



'''simple docstring'''




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
						import PIL


a     :    int      						=					logging.get_logger(__name__)



def    __lowerCamelCase						(      _lowercase	)   ->    List[List[ImageInput]]:
							if isinstance(_lowercase			,       (list, tuple)	) and isinstance(videos[0]			,       (list, tuple)	) and is_valid_image(videos[0][0]	):
														return videos

							elif isinstance(_lowercase			,       (list, tuple)	) and is_valid_image(videos[0]	):
														return [videos]

							elif is_valid_image(_lowercase	):
														return [[videos]]

							raise ValueError(F'''Could not make batched video from {videos}'''	)


class     UpperCamelCase_				(							__magic_name__    ):
	lowercase    =				['pixel_values']





	def __init__(						self	,	A = True	,	A = None	,	A = PILImageResampling.BILINEAR	,	A = True	,	A = None	,	A = True	,	A = 1 / 255	,	A = True	,	A = None	,	A = None	,	**A	,	)    ->    None:
								super().__init__(**A					)
								UpperCAmelCase  :	Dict							  =	size if size is not None else {"""shortest_edge""": 224}
								UpperCAmelCase  :	List[Any]							  =	get_size_dict(A	,	default_to_square=A					)
								UpperCAmelCase  :	Union[str, Any]							  =	crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
								UpperCAmelCase  :	Any							  =	get_size_dict(A	,	param_name="""crop_size"""					)

								UpperCAmelCase  :	str							  =	do_resize
								UpperCAmelCase  :	str							  =	size
								UpperCAmelCase  :	Optional[Any]							  =	do_center_crop
								UpperCAmelCase  :	Union[str, Any]							  =	crop_size
								UpperCAmelCase  :	Dict							  =	resample
								UpperCAmelCase  :	str							  =	do_rescale
								UpperCAmelCase  :	Tuple							  =	rescale_factor
								UpperCAmelCase  :	Optional[int]							  =	do_normalize
								UpperCAmelCase  :	str							  =	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								UpperCAmelCase  :	Any							  =	image_std if image_std is not None else IMAGENET_STANDARD_STD





	def 				_lowercase(						self	,	A	,	A	,	A = PILImageResampling.BILINEAR	,	A = None	,	**A	,	)    ->    np.ndarray:
								UpperCAmelCase  :	List[str]							  =	get_size_dict(A	,	default_to_square=A					)
								if "shortest_edge" in size:
															UpperCAmelCase  :	List[str]							  =	get_resize_output_image_size(A	,	size["""shortest_edge"""]	,	default_to_square=A					)
								elif "height" in size and "width" in size:
															UpperCAmelCase  :	List[str]							  =	(size["""height"""], size["""width"""])
								else:
															raise ValueError(f'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''					)
								return resize(A	,	size=A	,	resample=A	,	data_format=A	,	**A					)





	def 				_lowercase(						self	,	A	,	A	,	A = None	,	**A	,	)    ->    np.ndarray:
								UpperCAmelCase  :	List[str]							  =	get_size_dict(A					)
								if "height" not in size or "width" not in size:
															raise ValueError(f'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}'''					)
								return center_crop(A	,	size=(size["""height"""], size["""width"""])	,	data_format=A	,	**A					)





	def 				_lowercase(						self	,	A	,	A	,	A = None	,	**A	,	)    ->    List[str]:
								return rescale(A	,	scale=A	,	data_format=A	,	**A					)





	def 				_lowercase(						self	,	A	,	A	,	A	,	A = None	,	**A	,	)    ->    np.ndarray:
								return normalize(A	,	mean=A	,	std=A	,	data_format=A	,	**A					)





	def 				_lowercase(						self	,	A	,	A = None	,	A = None	,	A = None	,	A = None	,	A = None	,	A = None	,	A = None	,	A = None	,	A = None	,	A = None	,	A = ChannelDimension.FIRST	,	)    ->    np.ndarray:
								if do_resize and size is None or resample is None:
															raise ValueError("""Size and resample must be specified if do_resize is True."""					)

								if do_center_crop and crop_size is None:
															raise ValueError("""Crop size must be specified if do_center_crop is True."""					)

								if do_rescale and rescale_factor is None:
															raise ValueError("""Rescale factor must be specified if do_rescale is True."""					)

								if do_normalize and (image_mean is None or image_std is None):
															raise ValueError("""Image mean and std must be specified if do_normalize is True."""					)

