




"""simple docstring"""




import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


lowerCAmelCase_						=			False
class 						_A (  unittest.TestCase				):
  pass


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






  def  __a      (  self		:     Optional[int]				)		->   str:






       """simple docstring"""







       lowercase :       Dict       =			VersatileDiffusionImageVariationPipeline.from_pretrained('''shi-labs/versatile-diffusion'''				)
       pipe.to(__UpperCamelCase				)
       pipe.set_progress_bar_config(disable=__UpperCamelCase				)

       lowercase :       List[Any]       =			load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''				)
       lowercase :       Optional[int]       =			torch.manual_seed(0				)
       lowercase :       Tuple       =			pipe(
           image=__UpperCamelCase				,						generator=__UpperCamelCase				,						guidance_scale=7.5				,						num_inference_steps=50				,						output_type='''numpy'''				,						).images

       lowercase :       List[Any]       =			image[0, 253:256, 253:256, -1]

       assert image.shape == (1, 512, 512, 3)
       lowercase :       str       =			np.array([0.0_441, 0.0_469, 0.0_507, 0.0_575, 0.0_632, 0.0_650, 0.0_865, 0.0_909, 0.0_945]				)

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





from sklearn.metrics import mean_squared_error

import datasets


lowerCAmelCase_						=			'\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n'

lowerCAmelCase_						=			'\\nMean Squared Error(MSE) is the average of the square of difference between the predicted\nand actual values.\n'


lowerCAmelCase_						=			'\nArgs:\n    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Estimated target values.\n    references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Ground truth (correct) target values.\n    sample_weight: array-like of shape (n_samples,), default=None\n        Sample weights.\n    multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"\n        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n        "raw_values" : Returns a full set of errors in case of multioutput input.\n\n        "uniform_average" : Errors of all outputs are averaged with uniform weight.\n\n    squared : bool, default=True\n        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.\n\nReturns:\n    mse : mean squared error.\nExamples:\n\n    >>> mse_metric = datasets.load_metric("mse")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mse\': 0.375}\n    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)\n    >>> print(rmse_result)\n    {\'mse\': 0.6123724356957945}\n\n    If you\'re using multi-dimensional lists, then set the config as follows :\n\n    >>> mse_metric = datasets.load_metric("mse", "multilist")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mse\': 0.7083333333333334}\n    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {\'mse\': array([0.41666667, 1. ])}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,       _KWARGS_DESCRIPTION				)
class 						_A (  datasets.Metric				):






  def  __a      (  self		:     List[Any]				)		->   Optional[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_squared_error.html'''
           ]				,						)






  def  __a      (  self		:     List[Any]				)		->   int:






       """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  __a      (  self		:     Any				,						_A		:     Dict				,						_A		:     Any				,						_A		:     Any=None				,						_A		:     Any="uniform_average"				,						_A		:     Optional[Any]=True				)		->   Dict:






       """simple docstring"""







       lowercase :       Any       =			mean_squared_error(
           _A				,						_A				,						sample_weight=_A				,						multioutput=_A				,						squared=_A				)

       return {"mse": mse}