


"""simple docstring"""
from typing import TYPE_CHECKING

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


__SCREAMING_SNAKE_CASE							:      Optional[int]						=      {
    'configuration_distilbert': [
        'DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'DistilBertConfig',
        'DistilBertOnnxConfig',
    ],
    'tokenization_distilbert': ['DistilBertTokenizer'],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE							:      Dict						=      ['DistilBertTokenizerFast']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE							:      Union[str, Any]						=      [
				    'DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'DistilBertForMaskedLM',
				    'DistilBertForMultipleChoice',
				    'DistilBertForQuestionAnswering',
				    'DistilBertForSequenceClassification',
				    'DistilBertForTokenClassification',
				    'DistilBertModel',
				    'DistilBertPreTrainedModel',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE							:      List[str]						=      [
				    'TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'TFDistilBertForMaskedLM',
				    'TFDistilBertForMultipleChoice',
				    'TFDistilBertForQuestionAnswering',
				    'TFDistilBertForSequenceClassification',
				    'TFDistilBertForTokenClassification',
				    'TFDistilBertMainLayer',
				    'TFDistilBertModel',
				    'TFDistilBertPreTrainedModel',
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE							:      Dict						=      [
				    'FlaxDistilBertForMaskedLM',
				    'FlaxDistilBertForMultipleChoice',
				    'FlaxDistilBertForQuestionAnswering',
				    'FlaxDistilBertForSequenceClassification',
				    'FlaxDistilBertForTokenClassification',
				    'FlaxDistilBertModel',
				    'FlaxDistilBertPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_distilbert import (
				    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    DistilBertConfig,
				    DistilBertOnnxConfig,
				)
				from .tokenization_distilbert import DistilBertTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_distilbert_fast import DistilBertTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_distilbert import (
								    DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    DistilBertForMaskedLM,
								    DistilBertForMultipleChoice,
								    DistilBertForQuestionAnswering,
								    DistilBertForSequenceClassification,
								    DistilBertForTokenClassification,
								    DistilBertModel,
								    DistilBertPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_distilbert import (
								    TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFDistilBertForMaskedLM,
								    TFDistilBertForMultipleChoice,
								    TFDistilBertForQuestionAnswering,
								    TFDistilBertForSequenceClassification,
								    TFDistilBertForTokenClassification,
								    TFDistilBertMainLayer,
								    TFDistilBertModel,
								    TFDistilBertPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_distilbert import (
								    FlaxDistilBertForMaskedLM,
								    FlaxDistilBertForMultipleChoice,
								    FlaxDistilBertForQuestionAnswering,
								    FlaxDistilBertForSequenceClassification,
								    FlaxDistilBertForTokenClassification,
								    FlaxDistilBertModel,
								    FlaxDistilBertPreTrainedModel,
								)

else:
				import sys

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







"""simple docstring"""
from dataclasses import dataclass, field
from typing import Optional





@dataclass
class        __A       :





							'''simple docstring'''

							__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=1_00_00  ,						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=7_50  ,						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=snake_case__  ,						metadata={"""help""": """Use gradient checkpointing to reduce memory footprint."""})
							__lowercase:						Optional[int]							 =      field(default=5_00_00  ,						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=10_24  ,						metadata={"""help""": """Sequence lengths used for training."""})
							__lowercase:						Optional[int]							 =      field(default=1  ,						metadata={"""help""": """Training seed."""})
							__lowercase:						Optional[int]							 =      field(
							    default=10_24  ,						metadata={"""help""": """Interval to save checkpoints. Measured as number of forward passes not training steps."""}  ,						)
							__lowercase:						Optional[str]							 =      field(
							    default=snake_case__  ,						metadata={"""help""": """States path if the training should continue from a checkpoint folder."""})
							__lowercase:						Optional[bool]							 =      field(default=snake_case__  ,						metadata={"""help""": """If True the data is pretokenized."""})





@dataclass
class        __A       :





							'''simple docstring'''

							__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=10_24  ,						metadata={"""help""": """Length of sequences to be evaluated."""})
							__lowercase:						Optional[int]							 =      field(default=1  ,						metadata={"""help""": """Random seed used for evaluation."""})





@dataclass
class        __A       :





							'''simple docstring'''

							__lowercase:						Optional[str]							 =      field(
							    default="""codeparrot/codeparrot"""  ,						metadata={"""help""": """Model name or path of model to be evaluated."""})
							__lowercase:						Optional[int]							 =      field(default=snake_case__  ,						metadata={"""help""": """Number of workers used for code evaluation."""})
							__lowercase:						Optional[int]							 =      field(
							    default=snake_case__  ,						metadata={"""help""": """The number of human-eval tasks to run. If not included all tasks are evaluated."""}  ,						)
							__lowercase:						Optional[bool]							 =      field(
							    default=snake_case__  ,						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=2_56  ,						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=0.9_5  ,						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=2_00  ,						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        __A       :





							'''simple docstring'''

							__lowercase:						Optional[int]							 =      field(
							    default=snake_case__  ,						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=10_00_00  ,						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=10_00  ,						metadata={"""help""": """Maximum line length in file, otherwise file is filtered."""})
							__lowercase:						Optional[float]							 =      field(
							    default=1_00  ,						metadata={"""help""": """Maximum mean line length in file, otherwise file is filtered."""})
							__lowercase:						Optional[float]							 =      field(
							    default=0.2_5  ,						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=snake_case__  ,						metadata={"""help""": """If True, near-duplicate samples are removed."""})
							__lowercase:						Optional[float]							 =      field(
							    default=0.8_5  ,						metadata={"""help""": """Jaccard threshold for near-duplicate samples."""})





@dataclass
class        __A       :





							'''simple docstring'''

							__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=20_00_00  ,						metadata={"""help""": """Number of examples to train tokenizer on."""})
							__lowercase:						Optional[int]							 =      field(
							    default=3_27_68  ,						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=snake_case__  ,						metadata={"""help""": """Push saved tokenizer to the hub."""})





@dataclass
class        __A       :





							'''simple docstring'''

							__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=snake_case__  ,						metadata={"""help""": """Number of workers used for code evaluation."""})





@dataclass
class        __A       :





							'''simple docstring'''

							__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=snake_case__  ,						metadata={"""help""": """Push saved tokenizer to the hub."""})



