


"""simple docstring"""




from dataclasses import dataclass, field
from typing import Optional
@dataclass
class 					lowerCAmelCase	:




		'''simple docstring'''







		SCREAMING_SNAKE_CASE_						:     Optional[str]								=     field(
		    default="""codeparrot/codeparrot"""	,    metadata={"""help""": """Model name or path of model to be trained."""}    )
		SCREAMING_SNAKE_CASE_						:     Optional[str]								=     field(
		    default="""./"""	,    metadata={"""help""": """Save dir where model repo is cloned and models updates are saved to."""}    )
		SCREAMING_SNAKE_CASE_						:     Optional[str]								=     field(
		    default="""codeparrot/codeparrot-clean-train"""	,    metadata={"""help""": """Name or path of training dataset."""}    )
		SCREAMING_SNAKE_CASE_						:     Optional[str]								=     field(
		    default="""codeparrot/codeparrot-clean-valid"""	,    metadata={"""help""": """Name or path of validation dataset."""}    )
		SCREAMING_SNAKE_CASE_						:     Optional[int]								=     field(default=2	,    metadata={"""help""": """Batch size for training."""}    )
		SCREAMING_SNAKE_CASE_						:     Optional[int]								=     field(default=2	,    metadata={"""help""": """Batch size for evaluation."""}    )
		SCREAMING_SNAKE_CASE_						:     Optional[float]								=     field(default=0.1	,    metadata={"""help""": """Value of weight decay."""}    )
		SCREAMING_SNAKE_CASE_						:     Optional[int]								=     field(
		    default=1_0_0_0_0	,    metadata={"""help""": """Size of buffer used to shuffle streaming dataset."""}    )
		SCREAMING_SNAKE_CASE_						:     Optional[float]								=     field(default=2e-4	,    metadata={"""help""": """Learning rate fo training."""}    )
		SCREAMING_SNAKE_CASE_						:     Optional[str]								=     field(default="""cosine"""	,    metadata={"""help""": """Learning rate."""}    )
		SCREAMING_SNAKE_CASE_						:     Optional[int]								=     field(
		    default=7_5_0	,    metadata={"""help""": """Number of warmup steps in the learning rate schedule."""}    )
		SCREAMING_SNAKE_CASE_						:     Optional[int]								=     field(
		    default=1_6	,    metadata={"""help""": """Number of gradient accumulation steps."""}    )
		SCREAMING_SNAKE_CASE_						:     Optional[bool]								=     field(
		    default=lowerCamelCase_	,    metadata={"""help""": """Use gradient checkpointing to reduce memory footprint."""}    )
		SCREAMING_SNAKE_CASE_						:     Optional[int]								=     field(default=5_0_0_0_0	,    metadata={"""help""": """Maximum number of training steps."""}    )
		SCREAMING_SNAKE_CASE_						:     Optional[int]								=     field(
		    default=-1	,    metadata={"""help""": """Maximum number of evaluation steps. If -1 the full dataset is evaluated."""}    )
		SCREAMING_SNAKE_CASE_						:     Optional[int]								=     field(default=1_0_2_4	,    metadata={"""help""": """Sequence lengths used for training."""}    )
		SCREAMING_SNAKE_CASE_						:     Optional[int]								=     field(default=1	,    metadata={"""help""": """Training seed."""}    )
		SCREAMING_SNAKE_CASE_						:     Optional[int]								=     field(
		    default=1_0_2_4	,    metadata={"""help""": """Interval to save checkpoints. Measured as number of forward passes not training steps."""}	,    )
		SCREAMING_SNAKE_CASE_						:     Optional[str]								=     field(
		    default=lowerCamelCase_	,    metadata={"""help""": """States path if the training should continue from a checkpoint folder."""}    )
		SCREAMING_SNAKE_CASE_						:     Optional[bool]								=     field(default=lowerCamelCase_	,    metadata={"""help""": """If True the data is pretokenized."""}    )





@dataclass
class 					lowerCAmelCase	:




		'''simple docstring'''







