



"""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      A__  (    __SCREAMING_SNAKE_CASE    ,						unittest.TestCase				):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE        =      'hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'

	def 				_SCREAMING_SNAKE_CASE  (	self:						Optional[int]			,     _SCREAMING_SNAKE_CASE:						Tuple=0)							->			Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase :    Dict				=			floats_tensor((1, 3, 128, 128)			,     rng=random.Random(_SCREAMING_SNAKE_CASE))
				__lowerCAmelCase :    List[Any]				=			np.random.RandomState(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    Union[str, Any]				=			{
				    "prompt": "A painting of a squirrel eating a burger",
				    "image": image,
				    "generator": generator,
				    "num_inference_steps": 3,
				    "strength": 0.75,
				    "guidance_scale": 7.5,
				    "output_type": "numpy",
				}
				return inputs

	def 				_SCREAMING_SNAKE_CASE  (	self:						List[Any])							->			Any:

				"""simple docstring"""

				__lowerCAmelCase :    Optional[Any]				=			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint			,     provider="CPUExecutionProvider")
				pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    Optional[Any]				=			self.get_dummy_inputs()
				__lowerCAmelCase :    int				=			pipe(**_SCREAMING_SNAKE_CASE).images
				__lowerCAmelCase :    List[Any]				=			image[0, -3:, -3:, -1].flatten()

				assert image.shape == (1, 128, 128, 3)
				__lowerCAmelCase :    Any				=			np.array([0.6_9643, 0.5_8484, 0.5_0314, 0.5_8760, 0.5_5368, 0.5_9643, 0.5_1529, 0.4_1217, 0.4_9087])
				assert np.abs(image_slice - expected_slice).max() < 1e-1

	def 				_SCREAMING_SNAKE_CASE  (	self:						str)							->			str:

				"""simple docstring"""

				__lowerCAmelCase :    Optional[Any]				=			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint			,     provider="CPUExecutionProvider")
				__lowerCAmelCase :    Union[str, Any]				=			PNDMScheduler.from_config(pipe.scheduler.config			,     skip_prk_steps=_SCREAMING_SNAKE_CASE)
				pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    Any				=			self.get_dummy_inputs()
				__lowerCAmelCase :    Dict				=			pipe(**_SCREAMING_SNAKE_CASE).images
				__lowerCAmelCase :    List[Any]				=			image[0, -3:, -3:, -1]

				assert image.shape == (1, 128, 128, 3)
				__lowerCAmelCase :    List[str]				=			np.array([0.6_1737, 0.5_4642, 0.5_3183, 0.5_4465, 0.5_2742, 0.6_0525, 0.4_9969, 0.4_0655, 0.4_8154])

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

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

				"""simple docstring"""

				__lowerCAmelCase :    Any				=			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint			,     provider="CPUExecutionProvider")
				__lowerCAmelCase :    Dict				=			LMSDiscreteScheduler.from_config(pipe.scheduler.config)
				pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE)

				# warmup pass to apply optimizations
				__lowerCAmelCase :    str				=			pipe(**self.get_dummy_inputs())

				__lowerCAmelCase :    List[Any]				=			self.get_dummy_inputs()
				__lowerCAmelCase :    Tuple				=			pipe(**_SCREAMING_SNAKE_CASE).images
				__lowerCAmelCase :    Union[str, Any]				=			image[0, -3:, -3:, -1]

				assert image.shape == (1, 128, 128, 3)
				__lowerCAmelCase :    Optional[int]				=			np.array([0.5_2761, 0.5_9977, 0.4_9033, 0.4_9619, 0.5_4282, 0.5_0311, 0.4_7600, 0.4_0918, 0.4_5203])

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

	def 				_SCREAMING_SNAKE_CASE  (	self:						Dict)							->			Union[str, Any]:

				"""simple docstring"""

