
'''simple docstring'''



import unittest

from huggingface_hub import hf_hub_download

from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_decord,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
)

from .test_pipelines_common import ANY




@is_pipeline_test
@require_torch_or_tf
@require_vision
@require_decord
class     UpperCAmelCase_	(						unittest.TestCase					):






						'''simple docstring'''






						_lowercase						: Optional[int]										=  MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase       ,  _lowercase ):

												"""simple docstring"""



												_lowerCAmelCase    		=					hf_hub_download(
												    repo_id="""nateraw/video-demo"""       ,  filename="""archery.mp4"""       ,  repo_type="""dataset""" )
												_lowerCAmelCase    		=					VideoClassificationPipeline(model=_lowercase       ,  image_processor=_lowercase       ,  top_k=2 )
												_lowerCAmelCase    		=					[
												    example_video_filepath,
												    """https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4""",
												]
												return video_classifier, examples




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase ):

												"""simple docstring"""



												for example in examples:
																		_lowerCAmelCase    		=					video_classifier(_lowercase )

																		self.assertEqual(
																		    _lowercase       ,  [
																		        {"""score""": ANY(_lowercase ), """label""": ANY(_lowercase )},
																		        {"""score""": ANY(_lowercase ), """label""": ANY(_lowercase )},
																		    ]       ,  )




						@require_torch
						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					"""hf-internal-testing/tiny-random-VideoMAEForVideoClassification"""
												_lowerCAmelCase    		=					VideoMAEFeatureExtractor(
												    size={"""shortest_edge""": 10}       ,  crop_size={"""height""": 10, """width""": 10} )
												_lowerCAmelCase    		=					pipeline(
												    """video-classification"""       ,  model=_lowercase       ,  feature_extractor=_lowercase       ,  frame_sampling_rate=4 )

												_lowerCAmelCase    		=					hf_hub_download(repo_id="""nateraw/video-demo"""       ,  filename="""archery.mp4"""       ,  repo_type="""dataset""" )
												_lowerCAmelCase    		=					video_classifier(_lowercase       ,  top_k=2 )
												self.assertEqual(
												    nested_simplify(_lowercase       ,  decimals=4 )       ,  [{"""score""": 0.5199, """label""": """LABEL_0"""}, {"""score""": 0.4801, """label""": """LABEL_1"""}]       ,  )

												_lowerCAmelCase    		=					video_classifier(
												    [
												        video_file_path,
												        video_file_path,
												    ]       ,  top_k=2       ,  )
												self.assertEqual(
												    nested_simplify(_lowercase       ,  decimals=4 )       ,  [
												        [{"""score""": 0.5199, """label""": """LABEL_0"""}, {"""score""": 0.4801, """label""": """LABEL_1"""}],
												        [{"""score""": 0.5199, """label""": """LABEL_0"""}, {"""score""": 0.4801, """label""": """LABEL_1"""}],
												    ]       ,  )




						@require_tf
						def    _lowercase      (   self ):

												"""simple docstring"""



												pass


'''simple docstring'''



from sklearn.metrics import mean_squared_error

import datasets


_lowercase          =						"""\
@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}
}
"""

_lowercase          =						"""\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
"""


_lowercase          =						"""
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.

    squared : bool, default=True
        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.

Returns:
    mse : mean squared error.
Examples:

    >>> mse_metric = datasets.load_metric(\"mse\")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mse': 0.375}
    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)
    >>> print(rmse_result)
    {'mse': 0.6123724356957945}

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

    >>> mse_metric = datasets.load_metric(\"mse\", \"multilist\")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mse': 0.7083333333333334}
    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput='raw_values')
    >>> print(results) # doctest: +NORMALIZE_WHITESPACE
    {'mse': array([0.41666667, 1. ])}
"""




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






						'''simple docstring'''






						def    _lowercase      (   self ):

												"""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    _lowercase      (   self ):

												"""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    _lowercase      (   self       ,  _lowercase       ,  _lowercase       ,  _lowercase=None       ,  _lowercase="uniform_average"       ,  _lowercase=True ):

												"""simple docstring"""



												_lowerCAmelCase    		=					mean_squared_error(
												    _lowercase       ,  _lowercase       ,  sample_weight=_lowercase       ,  multioutput=_lowercase       ,  squared=_lowercase )

												return {"mse": mse}
