






from dataclasses import dataclass, field
from typing import Optional
@dataclass
class     lowerCamelCase_					:
				a__       							=	field(
				    default='''codeparrot/codeparrot'''      ,					metadata={'''help''': '''Model name or path of model to be trained.'''}			)
				a__       							=	field(
				    default='''./'''      ,					metadata={'''help''': '''Save dir where model repo is cloned and models updates are saved to.'''}			)
				a__       							=	field(
				    default='''codeparrot/codeparrot-clean-train'''      ,					metadata={'''help''': '''Name or path of training dataset.'''}			)
				a__       							=	field(
				    default='''codeparrot/codeparrot-clean-valid'''      ,					metadata={'''help''': '''Name or path of validation dataset.'''}			)
				a__       							=	field(default=2      ,					metadata={'''help''': '''Batch size for training.'''}			)
				a__       							=	field(default=2      ,					metadata={'''help''': '''Batch size for evaluation.'''}			)
				a__       							=	field(default=0.1      ,					metadata={'''help''': '''Value of weight decay.'''}			)
				a__       							=	field(
				    default=1_00_00      ,					metadata={'''help''': '''Size of buffer used to shuffle streaming dataset.'''}			)
				a__       							=	field(default=2e-4      ,					metadata={'''help''': '''Learning rate fo training.'''}			)
				a__       							=	field(default='''cosine'''      ,					metadata={'''help''': '''Learning rate.'''}			)
				a__       							=	field(
				    default=7_50      ,					metadata={'''help''': '''Number of warmup steps in the learning rate schedule.'''}			)
				a__       							=	field(
				    default=16      ,					metadata={'''help''': '''Number of gradient accumulation steps.'''}			)
				a__       							=	field(
				    default=lowerCamelCase      ,					metadata={'''help''': '''Use gradient checkpointing to reduce memory footprint.'''}			)
				a__       							=	field(default=5_00_00      ,					metadata={'''help''': '''Maximum number of training steps.'''}			)
				a__       							=	field(
				    default=-1      ,					metadata={'''help''': '''Maximum number of evaluation steps. If -1 the full dataset is evaluated.'''}			)
				a__       							=	field(default=10_24      ,					metadata={'''help''': '''Sequence lengths used for training.'''}			)
				a__       							=	field(default=1      ,					metadata={'''help''': '''Training seed.'''}			)
				a__       							=	field(
				    default=10_24      ,					metadata={'''help''': '''Interval to save checkpoints. Measured as number of forward passes not training steps.'''}      ,					)
				a__       							=	field(
				    default=lowerCamelCase      ,					metadata={'''help''': '''States path if the training should continue from a checkpoint folder.'''}			)
				a__       							=	field(default=lowerCamelCase      ,					metadata={'''help''': '''If True the data is pretokenized.'''}			)







@dataclass
class     lowerCamelCase_					:
				a__       							=	field(
				    default='''codeparrot/codeparrot'''      ,					metadata={'''help''': '''Model name or path of model to be evaluated.'''}			)
				a__       							=	field(
				    default='''codeparrot/codeparrot-clean-valid'''      ,					metadata={'''help''': '''Name or path of validation dataset.'''}			)
				a__       							=	field(default=2      ,					metadata={'''help''': '''Batch size used for evaluation.'''}			)
				a__       							=	field(
				    default=-1      ,					metadata={'''help''': '''Maximum number of evaluation steps. If -1 the full dataset is evaluated.'''}			)
				a__       							=	field(default=10_24      ,					metadata={'''help''': '''Length of sequences to be evaluated.'''}			)
				a__       							=	field(default=1      ,					metadata={'''help''': '''Random seed used for evaluation.'''}			)







