


"""simple docstring"""
from sklearn.metrics import mean_squared_error

import datasets


SCREAMING_SNAKE_CASE :    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}
}
"""

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


SCREAMING_SNAKE_CASE :    Tuple									=				"""
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 				SCREAMING_SNAKE_CASE  (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 				SCREAMING_SNAKE_CASE  (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 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_				,       a_=None				,       a_="uniform_average"				,       a_=True					):





			'''simple docstring'''

			__snake_case	:						Dict						=				mean_squared_error(
			    a_				,       a_				,       sample_weight=a_				,       multioutput=a_				,       squared=a_					)

			return {"mse": mse}









"""simple docstring"""
import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()



@slow
@require_torch_gpu
class 			_UpperCAmelCase (							unittest.TestCase				):






		'''simple docstring'''

		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Tuple						=				StableDiffusionKDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''					)
			__snake_case	:						List[str]						=				sd_pipe.to(a_					)
			sd_pipe.set_progress_bar_config(disable=a_					)

			sd_pipe.set_scheduler('''sample_euler'''					)

			__snake_case	:						Tuple						=				'''A painting of a squirrel eating a burger'''
			__snake_case	:						str						=				torch.manual_seed(0					)
			__snake_case	:						int						=				sd_pipe([prompt]				,       generator=a_				,       guidance_scale=9.0				,       num_inference_steps=20				,       output_type='''np'''					)

			__snake_case	:						List[str]						=				output.images

			__snake_case	:						Tuple						=				image[0, -3:, -3:, -1]

			assert image.shape == (1, 5_12, 5_12, 3)
			__snake_case	:						Dict						=				np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339]					)

			assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Optional[Any]						=				StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''					)
			__snake_case	:						Union[str, Any]						=				sd_pipe.to(a_					)
			sd_pipe.set_progress_bar_config(disable=a_					)

			sd_pipe.set_scheduler('''sample_euler'''					)

			__snake_case	:						str						=				'''A painting of a squirrel eating a burger'''
			__snake_case	:						List[str]						=				torch.manual_seed(0					)
			__snake_case	:						Dict						=				sd_pipe([prompt]				,       generator=a_				,       guidance_scale=9.0				,       num_inference_steps=20				,       output_type='''np'''					)

			__snake_case	:						Any						=				output.images

			__snake_case	:						Tuple						=				image[0, -3:, -3:, -1]

			assert image.shape == (1, 5_12, 5_12, 3)
			__snake_case	:						Tuple						=				np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112]					)

			assert np.abs(image_slice.flatten() - expected_slice					).max() < 5E-1



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Any						=				StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''					)
			__snake_case	:						Union[str, Any]						=				sd_pipe.to(a_					)
			sd_pipe.set_progress_bar_config(disable=a_					)

			sd_pipe.set_scheduler('''sample_dpmpp_2m'''					)

			__snake_case	:						int						=				'''A painting of a squirrel eating a burger'''
			__snake_case	:						Optional[Any]						=				torch.manual_seed(0					)
			__snake_case	:						Dict						=				sd_pipe(
			    [prompt]				,       generator=a_				,       guidance_scale=7.5				,       num_inference_steps=15				,       output_type='''np'''				,       use_karras_sigmas=a_				,       )

			__snake_case	:						Optional[int]						=				output.images

			__snake_case	:						Any						=				image[0, -3:, -3:, -1]

			assert image.shape == (1, 5_12, 5_12, 3)
			__snake_case	:						Any						=				np.array(
			    [0.1138_1689, 0.1211_2921, 0.138_9457, 0.1254_9606, 0.124_4964, 0.1083_1517, 0.1156_2866, 0.1086_7816, 0.1049_9048]					)

			assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2





