
'''simple docstring'''





from dataclasses import dataclass, field
from typing import Optional






@dataclass
class 							UpperCAmelCase_							:

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






@dataclass
class 							UpperCAmelCase_							:

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






@dataclass
class 							UpperCAmelCase_							:

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

 '''simple docstring'''
 lowercase_					:    Optional[int]				=							field(
     default=A					,				metadata={
         "help": "The number of CPU cores to use for parallel preprocessing. Default uses the maximum available."
     }					,				)
 lowercase_					:    Optional[str]				=							field(
     default="transformersbook/codeparrot"					,				metadata={"help": "Folder or name of dataset to process."}				)
 lowercase_					:    Optional[str]				=							field(
     default="codeparrot-clean"					,				metadata={"help": "Folder to save processed processed dataset."}				)
 lowercase_					:    Optional[int]				=							field(
     default=100_000					,				metadata={"help": "Number of files to save per JSON output file."}				)
 lowercase_					:    Optional[str]				=							field(default="content"					,				metadata={"help": "Column containing text data to process."}				)
 lowercase_					:    Optional[float]				=							field(
     default=1_000					,				metadata={"help": "Maximum line length in file, otherwise file is filtered."}				)
 lowercase_					:    Optional[float]				=							field(
     default=100					,				metadata={"help": "Maximum mean line length in file, otherwise file is filtered."}				)
 lowercase_					:    Optional[float]				=							field(
     default=0.25					,				metadata={"help": "Maximum fraction of non-alphanumeric characters, otherwise file is filtered."}				)
 lowercase_					:    Optional[float]				=							field(
     default=1.5					,				metadata={"help": "Minimum character token ratio for the file, otherwise file is filtered."}				)
 lowercase_					:    Optional[float]				=							field(
     default=0.7					,				metadata={"help": "Probability for filtering config, test and uncommon files."}				)
 lowercase_					:    Optional[str]				=							field(
     default="codeparrot/codeparrot"					,				metadata={"help": "Name or path to the tokenizer."}					,				)
 lowercase_					:    Optional[bool]				=							field(
     default=A					,				metadata={"help": "If True, near-duplicate samples are removed."}				)
 lowercase_					:    Optional[float]				=							field(
     default=0.85					,				metadata={"help": "Jaccard threshold for near-duplicate samples."}				)






@dataclass
class 							UpperCAmelCase_							:

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






@dataclass
class 							UpperCAmelCase_							:

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






@dataclass
class 							UpperCAmelCase_							:

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





'''simple docstring'''





SCREAMING_SNAKE_CASE     =       {
    """meter""": """m""",
    """kilometer""": """km""",
    """megametre""": """Mm""",
    """gigametre""": """Gm""",
    """terametre""": """Tm""",
    """petametre""": """Pm""",
    """exametre""": """Em""",
    """zettametre""": """Zm""",
    """yottametre""": """Ym""",
}
# Exponent of the factor(meter)
SCREAMING_SNAKE_CASE     =       {
    """m""": 0,
    """km""": 3,
    """Mm""": 6,
    """Gm""": 9,
    """Tm""": 1_2,
    """Pm""": 1_5,
    """Em""": 1_8,
    """Zm""": 2_1,
    """Ym""": 2_4,
}







def snake_case_     (					lowercase__   ,       lowercase__   ,       lowercase__		):
  UpperCAmelCase__      :					str				       =							from_type.lower().strip("s"		)
  UpperCAmelCase__      :					Any				       =							to_type.lower().strip("s"		)

  UpperCAmelCase__      :					Union[str, Any]				       =							UNIT_SYMBOL.get(lowercase__   ,       lowercase__		)
  UpperCAmelCase__      :					Any				       =							UNIT_SYMBOL.get(lowercase__   ,       lowercase__		)

  if from_sanitized not in METRIC_CONVERSION:
    UpperCAmelCase__      :					str				       =							(
        F"""Invalid 'from_type' value: {from_type!r}.\n"""
        F"""Conversion abbreviations are: {", ".join(lowercase__		)}"""
    )
    raise ValueError(lowercase__		)
  if to_sanitized not in METRIC_CONVERSION:
    UpperCAmelCase__      :					List[str]				       =							(
        F"""Invalid 'to_type' value: {to_type!r}.\n"""
        F"""Conversion abbreviations are: {", ".join(lowercase__		)}"""
    )
    raise ValueError(lowercase__		)
  UpperCAmelCase__      :					Optional[int]				       =							METRIC_CONVERSION[from_sanitized]
  UpperCAmelCase__      :					str				       =							METRIC_CONVERSION[to_sanitized]
  UpperCAmelCase__      :					Tuple				       =							1

  if from_exponent > to_exponent:
    UpperCAmelCase__      :					Tuple				       =							from_exponent - to_exponent
  else:
    UpperCAmelCase__      :					Optional[int]				       =							-(to_exponent - from_exponent)

  return value * pow(1_0   ,       lowercase__		)


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

      testmod()



