"""simple docstring"""






import argparse
from collections import defaultdict

import yaml


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



def   a_						(					lowerCamelCase       ):
				UpperCAmelCase__             =							defaultdict(SCREAMING_SNAKE_CASE__       )
				UpperCAmelCase__             =							[]
				UpperCAmelCase__             =							[]
				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(SCREAMING_SNAKE_CASE__       )

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

				UpperCAmelCase__             =							[]
				for duplicate_key in duplicates:
								UpperCAmelCase__             =							list({doc['title'] for doc in doc_list if doc['local'] == duplicate_key}       )
								if len(SCREAMING_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]       )
				UpperCAmelCase__             =							sorted(SCREAMING_SNAKE_CASE__					,			key=lambda lowerCamelCase       : s["title"].lower()       )

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

				overview_doc.extend(SCREAMING_SNAKE_CASE__       )

				# Sort
				return overview_doc



def   a_						(					lowerCamelCase=False       ):





				with open(SCREAMING_SNAKE_CASE__					,			encoding='utf-8'       ) as f:
								UpperCAmelCase__             =							yaml.safe_load(f.read()       )

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

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

				UpperCAmelCase__             =							api_doc[scheduler_idx]["""sections"""]
				UpperCAmelCase__             =							clean_doc_toc(SCREAMING_SNAKE_CASE__       )

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

				if diff:
								if overwrite:
												UpperCAmelCase__             =							api_doc
												with open(SCREAMING_SNAKE_CASE__					,			'w'					,			encoding='utf-8'       ) as f:
																f.write(yaml.dump(SCREAMING_SNAKE_CASE__					,			allow_unicode=SCREAMING_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   a_						(					lowerCamelCase=False       ):





				with open(SCREAMING_SNAKE_CASE__					,			encoding='utf-8'       ) as f:
								UpperCAmelCase__             =							yaml.safe_load(f.read()       )

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

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

				UpperCAmelCase__             =							False
				UpperCAmelCase__             =							api_doc[pipeline_idx]["""sections"""]
				UpperCAmelCase__             =							[]

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

				# sort overall pipeline doc
				UpperCAmelCase__             =							clean_doc_toc(SCREAMING_SNAKE_CASE__       )

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

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

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







'''simple docstring'''


from sklearn.metrics import fa_score

import datasets


UpperCAmelCase_		:   Optional[int]							   =		'\nThe F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:\nF1 = 2 * (precision * recall) / (precision + recall)\n'


UpperCAmelCase_		:   Tuple							   =		'\nArgs:\n    predictions (`list` of `int`): Predicted labels.\n    references (`list` of `int`): Ground truth labels.\n    labels (`list` of `int`): The set of labels to include when `average` is not set to `\'binary\'`, and the order of the labels if `average` is `None`. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. Labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in `predictions` and `references` are used in sorted order. Defaults to None.\n    pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.\n    average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `\'binary\'`.\n\n        - \'binary\': Only report results for the class specified by `pos_label`. This is applicable only if the classes found in `predictions` and `references` are binary.\n        - \'micro\': Calculate metrics globally by counting the total true positives, false negatives and false positives.\n        - \'macro\': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.\n        - \'weighted\': Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `\'macro\'` to account for label imbalance. This option can result in an F-score that is not between precision and recall.\n        - \'samples\': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).\n    sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n    f1 (`float` or `array` of `float`): F1 score or list of f1 scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher f1 scores are better.\n\nExamples:\n\n    Example 1-A simple binary example\n        >>> f1_metric = datasets.load_metric("f1")\n        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])\n        >>> print(results)\n        {\'f1\': 0.5}\n\n    Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.\n        >>> f1_metric = datasets.load_metric("f1")\n        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)\n        >>> print(round(results[\'f1\'], 2))\n        0.67\n\n    Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.\n        >>> f1_metric = datasets.load_metric("f1")\n        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])\n        >>> print(round(results[\'f1\'], 2))\n        0.35\n\n    Example 4-A multiclass example, with different values for the `average` input.\n        >>> predictions = [0, 2, 1, 0, 0, 1]\n        >>> references = [0, 1, 2, 0, 1, 2]\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average="macro")\n        >>> print(round(results[\'f1\'], 2))\n        0.27\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average="micro")\n        >>> print(round(results[\'f1\'], 2))\n        0.33\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average="weighted")\n        >>> print(round(results[\'f1\'], 2))\n        0.27\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average=None)\n        >>> print(results)\n        {\'f1\': array([0.8, 0. , 0. ])}\n'


UpperCAmelCase_		:   Dict							   =		'\n@article{scikit-learn,\n    title={Scikit-learn: Machine Learning in {P}ython},\n    author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n           and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n           and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n           Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n    journal={Journal of Machine Learning Research},\n    volume={12},\n    pages={2825--2830},\n    year={2011}\n}\n'





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





							'''simple docstring'''


							def UpperCAmelCase_	(    self  ):
											return datasets.MetricInfo(
											    description=_DESCRIPTION    ,					citation=_CITATION    ,					inputs_description=_KWARGS_DESCRIPTION    ,					features=datasets.Features(
											        {
											            """predictions""": datasets.Sequence(datasets.Value("""int32"""  )  ),
											            """references""": datasets.Sequence(datasets.Value("""int32"""  )  ),
											        }
											        if self.config_name == """multilabel"""
											        else {
											            """predictions""": datasets.Value("""int32"""  ),
											            """references""": datasets.Value("""int32"""  ),
											        }  )    ,					reference_urls=["""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html"""]    ,					)






							def UpperCAmelCase_	(    self    ,					__snake_case    ,					__snake_case    ,					__snake_case=None    ,					__snake_case=1    ,					__snake_case="binary"    ,					__snake_case=None  ):
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      fa_score(
											    __snake_case    ,					__snake_case    ,					labels=__snake_case    ,					pos_label=__snake_case    ,					average=__snake_case    ,					sample_weight=__snake_case  )
											return {"f1": float(__snake_case  ) if score.size == 1 else score}




