





"""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_video_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import VideoMAEImageProcessor




class  _a						(      unittest.TestCase				):






				def __init__( self		:    Optional[Any], lowerCAmelCase__		:    Union[str, Any], lowerCAmelCase__		:    List[str]=7, lowerCAmelCase__		:    Optional[Any]=3, lowerCAmelCase__		:    Optional[Any]=1_0, lowerCAmelCase__		:    List[Any]=1_8, lowerCAmelCase__		:    List[Any]=3_0, lowerCAmelCase__		:    Dict=4_0_0, lowerCAmelCase__		:    Tuple=True, lowerCAmelCase__		:    Optional[int]=None, lowerCAmelCase__		:    Optional[int]=True, lowerCAmelCase__		:    List[str]=[0.5, 0.5, 0.5], lowerCAmelCase__		:    Optional[Any]=[0.5, 0.5, 0.5], lowerCAmelCase__		:    Optional[Any]=None, )	->      Optional[int]:




										'''simple docstring'''



										_UpperCamelCase			:					Optional[Any]     =    size if size is not None else {'''shortest_edge''': 1_8}
										_UpperCamelCase			:					str     =    crop_size if crop_size is not None else {'''height''': 1_8, '''width''': 1_8}

										_UpperCamelCase			:					Tuple     =    parent
										_UpperCamelCase			:					Dict     =    batch_size
										_UpperCamelCase			:					str     =    num_channels
										_UpperCamelCase			:					str     =    num_frames
										_UpperCamelCase			:					Optional[int]     =    image_size
										_UpperCamelCase			:					Union[str, Any]     =    min_resolution
										_UpperCamelCase			:					Tuple     =    max_resolution
										_UpperCamelCase			:					Optional[Any]     =    do_resize
										_UpperCamelCase			:					List[str]     =    size
										_UpperCamelCase			:					str     =    do_normalize
										_UpperCamelCase			:					str     =    image_mean
										_UpperCamelCase			:					str     =    image_std
										_UpperCamelCase			:					str     =    crop_size






				def 				snake_case	( self		:    Tuple							)	->      Tuple:




										'''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,
										    "crop_size": self.crop_size,
										}




@require_torch
@require_vision
class  _a						(      _lowerCAmelCase	,				unittest.TestCase				):
				UpperCamelCase								=    VideoMAEImageProcessor if is_vision_available() else None






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




										'''simple docstring'''



										_UpperCamelCase			:					Optional[Any]     =    VideoMAEImageProcessingTester(self							)






				@property
				def 				snake_case	( self		:    Union[str, Any]							)	->      Dict:




										'''simple docstring'''



										return self.image_processor_tester.prepare_image_processor_dict()






				def 				snake_case	( self		:    Optional[Any]							)	->      List[str]:




										'''simple docstring'''



										_UpperCamelCase			:					Union[str, Any]     =    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__, '''do_center_crop'''							)							)
										self.assertTrue(hasattr(lowerCAmelCase__, '''size'''							)							)






				def 				snake_case	( self		:    Union[str, Any]							)	->      str:




										'''simple docstring'''



										_UpperCamelCase			:					List[Any]     =    self.image_processing_class.from_dict(self.image_processor_dict							)
										self.assertEqual(image_processor.size, {'''shortest_edge''': 1_8}							)
										self.assertEqual(image_processor.crop_size, {'''height''': 1_8, '''width''': 1_8}							)

										_UpperCamelCase			:					Any     =    self.image_processing_class.from_dict(self.image_processor_dict, size=4_2, crop_size=8_4							)
										self.assertEqual(image_processor.size, {'''shortest_edge''': 4_2}							)
										self.assertEqual(image_processor.crop_size, {'''height''': 8_4, '''width''': 8_4}							)






				def 				snake_case	( self		:    Any							)	->      List[str]:




										'''simple docstring'''



										pass






				def 				snake_case	( self		:    int							)	->      Dict:




										'''simple docstring'''



