


"""simple docstring"""




import torch

from transformers import AutoTokenizer







class 				lowerCamelCase__       (	lowerCAmelCase):





     def __init__(self		,					UpperCAmelCase	)					->       int:
      _lowercase											=AutoTokenizer.from_pretrained(lowercase__	)





     def 					__A (self		,					UpperCAmelCase	)					->       List[Any]:







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

       _lowercase											=torch.tensor(self.tokenizer.convert_tokens_to_ids('''[E]'''	)	)
       _lowercase											=torch.tensor(self.tokenizer.convert_tokens_to_ids('''[/E]'''	)	)

      elif isinstance(lowercase__		,					lowercase__	) and all(isinstance(_x		,					lowercase__	) for _x in x	):
       _lowercase											=self.tokenizer(
           lowercase__		,					padding='''max_length'''		,					max_length=3_8_4		,					truncation=lowercase__		,					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		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase=0.70	)					->       int:
      _lowercase											=[]
      for idx in range(len(W_query['''input_ids''']	)	):
       _lowercase											=_lowercase											=range(p_start.shape[1]	)

       _lowercase											=[]
       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 UpperCAmelCase	: (tup[2] * tup[3])		,					reverse=lowercase__	)
       _lowercase											=[]
       for k in range(len(lowercase__	)	):
        if output[k][2] * output[k][3] >= thresh:
         _lowercase				,   _lowercase											=output[k][0], output[k][1]
         _lowercase											=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(lowercase__	)

      return final_outputs





import argparse

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


logging.set_verbosity_info()
def     UpperCAmelCase_ (							__snake_case					,						__snake_case					,						__snake_case					,						__snake_case					,						__snake_case						)					->      Tuple:







 """simple docstring"""


 _lowercase											=BertConfig.from_pretrained(
     '''bert-large-cased'''					,						vocab_size=__snake_case					,						max_position_embeddings=512					,						is_decoder=__snake_case					,						add_cross_attention=__snake_case					,						)
 _lowercase											=bert_config.to_dict()
 del bert_config_dict["type_vocab_size"]
 _lowercase											=BertGenerationConfig(**__snake_case						)
 if is_encoder:
  _lowercase											=BertGenerationEncoder(__snake_case						)
 else:
  _lowercase											=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__            =							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_0358, type=int, help='''Vocab size of model''')
  UpperCAmelCase__            =							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,
  )

