




'''simple docstring'''





import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()





def 			__a			(     _UpperCamelCase:					Union[str, Any]      ,		_UpperCamelCase:					List[str]      ,		_UpperCamelCase:					int      ,		_UpperCamelCase:					Optional[Any]      ,		_UpperCamelCase:					Optional[int]					)		->				Union[str, Any]:

     """simple docstring"""




     _snake_case							=   BertConfig.from_pretrained(
         "bert-large-cased"      ,		vocab_size=_UpperCamelCase      ,		max_position_embeddings=512      ,		is_decoder=_UpperCamelCase      ,		add_cross_attention=_UpperCamelCase      ,		)
     _snake_case							=   bert_config.to_dict()
     del bert_config_dict["type_vocab_size"]
     _snake_case							=   BertGenerationConfig(**_UpperCamelCase					)
     if is_encoder:
          _snake_case							=   BertGenerationEncoder(_UpperCamelCase					)
     else:
          _snake_case							=   BertGenerationDecoder(_UpperCamelCase					)
     print(F"""Building PyTorch model from configuration: {config}"""					)

     # Load weights from tf checkpoint
     load_tf_weights_in_bert_generation(
         _UpperCamelCase      ,		_UpperCamelCase      ,		model_class="bert"      ,		is_encoder_named_decoder=_UpperCamelCase      ,		is_encoder=_UpperCamelCase      ,		)

     # Save pytorch-model
     print(F"""Save PyTorch model and config to {pytorch_dump_path}"""					)
     model.save_pretrained(_UpperCamelCase					)


if __name__ == "__main__":
   UpperCamelCase_		:    str									=		argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--tf_hub_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
   )
   parser.add_argument(
       '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
   )
   parser.add_argument(
       '''--is_encoder_named_decoder''',
       action='''store_true''',
       help='''If decoder has to be renamed to encoder in PyTorch model.''',
   )
   parser.add_argument('''--is_encoder''', action='''store_true''', help='''If model is an encoder.''')
   parser.add_argument('''--vocab_size''', default=50358, type=int, help='''Vocab size of model''')
   UpperCamelCase_		:    Optional[int]									=		parser.parse_args()
   convert_tf_checkpoint_to_pytorch(
       args.tf_hub_path,
       args.pytorch_dump_path,
       args.is_encoder_named_decoder,
       args.vocab_size,
       is_encoder=args.is_encoder,
   )








'''simple docstring'''





import enum
import inspect
from typing import Iterable, List, Optional, Tuple, Union

class      _a       (		enum.Enum							):
     SCREAMING_SNAKE_CASE_							:					Optional[int]					       =		"""timm"""
     SCREAMING_SNAKE_CASE_							:					int					       =		"""transformers"""







def 			__a			(     _UpperCamelCase:					Optional[Iterable[str]]      ,		_UpperCamelCase:					Optional[Iterable[int]]      ,		_UpperCamelCase:					Optional[Iterable[str]]					)		->				Union[str, Any]:

     """simple docstring"""




     if stage_names is None:
          raise ValueError("Stage_names must be set for transformers backbones"					)

     if out_features is not None:
          if not isinstance(_UpperCamelCase      ,		(list,)					):
               raise ValueError(F"""out_features must be a list {type(_UpperCamelCase					)}"""					)
          if any(feat not in stage_names for feat in out_features					):
               raise ValueError(F"""out_features must be a subset of stage_names: {stage_names} got {out_features}"""					)

     if out_indices is not None:
          if not isinstance(_UpperCamelCase      ,		(list, tuple)					):
               raise ValueError(F"""out_indices must be a list or tuple, got {type(_UpperCamelCase					)}"""					)
          if any(idx >= len(_UpperCamelCase					) for idx in out_indices					):
               raise ValueError("out_indices must be valid indices for stage_names {stage_names}, got {out_indices}"					)

     if out_features is not None and out_indices is not None:
          if len(_UpperCamelCase					) != len(_UpperCamelCase					):
               raise ValueError("out_features and out_indices should have the same length if both are set"					)
          if out_features != [stage_names[idx] for idx in out_indices]:
               raise ValueError("out_features and out_indices should correspond to the same stages if both are set"					)





def 			__a			(     _UpperCamelCase:					Optional[List[str]]      ,		_UpperCamelCase:					Optional[Union[List[int], Tuple[int]]]      ,		_UpperCamelCase:					List[str]      ,		)		->				Any:

     """simple docstring"""




     if out_indices is None and out_features is None:
          _snake_case							=   [len(_UpperCamelCase					) - 1]
          _snake_case							=   [stage_names[-1]]
     elif out_indices is None and out_features is not None:
          _snake_case							=   [stage_names.index(_UpperCamelCase					) for layer in out_features]
     elif out_features is None and out_indices is not None:
          _snake_case							=   [stage_names[idx] for idx in out_indices]
     return out_features, out_indices





def 			__a			(     _UpperCamelCase:					Optional[List[str]]      ,		_UpperCamelCase:					Optional[Union[List[int], Tuple[int]]]      ,		_UpperCamelCase:					List[str]      ,		)		->				Tuple[List[str], List[int]]:

     """simple docstring"""




     verify_out_features_out_indices(out_features=_UpperCamelCase      ,		out_indices=_UpperCamelCase      ,		stage_names=_UpperCamelCase					)
     _snake_case							,      _snake_case							=   _align_output_features_output_indices(
         out_features=_UpperCamelCase      ,		out_indices=_UpperCamelCase      ,		stage_names=_UpperCamelCase					)
     # Verify that the aligned out_features and out_indices are valid
     verify_out_features_out_indices(out_features=_UpperCamelCase      ,		out_indices=_UpperCamelCase      ,		stage_names=_UpperCamelCase					)
     return output_features, output_indices

class      _a       :
     SCREAMING_SNAKE_CASE_							:					Optional[BackboneType]					       =		None






