

from dataclasses import dataclass, field
from typing import Optional






@dataclass
class 	A_		:
							_UpperCAmelCase   :  Optional[str]										=				field(
							    default='''codeparrot/codeparrot'''      ,							metadata={'''help''': '''Model name or path of model to be trained.'''}    )
							_UpperCAmelCase   :  Optional[str]										=				field(
							    default='''./'''      ,							metadata={'''help''': '''Save dir where model repo is cloned and models updates are saved to.'''}    )
							_UpperCAmelCase   :  Optional[str]										=				field(
							    default='''codeparrot/codeparrot-clean-train'''      ,							metadata={'''help''': '''Name or path of training dataset.'''}    )
							_UpperCAmelCase   :  Optional[str]										=				field(
							    default='''codeparrot/codeparrot-clean-valid'''      ,							metadata={'''help''': '''Name or path of validation dataset.'''}    )
							_UpperCAmelCase   :  Optional[int]										=				field(default=2      ,							metadata={'''help''': '''Batch size for training.'''}    )
							_UpperCAmelCase   :  Optional[int]										=				field(default=2      ,							metadata={'''help''': '''Batch size for evaluation.'''}    )
							_UpperCAmelCase   :  Optional[float]										=				field(default=0.1      ,							metadata={'''help''': '''Value of weight decay.'''}    )
							_UpperCAmelCase   :  Optional[int]										=				field(
							    default=10_000      ,							metadata={'''help''': '''Size of buffer used to shuffle streaming dataset.'''}    )
							_UpperCAmelCase   :  Optional[float]										=				field(default=2E-4      ,							metadata={'''help''': '''Learning rate fo training.'''}    )
							_UpperCAmelCase   :  Optional[str]										=				field(default='''cosine'''      ,							metadata={'''help''': '''Learning rate.'''}    )
							_UpperCAmelCase   :  Optional[int]										=				field(
							    default=750      ,							metadata={'''help''': '''Number of warmup steps in the learning rate schedule.'''}    )
							_UpperCAmelCase   :  Optional[int]										=				field(
							    default=16      ,							metadata={'''help''': '''Number of gradient accumulation steps.'''}    )
							_UpperCAmelCase   :  Optional[bool]										=				field(
							    default=SCREAMING_SNAKE_CASE      ,							metadata={'''help''': '''Use gradient checkpointing to reduce memory footprint.'''}    )
							_UpperCAmelCase   :  Optional[int]										=				field(default=50_000      ,							metadata={'''help''': '''Maximum number of training steps.'''}    )
							_UpperCAmelCase   :  Optional[int]										=				field(
							    default=-1      ,							metadata={'''help''': '''Maximum number of evaluation steps. If -1 the full dataset is evaluated.'''}    )
							_UpperCAmelCase   :  Optional[int]										=				field(default=1_024      ,							metadata={'''help''': '''Sequence lengths used for training.'''}    )
							_UpperCAmelCase   :  Optional[int]										=				field(default=1      ,							metadata={'''help''': '''Training seed.'''}    )
							_UpperCAmelCase   :  Optional[int]										=				field(
							    default=1_024      ,							metadata={'''help''': '''Interval to save checkpoints. Measured as number of forward passes not training steps.'''}      ,							)
							_UpperCAmelCase   :  Optional[str]										=				field(
							    default=SCREAMING_SNAKE_CASE      ,							metadata={'''help''': '''States path if the training should continue from a checkpoint folder.'''}    )
							_UpperCAmelCase   :  Optional[bool]										=				field(default=SCREAMING_SNAKE_CASE      ,							metadata={'''help''': '''If True the data is pretokenized.'''}    )






@dataclass
class 	A_		:
							_UpperCAmelCase   :  Optional[str]										=				field(
							    default='''codeparrot/codeparrot'''      ,							metadata={'''help''': '''Model name or path of model to be evaluated.'''}    )
							_UpperCAmelCase   :  Optional[str]										=				field(
							    default='''codeparrot/codeparrot-clean-valid'''      ,							metadata={'''help''': '''Name or path of validation dataset.'''}    )
							_UpperCAmelCase   :  Optional[int]										=				field(default=2      ,							metadata={'''help''': '''Batch size used for evaluation.'''}    )
							_UpperCAmelCase   :  Optional[int]										=				field(
							    default=-1      ,							metadata={'''help''': '''Maximum number of evaluation steps. If -1 the full dataset is evaluated.'''}    )
							_UpperCAmelCase   :  Optional[int]										=				field(default=1_024      ,							metadata={'''help''': '''Length of sequences to be evaluated.'''}    )
							_UpperCAmelCase   :  Optional[int]										=				field(default=1      ,							metadata={'''help''': '''Random seed used for evaluation.'''}    )






