'''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 				lowercase    (    unittest.TestCase			):


   def __init__( self					:						Any ,		__lowerCAmelCase					:						Union[str, Any] ,		__lowerCAmelCase					:						Optional[Any]=7 ,		__lowerCAmelCase					:						int=3 ,		__lowerCAmelCase					:						str=18 ,		__lowerCAmelCase					:						Union[str, Any]=30 ,		__lowerCAmelCase					:						Dict=400 ,		__lowerCAmelCase					:						Any=True ,		__lowerCAmelCase					:						Optional[int]=None ,		__lowerCAmelCase					:						str=2 ,		__lowerCAmelCase					:						Optional[Any]=True ,		__lowerCAmelCase					:						Tuple=[0.5, 0.5, 0.5] ,		__lowerCAmelCase					:						Union[str, Any]=[0.5, 0.5, 0.5] ,		)   ->       Dict:
         lowercase_     			=   size if size is not None else {"""shortest_edge""": 30}
         lowercase_     			=   parent
         lowercase_     			=   batch_size
         lowercase_     			=   num_channels
         lowercase_     			=   image_size
         lowercase_     			=   min_resolution
         lowercase_     			=   max_resolution
         lowercase_     			=   do_resize
         lowercase_     			=   size
         lowercase_     			=   size_divisor
         lowercase_     			=   do_normalize
         lowercase_     			=   image_mean
         lowercase_     			=   image_std


   def __UpperCAmelCase							( self					:						Dict)   ->       Optional[int]:
         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					:						str ,		__lowerCAmelCase					:						Any ,		__lowerCAmelCase					:						Union[str, Any]=False)   ->       Union[str, Any]:





         if not batched:
               lowercase_     			=   self.size["""shortest_edge"""]
               lowercase_     			=   image_inputs[0]
               if isinstance(lowerCAmelCase__ ,		Image.Image):
                     lowercase_     			=   image.size
               else:
                     lowercase_     			=   image.shape[1], image.shape[2]
               lowercase_     			=   size / min(lowerCAmelCase__ ,		lowerCAmelCase__)
               if h < w:
                     lowercase_     			=   size, scale * w
               else:
                     lowercase_     			=   scale * h, size

               lowercase_     			=   int((1333 / 800) * size)
               if max(lowerCAmelCase__ ,		lowerCAmelCase__) > max_size:
                     lowercase_     			=   max_size / max(lowerCAmelCase__ ,		lowerCAmelCase__)
                     lowercase_     			=   newh * scale
                     lowercase_     			=   neww * scale

               lowercase_     			=   int(newh + 0.5), int(neww + 0.5)
               lowercase_     			=   (
                   newh // self.size_divisor * self.size_divisor,
                   neww // self.size_divisor * self.size_divisor,
               )

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

         return expected_height, expected_width





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


   def __UpperCAmelCase							( self					:						Dict)   ->       List[str]:
         lowercase_     			=   ViltImageProcessingTester(self)


   @property
   def __UpperCAmelCase							( self					:						str)   ->       str:
         return self.image_processor_tester.prepare_image_processor_dict()


   def __UpperCAmelCase							( self					:						str)   ->       Tuple:
         lowercase_     			=   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[str])   ->       List[Any]:
         lowercase_     			=   self.image_processing_class.from_dict(self.image_processor_dict)
         self.assertEqual(image_processor.size ,		{"shortest_edge": 30})

         lowercase_     			=   self.image_processing_class.from_dict(self.image_processor_dict ,		size=42)
         self.assertEqual(image_processor.size ,		{"shortest_edge": 42})


   def __UpperCAmelCase							( self					:						List[Any])   ->       int:
         pass


   def __UpperCAmelCase							( self					:						Tuple)   ->       str:
         lowercase_     			=   self.image_processing_class(**self.image_processor_dict)
         # create random PIL images
         lowercase_     			=   prepare_image_inputs(self.image_processor_tester ,		equal_resolution=lowerCAmelCase__)
         for image in image_inputs:
               self.assertIsInstance(lowerCAmelCase__ ,		Image.Image)

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

         lowercase_     			=   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
         lowercase_     			=   image_processing(lowerCAmelCase__ ,		return_tensors="pt").pixel_values

         lowercase_     			=   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					:						str)   ->       Any:
         lowercase_     			=   self.image_processing_class(**self.image_processor_dict)
         # create random numpy tensors
         lowercase_     			=   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
         lowercase_     			=   image_processing(image_inputs[0] ,		return_tensors="pt").pixel_values

         lowercase_     			=   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
         lowercase_     			=   image_processing(lowerCAmelCase__ ,		return_tensors="pt").pixel_values

         lowercase_     			=   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					:						Dict)   ->       List[str]:
         lowercase_     			=   self.image_processing_class(**self.image_processor_dict)
         # create random PyTorch tensors
         lowercase_     			=   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
         lowercase_     			=   image_processing(image_inputs[0] ,		return_tensors="pt").pixel_values

         lowercase_     			=   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
         lowercase_     			=   image_processing(lowerCAmelCase__ ,		return_tensors="pt").pixel_values

         lowercase_     			=   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}