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."""}     )





import functools
import logging
import os
import sys
import threading
from logging import (
    CRITICAL,  # NOQA
    DEBUG,  # NOQA
    ERROR,  # NOQA
    FATAL,  # NOQA
    INFO,  # NOQA
    NOTSET,  # NOQA
    WARN,  # NOQA
    WARNING,  # NOQA
)
from typing import Optional

import huggingface_hub.utils as hf_hub_utils
from tqdm import auto as tqdm_lib


_lowerCamelCase					:   str							=		threading.Lock()
_lowerCamelCase					:   Optional[logging.Handler]							=		None

_lowerCamelCase					:   Any							=		{
    '''debug''': logging.DEBUG,
    '''info''': logging.INFO,
    '''warning''': logging.WARNING,
    '''error''': logging.ERROR,
    '''critical''': logging.CRITICAL,
}

_lowerCamelCase					:   Union[str, Any]							=		logging.WARNING

_lowerCamelCase					:   List[Any]							=		True





def 	__lowerCamelCase							():
					SCREAMING_SNAKE_CASE    			=       os.getenv("TRANSFORMERS_VERBOSITY"      , UpperCAmelCase__  )
					if env_level_str:
										if env_level_str in log_levels:
															return log_levels[env_level_str]
										else:
															logging.getLogger().warning(
															    F"Unknown option TRANSFORMERS_VERBOSITY={env_level_str}, "
															    F"has to be one of: { ', '.join(log_levels.keys()  ) }"  )
					return _default_log_level





def 	__lowerCamelCase							():
					return __name__.split("."  )[0]





def 	__lowerCamelCase							():
					return logging.getLogger(_get_library_name()  )





def 	__lowerCamelCase							():
					global _default_handler

					with _lock:
										if _default_handler:
															# This library has already configured the library root logger.
															return
										SCREAMING_SNAKE_CASE    			=       logging.StreamHandler()  # Set sys.stderr as stream.
										SCREAMING_SNAKE_CASE    			=       sys.stderr.flush

										# Apply our default configuration to the library root logger.
										SCREAMING_SNAKE_CASE    			=       _get_library_root_logger()
										library_root_logger.addHandler(_default_handler  )
										library_root_logger.setLevel(_get_default_logging_level()  )
										SCREAMING_SNAKE_CASE    			=       False





def 	__lowerCamelCase							():
					global _default_handler

					with _lock:
										if not _default_handler:
															return

										SCREAMING_SNAKE_CASE    			=       _get_library_root_logger()
										library_root_logger.removeHandler(_default_handler  )
										library_root_logger.setLevel(logging.NOTSET  )
										SCREAMING_SNAKE_CASE    			=       None





def 	__lowerCamelCase							():
					return log_levels





def 	__lowerCamelCase							(UpperCAmelCase__     :							Optional[str] = None  ):

					if name is None:
										SCREAMING_SNAKE_CASE    			=       _get_library_name()

					_configure_library_root_logger()
					return logging.getLogger(UpperCAmelCase__  )





def 	__lowerCamelCase							():
					_configure_library_root_logger()
					return _get_library_root_logger().getEffectiveLevel()





def 	__lowerCamelCase							(UpperCAmelCase__     :							int  ):
					_configure_library_root_logger()
					_get_library_root_logger().setLevel(UpperCAmelCase__  )





def 	__lowerCamelCase							():
					return set_verbosity(UpperCAmelCase__  )





def 	__lowerCamelCase							():
					return set_verbosity(UpperCAmelCase__  )





def 	__lowerCamelCase							():
					return set_verbosity(UpperCAmelCase__  )





def 	__lowerCamelCase							():
					return set_verbosity(UpperCAmelCase__  )





def 	__lowerCamelCase							():
					_configure_library_root_logger()

					assert _default_handler is not None
					_get_library_root_logger().removeHandler(_default_handler  )





def 	__lowerCamelCase							():
					_configure_library_root_logger()

					assert _default_handler is not None
					_get_library_root_logger().addHandler(_default_handler  )





def 	__lowerCamelCase							(UpperCAmelCase__     :							logging.Handler  ):
					_configure_library_root_logger()

					assert handler is not None
					_get_library_root_logger().addHandler(UpperCAmelCase__  )





def 	__lowerCamelCase							(UpperCAmelCase__     :							logging.Handler  ):
					_configure_library_root_logger()

