






'''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    A_	(a_						,					unittest.TestCase							):
							"""simple docstring"""




							pass







@nightly
@require_onnxruntime
@require_torch_gpu
class    A_	(unittest.TestCase							):
							"""simple docstring"""




							@property
							def 							_A	(		self			:List[Any]				)				->  str:


										'''simple docstring'''




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

							@property
							def 							_A	(		self			:int				)				->  int:


										'''simple docstring'''




										snake_case_				:					str   =       ort.SessionOptions()
										snake_case_				:					List[str]   =       False
										return options

							def 							_A	(		self			:Tuple				)				->  Optional[Any]:


										'''simple docstring'''




										snake_case_				:					List[str]   =       load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/in_paint/overture-creations-5sI6fQgYIuo.png"				)
										snake_case_				:					Optional[Any]   =       load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"				)
										snake_case_				:					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__				)

										snake_case_				:					int   =       "A red cat sitting on a park bench"

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

										assert images.shape == (1, 512, 512, 3)
										snake_case_				:					Dict   =       np.array([0.2_5_1_4, 0.3_0_0_7, 0.3_5_1_7, 0.1_7_9_0, 0.2_3_8_2, 0.3_1_6_7, 0.1_9_4_4, 0.2_2_7_3, 0.2_4_6_4]				)

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






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


										'''simple docstring'''




										snake_case_				:					Dict   =       load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/in_paint/overture-creations-5sI6fQgYIuo.png"				)
										snake_case_				:					List[Any]   =       load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"				)
										snake_case_				:					Optional[Any]   =       LMSDiscreteScheduler.from_pretrained(
										    "runwayml/stable-diffusion-inpainting"       ,							subfolder="scheduler"       ,							revision="onnx"				)
										snake_case_				:					Dict   =       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__				)

										snake_case_				:					Union[str, Any]   =       "A red cat sitting on a park bench"

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

										assert images.shape == (1, 512, 512, 3)
										snake_case_				:					Dict   =       np.array([0.0_0_8_6, 0.0_0_7_7, 0.0_0_8_3, 0.0_0_9_3, 0.0_1_0_7, 0.0_1_3_9, 0.0_0_9_4, 0.0_0_9_7, 0.0_1_2_5]				)

										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 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__						)->    float:



			"""simple docstring"""



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

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__						)->    list[list[list[float] | float]]:



			"""simple docstring"""



			if dataset.ndim != value_array.ndim:
						snake_case_				:					int   =       (
						    "Wrong input data's dimensions... "
						    F'''dataset : {dataset.ndim}, value_array : {value_array.ndim}'''
						)
						raise ValueError(__magic_name__						)

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

			if dataset.dtype != value_array.dtype:
						snake_case_				:					Dict   =       (
						    "Input data have different datatype... "
						    F'''dataset : {dataset.dtype}, value_array : {value_array.dtype}'''
						)
						raise TypeError(__magic_name__						)

			snake_case_				:					Optional[int]   =       []

			for value in value_array:
						snake_case_				:					List[str]   =       euclidean(__magic_name__     ,dataset[0]						)
						snake_case_				:					int   =       dataset[0].tolist()

						for dataset_value in dataset[1:]:
									snake_case_				:					Optional[Any]   =       euclidean(__magic_name__     ,__magic_name__						)

									if dist > temp_dist:
												snake_case_				:					Tuple   =       temp_dist
												snake_case_				:					Optional[int]   =       dataset_value.tolist()

						answer.append([vector, dist]						)

			return answer

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__						)->    float:



			"""simple docstring"""



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


if __name__ == "__main__":
		import doctest

		doctest.testmod()




