'''simple docstring'''

import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import ViltImageProcessor




class    lowerCamelCase__  (   unittest.TestCase     ):



   def __init__(    self					,      lowerCAmelCase__					,      lowerCAmelCase__=7					,      lowerCAmelCase__=3					,      lowerCAmelCase__=18					,      lowerCAmelCase__=30					,      lowerCAmelCase__=400					,      lowerCAmelCase__=True					,      lowerCAmelCase__=None					,      lowerCAmelCase__=2					,      lowerCAmelCase__=True					,      lowerCAmelCase__=[0.5, 0.5, 0.5]					,      lowerCAmelCase__=[0.5, 0.5, 0.5]					,      )				->     Dict:
        """simple docstring"""





        _UpperCamelCase  :List[Any]       =size if size is not None else {"""shortest_edge""": 30}
        _UpperCamelCase  :Optional[Any]       =parent
        _UpperCamelCase  :int       =batch_size
        _UpperCamelCase  :int       =num_channels
        _UpperCamelCase  :List[Any]       =image_size
        _UpperCamelCase  :List[Any]       =min_resolution
        _UpperCamelCase  :Tuple       =max_resolution
        _UpperCamelCase  :Union[str, Any]       =do_resize
        _UpperCamelCase  :Tuple       =size
        _UpperCamelCase  :Tuple       =size_divisor
        _UpperCamelCase  :int       =do_normalize
        _UpperCamelCase  :Union[str, Any]       =image_mean
        _UpperCamelCase  :Tuple       =image_std



   def  _UpperCamelCase		(    self     )				->     Optional[int]:
        """simple docstring"""





        return {
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_normalize": self.do_normalize,
            "do_resize": self.do_resize,
            "size": self.size,
            "size_divisor": self.size_divisor,
        }




   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__=False     )				->     Union[str, Any]:
        """simple docstring"""





        if not batched:
             _UpperCamelCase  :str       =self.size["""shortest_edge"""]
             _UpperCamelCase  :Any       =image_inputs[0]
             if isinstance(lowerCAmelCase__					,      Image.Image     ):
                  _UpperCamelCase       ,      _UpperCamelCase  :Union[str, Any]       =image.size
             else:
                  _UpperCamelCase       ,      _UpperCamelCase  :Dict       =image.shape[1], image.shape[2]
             _UpperCamelCase  :Optional[int]       =size / min(lowerCAmelCase__					,      lowerCAmelCase__     )
             if h < w:
                  _UpperCamelCase       ,      _UpperCamelCase  :List[str]       =size, scale * w
             else:
                  _UpperCamelCase       ,      _UpperCamelCase  :Optional[int]       =scale * h, size

             _UpperCamelCase  :List[Any]       =int((1_333 / 800) * size     )
             if max(lowerCAmelCase__					,      lowerCAmelCase__     ) > max_size:
                  _UpperCamelCase  :Optional[Any]       =max_size / max(lowerCAmelCase__					,      lowerCAmelCase__     )
                  _UpperCamelCase  :Any       =newh * scale
                  _UpperCamelCase  :Dict       =neww * scale

             _UpperCamelCase       ,      _UpperCamelCase  :Any       =int(newh + 0.5     ), int(neww + 0.5     )
             _UpperCamelCase       ,      _UpperCamelCase  :List[Any]       =(
                 newh // self.size_divisor * self.size_divisor,
                 neww // self.size_divisor * self.size_divisor,
             )

        else:
             _UpperCamelCase  :List[Any]       =[]
             for image in image_inputs:
                  _UpperCamelCase       ,      _UpperCamelCase  :int       =self.get_expected_values([image]     )
                  expected_values.append((expected_height, expected_width)     )
             _UpperCamelCase  :Any       =max(lowerCAmelCase__					,      key=lambda lowerCAmelCase__     : item[0]     )[0]
             _UpperCamelCase  :int       =max(lowerCAmelCase__					,      key=lambda lowerCAmelCase__     : item[1]     )[1]

        return expected_height, expected_width




@require_torch
@require_vision
class    lowerCamelCase__  (   __snake_case       ,				unittest.TestCase     ):
   __UpperCAmelCase           =						ViltImageProcessor if is_vision_available() else None



   def  _UpperCamelCase		(    self     )				->     List[str]:
        """simple docstring"""





