






'''simple docstring'''



from dataclasses import dataclass, field
from typing import Optional
@dataclass
class UpperCAmelCase_					:




      """simple docstring"""




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





@dataclass
class UpperCAmelCase_					:




      """simple docstring"""




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





@dataclass
class UpperCAmelCase_					:




      """simple docstring"""




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




      """simple docstring"""




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





@dataclass
class UpperCAmelCase_					:




      """simple docstring"""




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





@dataclass
class UpperCAmelCase_					:




      """simple docstring"""




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





@dataclass
class UpperCAmelCase_					:




      """simple docstring"""




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











'''simple docstring'''



from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE						=	{
    'configuration_mega': ['MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegaConfig', 'MegaOnnxConfig'],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE						=	[
          'MEGA_PRETRAINED_MODEL_ARCHIVE_LIST',
          'MegaForCausalLM',
          'MegaForMaskedLM',
          'MegaForMultipleChoice',
          'MegaForQuestionAnswering',
          'MegaForSequenceClassification',
          'MegaForTokenClassification',
          'MegaModel',
          'MegaPreTrainedModel',
      ]

if TYPE_CHECKING:
      from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mega import (
                MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
                MegaForCausalLM,
                MegaForMaskedLM,
                MegaForMultipleChoice,
                MegaForQuestionAnswering,
                MegaForSequenceClassification,
                MegaForTokenClassification,
                MegaModel,
                MegaPreTrainedModel,
            )

else:
      import sys

      SCREAMING_SNAKE_CASE						=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



