




'''simple docstring'''

import argparse

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


logging.set_verbosity_info()




def    snake_case__     (  a       ,      a       ,      a       ,      a       ,      a	)			->       List[str]:





			'''simple docstring'''






			snake_case__          =						BertConfig.from_pretrained(
			    """bert-large-cased"""       ,      vocab_size=a       ,      max_position_embeddings=512       ,      is_decoder=a       ,      add_cross_attention=a       ,      )
			snake_case__          =						bert_config.to_dict()
			del bert_config_dict["type_vocab_size"]
			snake_case__          =						BertGenerationConfig(**a	)
			if is_encoder:
						snake_case__          =						BertGenerationEncoder(a	)
			else:
						snake_case__          =						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=50_358, 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,
		)




def 		__snake_case						(    _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase				):
					"""simple docstring"""






					if right <= left:
										return
					lowercase						   =       lowercase						   =       left
					lowercase						   =       right
					lowercase						   =       sorting[left]
					while i <= b:
										if sorting[i] < pivot:
															lowercase   ,      lowercase						   =       sorting[i], sorting[a]
															a += 1
															i += 1
										elif sorting[i] > pivot:
															lowercase   ,      lowercase						   =       sorting[i], sorting[b]
															b -= 1
										else:
															i += 1
					quick_sort_apartition(_UpperCAmelCase       ,			_UpperCAmelCase       ,			a - 1				)
					quick_sort_apartition(_UpperCAmelCase       ,			b + 1       ,			_UpperCAmelCase				)





def 		__snake_case						(    _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase				):
					"""simple docstring"""






					if left < right:
										lowercase						   =       lomuto_partition(_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase				)
										quick_sort_lomuto_partition(_UpperCAmelCase       ,			_UpperCAmelCase       ,			pivot_index - 1				)
										quick_sort_lomuto_partition(_UpperCAmelCase       ,			pivot_index + 1       ,			_UpperCAmelCase				)





def 		__snake_case						(    _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase				):
					"""simple docstring"""






					lowercase						   =       sorting[right]
					lowercase						   =       left
					for i in range(_UpperCAmelCase       ,			_UpperCAmelCase				):
										if sorting[i] < pivot:
															lowercase   ,      lowercase						   =       sorting[i], sorting[store_index]
															store_index += 1
					lowercase   ,      lowercase						   =       sorting[store_index], sorting[right]
					return store_index





def 		__snake_case						(    _UpperCAmelCase				):
					"""simple docstring"""






					if len(_UpperCAmelCase				) <= 1:
										return sorting
					return (
					    three_way_radix_quicksort([i for i in sorting if i < sorting[0]]				)
					    + [i for i in sorting if i == sorting[0]]
					    + three_way_radix_quicksort([i for i in sorting if i > sorting[0]]				)
					)


if __name__ == "__main__":
		import doctest

		doctest.testmod(verbose=True)

		__magic_name__       =     input('''Enter numbers separated by a comma:\n''').strip()
		__magic_name__       =     [int(item) for item in user_input.split(''',''')]
		quick_sort_apartition(unsorted, 0, len(unsorted) - 1)
		print(unsorted)