        _UpperCamelCase  :int       =ViltImageProcessingTester(self     )



   @property
   def  _UpperCamelCase		(    self     )				->     str:
        """simple docstring"""





        return self.image_processor_tester.prepare_image_processor_dict()



   def  _UpperCamelCase		(    self     )				->     Tuple:
        """simple docstring"""





        _UpperCamelCase  :Dict       =self.image_processing_class(**self.image_processor_dict     )
        self.assertTrue(hasattr(lowerCAmelCase__					,      """image_mean"""     )     )
        self.assertTrue(hasattr(lowerCAmelCase__					,      """image_std"""     )     )
        self.assertTrue(hasattr(lowerCAmelCase__					,      """do_normalize"""     )     )
        self.assertTrue(hasattr(lowerCAmelCase__					,      """do_resize"""     )     )
        self.assertTrue(hasattr(lowerCAmelCase__					,      """size"""     )     )
        self.assertTrue(hasattr(lowerCAmelCase__					,      """size_divisor"""     )     )



   def  _UpperCamelCase		(    self     )				->     List[Any]:
        """simple docstring"""





        _UpperCamelCase  :Any       =self.image_processing_class.from_dict(self.image_processor_dict     )
        self.assertEqual(image_processor.size					,      {"""shortest_edge""": 30}     )

        _UpperCamelCase  :List[str]       =self.image_processing_class.from_dict(self.image_processor_dict					,      size=42     )
        self.assertEqual(image_processor.size					,      {"""shortest_edge""": 42}     )



   def  _UpperCamelCase		(    self     )				->     int:
        """simple docstring"""





        pass



   def  _UpperCamelCase		(    self     )				->     str:
        """simple docstring"""





        _UpperCamelCase  :int       =self.image_processing_class(**self.image_processor_dict     )
        # create random PIL images
        _UpperCamelCase  :Union[str, Any]       =prepare_image_inputs(self.image_processor_tester					,      equal_resolution=lowerCAmelCase__     )
        for image in image_inputs:
             self.assertIsInstance(lowerCAmelCase__					,      Image.Image     )

        # Test not batched input
        _UpperCamelCase  :List[Any]       =image_processing(image_inputs[0]					,      return_tensors="""pt"""     ).pixel_values

        _UpperCamelCase       ,      _UpperCamelCase  :Optional[Any]       =self.image_processor_tester.get_expected_values(lowerCAmelCase__     )
        self.assertEqual(
            encoded_images.shape					,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)					,      )

        # Test batched
        _UpperCamelCase  :List[Any]       =image_processing(lowerCAmelCase__					,      return_tensors="""pt"""     ).pixel_values

        _UpperCamelCase       ,      _UpperCamelCase  :Any       =self.image_processor_tester.get_expected_values(lowerCAmelCase__					,      batched=lowerCAmelCase__     )
        self.assertEqual(
            encoded_images.shape					,      (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )					,      )



   def  _UpperCamelCase		(    self     )				->     Any:
        """simple docstring"""





        _UpperCamelCase  :List[Any]       =self.image_processing_class(**self.image_processor_dict     )
        # create random numpy tensors
        _UpperCamelCase  :Optional[Any]       =prepare_image_inputs(self.image_processor_tester					,      equal_resolution=lowerCAmelCase__					,      numpify=lowerCAmelCase__     )
        for image in image_inputs:
             self.assertIsInstance(lowerCAmelCase__					,      np.ndarray     )

