






from dataclasses import dataclass, field
from typing import Optional


@dataclass
class    __lowercase   :







     '''simple docstring'''
     SCREAMING_SNAKE_CASE     =					field(
         default="codeparrot/codeparrot"      ,   metadata={"help": "Model name or path of model to be trained."}	)
     SCREAMING_SNAKE_CASE     =					field(
         default="./"      ,   metadata={"help": "Save dir where model repo is cloned and models updates are saved to."}	)
     SCREAMING_SNAKE_CASE     =					field(
         default="codeparrot/codeparrot-clean-train"      ,   metadata={"help": "Name or path of training dataset."}	)
     SCREAMING_SNAKE_CASE     =					field(
         default="codeparrot/codeparrot-clean-valid"      ,   metadata={"help": "Name or path of validation dataset."}	)
     SCREAMING_SNAKE_CASE     =					field(default=2      ,   metadata={"help": "Batch size for training."}	)
     SCREAMING_SNAKE_CASE     =					field(default=2      ,   metadata={"help": "Batch size for evaluation."}	)
     SCREAMING_SNAKE_CASE     =					field(default=0.1      ,   metadata={"help": "Value of weight decay."}	)
     SCREAMING_SNAKE_CASE     =					field(
         default=1_00_00      ,   metadata={"help": "Size of buffer used to shuffle streaming dataset."}	)
     SCREAMING_SNAKE_CASE     =					field(default=2E-4      ,   metadata={"help": "Learning rate fo training."}	)
     SCREAMING_SNAKE_CASE     =					field(default="cosine"      ,   metadata={"help": "Learning rate."}	)
     SCREAMING_SNAKE_CASE     =					field(
         default=7_50      ,   metadata={"help": "Number of warmup steps in the learning rate schedule."}	)
     SCREAMING_SNAKE_CASE     =					field(
         default=16      ,   metadata={"help": "Number of gradient accumulation steps."}	)
     SCREAMING_SNAKE_CASE     =					field(
         default=lowercase_      ,   metadata={"help": "Use gradient checkpointing to reduce memory footprint."}	)
     SCREAMING_SNAKE_CASE     =					field(default=5_00_00      ,   metadata={"help": "Maximum number of training steps."}	)
     SCREAMING_SNAKE_CASE     =					field(
         default=-1      ,   metadata={"help": "Maximum number of evaluation steps. If -1 the full dataset is evaluated."}	)
     SCREAMING_SNAKE_CASE     =					field(default=10_24      ,   metadata={"help": "Sequence lengths used for training."}	)
     SCREAMING_SNAKE_CASE     =					field(default=1      ,   metadata={"help": "Training seed."}	)
     SCREAMING_SNAKE_CASE     =					field(
         default=10_24      ,   metadata={"help": "Interval to save checkpoints. Measured as number of forward passes not training steps."}      ,   )
     SCREAMING_SNAKE_CASE     =					field(
         default=lowercase_      ,   metadata={"help": "States path if the training should continue from a checkpoint folder."}	)
     SCREAMING_SNAKE_CASE     =					field(default=lowercase_      ,   metadata={"help": "If True the data is pretokenized."}	)


@dataclass
class    __lowercase   :







     '''simple docstring'''
     SCREAMING_SNAKE_CASE     =					field(
         default="codeparrot/codeparrot"      ,   metadata={"help": "Model name or path of model to be evaluated."}	)
     SCREAMING_SNAKE_CASE     =					field(
         default="codeparrot/codeparrot-clean-valid"      ,   metadata={"help": "Name or path of validation dataset."}	)
     SCREAMING_SNAKE_CASE     =					field(default=2      ,   metadata={"help": "Batch size used for evaluation."}	)
     SCREAMING_SNAKE_CASE     =					field(
         default=-1      ,   metadata={"help": "Maximum number of evaluation steps. If -1 the full dataset is evaluated."}	)
     SCREAMING_SNAKE_CASE     =					field(default=10_24      ,   metadata={"help": "Length of sequences to be evaluated."}	)
     SCREAMING_SNAKE_CASE     =					field(default=1      ,   metadata={"help": "Random seed used for evaluation."}	)


@dataclass
class    __lowercase   :







     '''simple docstring'''
     SCREAMING_SNAKE_CASE     =					field(
         default="codeparrot/codeparrot"      ,   metadata={"help": "Model name or path of model to be evaluated."}	)
     SCREAMING_SNAKE_CASE     =					field(default=lowercase_      ,   metadata={"help": "Number of workers used for code evaluation."}	)
     SCREAMING_SNAKE_CASE     =					field(
         default=lowercase_      ,   metadata={"help": "The number of human-eval tasks to run. If not included all tasks are evaluated."}      ,   )
     SCREAMING_SNAKE_CASE     =					field(
         default=lowercase_      ,   metadata={"help": "Sample from the language model's output distribution."}	)
     SCREAMING_SNAKE_CASE     =					field(default=0.2      ,   metadata={"help": "Sampling temperature used for generation."}	)
     SCREAMING_SNAKE_CASE     =					field(default=2_56      ,   metadata={"help": "Maximum number of newly generated tokens."}	)
     SCREAMING_SNAKE_CASE     =					field(default=0      ,   metadata={"help": "Top-k parameter used for generation."}	)
     SCREAMING_SNAKE_CASE     =					field(default=0.95      ,   metadata={"help": "Top-p parameter used for nucleus sampling."}	)
     SCREAMING_SNAKE_CASE     =					field(default=10      ,   metadata={"help": "Number of generations to run in parallel."}	)
     SCREAMING_SNAKE_CASE     =					field(
         default=2_00      ,   metadata={"help": "Number of completions to generate for each sample."}	)
     SCREAMING_SNAKE_CASE     =					field(default=1      ,   metadata={"help": "Random seed used for evaluation."}	)
     SCREAMING_SNAKE_CASE     =					field(
         default="eval_results.json"      ,   metadata={"help": "Random seed used for evaluation."}	)
     SCREAMING_SNAKE_CASE     =					field(
         default="0"      ,   metadata={"help": "Allow `code_eval` to execute Python code on machine"}	)
     SCREAMING_SNAKE_CASE     =					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    __lowercase   :







