




from dataclasses import dataclass, field
from typing import Optional







@dataclass
class 						A:



 '''simple docstring'''


 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=1_0000							,   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=UpperCAmelCase__							,   metadata={'''help''': '''Use gradient checkpointing to reduce memory footprint.'''}     )
 UpperCamelCase		     =		field(default=5_0000							,   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=UpperCAmelCase__							,   metadata={'''help''': '''States path if the training should continue from a checkpoint folder.'''}     )
 UpperCamelCase		     =		field(default=UpperCAmelCase__							,   metadata={'''help''': '''If True the data is pretokenized.'''}     )







@dataclass
class 						A:



 '''simple docstring'''


 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:



 '''simple docstring'''


 UpperCamelCase		     =		field(
     default='''codeparrot/codeparrot'''							,   metadata={'''help''': '''Model name or path of model to be evaluated.'''}     )
 UpperCamelCase		     =		field(default=UpperCAmelCase__							,   metadata={'''help''': '''Number of workers used for code evaluation.'''}     )
 UpperCamelCase		     =		field(
     default=UpperCAmelCase__							,   metadata={'''help''': '''The number of human-eval tasks to run. If not included all tasks are evaluated.'''}							,   )
 UpperCamelCase		     =		field(
     default=UpperCAmelCase__							,   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:



 '''simple docstring'''


 UpperCamelCase		     =		field(
     default=UpperCAmelCase__							,   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=10_0000							,   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=UpperCAmelCase__							,   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:



 '''simple docstring'''


 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=20_0000							,   metadata={'''help''': '''Number of examples to train tokenizer on.'''}     )
 UpperCamelCase		     =		field(
     default=3_2768							,   metadata={'''help''': '''Number of examples to train the tokenizer on.'''}     )
 UpperCamelCase		     =		field(default='''codeparrot'''							,   metadata={'''help''': '''Name of new tokenizer.'''}     )
 UpperCamelCase		     =		field(default=UpperCAmelCase__							,   metadata={'''help''': '''Push saved tokenizer to the hub.'''}     )







@dataclass
class 						A:



 '''simple docstring'''


 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=UpperCAmelCase__							,   metadata={'''help''': '''Number of workers used for code evaluation.'''}     )







@dataclass
class 						A:



 '''simple docstring'''


 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=UpperCAmelCase__							,   metadata={'''help''': '''Push saved tokenizer to the hub.'''}     )









from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate







@dataclass(frozen=UpperCamelCase     )
class 						A(		UpperCamelCase     ):



 '''simple docstring'''


 UpperCamelCase		     =		field(default='''language-modeling'''							,   metadata={'''include_in_asdict_even_if_is_default''': True}     )

 UpperCamelCase		     =		Features({'''text''': Value('''string'''     )}     )
 UpperCamelCase		     =		Features({}     )
 UpperCamelCase		     =		"text"

 @property
 def        a__				(       self      :						List[Any]						)     ->			Dict[str, str]:

       """simple docstring"""






       return {self.text_column: "text"}



