




from dataclasses import dataclass, field
from typing import Optional

@dataclass
class 		__lowerCAmelCase	:
							UpperCamelCase__					     =  field(
							    default='''codeparrot/codeparrot'''      ,       metadata={'''help''': '''Model name or path of model to be trained.'''} )
							UpperCamelCase__					     =  field(
							    default='''./'''      ,       metadata={'''help''': '''Save dir where model repo is cloned and models updates are saved to.'''} )
							UpperCamelCase__					     =  field(
							    default='''codeparrot/codeparrot-clean-train'''      ,       metadata={'''help''': '''Name or path of training dataset.'''} )
							UpperCamelCase__					     =  field(
							    default='''codeparrot/codeparrot-clean-valid'''      ,       metadata={'''help''': '''Name or path of validation dataset.'''} )
							UpperCamelCase__					     =  field(default=2      ,       metadata={'''help''': '''Batch size for training.'''} )
							UpperCamelCase__					     =  field(default=2      ,       metadata={'''help''': '''Batch size for evaluation.'''} )
							UpperCamelCase__					     =  field(default=0.1      ,       metadata={'''help''': '''Value of weight decay.'''} )
							UpperCamelCase__					     =  field(
							    default=10000      ,       metadata={'''help''': '''Size of buffer used to shuffle streaming dataset.'''} )
							UpperCamelCase__					     =  field(default=2e-4      ,       metadata={'''help''': '''Learning rate fo training.'''} )
							UpperCamelCase__					     =  field(default='''cosine'''      ,       metadata={'''help''': '''Learning rate.'''} )
							UpperCamelCase__					     =  field(
							    default=750      ,       metadata={'''help''': '''Number of warmup steps in the learning rate schedule.'''} )
							UpperCamelCase__					     =  field(
							    default=16      ,       metadata={'''help''': '''Number of gradient accumulation steps.'''} )
							UpperCamelCase__					     =  field(
							    default=__magic_name__      ,       metadata={'''help''': '''Use gradient checkpointing to reduce memory footprint.'''} )
							UpperCamelCase__					     =  field(default=50000      ,       metadata={'''help''': '''Maximum number of training steps.'''} )
							UpperCamelCase__					     =  field(
							    default=-1      ,       metadata={'''help''': '''Maximum number of evaluation steps. If -1 the full dataset is evaluated.'''} )
							UpperCamelCase__					     =  field(default=1024      ,       metadata={'''help''': '''Sequence lengths used for training.'''} )
							UpperCamelCase__					     =  field(default=1      ,       metadata={'''help''': '''Training seed.'''} )
							UpperCamelCase__					     =  field(
							    default=1024      ,       metadata={'''help''': '''Interval to save checkpoints. Measured as number of forward passes not training steps.'''}      ,       )
							UpperCamelCase__					     =  field(
							    default=__magic_name__      ,       metadata={'''help''': '''States path if the training should continue from a checkpoint folder.'''} )
							UpperCamelCase__					     =  field(default=__magic_name__      ,       metadata={'''help''': '''If True the data is pretokenized.'''} )







@dataclass
class 		__lowerCAmelCase	:
							UpperCamelCase__					     =  field(
							    default='''codeparrot/codeparrot'''      ,       metadata={'''help''': '''Model name or path of model to be evaluated.'''} )
							UpperCamelCase__					     =  field(
							    default='''codeparrot/codeparrot-clean-valid'''      ,       metadata={'''help''': '''Name or path of validation dataset.'''} )
							UpperCamelCase__					     =  field(default=2      ,       metadata={'''help''': '''Batch size used for evaluation.'''} )
							UpperCamelCase__					     =  field(
							    default=-1      ,       metadata={'''help''': '''Maximum number of evaluation steps. If -1 the full dataset is evaluated.'''} )
							UpperCamelCase__					     =  field(default=1024      ,       metadata={'''help''': '''Length of sequences to be evaluated.'''} )
							UpperCamelCase__					     =  field(default=1      ,       metadata={'''help''': '''Random seed used for evaluation.'''} )