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE					)							->    None:
          if getattr(self      ,"_backbone"      ,_SCREAMING_SNAKE_CASE					) is None:
               raise ValueError("self._backbone must be set before calling _init_timm_backbone"					)

          # These will diagree with the defaults for the transformers models e.g. for resnet50
          # the transformer model has out_features = ['stem', 'stage1', 'stage2', 'stage3', 'stage4']
          # the timm model has out_features = ['act', 'layer1', 'layer2', 'layer3', 'layer4']
          _snake_case							=   [stage["module"] for stage in self._backbone.feature_info.info]
          _snake_case							=   [stage["num_chs"] for stage in self._backbone.feature_info.info]
          _snake_case							=   self._backbone.feature_info.out_indices
          _snake_case							=   self._backbone.feature_info.module_name()

          # We verify the out indices and out features are valid
          verify_out_features_out_indices(
              out_features=_SCREAMING_SNAKE_CASE      ,out_indices=_SCREAMING_SNAKE_CASE      ,stage_names=self.stage_names					)
          _snake_case							,      _snake_case							=   out_features, out_indices






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE					)							->    None:
          _snake_case							=   getattr(_SCREAMING_SNAKE_CASE      ,"stage_names"					)
          _snake_case							=   getattr(_SCREAMING_SNAKE_CASE      ,"out_features"      ,_SCREAMING_SNAKE_CASE					)
          _snake_case							=   getattr(_SCREAMING_SNAKE_CASE      ,"out_indices"      ,_SCREAMING_SNAKE_CASE					)

          _snake_case							=   stage_names
          _snake_case							,      _snake_case							=   get_aligned_output_features_output_indices(
              out_features=_SCREAMING_SNAKE_CASE      ,out_indices=_SCREAMING_SNAKE_CASE      ,stage_names=_SCREAMING_SNAKE_CASE					)
          # Number of channels for each stage. This is set in the transformer backbone model init
          _snake_case							=   None






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE					)							->    None:
          _snake_case							=   config

          _snake_case							=   getattr(_SCREAMING_SNAKE_CASE      ,"use_timm_backbone"      ,_SCREAMING_SNAKE_CASE					)
          _snake_case							=   BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

          if self.backbone_type == BackboneType.TIMM:
               self._init_timm_backbone(_SCREAMING_SNAKE_CASE					)
          elif self.backbone_type == BackboneType.TRANSFORMERS:
               self._init_transformers_backbone(_SCREAMING_SNAKE_CASE					)
          else:
               raise ValueError(f"""backbone_type {self.backbone_type} not supported."""					)






     @property
     def        _lowercase			(				self					)							->    int:
          return self._out_features






     @out_features.setter
     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE					)							->    str:
          _snake_case							,      _snake_case							=   get_aligned_output_features_output_indices(
              out_features=_SCREAMING_SNAKE_CASE      ,out_indices=_SCREAMING_SNAKE_CASE      ,stage_names=self.stage_names					)






     @property
     def        _lowercase			(				self					)							->    Any:
          return self._out_indices






     @out_indices.setter
     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE					)							->    int:
          _snake_case							,      _snake_case							=   get_aligned_output_features_output_indices(
              out_features=_SCREAMING_SNAKE_CASE      ,out_indices=_SCREAMING_SNAKE_CASE      ,stage_names=self.stage_names					)






     @property
     def        _lowercase			(				self					)							->    List[str]:
          # the current backbones will output the number of channels for each stage
          # even if that stage is not in the out_features list.
          return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names					)}






     @property
     def        _lowercase			(				self					)							->    Any:
          return [self.out_feature_channels[name] for name in self.out_features]






     def        _lowercase			(				self      ,*_SCREAMING_SNAKE_CASE      ,**_SCREAMING_SNAKE_CASE					)							->    str:
          _snake_case							=   dict(inspect.signature(self.forward					).parameters					)
          _snake_case							=   {k: v for k, v in kwargs.items() if k in signature}
          return self(*_SCREAMING_SNAKE_CASE      ,**_SCREAMING_SNAKE_CASE					)






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE = None      ,_SCREAMING_SNAKE_CASE = None      ,_SCREAMING_SNAKE_CASE = None      ,)							->    Any:
          raise NotImplementedError("This method should be implemented by the derived class."					)






     def        _lowercase			(				self					)							->    Tuple:
          _snake_case							=   super().to_dict()
          _snake_case							=   output.pop("_out_features"					)
          _snake_case							=   output.pop("_out_indices"					)
          return output







class      _a       :






     @property
     def        _lowercase			(				self					)							->    List[str]:
          return self._out_features






     @out_features.setter
     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE					)							->    str:
          _snake_case							,      _snake_case							=   get_aligned_output_features_output_indices(
              out_features=_SCREAMING_SNAKE_CASE      ,out_indices=_SCREAMING_SNAKE_CASE      ,stage_names=self.stage_names					)






     @property
     def        _lowercase			(				self					)							->    Any:
          return self._out_indices






     @out_indices.setter
     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE					)							->    Optional[Any]:
          _snake_case							,      _snake_case							=   get_aligned_output_features_output_indices(
              out_features=_SCREAMING_SNAKE_CASE      ,out_indices=_SCREAMING_SNAKE_CASE      ,stage_names=self.stage_names					)






     def        _lowercase			(				self					)							->    List[str]:
          _snake_case							=   super().to_dict()
          _snake_case							=   output.pop("_out_features"					)
          _snake_case							=   output.pop("_out_indices"					)
          return output


