





import argparse

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


logging.set_verbosity_info()




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

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

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


if __name__ == "__main__":
				UpperCamelCase__ :			List[Any]       	=			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_03_58, type=int, help="""Vocab size of model""")
				UpperCamelCase__ :			str       	=			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"""






def    UpperCamelCase						(					UpperCAmelCase ,   UpperCAmelCase = 1_000     )						->str:

   """simple docstring"""



   if (not isinstance(UpperCAmelCase ,   UpperCAmelCase     )) or (digit_position <= 0):
      raise ValueError("Digit position must be a positive integer"     )
   elif (not isinstance(UpperCAmelCase ,   UpperCAmelCase     )) or (precision < 0):
      raise ValueError("Precision must be a nonnegative integer"     )

   # compute an approximation of (16 ** (n - 1)) * pi whose fractional part is mostly
   # accurate
   a_						   =     (
       4 * _subsum(UpperCAmelCase ,   1 ,   UpperCAmelCase     )
       - 2 * _subsum(UpperCAmelCase ,   4 ,   UpperCAmelCase     )
       - _subsum(UpperCAmelCase ,   5 ,   UpperCAmelCase     )
       - _subsum(UpperCAmelCase ,   6 ,   UpperCAmelCase     )
   )

   # return the first hex digit of the fractional part of the result
   return hex(int((sum_result % 1) * 16     )     )[2:]
def    UpperCamelCase						(					UpperCAmelCase ,   UpperCAmelCase ,   UpperCAmelCase     )						->float:

   """simple docstring"""



   a_						   =     0.0
   for sum_index in range(digit_pos_to_extract + precision     ):
      a_						   =     8 * sum_index + denominator_addend
      if sum_index < digit_pos_to_extract:
         # if the exponential term is an integer and we mod it by the denominator
         # before dividing, only the integer part of the sum will change;
         # the fractional part will not
         a_						   =     pow(
             16 ,   digit_pos_to_extract - 1 - sum_index ,   UpperCAmelCase     )
      else:
         a_						   =     pow(16 ,   digit_pos_to_extract - 1 - sum_index     )
      total += exponential_term / denominator
   return total


if __name__ == "__main__":
      import doctest

      doctest.testmod()