


from dataclasses import dataclass, field
from typing import Optional


@dataclass
class        _a							:
							_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=10000     ,	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=750     ,	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=UpperCamelCase__     ,	metadata={'''help''': '''Use gradient checkpointing to reduce memory footprint.'''}			)
							_lowercase	:			Optional[int]        = field(default=50000     ,	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=1024     ,	metadata={'''help''': '''Sequence lengths used for training.'''}			)
							_lowercase	:			Optional[int]        = field(default=1     ,	metadata={'''help''': '''Training seed.'''}			)
							_lowercase	:			Optional[int]        = field(
							    default=1024     ,	metadata={'''help''': '''Interval to save checkpoints. Measured as number of forward passes not training steps.'''}     ,	)
							_lowercase	:			Optional[str]        = field(
							    default=UpperCamelCase__     ,	metadata={'''help''': '''States path if the training should continue from a checkpoint folder.'''}			)
							_lowercase	:			Optional[bool]        = field(default=UpperCamelCase__     ,	metadata={'''help''': '''If True the data is pretokenized.'''}			)


@dataclass
class        _a							:
							_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=1024     ,	metadata={'''help''': '''Length of sequences to be evaluated.'''}			)
							_lowercase	:			Optional[int]        = field(default=1     ,	metadata={'''help''': '''Random seed used for evaluation.'''}			)


@dataclass
class        _a							:
							_lowercase	:			Optional[str]        = field(
							    default='''codeparrot/codeparrot'''     ,	metadata={'''help''': '''Model name or path of model to be evaluated.'''}			)
							_lowercase	:			Optional[int]        = field(default=UpperCamelCase__     ,	metadata={'''help''': '''Number of workers used for code evaluation.'''}			)
							_lowercase	:			Optional[int]        = field(
							    default=UpperCamelCase__     ,	metadata={'''help''': '''The number of human-eval tasks to run. If not included all tasks are evaluated.'''}     ,	)
							_lowercase	:			Optional[bool]        = field(
							    default=UpperCamelCase__     ,	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=256     ,	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=0.95     ,	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=200     ,	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							:
							_lowercase	:			Optional[int]        = field(
							    default=UpperCamelCase__     ,	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=100000     ,	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=1000     ,	metadata={'''help''': '''Maximum line length in file, otherwise file is filtered.'''}			)
							_lowercase	:			Optional[float]        = field(
							    default=100     ,	metadata={'''help''': '''Maximum mean line length in file, otherwise file is filtered.'''}			)
							_lowercase	:			Optional[float]        = field(
							    default=0.25     ,	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=UpperCamelCase__     ,	metadata={'''help''': '''If True, near-duplicate samples are removed.'''}			)
							_lowercase	:			Optional[float]        = field(
							    default=0.85     ,	metadata={'''help''': '''Jaccard threshold for near-duplicate samples.'''}			)


@dataclass
class        _a							:
							_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=200000     ,	metadata={'''help''': '''Number of examples to train tokenizer on.'''}			)
							_lowercase	:			Optional[int]        = field(
							    default=32768     ,	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=UpperCamelCase__     ,	metadata={'''help''': '''Push saved tokenizer to the hub.'''}			)


@dataclass
class        _a							:
							_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=UpperCamelCase__     ,	metadata={'''help''': '''Number of workers used for code evaluation.'''}			)


@dataclass
class        _a							:
							_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=UpperCamelCase__     ,	metadata={'''help''': '''Push saved tokenizer to the hub.'''}			)






'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowerCamelCase							:   str  						=	{
    'configuration_rag': ['RagConfig'],
    'retrieval_rag': ['RagRetriever'],
    'tokenization_rag': ['RagTokenizer'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase							:   Dict  						=	[
     'RagModel',
     'RagPreTrainedModel',
     'RagSequenceForGeneration',
     'RagTokenForGeneration',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase							:   Optional[int]  						=	[
     'TFRagModel',
     'TFRagPreTrainedModel',
     'TFRagSequenceForGeneration',
     'TFRagTokenForGeneration',
 ]


if TYPE_CHECKING:
 from .configuration_rag import RagConfig
 from .retrieval_rag import RagRetriever
 from .tokenization_rag import RagTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_rag import (
      TFRagModel,
      TFRagPreTrainedModel,
      TFRagSequenceForGeneration,
      TFRagTokenForGeneration,
  )

else:
 import sys

 lowerCamelCase							:   Tuple  						=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




