






import argparse

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


logging.set_verbosity_info()


def __A					(   __lowerCAmelCase				,	__lowerCAmelCase				,	__lowerCAmelCase				,	__lowerCAmelCase				,	__lowerCAmelCase			)->						List[str]:





						"""simple docstring"""



						_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__":
		_a   			=						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''')
		_a   			=						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,
		)













import math
from timeit import timeit


def __A					(   __lowerCAmelCase			)->						int:





						"""simple docstring"""



						_UpperCAmelCase             =    0
						_UpperCAmelCase             =    abs(__lowerCAmelCase			)
						while True:
												_UpperCAmelCase             =    n // 10
												digits += 1
												if n == 0:
																		break
						return digits


def __A					(   __lowerCAmelCase			)->						int:





						"""simple docstring"""



						return 1 if n == 0 else math.floor(math.log(abs(__lowerCAmelCase			)				,	10			) + 1			)


def __A					(   __lowerCAmelCase			)->						int:





						"""simple docstring"""



						return len(str(abs(__lowerCAmelCase			)			)			)


def __A					(   )->						None:





						"""simple docstring"""



						from collections.abc import Callable

						def benchmark_a_function(__lowerCAmelCase				,	__lowerCAmelCase			) -> None:
												_UpperCAmelCase             =    F"""{func.__name__}({value})"""
												_UpperCAmelCase             =    timeit(F"""__main__.{call}"""				,	setup='import __main__'			)
												print(F"""{call}: {func(__lowerCAmelCase			)} -- {timing} seconds"""			)

						for value in (262_144, 1_125_899_906_842_624, 1_267_650_600_228_229_401_496_703_205_376):
												for func in (num_digits, num_digits_fast, num_digits_faster):
																		benchmark_a_function(__lowerCAmelCase				,	__lowerCAmelCase			)
												print()


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		benchmark()
