





from dataclasses import dataclass, field
from typing import Optional


@dataclass
class   A      :
			'''simple docstring'''




			__lowerCamelCase     :	Optional[str]          =   field(
			    default='''codeparrot/codeparrot'''       ,					metadata={'''help''': '''Model name or path of model to be trained.'''}   )
			__lowerCamelCase     :	Optional[str]          =   field(
			    default='''./'''       ,					metadata={'''help''': '''Save dir where model repo is cloned and models updates are saved to.'''}   )
			__lowerCamelCase     :	Optional[str]          =   field(
			    default='''codeparrot/codeparrot-clean-train'''       ,					metadata={'''help''': '''Name or path of training dataset.'''}   )
			__lowerCamelCase     :	Optional[str]          =   field(
			    default='''codeparrot/codeparrot-clean-valid'''       ,					metadata={'''help''': '''Name or path of validation dataset.'''}   )
			__lowerCamelCase     :	Optional[int]          =   field(default=2       ,					metadata={'''help''': '''Batch size for training.'''}   )
			__lowerCamelCase     :	Optional[int]          =   field(default=2       ,					metadata={'''help''': '''Batch size for evaluation.'''}   )
			__lowerCamelCase     :	Optional[float]          =   field(default=0.1       ,					metadata={'''help''': '''Value of weight decay.'''}   )
			__lowerCamelCase     :	Optional[int]          =   field(
			    default=10000       ,					metadata={'''help''': '''Size of buffer used to shuffle streaming dataset.'''}   )
			__lowerCamelCase     :	Optional[float]          =   field(default=2E-4       ,					metadata={'''help''': '''Learning rate fo training.'''}   )
			__lowerCamelCase     :	Optional[str]          =   field(default='''cosine'''       ,					metadata={'''help''': '''Learning rate.'''}   )
			__lowerCamelCase     :	Optional[int]          =   field(
			    default=750       ,					metadata={'''help''': '''Number of warmup steps in the learning rate schedule.'''}   )
			__lowerCamelCase     :	Optional[int]          =   field(
			    default=16       ,					metadata={'''help''': '''Number of gradient accumulation steps.'''}   )
			__lowerCamelCase     :	Optional[bool]          =   field(
			    default=SCREAMING_SNAKE_CASE       ,					metadata={'''help''': '''Use gradient checkpointing to reduce memory footprint.'''}   )
			__lowerCamelCase     :	Optional[int]          =   field(default=50000       ,					metadata={'''help''': '''Maximum number of training steps.'''}   )
			__lowerCamelCase     :	Optional[int]          =   field(
			    default=-1       ,					metadata={'''help''': '''Maximum number of evaluation steps. If -1 the full dataset is evaluated.'''}   )
			__lowerCamelCase     :	Optional[int]          =   field(default=1024       ,					metadata={'''help''': '''Sequence lengths used for training.'''}   )
			__lowerCamelCase     :	Optional[int]          =   field(default=1       ,					metadata={'''help''': '''Training seed.'''}   )
			__lowerCamelCase     :	Optional[int]          =   field(
			    default=1024       ,					metadata={'''help''': '''Interval to save checkpoints. Measured as number of forward passes not training steps.'''}       ,					)
			__lowerCamelCase     :	Optional[str]          =   field(
			    default=SCREAMING_SNAKE_CASE       ,					metadata={'''help''': '''States path if the training should continue from a checkpoint folder.'''}   )
			__lowerCamelCase     :	Optional[bool]          =   field(default=SCREAMING_SNAKE_CASE       ,					metadata={'''help''': '''If True the data is pretokenized.'''}   )


@dataclass
class   A      :
			'''simple docstring'''




