
'''simple docstring'''







from dataclasses import dataclass, field
from typing import Optional


@dataclass
class 			lowerCAmelCase :
		a							:					Optional[str]       =					field(
		    default="""codeparrot/codeparrot"""		,     metadata={"""help""": """Model name or path of model to be trained."""}			)
		a							:					Optional[str]       =					field(
		    default="""./"""		,     metadata={"""help""": """Save dir where model repo is cloned and models updates are saved to."""}			)
		a							:					Optional[str]       =					field(
		    default="""codeparrot/codeparrot-clean-train"""		,     metadata={"""help""": """Name or path of training dataset."""}			)
		a							:					Optional[str]       =					field(
		    default="""codeparrot/codeparrot-clean-valid"""		,     metadata={"""help""": """Name or path of validation dataset."""}			)
		a							:					Optional[int]       =					field(default=2		,     metadata={"""help""": """Batch size for training."""}			)
		a							:					Optional[int]       =					field(default=2		,     metadata={"""help""": """Batch size for evaluation."""}			)
		a							:					Optional[float]       =					field(default=0.1		,     metadata={"""help""": """Value of weight decay."""}			)
		a							:					Optional[int]       =					field(
		    default=10_000		,     metadata={"""help""": """Size of buffer used to shuffle streaming dataset."""}			)
		a							:					Optional[float]       =					field(default=2e-4		,     metadata={"""help""": """Learning rate fo training."""}			)
		a							:					Optional[str]       =					field(default="""cosine"""		,     metadata={"""help""": """Learning rate."""}			)
		a							:					Optional[int]       =					field(
		    default=750		,     metadata={"""help""": """Number of warmup steps in the learning rate schedule."""}			)
		a							:					Optional[int]       =					field(
		    default=16		,     metadata={"""help""": """Number of gradient accumulation steps."""}			)
		a							:					Optional[bool]       =					field(
		    default=__UpperCAmelCase		,     metadata={"""help""": """Use gradient checkpointing to reduce memory footprint."""}			)
		a							:					Optional[int]       =					field(default=50_000		,     metadata={"""help""": """Maximum number of training steps."""}			)
		a							:					Optional[int]       =					field(
		    default=-1		,     metadata={"""help""": """Maximum number of evaluation steps. If -1 the full dataset is evaluated."""}			)
		a							:					Optional[int]       =					field(default=1_024		,     metadata={"""help""": """Sequence lengths used for training."""}			)
		a							:					Optional[int]       =					field(default=1		,     metadata={"""help""": """Training seed."""}			)
		a							:					Optional[int]       =					field(
		    default=1_024		,     metadata={"""help""": """Interval to save checkpoints. Measured as number of forward passes not training steps."""}		,     )
		a							:					Optional[str]       =					field(
		    default=__UpperCAmelCase		,     metadata={"""help""": """States path if the training should continue from a checkpoint folder."""}			)
		a							:					Optional[bool]       =					field(default=__UpperCAmelCase		,     metadata={"""help""": """If True the data is pretokenized."""}			)


@dataclass
class 			lowerCAmelCase :
		a							:					Optional[str]       =					field(
		    default="""codeparrot/codeparrot"""		,     metadata={"""help""": """Model name or path of model to be evaluated."""}			)
		a							:					Optional[str]       =					field(
		    default="""codeparrot/codeparrot-clean-valid"""		,     metadata={"""help""": """Name or path of validation dataset."""}			)
		a							:					Optional[int]       =					field(default=2		,     metadata={"""help""": """Batch size used for evaluation."""}			)
		a							:					Optional[int]       =					field(
		    default=-1		,     metadata={"""help""": """Maximum number of evaluation steps. If -1 the full dataset is evaluated."""}			)
		a							:					Optional[int]       =					field(default=1_024		,     metadata={"""help""": """Length of sequences to be evaluated."""}			)
		a							:					Optional[int]       =					field(default=1		,     metadata={"""help""": """Random seed used for evaluation."""}			)