				__lowerCAmelCase :    Union[str, Any]				=			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint			,     provider="CPUExecutionProvider")
				__lowerCAmelCase :    Optional[int]				=			EulerDiscreteScheduler.from_config(pipe.scheduler.config)
				pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    Union[str, Any]				=			self.get_dummy_inputs()
				__lowerCAmelCase :    Any				=			pipe(**_SCREAMING_SNAKE_CASE).images
				__lowerCAmelCase :    List[Any]				=			image[0, -3:, -3:, -1]

				assert image.shape == (1, 128, 128, 3)
				__lowerCAmelCase :    Optional[Any]				=			np.array([0.5_2911, 0.6_0004, 0.4_9229, 0.4_9805, 0.5_4502, 0.5_0680, 0.4_7777, 0.4_1028, 0.4_5304])

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

	def 				_SCREAMING_SNAKE_CASE  (	self:						List[Any])							->			List[str]:

				"""simple docstring"""

				__lowerCAmelCase :    Any				=			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint			,     provider="CPUExecutionProvider")
				__lowerCAmelCase :    str				=			EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
				pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    Any				=			self.get_dummy_inputs()
				__lowerCAmelCase :    str				=			pipe(**_SCREAMING_SNAKE_CASE).images
				__lowerCAmelCase :    Union[str, Any]				=			image[0, -3:, -3:, -1]

				assert image.shape == (1, 128, 128, 3)
				__lowerCAmelCase :    Dict				=			np.array([0.5_2911, 0.6_0004, 0.4_9229, 0.4_9805, 0.5_4502, 0.5_0680, 0.4_7777, 0.4_1028, 0.4_5304])

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







	def 				_SCREAMING_SNAKE_CASE  (	self:						Optional[Any])							->			Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase :    Optional[int]				=			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint			,     provider="CPUExecutionProvider")
				__lowerCAmelCase :    Any				=			DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
				pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    Optional[Any]				=			self.get_dummy_inputs()
				__lowerCAmelCase :    Any				=			pipe(**_SCREAMING_SNAKE_CASE).images
				__lowerCAmelCase :    Any				=			image[0, -3:, -3:, -1]

				assert image.shape == (1, 128, 128, 3)
				__lowerCAmelCase :    int				=			np.array([0.6_5331, 0.5_8277, 0.4_8204, 0.5_6059, 0.5_3665, 0.5_6235, 0.5_0969, 0.4_0009, 0.4_6552])

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






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




	'''simple docstring'''





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

				"""simple docstring"""

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

	@property
	def 				_SCREAMING_SNAKE_CASE  (	self:						Optional[Any])							->			Any:

				"""simple docstring"""

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

	def 				_SCREAMING_SNAKE_CASE  (	self:						List[Any])							->			Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase :    Any				=			load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
				    "/img2img/sketch-mountains-input.jpg")
				__lowerCAmelCase :    str				=			init_image.resize((768, 512))
				# using the PNDM scheduler by default
				__lowerCAmelCase :    Optional[int]				=			OnnxStableDiffusionImgaImgPipeline.from_pretrained(
				    "CompVis/stable-diffusion-v1-4"			,     revision="onnx"			,     safety_checker=_SCREAMING_SNAKE_CASE			,     feature_extractor=_SCREAMING_SNAKE_CASE			,     provider=self.gpu_provider			,     sess_options=self.gpu_options			,     )
				pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    List[Any]				=			"A fantasy landscape, trending on artstation"

