

from dataclasses import dataclass, field
from typing import Optional






@dataclass
class 						__magic_name__    :
 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=1_0_0_0_0	,						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=7_5_0	,						metadata={"""help""": """Number of warmup steps in the learning rate schedule."""}    )
 UpperCAmelCase							    =						field(
     default=1_6	,						metadata={"""help""": """Number of gradient accumulation steps."""}    )
 UpperCAmelCase							    =						field(
     default=__snake_case	,						metadata={"""help""": """Use gradient checkpointing to reduce memory footprint."""}    )
 UpperCAmelCase							    =						field(default=5_0_0_0_0	,						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=1_0_2_4	,						metadata={"""help""": """Sequence lengths used for training."""}    )
 UpperCAmelCase							    =						field(default=1	,						metadata={"""help""": """Training seed."""}    )
 UpperCAmelCase							    =						field(
     default=1_0_2_4	,						metadata={"""help""": """Interval to save checkpoints. Measured as number of forward passes not training steps."""}	,						)
 UpperCAmelCase							    =						field(
     default=__snake_case	,						metadata={"""help""": """States path if the training should continue from a checkpoint folder."""}    )
 UpperCAmelCase							    =						field(default=__snake_case	,						metadata={"""help""": """If True the data is pretokenized."""}    )






@dataclass
class 						__magic_name__    :
 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=1_0_2_4	,						metadata={"""help""": """Length of sequences to be evaluated."""}    )
 UpperCAmelCase							    =						field(default=1	,						metadata={"""help""": """Random seed used for evaluation."""}    )






@dataclass
class 						__magic_name__    :
 UpperCAmelCase							    =						field(
     default="""codeparrot/codeparrot"""	,						metadata={"""help""": """Model name or path of model to be evaluated."""}    )
 UpperCAmelCase							    =						field(default=__snake_case	,						metadata={"""help""": """Number of workers used for code evaluation."""}    )
 UpperCAmelCase							    =						field(
     default=__snake_case	,						metadata={"""help""": """The number of human-eval tasks to run. If not included all tasks are evaluated."""}	,						)
 UpperCAmelCase							    =						field(
     default=__snake_case	,						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=2_5_6	,						metadata={"""help""": """Maximum number of newly generated tokens."""}    )
 UpperCAmelCase							    =						field(default=0	,						metadata={"""help""": """Top-k parameter used for generation."""}    )
 UpperCAmelCase							    =						field(default=0.95	,						metadata={"""help""": """Top-p parameter used for nucleus sampling."""}    )
 UpperCAmelCase							    =						field(default=1_0	,						metadata={"""help""": """Number of generations to run in parallel."""}    )
 UpperCAmelCase							    =						field(
     default=2_0_0	,						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 						__magic_name__    :
 UpperCAmelCase							    =						field(
     default=__snake_case	,						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=1_0_0_0_0_0	,						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=1_0_0_0	,						metadata={"""help""": """Maximum line length in file, otherwise file is filtered."""}    )
 UpperCAmelCase							    =						field(
     default=1_0_0	,						metadata={"""help""": """Maximum mean line length in file, otherwise file is filtered."""}    )
 UpperCAmelCase							    =						field(
     default=0.25	,						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=__snake_case	,						metadata={"""help""": """If True, near-duplicate samples are removed."""}    )
 UpperCAmelCase							    =						field(
     default=0.85	,						metadata={"""help""": """Jaccard threshold for near-duplicate samples."""}    )






@dataclass
class 						__magic_name__    :
 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=2_0_0_0_0_0	,						metadata={"""help""": """Number of examples to train tokenizer on."""}    )
 UpperCAmelCase							    =						field(
     default=3_2_7_6_8	,						metadata={"""help""": """Number of examples to train the tokenizer on."""}    )
 UpperCAmelCase							    =						field(default="""codeparrot"""	,						metadata={"""help""": """Name of new tokenizer."""}    )
 UpperCAmelCase							    =						field(default=__snake_case	,						metadata={"""help""": """Push saved tokenizer to the hub."""}    )






@dataclass
class 						__magic_name__    :
 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=__snake_case	,						metadata={"""help""": """Number of workers used for code evaluation."""}    )






@dataclass
class 						__magic_name__    :
 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=__snake_case	,						metadata={"""help""": """Push saved tokenizer to the hub."""}    )






def      _lowerCAmelCase(	__A			):
  assert column_title.isupper()
  UpperCAmelCase					      =  0
  UpperCAmelCase					      =  len(__A			) - 1
  UpperCAmelCase					      =  0

  while index >= 0:
    UpperCAmelCase					      =  (ord(column_title[index]			) - 64) * pow(26   ,       __A			)
    answer += value
    power += 1
    index -= 1

  return answer


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

      testmod()