@dataclass
class 	A_		:
							_UpperCAmelCase   :  Optional[str]										=				field(
							    default='''codeparrot/codeparrot'''      ,							metadata={'''help''': '''Model name or path of model to be evaluated.'''}    )
							_UpperCAmelCase   :  Optional[int]										=				field(default=SCREAMING_SNAKE_CASE      ,							metadata={'''help''': '''Number of workers used for code evaluation.'''}    )
							_UpperCAmelCase   :  Optional[int]										=				field(
							    default=SCREAMING_SNAKE_CASE      ,							metadata={'''help''': '''The number of human-eval tasks to run. If not included all tasks are evaluated.'''}      ,							)
							_UpperCAmelCase   :  Optional[bool]										=				field(
							    default=SCREAMING_SNAKE_CASE      ,							metadata={'''help''': '''Sample from the language model\'s output distribution.'''}    )
							_UpperCAmelCase   :  Optional[float]										=				field(default=0.2      ,							metadata={'''help''': '''Sampling temperature used for generation.'''}    )
							_UpperCAmelCase   :  Optional[int]										=				field(default=256      ,							metadata={'''help''': '''Maximum number of newly generated tokens.'''}    )
							_UpperCAmelCase   :  Optional[int]										=				field(default=0      ,							metadata={'''help''': '''Top-k parameter used for generation.'''}    )
							_UpperCAmelCase   :  Optional[float]										=				field(default=0.95      ,							metadata={'''help''': '''Top-p parameter used for nucleus sampling.'''}    )
							_UpperCAmelCase   :  Optional[int]										=				field(default=10      ,							metadata={'''help''': '''Number of generations to run in parallel.'''}    )
							_UpperCAmelCase   :  Optional[int]										=				field(
							    default=200      ,							metadata={'''help''': '''Number of completions to generate for each sample.'''}    )
							_UpperCAmelCase   :  Optional[int]										=				field(default=1      ,							metadata={'''help''': '''Random seed used for evaluation.'''}    )
							_UpperCAmelCase   :  Optional[str]										=				field(
							    default='''eval_results.json'''      ,							metadata={'''help''': '''Random seed used for evaluation.'''}    )
							_UpperCAmelCase   :  Optional[str]										=				field(
							    default='''0'''      ,							metadata={'''help''': '''Allow `code_eval` to execute Python code on machine'''}    )
							_UpperCAmelCase   :  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_		:
							_UpperCAmelCase   :  Optional[int]										=				field(
							    default=SCREAMING_SNAKE_CASE      ,							metadata={
							        '''help''': '''The number of CPU cores to use for parallel preprocessing. Default uses the maximum available.'''
							    }      ,							)
							_UpperCAmelCase   :  Optional[str]										=				field(
							    default='''transformersbook/codeparrot'''      ,							metadata={'''help''': '''Folder or name of dataset to process.'''}    )
							_UpperCAmelCase   :  Optional[str]										=				field(
							    default='''codeparrot-clean'''      ,							metadata={'''help''': '''Folder to save processed processed dataset.'''}    )
							_UpperCAmelCase   :  Optional[int]										=				field(
							    default=100_000      ,							metadata={'''help''': '''Number of files to save per JSON output file.'''}    )
							_UpperCAmelCase   :  Optional[str]										=				field(default='''content'''      ,							metadata={'''help''': '''Column containing text data to process.'''}    )
							_UpperCAmelCase   :  Optional[float]										=				field(
							    default=1_000      ,							metadata={'''help''': '''Maximum line length in file, otherwise file is filtered.'''}    )
							_UpperCAmelCase   :  Optional[float]										=				field(
							    default=100      ,							metadata={'''help''': '''Maximum mean line length in file, otherwise file is filtered.'''}    )
							_UpperCAmelCase   :  Optional[float]										=				field(
							    default=0.25      ,							metadata={'''help''': '''Maximum fraction of non-alphanumeric characters, otherwise file is filtered.'''}    )
							_UpperCAmelCase   :  Optional[float]										=				field(
							    default=1.5      ,							metadata={'''help''': '''Minimum character token ratio for the file, otherwise file is filtered.'''}    )
							_UpperCAmelCase   :  Optional[float]										=				field(
							    default=0.7      ,							metadata={'''help''': '''Probability for filtering config, test and uncommon files.'''}    )
							_UpperCAmelCase   :  Optional[str]										=				field(
							    default='''codeparrot/codeparrot'''      ,							metadata={'''help''': '''Name or path to the tokenizer.'''}      ,							)
							_UpperCAmelCase   :  Optional[bool]										=				field(
							    default=SCREAMING_SNAKE_CASE      ,							metadata={'''help''': '''If True, near-duplicate samples are removed.'''}    )
							_UpperCAmelCase   :  Optional[float]										=				field(
							    default=0.85      ,							metadata={'''help''': '''Jaccard threshold for near-duplicate samples.'''}    )