				__lowerCAmelCase :    str				=			np.random.RandomState(0)
				__lowerCAmelCase :    Optional[int]				=			pipe(
				    prompt=_SCREAMING_SNAKE_CASE			,     image=_SCREAMING_SNAKE_CASE			,     strength=0.75			,     guidance_scale=7.5			,     num_inference_steps=10			,     generator=_SCREAMING_SNAKE_CASE			,     output_type="np"			,     )
				__lowerCAmelCase :    List[Any]				=			output.images
				__lowerCAmelCase :    Dict				=			images[0, 255:258, 383:386, -1]

				assert images.shape == (1, 512, 768, 3)
				__lowerCAmelCase :    str				=			np.array([0.4909, 0.5059, 0.5372, 0.4623, 0.4876, 0.5049, 0.4820, 0.4956, 0.5019])
				# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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







	def 				_SCREAMING_SNAKE_CASE  (	self:						List[str])							->			Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase :    Dict				=			load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
				    "/img2img/sketch-mountains-input.jpg")
				__lowerCAmelCase :    Any				=			init_image.resize((768, 512))
				__lowerCAmelCase :    Union[str, Any]				=			LMSDiscreteScheduler.from_pretrained(
				    "runwayml/stable-diffusion-v1-5"			,     subfolder="scheduler"			,     revision="onnx")
				__lowerCAmelCase :    Any				=			OnnxStableDiffusionImgaImgPipeline.from_pretrained(
				    "runwayml/stable-diffusion-v1-5"			,     revision="onnx"			,     scheduler=_SCREAMING_SNAKE_CASE			,     safety_checker=_SCREAMING_SNAKE_CASE			,     feature_extractor=_SCREAMING_SNAKE_CASE			,     provider=self.gpu_provider			,     sess_options=self.gpu_options			,     )
				pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    int				=			"A fantasy landscape, trending on artstation"

				__lowerCAmelCase :    Tuple				=			np.random.RandomState(0)
				__lowerCAmelCase :    List[Any]				=			pipe(
				    prompt=_SCREAMING_SNAKE_CASE			,     image=_SCREAMING_SNAKE_CASE			,     strength=0.75			,     guidance_scale=7.5			,     num_inference_steps=20			,     generator=_SCREAMING_SNAKE_CASE			,     output_type="np"			,     )
				__lowerCAmelCase :    Optional[Any]				=			output.images
				__lowerCAmelCase :    int				=			images[0, 255:258, 383:386, -1]

				assert images.shape == (1, 512, 768, 3)
				__lowerCAmelCase :    List[str]				=			np.array([0.8043, 0.926, 0.9581, 0.8119, 0.8954, 0.913, 0.7209, 0.7463, 0.7431])
				# 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"""






def      _lowercase							(    __snake_case   ,__snake_case      )		->  Tuple:
			# "extended trapezoidal rule"
			# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
			__lowerCAmelCase :    Optional[Any]				=			(boundary[1] - boundary[0]) / steps
			__lowerCAmelCase :    List[Any]				=			boundary[0]
			__lowerCAmelCase :    Any				=			boundary[1]
			__lowerCAmelCase :    Dict				=			make_points(__snake_case   ,__snake_case   ,__snake_case      )
			__lowerCAmelCase :    List[str]				=			0.0
			y += (h / 2.0) * f(__snake_case      )
			for i in x_i:
						# print(i)
						y += h * f(__snake_case      )
			y += (h / 2.0) * f(__snake_case      )
			return y


def      _lowercase							(    __snake_case   ,__snake_case   ,__snake_case      )		->  Union[str, Any]:
			__lowerCAmelCase :    int				=			a + h
			while x < (b - h):
						yield x
						__lowerCAmelCase :    List[str]				=			x + h


def      _lowercase							(    __snake_case      )		->  Tuple:  # enter your function here
			__lowerCAmelCase :    int				=			(x - 0) * (x - 0)
			return y


def      _lowercase							(    )		->  int:
			__lowerCAmelCase :    Tuple				=			0.0  # Lower bound of integration
			__lowerCAmelCase :    Union[str, Any]				=			1.0  # Upper bound of integration
			__lowerCAmelCase :    Dict				=			10.0  # define number of steps or resolution
			__lowerCAmelCase :    Optional[Any]				=			[a, b]  # define boundary of integration
			__lowerCAmelCase :    Optional[int]				=			method_a(__snake_case   ,__snake_case      )
			print(F"""y = {y}"""      )


if __name__ == "__main__":
	main()