





'''simple docstring'''






from scipy.stats import spearmanr

import datasets


A   : Dict             =			'\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   : str             =			'\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[Any]             =			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  ):


					def    SCREAMING_SNAKE_CASE_		(					self	:  Dict       ):





											'''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    SCREAMING_SNAKE_CASE_		(					self	:  List[str]       ,     __snake_case	:  Any       ,     __snake_case	:  List[Any]       ,     __snake_case	:  Tuple=False       ):





											'''simple docstring'''





											_snake_case:   Dict  					=		spearmanr(__snake_case       ,     __snake_case       )
											if return_pvalue:
																	return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
											else:
																	return {"spearmanr": results[0]}












'''simple docstring'''






import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin







class 			lowerCamelCase					(						__UpperCAmelCase  ):
					_SCREAMING_SNAKE_CASE							   =    "Speech2TextFeatureExtractor"
					_SCREAMING_SNAKE_CASE							   =    "Speech2TextTokenizer"


					def __init__(					self	:  List[str]       ,     __snake_case	:  Union[str, Any]       ,     __snake_case	:  List[str]       ):





											'''simple docstring'''





											super().__init__(__snake_case       ,     __snake_case       )
											_snake_case:   Optional[int]  					=		self.feature_extractor
											_snake_case:   Dict  					=		False


					def __call__(					self	:  Optional[int]       ,     *__snake_case	:  Optional[Any]       ,     **__snake_case	:  Union[str, Any]       ):





											'''simple docstring'''





											if self._in_target_context_manager:
																	return self.current_processor(*__snake_case       ,     **__snake_case       )

											if "raw_speech" in kwargs:
																	warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'       )
																	_snake_case:   int  					=		kwargs.pop('raw_speech'       )
											else:
																	_snake_case:   Tuple  					=		kwargs.pop('audio'       ,     __snake_case       )
											_snake_case:   Optional[Any]  					=		kwargs.pop('sampling_rate'       ,     __snake_case       )
											_snake_case:   Optional[Any]  					=		kwargs.pop('text'       ,     __snake_case       )
											if len(__snake_case       ) > 0:
																	_snake_case:   Optional[Any]  					=		args[0]
																	_snake_case:   Union[str, Any]  					=		args[1:]

											if audio is None and text is None:
																	raise ValueError('You need to specify either an `audio` or `text` input to process.'       )

											if audio is not None:
																	_snake_case:   List[Any]  					=		self.feature_extractor(__snake_case       ,     *__snake_case       ,     sampling_rate=__snake_case       ,     **__snake_case       )
											if text is not None:
																	_snake_case:   Tuple  					=		self.tokenizer(__snake_case       ,     **__snake_case       )

											if text is None:
																	return inputs
											elif audio is None:
																	return encodings
											else:
																	_snake_case:   List[Any]  					=		encodings['input_ids']
																	return inputs


					def    SCREAMING_SNAKE_CASE_		(					self	:  Dict       ,     *__snake_case	:  Optional[int]       ,     **__snake_case	:  str       ):





											'''simple docstring'''





											return self.tokenizer.batch_decode(*__snake_case       ,     **__snake_case       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Dict       ,     *__snake_case	:  List[str]       ,     **__snake_case	:  Tuple       ):





											'''simple docstring'''





											return self.tokenizer.decode(*__snake_case       ,     **__snake_case       )




					@contextmanager
					def    SCREAMING_SNAKE_CASE_		(					self	:  List[str]       ):





											'''simple docstring'''





											warnings.warn(
											    '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
											    'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
											    'your audio inputs, or in a separate call.'       )
											_snake_case:   Any  					=		True
											_snake_case:   str  					=		self.tokenizer
											yield
											_snake_case:   Union[str, Any]  					=		self.feature_extractor
											_snake_case:   Tuple  					=		False





