





'''simple docstring'''




from dataclasses import dataclass, field
from typing import Optional




@dataclass
class      UpperCamelCase__					:




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	field(
					    default='''codeparrot/codeparrot'''						,					metadata={'''help''': '''Model name or path of model to be trained.'''}			)
					SCREAMING_SNAKE_CASE__				=	field(
					    default='''./'''						,					metadata={'''help''': '''Save dir where model repo is cloned and models updates are saved to.'''}			)
					SCREAMING_SNAKE_CASE__				=	field(
					    default='''codeparrot/codeparrot-clean-train'''						,					metadata={'''help''': '''Name or path of training dataset.'''}			)
					SCREAMING_SNAKE_CASE__				=	field(
					    default='''codeparrot/codeparrot-clean-valid'''						,					metadata={'''help''': '''Name or path of validation dataset.'''}			)
					SCREAMING_SNAKE_CASE__				=	field(default=2						,					metadata={'''help''': '''Batch size for training.'''}			)
					SCREAMING_SNAKE_CASE__				=	field(default=2						,					metadata={'''help''': '''Batch size for evaluation.'''}			)
					SCREAMING_SNAKE_CASE__				=	field(default=0.1						,					metadata={'''help''': '''Value of weight decay.'''}			)
					SCREAMING_SNAKE_CASE__				=	field(
					    default=1_0000						,					metadata={'''help''': '''Size of buffer used to shuffle streaming dataset.'''}			)
					SCREAMING_SNAKE_CASE__				=	field(default=2e-4						,					metadata={'''help''': '''Learning rate fo training.'''}			)
					SCREAMING_SNAKE_CASE__				=	field(default='''cosine'''						,					metadata={'''help''': '''Learning rate.'''}			)
					SCREAMING_SNAKE_CASE__				=	field(
					    default=750						,					metadata={'''help''': '''Number of warmup steps in the learning rate schedule.'''}			)
					SCREAMING_SNAKE_CASE__				=	field(
					    default=16						,					metadata={'''help''': '''Number of gradient accumulation steps.'''}			)
					SCREAMING_SNAKE_CASE__				=	field(
					    default=lowercase_						,					metadata={'''help''': '''Use gradient checkpointing to reduce memory footprint.'''}			)
					SCREAMING_SNAKE_CASE__				=	field(default=5_0000						,					metadata={'''help''': '''Maximum number of training steps.'''}			)
					SCREAMING_SNAKE_CASE__				=	field(
					    default=-1						,					metadata={'''help''': '''Maximum number of evaluation steps. If -1 the full dataset is evaluated.'''}			)
					SCREAMING_SNAKE_CASE__				=	field(default=1024						,					metadata={'''help''': '''Sequence lengths used for training.'''}			)
					SCREAMING_SNAKE_CASE__				=	field(default=1						,					metadata={'''help''': '''Training seed.'''}			)
					SCREAMING_SNAKE_CASE__				=	field(
					    default=1024						,					metadata={'''help''': '''Interval to save checkpoints. Measured as number of forward passes not training steps.'''}						,					)
					SCREAMING_SNAKE_CASE__				=	field(
					    default=lowercase_						,					metadata={'''help''': '''States path if the training should continue from a checkpoint folder.'''}			)
					SCREAMING_SNAKE_CASE__				=	field(default=lowercase_						,					metadata={'''help''': '''If True the data is pretokenized.'''}			)







@dataclass
class      UpperCamelCase__					:




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







@dataclass
class      UpperCamelCase__					:




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	field(
					    default='''codeparrot/codeparrot'''						,					metadata={'''help''': '''Model name or path of model to be evaluated.'''}			)
					SCREAMING_SNAKE_CASE__				=	field(default=lowercase_						,					metadata={'''help''': '''Number of workers used for code evaluation.'''}			)
					SCREAMING_SNAKE_CASE__				=	field(
					    default=lowercase_						,					metadata={'''help''': '''The number of human-eval tasks to run. If not included all tasks are evaluated.'''}						,					)
					SCREAMING_SNAKE_CASE__				=	field(
					    default=lowercase_						,					metadata={'''help''': '''Sample from the language model\'s output distribution.'''}			)
					SCREAMING_SNAKE_CASE__				=	field(default=0.2						,					metadata={'''help''': '''Sampling temperature used for generation.'''}			)
					SCREAMING_SNAKE_CASE__				=	field(default=256						,					metadata={'''help''': '''Maximum number of newly generated tokens.'''}			)
					SCREAMING_SNAKE_CASE__				=	field(default=0						,					metadata={'''help''': '''Top-k parameter used for generation.'''}			)
					SCREAMING_SNAKE_CASE__				=	field(default=0.95						,					metadata={'''help''': '''Top-p parameter used for nucleus sampling.'''}			)
					SCREAMING_SNAKE_CASE__				=	field(default=10						,					metadata={'''help''': '''Number of generations to run in parallel.'''}			)
					SCREAMING_SNAKE_CASE__				=	field(
					    default=200						,					metadata={'''help''': '''Number of completions to generate for each sample.'''}			)
					SCREAMING_SNAKE_CASE__				=	field(default=1						,					metadata={'''help''': '''Random seed used for evaluation.'''}			)
					SCREAMING_SNAKE_CASE__				=	field(
					    default='''eval_results.json'''						,					metadata={'''help''': '''Random seed used for evaluation.'''}			)
					SCREAMING_SNAKE_CASE__				=	field(
					    default='''0'''						,					metadata={'''help''': '''Allow `code_eval` to execute Python code on machine'''}			)
					SCREAMING_SNAKE_CASE__				=	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      UpperCamelCase__					:




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	field(
					    default=lowercase_						,					metadata={
					        '''help''': '''The number of CPU cores to use for parallel preprocessing. Default uses the maximum available.'''
					    }						,					)
					SCREAMING_SNAKE_CASE__				=	field(
					    default='''transformersbook/codeparrot'''						,					metadata={'''help''': '''Folder or name of dataset to process.'''}			)
					SCREAMING_SNAKE_CASE__				=	field(
					    default='''codeparrot-clean'''						,					metadata={'''help''': '''Folder to save processed processed dataset.'''}			)
					SCREAMING_SNAKE_CASE__				=	field(
					    default=10_0000						,					metadata={'''help''': '''Number of files to save per JSON output file.'''}			)
					SCREAMING_SNAKE_CASE__				=	field(default='''content'''						,					metadata={'''help''': '''Column containing text data to process.'''}			)
					SCREAMING_SNAKE_CASE__				=	field(
					    default=1000						,					metadata={'''help''': '''Maximum line length in file, otherwise file is filtered.'''}			)
					SCREAMING_SNAKE_CASE__				=	field(
					    default=100						,					metadata={'''help''': '''Maximum mean line length in file, otherwise file is filtered.'''}			)
					SCREAMING_SNAKE_CASE__				=	field(
					    default=0.25						,					metadata={'''help''': '''Maximum fraction of non-alphanumeric characters, otherwise file is filtered.'''}			)
					SCREAMING_SNAKE_CASE__				=	field(
					    default=1.5						,					metadata={'''help''': '''Minimum character token ratio for the file, otherwise file is filtered.'''}			)
					SCREAMING_SNAKE_CASE__				=	field(
					    default=0.7						,					metadata={'''help''': '''Probability for filtering config, test and uncommon files.'''}			)
					SCREAMING_SNAKE_CASE__				=	field(
					    default='''codeparrot/codeparrot'''						,					metadata={'''help''': '''Name or path to the tokenizer.'''}						,					)
					SCREAMING_SNAKE_CASE__				=	field(
					    default=lowercase_						,					metadata={'''help''': '''If True, near-duplicate samples are removed.'''}			)
					SCREAMING_SNAKE_CASE__				=	field(
					    default=0.85						,					metadata={'''help''': '''Jaccard threshold for near-duplicate samples.'''}			)







@dataclass
class      UpperCamelCase__					:




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	field(
					    default='''gpt2'''						,					metadata={'''help''': '''Base tokenizer to build new tokenizer from.'''}			)
					SCREAMING_SNAKE_CASE__				=	field(
					    default='''transformersbook/codeparrot-train'''						,					metadata={'''help''': '''Dataset to train tokenizer on.'''}			)
					SCREAMING_SNAKE_CASE__				=	field(default='''content'''						,					metadata={'''help''': '''Column containing text data to process.'''}			)
					SCREAMING_SNAKE_CASE__				=	field(default=20_0000						,					metadata={'''help''': '''Number of examples to train tokenizer on.'''}			)
					SCREAMING_SNAKE_CASE__				=	field(
					    default=3_2768						,					metadata={'''help''': '''Number of examples to train the tokenizer on.'''}			)
					SCREAMING_SNAKE_CASE__				=	field(default='''codeparrot'''						,					metadata={'''help''': '''Name of new tokenizer.'''}			)
					SCREAMING_SNAKE_CASE__				=	field(default=lowercase_						,					metadata={'''help''': '''Push saved tokenizer to the hub.'''}			)







@dataclass
class      UpperCamelCase__					:




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	field(
					    default='''codeparrot/codeparrot'''						,					metadata={'''help''': '''Name or path to the tokenizer.'''}			)
					SCREAMING_SNAKE_CASE__				=	field(
					    default='''codeparrot/codeparrot-clean-train'''						,					metadata={'''help''': '''Name or path to the dataset to pretokenize.'''}			)
					SCREAMING_SNAKE_CASE__				=	field(
					    default='''tokenized-codeparrot-train'''						,					metadata={'''help''': '''Repo name of the pretokenized data.'''}			)
					SCREAMING_SNAKE_CASE__				=	field(default=lowercase_						,					metadata={'''help''': '''Number of workers used for code evaluation.'''}			)







@dataclass
class      UpperCamelCase__					:




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	field(
					    default='''gpt2-large'''						,					metadata={'''help''': '''Configuration to use for model initialization.'''}			)
					SCREAMING_SNAKE_CASE__				=	field(
					    default='''codeparrot/codeparrot'''						,					metadata={'''help''': '''Tokenizer attached to model.'''}			)
					SCREAMING_SNAKE_CASE__				=	field(default='''codeparrot'''						,					metadata={'''help''': '''Name of the created model.'''}			)
					SCREAMING_SNAKE_CASE__				=	field(default=lowercase_						,					metadata={'''help''': '''Push saved tokenizer to the hub.'''}			)





'''simple docstring'''





def snake_case_     (					lowercase__ = "The quick brown fox jumps over the lazy dog"   ,       ):
  UpperCAmelCase__      :					Dict				       =							set()

  # Replace all the whitespace in our sentence
  UpperCAmelCase__      :					str				       =							input_str.replace(" "   ,       ""		)
  for alpha in input_str:
    if "a" <= alpha.lower() <= "z":
      frequency.add(alpha.lower()		)
  return len(lowercase__		) == 2_6







def snake_case_     (					lowercase__ = "The quick brown fox jumps over the lazy dog"   ,       ):
  UpperCAmelCase__      :					str				       =							[False] * 2_6
  for char in input_str:
    if char.islower():
      UpperCAmelCase__      :					List[Any]				       =							True
    elif char.isupper():
      UpperCAmelCase__      :					List[Any]				       =							True
  return all(lowercase__		)







def snake_case_     (					lowercase__ = "The quick brown fox jumps over the lazy dog"   ,       ):
  return len({char for char in input_str.lower() if char.isalpha()}		) == 2_6







def snake_case_     (					):
  from timeit import timeit

  UpperCAmelCase__      :					Union[str, Any]				       =							"from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest"
  print(timeit("is_pangram()"   ,       setup=lowercase__		)		)
  print(timeit("is_pangram_faster()"   ,       setup=lowercase__		)		)
  print(timeit("is_pangram_fastest()"   ,       setup=lowercase__		)		)
  # 5.348480500048026, 2.6477354579837993, 1.8470395830227062
  # 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      benchmark()



