from dataclasses import dataclass, field
from typing import Optional


@dataclass
class 						lowercase    :
							lowercase__						:       Optional[str]								=							field(
							    default="""codeparrot/codeparrot"""				,       metadata={"""help""": """Model name or path of model to be trained."""}     )
							lowercase__						:       Optional[str]								=							field(
							    default="""./"""				,       metadata={"""help""": """Save dir where model repo is cloned and models updates are saved to."""}     )
							lowercase__						:       Optional[str]								=							field(
							    default="""codeparrot/codeparrot-clean-train"""				,       metadata={"""help""": """Name or path of training dataset."""}     )
							lowercase__						:       Optional[str]								=							field(
							    default="""codeparrot/codeparrot-clean-valid"""				,       metadata={"""help""": """Name or path of validation dataset."""}     )
							lowercase__						:       Optional[int]								=							field(default=2				,       metadata={"""help""": """Batch size for training."""}     )
							lowercase__						:       Optional[int]								=							field(default=2				,       metadata={"""help""": """Batch size for evaluation."""}     )
							lowercase__						:       Optional[float]								=							field(default=0.1				,       metadata={"""help""": """Value of weight decay."""}     )
							lowercase__						:       Optional[int]								=							field(
							    default=10_000				,       metadata={"""help""": """Size of buffer used to shuffle streaming dataset."""}     )
							lowercase__						:       Optional[float]								=							field(default=2e-4				,       metadata={"""help""": """Learning rate fo training."""}     )
							lowercase__						:       Optional[str]								=							field(default="""cosine"""				,       metadata={"""help""": """Learning rate."""}     )
							lowercase__						:       Optional[int]								=							field(
							    default=750				,       metadata={"""help""": """Number of warmup steps in the learning rate schedule."""}     )
							lowercase__						:       Optional[int]								=							field(
							    default=16				,       metadata={"""help""": """Number of gradient accumulation steps."""}     )
							lowercase__						:       Optional[bool]								=							field(
							    default=a				,       metadata={"""help""": """Use gradient checkpointing to reduce memory footprint."""}     )
							lowercase__						:       Optional[int]								=							field(default=50_000				,       metadata={"""help""": """Maximum number of training steps."""}     )
							lowercase__						:       Optional[int]								=							field(
							    default=-1				,       metadata={"""help""": """Maximum number of evaluation steps. If -1 the full dataset is evaluated."""}     )
							lowercase__						:       Optional[int]								=							field(default=1_024				,       metadata={"""help""": """Sequence lengths used for training."""}     )
							lowercase__						:       Optional[int]								=							field(default=1				,       metadata={"""help""": """Training seed."""}     )
							lowercase__						:       Optional[int]								=							field(
							    default=1_024				,       metadata={"""help""": """Interval to save checkpoints. Measured as number of forward passes not training steps."""}				,       )
							lowercase__						:       Optional[str]								=							field(
							    default=a				,       metadata={"""help""": """States path if the training should continue from a checkpoint folder."""}     )
							lowercase__						:       Optional[bool]								=							field(default=a				,       metadata={"""help""": """If True the data is pretokenized."""}     )



@dataclass
class 						lowercase    :
							lowercase__						:       Optional[str]								=							field(
							    default="""codeparrot/codeparrot"""				,       metadata={"""help""": """Model name or path of model to be evaluated."""}     )
							lowercase__						:       Optional[str]								=							field(
							    default="""codeparrot/codeparrot-clean-valid"""				,       metadata={"""help""": """Name or path of validation dataset."""}     )
							lowercase__						:       Optional[int]								=							field(default=2				,       metadata={"""help""": """Batch size used for evaluation."""}     )
							lowercase__						:       Optional[int]								=							field(
							    default=-1				,       metadata={"""help""": """Maximum number of evaluation steps. If -1 the full dataset is evaluated."""}     )
							lowercase__						:       Optional[int]								=							field(default=1_024				,       metadata={"""help""": """Length of sequences to be evaluated."""}     )
							lowercase__						:       Optional[int]								=							field(default=1				,       metadata={"""help""": """Random seed used for evaluation."""}     )