     '''simple docstring'''
     SCREAMING_SNAKE_CASE     =					field(
         default=lowercase_      ,   metadata={
             "help": "The number of CPU cores to use for parallel preprocessing. Default uses the maximum available."
         }      ,   )
     SCREAMING_SNAKE_CASE     =					field(
         default="transformersbook/codeparrot"      ,   metadata={"help": "Folder or name of dataset to process."}	)
     SCREAMING_SNAKE_CASE     =					field(
         default="codeparrot-clean"      ,   metadata={"help": "Folder to save processed processed dataset."}	)
     SCREAMING_SNAKE_CASE     =					field(
         default=10_00_00      ,   metadata={"help": "Number of files to save per JSON output file."}	)
     SCREAMING_SNAKE_CASE     =					field(default="content"      ,   metadata={"help": "Column containing text data to process."}	)
     SCREAMING_SNAKE_CASE     =					field(
         default=10_00      ,   metadata={"help": "Maximum line length in file, otherwise file is filtered."}	)
     SCREAMING_SNAKE_CASE     =					field(
         default=1_00      ,   metadata={"help": "Maximum mean line length in file, otherwise file is filtered."}	)
     SCREAMING_SNAKE_CASE     =					field(
         default=0.25      ,   metadata={"help": "Maximum fraction of non-alphanumeric characters, otherwise file is filtered."}	)
     SCREAMING_SNAKE_CASE     =					field(
         default=1.5      ,   metadata={"help": "Minimum character token ratio for the file, otherwise file is filtered."}	)
     SCREAMING_SNAKE_CASE     =					field(
         default=0.7      ,   metadata={"help": "Probability for filtering config, test and uncommon files."}	)
     SCREAMING_SNAKE_CASE     =					field(
         default="codeparrot/codeparrot"      ,   metadata={"help": "Name or path to the tokenizer."}      ,   )
     SCREAMING_SNAKE_CASE     =					field(
         default=lowercase_      ,   metadata={"help": "If True, near-duplicate samples are removed."}	)
     SCREAMING_SNAKE_CASE     =					field(
         default=0.85      ,   metadata={"help": "Jaccard threshold for near-duplicate samples."}	)


@dataclass
class    __lowercase   :







     '''simple docstring'''
     SCREAMING_SNAKE_CASE     =					field(
         default="gpt2"      ,   metadata={"help": "Base tokenizer to build new tokenizer from."}	)
     SCREAMING_SNAKE_CASE     =					field(
         default="transformersbook/codeparrot-train"      ,   metadata={"help": "Dataset to train tokenizer on."}	)
     SCREAMING_SNAKE_CASE     =					field(default="content"      ,   metadata={"help": "Column containing text data to process."}	)
     SCREAMING_SNAKE_CASE     =					field(default=20_00_00      ,   metadata={"help": "Number of examples to train tokenizer on."}	)
     SCREAMING_SNAKE_CASE     =					field(
         default=3_27_68      ,   metadata={"help": "Number of examples to train the tokenizer on."}	)
     SCREAMING_SNAKE_CASE     =					field(default="codeparrot"      ,   metadata={"help": "Name of new tokenizer."}	)
     SCREAMING_SNAKE_CASE     =					field(default=lowercase_      ,   metadata={"help": "Push saved tokenizer to the hub."}	)


@dataclass
class    __lowercase   :







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


@dataclass
class    __lowercase   :







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












def       _SCREAMING_SNAKE_CASE    (			__lowercase :      List[Any]     )    ->   Any:





    """simple docstring"""

    stooge(__lowercase      ,					0      ,					len(__lowercase     ) - 1     )
    return arr



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      str      ,					__lowercase :      Dict      ,					__lowercase :      Dict     )    ->   int:





    """simple docstring"""

    if i >= h:
        return

    # If first element is smaller than the last then swap them
    if arr[i] > arr[h]:
        __A     ,						__A       =						arr[h], arr[i]

    # If there are more than 2 elements in the array
    if h - i + 1 > 2:
        __A       =						(int)((h - i + 1) / 3     )

        # Recursively sort first 2/3 elements
        stooge(__lowercase      ,					__lowercase      ,					(h - t)     )

        # Recursively sort last 2/3 elements
        stooge(__lowercase      ,					i + t      ,					(__lowercase)     )

        # Recursively sort first 2/3 elements
        stooge(__lowercase      ,					__lowercase      ,					(h - t)     )


if __name__ == "__main__":
  __a   :							List[Any]						= input("Enter numbers separated by a comma:\n").strip()
  __a   :							List[str]						= [int(item) for item in user_input.split(",")]
  print(stooge_sort(unsorted))




