




'''simple docstring'''





import argparse

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


logging.set_verbosity_info()





def 			__a			(     _UpperCamelCase:					Union[str, Any]      ,		_UpperCamelCase:					List[str]      ,		_UpperCamelCase:					int      ,		_UpperCamelCase:					Optional[Any]      ,		_UpperCamelCase:					Optional[int]					)		->				Union[str, Any]:

					"""simple docstring"""




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

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

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


if __name__ == "__main__":
			UpperCamelCase_		:    str									=		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_		:    Optional[int]									=		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'''





UpperCamelCase_		:    Optional[Any]									=		'''
# Diffusers installation
! pip install diffusers transformers datasets accelerate
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/diffusers.git
'''

UpperCamelCase_		:    List[Any]									=		[{'''type''': '''code''', '''content''': INSTALL_CONTENT}]