@dataclass
class 						lowercase    :
							lowercase__						:       Optional[str]								=							field(
							    default="""codeparrot/codeparrot"""				,       metadata={"""help""": """Model name or path of model to be evaluated."""}     )
							lowercase__						:       Optional[int]								=							field(default=a				,       metadata={"""help""": """Number of workers used for code evaluation."""}     )
							lowercase__						:       Optional[int]								=							field(
							    default=a				,       metadata={"""help""": """The number of human-eval tasks to run. If not included all tasks are evaluated."""}				,       )
							lowercase__						:       Optional[bool]								=							field(
							    default=a				,       metadata={"""help""": """Sample from the language model's output distribution."""}     )
							lowercase__						:       Optional[float]								=							field(default=0.2				,       metadata={"""help""": """Sampling temperature used for generation."""}     )
							lowercase__						:       Optional[int]								=							field(default=256				,       metadata={"""help""": """Maximum number of newly generated tokens."""}     )
							lowercase__						:       Optional[int]								=							field(default=0				,       metadata={"""help""": """Top-k parameter used for generation."""}     )
							lowercase__						:       Optional[float]								=							field(default=0.95				,       metadata={"""help""": """Top-p parameter used for nucleus sampling."""}     )
							lowercase__						:       Optional[int]								=							field(default=10				,       metadata={"""help""": """Number of generations to run in parallel."""}     )
							lowercase__						:       Optional[int]								=							field(
							    default=200				,       metadata={"""help""": """Number of completions to generate for each sample."""}     )
							lowercase__						:       Optional[int]								=							field(default=1				,       metadata={"""help""": """Random seed used for evaluation."""}     )
							lowercase__						:       Optional[str]								=							field(
							    default="""eval_results.json"""				,       metadata={"""help""": """Random seed used for evaluation."""}     )
							lowercase__						:       Optional[str]								=							field(
							    default="""0"""				,       metadata={"""help""": """Allow `code_eval` to execute Python code on machine"""}     )
							lowercase__						:       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 						lowercase    :
							lowercase__						:       Optional[int]								=							field(
							    default=a				,       metadata={
							        """help""": """The number of CPU cores to use for parallel preprocessing. Default uses the maximum available."""
							    }				,       )
							lowercase__						:       Optional[str]								=							field(
							    default="""transformersbook/codeparrot"""				,       metadata={"""help""": """Folder or name of dataset to process."""}     )
							lowercase__						:       Optional[str]								=							field(
							    default="""codeparrot-clean"""				,       metadata={"""help""": """Folder to save processed processed dataset."""}     )
							lowercase__						:       Optional[int]								=							field(
							    default=100_000				,       metadata={"""help""": """Number of files to save per JSON output file."""}     )
							lowercase__						:       Optional[str]								=							field(default="""content"""				,       metadata={"""help""": """Column containing text data to process."""}     )
							lowercase__						:       Optional[float]								=							field(
							    default=1_000				,       metadata={"""help""": """Maximum line length in file, otherwise file is filtered."""}     )
							lowercase__						:       Optional[float]								=							field(
							    default=100				,       metadata={"""help""": """Maximum mean line length in file, otherwise file is filtered."""}     )
							lowercase__						:       Optional[float]								=							field(
							    default=0.25				,       metadata={"""help""": """Maximum fraction of non-alphanumeric characters, otherwise file is filtered."""}     )
							lowercase__						:       Optional[float]								=							field(
							    default=1.5				,       metadata={"""help""": """Minimum character token ratio for the file, otherwise file is filtered."""}     )
							lowercase__						:       Optional[float]								=							field(
							    default=0.7				,       metadata={"""help""": """Probability for filtering config, test and uncommon files."""}     )
							lowercase__						:       Optional[str]								=							field(
							    default="""codeparrot/codeparrot"""				,       metadata={"""help""": """Name or path to the tokenizer."""}				,       )
							lowercase__						:       Optional[bool]								=							field(
							    default=a				,       metadata={"""help""": """If True, near-duplicate samples are removed."""}     )
							lowercase__						:       Optional[float]								=							field(
							    default=0.85				,       metadata={"""help""": """Jaccard threshold for near-duplicate samples."""}     )