        # Test not batched input
        _UpperCamelCase  :Any       =image_processing(image_inputs[0]					,      return_tensors="""pt"""     ).pixel_values

        _UpperCamelCase       ,      _UpperCamelCase  :Tuple       =self.image_processor_tester.get_expected_values(lowerCAmelCase__     )
        self.assertEqual(
            encoded_images.shape					,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)					,      )

        # Test batched
        _UpperCamelCase  :Dict       =image_processing(lowerCAmelCase__					,      return_tensors="""pt"""     ).pixel_values

        _UpperCamelCase       ,      _UpperCamelCase  :Optional[Any]       =self.image_processor_tester.get_expected_values(lowerCAmelCase__					,      batched=lowerCAmelCase__     )
        self.assertEqual(
            encoded_images.shape					,      (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )					,      )




   def  _UpperCamelCase		(    self     )				->     List[str]:
        """simple docstring"""





        _UpperCamelCase  :Any       =self.image_processing_class(**self.image_processor_dict     )
        # create random PyTorch tensors
        _UpperCamelCase  :int       =prepare_image_inputs(self.image_processor_tester					,      equal_resolution=lowerCAmelCase__					,      torchify=lowerCAmelCase__     )
        for image in image_inputs:
             self.assertIsInstance(lowerCAmelCase__					,      torch.Tensor     )

        # Test not batched input
        _UpperCamelCase  :Union[str, Any]       =image_processing(image_inputs[0]					,      return_tensors="""pt"""     ).pixel_values

        _UpperCamelCase       ,      _UpperCamelCase  :str       =self.image_processor_tester.get_expected_values(lowerCAmelCase__     )
        self.assertEqual(
            encoded_images.shape					,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)					,      )

        # Test batched
        _UpperCamelCase  :Tuple       =image_processing(lowerCAmelCase__					,      return_tensors="""pt"""     ).pixel_values

        _UpperCamelCase       ,      _UpperCamelCase  :Tuple       =self.image_processor_tester.get_expected_values(lowerCAmelCase__					,      batched=lowerCAmelCase__     )
        self.assertEqual(
            encoded_images.shape					,      (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )					,      )
'''simple docstring'''

from sklearn.metrics import mean_absolute_error

import datasets


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

_lowerCamelCase :				List[Any]														= """\
Mean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual
values.
"""


_lowerCamelCase :				Union[str, Any]														= """
Args:
    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Estimated target values.
    references: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Ground truth (correct) target values.
    sample_weight: array-like of shape (n_samples,), default=None
        Sample weights.
    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"
        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.

                 \"raw_values\" : Returns a full set of errors in case of multioutput input.

                 \"uniform_average\" : Errors of all outputs are averaged with uniform weight.

Returns:
    mae : mean absolute error.
        If multioutput is \"raw_values\", then mean absolute error is returned for each output separately. If multioutput is \"uniform_average\" or an ndarray of weights, then the weighted average of all output errors is returned.
        MAE output is non-negative floating point. The best value is 0.0.
Examples:

    >>> mae_metric = datasets.load_metric(\"mae\")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mae': 0.5}

    If you're using multi-dimensional lists, then set the config as follows :

    >>> mae_metric = datasets.load_metric(\"mae\", \"multilist\")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mae': 0.75}
    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput='raw_values')
    >>> print(results)
    {'mae': array([0.5, 1. ])}
"""




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



   def  _UpperCamelCase		(    self     )				->     int:
        """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_absolute_error.html"""
            ]					,      )



   def  _UpperCamelCase		(    self     )				->     Tuple:
        """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  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__=None					,      lowerCAmelCase__="uniform_average"     )				->     List[str]:
        """simple docstring"""





        _UpperCamelCase  :Tuple       =mean_absolute_error(lowerCAmelCase__					,      lowerCAmelCase__					,      sample_weight=lowerCAmelCase__					,      multioutput=lowerCAmelCase__     )

        return {"mae": mae_score}