

import argparse

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


logging.set_verbosity_info()



def  snake_case_(_UpperCamelCase					,			_UpperCamelCase					,			_UpperCamelCase					,			_UpperCamelCase					,			_UpperCamelCase	)				->      Dict:



  """simple docstring"""






  _snake_case		=				BertConfig.from_pretrained(
      '''bert-large-cased'''					,			vocab_size=__lowercase					,			max_position_embeddings=512					,			is_decoder=__lowercase					,			add_cross_attention=__lowercase					,			)
  _snake_case		=				bert_config.to_dict()
  del bert_config_dict["type_vocab_size"]
  _snake_case		=				BertGenerationConfig(**__lowercase	)
  if is_encoder:
    _snake_case		=				BertGenerationEncoder(__lowercase	)
  else:
    _snake_case		=				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__":
 __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=5_03_58, 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,
 )






class 				lowerCAmelCase__	:







					def __init__(							self   :  Union[str, Any]):
											A__						:				Optional[int]         =			[0]
											A__						:				Any         =			0







					def 			_lowercase      (							self   :  Optional[int]      ,		_A   :  int):
											A__						:				Optional[Any]         =			self.__heap[i]
											while i // 2 > 0:
																	if self.__heap[i] > self.__heap[i // 2]:
																							A__						:				Union[str, Any]         =			self.__heap[i // 2]
																							A__						:				List[str]         =			temporary
																	i //= 2







					def 			_lowercase      (							self   :  Union[str, Any]      ,		_A   :  int):
											self.__heap.append(_A)
											self.__size += 1
											self.__swap_up(self.__size)







					def 			_lowercase      (							self   :  Any      ,		_A   :  int):
											while self.__size >= 2 * i:
																	if 2 * i + 1 > self.__size:
																							A__						:				str         =			2 * i
																	else:
																							if self.__heap[2 * i] > self.__heap[2 * i + 1]:
																													A__						:				Tuple         =			2 * i
																							else:
																													A__						:				Tuple         =			2 * i + 1
																	A__						:				Any         =			self.__heap[i]
																	if self.__heap[i] < self.__heap[bigger_child]:
																							A__						:				List[str]         =			self.__heap[bigger_child]
																							A__						:				Tuple         =			temporary
																	A__						:				Tuple         =			bigger_child







					def 			_lowercase      (							self   :  int):
											A__						:				str         =			self.__heap[1]
											A__						:				Tuple         =			self.__heap[self.__size]
											self.__size -= 1
											self.__heap.pop()
											self.__swap_down(1)
											return max_value







					@property
					def 			_lowercase      (							self   :  Any):
											return self.__heap[1:]







					def __len__(							self   :  Union[str, Any]):
											return self.__size


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		# create an instance of BinaryHeap
		snake_case     :    List[Any]						=    BinaryHeap()
		binary_heap.insert(6)
		binary_heap.insert(1_0)
		binary_heap.insert(1_5)
		binary_heap.insert(1_2)
		# pop root(max-values because it is max heap)
		print(binary_heap.pop())  # 15
		print(binary_heap.pop())  # 12
		# get the list and size after operations
		print(binary_heap.get_list)
		print(len(binary_heap))