


'''simple docstring'''





from dataclasses import dataclass, field
from typing import Optional

@dataclass
class 						A__							:
						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=__snake_case   ,     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=__snake_case   ,     metadata={"help": "States path if the training should continue from a checkpoint folder."}  )
						UpperCAmelCase             =						field(default=__snake_case   ,     metadata={"help": "If True the data is pretokenized."}  )

@dataclass
class 						A__							:
						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 						A__							:
						UpperCAmelCase             =						field(
						    default="codeparrot/codeparrot"   ,     metadata={"help": "Model name or path of model to be evaluated."}  )
						UpperCAmelCase             =						field(default=__snake_case   ,     metadata={"help": "Number of workers used for code evaluation."}  )
						UpperCAmelCase             =						field(
						    default=__snake_case   ,     metadata={"help": "The number of human-eval tasks to run. If not included all tasks are evaluated."}   ,     )
						UpperCAmelCase             =						field(
						    default=__snake_case   ,     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 						A__							:
						UpperCAmelCase             =						field(
						    default=__snake_case   ,     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=__snake_case   ,     metadata={"help": "If True, near-duplicate samples are removed."}  )
						UpperCAmelCase             =						field(
						    default=0.85   ,     metadata={"help": "Jaccard threshold for near-duplicate samples."}  )

@dataclass
class 						A__							:
						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=__snake_case   ,     metadata={"help": "Push saved tokenizer to the hub."}  )

@dataclass
class 						A__							:
						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=__snake_case   ,     metadata={"help": "Number of workers used for code evaluation."}  )

@dataclass
class 						A__							:
						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=__snake_case   ,     metadata={"help": "Push saved tokenizer to the hub."}  )



from typing import TYPE_CHECKING

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


snake_case_						:			Optional[Any]													=							{
    '''configuration_audio_spectrogram_transformer''': [
        '''AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''ASTConfig''',
    ]
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							snake_case_						:			Optional[Any]													=							[
							    '''AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''ASTForAudioClassification''',
							    '''ASTModel''',
							    '''ASTPreTrainedModel''',
							]

try:
							if not is_speech_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							snake_case_						:			List[Any]													=							['''ASTFeatureExtractor''']

if TYPE_CHECKING:
							from .configuration_audio_spectrogram_transformer import (
							    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    ASTConfig,
							)

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_audio_spectrogram_transformer import (
														    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ASTForAudioClassification,
														    ASTModel,
														    ASTPreTrainedModel,
														)

							try:
														if not is_speech_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor


else:
							import sys

							snake_case_						:			Optional[Any]													=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)