


"""simple docstring"""




from .number_theory.prime_numbers import next_prime







class 				lowerCamelCase__       :





     def __init__(self		,					UpperCAmelCase		,					UpperCAmelCase = None		,					UpperCAmelCase = None		,					)					->       None:
      _lowercase											=size_table
      _lowercase											=[None] * self.size_table
      _lowercase											=0.75 if lim_charge is None else lim_charge
      _lowercase											=1 if charge_factor is None else charge_factor
      _lowercase											=[]
      _lowercase											={}





     def 					__A (self	)					->       Any:
      return self._keys





     def 					__A (self	)					->       Union[str, Any]:
      return sum(1 for slot in self.values if slot is not None	) / (
          self.size_table * self.charge_factor
      )





     def 					__A (self		,					UpperCAmelCase	)					->       Tuple:
      return key % self.size_table





     def 					__A (self		,					UpperCAmelCase	)					->       Tuple:
      print(f"step {step_ord}"	)
      print(list(range(len(self.values	)	)	)	)
      print(self.values	)





     def 					__A (self		,					UpperCAmelCase	)					->       str:
      _lowercase											=1
      _lowercase											=values
      for value in values:
       self.insert_data(lowerCAmelCase__	)
       self._step_by_step(lowerCAmelCase__	)
       i += 1





     def 					__A (self		,					UpperCAmelCase		,					UpperCAmelCase	)					->       int:
      _lowercase											=data
      _lowercase											=data





     def 					__A (self		,					UpperCAmelCase		,					UpperCAmelCase=None	)					->       Optional[Any]:
      _lowercase											=self.hash_function(key + 1	)

      while self.values[new_key] is not None and self.values[new_key] != key:
       if self.values.count(lowerCAmelCase__	) > 0:
        _lowercase											=self.hash_function(new_key + 1	)
       else:
        _lowercase											=None
        break

      return new_key





     def 					__A (self	)					->       str:
      _lowercase											=[value for value in self.values if value is not None]
      _lowercase											=next_prime(self.size_table		,					factor=2	)
      self._keys.clear()
      _lowercase											=[None] * self.size_table  # hell's pointers D: don't DRY ;/
      for value in survivor_values:
       self.insert_data(lowerCAmelCase__	)





     def 					__A (self		,					UpperCAmelCase	)					->       int:
      _lowercase											=self.hash_function(lowerCAmelCase__	)

      if self.values[key] is None:
       self._set_value(lowerCAmelCase__		,					lowerCAmelCase__	)

      elif self.values[key] == data:
       pass

      else:
       _lowercase											=self._collision_resolution(lowerCAmelCase__		,					lowerCAmelCase__	)
       if collision_resolution is not None:
        self._set_value(lowerCAmelCase__		,					lowerCAmelCase__	)
       else:
        self.rehashing()
        self.insert_data(lowerCAmelCase__	)





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,
  )