			__lowerCamelCase     :	Optional[str]          =   field(
			    default='''codeparrot/codeparrot'''       ,					metadata={'''help''': '''Model name or path of model to be evaluated.'''}   )
			__lowerCamelCase     :	Optional[str]          =   field(
			    default='''codeparrot/codeparrot-clean-valid'''       ,					metadata={'''help''': '''Name or path of validation dataset.'''}   )
			__lowerCamelCase     :	Optional[int]          =   field(default=2       ,					metadata={'''help''': '''Batch size used for evaluation.'''}   )
			__lowerCamelCase     :	Optional[int]          =   field(
			    default=-1       ,					metadata={'''help''': '''Maximum number of evaluation steps. If -1 the full dataset is evaluated.'''}   )
			__lowerCamelCase     :	Optional[int]          =   field(default=1024       ,					metadata={'''help''': '''Length of sequences to be evaluated.'''}   )
			__lowerCamelCase     :	Optional[int]          =   field(default=1       ,					metadata={'''help''': '''Random seed used for evaluation.'''}   )


@dataclass
class   A      :
			'''simple docstring'''




			__lowerCamelCase     :	Optional[str]          =   field(
			    default='''codeparrot/codeparrot'''       ,					metadata={'''help''': '''Model name or path of model to be evaluated.'''}   )
			__lowerCamelCase     :	Optional[int]          =   field(default=SCREAMING_SNAKE_CASE       ,					metadata={'''help''': '''Number of workers used for code evaluation.'''}   )
			__lowerCamelCase     :	Optional[int]          =   field(
			    default=SCREAMING_SNAKE_CASE       ,					metadata={'''help''': '''The number of human-eval tasks to run. If not included all tasks are evaluated.'''}       ,					)
			__lowerCamelCase     :	Optional[bool]          =   field(
			    default=SCREAMING_SNAKE_CASE       ,					metadata={'''help''': '''Sample from the language model\'s output distribution.'''}   )
			__lowerCamelCase     :	Optional[float]          =   field(default=0.2       ,					metadata={'''help''': '''Sampling temperature used for generation.'''}   )
			__lowerCamelCase     :	Optional[int]          =   field(default=256       ,					metadata={'''help''': '''Maximum number of newly generated tokens.'''}   )
			__lowerCamelCase     :	Optional[int]          =   field(default=0       ,					metadata={'''help''': '''Top-k parameter used for generation.'''}   )
			__lowerCamelCase     :	Optional[float]          =   field(default=0.95       ,					metadata={'''help''': '''Top-p parameter used for nucleus sampling.'''}   )
			__lowerCamelCase     :	Optional[int]          =   field(default=10       ,					metadata={'''help''': '''Number of generations to run in parallel.'''}   )
			__lowerCamelCase     :	Optional[int]          =   field(
			    default=200       ,					metadata={'''help''': '''Number of completions to generate for each sample.'''}   )
			__lowerCamelCase     :	Optional[int]          =   field(default=1       ,					metadata={'''help''': '''Random seed used for evaluation.'''}   )
			__lowerCamelCase     :	Optional[str]          =   field(
			    default='''eval_results.json'''       ,					metadata={'''help''': '''Random seed used for evaluation.'''}   )
			__lowerCamelCase     :	Optional[str]          =   field(
			    default='''0'''       ,					metadata={'''help''': '''Allow `code_eval` to execute Python code on machine'''}   )
			__lowerCamelCase     :	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   A      :
			'''simple docstring'''