@dataclass
class 						lowercase    :
							lowercase__						:       Optional[str]								=							field(
							    default="""gpt2"""				,       metadata={"""help""": """Base tokenizer to build new tokenizer from."""}     )
							lowercase__						:       Optional[str]								=							field(
							    default="""transformersbook/codeparrot-train"""				,       metadata={"""help""": """Dataset to train tokenizer on."""}     )
							lowercase__						:       Optional[str]								=							field(default="""content"""				,       metadata={"""help""": """Column containing text data to process."""}     )
							lowercase__						:       Optional[int]								=							field(default=200_000				,       metadata={"""help""": """Number of examples to train tokenizer on."""}     )
							lowercase__						:       Optional[int]								=							field(
							    default=32_768				,       metadata={"""help""": """Number of examples to train the tokenizer on."""}     )
							lowercase__						:       Optional[str]								=							field(default="""codeparrot"""				,       metadata={"""help""": """Name of new tokenizer."""}     )
							lowercase__						:       Optional[bool]								=							field(default=a				,       metadata={"""help""": """Push saved tokenizer to the hub."""}     )



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



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





from __future__ import annotations

from collections.abc import Iterator
from typing import Generic, TypeVar

_lowerCamelCase					:   Optional[Any]							=		TypeVar('''T''')


class 						lowercase    (     Generic[T]     ):




							def __init__(						self   :					Any	,							_UpperCamelCase   :					T      )					->				Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       data
												SCREAMING_SNAKE_CASE    			=       None





							def __str__(						self   :					Union[str, Any]      )					->				str:



												'''simple docstring'''

												return F"{self.data}"



class 						lowercase    (     Generic[T]     ):




							def __init__(						self   :					Optional[int]      )					->				None:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       None




							def __iter__(						self   :					str      )					->				Iterator[T]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.top
												while node:
																	yield node.data
																	SCREAMING_SNAKE_CASE    			=       node.next




							def __str__(						self   :					int      )					->				str:



												'''simple docstring'''

												return "->".join([str(_UpperCamelCase      ) for item in self]      )




							def __len__(						self   :					Tuple      )					->				int:



												'''simple docstring'''

												return len(tuple(iter(self      )      )      )




							def 	__snake_case(						self   :					Union[str, Any]      )					->				bool:



												'''simple docstring'''

												return self.top is None




							def 	__snake_case(						self   :					str	,							_UpperCamelCase   :					T      )					->				None:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       Node(_UpperCamelCase      )
												if not self.is_empty():
																	SCREAMING_SNAKE_CASE    			=       self.top
												SCREAMING_SNAKE_CASE    			=       node




							def 	__snake_case(						self   :					Union[str, Any]      )					->				T:



												'''simple docstring'''

												if self.is_empty():
																	raise IndexError("pop from empty stack"      )
												assert isinstance(self.top	,							_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       self.top
												SCREAMING_SNAKE_CASE    			=       self.top.next
												return pop_node.data




							def 	__snake_case(						self   :					Union[str, Any]      )					->				T:



												'''simple docstring'''

												if self.is_empty():
																	raise IndexError("peek from empty stack"      )

												assert self.top is not None
												return self.top.data





							def 	__snake_case(						self   :					Dict      )					->				None:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       None


if __name__ == "__main__":
							from doctest import testmod

							testmod()




