





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'''


      def get_matched_characters(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			) -> str:
            SCREAMING_SNAKE_CASE	=		[]
            SCREAMING_SNAKE_CASE	=		min(len(_stra			)				,		len(_stra			)			) // 2
            for i, l in enumerate(_stra			):
                  SCREAMING_SNAKE_CASE	=		int(max(0				,		i - limit			)			)
                  SCREAMING_SNAKE_CASE	=		int(min(i + limit + 1				,		len(_stra			)			)			)
                  if l in _stra[left:right]:
                        matched.append(_SCREAMING_SNAKE_CASE			)
                        SCREAMING_SNAKE_CASE	=		F"""{_stra[0:_stra.index(_SCREAMING_SNAKE_CASE			)]} {_stra[_stra.index(_SCREAMING_SNAKE_CASE			) + 1:]}"""

            return "".join(_SCREAMING_SNAKE_CASE			)

      # matching characters
      SCREAMING_SNAKE_CASE	=		get_matched_characters(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)
      SCREAMING_SNAKE_CASE	=		get_matched_characters(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)
      SCREAMING_SNAKE_CASE	=		len(_SCREAMING_SNAKE_CASE			)

      # transposition
      SCREAMING_SNAKE_CASE	=		(
          len([(ca, ca) for ca, ca in zip(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			) if ca != ca]			) // 2
      )

      if not match_count:
            SCREAMING_SNAKE_CASE	=		0.0
      else:
            SCREAMING_SNAKE_CASE	=		(
                1
                / 3
                * (
                    match_count / len(_SCREAMING_SNAKE_CASE			)
                    + match_count / len(_SCREAMING_SNAKE_CASE			)
                    + (match_count - transpositions) / match_count
                )
            )

      # common prefix up to 4 characters
      SCREAMING_SNAKE_CASE	=		0
      for ca, ca in zip(stra[:4]				,		stra[:4]			):
            if ca == ca:
                  prefix_len += 1
            else:
                  break

      return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      print(jaro_winkler("""hello""", """world"""))


