




"""simple docstring"""







import argparse
from collections import defaultdict

import yaml


__A   :  Optional[int]					=      'docs/source/en/_toctree.yml'

def 						snake_case__       (					_lowerCamelCase				)			->Union[str, Any]:






		"""simple docstring"""


		__lowercase				:		Optional[Any]												=				defaultdict(_lowerCamelCase				)
		__lowercase				:		str												=				[]
		__lowercase				:		List[str]												=				[]
		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(_lowerCamelCase				)

		__lowercase				:		Dict												=				new_doc_list
		__lowercase				:		Optional[Any]												=				[key for key, value in counts.items() if value > 1]

		__lowercase				:		Optional[Any]												=				[]
		for duplicate_key in duplicates:
				__lowercase				:		List[Any]												=				list({doc["title"] for doc in doc_list 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 doc_list if "local" not in counts or counts[doc["local"]] == 1]				)
		__lowercase				:		List[str]												=				sorted(_lowerCamelCase,  key=lambda _lowerCamelCase				: s["title"].lower()				)

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

		overview_doc.extend(_lowerCamelCase				)

		# Sort
		return overview_doc

def 						snake_case__       (					_lowerCamelCase=False				)			->int:






		"""simple docstring"""


		with open(_lowerCamelCase,  encoding="utf-8"				) as f:
				__lowercase				:		Any												=				yaml.safe_load(f.read()				)

		# Get to the API doc
		__lowercase				:		Optional[int]												=				0
		while content[api_idx]["title"] != "API":
				api_idx += 1
		__lowercase				:		List[str]												=				content[api_idx]["sections"]

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

		__lowercase				:		str												=				api_doc[scheduler_idx]["sections"]
		__lowercase				:		List[str]												=				clean_doc_toc(_lowerCamelCase				)

		__lowercase				:		str												=				False
		if new_scheduler_doc != scheduler_doc:
				__lowercase				:		Dict												=				True
				if overwrite:
						__lowercase				:		str												=				new_scheduler_doc

		if diff:
				if overwrite:
						__lowercase				:		List[str]												=				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."				)

def 						snake_case__       (					_lowerCamelCase=False				)			->str:






		"""simple docstring"""


		with open(_lowerCamelCase,  encoding="utf-8"				) as f:
				__lowercase				:		Optional[int]												=				yaml.safe_load(f.read()				)

		# Get to the API doc
		__lowercase				:		Union[str, Any]												=				0
		while content[api_idx]["title"] != "API":
				api_idx += 1
		__lowercase				:		List[str]												=				content[api_idx]["sections"]

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

		__lowercase				:		Union[str, Any]												=				False
		__lowercase				:		int												=				api_doc[pipeline_idx]["sections"]
		__lowercase				:		List[Any]												=				[]

		# sort sub pipeline docs
		for pipeline_doc in pipeline_docs:
				if "section" in pipeline_doc:
						__lowercase				:		Optional[int]												=				pipeline_doc["section"]
						__lowercase				:		List[str]												=				clean_doc_toc(_lowerCamelCase				)
						if overwrite:
								__lowercase				:		Any												=				new_sub_pipeline_doc
				new_pipeline_docs.append(_lowerCamelCase				)

		# sort overall pipeline doc
		__lowercase				:		List[Any]												=				clean_doc_toc(_lowerCamelCase				)

		if new_pipeline_docs != pipeline_docs:
				__lowercase				:		List[str]												=				True
				if overwrite:
						__lowercase				:		Optional[int]												=				new_pipeline_docs

		if diff:
				if overwrite:
						__lowercase				:		Tuple												=				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__":
						__A   :  Optional[int]					=      argparse.ArgumentParser()
						parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
						__A   :  Optional[int]					=      parser.parse_args()

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






"""simple docstring"""







from scipy.stats import spearmanr

import datasets


__A   :  Optional[int]					=      '\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   :  List[Any]					=      '\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   :  Optional[int]					=      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       lowerCAmelCase__       (			datasets.Metric    ):


				"""simple docstring"""





				def       snake_case    (     self					:	List[str]						):
						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					:	Optional[int]    ,   lowercase__					:	str    ,   lowercase__					:	Any    ,   lowercase__					:	int=False						):
						__lowercase				:		Optional[int]												=				spearmanr(lowercase__    ,   lowercase__						)
						if return_pvalue:
								return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
						else:
								return {"spearmanr": results[0]}