					assert handler is not None and handler not in _get_library_root_logger().handlers
					_get_library_root_logger().removeHandler(UpperCAmelCase__  )





def 	__lowerCamelCase							():
					_configure_library_root_logger()
					SCREAMING_SNAKE_CASE    			=       False





def 	__lowerCamelCase							():
					_configure_library_root_logger()
					SCREAMING_SNAKE_CASE    			=       True





def 	__lowerCamelCase							():
					SCREAMING_SNAKE_CASE    			=       _get_library_root_logger().handlers

					for handler in handlers:
										SCREAMING_SNAKE_CASE    			=       logging.Formatter("[%(levelname)s|%(filename)s:%(lineno)s] %(asctime)s >> %(message)s"  )
										handler.setFormatter(UpperCAmelCase__  )





def 	__lowerCamelCase							():
					SCREAMING_SNAKE_CASE    			=       _get_library_root_logger().handlers

					for handler in handlers:
										handler.setFormatter(UpperCAmelCase__  )





def 	__lowerCamelCase							(self     :							str      , *UpperCAmelCase__     :							Union[str, Any]      , **UpperCAmelCase__     :							List[str]  ):
					SCREAMING_SNAKE_CASE    			=       os.getenv("TRANSFORMERS_NO_ADVISORY_WARNINGS"      , UpperCAmelCase__  )
					if no_advisory_warnings:
										return
					self.warning(*UpperCAmelCase__      , **UpperCAmelCase__  )


_lowerCamelCase					:   str							=		warning_advice





@functools.lru_cache(UpperCAmelCase__  )
def 	__lowerCamelCase							(self     :							List[str]      , *UpperCAmelCase__     :							Tuple      , **UpperCAmelCase__     :							int  ):
					self.warning(*UpperCAmelCase__      , **UpperCAmelCase__  )


_lowerCamelCase					:   Dict							=		warning_once


class 						lowercase    :




							def __init__(						self   :					List[Any]	,							*_UpperCamelCase   :					Union[str, Any]	,							**_UpperCamelCase   :					str      )					->				List[Any]:  # pylint: disable=unused-argument



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       args[0] if args else None




							def __iter__(						self   :					Optional[Any]      )					->				str:



												'''simple docstring'''

												return iter(self._iterator      )




							def __getattr__(						self   :					List[str]	,							_UpperCamelCase   :					Any      )					->				List[Any]:



												'''simple docstring'''

												def empty_fn(*_UpperCamelCase   :					List[str]	,							**_UpperCamelCase   :					Union[str, Any]      ):  # pylint: disable=unused-argument
																	return

												return empty_fn




							def __enter__(						self   :					Any      )					->				Optional[Any]:



												'''simple docstring'''

												return self





							def __exit__(						self   :					Optional[Any]	,							_UpperCamelCase   :					Tuple	,							_UpperCamelCase   :					str	,							_UpperCamelCase   :					List[str]      )					->				Union[str, Any]:



												'''simple docstring'''

												return



class 						lowercase    :




							def __call__(						self   :					Union[str, Any]	,							*_UpperCamelCase   :					Optional[Any]	,							**_UpperCamelCase   :					Tuple      )					->				Tuple:



												'''simple docstring'''

												if _tqdm_active:
																	return tqdm_lib.tqdm(*_UpperCamelCase	,							**_UpperCamelCase      )
												else:
																	return EmptyTqdm(*_UpperCamelCase	,							**_UpperCamelCase      )




							def 	__snake_case(						self   :					Dict	,							*_UpperCamelCase   :					Dict	,							**_UpperCamelCase   :					Union[str, Any]      )					->				Union[str, Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       None
												if _tqdm_active:
																	return tqdm_lib.tqdm.set_lock(*_UpperCamelCase	,							**_UpperCamelCase      )





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



												'''simple docstring'''

												if _tqdm_active:
																	return tqdm_lib.tqdm.get_lock()



_lowerCamelCase					:   Union[str, Any]							=		_tqdm_cls()





def 	__lowerCamelCase							():
					global _tqdm_active
					return bool(_tqdm_active  )





def 	__lowerCamelCase							():
					global _tqdm_active
					SCREAMING_SNAKE_CASE    			=       True
					hf_hub_utils.enable_progress_bars()





def 	__lowerCamelCase							():
					global _tqdm_active
					SCREAMING_SNAKE_CASE    			=       False
					hf_hub_utils.disable_progress_bars()




