


from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available







@dataclass
class 							UpperCamelCase__ (						__magic_name__ ):
					__SCREAMING_SNAKE_CASE							:	Union[List[PIL.Image.Image], np.ndarray]
					__SCREAMING_SNAKE_CASE							:	Optional[List[bool]]


if is_transformers_available() and is_torch_available():
							from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline









'''simple docstring'''


from sklearn.metrics import mean_squared_error

import datasets


_SCREAMING_SNAKE_CASE									=				"\\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"

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


_SCREAMING_SNAKE_CASE									=				"\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  lowerCAmelCase_		(		datasets.Metric		):




 def 				_snake_case (						self		)     ->				Dict:
     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 				_snake_case (						self		)     ->				Tuple:
     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      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase=None      ,  _lowerCAmelCase="uniform_average"      ,  _lowerCAmelCase=True		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   mean_squared_error(
         _lowerCAmelCase      ,  _lowerCAmelCase      ,  sample_weight=_lowerCAmelCase      ,  multioutput=_lowerCAmelCase      ,  squared=_lowerCAmelCase		)

     return {"mse": mse}