		SCREAMING_SNAKE_CASE_						:     Optional[str]								=     field(
		    default="""codeparrot/codeparrot"""	,    metadata={"""help""": """Model name or path of model to be evaluated."""}    )
		SCREAMING_SNAKE_CASE_						:     Optional[str]								=     field(
		    default="""codeparrot/codeparrot-clean-valid"""	,    metadata={"""help""": """Name or path of validation dataset."""}    )
		SCREAMING_SNAKE_CASE_						:     Optional[int]								=     field(default=2	,    metadata={"""help""": """Batch size used for evaluation."""}    )
		SCREAMING_SNAKE_CASE_						:     Optional[int]								=     field(
		    default=-1	,    metadata={"""help""": """Maximum number of evaluation steps. If -1 the full dataset is evaluated."""}    )
		SCREAMING_SNAKE_CASE_						:     Optional[int]								=     field(default=1_0_2_4	,    metadata={"""help""": """Length of sequences to be evaluated."""}    )
		SCREAMING_SNAKE_CASE_						:     Optional[int]								=     field(default=1	,    metadata={"""help""": """Random seed used for evaluation."""}    )





@dataclass
class 					lowerCAmelCase	:




		'''simple docstring'''







		SCREAMING_SNAKE_CASE_						:     Optional[str]								=     field(
		    default="""codeparrot/codeparrot"""	,    metadata={"""help""": """Model name or path of model to be evaluated."""}    )
		SCREAMING_SNAKE_CASE_						:     Optional[int]								=     field(default=lowerCamelCase_	,    metadata={"""help""": """Number of workers used for code evaluation."""}    )
		SCREAMING_SNAKE_CASE_						:     Optional[int]								=     field(
		    default=lowerCamelCase_	,    metadata={"""help""": """The number of human-eval tasks to run. If not included all tasks are evaluated."""}	,    )
		SCREAMING_SNAKE_CASE_						:     Optional[bool]								=     field(
		    default=lowerCamelCase_	,    metadata={"""help""": """Sample from the language model's output distribution."""}    )
		SCREAMING_SNAKE_CASE_						:     Optional[float]								=     field(default=0.2	,    metadata={"""help""": """Sampling temperature used for generation."""}    )
		SCREAMING_SNAKE_CASE_						:     Optional[int]								=     field(default=2_5_6	,    metadata={"""help""": """Maximum number of newly generated tokens."""}    )
		SCREAMING_SNAKE_CASE_						:     Optional[int]								=     field(default=0	,    metadata={"""help""": """Top-k parameter used for generation."""}    )
		SCREAMING_SNAKE_CASE_						:     Optional[float]								=     field(default=0.9_5	,    metadata={"""help""": """Top-p parameter used for nucleus sampling."""}    )
		SCREAMING_SNAKE_CASE_						:     Optional[int]								=     field(default=1_0	,    metadata={"""help""": """Number of generations to run in parallel."""}    )
		SCREAMING_SNAKE_CASE_						:     Optional[int]								=     field(
		    default=2_0_0	,    metadata={"""help""": """Number of completions to generate for each sample."""}    )
		SCREAMING_SNAKE_CASE_						:     Optional[int]								=     field(default=1	,    metadata={"""help""": """Random seed used for evaluation."""}    )
		SCREAMING_SNAKE_CASE_						:     Optional[str]								=     field(
		    default="""eval_results.json"""	,    metadata={"""help""": """Random seed used for evaluation."""}    )
		SCREAMING_SNAKE_CASE_						:     Optional[str]								=     field(
		    default="""0"""	,    metadata={"""help""": """Allow `code_eval` to execute Python code on machine"""}    )
		SCREAMING_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 					lowerCAmelCase	:




		'''simple docstring'''