@dataclass
class     lowerCamelCase_					:
				a__       							=	field(
				    default='''codeparrot/codeparrot'''      ,					metadata={'''help''': '''Model name or path of model to be evaluated.'''}			)
				a__       							=	field(default=lowerCamelCase      ,					metadata={'''help''': '''Number of workers used for code evaluation.'''}			)
				a__       							=	field(
				    default=lowerCamelCase      ,					metadata={'''help''': '''The number of human-eval tasks to run. If not included all tasks are evaluated.'''}      ,					)
				a__       							=	field(
				    default=lowerCamelCase      ,					metadata={'''help''': '''Sample from the language model\'s output distribution.'''}			)
				a__       							=	field(default=0.2      ,					metadata={'''help''': '''Sampling temperature used for generation.'''}			)
				a__       							=	field(default=2_56      ,					metadata={'''help''': '''Maximum number of newly generated tokens.'''}			)
				a__       							=	field(default=0      ,					metadata={'''help''': '''Top-k parameter used for generation.'''}			)
				a__       							=	field(default=0.95      ,					metadata={'''help''': '''Top-p parameter used for nucleus sampling.'''}			)
				a__       							=	field(default=10      ,					metadata={'''help''': '''Number of generations to run in parallel.'''}			)
				a__       							=	field(
				    default=2_00      ,					metadata={'''help''': '''Number of completions to generate for each sample.'''}			)
				a__       							=	field(default=1      ,					metadata={'''help''': '''Random seed used for evaluation.'''}			)
				a__       							=	field(
				    default='''eval_results.json'''      ,					metadata={'''help''': '''Random seed used for evaluation.'''}			)
				a__       							=	field(
				    default='''0'''      ,					metadata={'''help''': '''Allow `code_eval` to execute Python code on machine'''}			)
				a__       							=	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     lowerCamelCase_					:
				a__       							=	field(
				    default=lowerCamelCase      ,					metadata={
				        '''help''': '''The number of CPU cores to use for parallel preprocessing. Default uses the maximum available.'''
				    }      ,					)
				a__       							=	field(
				    default='''transformersbook/codeparrot'''      ,					metadata={'''help''': '''Folder or name of dataset to process.'''}			)
				a__       							=	field(
				    default='''codeparrot-clean'''      ,					metadata={'''help''': '''Folder to save processed processed dataset.'''}			)
				a__       							=	field(
				    default=10_00_00      ,					metadata={'''help''': '''Number of files to save per JSON output file.'''}			)
				a__       							=	field(default='''content'''      ,					metadata={'''help''': '''Column containing text data to process.'''}			)
				a__       							=	field(
				    default=10_00      ,					metadata={'''help''': '''Maximum line length in file, otherwise file is filtered.'''}			)
				a__       							=	field(
				    default=1_00      ,					metadata={'''help''': '''Maximum mean line length in file, otherwise file is filtered.'''}			)
				a__       							=	field(
				    default=0.25      ,					metadata={'''help''': '''Maximum fraction of non-alphanumeric characters, otherwise file is filtered.'''}			)
				a__       							=	field(
				    default=1.5      ,					metadata={'''help''': '''Minimum character token ratio for the file, otherwise file is filtered.'''}			)
				a__       							=	field(
				    default=0.7      ,					metadata={'''help''': '''Probability for filtering config, test and uncommon files.'''}			)
				a__       							=	field(
				    default='''codeparrot/codeparrot'''      ,					metadata={'''help''': '''Name or path to the tokenizer.'''}      ,					)
				a__       							=	field(
				    default=lowerCamelCase      ,					metadata={'''help''': '''If True, near-duplicate samples are removed.'''}			)
				a__       							=	field(
				    default=0.85      ,					metadata={'''help''': '''Jaccard threshold for near-duplicate samples.'''}			)







@dataclass
class     lowerCamelCase_					:
				a__       							=	field(
				    default='''gpt2'''      ,					metadata={'''help''': '''Base tokenizer to build new tokenizer from.'''}			)
				a__       							=	field(
				    default='''transformersbook/codeparrot-train'''      ,					metadata={'''help''': '''Dataset to train tokenizer on.'''}			)
				a__       							=	field(default='''content'''      ,					metadata={'''help''': '''Column containing text data to process.'''}			)
				a__       							=	field(default=20_00_00      ,					metadata={'''help''': '''Number of examples to train tokenizer on.'''}			)
				a__       							=	field(
				    default=3_27_68      ,					metadata={'''help''': '''Number of examples to train the tokenizer on.'''}			)
				a__       							=	field(default='''codeparrot'''      ,					metadata={'''help''': '''Name of new tokenizer.'''}			)
				a__       							=	field(default=lowerCamelCase      ,					metadata={'''help''': '''Push saved tokenizer to the hub.'''}			)







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







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














from typing import TYPE_CHECKING

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


UpperCAmelCase__							  =						{
    "configuration_rag": ["RagConfig"],
    "retrieval_rag": ["RagRetriever"],
    "tokenization_rag": ["RagTokenizer"],
}

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

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase__							  =						[
         "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

     UpperCAmelCase__							  =						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