										_UpperCamelCase			:					Union[str, Any]     =    self.image_processing_class(**self.image_processor_dict							)
										# create random PIL videos
										_UpperCamelCase			:					Union[str, Any]     =    prepare_video_inputs(self.image_processor_tester, equal_resolution=lowerCAmelCase__							)
										for video in video_inputs:
																self.assertIsInstance(lowerCAmelCase__, lowerCAmelCase__							)
																self.assertIsInstance(video[0], Image.Image							)

										# Test not batched input
										_UpperCamelCase			:					Union[str, Any]     =    image_processing(video_inputs[0], return_tensors='''pt'''							).pixel_values
										self.assertEqual(
										    encoded_videos.shape, (
										        1,
										        self.image_processor_tester.num_frames,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    ), )

										# Test batched
										_UpperCamelCase			:					int     =    image_processing(lowerCAmelCase__, return_tensors='''pt'''							).pixel_values
										self.assertEqual(
										    encoded_videos.shape, (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_frames,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    ), )






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




										'''simple docstring'''



										_UpperCamelCase			:					Tuple     =    self.image_processing_class(**self.image_processor_dict							)
										# create random numpy tensors
										_UpperCamelCase			:					Optional[Any]     =    prepare_video_inputs(self.image_processor_tester, equal_resolution=lowerCAmelCase__, numpify=lowerCAmelCase__							)
										for video in video_inputs:
																self.assertIsInstance(lowerCAmelCase__, lowerCAmelCase__							)
																self.assertIsInstance(video[0], np.ndarray							)

										# Test not batched input
										_UpperCamelCase			:					Optional[int]     =    image_processing(video_inputs[0], return_tensors='''pt'''							).pixel_values
										self.assertEqual(
										    encoded_videos.shape, (
										        1,
										        self.image_processor_tester.num_frames,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    ), )

										# Test batched
										_UpperCamelCase			:					Union[str, Any]     =    image_processing(lowerCAmelCase__, return_tensors='''pt'''							).pixel_values
										self.assertEqual(
										    encoded_videos.shape, (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_frames,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    ), )






				def 				snake_case	( self		:    int							)	->      Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase			:					Dict     =    self.image_processing_class(**self.image_processor_dict							)
										# create random PyTorch tensors
										_UpperCamelCase			:					Union[str, Any]     =    prepare_video_inputs(self.image_processor_tester, equal_resolution=lowerCAmelCase__, torchify=lowerCAmelCase__							)
										for video in video_inputs:
																self.assertIsInstance(lowerCAmelCase__, lowerCAmelCase__							)
																self.assertIsInstance(video[0], torch.Tensor							)

										# Test not batched input
										_UpperCamelCase			:					Tuple     =    image_processing(video_inputs[0], return_tensors='''pt'''							).pixel_values
										self.assertEqual(
										    encoded_videos.shape, (
										        1,
										        self.image_processor_tester.num_frames,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    ), )

										# Test batched
										_UpperCamelCase			:					Dict     =    image_processing(lowerCAmelCase__, return_tensors='''pt'''							).pixel_values
										self.assertEqual(
										    encoded_videos.shape, (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_frames,
										        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


UpperCamelCase_           ="""\
@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}
}
"""

UpperCamelCase_           ="""\
Mean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual
values.
"""


UpperCamelCase_           ="""
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  _a						(      datasets.Metric				):






				def 				snake_case	( self		:    Optional[int]							)	->      List[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_absolute_error.html'''
										    ], )






				def 				snake_case	( self		:    Dict							)	->      Union[str, 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 				snake_case	( self		:    List[Any], lowerCAmelCase__		:    Dict, lowerCAmelCase__		:    Union[str, Any], lowerCAmelCase__		:    Tuple=None, lowerCAmelCase__		:    Tuple="uniform_average"							)	->      Optional[int]:




										'''simple docstring'''



										_UpperCamelCase			:					List[str]     =    mean_absolute_error(lowerCAmelCase__, lowerCAmelCase__, sample_weight=lowerCAmelCase__, multioutput=lowerCAmelCase__							)

										return {"mae": mae_score}