@dataclass
class 	A_		:
							_UpperCAmelCase   :  Optional[str]										=				field(
							    default='''gpt2'''      ,							metadata={'''help''': '''Base tokenizer to build new tokenizer from.'''}    )
							_UpperCAmelCase   :  Optional[str]										=				field(
							    default='''transformersbook/codeparrot-train'''      ,							metadata={'''help''': '''Dataset to train tokenizer on.'''}    )
							_UpperCAmelCase   :  Optional[str]										=				field(default='''content'''      ,							metadata={'''help''': '''Column containing text data to process.'''}    )
							_UpperCAmelCase   :  Optional[int]										=				field(default=200_000      ,							metadata={'''help''': '''Number of examples to train tokenizer on.'''}    )
							_UpperCAmelCase   :  Optional[int]										=				field(
							    default=32_768      ,							metadata={'''help''': '''Number of examples to train the tokenizer on.'''}    )
							_UpperCAmelCase   :  Optional[str]										=				field(default='''codeparrot'''      ,							metadata={'''help''': '''Name of new tokenizer.'''}    )
							_UpperCAmelCase   :  Optional[bool]										=				field(default=SCREAMING_SNAKE_CASE      ,							metadata={'''help''': '''Push saved tokenizer to the hub.'''}    )






@dataclass
class 	A_		:
							_UpperCAmelCase   :  Optional[str]										=				field(
							    default='''codeparrot/codeparrot'''      ,							metadata={'''help''': '''Name or path to the tokenizer.'''}    )
							_UpperCAmelCase   :  Optional[str]										=				field(
							    default='''codeparrot/codeparrot-clean-train'''      ,							metadata={'''help''': '''Name or path to the dataset to pretokenize.'''}    )
							_UpperCAmelCase   :  Optional[str]										=				field(
							    default='''tokenized-codeparrot-train'''      ,							metadata={'''help''': '''Repo name of the pretokenized data.'''}    )
							_UpperCAmelCase   :  Optional[int]										=				field(default=SCREAMING_SNAKE_CASE      ,							metadata={'''help''': '''Number of workers used for code evaluation.'''}    )






@dataclass
class 	A_		:
							_UpperCAmelCase   :  Optional[str]										=				field(
							    default='''gpt2-large'''      ,							metadata={'''help''': '''Configuration to use for model initialization.'''}    )
							_UpperCAmelCase   :  Optional[str]										=				field(
							    default='''codeparrot/codeparrot'''      ,							metadata={'''help''': '''Tokenizer attached to model.'''}    )
							_UpperCAmelCase   :  Optional[str]										=				field(default='''codeparrot'''      ,							metadata={'''help''': '''Name of the created model.'''}    )
							_UpperCAmelCase   :  Optional[bool]										=				field(default=SCREAMING_SNAKE_CASE      ,							metadata={'''help''': '''Push saved tokenizer to the hub.'''}    )







from typing import TYPE_CHECKING

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


a		={
    """configuration_roformer""": ["""ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RoFormerConfig""", """RoFormerOnnxConfig"""],
    """tokenization_roformer""": ["""RoFormerTokenizer"""],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=["""RoFormerTokenizerFast"""]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=[
					    """ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """RoFormerForCausalLM""",
					    """RoFormerForMaskedLM""",
					    """RoFormerForMultipleChoice""",
					    """RoFormerForQuestionAnswering""",
					    """RoFormerForSequenceClassification""",
					    """RoFormerForTokenClassification""",
					    """RoFormerLayer""",
					    """RoFormerModel""",
					    """RoFormerPreTrainedModel""",
					    """load_tf_weights_in_roformer""",
					]


try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=[
					    """TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFRoFormerForCausalLM""",
					    """TFRoFormerForMaskedLM""",
					    """TFRoFormerForMultipleChoice""",
					    """TFRoFormerForQuestionAnswering""",
					    """TFRoFormerForSequenceClassification""",
					    """TFRoFormerForTokenClassification""",
					    """TFRoFormerLayer""",
					    """TFRoFormerModel""",
					    """TFRoFormerPreTrainedModel""",
					]


try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=[
					    """FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """FlaxRoFormerForMaskedLM""",
					    """FlaxRoFormerForMultipleChoice""",
					    """FlaxRoFormerForQuestionAnswering""",
					    """FlaxRoFormerForSequenceClassification""",
					    """FlaxRoFormerForTokenClassification""",
					    """FlaxRoFormerModel""",
					    """FlaxRoFormerPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
					from .tokenization_roformer import RoFormerTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_roformer_fast import RoFormerTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_roformer import (
										    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    RoFormerForCausalLM,
										    RoFormerForMaskedLM,
										    RoFormerForMultipleChoice,
										    RoFormerForQuestionAnswering,
										    RoFormerForSequenceClassification,
										    RoFormerForTokenClassification,
										    RoFormerLayer,
										    RoFormerModel,
										    RoFormerPreTrainedModel,
										    load_tf_weights_in_roformer,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_roformer import (
										    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFRoFormerForCausalLM,
										    TFRoFormerForMaskedLM,
										    TFRoFormerForMultipleChoice,
										    TFRoFormerForQuestionAnswering,
										    TFRoFormerForSequenceClassification,
										    TFRoFormerForTokenClassification,
										    TFRoFormerLayer,
										    TFRoFormerModel,
										    TFRoFormerPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_roformer import (
										    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    FlaxRoFormerForMaskedLM,
										    FlaxRoFormerForMultipleChoice,
										    FlaxRoFormerForQuestionAnswering,
										    FlaxRoFormerForSequenceClassification,
										    FlaxRoFormerForTokenClassification,
										    FlaxRoFormerModel,
										    FlaxRoFormerPreTrainedModel,
										)


else:
					import sys

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




