





from dataclasses import dataclass, field
from typing import Optional

@dataclass
class 	UpperCamelCase__				:



					'''simple docstring'''

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

@dataclass
class 	UpperCamelCase__				:



					'''simple docstring'''

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

@dataclass
class 	UpperCamelCase__				:



					'''simple docstring'''

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

					__snake_case					:			Optional[int]				=    field(
					    default=lowerCAmelCase_							,   metadata={
					        "help": "The number of CPU cores to use for parallel preprocessing. Default uses the maximum available."
					    }							,   )
					__snake_case					:			Optional[str]				=    field(
					    default="transformersbook/codeparrot"							,   metadata={"help": "Folder or name of dataset to process."}     )
					__snake_case					:			Optional[str]				=    field(
					    default="codeparrot-clean"							,   metadata={"help": "Folder to save processed processed dataset."}     )
					__snake_case					:			Optional[int]				=    field(
					    default=100000							,   metadata={"help": "Number of files to save per JSON output file."}     )
					__snake_case					:			Optional[str]				=    field(default="content"							,   metadata={"help": "Column containing text data to process."}     )
					__snake_case					:			Optional[float]				=    field(
					    default=1000							,   metadata={"help": "Maximum line length in file, otherwise file is filtered."}     )
					__snake_case					:			Optional[float]				=    field(
					    default=100							,   metadata={"help": "Maximum mean line length in file, otherwise file is filtered."}     )
					__snake_case					:			Optional[float]				=    field(
					    default=0.25							,   metadata={"help": "Maximum fraction of non-alphanumeric characters, otherwise file is filtered."}     )
					__snake_case					:			Optional[float]				=    field(
					    default=1.5							,   metadata={"help": "Minimum character token ratio for the file, otherwise file is filtered."}     )
					__snake_case					:			Optional[float]				=    field(
					    default=0.7							,   metadata={"help": "Probability for filtering config, test and uncommon files."}     )
					__snake_case					:			Optional[str]				=    field(
					    default="codeparrot/codeparrot"							,   metadata={"help": "Name or path to the tokenizer."}							,   )
					__snake_case					:			Optional[bool]				=    field(
					    default=lowerCAmelCase_							,   metadata={"help": "If True, near-duplicate samples are removed."}     )
					__snake_case					:			Optional[float]				=    field(
					    default=0.85							,   metadata={"help": "Jaccard threshold for near-duplicate samples."}     )

@dataclass
class 	UpperCamelCase__				:



					'''simple docstring'''

					__snake_case					:			Optional[str]				=    field(
					    default="gpt2"							,   metadata={"help": "Base tokenizer to build new tokenizer from."}     )
					__snake_case					:			Optional[str]				=    field(
					    default="transformersbook/codeparrot-train"							,   metadata={"help": "Dataset to train tokenizer on."}     )
					__snake_case					:			Optional[str]				=    field(default="content"							,   metadata={"help": "Column containing text data to process."}     )
					__snake_case					:			Optional[int]				=    field(default=200000							,   metadata={"help": "Number of examples to train tokenizer on."}     )
					__snake_case					:			Optional[int]				=    field(
					    default=32768							,   metadata={"help": "Number of examples to train the tokenizer on."}     )
					__snake_case					:			Optional[str]				=    field(default="codeparrot"							,   metadata={"help": "Name of new tokenizer."}     )
					__snake_case					:			Optional[bool]				=    field(default=lowerCAmelCase_							,   metadata={"help": "Push saved tokenizer to the hub."}     )

@dataclass
class 	UpperCamelCase__				:



					'''simple docstring'''

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

@dataclass
class 	UpperCamelCase__				:



					'''simple docstring'''

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









def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)				->     float:


      '''simple docstring'''


      _validate_point(_SCREAMING_SNAKE_CASE			)
      _validate_point(_SCREAMING_SNAKE_CASE			)
      if len(_SCREAMING_SNAKE_CASE			) != len(_SCREAMING_SNAKE_CASE			):
            raise ValueError("""Both points must be in the same n-dimensional space"""			)

      return float(sum(abs(a - b			) for a, b in zip(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)			)			)



def      __lowercase     (   _SCREAMING_SNAKE_CASE			)				->     None:


      '''simple docstring'''


      if point:
            if isinstance(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			):
                  for item in point:
                        if not isinstance(_SCREAMING_SNAKE_CASE				,		(int, float)			):
                              SCREAMING_SNAKE_CASE	=		(
                                  """Expected a list of numbers as input, found """
                                  F"""{type(_SCREAMING_SNAKE_CASE			).__name__}"""
                              )
                              raise TypeError(_SCREAMING_SNAKE_CASE			)
            else:
                  SCREAMING_SNAKE_CASE	=		F"""Expected a list of numbers as input, found {type(_SCREAMING_SNAKE_CASE			).__name__}"""
                  raise TypeError(_SCREAMING_SNAKE_CASE			)
      else:
            raise ValueError("""Missing an input"""			)



def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)				->     float:


      '''simple docstring'''


      _validate_point(_SCREAMING_SNAKE_CASE			)
      _validate_point(_SCREAMING_SNAKE_CASE			)
      if len(_SCREAMING_SNAKE_CASE			) != len(_SCREAMING_SNAKE_CASE			):
            raise ValueError("""Both points must be in the same n-dimensional space"""			)

      return float(sum(abs(x - y			) for x, y in zip(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)			)			)


if __name__ == "__main__":
      import doctest

      doctest.testmod()


