



import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
				import onnxruntime as ort

class 						__UpperCamelCase    (					lowercase     ,     unittest.TestCase						):
	# FIXME: add fast tests
	pass


@nightly
@require_onnxruntime
@require_torch_gpu
class 						__UpperCamelCase    (					unittest.TestCase						):




	@property
	def       __A					(		self	:						Any							):


		'''simple docstring'''







		return (
		    "CUDAExecutionProvider",
		    {
		        "gpu_mem_limit": "15000000000",  # 15GB
		        "arena_extend_strategy": "kSameAsRequested",
		    },
		)




	@property
	def       __A					(		self	:						List[str]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			ort.SessionOptions()
		UpperCAmelCase_       				=			False
		return options




	def       __A					(		self	:						Tuple							):


		'''simple docstring'''







		UpperCAmelCase_       				=			load_image(
		    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
		    "/in_paint/overture-creations-5sI6fQgYIuo.png"							)
		UpperCAmelCase_       				=			load_image(
		    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
		    "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"							)
		UpperCAmelCase_       				=			OnnxStableDiffusionInpaintPipeline.from_pretrained(
		    "runwayml/stable-diffusion-inpainting"		,	revision="onnx"		,	safety_checker=lowerCAmelCase		,	feature_extractor=lowerCAmelCase		,	provider=self.gpu_provider		,	sess_options=self.gpu_options		,	)
		pipe.set_progress_bar_config(disable=lowerCAmelCase							)

		UpperCAmelCase_       				=			"A red cat sitting on a park bench"

		UpperCAmelCase_       				=			np.random.RandomState(0							)
		UpperCAmelCase_       				=			pipe(
		    prompt=lowerCAmelCase		,	image=lowerCAmelCase		,	mask_image=lowerCAmelCase		,	guidance_scale=7.5		,	num_inference_steps=10		,	generator=lowerCAmelCase		,	output_type="np"		,	)
		UpperCAmelCase_       				=			output.images
		UpperCAmelCase_       				=			images[0, 255:258, 255:258, -1]

		assert images.shape == (1, 512, 512, 3)
		UpperCAmelCase_       				=			np.array([0.2_514, 0.3_007, 0.3_517, 0.1_790, 0.2_382, 0.3_167, 0.1_944, 0.2_273, 0.2_464]							)

		assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-3




	def       __A					(		self	:						Optional[Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			load_image(
		    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
		    "/in_paint/overture-creations-5sI6fQgYIuo.png"							)
		UpperCAmelCase_       				=			load_image(
		    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
		    "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"							)
		UpperCAmelCase_       				=			LMSDiscreteScheduler.from_pretrained(
		    "runwayml/stable-diffusion-inpainting"		,	subfolder="scheduler"		,	revision="onnx"							)
		UpperCAmelCase_       				=			OnnxStableDiffusionInpaintPipeline.from_pretrained(
		    "runwayml/stable-diffusion-inpainting"		,	revision="onnx"		,	scheduler=lowerCAmelCase		,	safety_checker=lowerCAmelCase		,	feature_extractor=lowerCAmelCase		,	provider=self.gpu_provider		,	sess_options=self.gpu_options		,	)
		pipe.set_progress_bar_config(disable=lowerCAmelCase							)

		UpperCAmelCase_       				=			"A red cat sitting on a park bench"

		UpperCAmelCase_       				=			np.random.RandomState(0							)
		UpperCAmelCase_       				=			pipe(
		    prompt=lowerCAmelCase		,	image=lowerCAmelCase		,	mask_image=lowerCAmelCase		,	guidance_scale=7.5		,	num_inference_steps=20		,	generator=lowerCAmelCase		,	output_type="np"		,	)
		UpperCAmelCase_       				=			output.images
		UpperCAmelCase_       				=			images[0, 255:258, 255:258, -1]

		assert images.shape == (1, 512, 512, 3)
		UpperCAmelCase_       				=			np.array([0.0_086, 0.0_077, 0.0_083, 0.0_093, 0.0_107, 0.0_139, 0.0_094, 0.0_097, 0.0_125]							)

		assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-3


from sklearn.metrics import mean_squared_error

import datasets


_a:    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:    List[Any] 							=			"""\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
"""


_a:    List[str] 							=			"""
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       __A					(		self	:						List[str]							):


		'''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	:						Optional[Any]							):


		'''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	:						Tuple		,	lowerCAmelCase	:						int		,	lowerCAmelCase	:						Dict		,	lowerCAmelCase	:						str=None		,	lowerCAmelCase	:						str="uniform_average"		,	lowerCAmelCase	:						Any=True							):


		'''simple docstring'''







		UpperCAmelCase_       				=			mean_squared_error(
		    lowerCAmelCase		,	lowerCAmelCase		,	sample_weight=lowerCAmelCase		,	multioutput=lowerCAmelCase		,	squared=lowerCAmelCase							)

		return {"mse": mse}