





'''simple docstring'''

import random
import unittest

import numpy as np

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionImgaImgPipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
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 (						UpperCAmelCase_			,       unittest.TestCase  ):
		UpperCAmelCase					:	Union[str, Any] 			=		'''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''




		def 				UpperCAmelCase__   (self      :	str							,			A__      :	int=0				)					-> List[Any]:
						lowercase         = floats_tensor((1, 3, 1_2_8, 1_2_8)							,			rng=random.Random(_lowercase				)				)
						lowercase         = np.random.RandomState(_lowercase				)
						lowercase         = {
						    "prompt": "A painting of a squirrel eating a burger",
						    "image": image,
						    "generator": generator,
						    "num_inference_steps": 3,
						    "strength": 0.7_5,
						    "guidance_scale": 7.5,
						    "output_type": "numpy",
						}
						return inputs




		def 				UpperCAmelCase__   (self      :	Optional[int]				)					-> int:
						lowercase         = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint							,			provider="CPUExecutionProvider"				)
						pipe.set_progress_bar_config(disable=_lowercase				)

						lowercase         = self.get_dummy_inputs()
						lowercase         = pipe(**_lowercase				).images
						lowercase         = image[0, -3:, -3:, -1].flatten()

						assert image.shape == (1, 1_2_8, 1_2_8, 3)
						lowercase         = np.array([0.6_9_6_4_3, 0.5_8_4_8_4, 0.5_0_3_1_4, 0.5_8_7_6_0, 0.5_5_3_6_8, 0.5_9_6_4_3, 0.5_1_5_2_9, 0.4_1_2_1_7, 0.4_9_0_8_7]				)
						assert np.abs(image_slice - expected_slice				).max() < 1e-1




		def 				UpperCAmelCase__   (self      :	List[Any]				)					-> int:
						lowercase         = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint							,			provider="CPUExecutionProvider"				)
						lowercase         = PNDMScheduler.from_config(pipe.scheduler.config							,			skip_prk_steps=_lowercase				)
						pipe.set_progress_bar_config(disable=_lowercase				)

						lowercase         = self.get_dummy_inputs()
						lowercase         = pipe(**_lowercase				).images
						lowercase         = image[0, -3:, -3:, -1]

						assert image.shape == (1, 1_2_8, 1_2_8, 3)
						lowercase         = np.array([0.6_1_7_3_7, 0.5_4_6_4_2, 0.5_3_1_8_3, 0.5_4_4_6_5, 0.5_2_7_4_2, 0.6_0_5_2_5, 0.4_9_9_6_9, 0.4_0_6_5_5, 0.4_8_1_5_4]				)

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




		def 				UpperCAmelCase__   (self      :	List[Any]				)					-> Tuple:
						lowercase         = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint							,			provider="CPUExecutionProvider"				)
						lowercase         = LMSDiscreteScheduler.from_config(pipe.scheduler.config				)
						pipe.set_progress_bar_config(disable=_lowercase				)

						# warmup pass to apply optimizations
						lowercase         = pipe(**self.get_dummy_inputs()				)

						lowercase         = self.get_dummy_inputs()
						lowercase         = pipe(**_lowercase				).images
						lowercase         = image[0, -3:, -3:, -1]

						assert image.shape == (1, 1_2_8, 1_2_8, 3)
						lowercase         = np.array([0.5_2_7_6_1, 0.5_9_9_7_7, 0.4_9_0_3_3, 0.4_9_6_1_9, 0.5_4_2_8_2, 0.5_0_3_1_1, 0.4_7_6_0_0, 0.4_0_9_1_8, 0.4_5_2_0_3]				)

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




		def 				UpperCAmelCase__   (self      :	Tuple				)					-> List[str]:
						lowercase         = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint							,			provider="CPUExecutionProvider"				)
						lowercase         = EulerDiscreteScheduler.from_config(pipe.scheduler.config				)
						pipe.set_progress_bar_config(disable=_lowercase				)

						lowercase         = self.get_dummy_inputs()
						lowercase         = pipe(**_lowercase				).images
						lowercase         = image[0, -3:, -3:, -1]

						assert image.shape == (1, 1_2_8, 1_2_8, 3)
						lowercase         = np.array([0.5_2_9_1_1, 0.6_0_0_0_4, 0.4_9_2_2_9, 0.4_9_8_0_5, 0.5_4_5_0_2, 0.5_0_6_8_0, 0.4_7_7_7_7, 0.4_1_0_2_8, 0.4_5_3_0_4]				)

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




		def 				UpperCAmelCase__   (self      :	Any				)					-> Tuple:
						lowercase         = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint							,			provider="CPUExecutionProvider"				)
						lowercase         = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config				)
						pipe.set_progress_bar_config(disable=_lowercase				)

						lowercase         = self.get_dummy_inputs()
						lowercase         = pipe(**_lowercase				).images
						lowercase         = image[0, -3:, -3:, -1]

						assert image.shape == (1, 1_2_8, 1_2_8, 3)
						lowercase         = np.array([0.5_2_9_1_1, 0.6_0_0_0_4, 0.4_9_2_2_9, 0.4_9_8_0_5, 0.5_4_5_0_2, 0.5_0_6_8_0, 0.4_7_7_7_7, 0.4_1_0_2_8, 0.4_5_3_0_4]				)

