





"""simple docstring"""




import torch

from transformers import AutoTokenizer







class 					_UpperCamelCase		( __snake_case):






    def __init__(self						,      lowerCamelCase__   ):


      """simple docstring"""
      A__											=							AutoTokenizer.from_pretrained(lowerCamelCase__   )






    def  A    (self						,      lowerCamelCase__   ):


      """simple docstring"""

      if isinstance(lowerCamelCase__						,      lowerCamelCase__   ) and all(isinstance(_x						,      lowerCamelCase__   ) for _x in x   ):
        A__											=							None
        for l in x:
          A__											=							self.tokenizer(
              lowerCamelCase__						,      padding="""max_length"""						,      max_length=3_8_4						,      truncation=lowerCamelCase__						,      return_tensors="""pt"""						,      )
          A__											=							torch.tensor([len(lowerCamelCase__   )]   )
          if d is not None:
            for k in d.keys():
              A__											=							torch.cat((d[k], t[k])						,      0   )
          else:
            A__											=							t

        A__											=							torch.tensor(self.tokenizer.convert_tokens_to_ids("""[E]"""   )   )
        A__											=							torch.tensor(self.tokenizer.convert_tokens_to_ids("""[/E]"""   )   )

      elif isinstance(lowerCamelCase__						,      lowerCamelCase__   ) and all(isinstance(_x						,      lowerCamelCase__   ) for _x in x   ):
        A__											=							self.tokenizer(
            lowerCamelCase__						,      padding="""max_length"""						,      max_length=3_8_4						,      truncation=lowerCamelCase__						,      return_tensors="""pt"""						,      )

      else:
        raise Exception(
            """Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of"""
            """ strings` for supports are supported."""   )

      return d






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__=0.7_0   ):


      """simple docstring"""
      A__											=							[]
      for idx in range(len(W_query["""input_ids"""]   )   ):
        A__											=							A__											=							range(p_start.shape[1]   )

        A__											=							[]
        for start_id in start_indexes:
          for end_id in end_indexes:
            if start_id < end_id:
              output.append(
                  (
                      start_id,
                      end_id,
                      p_start[idx][start_id].item(),
                      p_end[idx][end_id].item(),
                  )   )

        output.sort(key=lambda lowerCamelCase__   : (tup[2] * tup[3])						,      reverse=lowerCamelCase__   )
        A__											=							[]
        for k in range(len(lowerCamelCase__   )   ):
          if output[k][2] * output[k][3] >= thresh:
            A__						,A__											=							output[k][0], output[k][1]
            A__											=							self.tokenizer.decode(W_query["""input_ids"""][idx][c_start_pos:c_end_pos]   )
            temp.append((decoded, output[k][2] * output[k][3])   )

        final_outputs.append(lowerCamelCase__   )

      return final_outputs












"""simple docstring"""




import argparse

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


logging.set_verbosity_info()






def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	List[str]		,				UpperCamelCase     :	Tuple		,				UpperCamelCase     :	Optional[int]		,				UpperCamelCase     :	Union[str, Any]		,				UpperCamelCase     :	Optional[int]    ):
  # Initialise PyTorch model
  A__											=							BertConfig.from_pretrained(
      """bert-large-cased"""		,				vocab_size=UpperCamelCase		,				max_position_embeddings=512		,				is_decoder=UpperCamelCase		,				add_cross_attention=UpperCamelCase		,				)
  A__											=							bert_config.to_dict()
  del bert_config_dict["type_vocab_size"]
  A__											=							BertGenerationConfig(**UpperCamelCase    )
  if is_encoder:
    A__											=							BertGenerationEncoder(UpperCamelCase    )
  else:
    A__											=							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__":
      lowerCamelCase__			=   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=50_358, type=int, help="Vocab size of model")
      lowerCamelCase__			=   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,
      )




