




from dataclasses import dataclass, field
from typing import Optional
@dataclass
class 			a__    :
						_a							:    Optional[str]					=    field(
						    default="""codeparrot/codeparrot""" ,				metadata={"""help""": """Model name or path of model to be trained."""}      )
						_a							:    Optional[str]					=    field(
						    default="""./""" ,				metadata={"""help""": """Save dir where model repo is cloned and models updates are saved to."""}      )
						_a							:    Optional[str]					=    field(
						    default="""codeparrot/codeparrot-clean-train""" ,				metadata={"""help""": """Name or path of training dataset."""}      )
						_a							:    Optional[str]					=    field(
						    default="""codeparrot/codeparrot-clean-valid""" ,				metadata={"""help""": """Name or path of validation dataset."""}      )
						_a							:    Optional[int]					=    field(default=2 ,				metadata={"""help""": """Batch size for training."""}      )
						_a							:    Optional[int]					=    field(default=2 ,				metadata={"""help""": """Batch size for evaluation."""}      )
						_a							:    Optional[float]					=    field(default=0.1 ,				metadata={"""help""": """Value of weight decay."""}      )
						_a							:    Optional[int]					=    field(
						    default=1_0_0_0_0 ,				metadata={"""help""": """Size of buffer used to shuffle streaming dataset."""}      )
						_a							:    Optional[float]					=    field(default=2E-4 ,				metadata={"""help""": """Learning rate fo training."""}      )
						_a							:    Optional[str]					=    field(default="""cosine""" ,				metadata={"""help""": """Learning rate."""}      )
						_a							:    Optional[int]					=    field(
						    default=7_5_0 ,				metadata={"""help""": """Number of warmup steps in the learning rate schedule."""}      )
						_a							:    Optional[int]					=    field(
						    default=1_6 ,				metadata={"""help""": """Number of gradient accumulation steps."""}      )
						_a							:    Optional[bool]					=    field(
						    default=UpperCamelCase__ ,				metadata={"""help""": """Use gradient checkpointing to reduce memory footprint."""}      )
						_a							:    Optional[int]					=    field(default=5_0_0_0_0 ,				metadata={"""help""": """Maximum number of training steps."""}      )
						_a							:    Optional[int]					=    field(
						    default=-1 ,				metadata={"""help""": """Maximum number of evaluation steps. If -1 the full dataset is evaluated."""}      )
						_a							:    Optional[int]					=    field(default=1_0_2_4 ,				metadata={"""help""": """Sequence lengths used for training."""}      )
						_a							:    Optional[int]					=    field(default=1 ,				metadata={"""help""": """Training seed."""}      )
						_a							:    Optional[int]					=    field(
						    default=1_0_2_4 ,				metadata={"""help""": """Interval to save checkpoints. Measured as number of forward passes not training steps."""} ,				)
						_a							:    Optional[str]					=    field(
						    default=UpperCamelCase__ ,				metadata={"""help""": """States path if the training should continue from a checkpoint folder."""}      )
						_a							:    Optional[bool]					=    field(default=UpperCamelCase__ ,				metadata={"""help""": """If True the data is pretokenized."""}      )






@dataclass
class 			a__    :
						_a							:    Optional[str]					=    field(
						    default="""codeparrot/codeparrot""" ,				metadata={"""help""": """Model name or path of model to be evaluated."""}      )
						_a							:    Optional[str]					=    field(
						    default="""codeparrot/codeparrot-clean-valid""" ,				metadata={"""help""": """Name or path of validation dataset."""}      )
						_a							:    Optional[int]					=    field(default=2 ,				metadata={"""help""": """Batch size used for evaluation."""}      )
						_a							:    Optional[int]					=    field(
						    default=-1 ,				metadata={"""help""": """Maximum number of evaluation steps. If -1 the full dataset is evaluated."""}      )
						_a							:    Optional[int]					=    field(default=1_0_2_4 ,				metadata={"""help""": """Length of sequences to be evaluated."""}      )
						_a							:    Optional[int]					=    field(default=1 ,				metadata={"""help""": """Random seed used for evaluation."""}      )