								# All transformations expect numpy arrays.
								UpperCAmelCase  :	List[str]							  =	to_numpy_array(A					)

								if do_resize:
															UpperCAmelCase  :	str							  =	self.resize(image=A	,	size=A	,	resample=A					)

								if do_center_crop:
															UpperCAmelCase  :	Tuple							  =	self.center_crop(A	,	size=A					)

								if do_rescale:
															UpperCAmelCase  :	Optional[Any]							  =	self.rescale(image=A	,	scale=A					)

								if do_normalize:
															UpperCAmelCase  :	List[Any]							  =	self.normalize(image=A	,	mean=A	,	std=A					)

								UpperCAmelCase  :	int							  =	to_channel_dimension_format(A	,	A					)
								return image






	def 				_lowercase(						self	,	A	,	A = None	,	A = None	,	A = None	,	A = None	,	A = None	,	A = None	,	A = None	,	A = None	,	A = None	,	A = None	,	A = None	,	A = ChannelDimension.FIRST	,	**A	,	)    ->    PIL.Image.Image:
								UpperCAmelCase  :	Any							  =	do_resize if do_resize is not None else self.do_resize
								UpperCAmelCase  :	int							  =	resample if resample is not None else self.resample
								UpperCAmelCase  :	Union[str, Any]							  =	do_center_crop if do_center_crop is not None else self.do_center_crop
								UpperCAmelCase  :	Tuple							  =	do_rescale if do_rescale is not None else self.do_rescale
								UpperCAmelCase  :	Optional[Any]							  =	rescale_factor if rescale_factor is not None else self.rescale_factor
								UpperCAmelCase  :	Tuple							  =	do_normalize if do_normalize is not None else self.do_normalize
								UpperCAmelCase  :	Optional[int]							  =	image_mean if image_mean is not None else self.image_mean
								UpperCAmelCase  :	Tuple							  =	image_std if image_std is not None else self.image_std

								UpperCAmelCase  :	Optional[int]							  =	size if size is not None else self.size
								UpperCAmelCase  :	Union[str, Any]							  =	get_size_dict(A	,	default_to_square=A					)
								UpperCAmelCase  :	Optional[Any]							  =	crop_size if crop_size is not None else self.crop_size
								UpperCAmelCase  :	Dict							  =	get_size_dict(A	,	param_name="""crop_size"""					)

								if not valid_images(A					):
															raise ValueError(
															    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
															    """torch.Tensor, tf.Tensor or jax.ndarray."""					)

								UpperCAmelCase  :	List[Any]							  =	make_batched(A					)

								UpperCAmelCase  :	Dict							  =	[
								    [
								        self._preprocess_image(
								            image=A	,	do_resize=A	,	size=A	,	resample=A	,	do_center_crop=A	,	crop_size=A	,	do_rescale=A	,	rescale_factor=A	,	do_normalize=A	,	image_mean=A	,	image_std=A	,	data_format=A	,	)
								        for img in video
								    ]
								    for video in videos
								]

								UpperCAmelCase  :	int							  =	{"""pixel_values""": videos}
								return BatchFeature(data=A	,	tensor_type=A					)






'''simple docstring'''




from sklearn.metrics import mean_squared_error

import datasets


a     :    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}
}
"""

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


a     :    int      						=					"""
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    ):





	def 				_lowercase(						self					)    ->    List[str]:
								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					)    ->    List[Any]:
								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	,	A	,	A	,	A=None	,	A="uniform_average"	,	A=True					)    ->    List[Any]:
								UpperCAmelCase  :	List[Any]							  =	mean_squared_error(
								    A	,	A	,	sample_weight=A	,	multioutput=A	,	squared=A					)

								return {"mse": mse}

