




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

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

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


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=50358, 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,
     )












'''simple docstring'''


def 				SCREAMING_SNAKE_CASE(      __lowercase		,							__lowercase		,							__lowercase  ) ->   float:
     A:		str        =		[1 / first_term]
     A:		Optional[int]        =		1 / first_term
     for _ in range(number_of_terms - 1  ):
          first_term += common_difference
          arithmetic_progression.append(__lowercase  )
     A:		int        =		[1 / step for step in arithmetic_progression]
     return sum(__lowercase  )


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     print(sum_of_harmonic_progression(1 / 2, 2, 2))






