



"""simple docstring"""




from collections import UserDict
from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
     from PIL import Image

     from ..image_utils import load_image

if is_torch_available():
     from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING

if is_tf_available():
     from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
     from ..tf_utils import stable_softmax

_UpperCamelCase: str										=     logging.get_logger(__name__)


@add_end_docstrings(A__   )
class      a__						(  A__   ):


   def __init__(							self					:							Optional[Any],   **lowerCAmelCase					:							Tuple	)			->				Dict:
         super().__init__(**UpperCamelCase_	)

         requires_backends(self,   'vision'	)
         self.check_model_type(
             TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
             if self.framework == 'tf'
             else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING	)


   def __call__(							self					:							int,   lowerCAmelCase					:							str,   **lowerCAmelCase					:							str	)			->				List[Any]:
         return super().__call__(UpperCamelCase_,   **UpperCamelCase_	)


   def 				lowercase (							self					:							Any,   **lowerCAmelCase					:							str	)			->				Tuple:
         lowercase     :					Union[str, Any] 		=      {}
         if "candidate_labels" in kwargs:
               lowercase     :					int 		=      kwargs['''candidate_labels''']
         if "hypothesis_template" in kwargs:
               lowercase     :					Dict 		=      kwargs['''hypothesis_template''']

         return preprocess_params, {}, {}


   def 				lowercase (							self					:							int,   lowerCAmelCase					:							List[str],   lowerCAmelCase					:							Dict=None,   lowerCAmelCase					:							Union[str, Any]="This is a photo of {}."	)			->				Tuple:
         lowercase     :					List[Any] 		=      load_image(UpperCamelCase_	)
         lowercase     :					List[Any] 		=      self.image_processor(images=[image],   return_tensors=self.framework	)
         lowercase     :					List[Any] 		=      candidate_labels
         lowercase     :					Optional[int] 		=      [hypothesis_template.format(UpperCamelCase_	) for x in candidate_labels]
         lowercase     :					List[Any] 		=      self.tokenizer(UpperCamelCase_,   return_tensors=self.framework,   padding=UpperCamelCase_	)
         lowercase     :					Any 		=      [text_inputs]
         return inputs


   def 				lowercase (							self					:							Any,   lowerCAmelCase					:							int	)			->				str:
         lowercase     :					Optional[int] 		=      model_inputs.pop('candidate_labels'	)
         lowercase     :					Any 		=      model_inputs.pop('text_inputs'	)
         if isinstance(text_inputs[0],   UpperCamelCase_	):
               lowercase     :					Dict 		=      text_inputs[0]
         else:
               # Batching case.
               lowercase     :					Any 		=      text_inputs[0][0]

         lowercase     :					str 		=      self.model(**UpperCamelCase_,   **UpperCamelCase_	)

         lowercase     :					Optional[Any] 		=      {
             '''candidate_labels''': candidate_labels,
             '''logits''': outputs.logits_per_image,
         }
         return model_outputs






   def 				lowercase (							self					:							List[str],   lowerCAmelCase					:							List[Any]	)			->				Optional[Any]:
         lowercase     :					Optional[int] 		=      model_outputs.pop('candidate_labels'	)
         lowercase     :					List[Any] 		=      model_outputs['''logits'''][0]
         if self.framework == "pt":
               lowercase     :					Tuple 		=      logits.softmax(dim=-1	).squeeze(-1	)
               lowercase     :					str 		=      probs.tolist()
               if not isinstance(UpperCamelCase_,   UpperCamelCase_	):
                     lowercase     :					Optional[int] 		=      [scores]
         elif self.framework == "tf":
               lowercase     :					Any 		=      stable_softmax(UpperCamelCase_,   axis=-1	)
               lowercase     :					Tuple 		=      probs.numpy().tolist()
         else:
               raise ValueError(f'''Unsupported framework: {self.framework}'''	)

         lowercase     :					str 		=      [
             {'''score''': score, '''label''': candidate_label}
             for score, candidate_label in sorted(zip(UpperCamelCase_,   UpperCamelCase_	),   key=lambda lowerCAmelCase	: -x[0]	)
         ]
         return result




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

import datasets


__snake_case							      =     '''
Pearson correlation coefficient and p-value for testing non-correlation.
The 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.
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.
'''


__snake_case							      =     '''
Args:
    predictions (`list` of `int`): Predicted class labels, as returned by a model.
    references (`list` of `int`): Ground truth labels.
    return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.

Returns:
    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.
    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.

Examples:

    Example 1-A simple example using only predictions and references.
        >>> pearsonr_metric = datasets.load_metric("pearsonr")
        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])
        >>> print(round(results[\'pearsonr\'], 2))
        -0.74

    Example 2-The same as Example 1, but that also returns the `p-value`.
        >>> pearsonr_metric = datasets.load_metric("pearsonr")
        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)
        >>> print(sorted(list(results.keys())))
        [\'p-value\', \'pearsonr\']
        >>> print(round(results[\'pearsonr\'], 2))
        -0.74
        >>> print(round(results[\'p-value\'], 2))
        0.15
'''


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

		"""simple docstring"""

		def     lowerCAmelCase__				(		self							):

							'''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.pearsonr.html''']    ,   )




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_=False							):

							'''simple docstring'''


							if return_pvalue:
												UpperCamelCase__   :Any												= pearsonr(UpperCamelCase_    ,   UpperCamelCase_							)
												return {"pearsonr": results[0], "p-value": results[1]}
							else:
												return {"pearsonr": float(pearsonr(UpperCamelCase_    ,   UpperCamelCase_							)[0]							)}