
from __future__ import annotations

from math import gcd


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__ = 2			,		UpperCamelCase__ = 1			,		UpperCamelCase__ = 3			,		):


      if num < 2:
            raise ValueError('''The input value cannot be less than 2'''		)

      # Because of the relationship between ``f(f(x))`` and ``f(x)``, this
      # algorithm struggles to find factors that are divisible by two.
      # As a workaround, we specifically check for two and even inputs.
      #   See: https://math.stackexchange.com/a/2856214/165820
      if num > 2 and num % 2 == 0:
            return 2

      # Pollard's Rho algorithm requires a function that returns pseudorandom
      # values between 0 <= X < ``num``.  It doesn't need to be random in the
      # sense that the output value is cryptographically secure or difficult
      # to calculate, it only needs to be random in the sense that all output
      # values should be equally likely to appear.
      # For this reason, Pollard suggested using ``f(x) = (x**2 - 1) % num``
      # However, the success of Pollard's algorithm isn't guaranteed and is
      # determined in part by the initial seed and the chosen random function.
      # To make retries easier, we will instead use ``f(x) = (x**2 + C) % num``
      # where ``C`` is a value that we can modify between each attempt.
      def rand_fn(UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__		) -> int:
            return (pow(_UpperCamelCase			,		2		) + step) % modulus

      for _ in range(_UpperCamelCase		):
            # These track the position within the cycle detection logic.
            UpperCamelCase__     : Dict											=					seed
            UpperCamelCase__     : List[str]											=					seed

            while True:
                  # At each iteration, the tortoise moves one step and the hare moves two.
                  UpperCamelCase__     : Tuple											=					rand_fn(_UpperCamelCase			,		_UpperCamelCase			,		_UpperCamelCase		)
                  UpperCamelCase__     : Union[str, Any]											=					rand_fn(_UpperCamelCase			,		_UpperCamelCase			,		_UpperCamelCase		)
                  UpperCamelCase__     : Any											=					rand_fn(_UpperCamelCase			,		_UpperCamelCase			,		_UpperCamelCase		)

                  # At some point both the tortoise and the hare will enter a cycle whose
                  # length ``p`` is a divisor of ``num``.  Once in that cycle, at some point
                  # the tortoise and hare will end up on the same value modulo ``p``.
                  # We can detect when this happens because the position difference between
                  # the tortoise and the hare will share a common divisor with ``num``.
                  UpperCamelCase__     : int											=					gcd(hare - tortoise			,		_UpperCamelCase		)

                  if divisor == 1:
                        # No common divisor yet, just keep searching.
                        continue
                  else:
                        # We found a common divisor!
                        if divisor == num:
                              # Unfortunately, the divisor is ``num`` itself and is useless.
                              break
                        else:
                              # The divisor is a nontrivial factor of ``num``!
                              return divisor

                                 # If we made it here, then this attempt failed.
                                 # We need to pick a new starting seed for the tortoise and hare
                                 # in addition to a new step value for the random function.
                                 # To keep this example implementation deterministic, the
                                 # new values will be generated based on currently available
                                 # values instead of using something like ``random.randint``.

                                 # We can use the hare's position as the new seed.
                                 # This is actually what Richard Brent's the "optimized" variant does.
            UpperCamelCase__     : Tuple											=					hare

            # The new step value for the random function can just be incremented.
            # At first the results will be similar to what the old function would
            # have produced, but the value will quickly diverge after a bit.
            step += 1

      # We haven't found a divisor within the requested number of attempts.
      # We were unlucky or ``num`` itself is actually prime.
      return None


if __name__ == "__main__":
    import argparse

    lowerCamelCase  =argparse.ArgumentParser()
    parser.add_argument(
        "num",
        type=int,
        help="The value to find a divisor of",
    )
    parser.add_argument(
        "--attempts",
        type=int,
        default=3,
        help="The number of attempts before giving up",
    )
    lowerCamelCase  =parser.parse_args()

    lowerCamelCase  =pollard_rho(args.num, attempts=args.attempts)
    if divisor is None:
        print(F'''{args.num} is probably prime''')
    else:
        lowerCamelCase  =args.num // divisor
        print(F'''{args.num} = {divisor} * {quotient}''')





import argparse

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


logging.set_verbosity_info()


def 							__lowerCAmelCase     (							_UpperCamelCase		:	str		,				_UpperCamelCase		:	Union[str, Any]		,				_UpperCamelCase		:	Tuple		,				_UpperCamelCase		:	Tuple						)		->				Dict:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      BigBirdConfig.from_json_file(_UpperCamelCase						)
 print(f"""Building PyTorch model from configuration: {config}"""						)

 if is_trivia_qa:
  SCREAMING_SNAKE_CASE		    =      BigBirdForQuestionAnswering(_UpperCamelCase						)
 else:
  SCREAMING_SNAKE_CASE		    =      BigBirdForPreTraining(_UpperCamelCase						)

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

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


if __name__ == "__main__":
      a_     :    int     		=   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."
      )
      a_     :    Any     		=   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
      )