		SCREAMING_SNAKE_CASE_						:     Optional[int]								=     field(
		    default=lowerCamelCase_	,    metadata={
		        """help""": """The number of CPU cores to use for parallel preprocessing. Default uses the maximum available."""
		    }	,    )
		SCREAMING_SNAKE_CASE_						:     Optional[str]								=     field(
		    default="""transformersbook/codeparrot"""	,    metadata={"""help""": """Folder or name of dataset to process."""}    )
		SCREAMING_SNAKE_CASE_						:     Optional[str]								=     field(
		    default="""codeparrot-clean"""	,    metadata={"""help""": """Folder to save processed processed dataset."""}    )
		SCREAMING_SNAKE_CASE_						:     Optional[int]								=     field(
		    default=1_0_0_0_0_0	,    metadata={"""help""": """Number of files to save per JSON output file."""}    )
		SCREAMING_SNAKE_CASE_						:     Optional[str]								=     field(default="""content"""	,    metadata={"""help""": """Column containing text data to process."""}    )
		SCREAMING_SNAKE_CASE_						:     Optional[float]								=     field(
		    default=1_0_0_0	,    metadata={"""help""": """Maximum line length in file, otherwise file is filtered."""}    )
		SCREAMING_SNAKE_CASE_						:     Optional[float]								=     field(
		    default=1_0_0	,    metadata={"""help""": """Maximum mean line length in file, otherwise file is filtered."""}    )
		SCREAMING_SNAKE_CASE_						:     Optional[float]								=     field(
		    default=0.2_5	,    metadata={"""help""": """Maximum fraction of non-alphanumeric characters, otherwise file is filtered."""}    )
		SCREAMING_SNAKE_CASE_						:     Optional[float]								=     field(
		    default=1.5	,    metadata={"""help""": """Minimum character token ratio for the file, otherwise file is filtered."""}    )
		SCREAMING_SNAKE_CASE_						:     Optional[float]								=     field(
		    default=0.7	,    metadata={"""help""": """Probability for filtering config, test and uncommon files."""}    )
		SCREAMING_SNAKE_CASE_						:     Optional[str]								=     field(
		    default="""codeparrot/codeparrot"""	,    metadata={"""help""": """Name or path to the tokenizer."""}	,    )
		SCREAMING_SNAKE_CASE_						:     Optional[bool]								=     field(
		    default=lowerCamelCase_	,    metadata={"""help""": """If True, near-duplicate samples are removed."""}    )
		SCREAMING_SNAKE_CASE_						:     Optional[float]								=     field(
		    default=0.8_5	,    metadata={"""help""": """Jaccard threshold for near-duplicate samples."""}    )





@dataclass
class 					lowerCAmelCase	:




		'''simple docstring'''







		SCREAMING_SNAKE_CASE_						:     Optional[str]								=     field(
		    default="""gpt2"""	,    metadata={"""help""": """Base tokenizer to build new tokenizer from."""}    )
		SCREAMING_SNAKE_CASE_						:     Optional[str]								=     field(
		    default="""transformersbook/codeparrot-train"""	,    metadata={"""help""": """Dataset to train tokenizer on."""}    )
		SCREAMING_SNAKE_CASE_						:     Optional[str]								=     field(default="""content"""	,    metadata={"""help""": """Column containing text data to process."""}    )
		SCREAMING_SNAKE_CASE_						:     Optional[int]								=     field(default=2_0_0_0_0_0	,    metadata={"""help""": """Number of examples to train tokenizer on."""}    )
		SCREAMING_SNAKE_CASE_						:     Optional[int]								=     field(
		    default=3_2_7_6_8	,    metadata={"""help""": """Number of examples to train the tokenizer on."""}    )
		SCREAMING_SNAKE_CASE_						:     Optional[str]								=     field(default="""codeparrot"""	,    metadata={"""help""": """Name of new tokenizer."""}    )
		SCREAMING_SNAKE_CASE_						:     Optional[bool]								=     field(default=lowerCamelCase_	,    metadata={"""help""": """Push saved tokenizer to the hub."""}    )





@dataclass
class 					lowerCAmelCase	:




		'''simple docstring'''







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





@dataclass
class 					lowerCAmelCase	:




		'''simple docstring'''







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






"""simple docstring"""




__UpperCamelCase						=      0  # The first color of the flag.
__UpperCamelCase						=      1  # The second color of the flag.
__UpperCamelCase						=      2  # The third color of the flag.
__UpperCamelCase						=      (red, white, blue)


def     lowercase			(SCREAMING_SNAKE_CASE_		:				list  )   ->						list:
						if not sequence:
												return []
						if len(SCREAMING_SNAKE_CASE_  ) == 1:
												return list(SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					0
						SCREAMING_SNAKE_CASE													=					len(SCREAMING_SNAKE_CASE_  ) - 1
						SCREAMING_SNAKE_CASE													=					0
						while mid <= high:
												if sequence[mid] == colors[0]:
																		SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					sequence[mid], sequence[low]
																		low += 1
																		mid += 1
												elif sequence[mid] == colors[1]:
																		mid += 1
												elif sequence[mid] == colors[2]:
																		SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					sequence[high], sequence[mid]
																		high -= 1
												else:
																		SCREAMING_SNAKE_CASE													=					F'The elements inside the sequence must contains only {colors} values'
																		raise ValueError(SCREAMING_SNAKE_CASE_  )
						return sequence


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	__UpperCamelCase						=      input('''Enter numbers separated by commas:\n''').strip()
	__UpperCamelCase						=      [int(item.strip()) for item in user_input.split(''',''')]
	print(f'''{dutch_national_flag_sort(unsorted)}''')


