


'''simple docstring'''



from dataclasses import dataclass, field
from typing import Optional






@dataclass
class 			UpperCamelCase__:
				__magic_name__							: int    						=			field(
				    default="codeparrot/codeparrot"		,							metadata={"help": "Model name or path of model to be trained."}			)
				__magic_name__							: Tuple    						=			field(
				    default="./"		,							metadata={"help": "Save dir where model repo is cloned and models updates are saved to."}			)
				__magic_name__							: str    						=			field(
				    default="codeparrot/codeparrot-clean-train"		,							metadata={"help": "Name or path of training dataset."}			)
				__magic_name__							: List[str]    						=			field(
				    default="codeparrot/codeparrot-clean-valid"		,							metadata={"help": "Name or path of validation dataset."}			)
				__magic_name__							: str    						=			field(default=2		,							metadata={"help": "Batch size for training."}			)
				__magic_name__							: Tuple    						=			field(default=2		,							metadata={"help": "Batch size for evaluation."}			)
				__magic_name__							: Optional[Any]    						=			field(default=0.1		,							metadata={"help": "Value of weight decay."}			)
				__magic_name__							: Any    						=			field(
				    default=1_0000		,							metadata={"help": "Size of buffer used to shuffle streaming dataset."}			)
				__magic_name__							: List[Any]    						=			field(default=2e-4		,							metadata={"help": "Learning rate fo training."}			)
				__magic_name__							: List[str]    						=			field(default="cosine"		,							metadata={"help": "Learning rate."}			)
				__magic_name__							: Union[str, Any]    						=			field(
				    default=750		,							metadata={"help": "Number of warmup steps in the learning rate schedule."}			)
				__magic_name__							: Any    						=			field(
				    default=16		,							metadata={"help": "Number of gradient accumulation steps."}			)
				__magic_name__							: List[str]    						=			field(
				    default=__snake_case		,							metadata={"help": "Use gradient checkpointing to reduce memory footprint."}			)
				__magic_name__							: List[Any]    						=			field(default=5_0000		,							metadata={"help": "Maximum number of training steps."}			)
				__magic_name__							: Optional[Any]    						=			field(
				    default=-1		,							metadata={"help": "Maximum number of evaluation steps. If -1 the full dataset is evaluated."}			)
				__magic_name__							: Tuple    						=			field(default=1024		,							metadata={"help": "Sequence lengths used for training."}			)
				__magic_name__							: Union[str, Any]    						=			field(default=1		,							metadata={"help": "Training seed."}			)
				__magic_name__							: List[Any]    						=			field(
				    default=1024		,							metadata={"help": "Interval to save checkpoints. Measured as number of forward passes not training steps."}		,							)
				__magic_name__							: List[Any]    						=			field(
				    default=__snake_case		,							metadata={"help": "States path if the training should continue from a checkpoint folder."}			)
				__magic_name__							: str    						=			field(default=__snake_case		,							metadata={"help": "If True the data is pretokenized."}			)







@dataclass
class 			UpperCamelCase__:
				__magic_name__							: int    						=			field(
				    default="codeparrot/codeparrot"		,							metadata={"help": "Model name or path of model to be evaluated."}			)
				__magic_name__							: Dict    						=			field(
				    default="codeparrot/codeparrot-clean-valid"		,							metadata={"help": "Name or path of validation dataset."}			)
				__magic_name__							: Union[str, Any]    						=			field(default=2		,							metadata={"help": "Batch size used for evaluation."}			)
				__magic_name__							: Tuple    						=			field(
				    default=-1		,							metadata={"help": "Maximum number of evaluation steps. If -1 the full dataset is evaluated."}			)
				__magic_name__							: List[str]    						=			field(default=1024		,							metadata={"help": "Length of sequences to be evaluated."}			)
				__magic_name__							: List[str]    						=			field(default=1		,							metadata={"help": "Random seed used for evaluation."}			)







