



def  _snake_case			(							__snake_case       ,	__snake_case       ,	__snake_case       ,	__snake_case     )  ->  Optional[int]:






      '''simple docstring'''







      if start == end:
            return start if lst[start] > item else start + 1
      if start > end:
            return start

      UpperCAmelCase_	:						List[Any]					   =		(start + end) // 2
      if lst[mid] < item:
            return binary_search(__snake_case       ,	__snake_case       ,	mid + 1       ,	__snake_case     )
      elif lst[mid] > item:
            return binary_search(__snake_case       ,	__snake_case       ,	__snake_case       ,	mid - 1     )
      else:
            return mid







def  _snake_case			(							__snake_case     )  ->  Optional[Any]:






      '''simple docstring'''







      UpperCAmelCase_	:						int					   =		len(__snake_case     )

      for index in range(1       ,	__snake_case     ):
            UpperCAmelCase_	:						List[Any]					   =		lst[index]
            UpperCAmelCase_	:						Optional[int]					   =		binary_search(__snake_case       ,	__snake_case       ,	0       ,	index - 1     )
            UpperCAmelCase_	:						Optional[int]					   =		lst[:pos] + [value] + lst[pos:index] + lst[index + 1 :]

      return lst







def  _snake_case			(							__snake_case       ,	__snake_case     )  ->  List[Any]:






      '''simple docstring'''







      if not left:
            return right

      if not right:
            return left

      if left[0] < right[0]:
            return [left[0], *merge(left[1:]       ,	__snake_case     )]

      return [right[0], *merge(__snake_case       ,	right[1:]     )]







def  _snake_case			(							__snake_case     )  ->  Union[str, Any]:






      '''simple docstring'''







      UpperCAmelCase_	:						Any					   =		len(__snake_case     )
      UpperCAmelCase_   ,   UpperCAmelCase_	:						List[str]					   =		[], []
      UpperCAmelCase_	:						Tuple					   =		[lst[0]]
      UpperCAmelCase_	:						Dict					   =		[]
      UpperCAmelCase_	:						Dict					   =		1
      while i < length:
            if lst[i] < lst[i - 1]:
                  runs.append(__snake_case     )
                  UpperCAmelCase_	:						Union[str, Any]					   =		[lst[i]]
            else:
                  new_run.append(lst[i]     )
            i += 1
      runs.append(__snake_case     )

      for run in runs:
            sorted_runs.append(insertion_sort(__snake_case     )     )
      for run in sorted_runs:
            UpperCAmelCase_	:						List[str]					   =		merge(__snake_case       ,	__snake_case     )

      return sorted_array







def  _snake_case			(							)  ->  List[str]:






      '''simple docstring'''







      UpperCAmelCase_	:						Optional[int]					   =		[5, 9, 1_0, 3, -4, 5, 1_7_8, 9_2, 4_6, -1_8, 0, 7]
      UpperCAmelCase_	:						Tuple					   =		tim_sort(__snake_case     )
      print(__snake_case     )


if __name__ == "__main__":
   main()


"""simple docstring"""

import argparse

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


logging.set_verbosity_info()






def 				A		(							snake_case :Dict							,   snake_case :int							,   snake_case :List[str]							,   snake_case :int							,   snake_case :List[str]						) ->      List[str]:
 # Initialise PyTorch model
 __UpperCamelCase							    =			BertConfig.from_pretrained(
     'bert-large-cased'							,   vocab_size=snake_case							,   max_position_embeddings=5_1_2							,   is_decoder=snake_case							,   add_cross_attention=snake_case							,   )
 __UpperCamelCase							    =			bert_config.to_dict()
 del bert_config_dict["type_vocab_size"]
 __UpperCamelCase							    =			BertGenerationConfig(**snake_case						)
 if is_encoder:
  __UpperCamelCase							    =			BertGenerationEncoder(snake_case						)
 else:
  __UpperCamelCase							    =			BertGenerationDecoder(snake_case						)
 print(f'Building PyTorch model from configuration: {config}'						)

 # Load weights from tf checkpoint
 load_tf_weights_in_bert_generation(
     snake_case							,   snake_case							,   model_class='bert'							,   is_encoder_named_decoder=snake_case							,   is_encoder=snake_case							,   )

 # Save pytorch-model
 print(f'Save PyTorch model and config to {pytorch_dump_path}'						)
 model.save_pretrained(snake_case						)


if __name__ == "__main__":
      UpperCamelCase      :							List[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=5_0_3_5_8, type=int, help="Vocab size of model")
      UpperCamelCase      :							Dict          =					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,
      )



