


'''simple docstring'''






from sklearn.metrics import mean_squared_error

import datasets


lowercase							='\\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'

lowercase							='\\nMean Squared Error(MSE) is the average of the square of difference between the predicted\nand actual values.\n'


lowercase							='\nArgs:\n    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Estimated target values.\n    references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Ground truth (correct) target values.\n    sample_weight: array-like of shape (n_samples,), default=None\n        Sample weights.\n    multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"\n        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n        "raw_values" : Returns a full set of errors in case of multioutput input.\n\n        "uniform_average" : Errors of all outputs are averaged with uniform weight.\n\n    squared : bool, default=True\n        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.\n\nReturns:\n    mse : mean squared error.\nExamples:\n\n    >>> mse_metric = datasets.load_metric("mse")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mse\': 0.375}\n    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)\n    >>> print(rmse_result)\n    {\'mse\': 0.6123724356957945}\n\n    If you\'re using multi-dimensional lists, then set the config as follows :\n\n    >>> mse_metric = datasets.load_metric("mse", "multilist")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mse\': 0.7083333333333334}\n    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {\'mse\': array([0.41666667, 1. ])}\n'






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






       def       lowerCAmelCase		(      self)						->  Any:




          '''simple docstring'''
          return datasets.MetricInfo(
              description=_DESCRIPTION  ,							citation=_CITATION  ,							inputs_description=_KWARGS_DESCRIPTION  ,							features=datasets.Features(self._get_feature_types())  ,							reference_urls=[
                  'https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html'
              ]  ,							)






       def       lowerCAmelCase		(      self)						->  Optional[Any]:




          '''simple docstring'''
          if self.config_name == "multilist":
             return {
                 "predictions": datasets.Sequence(datasets.Value('float')),
                 "references": datasets.Sequence(datasets.Value('float')),
             }
          else:
             return {
                 "predictions": datasets.Value('float'),
                 "references": datasets.Value('float'),
             }






       def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case  ,							snake_case=None  ,							snake_case="uniform_average"  ,							snake_case=True)						->  Tuple:




          '''simple docstring'''
          _UpperCAmelCase       :       str							   =mean_squared_error(
              snake_case  ,							snake_case  ,							sample_weight=snake_case  ,							multioutput=snake_case  ,							squared=snake_case)

          return {"mse": mse}





'''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

lowercase							=logging.get_logger(__name__)






@add_end_docstrings(lowerCAmelCase    )
class  __magic_name__							(		lowerCAmelCase    ):






       def __init__(      self  ,							**snake_case)						->  Optional[int]:




          '''simple docstring'''
          super().__init__(**snake_case)

          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  ,							snake_case  ,							**snake_case)						->  str:




          '''simple docstring'''
          return super().__call__(snake_case  ,							**snake_case)






       def       lowerCAmelCase		(      self  ,							**snake_case)						->  int:




          '''simple docstring'''
          _UpperCAmelCase       :       str							   ={}
          if "candidate_labels" in kwargs:
             _UpperCAmelCase       :       Union[str, Any]							   =kwargs['candidate_labels']
          if "hypothesis_template" in kwargs:
             _UpperCAmelCase       :       List[Any]							   =kwargs['hypothesis_template']

          return preprocess_params, {}, {}






       def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case=None  ,							snake_case="This is a photo of {}.")						->  Any:




          '''simple docstring'''
          _UpperCAmelCase       :       Optional[Any]							   =load_image(snake_case)
          _UpperCAmelCase       :       Union[str, Any]							   =self.image_processor(images=[image]  ,							return_tensors=self.framework)
          _UpperCAmelCase       :       Union[str, Any]							   =candidate_labels
          _UpperCAmelCase       :       List[Any]							   =[hypothesis_template.format(snake_case) for x in candidate_labels]
          _UpperCAmelCase       :       str							   =self.tokenizer(snake_case  ,							return_tensors=self.framework  ,							padding=snake_case)
          _UpperCAmelCase       :       Any							   =[text_inputs]
          return inputs






       def       lowerCAmelCase		(      self  ,							snake_case)						->  str:




          '''simple docstring'''
          _UpperCAmelCase       :       List[str]							   =model_inputs.pop('candidate_labels')
          _UpperCAmelCase       :       Tuple							   =model_inputs.pop('text_inputs')
          if isinstance(text_inputs[0]  ,							snake_case):
             _UpperCAmelCase       :       Any							   =text_inputs[0]
          else:
             # Batching case.
             _UpperCAmelCase       :       str							   =text_inputs[0][0]

          _UpperCAmelCase       :       Any							   =self.model(**snake_case  ,							**snake_case)

          _UpperCAmelCase       :       List[str]							   ={
              'candidate_labels': candidate_labels,
              'logits': outputs.logits_per_image,
          }
          return model_outputs






       def       lowerCAmelCase		(      self  ,							snake_case)						->  Optional[int]:




          '''simple docstring'''
          _UpperCAmelCase       :       str							   =model_outputs.pop('candidate_labels')
          _UpperCAmelCase       :       Union[str, Any]							   =model_outputs['logits'][0]
          if self.framework == "pt":
             _UpperCAmelCase       :       Dict							   =logits.softmax(dim=-1).squeeze(-1)
             _UpperCAmelCase       :       Union[str, Any]							   =probs.tolist()
             if not isinstance(snake_case  ,							snake_case):
                _UpperCAmelCase       :       Union[str, Any]							   =[scores]
          elif self.framework == "tf":
             _UpperCAmelCase       :       Dict							   =stable_softmax(snake_case  ,							axis=-1)
             _UpperCAmelCase       :       str							   =probs.numpy().tolist()
          else:
             raise ValueError(f"Unsupported framework: {self.framework}")

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

