





"""simple docstring"""




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				(						a__		,       unittest.TestCase						):
							# FIXME: add fast tests
							pass


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






							@property
							def     __a   (   self      )	-> Optional[int]:
								return (
								    "CUDAExecutionProvider",
								    {
								        "gpu_mem_limit": "15000000000",  # 15GB
								        "arena_extend_strategy": "kSameAsRequested",
								    },
								)






							@property
							def     __a   (   self      )	-> Any:
								a      :		Union[str, Any]	=				ort.SessionOptions()
								a      :		Any	=				False
								return options






							def     __a   (   self      )	-> Dict:
								a      :		List[str]	=				load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/in_paint/overture-creations-5sI6fQgYIuo.png"      )
								a      :		List[Any]	=				load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"      )
								a      :		int	=				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__      )

								a      :		Optional[int]	=				"A red cat sitting on a park bench"

								a      :		Dict	=				np.random.RandomState(0      )
								a      :		str	=				pipe(
								    prompt=lowerCAmelCase__      ,					image=lowerCAmelCase__      ,					mask_image=lowerCAmelCase__      ,					guidance_scale=7.5      ,					num_inference_steps=10      ,					generator=lowerCAmelCase__      ,					output_type="np"      ,					)
								a      :		Union[str, Any]	=				output.images
								a      :		str	=				images[0, 255:258, 255:258, -1]

								assert images.shape == (1, 512, 512, 3)
								a      :		int	=				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      )	-> Tuple:
								a      :		Union[str, Any]	=				load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/in_paint/overture-creations-5sI6fQgYIuo.png"      )
								a      :		Union[str, Any]	=				load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"      )
								a      :		Dict	=				LMSDiscreteScheduler.from_pretrained(
								    "runwayml/stable-diffusion-inpainting"      ,					subfolder="scheduler"      ,					revision="onnx"      )
								a      :		Union[str, Any]	=				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__      )

								a      :		List[Any]	=				"A red cat sitting on a park bench"

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

								assert images.shape == (1, 512, 512, 3)
								a      :		Any	=				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









"""simple docstring"""




from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    np.ndarray    ,       _lowercase						:    np.ndarray					) ->float:






	'''simple docstring'''






	return math.sqrt(sum(pow(a - b    ,       2					) for a, b in zip(_lowercase    ,       _lowercase					)					)					)






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    np.ndarray    ,       _lowercase						:    np.ndarray					) ->list[list[list[float] | float]]:






	'''simple docstring'''






	if dataset.ndim != value_array.ndim:
		a      :		str	=				(
		    "Wrong input data's dimensions... "
		    F"""dataset : {dataset.ndim}, value_array : {value_array.ndim}"""
		)
		raise ValueError(_lowercase					)

	try:
		if dataset.shape[1] != value_array.shape[1]:
			a      :		int	=				(
			    "Wrong input data's shape... "
			    F"""dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}"""
			)
			raise ValueError(_lowercase					)
	except IndexError:
		if dataset.ndim != value_array.ndim:
			raise TypeError("Wrong shape"					)

	if dataset.dtype != value_array.dtype:
		a      :		Union[str, Any]	=				(
		    "Input data have different datatype... "
		    F"""dataset : {dataset.dtype}, value_array : {value_array.dtype}"""
		)
		raise TypeError(_lowercase					)

	a      :		str	=				[]

	for value in value_array:
		a      :		List[Any]	=				euclidean(_lowercase    ,       dataset[0]					)
		a      :		str	=				dataset[0].tolist()

		for dataset_value in dataset[1:]:
			a      :		Tuple	=				euclidean(_lowercase    ,       _lowercase					)

			if dist > temp_dist:
				a      :		Dict	=				temp_dist
				a      :		Optional[int]	=				dataset_value.tolist()

		answer.append([vector, dist]					)

	return answer






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    np.ndarray    ,       _lowercase						:    np.ndarray					) ->float:






	'''simple docstring'''






	return np.dot(_lowercase    ,       _lowercase					) / (norm(_lowercase					) * norm(_lowercase					))


if __name__ == "__main__":
		import doctest

		doctest.testmod()