			__lowerCamelCase     :	Optional[int]          =   field(
			    default=SCREAMING_SNAKE_CASE       ,					metadata={
			        '''help''': '''The number of CPU cores to use for parallel preprocessing. Default uses the maximum available.'''
			    }       ,					)
			__lowerCamelCase     :	Optional[str]          =   field(
			    default='''transformersbook/codeparrot'''       ,					metadata={'''help''': '''Folder or name of dataset to process.'''}   )
			__lowerCamelCase     :	Optional[str]          =   field(
			    default='''codeparrot-clean'''       ,					metadata={'''help''': '''Folder to save processed processed dataset.'''}   )
			__lowerCamelCase     :	Optional[int]          =   field(
			    default=100000       ,					metadata={'''help''': '''Number of files to save per JSON output file.'''}   )
			__lowerCamelCase     :	Optional[str]          =   field(default='''content'''       ,					metadata={'''help''': '''Column containing text data to process.'''}   )
			__lowerCamelCase     :	Optional[float]          =   field(
			    default=1000       ,					metadata={'''help''': '''Maximum line length in file, otherwise file is filtered.'''}   )
			__lowerCamelCase     :	Optional[float]          =   field(
			    default=100       ,					metadata={'''help''': '''Maximum mean line length in file, otherwise file is filtered.'''}   )
			__lowerCamelCase     :	Optional[float]          =   field(
			    default=0.25       ,					metadata={'''help''': '''Maximum fraction of non-alphanumeric characters, otherwise file is filtered.'''}   )
			__lowerCamelCase     :	Optional[float]          =   field(
			    default=1.5       ,					metadata={'''help''': '''Minimum character token ratio for the file, otherwise file is filtered.'''}   )
			__lowerCamelCase     :	Optional[float]          =   field(
			    default=0.7       ,					metadata={'''help''': '''Probability for filtering config, test and uncommon files.'''}   )
			__lowerCamelCase     :	Optional[str]          =   field(
			    default='''codeparrot/codeparrot'''       ,					metadata={'''help''': '''Name or path to the tokenizer.'''}       ,					)
			__lowerCamelCase     :	Optional[bool]          =   field(
			    default=SCREAMING_SNAKE_CASE       ,					metadata={'''help''': '''If True, near-duplicate samples are removed.'''}   )
			__lowerCamelCase     :	Optional[float]          =   field(
			    default=0.85       ,					metadata={'''help''': '''Jaccard threshold for near-duplicate samples.'''}   )


@dataclass
class   A      :
			'''simple docstring'''




			__lowerCamelCase     :	Optional[str]          =   field(
			    default='''gpt2'''       ,					metadata={'''help''': '''Base tokenizer to build new tokenizer from.'''}   )
			__lowerCamelCase     :	Optional[str]          =   field(
			    default='''transformersbook/codeparrot-train'''       ,					metadata={'''help''': '''Dataset to train tokenizer on.'''}   )
			__lowerCamelCase     :	Optional[str]          =   field(default='''content'''       ,					metadata={'''help''': '''Column containing text data to process.'''}   )
			__lowerCamelCase     :	Optional[int]          =   field(default=200000       ,					metadata={'''help''': '''Number of examples to train tokenizer on.'''}   )
			__lowerCamelCase     :	Optional[int]          =   field(
			    default=32768       ,					metadata={'''help''': '''Number of examples to train the tokenizer on.'''}   )
			__lowerCamelCase     :	Optional[str]          =   field(default='''codeparrot'''       ,					metadata={'''help''': '''Name of new tokenizer.'''}   )
			__lowerCamelCase     :	Optional[bool]          =   field(default=SCREAMING_SNAKE_CASE       ,					metadata={'''help''': '''Push saved tokenizer to the hub.'''}   )


@dataclass
class   A      :
			'''simple docstring'''




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


@dataclass
class   A      :
			'''simple docstring'''




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










from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


A   : Optional[Any]			=				{'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMAEConfig''']}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A   : List[str]			=				[
					    '''VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''ViTMAEForPreTraining''',
					    '''ViTMAELayer''',
					    '''ViTMAEModel''',
					    '''ViTMAEPreTrainedModel''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A   : Any			=				[
					    '''TFViTMAEForPreTraining''',
					    '''TFViTMAEModel''',
					    '''TFViTMAEPreTrainedModel''',
					]

if TYPE_CHECKING:
					from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_vit_mae import (
										    VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ViTMAEForPreTraining,
										    ViTMAELayer,
										    ViTMAEModel,
										    ViTMAEPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
					import sys

					A   : str			=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



