





from dataclasses import dataclass, field
from typing import Optional

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

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

@dataclass
class   A							:
      _snake_case				  =field(
          default='''codeparrot/codeparrot'''		,   metadata={'''help''': '''Model name or path of model to be evaluated.'''}    )
      _snake_case				  =field(default=__lowercase		,   metadata={'''help''': '''Number of workers used for code evaluation.'''}    )
      _snake_case				  =field(
          default=__lowercase		,   metadata={'''help''': '''The number of human-eval tasks to run. If not included all tasks are evaluated.'''}		,   )
      _snake_case				  =field(
          default=__lowercase		,   metadata={'''help''': '''Sample from the language model\'s output distribution.'''}    )
      _snake_case				  =field(default=0.2		,   metadata={'''help''': '''Sampling temperature used for generation.'''}    )
      _snake_case				  =field(default=256		,   metadata={'''help''': '''Maximum number of newly generated tokens.'''}    )
      _snake_case				  =field(default=0		,   metadata={'''help''': '''Top-k parameter used for generation.'''}    )
      _snake_case				  =field(default=0.95		,   metadata={'''help''': '''Top-p parameter used for nucleus sampling.'''}    )
      _snake_case				  =field(default=10		,   metadata={'''help''': '''Number of generations to run in parallel.'''}    )
      _snake_case				  =field(
          default=200		,   metadata={'''help''': '''Number of completions to generate for each sample.'''}    )
      _snake_case				  =field(default=1		,   metadata={'''help''': '''Random seed used for evaluation.'''}    )
      _snake_case				  =field(
          default='''eval_results.json'''		,   metadata={'''help''': '''Random seed used for evaluation.'''}    )
      _snake_case				  =field(
          default='''0'''		,   metadata={'''help''': '''Allow `code_eval` to execute Python code on machine'''}    )
      _snake_case				  =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							:
      _snake_case				  =field(
          default=__lowercase		,   metadata={
              '''help''': '''The number of CPU cores to use for parallel preprocessing. Default uses the maximum available.'''
          }		,   )
      _snake_case				  =field(
          default='''transformersbook/codeparrot'''		,   metadata={'''help''': '''Folder or name of dataset to process.'''}    )
      _snake_case				  =field(
          default='''codeparrot-clean'''		,   metadata={'''help''': '''Folder to save processed processed dataset.'''}    )
      _snake_case				  =field(
          default=100000		,   metadata={'''help''': '''Number of files to save per JSON output file.'''}    )
      _snake_case				  =field(default='''content'''		,   metadata={'''help''': '''Column containing text data to process.'''}    )
      _snake_case				  =field(
          default=1000		,   metadata={'''help''': '''Maximum line length in file, otherwise file is filtered.'''}    )
      _snake_case				  =field(
          default=100		,   metadata={'''help''': '''Maximum mean line length in file, otherwise file is filtered.'''}    )
      _snake_case				  =field(
          default=0.25		,   metadata={'''help''': '''Maximum fraction of non-alphanumeric characters, otherwise file is filtered.'''}    )
      _snake_case				  =field(
          default=1.5		,   metadata={'''help''': '''Minimum character token ratio for the file, otherwise file is filtered.'''}    )
      _snake_case				  =field(
          default=0.7		,   metadata={'''help''': '''Probability for filtering config, test and uncommon files.'''}    )
      _snake_case				  =field(
          default='''codeparrot/codeparrot'''		,   metadata={'''help''': '''Name or path to the tokenizer.'''}		,   )
      _snake_case				  =field(
          default=__lowercase		,   metadata={'''help''': '''If True, near-duplicate samples are removed.'''}    )
      _snake_case				  =field(
          default=0.85		,   metadata={'''help''': '''Jaccard threshold for near-duplicate samples.'''}    )

@dataclass
class   A							:
      _snake_case				  =field(
          default='''gpt2'''		,   metadata={'''help''': '''Base tokenizer to build new tokenizer from.'''}    )
      _snake_case				  =field(
          default='''transformersbook/codeparrot-train'''		,   metadata={'''help''': '''Dataset to train tokenizer on.'''}    )
      _snake_case				  =field(default='''content'''		,   metadata={'''help''': '''Column containing text data to process.'''}    )
      _snake_case				  =field(default=200000		,   metadata={'''help''': '''Number of examples to train tokenizer on.'''}    )
      _snake_case				  =field(
          default=32768		,   metadata={'''help''': '''Number of examples to train the tokenizer on.'''}    )
      _snake_case				  =field(default='''codeparrot'''		,   metadata={'''help''': '''Name of new tokenizer.'''}    )
      _snake_case				  =field(default=__lowercase		,   metadata={'''help''': '''Push saved tokenizer to the hub.'''}    )

