



'''simple docstring'''

from dataclasses import dataclass, field
from typing import Optional

@dataclass
class       __magic_name__		:
							lowerCAmelCase			:       Optional[str]  							=   field(
							    default='codeparrot/codeparrot'     ,		metadata={'help': 'Model name or path of model to be trained.'}  )
							lowerCAmelCase			:       Optional[str]  							=   field(
							    default='./'     ,		metadata={'help': 'Save dir where model repo is cloned and models updates are saved to.'}  )
							lowerCAmelCase			:       Optional[str]  							=   field(
							    default='codeparrot/codeparrot-clean-train'     ,		metadata={'help': 'Name or path of training dataset.'}  )
							lowerCAmelCase			:       Optional[str]  							=   field(
							    default='codeparrot/codeparrot-clean-valid'     ,		metadata={'help': 'Name or path of validation dataset.'}  )
							lowerCAmelCase			:       Optional[int]  							=   field(default=2     ,		metadata={'help': 'Batch size for training.'}  )
							lowerCAmelCase			:       Optional[int]  							=   field(default=2     ,		metadata={'help': 'Batch size for evaluation.'}  )
							lowerCAmelCase			:       Optional[float]  							=   field(default=0.1     ,		metadata={'help': 'Value of weight decay.'}  )
							lowerCAmelCase			:       Optional[int]  							=   field(
							    default=1_0_0_0_0     ,		metadata={'help': 'Size of buffer used to shuffle streaming dataset.'}  )
							lowerCAmelCase			:       Optional[float]  							=   field(default=2e-4     ,		metadata={'help': 'Learning rate fo training.'}  )
							lowerCAmelCase			:       Optional[str]  							=   field(default='cosine'     ,		metadata={'help': 'Learning rate.'}  )
							lowerCAmelCase			:       Optional[int]  							=   field(
							    default=7_5_0     ,		metadata={'help': 'Number of warmup steps in the learning rate schedule.'}  )
							lowerCAmelCase			:       Optional[int]  							=   field(
							    default=1_6     ,		metadata={'help': 'Number of gradient accumulation steps.'}  )
							lowerCAmelCase			:       Optional[bool]  							=   field(
							    default=_UpperCamelCase     ,		metadata={'help': 'Use gradient checkpointing to reduce memory footprint.'}  )
							lowerCAmelCase			:       Optional[int]  							=   field(default=5_0_0_0_0     ,		metadata={'help': 'Maximum number of training steps.'}  )
							lowerCAmelCase			:       Optional[int]  							=   field(
							    default=-1     ,		metadata={'help': 'Maximum number of evaluation steps. If -1 the full dataset is evaluated.'}  )
							lowerCAmelCase			:       Optional[int]  							=   field(default=1_0_2_4     ,		metadata={'help': 'Sequence lengths used for training.'}  )
							lowerCAmelCase			:       Optional[int]  							=   field(default=1     ,		metadata={'help': 'Training seed.'}  )
							lowerCAmelCase			:       Optional[int]  							=   field(
							    default=1_0_2_4     ,		metadata={'help': 'Interval to save checkpoints. Measured as number of forward passes not training steps.'}     ,		)
							lowerCAmelCase			:       Optional[str]  							=   field(
							    default=_UpperCamelCase     ,		metadata={'help': 'States path if the training should continue from a checkpoint folder.'}  )
							lowerCAmelCase			:       Optional[bool]  							=   field(default=_UpperCamelCase     ,		metadata={'help': 'If True the data is pretokenized.'}  )

@dataclass
class       __magic_name__		:
							lowerCAmelCase			:       Optional[str]  							=   field(
							    default='codeparrot/codeparrot'     ,		metadata={'help': 'Model name or path of model to be evaluated.'}  )
							lowerCAmelCase			:       Optional[str]  							=   field(
							    default='codeparrot/codeparrot-clean-valid'     ,		metadata={'help': 'Name or path of validation dataset.'}  )
							lowerCAmelCase			:       Optional[int]  							=   field(default=2     ,		metadata={'help': 'Batch size used for evaluation.'}  )
							lowerCAmelCase			:       Optional[int]  							=   field(
							    default=-1     ,		metadata={'help': 'Maximum number of evaluation steps. If -1 the full dataset is evaluated.'}  )
							lowerCAmelCase			:       Optional[int]  							=   field(default=1_0_2_4     ,		metadata={'help': 'Length of sequences to be evaluated.'}  )
							lowerCAmelCase			:       Optional[int]  							=   field(default=1     ,		metadata={'help': 'Random seed used for evaluation.'}  )