@dataclass
class 			a__    :
						_a							:    Optional[str]					=    field(
						    default="""codeparrot/codeparrot""" ,				metadata={"""help""": """Model name or path of model to be evaluated."""}      )
						_a							:    Optional[int]					=    field(default=UpperCamelCase__ ,				metadata={"""help""": """Number of workers used for code evaluation."""}      )
						_a							:    Optional[int]					=    field(
						    default=UpperCamelCase__ ,				metadata={"""help""": """The number of human-eval tasks to run. If not included all tasks are evaluated."""} ,				)
						_a							:    Optional[bool]					=    field(
						    default=UpperCamelCase__ ,				metadata={"""help""": """Sample from the language model\'s output distribution."""}      )
						_a							:    Optional[float]					=    field(default=0.2 ,				metadata={"""help""": """Sampling temperature used for generation."""}      )
						_a							:    Optional[int]					=    field(default=2_5_6 ,				metadata={"""help""": """Maximum number of newly generated tokens."""}      )
						_a							:    Optional[int]					=    field(default=0 ,				metadata={"""help""": """Top-k parameter used for generation."""}      )
						_a							:    Optional[float]					=    field(default=0.95 ,				metadata={"""help""": """Top-p parameter used for nucleus sampling."""}      )
						_a							:    Optional[int]					=    field(default=1_0 ,				metadata={"""help""": """Number of generations to run in parallel."""}      )
						_a							:    Optional[int]					=    field(
						    default=2_0_0 ,				metadata={"""help""": """Number of completions to generate for each sample."""}      )
						_a							:    Optional[int]					=    field(default=1 ,				metadata={"""help""": """Random seed used for evaluation."""}      )
						_a							:    Optional[str]					=    field(
						    default="""eval_results.json""" ,				metadata={"""help""": """Random seed used for evaluation."""}      )
						_a							:    Optional[str]					=    field(
						    default="""0""" ,				metadata={"""help""": """Allow `code_eval` to execute Python code on machine"""}      )
						_a							:    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__    :
						_a							:    Optional[int]					=    field(
						    default=UpperCamelCase__ ,				metadata={
						        """help""": """The number of CPU cores to use for parallel preprocessing. Default uses the maximum available."""
						    } ,				)
						_a							:    Optional[str]					=    field(
						    default="""transformersbook/codeparrot""" ,				metadata={"""help""": """Folder or name of dataset to process."""}      )
						_a							:    Optional[str]					=    field(
						    default="""codeparrot-clean""" ,				metadata={"""help""": """Folder to save processed processed dataset."""}      )
						_a							:    Optional[int]					=    field(
						    default=1_0_0_0_0_0 ,				metadata={"""help""": """Number of files to save per JSON output file."""}      )
						_a							:    Optional[str]					=    field(default="""content""" ,				metadata={"""help""": """Column containing text data to process."""}      )
						_a							:    Optional[float]					=    field(
						    default=1_0_0_0 ,				metadata={"""help""": """Maximum line length in file, otherwise file is filtered."""}      )
						_a							:    Optional[float]					=    field(
						    default=1_0_0 ,				metadata={"""help""": """Maximum mean line length in file, otherwise file is filtered."""}      )
						_a							:    Optional[float]					=    field(
						    default=0.25 ,				metadata={"""help""": """Maximum fraction of non-alphanumeric characters, otherwise file is filtered."""}      )
						_a							:    Optional[float]					=    field(
						    default=1.5 ,				metadata={"""help""": """Minimum character token ratio for the file, otherwise file is filtered."""}      )
						_a							:    Optional[float]					=    field(
						    default=0.7 ,				metadata={"""help""": """Probability for filtering config, test and uncommon files."""}      )
						_a							:    Optional[str]					=    field(
						    default="""codeparrot/codeparrot""" ,				metadata={"""help""": """Name or path to the tokenizer."""} ,				)
						_a							:    Optional[bool]					=    field(
						    default=UpperCamelCase__ ,				metadata={"""help""": """If True, near-duplicate samples are removed."""}      )
						_a							:    Optional[float]					=    field(
						    default=0.85 ,				metadata={"""help""": """Jaccard threshold for near-duplicate samples."""}      )






