
from dataclasses import dataclass, field
from typing import Optional

@dataclass
class     _UpperCamelCase			:






			'''simple docstring'''

			__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=_A       ,    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=_A       ,    metadata={"""help""": """States path if the training should continue from a checkpoint folder."""}					)
			__UpperCamelCase :  Optional[bool]    =	field(default=_A       ,    metadata={"""help""": """If True the data is pretokenized."""}					)




@dataclass
class     _UpperCamelCase			:






			'''simple docstring'''

			__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     _UpperCamelCase			:






			'''simple docstring'''

			__UpperCamelCase :  Optional[str]    =	field(
			    default="""codeparrot/codeparrot"""       ,    metadata={"""help""": """Model name or path of model to be evaluated."""}					)
			__UpperCamelCase :  Optional[int]    =	field(default=_A       ,    metadata={"""help""": """Number of workers used for code evaluation."""}					)
			__UpperCamelCase :  Optional[int]    =	field(
			    default=_A       ,    metadata={"""help""": """The number of human-eval tasks to run. If not included all tasks are evaluated."""}       ,    )
			__UpperCamelCase :  Optional[bool]    =	field(
			    default=_A       ,    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     _UpperCamelCase			:






			'''simple docstring'''

			__UpperCamelCase :  Optional[int]    =	field(
			    default=_A       ,    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=_A       ,    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     _UpperCamelCase			:






			'''simple docstring'''

			__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=_A       ,    metadata={"""help""": """Push saved tokenizer to the hub."""}					)




@dataclass
class     _UpperCamelCase			:






			'''simple docstring'''

			__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=_A       ,    metadata={"""help""": """Number of workers used for code evaluation."""}					)




@dataclass
class     _UpperCamelCase			:






			'''simple docstring'''

			__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=_A       ,    metadata={"""help""": """Push saved tokenizer to the hub."""}					)








from typing import TYPE_CHECKING

from ...utils import _LazyModule


lowerCamelCase_  :	Dict 				=					{"""tokenization_byt5""": ["""ByT5Tokenizer"""]}


if TYPE_CHECKING:
							from .tokenization_byta import ByTaTokenizer
else:
							import sys

							lowerCamelCase_  :	Any 				=					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






