
"""simple docstring"""






import argparse

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


logging.set_verbosity_info()



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

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

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


if __name__ == "__main__":
					_lowercase							 =							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=50_358, type=int, help='''Vocab size of model''')
					_lowercase							 =							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     lowerCAmelCase__ (		__magic_name__ ,      __magic_name__			)	->list:
			__lowercase						      =					len(__magic_name__			)
			__lowercase						      =					len(__magic_name__			)
			__lowercase						      =					(
			    first_input_list_length
			    if first_input_list_length > second_input_list_length
			    else second_input_list_length
			)
			__lowercase						      =					[]
			for char_count in range(__magic_name__			):
						if char_count < first_input_list_length:
									output_result_list.append(first_input_list[char_count]			)
						if char_count < second_input_list_length:
									output_result_list.append(second_input_list[char_count]			)

			return output_result_list


if __name__ == "__main__":
					print(alternative_list_arrange(['''A''', '''B''', '''C'''], [1, 2, 3, 4, 5]), end=''' ''')





