



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
UpperCAmelCase_							:      Union[str, Any]      ="src/transformers"


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

UpperCAmelCase_							:      Dict      =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)`
UpperCAmelCase_							:      Dict      =re.compile(R"\[(.+?)\]\((https://huggingface\.co/.+?)\)")


UpperCAmelCase_							:      str      ={
    "DecisionTransformerConfig",
    "EncoderDecoderConfig",
    "MusicgenConfig",
    "RagConfig",
    "SpeechEncoderDecoderConfig",
    "TimmBackboneConfig",
    "VisionEncoderDecoderConfig",
    "VisionTextDualEncoderConfig",
    "LlamaConfig",
}
def  UpperCamelCase						(       _A			:				int    )->       Optional[Any]:





						"""simple docstring"""





						A__							=       None

						# source code of `config_class`
						A__							=       inspect.getsource(_A    )
						A__							=       _re_checkpoint.findall(_A    )

						# 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("/"    ):
																		A__							=       ckpt_link[:-1]

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

						return checkpoint
def  UpperCamelCase						(       )->       Any:





						"""simple docstring"""





						A__							=       []

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

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

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


if __name__ == "__main__":
						check_config_docstrings_have_checkpoints()










import datasets

from .evaluate import evaluate


UpperCAmelCase_							:      List[Any]      =  "\\n@inproceedings{Rajpurkar2016SQuAD10,\n  title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n  author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n  booktitle={EMNLP},\n  year={2016}\n}\n"

UpperCAmelCase_							:      Any      =  "\nThis metric wrap the official scoring script for version 1 of the Stanford Question Answering Dataset (SQuAD).\n\nStanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by\ncrowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,\nfrom the corresponding reading passage, or the question might be unanswerable.\n"

UpperCAmelCase_							:      Tuple      =  "\nComputes SQuAD scores (F1 and EM).\nArgs:\n    predictions: List of question-answers dictionaries with the following key-values:\n        - 'id': id of the question-answer pair as given in the references (see below)\n        - 'prediction_text': the text of the answer\n    references: List of question-answers dictionaries with the following key-values:\n        - 'id': id of the question-answer pair (see above),\n        - 'answers': a Dict in the SQuAD dataset format\n            {\n                'text': list of possible texts for the answer, as a list of strings\n                'answer_start': list of start positions for the answer, as a list of ints\n            }\n            Note that answer_start values are not taken into account to compute the metric.\nReturns:\n    'exact_match': Exact match (the normalized answer exactly match the gold answer)\n    'f1': The F-score of predicted tokens versus the gold answer\nExamples:\n\n    >>> predictions = [{'prediction_text': '1976', 'id': '56e10a3be3433e1400422b22'}]\n    >>> references = [{'answers': {'answer_start': [97], 'text': ['1976']}, 'id': '56e10a3be3433e1400422b22'}]\n    >>> squad_metric = datasets.load_metric(\"squad\")\n    >>> results = squad_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'exact_match': 100.0, 'f1': 100.0}\n"






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION       ,    _KWARGS_DESCRIPTION     )
class      UpperCamelCase    (						datasets.Metric     ):



	def 			__A							(       self		):
							return datasets.MetricInfo(
							    description=_DESCRIPTION			,	citation=_CITATION			,	inputs_description=_KWARGS_DESCRIPTION			,	features=datasets.Features(
							        {
							            "predictions": {"id": datasets.Value("string"		), "prediction_text": datasets.Value("string"		)},
							            "references": {
							                "id": datasets.Value("string"		),
							                "answers": datasets.features.Sequence(
							                    {
							                        "text": datasets.Value("string"		),
							                        "answer_start": datasets.Value("int32"		),
							                    }		),
							            },
							        }		)			,	codebase_urls=["https://rajpurkar.github.io/SQuAD-explorer/"]			,	reference_urls=["https://rajpurkar.github.io/SQuAD-explorer/"]			,	)



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__		):
							A__							=       {prediction["id"]: prediction["prediction_text"] for prediction in predictions}
							A__							=       [
							    {
							        "paragraphs": [
							            {
							                "qas": [
							                    {
							                        "answers": [{"text": answer_text} for answer_text in ref["answers"]["text"]],
							                        "id": ref["id"],
							                    }
							                    for ref in references
							                ]
							            }
							        ]
							    }
							]
							A__							=       evaluate(dataset=UpperCAmelCase__			,	predictions=UpperCAmelCase__		)
							return score