@dataclass
class 		__lowerCAmelCase	:
							UpperCamelCase__					     =  field(
							    default='''codeparrot/codeparrot'''      ,       metadata={'''help''': '''Model name or path of model to be evaluated.'''} )
							UpperCamelCase__					     =  field(default=__magic_name__      ,       metadata={'''help''': '''Number of workers used for code evaluation.'''} )
							UpperCamelCase__					     =  field(
							    default=__magic_name__      ,       metadata={'''help''': '''The number of human-eval tasks to run. If not included all tasks are evaluated.'''}      ,       )
							UpperCamelCase__					     =  field(
							    default=__magic_name__      ,       metadata={'''help''': '''Sample from the language model\'s output distribution.'''} )
							UpperCamelCase__					     =  field(default=0.2      ,       metadata={'''help''': '''Sampling temperature used for generation.'''} )
							UpperCamelCase__					     =  field(default=256      ,       metadata={'''help''': '''Maximum number of newly generated tokens.'''} )
							UpperCamelCase__					     =  field(default=0      ,       metadata={'''help''': '''Top-k parameter used for generation.'''} )
							UpperCamelCase__					     =  field(default=0.9_5      ,       metadata={'''help''': '''Top-p parameter used for nucleus sampling.'''} )
							UpperCamelCase__					     =  field(default=10      ,       metadata={'''help''': '''Number of generations to run in parallel.'''} )
							UpperCamelCase__					     =  field(
							    default=200      ,       metadata={'''help''': '''Number of completions to generate for each sample.'''} )
							UpperCamelCase__					     =  field(default=1      ,       metadata={'''help''': '''Random seed used for evaluation.'''} )
							UpperCamelCase__					     =  field(
							    default='''eval_results.json'''      ,       metadata={'''help''': '''Random seed used for evaluation.'''} )
							UpperCamelCase__					     =  field(
							    default='''0'''      ,       metadata={'''help''': '''Allow `code_eval` to execute Python code on machine'''} )
							UpperCamelCase__					     =  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	:
							UpperCamelCase__					     =  field(
							    default=__magic_name__      ,       metadata={
							        '''help''': '''The number of CPU cores to use for parallel preprocessing. Default uses the maximum available.'''
							    }      ,       )
							UpperCamelCase__					     =  field(
							    default='''transformersbook/codeparrot'''      ,       metadata={'''help''': '''Folder or name of dataset to process.'''} )
							UpperCamelCase__					     =  field(
							    default='''codeparrot-clean'''      ,       metadata={'''help''': '''Folder to save processed processed dataset.'''} )
							UpperCamelCase__					     =  field(
							    default=100000      ,       metadata={'''help''': '''Number of files to save per JSON output file.'''} )
							UpperCamelCase__					     =  field(default='''content'''      ,       metadata={'''help''': '''Column containing text data to process.'''} )
							UpperCamelCase__					     =  field(
							    default=1000      ,       metadata={'''help''': '''Maximum line length in file, otherwise file is filtered.'''} )
							UpperCamelCase__					     =  field(
							    default=100      ,       metadata={'''help''': '''Maximum mean line length in file, otherwise file is filtered.'''} )
							UpperCamelCase__					     =  field(
							    default=0.2_5      ,       metadata={'''help''': '''Maximum fraction of non-alphanumeric characters, otherwise file is filtered.'''} )
							UpperCamelCase__					     =  field(
							    default=1.5      ,       metadata={'''help''': '''Minimum character token ratio for the file, otherwise file is filtered.'''} )
							UpperCamelCase__					     =  field(
							    default=0.7      ,       metadata={'''help''': '''Probability for filtering config, test and uncommon files.'''} )
							UpperCamelCase__					     =  field(
							    default='''codeparrot/codeparrot'''      ,       metadata={'''help''': '''Name or path to the tokenizer.'''}      ,       )
							UpperCamelCase__					     =  field(
							    default=__magic_name__      ,       metadata={'''help''': '''If True, near-duplicate samples are removed.'''} )
							UpperCamelCase__					     =  field(
							    default=0.8_5      ,       metadata={'''help''': '''Jaccard threshold for near-duplicate samples.'''} )







@dataclass
class 		__lowerCAmelCase	:
							UpperCamelCase__					     =  field(
							    default='''gpt2'''      ,       metadata={'''help''': '''Base tokenizer to build new tokenizer from.'''} )
							UpperCamelCase__					     =  field(
							    default='''transformersbook/codeparrot-train'''      ,       metadata={'''help''': '''Dataset to train tokenizer on.'''} )
							UpperCamelCase__					     =  field(default='''content'''      ,       metadata={'''help''': '''Column containing text data to process.'''} )
							UpperCamelCase__					     =  field(default=200000      ,       metadata={'''help''': '''Number of examples to train tokenizer on.'''} )
							UpperCamelCase__					     =  field(
							    default=32768      ,       metadata={'''help''': '''Number of examples to train the tokenizer on.'''} )
							UpperCamelCase__					     =  field(default='''codeparrot'''      ,       metadata={'''help''': '''Name of new tokenizer.'''} )
							UpperCamelCase__					     =  field(default=__magic_name__      ,       metadata={'''help''': '''Push saved tokenizer to the hub.'''} )







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







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







from math import pi, sqrt





def 		__A		(	__lowerCamelCase    )      ->       float:
	if num <= 0:
		raise ValueError("""math domain error"""    )
	if num > 171.5:
		raise OverflowError("""math range error"""    )
	elif num - int(__lowerCamelCase    ) not in (0, 0.5):
		raise NotImplementedError("""num must be an integer or a half-integer"""    )
	elif num == 0.5:
		return sqrt(__lowerCamelCase    )
	else:
		return 1.0 if num == 1 else (num - 1) * gamma(num - 1    )





def 		__A		(	)      ->       None:
	assert gamma(0.5    ) == sqrt(__lowerCamelCase    )
	assert gamma(1    ) == 1.0
	assert gamma(2    ) == 1.0


if __name__ == "__main__":
		from doctest import testmod

		testmod()
		__UpperCamelCase					:       str    					=      1.0
		while num:
				__UpperCamelCase					:       Dict    					=      float(input("Gamma of: "))
				print(F'gamma({num}) = {gamma(num)}')
				print("\nEnter 0 to exit...")

