






'''simple docstring'''
import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class        __SCREAMING_SNAKE_CASE (     _UpperCAmelCase			):
 snake_case_												=							["image_processor", "tokenizer"]
 snake_case_												=							"ViTImageProcessor"
 snake_case_												=							("CLIPTokenizer", "CLIPTokenizerFast")




 def __init__( self		:							str   , __lowercase		:							Dict=None   , __lowercase		:							List[Any]=None   , **__lowercase		:							Union[str, Any] )		->  List[str]:
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=None
       if "feature_extractor" in kwargs:
             warnings.warn(
                 '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                 ''' instead.'''   , _UpperCAmelCase   , )
             SCREAMING_SNAKE_CASE__       :			Optional[int]													=kwargs.pop('''feature_extractor''' )

       SCREAMING_SNAKE_CASE__       :			List[Any]													=image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
             raise ValueError('''You need to specify an `image_processor`.''' )
       if tokenizer is None:
             raise ValueError('''You need to specify a `tokenizer`.''' )

       super().__init__(_UpperCAmelCase   , _UpperCAmelCase )




 def __call__( self		:							int   , __lowercase		:							List[str]=None   , __lowercase		:							List[str]=None   , __lowercase		:							str=None   , __lowercase		:							Optional[Any]=None   , **__lowercase		:							List[str] )		->  List[str]:
       if text is None and visual_prompt is None and images is None:
             raise ValueError('''You have to specify either text, visual prompt or images.''' )

       if text is not None and visual_prompt is not None:
             raise ValueError('''You have to specify exactly one type of prompt. Either text or visual prompt.''' )

       if text is not None:
             SCREAMING_SNAKE_CASE__       :			Optional[Any]													=self.tokenizer(_UpperCAmelCase   , return_tensors=_UpperCAmelCase   , **_UpperCAmelCase )

       if visual_prompt is not None:
             SCREAMING_SNAKE_CASE__       :			str													=self.image_processor(_UpperCAmelCase   , return_tensors=_UpperCAmelCase   , **_UpperCAmelCase )

       if images is not None:
             SCREAMING_SNAKE_CASE__       :			Tuple													=self.image_processor(_UpperCAmelCase   , return_tensors=_UpperCAmelCase   , **_UpperCAmelCase )

       if visual_prompt is not None and images is not None:
             SCREAMING_SNAKE_CASE__       :			Any													={
                 '''pixel_values''': image_features.pixel_values,
                 '''conditional_pixel_values''': prompt_features.pixel_values,
             }
             return encoding
       elif text is not None and images is not None:
             SCREAMING_SNAKE_CASE__       :			str													=image_features.pixel_values
             return encoding
       elif text is not None:
             return encoding
       elif visual_prompt is not None:
             SCREAMING_SNAKE_CASE__       :			Union[str, Any]													={
                 '''conditional_pixel_values''': prompt_features.pixel_values,
             }
             return encoding
       else:
             return BatchEncoding(data=dict(**_UpperCAmelCase )   , tensor_type=_UpperCAmelCase )




 def        __magic_name__		( self		:							Optional[Any]   , *__lowercase		:							List[str]   , **__lowercase		:							str )		->  List[Any]:
       return self.tokenizer.batch_decode(*_UpperCAmelCase   , **_UpperCAmelCase )




 def        __magic_name__		( self		:							Tuple   , *__lowercase		:							Dict   , **__lowercase		:							List[str] )		->  Union[str, Any]:
       return self.tokenizer.decode(*_UpperCAmelCase   , **_UpperCAmelCase )




 @property
 def        __magic_name__		( self		:							Union[str, Any] )		->  Union[str, Any]:
       warnings.warn(
           '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''   , _UpperCAmelCase   , )
       return self.image_processor_class




 @property
 def        __magic_name__		( self		:							Optional[Any] )		->  Tuple:
       warnings.warn(
           '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''   , _UpperCAmelCase   , )
       return self.image_processor









'''simple docstring'''
from scipy.stats import pearsonr

import datasets


a_					=	'\nPearson correlation coefficient and p-value for testing non-correlation.\nThe Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that each dataset is normally distributed. Like other correlation coefficients, this one varies between -1 and +1 with 0 implying no correlation. Correlations of -1 or +1 imply an exact linear relationship. Positive correlations imply that as x increases, so does y. Negative correlations imply that as x increases, y decreases.\nThe p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets.\n'


a_					=	'\nArgs:\n    predictions (`list` of `int`): Predicted class labels, as returned by a model.\n    references (`list` of `int`): Ground truth labels.\n    return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.\n\nReturns:\n    pearsonr (`float`): Pearson correlation coefficient. Minimum possible value is -1. Maximum possible value is 1. Values of 1 and -1 indicate exact linear positive and negative relationships, respectively. A value of 0 implies no correlation.\n    p-value (`float`): P-value, which roughly indicates the probability of an The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate higher probabilities.\n\nExamples:\n\n    Example 1-A simple example using only predictions and references.\n        >>> pearsonr_metric = datasets.load_metric("pearsonr")\n        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])\n        >>> print(round(results[\'pearsonr\'], 2))\n        -0.74\n\n    Example 2-The same as Example 1, but that also returns the `p-value`.\n        >>> pearsonr_metric = datasets.load_metric("pearsonr")\n        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)\n        >>> print(sorted(list(results.keys())))\n        [\'p-value\', \'pearsonr\']\n        >>> print(round(results[\'pearsonr\'], 2))\n        -0.74\n        >>> print(round(results[\'p-value\'], 2))\n        0.15\n'


a_					=	'\n@article{2020SciPy-NMeth,\nauthor  = {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, Ilhan 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, Antonio H. and Pedregosa, Fabian and\n      {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},\ntitle   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific\n      Computing in Python}},\njournal = {Nature Methods},\nyear    = {2020},\nvolume  = {17},\npages   = {261--272},\nadsurl  = {https://rdcu.be/b08Wh},\ndoi = {10.1038/s41592-019-0686-2},\n}\n'






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




 def        __magic_name__		( self		:							List[Any] )		->  Any:
       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.pearsonr.html''']   , )




 def        __magic_name__		( self		:							Optional[int]   , __lowercase		:							List[Any]   , __lowercase		:							List[Any]   , __lowercase		:							str=False )		->  int:
       if return_pvalue:
             SCREAMING_SNAKE_CASE__       :			List[str]													=pearsonr(__lowercase   , __lowercase )
             return {"pearsonr": results[0], "p-value": results[1]}
       else:
             return {"pearsonr": float(pearsonr(__lowercase   , __lowercase )[0] )}