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





		def 				UpperCAmelCase__   (self      :	List[Any]				)					-> str:
						lowercase         = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint							,			provider="CPUExecutionProvider"				)
						lowercase         = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config				)
						pipe.set_progress_bar_config(disable=_lowercase				)

						lowercase         = self.get_dummy_inputs()
						lowercase         = pipe(**_lowercase				).images
						lowercase         = image[0, -3:, -3:, -1]

						assert image.shape == (1, 1_2_8, 1_2_8, 3)
						lowercase         = np.array([0.6_5_3_3_1, 0.5_8_2_7_7, 0.4_8_2_0_4, 0.5_6_0_5_9, 0.5_3_6_6_5, 0.5_6_2_3_5, 0.5_0_9_6_9, 0.4_0_0_0_9, 0.4_6_5_5_2]				)

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




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




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




		@property
		def 				UpperCAmelCase__   (self      :	str				)					-> Optional[int]:
						lowercase         = ort.SessionOptions()
						lowercase         = False
						return options




		def 				UpperCAmelCase__   (self      :	Union[str, Any]				)					-> Union[str, Any]:
						lowercase         = load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
						    "/img2img/sketch-mountains-input.jpg"				)
						lowercase         = init_image.resize((7_6_8, 5_1_2)				)
						# using the PNDM scheduler by default
						lowercase         = OnnxStableDiffusionImgaImgPipeline.from_pretrained(
						    "CompVis/stable-diffusion-v1-4"							,			revision="onnx"							,			safety_checker=_lowercase							,			feature_extractor=_lowercase							,			provider=self.gpu_provider							,			sess_options=self.gpu_options							,			)
						pipe.set_progress_bar_config(disable=_lowercase				)

						lowercase         = "A fantasy landscape, trending on artstation"

						lowercase         = np.random.RandomState(0				)
						lowercase         = pipe(
						    prompt=_lowercase							,			image=_lowercase							,			strength=0.7_5							,			guidance_scale=7.5							,			num_inference_steps=1_0							,			generator=_lowercase							,			output_type="np"							,			)
						lowercase         = output.images
						lowercase         = images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

						assert images.shape == (1, 5_1_2, 7_6_8, 3)
						lowercase         = np.array([0.4_9_0_9, 0.5_0_5_9, 0.5_3_7_2, 0.4_6_2_3, 0.4_8_7_6, 0.5_0_4_9, 0.4_8_2_0, 0.4_9_5_6, 0.5_0_1_9]				)
						# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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





		def 				UpperCAmelCase__   (self      :	int				)					-> Optional[int]:
						lowercase         = load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
						    "/img2img/sketch-mountains-input.jpg"				)
						lowercase         = init_image.resize((7_6_8, 5_1_2)				)
						lowercase         = LMSDiscreteScheduler.from_pretrained(
						    "runwayml/stable-diffusion-v1-5"							,			subfolder="scheduler"							,			revision="onnx"				)
						lowercase         = OnnxStableDiffusionImgaImgPipeline.from_pretrained(
						    "runwayml/stable-diffusion-v1-5"							,			revision="onnx"							,			scheduler=_lowercase							,			safety_checker=_lowercase							,			feature_extractor=_lowercase							,			provider=self.gpu_provider							,			sess_options=self.gpu_options							,			)
						pipe.set_progress_bar_config(disable=_lowercase				)

						lowercase         = "A fantasy landscape, trending on artstation"

						lowercase         = np.random.RandomState(0				)
						lowercase         = pipe(
						    prompt=_lowercase							,			image=_lowercase							,			strength=0.7_5							,			guidance_scale=7.5							,			num_inference_steps=2_0							,			generator=_lowercase							,			output_type="np"							,			)
						lowercase         = output.images
						lowercase         = images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

						assert images.shape == (1, 5_1_2, 7_6_8, 3)
						lowercase         = np.array([0.8_0_4_3, 0.9_2_6, 0.9_5_8_1, 0.8_1_1_9, 0.8_9_5_4, 0.9_1_3, 0.7_2_0_9, 0.7_4_6_3, 0.7_4_3_1]				)
						# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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











'''simple docstring'''

# Algorithm for the pigeonhole sorting









def 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = min(lowerCAmelCase_							)  # min() finds the minimum value
				lowercase         = max(lowerCAmelCase_							)  # max() finds the maximum value

				lowercase         = max_val - min_val + 1  # size is difference of max and min values plus one

				# list of pigeonholes of size equal to the variable size
				lowercase         = [0] * size

				# Populate the pigeonholes.
				for x in a:
								assert isinstance(lowerCAmelCase_		,							lowerCAmelCase_							), "integers only please"
								holes[x - min_val] += 1

				# Putting the elements back into the array in an order.
				lowercase         = 0
				for count in range(lowerCAmelCase_							):
								while holes[count] > 0:
												holes[count] -= 1
												lowercase         = count + min_val
												i += 1







def 							UpperCAmelCase_    (	):

				"""simple docstring"""






				lowercase         = [8, 3, 2, 7, 4, 6, 8]
				pigeonhole_sort(lowerCAmelCase_							)
				print("Sorted order is:"		,							" ".join(lowerCAmelCase_							)							)


if __name__ == "__main__":
			main()




