


import datasets


__UpperCamelCase     		=							'''\
@InProceedings{conneau2018xnli,
  author = "Conneau, Alexis
                 and Rinott, Ruty
                 and Lample, Guillaume
                 and Williams, Adina
                 and Bowman, Samuel R.
                 and Schwenk, Holger
                 and Stoyanov, Veselin",
  title = "XNLI: Evaluating Cross-lingual Sentence Representations",
  booktitle = "Proceedings of the 2018 Conference on Empirical Methods
               in Natural Language Processing",
  year = "2018",
  publisher = "Association for Computational Linguistics",
  location = "Brussels, Belgium",
}
'''

__UpperCamelCase     		=							'''\
XNLI is a subset of a few thousand examples from MNLI which has been translated
into a 14 different languages (some low-ish resource). As with MNLI, the goal is
to predict textual entailment (does sentence A imply/contradict/neither sentence
B) and is a classification task (given two sentences, predict one of three
labels).
'''

__UpperCamelCase     		=							'''
Computes XNLI score which is just simple accuracy.
Args:
    predictions: Predicted labels.
    references: Ground truth labels.
Returns:
    \'accuracy\': accuracy
Examples:

    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> xnli_metric = datasets.load_metric("xnli")
    >>> results = xnli_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'accuracy\': 1.0}
'''
def 							UpperCamelCase_(					_A :Tuple					,  _A :List[Any]	)->		int:
			return (preds == labels).mean()







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






			"""simple docstring"""






			def 					snake_case__					(				self  ):

						'''simple docstring'''


						return datasets.MetricInfo(
						    description=_DESCRIPTION   ,    citation=_CITATION   ,    inputs_description=_KWARGS_DESCRIPTION   ,    features=datasets.Features(
						        {
						            "predictions": datasets.Value("int64" if self.config_name != "sts-b" else "float32"  ),
						            "references": datasets.Value("int64" if self.config_name != "sts-b" else "float32"  ),
						        }  )   ,    codebase_urls=[]   ,    reference_urls=[]   ,    format="numpy"   ,    )







			def 					snake_case__					(				self   ,    snake_case   ,    snake_case  ):

						'''simple docstring'''


						return {"accuracy": simple_accuracy(lowercase__   ,    lowercase__  )}



import argparse
from collections import defaultdict

import yaml


_lowercase:	List[Any]       			=		'''docs/source/en/_toctree.yml'''






def _lowerCamelCase  (		snake_case  ):
							_lowerCAmelCase							=							defaultdict(snake_case  )
							_lowerCAmelCase							=							[]
							_lowerCAmelCase							=							[]
							for doc in doc_list:
														if "local" in doc:
																					counts[doc["local"]] += 1

														if doc["title"].lower() == "overview":
																					overview_doc.append({'local': doc['local'], 'title': doc['title']}  )
														else:
																					new_doc_list.append(snake_case  )

							_lowerCAmelCase							=							new_doc_list
							_lowerCAmelCase							=							[key for key, value in counts.items() if value > 1]

							_lowerCAmelCase							=							[]
							for duplicate_key in duplicates:
														_lowerCAmelCase							=							list({doc['title'] for doc in doc_list if doc['local'] == duplicate_key}  )
														if len(snake_case  ) > 1:
																					raise ValueError(
																					    F'{duplicate_key} is present several times in the documentation table of content at '
																					    '`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '
																					    'others.'  )
														# Only add this once
														new_doc.append({'local': duplicate_key, 'title': titles[0]}  )

							# Add none duplicate-keys
							new_doc.extend([doc for doc in doc_list if 'local' not in counts or counts[doc['local']] == 1]  )
							_lowerCAmelCase							=							sorted(snake_case     , key=lambda snake_case  : s["title"].lower()  )

							# "overview" gets special treatment and is always first
							if len(snake_case  ) > 1:
														raise ValueError('{doc_list} has two \'overview\' docs which is not allowed.'  )

							overview_doc.extend(snake_case  )

							# Sort
							return overview_doc






def _lowerCamelCase  (		snake_case=False  ):
							with open(snake_case     , encoding='utf-8'  ) as f:
														_lowerCAmelCase							=							yaml.safe_load(f.read()  )

							# Get to the API doc
							_lowerCAmelCase							=							0
							while content[api_idx]["title"] != "API":
														api_idx += 1
							_lowerCAmelCase							=							content[api_idx]['sections']

							# Then to the model doc
							_lowerCAmelCase							=							0
							while api_doc[scheduler_idx]["title"] != "Schedulers":
														scheduler_idx += 1

							_lowerCAmelCase							=							api_doc[scheduler_idx]['sections']
							_lowerCAmelCase							=							clean_doc_toc(snake_case  )

							_lowerCAmelCase							=							False
							if new_scheduler_doc != scheduler_doc:
														_lowerCAmelCase							=							True
														if overwrite:
																					_lowerCAmelCase							=							new_scheduler_doc

							if diff:
														if overwrite:
																					_lowerCAmelCase							=							api_doc
																					with open(snake_case     , 'w'     , encoding='utf-8'  ) as f:
																												f.write(yaml.dump(snake_case     , allow_unicode=snake_case  )  )
														else:
																					raise ValueError(
																					    'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'  )






def _lowerCamelCase  (		snake_case=False  ):
							with open(snake_case     , encoding='utf-8'  ) as f:
														_lowerCAmelCase							=							yaml.safe_load(f.read()  )

							# Get to the API doc
							_lowerCAmelCase							=							0
							while content[api_idx]["title"] != "API":
														api_idx += 1
							_lowerCAmelCase							=							content[api_idx]['sections']

							# Then to the model doc
							_lowerCAmelCase							=							0
							while api_doc[pipeline_idx]["title"] != "Pipelines":
														pipeline_idx += 1

							_lowerCAmelCase							=							False
							_lowerCAmelCase							=							api_doc[pipeline_idx]['sections']
							_lowerCAmelCase							=							[]

							# sort sub pipeline docs
							for pipeline_doc in pipeline_docs:
														if "section" in pipeline_doc:
																					_lowerCAmelCase							=							pipeline_doc['section']
																					_lowerCAmelCase							=							clean_doc_toc(snake_case  )
																					if overwrite:
																												_lowerCAmelCase							=							new_sub_pipeline_doc
														new_pipeline_docs.append(snake_case  )

							# sort overall pipeline doc
							_lowerCAmelCase							=							clean_doc_toc(snake_case  )

							if new_pipeline_docs != pipeline_docs:
														_lowerCAmelCase							=							True
														if overwrite:
																					_lowerCAmelCase							=							new_pipeline_docs

							if diff:
														if overwrite:
																					_lowerCAmelCase							=							api_doc
																					with open(snake_case     , 'w'     , encoding='utf-8'  ) as f:
																												f.write(yaml.dump(snake_case     , allow_unicode=snake_case  )  )
														else:
																					raise ValueError(
																					    'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'  )


if __name__ == "__main__":
				_lowercase:	Tuple       			=		argparse.ArgumentParser()
				parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
				_lowercase:	Optional[Any]       			=		parser.parse_args()

				check_scheduler_doc(args.fix_and_overwrite)
				check_pipeline_doc(args.fix_and_overwrite)