@dataclass
class 			UpperCamelCase__:
				__magic_name__							: Optional[Any]    						=			field(
				    default="codeparrot/codeparrot"		,							metadata={"help": "Model name or path of model to be evaluated."}			)
				__magic_name__							: str    						=			field(default=__snake_case		,							metadata={"help": "Number of workers used for code evaluation."}			)
				__magic_name__							: List[str]    						=			field(
				    default=__snake_case		,							metadata={"help": "The number of human-eval tasks to run. If not included all tasks are evaluated."}		,							)
				__magic_name__							: Union[str, Any]    						=			field(
				    default=__snake_case		,							metadata={"help": "Sample from the language model's output distribution."}			)
				__magic_name__							: Optional[Any]    						=			field(default=0.2		,							metadata={"help": "Sampling temperature used for generation."}			)
				__magic_name__							: Dict    						=			field(default=256		,							metadata={"help": "Maximum number of newly generated tokens."}			)
				__magic_name__							: Union[str, Any]    						=			field(default=0		,							metadata={"help": "Top-k parameter used for generation."}			)
				__magic_name__							: Dict    						=			field(default=0.9_5		,							metadata={"help": "Top-p parameter used for nucleus sampling."}			)
				__magic_name__							: Dict    						=			field(default=10		,							metadata={"help": "Number of generations to run in parallel."}			)
				__magic_name__							: Optional[int]    						=			field(
				    default=200		,							metadata={"help": "Number of completions to generate for each sample."}			)
				__magic_name__							: Tuple    						=			field(default=1		,							metadata={"help": "Random seed used for evaluation."}			)
				__magic_name__							: Optional[int]    						=			field(
				    default="eval_results.json"		,							metadata={"help": "Random seed used for evaluation."}			)
				__magic_name__							: int    						=			field(
				    default="0"		,							metadata={"help": "Allow `code_eval` to execute Python code on machine"}			)
				__magic_name__							: 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 			UpperCamelCase__:
				__magic_name__							: str    						=			field(
				    default=__snake_case		,							metadata={
				        "help": "The number of CPU cores to use for parallel preprocessing. Default uses the maximum available."
				    }		,							)
				__magic_name__							: Optional[int]    						=			field(
				    default="transformersbook/codeparrot"		,							metadata={"help": "Folder or name of dataset to process."}			)
				__magic_name__							: Any    						=			field(
				    default="codeparrot-clean"		,							metadata={"help": "Folder to save processed processed dataset."}			)
				__magic_name__							: Optional[Any]    						=			field(
				    default=10_0000		,							metadata={"help": "Number of files to save per JSON output file."}			)
				__magic_name__							: Union[str, Any]    						=			field(default="content"		,							metadata={"help": "Column containing text data to process."}			)
				__magic_name__							: Optional[int]    						=			field(
				    default=1000		,							metadata={"help": "Maximum line length in file, otherwise file is filtered."}			)
				__magic_name__							: Optional[Any]    						=			field(
				    default=100		,							metadata={"help": "Maximum mean line length in file, otherwise file is filtered."}			)
				__magic_name__							: Dict    						=			field(
				    default=0.2_5		,							metadata={"help": "Maximum fraction of non-alphanumeric characters, otherwise file is filtered."}			)
				__magic_name__							: Optional[int]    						=			field(
				    default=1.5		,							metadata={"help": "Minimum character token ratio for the file, otherwise file is filtered."}			)
				__magic_name__							: Dict    						=			field(
				    default=0.7		,							metadata={"help": "Probability for filtering config, test and uncommon files."}			)
				__magic_name__							: Optional[int]    						=			field(
				    default="codeparrot/codeparrot"		,							metadata={"help": "Name or path to the tokenizer."}		,							)
				__magic_name__							: List[str]    						=			field(
				    default=__snake_case		,							metadata={"help": "If True, near-duplicate samples are removed."}			)
				__magic_name__							: List[Any]    						=			field(
				    default=0.8_5		,							metadata={"help": "Jaccard threshold for near-duplicate samples."}			)







@dataclass
class 			UpperCamelCase__:
				__magic_name__							: List[str]    						=			field(
				    default="gpt2"		,							metadata={"help": "Base tokenizer to build new tokenizer from."}			)
				__magic_name__							: List[str]    						=			field(
				    default="transformersbook/codeparrot-train"		,							metadata={"help": "Dataset to train tokenizer on."}			)
				__magic_name__							: Tuple    						=			field(default="content"		,							metadata={"help": "Column containing text data to process."}			)
				__magic_name__							: int    						=			field(default=20_0000		,							metadata={"help": "Number of examples to train tokenizer on."}			)
				__magic_name__							: Dict    						=			field(
				    default=3_2768		,							metadata={"help": "Number of examples to train the tokenizer on."}			)
				__magic_name__							: Optional[Any]    						=			field(default="codeparrot"		,							metadata={"help": "Name of new tokenizer."}			)
				__magic_name__							: Optional[int]    						=			field(default=__snake_case		,							metadata={"help": "Push saved tokenizer to the hub."}			)







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







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







from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase_						:     Optional[int]	       =							logging.get_logger(__name__)




class   __lowercase						(						__snake_case       ):
	_A       =   "timm_backbone"






	def __init__(self				: Any      ,	snake_case				: List[Any]=None      ,	snake_case				: int=3      ,	snake_case				: Dict=True      ,	snake_case				: Union[str, Any]=True      ,	snake_case				: Any=None      ,	**snake_case				: int      ,	) ->			Optional[int]:
							super().__init__(**snake_case    )
							_lowercase       :	Dict           =   backbone
							_lowercase       :	Optional[Any]           =   num_channels
							_lowercase       :	Union[str, Any]           =   features_only
							_lowercase       :	Tuple           =   use_pretrained_backbone
							_lowercase       :	List[str]           =   True
							_lowercase       :	Tuple           =   out_indices if out_indices is not None else (-1,)




