





from dataclasses import dataclass, field
from typing import Optional



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



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



@dataclass
class    A__  :
      _UpperCAmelCase      :Optional[str]		       =  field(
          default='codeparrot/codeparrot'			,  metadata={'help': 'Model name or path of model to be evaluated.'}     )
      _UpperCAmelCase      :Optional[int]		       =  field(default=__snake_case			,  metadata={'help': 'Number of workers used for code evaluation.'}     )
      _UpperCAmelCase      :Optional[int]		       =  field(
          default=__snake_case			,  metadata={'help': 'The number of human-eval tasks to run. If not included all tasks are evaluated.'}			,  )
      _UpperCAmelCase      :Optional[bool]		       =  field(
          default=__snake_case			,  metadata={'help': 'Sample from the language model\'s output distribution.'}     )
      _UpperCAmelCase      :Optional[float]		       =  field(default=0.2			,  metadata={'help': 'Sampling temperature used for generation.'}     )
      _UpperCAmelCase      :Optional[int]		       =  field(default=2_5_6			,  metadata={'help': 'Maximum number of newly generated tokens.'}     )
      _UpperCAmelCase      :Optional[int]		       =  field(default=0			,  metadata={'help': 'Top-k parameter used for generation.'}     )
      _UpperCAmelCase      :Optional[float]		       =  field(default=0.95			,  metadata={'help': 'Top-p parameter used for nucleus sampling.'}     )
      _UpperCAmelCase      :Optional[int]		       =  field(default=1_0			,  metadata={'help': 'Number of generations to run in parallel.'}     )
      _UpperCAmelCase      :Optional[int]		       =  field(
          default=2_0_0			,  metadata={'help': 'Number of completions to generate for each sample.'}     )
      _UpperCAmelCase      :Optional[int]		       =  field(default=1			,  metadata={'help': 'Random seed used for evaluation.'}     )
      _UpperCAmelCase      :Optional[str]		       =  field(
          default='eval_results.json'			,  metadata={'help': 'Random seed used for evaluation.'}     )
      _UpperCAmelCase      :Optional[str]		       =  field(
          default='0'			,  metadata={'help': 'Allow `code_eval` to execute Python code on machine'}     )
      _UpperCAmelCase      :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    A__  :
      _UpperCAmelCase      :Optional[int]		       =  field(
          default=__snake_case			,  metadata={
              'help': 'The number of CPU cores to use for parallel preprocessing. Default uses the maximum available.'
          }			,  )
      _UpperCAmelCase      :Optional[str]		       =  field(
          default='transformersbook/codeparrot'			,  metadata={'help': 'Folder or name of dataset to process.'}     )
      _UpperCAmelCase      :Optional[str]		       =  field(
          default='codeparrot-clean'			,  metadata={'help': 'Folder to save processed processed dataset.'}     )
      _UpperCAmelCase      :Optional[int]		       =  field(
          default=1_0_0_0_0_0			,  metadata={'help': 'Number of files to save per JSON output file.'}     )
      _UpperCAmelCase      :Optional[str]		       =  field(default='content'			,  metadata={'help': 'Column containing text data to process.'}     )
      _UpperCAmelCase      :Optional[float]		       =  field(
          default=1_0_0_0			,  metadata={'help': 'Maximum line length in file, otherwise file is filtered.'}     )
      _UpperCAmelCase      :Optional[float]		       =  field(
          default=1_0_0			,  metadata={'help': 'Maximum mean line length in file, otherwise file is filtered.'}     )
      _UpperCAmelCase      :Optional[float]		       =  field(
          default=0.25			,  metadata={'help': 'Maximum fraction of non-alphanumeric characters, otherwise file is filtered.'}     )
      _UpperCAmelCase      :Optional[float]		       =  field(
          default=1.5			,  metadata={'help': 'Minimum character token ratio for the file, otherwise file is filtered.'}     )
      _UpperCAmelCase      :Optional[float]		       =  field(
          default=0.7			,  metadata={'help': 'Probability for filtering config, test and uncommon files.'}     )
      _UpperCAmelCase      :Optional[str]		       =  field(
          default='codeparrot/codeparrot'			,  metadata={'help': 'Name or path to the tokenizer.'}			,  )
      _UpperCAmelCase      :Optional[bool]		       =  field(
          default=__snake_case			,  metadata={'help': 'If True, near-duplicate samples are removed.'}     )
      _UpperCAmelCase      :Optional[float]		       =  field(
          default=0.85			,  metadata={'help': 'Jaccard threshold for near-duplicate samples.'}     )



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



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



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







import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


__lowerCamelCase			:	Optional[Any]					    =	logging.get_logger(__name__)



class    A__  (			__snake_case     ):





      def __init__(					self			,				*A_			,				**A_  ):
       '''simple docstring'''




       warnings.warn(
           "The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
           " use BeitImageProcessor instead."			,				A_			,				)
       super().__init__(*A_			,				**A_  )