@dataclass
class 			lowerCAmelCase :
		a							:					Optional[str]       =					field(
		    default="""codeparrot/codeparrot"""		,     metadata={"""help""": """Model name or path of model to be evaluated."""}			)
		a							:					Optional[int]       =					field(default=__UpperCAmelCase		,     metadata={"""help""": """Number of workers used for code evaluation."""}			)
		a							:					Optional[int]       =					field(
		    default=__UpperCAmelCase		,     metadata={"""help""": """The number of human-eval tasks to run. If not included all tasks are evaluated."""}		,     )
		a							:					Optional[bool]       =					field(
		    default=__UpperCAmelCase		,     metadata={"""help""": """Sample from the language model's output distribution."""}			)
		a							:					Optional[float]       =					field(default=0.2		,     metadata={"""help""": """Sampling temperature used for generation."""}			)
		a							:					Optional[int]       =					field(default=256		,     metadata={"""help""": """Maximum number of newly generated tokens."""}			)
		a							:					Optional[int]       =					field(default=0		,     metadata={"""help""": """Top-k parameter used for generation."""}			)
		a							:					Optional[float]       =					field(default=0.9_5		,     metadata={"""help""": """Top-p parameter used for nucleus sampling."""}			)
		a							:					Optional[int]       =					field(default=10		,     metadata={"""help""": """Number of generations to run in parallel."""}			)
		a							:					Optional[int]       =					field(
		    default=200		,     metadata={"""help""": """Number of completions to generate for each sample."""}			)
		a							:					Optional[int]       =					field(default=1		,     metadata={"""help""": """Random seed used for evaluation."""}			)
		a							:					Optional[str]       =					field(
		    default="""eval_results.json"""		,     metadata={"""help""": """Random seed used for evaluation."""}			)
		a							:					Optional[str]       =					field(
		    default="""0"""		,     metadata={"""help""": """Allow `code_eval` to execute Python code on machine"""}			)
		a							:					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 			lowerCAmelCase :
		a							:					Optional[int]       =					field(
		    default=__UpperCAmelCase		,     metadata={
		        """help""": """The number of CPU cores to use for parallel preprocessing. Default uses the maximum available."""
		    }		,     )
		a							:					Optional[str]       =					field(
		    default="""transformersbook/codeparrot"""		,     metadata={"""help""": """Folder or name of dataset to process."""}			)
		a							:					Optional[str]       =					field(
		    default="""codeparrot-clean"""		,     metadata={"""help""": """Folder to save processed processed dataset."""}			)
		a							:					Optional[int]       =					field(
		    default=100_000		,     metadata={"""help""": """Number of files to save per JSON output file."""}			)
		a							:					Optional[str]       =					field(default="""content"""		,     metadata={"""help""": """Column containing text data to process."""}			)
		a							:					Optional[float]       =					field(
		    default=1_000		,     metadata={"""help""": """Maximum line length in file, otherwise file is filtered."""}			)
		a							:					Optional[float]       =					field(
		    default=100		,     metadata={"""help""": """Maximum mean line length in file, otherwise file is filtered."""}			)
		a							:					Optional[float]       =					field(
		    default=0.2_5		,     metadata={"""help""": """Maximum fraction of non-alphanumeric characters, otherwise file is filtered."""}			)
		a							:					Optional[float]       =					field(
		    default=1.5		,     metadata={"""help""": """Minimum character token ratio for the file, otherwise file is filtered."""}			)
		a							:					Optional[float]       =					field(
		    default=0.7		,     metadata={"""help""": """Probability for filtering config, test and uncommon files."""}			)
		a							:					Optional[str]       =					field(
		    default="""codeparrot/codeparrot"""		,     metadata={"""help""": """Name or path to the tokenizer."""}		,     )
		a							:					Optional[bool]       =					field(
		    default=__UpperCAmelCase		,     metadata={"""help""": """If True, near-duplicate samples are removed."""}			)
		a							:					Optional[float]       =					field(
		    default=0.8_5		,     metadata={"""help""": """Jaccard threshold for near-duplicate samples."""}			)


