

from string import ascii_uppercase

a_      =     {str(ord(c) - 55): c for c in ascii_uppercase}

def 			lowerCamelCase__			(       _a				,      _a):
	if isinstance(_a				,      _a):
		raise TypeError("int() can't convert non-string with explicit base")
	if num < 0:
		raise ValueError("parameter must be positive int")
	if isinstance(_a				,      _a):
		raise TypeError("'str' object cannot be interpreted as an integer")
	if isinstance(_a				,      _a):
		raise TypeError("'float' object cannot be interpreted as an integer")
	if base in (0, 1):
		raise ValueError("base must be >= 2")
	if base > 36:
		raise ValueError("base must be <= 36")
	SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							""
	SCREAMING_SNAKE_CASE :   Optional[Any]					  =							0
	SCREAMING_SNAKE_CASE :   Optional[Any]					  =							0
	while div != 1:
		SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Optional[int]					  =							divmod(_a				,      _a)
		if base >= 11 and 9 < mod < 36:
			SCREAMING_SNAKE_CASE :   Dict					  =							ALPHABET_VALUES[str(_a)]
		else:
			SCREAMING_SNAKE_CASE :   Dict					  =							str(_a)
		new_value += actual_value
		SCREAMING_SNAKE_CASE :   List[Any]					  =							num // base
		SCREAMING_SNAKE_CASE :   Tuple					  =							div
		if div == 0:
			return str(new_value[::-1])
		elif div == 1:
			new_value += str(_a)
			return str(new_value[::-1])

	return new_value[::-1]


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			for base in range(2, 37):
						for num in range(1000):
									assert int(decimal_to_any(num, base), base) == num, (
									    num,
									    base,
									    decimal_to_any(num, base),
									    int(decimal_to_any(num, base), base),
									)



'''simple docstring'''


import argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()


def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     __A	:       List[str]      =							BigBirdConfig.from_json_file(SCREAMING_SNAKE_CASE							)
     print(f"Building PyTorch model from configuration: {config}"							)

     if is_trivia_qa:
          __A	:       List[str]      =							BigBirdForQuestionAnswering(SCREAMING_SNAKE_CASE							)
     else:
          __A	:       List[Any]      =							BigBirdForPreTraining(SCREAMING_SNAKE_CASE							)

     # Load weights from tf checkpoint
     load_tf_weights_in_big_bird(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					is_trivia_qa=SCREAMING_SNAKE_CASE							)

     # Save pytorch-model
     print(f"Save PyTorch model to {pytorch_dump_path}"							)
     model.save_pretrained(SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
      _UpperCamelCase 							=  argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
      )
      parser.add_argument(
          """--big_bird_config_file""",
          default=None,
          type=str,
          required=True,
          help=(
              """The config json file corresponding to the pre-trained BERT model. \n"""
              """This specifies the model architecture."""
          ),
      )
      parser.add_argument(
          """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
      )
      parser.add_argument(
          """--is_trivia_qa""", action="""store_true""", help="""Whether to convert a model with a trivia_qa head."""
      )
      _UpperCamelCase 							=  parser.parse_args()
      convert_tf_checkpoint_to_pytorch(
          args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
      )