@dataclass
class 			a__    :
						_a							:    Optional[str]					=    field(
						    default="""gpt2""" ,				metadata={"""help""": """Base tokenizer to build new tokenizer from."""}      )
						_a							:    Optional[str]					=    field(
						    default="""transformersbook/codeparrot-train""" ,				metadata={"""help""": """Dataset to train tokenizer on."""}      )
						_a							:    Optional[str]					=    field(default="""content""" ,				metadata={"""help""": """Column containing text data to process."""}      )
						_a							:    Optional[int]					=    field(default=2_0_0_0_0_0 ,				metadata={"""help""": """Number of examples to train tokenizer on."""}      )
						_a							:    Optional[int]					=    field(
						    default=3_2_7_6_8 ,				metadata={"""help""": """Number of examples to train the tokenizer on."""}      )
						_a							:    Optional[str]					=    field(default="""codeparrot""" ,				metadata={"""help""": """Name of new tokenizer."""}      )
						_a							:    Optional[bool]					=    field(default=UpperCamelCase__ ,				metadata={"""help""": """Push saved tokenizer to the hub."""}      )






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






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








import inspect
import re

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
lowerCAmelCase       =							'src/transformers'


# This is to make sure the transformers module imported is the one in the repo.
lowerCAmelCase       =							direct_transformers_import(PATH_TO_TRANSFORMERS)

lowerCAmelCase       =							transformers.models.auto.configuration_auto.CONFIG_MAPPING

# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
lowerCAmelCase       =							re.compile(R'\[(.+?)\]\((https://huggingface\.co/.+?)\)')


lowerCAmelCase       =							{
    'DecisionTransformerConfig',
    'EncoderDecoderConfig',
    'MusicgenConfig',
    'RagConfig',
    'SpeechEncoderDecoderConfig',
    'TimmBackboneConfig',
    'VisionEncoderDecoderConfig',
    'VisionTextDualEncoderConfig',
    'LlamaConfig',
}
def 			_a (      SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		lowercase__            =      None

		# source code of `config_class`
		lowercase__            =      inspect.getsource(SCREAMING_SNAKE_CASE     )
		lowercase__            =      _re_checkpoint.findall(SCREAMING_SNAKE_CASE     )

		# Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
		# For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
		for ckpt_name, ckpt_link in checkpoints:
				# allow the link to end with `/`
				if ckpt_link.endswith('''/'''     ):
						lowercase__            =      ckpt_link[:-1]

				# verify the checkpoint name corresponds to the checkpoint link
				lowercase__            =      f'https://huggingface.co/{ckpt_name}'
				if ckpt_link == ckpt_link_from_name:
						lowercase__            =      ckpt_name
						break

		return checkpoint
def 			_a (      ):


		"""simple docstring"""




		lowercase__            =      []

		for config_class in list(CONFIG_MAPPING.values()     ):
				# Skip deprecated models
				if "models.deprecated" in config_class.__module__:
						continue
				lowercase__            =      get_checkpoint_from_config_class(SCREAMING_SNAKE_CASE     )

				lowercase__            =      config_class.__name__
				if checkpoint is None and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
						configs_without_checkpoint.append(SCREAMING_SNAKE_CASE     )

		if len(SCREAMING_SNAKE_CASE     ) > 0:
				lowercase__            =      '''\n'''.join(sorted(SCREAMING_SNAKE_CASE     )     )
				raise ValueError(f'The following configurations don\'t contain any valid checkpoint:\n{message}'     )


if __name__ == "__main__":
			check_config_docstrings_have_checkpoints()


