




def 		a_							( __lowerCAmelCase				):







  if len(__lowerCAmelCase				) < 2:
    return collection

  def circle_sort_util(__lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase				) -> bool:
    lowerCAmelCase__										=      False

    if low == high:
      return swapped

    lowerCAmelCase__										=      low
    lowerCAmelCase__										=      high

    while left < right:
      if collection[left] > collection[right]:
        lowerCAmelCase__										=      (
            collection[right],
            collection[left],
        )
        lowerCAmelCase__										=      True

      left += 1
      right -= 1

    if left == right and collection[left] > collection[right + 1]:
      lowerCAmelCase__										=      (
          collection[right + 1],
          collection[left],
      )

      lowerCAmelCase__										=      True

    lowerCAmelCase__										=      low + int((high - low) / 2				)
    lowerCAmelCase__										=      circle_sort_util(__lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase				)
    lowerCAmelCase__										=      circle_sort_util(__lowerCAmelCase   , mid + 1   , __lowerCAmelCase				)

    return swapped or left_swap or right_swap

  lowerCAmelCase__										=      True

  while is_not_sorted is True:
    lowerCAmelCase__										=      circle_sort_util(__lowerCAmelCase   , 0   , len(__lowerCAmelCase				) - 1				)

  return collection


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








"""simple docstring"""





from dataclasses import dataclass, field
from typing import Optional
@dataclass
class 		SCREAMING_SNAKE_CASE_				:
				"""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=1_00_00							,	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=7_50							,	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=snake_case__							,	metadata={"""help""": """Use gradient checkpointing to reduce memory footprint."""} )
				__snake_case :							Optional[int]									=		field(default=5_00_00							,	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=10_24							,	metadata={"""help""": """Sequence lengths used for training."""} )
				__snake_case :							Optional[int]									=		field(default=1							,	metadata={"""help""": """Training seed."""} )
				__snake_case :							Optional[int]									=		field(
				    default=10_24							,	metadata={"""help""": """Interval to save checkpoints. Measured as number of forward passes not training steps."""}							,	)
				__snake_case :							Optional[str]									=		field(
				    default=snake_case__							,	metadata={"""help""": """States path if the training should continue from a checkpoint folder."""} )
				__snake_case :							Optional[bool]									=		field(default=snake_case__							,	metadata={"""help""": """If True the data is pretokenized."""} )

@dataclass
class 		SCREAMING_SNAKE_CASE_				:
				"""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=10_24							,	metadata={"""help""": """Length of sequences to be evaluated."""} )
				__snake_case :							Optional[int]									=		field(default=1							,	metadata={"""help""": """Random seed used for evaluation."""} )

@dataclass
class 		SCREAMING_SNAKE_CASE_				:
				"""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=snake_case__							,	metadata={"""help""": """Number of workers used for code evaluation."""} )
				__snake_case :							Optional[int]									=		field(
				    default=snake_case__							,	metadata={"""help""": """The number of human-eval tasks to run. If not included all tasks are evaluated."""}							,	)
				__snake_case :							Optional[bool]									=		field(
				    default=snake_case__							,	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=2_56							,	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=2_00							,	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 		SCREAMING_SNAKE_CASE_				:
				"""simple docstring"""
				__snake_case :							Optional[int]									=		field(
				    default=snake_case__							,	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=10_00_00							,	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=10_00							,	metadata={"""help""": """Maximum line length in file, otherwise file is filtered."""} )
				__snake_case :							Optional[float]									=		field(
				    default=1_00							,	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=snake_case__							,	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 		SCREAMING_SNAKE_CASE_				:
				"""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=20_00_00							,	metadata={"""help""": """Number of examples to train tokenizer on."""} )
				__snake_case :							Optional[int]									=		field(
				    default=3_27_68							,	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=snake_case__							,	metadata={"""help""": """Push saved tokenizer to the hub."""} )

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

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




