






from __future__ import annotations

from math import gcd







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							int					,  snake_case_ :							int = 2					,  snake_case_ :							int = 1					,  snake_case_ :							int = 3					,  ):
	# A value less than 2 can cause an infinite loop in the algorithm.
	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(snake_case_ :							int					,  snake_case_ :							int					,  snake_case_ :							int       ) -> int:
		return (pow(snake_case_					,  2       ) + step) % modulus

	for _ in range(snake_case_       ):
		# These track the position within the cycle detection logic.
		snake_case__			:				str       =  seed
		snake_case__			:				Optional[Any]       =  seed

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

			# 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``.
			snake_case__			:				str       =  gcd(hare - tortoise					,  snake_case_       )

			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.
		snake_case__			:				Optional[int]       =  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 :      Optional[int]										=				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 :      Tuple										=				parser.parse_args()

	__lowerCamelCase :      Optional[int]										=				pollard_rho(args.num, attempts=args.attempts)
	if divisor is None:
		print(f"{args.num} is probably prime")
	else:
		__lowerCamelCase :      Union[str, Any]										=				args.num // divisor
		print(f"{args.num} = {divisor} * {quotient}")











import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Tuple					,  snake_case_ :							str					,  snake_case_ :							Union[str, Any]       ):
	# Initialise PyTorch model
	snake_case__			:				List[str]       =  MobileBertConfig.from_json_file(snake_case_       )
	print(F'''Building PyTorch model from configuration: {config}'''       )
	snake_case__			:				Dict       =  MobileBertForPreTraining(snake_case_       )
	# Load weights from tf checkpoint
	snake_case__			:				Any       =  load_tf_weights_in_mobilebert(snake_case_					,  snake_case_					,  snake_case_       )
	# Save pytorch-model
	print(F'''Save PyTorch model to {pytorch_dump_path}'''       )
	torch.save(model.state_dict()					,  snake_case_       )


if __name__ == "__main__":
	__lowerCamelCase :      List[Any]										=				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(
	    """--mobilebert_config_file""",
	    default=None,
	    type=str,
	    required=True,
	    help=(
	        """The config json file corresponding to the pre-trained MobileBERT 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."""
	)
	__lowerCamelCase :      int										=				parser.parse_args()
	convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)



