_CASE   ,"out_indices"   ,_SCREAMING_SNAKE_CASE       ) is not None else (-1,)

          _snake_case				= timm.create_model(
              config.backbone   ,pretrained=_SCREAMING_SNAKE_CASE   ,features_only=config.features_only   ,in_chans=config.num_channels   ,out_indices=_SCREAMING_SNAKE_CASE   ,**_SCREAMING_SNAKE_CASE   ,)
          # These are used to control the output of the model when called. If output_hidden_states is True, then
          # return_layers is modified to include all layers.
          _snake_case				= self._backbone.return_layers
          _snake_case				= {layer["module"]: str(_SCREAMING_SNAKE_CASE       ) for i, layer in enumerate(self._backbone.feature_info.info       )}
          super()._init_backbone(_SCREAMING_SNAKE_CASE       )






     @classmethod
     def 							_lowercase      (					cls   ,_SCREAMING_SNAKE_CASE   ,*_SCREAMING_SNAKE_CASE   ,**_SCREAMING_SNAKE_CASE       )				->     int:
          requires_backends(cls   ,["vision", "timm"]       )
          from ...models.timm_backbone import TimmBackboneConfig

          _snake_case				= kwargs.pop("config"   ,TimmBackboneConfig()       )

          _snake_case				= kwargs.pop("use_timm_backbone"   ,_SCREAMING_SNAKE_CASE       )
          if not use_timm:
               raise ValueError("use_timm_backbone must be True for timm backbones"       )

          _snake_case				= kwargs.pop("num_channels"   ,config.num_channels       )
          _snake_case				= kwargs.pop("features_only"   ,config.features_only       )
          _snake_case				= kwargs.pop("use_pretrained_backbone"   ,config.use_pretrained_backbone       )
          _snake_case				= kwargs.pop("out_indices"   ,config.out_indices       )
          _snake_case				= TimmBackboneConfig(
              backbone=_SCREAMING_SNAKE_CASE   ,num_channels=_SCREAMING_SNAKE_CASE   ,features_only=_SCREAMING_SNAKE_CASE   ,use_pretrained_backbone=_SCREAMING_SNAKE_CASE   ,out_indices=_SCREAMING_SNAKE_CASE   ,)
          return super()._from_config(_SCREAMING_SNAKE_CASE   ,**_SCREAMING_SNAKE_CASE       )






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE       )				->     Dict:
          pass






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE=None   ,_SCREAMING_SNAKE_CASE=None   ,_SCREAMING_SNAKE_CASE=None   ,**_SCREAMING_SNAKE_CASE       )				->     Union[BackboneOutput, Tuple[Tensor, ...]]:
          _snake_case				= return_dict if return_dict is not None else self.config.use_return_dict
          _snake_case				= (
              output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
          )
          _snake_case				= output_attentions if output_attentions is not None else self.config.output_attentions

          if output_attentions:
               raise ValueError("Cannot output attentions for timm backbones at the moment"       )

          if output_hidden_states:
               # We modify the return layers to include all the stages of the backbone
               _snake_case				= self._all_layers
               _snake_case				= self._backbone(_SCREAMING_SNAKE_CASE   ,**_SCREAMING_SNAKE_CASE       )
               _snake_case				= self._return_layers
               _snake_case				= tuple(hidden_states[i] for i in self.out_indices       )
          else:
               _snake_case				= self._backbone(_SCREAMING_SNAKE_CASE   ,**_SCREAMING_SNAKE_CASE       )
               _snake_case				= None

          _snake_case				= tuple(_SCREAMING_SNAKE_CASE       )
          _snake_case				= tuple(_SCREAMING_SNAKE_CASE       ) if hidden_states is not None else None

          if not return_dict:
               _snake_case				= (feature_maps,)
               if output_hidden_states:
                    _snake_case				= output + (hidden_states,)
               return output

          return BackboneOutput(feature_maps=_SCREAMING_SNAKE_CASE   ,hidden_states=_SCREAMING_SNAKE_CASE   ,attentions=_SCREAMING_SNAKE_CASE       )








'''simple docstring'''





from __future__ import annotations

import collections
import pprint
from pathlib import Path





def       __a      (  _UpperCamelCase:       str       )		->					str:

     """simple docstring"""




     return "".join(sorted(_UpperCamelCase       )       )





def       __a      (  _UpperCamelCase:       str       )		->					list[str]:

     """simple docstring"""




     return word_by_signature[signature(_UpperCamelCase       )]


UpperCamelCase_		:     str          =		Path(__file__).parent.joinpath('''words.txt''').read_text(encoding='''utf-8''')
UpperCamelCase_		:     Dict          =		sorted({word.strip().lower() for word in data.splitlines()})

UpperCamelCase_		:     Optional[Any]          =		collections.defaultdict(list)
for word in word_list:
   word_by_signature[signature(word)].append(word)

if __name__ == "__main__":
   UpperCamelCase_		:     List[str]          =		{word: anagram(word) for word in word_list if len(anagram(word)) > 1}

   with open('''anagrams.txt''', '''w''') as file:
      file.write('''all_anagrams = \n ''')
      file.write(pprint.pformat(all_anagrams))