@dataclass
class   A							:
      _snake_case				  =field(
          default='''codeparrot/codeparrot'''		,   metadata={'''help''': '''Name or path to the tokenizer.'''}    )
      _snake_case				  =field(
          default='''codeparrot/codeparrot-clean-train'''		,   metadata={'''help''': '''Name or path to the dataset to pretokenize.'''}    )
      _snake_case				  =field(
          default='''tokenized-codeparrot-train'''		,   metadata={'''help''': '''Repo name of the pretokenized data.'''}    )
      _snake_case				  =field(default=__lowercase		,   metadata={'''help''': '''Number of workers used for code evaluation.'''}    )

@dataclass
class   A							:
      _snake_case				  =field(
          default='''gpt2-large'''		,   metadata={'''help''': '''Configuration to use for model initialization.'''}    )
      _snake_case				  =field(
          default='''codeparrot/codeparrot'''		,   metadata={'''help''': '''Tokenizer attached to model.'''}    )
      _snake_case				  =field(default='''codeparrot'''		,   metadata={'''help''': '''Name of the created model.'''}    )
      _snake_case				  =field(default=__lowercase		,   metadata={'''help''': '''Push saved tokenizer to the hub.'''}    )








'''simple docstring'''
from typing import TYPE_CHECKING

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


lowerCAmelCase   : List[Any]             =				{
    """configuration_albert""": ["""ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """AlbertConfig""", """AlbertOnnxConfig"""],
}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase   : Any             =				["""AlbertTokenizer"""]

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase   : Optional[Any]             =				["""AlbertTokenizerFast"""]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase   : List[str]             =				[
					    """ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """AlbertForMaskedLM""",
					    """AlbertForMultipleChoice""",
					    """AlbertForPreTraining""",
					    """AlbertForQuestionAnswering""",
					    """AlbertForSequenceClassification""",
					    """AlbertForTokenClassification""",
					    """AlbertModel""",
					    """AlbertPreTrainedModel""",
					    """load_tf_weights_in_albert""",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase   : Tuple             =				[
					    """TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFAlbertForMaskedLM""",
					    """TFAlbertForMultipleChoice""",
					    """TFAlbertForPreTraining""",
					    """TFAlbertForQuestionAnswering""",
					    """TFAlbertForSequenceClassification""",
					    """TFAlbertForTokenClassification""",
					    """TFAlbertMainLayer""",
					    """TFAlbertModel""",
					    """TFAlbertPreTrainedModel""",
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase   : Optional[int]             =				[
					    """FlaxAlbertForMaskedLM""",
					    """FlaxAlbertForMultipleChoice""",
					    """FlaxAlbertForPreTraining""",
					    """FlaxAlbertForQuestionAnswering""",
					    """FlaxAlbertForSequenceClassification""",
					    """FlaxAlbertForTokenClassification""",
					    """FlaxAlbertModel""",
					    """FlaxAlbertPreTrainedModel""",
					]

if TYPE_CHECKING:
					from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_albert import AlbertTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_albert_fast import AlbertTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_albert import (
										    ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    AlbertForMaskedLM,
										    AlbertForMultipleChoice,
										    AlbertForPreTraining,
										    AlbertForQuestionAnswering,
										    AlbertForSequenceClassification,
										    AlbertForTokenClassification,
										    AlbertModel,
										    AlbertPreTrainedModel,
										    load_tf_weights_in_albert,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_albert import (
										    TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFAlbertForMaskedLM,
										    TFAlbertForMultipleChoice,
										    TFAlbertForPreTraining,
										    TFAlbertForQuestionAnswering,
										    TFAlbertForSequenceClassification,
										    TFAlbertForTokenClassification,
										    TFAlbertMainLayer,
										    TFAlbertModel,
										    TFAlbertPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_albert import (
										    FlaxAlbertForMaskedLM,
										    FlaxAlbertForMultipleChoice,
										    FlaxAlbertForPreTraining,
										    FlaxAlbertForQuestionAnswering,
										    FlaxAlbertForSequenceClassification,
										    FlaxAlbertForTokenClassification,
										    FlaxAlbertModel,
										    FlaxAlbertPreTrainedModel,
										)
else:
					import sys

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