@dataclass
class 			lowerCAmelCase :
		a							:					Optional[str]       =					field(
		    default="""gpt2"""		,     metadata={"""help""": """Base tokenizer to build new tokenizer from."""}			)
		a							:					Optional[str]       =					field(
		    default="""transformersbook/codeparrot-train"""		,     metadata={"""help""": """Dataset to train tokenizer on."""}			)
		a							:					Optional[str]       =					field(default="""content"""		,     metadata={"""help""": """Column containing text data to process."""}			)
		a							:					Optional[int]       =					field(default=200_000		,     metadata={"""help""": """Number of examples to train tokenizer on."""}			)
		a							:					Optional[int]       =					field(
		    default=32_768		,     metadata={"""help""": """Number of examples to train the tokenizer on."""}			)
		a							:					Optional[str]       =					field(default="""codeparrot"""		,     metadata={"""help""": """Name of new tokenizer."""}			)
		a							:					Optional[bool]       =					field(default=__UpperCAmelCase		,     metadata={"""help""": """Push saved tokenizer to the hub."""}			)


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


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

'''simple docstring'''







import os
import random
import sys

from . import cryptomath_module as cryptoMath  # noqa: N812
from . import rabin_miller as rabinMiller  # noqa: N812



def 			_a		(	):
						print("Making key files..."  )
						make_key_files("rsa"	,			1024  )
						print("Key files generation successful."  )



def 			_a		(	_SCREAMING_SNAKE_CASE		:   int  ):
						print("Generating prime p..."  )
						_SCREAMING_SNAKE_CASE				= rabinMiller.generate_large_prime(_SCREAMING_SNAKE_CASE  )
						print("Generating prime q..."  )
						_SCREAMING_SNAKE_CASE				= rabinMiller.generate_large_prime(_SCREAMING_SNAKE_CASE  )
						_SCREAMING_SNAKE_CASE				= p * q

						print("Generating e that is relatively prime to (p - 1) * (q - 1)..."  )
						while True:
												_SCREAMING_SNAKE_CASE				= random.randrange(2 ** (key_size - 1)	,			2 ** (key_size)  )
												if cryptoMath.gcd(_SCREAMING_SNAKE_CASE	,			(p - 1) * (q - 1)  ) == 1:
																		break

						print("Calculating d that is mod inverse of e..."  )
						_SCREAMING_SNAKE_CASE				= cryptoMath.find_mod_inverse(_SCREAMING_SNAKE_CASE	,			(p - 1) * (q - 1)  )

						_SCREAMING_SNAKE_CASE				= (n, e)
						_SCREAMING_SNAKE_CASE				= (n, d)
						return (public_key, private_key)



def 			_a		(	_SCREAMING_SNAKE_CASE		:   str	,			_SCREAMING_SNAKE_CASE		:   int  ):
						if os.path.exists(F'{name}_pubkey.txt'  ) or os.path.exists(F'{name}_privkey.txt'  ):
												print("\nWARNING:"  )
												print(
												    F'"{name}_pubkey.txt" or "{name}_privkey.txt" already exists. \n'
												    "Use a different name or delete these files and re-run this program."  )
												sys.exit()

						_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= generate_key(_SCREAMING_SNAKE_CASE  )
						print(F'\nWriting public key to file {name}_pubkey.txt...'  )
						with open(F'{name}_pubkey.txt'	,			"w"  ) as out_file:
												out_file.write(F'{key_size},{public_key[0]},{public_key[1]}'  )

						print(F'Writing private key to file {name}_privkey.txt...'  )
						with open(F'{name}_privkey.txt'	,			"w"  ) as out_file:
												out_file.write(F'{key_size},{private_key[0]},{private_key[1]}'  )


if __name__ == "__main__":
						main()