@dataclass
class       __magic_name__		:
							lowerCAmelCase			:       Optional[str]  							=   field(
							    default='codeparrot/codeparrot'     ,		metadata={'help': 'Model name or path of model to be evaluated.'}  )
							lowerCAmelCase			:       Optional[int]  							=   field(default=_UpperCamelCase     ,		metadata={'help': 'Number of workers used for code evaluation.'}  )
							lowerCAmelCase			:       Optional[int]  							=   field(
							    default=_UpperCamelCase     ,		metadata={'help': 'The number of human-eval tasks to run. If not included all tasks are evaluated.'}     ,		)
							lowerCAmelCase			:       Optional[bool]  							=   field(
							    default=_UpperCamelCase     ,		metadata={'help': 'Sample from the language model\'s output distribution.'}  )
							lowerCAmelCase			:       Optional[float]  							=   field(default=0.2     ,		metadata={'help': 'Sampling temperature used for generation.'}  )
							lowerCAmelCase			:       Optional[int]  							=   field(default=2_5_6     ,		metadata={'help': 'Maximum number of newly generated tokens.'}  )
							lowerCAmelCase			:       Optional[int]  							=   field(default=0     ,		metadata={'help': 'Top-k parameter used for generation.'}  )
							lowerCAmelCase			:       Optional[float]  							=   field(default=0.95     ,		metadata={'help': 'Top-p parameter used for nucleus sampling.'}  )
							lowerCAmelCase			:       Optional[int]  							=   field(default=1_0     ,		metadata={'help': 'Number of generations to run in parallel.'}  )
							lowerCAmelCase			:       Optional[int]  							=   field(
							    default=2_0_0     ,		metadata={'help': 'Number of completions to generate for each sample.'}  )
							lowerCAmelCase			:       Optional[int]  							=   field(default=1     ,		metadata={'help': 'Random seed used for evaluation.'}  )
							lowerCAmelCase			:       Optional[str]  							=   field(
							    default='eval_results.json'     ,		metadata={'help': 'Random seed used for evaluation.'}  )
							lowerCAmelCase			:       Optional[str]  							=   field(
							    default='0'     ,		metadata={'help': 'Allow `code_eval` to execute Python code on machine'}  )
							lowerCAmelCase			:       Optional[int]  							=   field(
							    default=-1     ,		metadata={
							        'help': (
							            'Determine which device to run the `text-generation` Pipeline on. -1 is CPU and any zero or positive'
							            ' number corresponds to which GPU device id to run on.'
							        )
							    }     ,		)

@dataclass
class       __magic_name__		:
							lowerCAmelCase			:       Optional[int]  							=   field(
							    default=_UpperCamelCase     ,		metadata={
							        'help': 'The number of CPU cores to use for parallel preprocessing. Default uses the maximum available.'
							    }     ,		)
							lowerCAmelCase			:       Optional[str]  							=   field(
							    default='transformersbook/codeparrot'     ,		metadata={'help': 'Folder or name of dataset to process.'}  )
							lowerCAmelCase			:       Optional[str]  							=   field(
							    default='codeparrot-clean'     ,		metadata={'help': 'Folder to save processed processed dataset.'}  )
							lowerCAmelCase			:       Optional[int]  							=   field(
							    default=1_0_0_0_0_0     ,		metadata={'help': 'Number of files to save per JSON output file.'}  )
							lowerCAmelCase			:       Optional[str]  							=   field(default='content'     ,		metadata={'help': 'Column containing text data to process.'}  )
							lowerCAmelCase			:       Optional[float]  							=   field(
							    default=1_0_0_0     ,		metadata={'help': 'Maximum line length in file, otherwise file is filtered.'}  )
							lowerCAmelCase			:       Optional[float]  							=   field(
							    default=1_0_0     ,		metadata={'help': 'Maximum mean line length in file, otherwise file is filtered.'}  )
							lowerCAmelCase			:       Optional[float]  							=   field(
							    default=0.25     ,		metadata={'help': 'Maximum fraction of non-alphanumeric characters, otherwise file is filtered.'}  )
							lowerCAmelCase			:       Optional[float]  							=   field(
							    default=1.5     ,		metadata={'help': 'Minimum character token ratio for the file, otherwise file is filtered.'}  )
							lowerCAmelCase			:       Optional[float]  							=   field(
							    default=0.7     ,		metadata={'help': 'Probability for filtering config, test and uncommon files.'}  )
							lowerCAmelCase			:       Optional[str]  							=   field(
							    default='codeparrot/codeparrot'     ,		metadata={'help': 'Name or path to the tokenizer.'}     ,		)
							lowerCAmelCase			:       Optional[bool]  							=   field(
							    default=_UpperCamelCase     ,		metadata={'help': 'If True, near-duplicate samples are removed.'}  )
							lowerCAmelCase			:       Optional[float]  							=   field(
							    default=0.85     ,		metadata={'help': 'Jaccard threshold for near-duplicate samples.'}  )

