from scipy.stats import spearmanr

import datasets


_a     						=			'\nThe Spearman rank-order correlation coefficient is a measure of the\nrelationship between two datasets. Like other correlation coefficients,\nthis one varies between -1 and +1 with 0 implying no correlation.\nPositive correlations imply that as data in dataset x increases, so\ndoes data in dataset y. Negative correlations imply that as x increases,\ny decreases. Correlations of -1 or +1 imply an exact monotonic relationship.\n\nUnlike the Pearson correlation, the Spearman correlation does not\nassume that both datasets are normally distributed.\n\nThe p-value roughly indicates the probability of an uncorrelated system\nproducing datasets that have a Spearman correlation at least as extreme\nas the one computed from these datasets. The p-values are not entirely\nreliable but are probably reasonable for datasets larger than 500 or so.\n'

_a     						=			'\nArgs:\n    predictions (`List[float]`): Predicted labels, as returned by a model.\n    references (`List[float]`): Ground truth labels.\n    return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns\n            only the spearmanr score. Defaults to `False`.\nReturns:\n    spearmanr (`float`): Spearman correlation coefficient.\n    p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.\nExamples:\n    Example 1:\n        >>> spearmanr_metric = datasets.load_metric("spearmanr")\n        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])\n        >>> print(results)\n        {\'spearmanr\': -0.7}\n\n    Example 2:\n        >>> spearmanr_metric = datasets.load_metric("spearmanr")\n        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],\n        ...                                     predictions=[10, 9, 2.5, 6, 4],\n        ...                                     return_pvalue=True)\n        >>> print(results[\'spearmanr\'])\n        -0.7\n        >>> print(round(results[\'spearmanr_pvalue\'], 2))\n        0.19\n'

_a     						=			r'\\n@book{kokoska2000crc,\n  title={CRC standard probability and statistics tables and formulae},\n  author={Kokoska, Stephen and Zwillinger, Daniel},\n  year={2000},\n  publisher={Crc Press}\n}\n@article{2020SciPy-NMeth,\n  author  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and\n            Haberland, Matt and Reddy, Tyler and Cournapeau, David and\n            Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and\n            Bright, Jonathan and {van der Walt}, St{\'e}fan J. and\n            Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and\n            Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and\n            Kern, Robert and Larson, Eric and Carey, C J and\n            Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and\n            {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and\n            Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and\n            Harris, Charles R. and Archibald, Anne M. and\n            Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and\n            {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},\n  title   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific\n            Computing in Python}},\n  journal = {Nature Methods},\n  year    = {2020},\n  volume  = {17},\n  pages   = {261--272},\n  adsurl  = {https://rdcu.be/b08Wh},\n  doi     = {10.1038/s41592-019-0686-2},\n}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	,					_KWARGS_DESCRIPTION						)
class     a_			(						datasets.Metric						):






 '''simple docstring'''






 def 		__snake_case						(      self :  Tuple):







        '''simple docstring'''



        return datasets.MetricInfo(
            description=_DESCRIPTION      ,   citation=_CITATION      ,   inputs_description=_KWARGS_DESCRIPTION      ,   features=datasets.Features(
                {
                    'predictions': datasets.Value('float'),
                    'references': datasets.Value('float'),
                })      ,   reference_urls=['https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html']      ,   )




 def 		__snake_case						(      self :  int      ,   lowercase__ :  str      ,   lowercase__ :  List[str]      ,   lowercase__ :  Any=False):







        '''simple docstring'''



        lowerCAmelCase__					     = spearmanr(lowercase__      ,   lowercase__)
        if return_pvalue:
               return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
        else:
               return {"spearmanr": results[0]}

import argparse
from collections import defaultdict

import yaml


lowerCAmelCase__     						=			'docs/source/en/_toctree.yml'






def 				__lowerCamelCase		(						lowerCAmelCase__     ):
       lowerCAmelCase__					     = defaultdict(lowerCAmelCase__     )
       for doc in model_doc:
              counts[doc["local"]] += 1
       lowerCAmelCase__					     = [key for key, value in counts.items() if value > 1]

       lowerCAmelCase__					     = []
       for duplicate_key in duplicates:
              lowerCAmelCase__					     = list({doc['title'] for doc in model_doc if doc['local'] == duplicate_key}     )
              if len(lowerCAmelCase__     ) > 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 model_doc if counts[doc['local']] == 1]     )

       # Sort
       return sorted(lowerCAmelCase__     ,  key=lambda lowerCAmelCase__     : s["title"].lower()     )






def 				__lowerCamelCase		(						lowerCAmelCase__=False     ):
       with open(lowerCAmelCase__     ,  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[model_idx]["title"] != "Models":
              model_idx += 1

       lowerCAmelCase__					     = api_doc[model_idx]['sections']

       lowerCAmelCase__					     = [(idx, section) for idx, section in enumerate(lowerCAmelCase__     ) if 'sections' in section]
       lowerCAmelCase__					     = False
       for idx, modality_doc in modalities_docs:
              lowerCAmelCase__					     = modality_doc['sections']
              lowerCAmelCase__					     = clean_model_doc_toc(lowerCAmelCase__     )

              if old_modality_doc != new_modality_doc:
                     lowerCAmelCase__					     = True
                     if overwrite:
                            lowerCAmelCase__					     = new_modality_doc

       if diff:
              if overwrite:
                     lowerCAmelCase__					     = model_doc
                     lowerCAmelCase__					     = api_doc
                     with open(lowerCAmelCase__     ,  'w'     ,  encoding='utf-8'     ) as f:
                            f.write(yaml.dump(lowerCAmelCase__     ,  allow_unicode=lowerCAmelCase__     )     )
              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__":
    lowerCAmelCase__     						=			argparse.ArgumentParser()
    parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
    lowerCAmelCase__     						=			parser.parse_args()

    check_model_doc(args.fix_and_overwrite)
