




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 MobileNetVaImageProcessor

class 		lowercase_       (	unittest.TestCase				):




					def __init__(   self:		Dict,     _lowercase:		List[Any],     _lowercase:		int=7,     _lowercase:		List[str]=3,     _lowercase:		Optional[int]=18,     _lowercase:		Dict=30,     _lowercase:		Union[str, Any]=400,     _lowercase:		Union[str, Any]=True,     _lowercase:		Any=None,     _lowercase:		Optional[Any]=True,     _lowercase:		Any=None,     ):


									'''simple docstring'''


									__lowerCAmelCase					=  size if size is not None else {"""shortest_edge""": 20}
									__lowerCAmelCase					=  crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
									__lowerCAmelCase					=  parent
									__lowerCAmelCase					=  batch_size
									__lowerCAmelCase					=  num_channels
									__lowerCAmelCase					=  image_size
									__lowerCAmelCase					=  min_resolution
									__lowerCAmelCase					=  max_resolution
									__lowerCAmelCase					=  do_resize
									__lowerCAmelCase					=  size
									__lowerCAmelCase					=  do_center_crop
									__lowerCAmelCase					=  crop_size







					def     _lowercase						(   self:		Dict):


									'''simple docstring'''


									return {
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "do_center_crop": self.do_center_crop,
									    "crop_size": self.crop_size,
									}





@require_torch
@require_vision
class 		lowercase_       (	lowerCAmelCase__						,							unittest.TestCase				):
					__UpperCamelCase     	= MobileNetVaImageProcessor if is_vision_available() else None




					def     _lowercase						(   self:		Union[str, Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  MobileNetVaImageProcessingTester(self)




					@property
					def     _lowercase						(   self:		Union[str, Any]):


									'''simple docstring'''


									return self.image_processor_tester.prepare_image_processor_dict()




					def     _lowercase						(   self:		int):


									'''simple docstring'''


									__lowerCAmelCase					=  self.image_processing_class(**self.image_processor_dict)
									self.assertTrue(hasattr(_lowercase,     """do_resize"""))
									self.assertTrue(hasattr(_lowercase,     """size"""))
									self.assertTrue(hasattr(_lowercase,     """do_center_crop"""))
									self.assertTrue(hasattr(_lowercase,     """center_crop"""))




					def     _lowercase						(   self:		str):


									'''simple docstring'''


									__lowerCAmelCase					=  self.image_processing_class.from_dict(self.image_processor_dict)
									self.assertEqual(image_processor.size,     {"""shortest_edge""": 20})
									self.assertEqual(image_processor.crop_size,     {"""height""": 18, """width""": 18})

									__lowerCAmelCase					=  self.image_processing_class.from_dict(self.image_processor_dict,     size=42,     crop_size=84)
									self.assertEqual(image_processor.size,     {"""shortest_edge""": 42})
									self.assertEqual(image_processor.crop_size,     {"""height""": 84, """width""": 84})




					def     _lowercase						(   self:		Optional[Any]):


									'''simple docstring'''


									pass




					def     _lowercase						(   self:		List[Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  self.image_processing_class(**self.image_processor_dict)
									# create random PIL images
									__lowerCAmelCase					=  prepare_image_inputs(self.image_processor_tester,     equal_resolution=_lowercase)
									for image in image_inputs:
													self.assertIsInstance(_lowercase,     Image.Image)

									# Test not batched input
									__lowerCAmelCase					=  image_processing(image_inputs[0],     return_tensors="""pt""").pixel_values
									self.assertEqual(
									    encoded_images.shape,     (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    ),     )

									# Test batched
									__lowerCAmelCase					=  image_processing(_lowercase,     return_tensors="""pt""").pixel_values
									self.assertEqual(
									    encoded_images.shape,     (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    ),     )




					def     _lowercase						(   self:		List[str]):


									'''simple docstring'''


									__lowerCAmelCase					=  self.image_processing_class(**self.image_processor_dict)
									# create random numpy tensors
									__lowerCAmelCase					=  prepare_image_inputs(self.image_processor_tester,     equal_resolution=_lowercase,     numpify=_lowercase)
									for image in image_inputs:
													self.assertIsInstance(_lowercase,     np.ndarray)

									# Test not batched input
									__lowerCAmelCase					=  image_processing(image_inputs[0],     return_tensors="""pt""").pixel_values
									self.assertEqual(
									    encoded_images.shape,     (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    ),     )

									# Test batched
									__lowerCAmelCase					=  image_processing(_lowercase,     return_tensors="""pt""").pixel_values
									self.assertEqual(
									    encoded_images.shape,     (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    ),     )







					def     _lowercase						(   self:		Union[str, Any]):


									'''simple docstring'''


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

									# Test not batched input
									__lowerCAmelCase					=  image_processing(image_inputs[0],     return_tensors="""pt""").pixel_values
									self.assertEqual(
									    encoded_images.shape,     (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    ),     )

									# Test batched
									__lowerCAmelCase					=  image_processing(_lowercase,     return_tensors="""pt""").pixel_values
									self.assertEqual(
									    encoded_images.shape,     (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""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}