@dataclass
class       __magic_name__		:
							lowerCAmelCase			:       Optional[str]  							=   field(
							    default='gpt2'     ,		metadata={'help': 'Base tokenizer to build new tokenizer from.'}  )
							lowerCAmelCase			:       Optional[str]  							=   field(
							    default='transformersbook/codeparrot-train'     ,		metadata={'help': 'Dataset to train tokenizer on.'}  )
							lowerCAmelCase			:       Optional[str]  							=   field(default='content'     ,		metadata={'help': 'Column containing text data to process.'}  )
							lowerCAmelCase			:       Optional[int]  							=   field(default=2_0_0_0_0_0     ,		metadata={'help': 'Number of examples to train tokenizer on.'}  )
							lowerCAmelCase			:       Optional[int]  							=   field(
							    default=3_2_7_6_8     ,		metadata={'help': 'Number of examples to train the tokenizer on.'}  )
							lowerCAmelCase			:       Optional[str]  							=   field(default='codeparrot'     ,		metadata={'help': 'Name of new tokenizer.'}  )
							lowerCAmelCase			:       Optional[bool]  							=   field(default=_UpperCamelCase     ,		metadata={'help': 'Push saved tokenizer to the hub.'}  )

@dataclass
class       __magic_name__		:
							lowerCAmelCase			:       Optional[str]  							=   field(
							    default='codeparrot/codeparrot'     ,		metadata={'help': 'Name or path to the tokenizer.'}  )
							lowerCAmelCase			:       Optional[str]  							=   field(
							    default='codeparrot/codeparrot-clean-train'     ,		metadata={'help': 'Name or path to the dataset to pretokenize.'}  )
							lowerCAmelCase			:       Optional[str]  							=   field(
							    default='tokenized-codeparrot-train'     ,		metadata={'help': 'Repo name of the pretokenized data.'}  )
							lowerCAmelCase			:       Optional[int]  							=   field(default=_UpperCamelCase     ,		metadata={'help': 'Number of workers used for code evaluation.'}  )

@dataclass
class       __magic_name__		:
							lowerCAmelCase			:       Optional[str]  							=   field(
							    default='gpt2-large'     ,		metadata={'help': 'Configuration to use for model initialization.'}  )
							lowerCAmelCase			:       Optional[str]  							=   field(
							    default='codeparrot/codeparrot'     ,		metadata={'help': 'Tokenizer attached to model.'}  )
							lowerCAmelCase			:       Optional[str]  							=   field(default='codeparrot'     ,		metadata={'help': 'Name of the created model.'}  )
							lowerCAmelCase			:       Optional[bool]  							=   field(default=_UpperCamelCase     ,		metadata={'help': 'Push saved tokenizer to the hub.'}  )









'''simple docstring'''

from __future__ import annotations

from math import gcd



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_ = 2		,     lowerCAmelCase_ = 1		,     lowerCAmelCase_ = 3		,     )     -> int | None:
		# 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(lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_    ) -> int:
				return (pow(lowerCAmelCase_		,     2    ) + step) % modulus

		for _ in range(lowerCAmelCase_    ):
				# These track the position within the cycle detection logic.
				_a	: Optional[int]								=				seed
				_a	: str								=				seed

				while True:
						# At each iteration, the tortoise moves one step and the hare moves two.
						_a	: str								=				rand_fn(lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_    )
						_a	: Dict								=				rand_fn(lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_    )
						_a	: Tuple								=				rand_fn(lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_    )

						# 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``.
						_a	: Optional[Any]								=				gcd(hare - tortoise		,     lowerCAmelCase_    )

						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.
				_a	: Union[str, Any]								=				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}""")




