



"""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):
			__magic_name__     					=     """hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"""



			def 				__lowercase					(			self							:				Dict   ,	_lowercase							:				int=0		)							->					str:
									snake_case   :			Any						    = floats_tensor((1, 3, 128, 128)   ,	rng=random.Random(_lowercase		)		)
									snake_case   :			List[Any]						    = np.random.RandomState(_lowercase		)
									snake_case   :			int						    = {
									    "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 				__lowercase					(			self							:				Dict		)							->					List[str]:
									snake_case   :			str						    = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint   ,	provider="CPUExecutionProvider"		)
									pipe.set_progress_bar_config(disable=_lowercase		)

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

									assert image.shape == (1, 128, 128, 3)
									snake_case   :			Optional[Any]						    = np.array([0.69643, 0.58484, 0.50314, 0.58760, 0.55368, 0.59643, 0.51529, 0.41217, 0.49087]		)
									assert np.abs(image_slice - expected_slice		).max() < 1E-1



			def 				__lowercase					(			self							:				Dict		)							->					Any:
									snake_case   :			Union[str, Any]						    = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint   ,	provider="CPUExecutionProvider"		)
									snake_case   :			int						    = PNDMScheduler.from_config(pipe.scheduler.config   ,	skip_prk_steps=_lowercase		)
									pipe.set_progress_bar_config(disable=_lowercase		)

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

									assert image.shape == (1, 128, 128, 3)
									snake_case   :			Any						    = np.array([0.61737, 0.54642, 0.53183, 0.54465, 0.52742, 0.60525, 0.49969, 0.40655, 0.48154]		)

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



			def 				__lowercase					(			self							:				Union[str, Any]		)							->					Tuple:
									snake_case   :			Dict						    = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint   ,	provider="CPUExecutionProvider"		)
									snake_case   :			Optional[int]						    = LMSDiscreteScheduler.from_config(pipe.scheduler.config		)
									pipe.set_progress_bar_config(disable=_lowercase		)

									# warmup pass to apply optimizations
									snake_case   :			Union[str, Any]						    = pipe(**self.get_dummy_inputs()		)

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

									assert image.shape == (1, 128, 128, 3)
									snake_case   :			Optional[Any]						    = np.array([0.52761, 0.59977, 0.49033, 0.49619, 0.54282, 0.50311, 0.47600, 0.40918, 0.45203]		)

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



			def 				__lowercase					(			self							:				int		)							->					str:
									snake_case   :			List[Any]						    = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint   ,	provider="CPUExecutionProvider"		)
									snake_case   :			Optional[Any]						    = EulerDiscreteScheduler.from_config(pipe.scheduler.config		)
									pipe.set_progress_bar_config(disable=_lowercase		)

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

									assert image.shape == (1, 128, 128, 3)
									snake_case   :			Any						    = np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304]		)

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



			def 				__lowercase					(			self							:				Optional[int]		)							->					Optional[int]:
									snake_case   :			Optional[int]						    = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint   ,	provider="CPUExecutionProvider"		)
									snake_case   :			List[str]						    = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config		)
									pipe.set_progress_bar_config(disable=_lowercase		)

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

									assert image.shape == (1, 128, 128, 3)
									snake_case   :			Any						    = np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304]		)

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



			def 				__lowercase					(			self							:				Optional[int]		)							->					Dict:
									snake_case   :			Tuple						    = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint   ,	provider="CPUExecutionProvider"		)
									snake_case   :			Tuple						    = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config		)
									pipe.set_progress_bar_config(disable=_lowercase		)

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

									assert image.shape == (1, 128, 128, 3)
									snake_case   :			Optional[Any]						    = np.array([0.65331, 0.58277, 0.48204, 0.56059, 0.53665, 0.56235, 0.50969, 0.40009, 0.46552]		)

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



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



			@property
			def 				__lowercase					(			self							:				int		)							->					Any:
									return (
									    "CUDAExecutionProvider",
									    {
									        "gpu_mem_limit": "15000000000",  # 15GB
									        "arena_extend_strategy": "kSameAsRequested",
									    },
									)



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



			def 				__lowercase					(			self							:				Optional[Any]		)							->					Tuple:
									snake_case   :			List[Any]						    = load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
									    "/img2img/sketch-mountains-input.jpg"		)
									snake_case   :			List[str]						    = init_image.resize((768, 512)		)
									# using the PNDM scheduler by default
									snake_case   :			Union[str, Any]						    = 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		)

									snake_case   :			Union[str, Any]						    = "A fantasy landscape, trending on artstation"

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

									assert images.shape == (1, 512, 768, 3)
									snake_case   :			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 				__lowercase					(			self							:				Any		)							->					Optional[Any]:
									snake_case   :			List[str]						    = load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
									    "/img2img/sketch-mountains-input.jpg"		)
									snake_case   :			str						    = init_image.resize((768, 512)		)
									snake_case   :			Dict						    = LMSDiscreteScheduler.from_pretrained(
									    "runwayml/stable-diffusion-v1-5"   ,	subfolder="scheduler"   ,	revision="onnx"		)
									snake_case   :			Optional[int]						    = 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		)

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

									snake_case   :			int						    = np.random.RandomState(0		)
									snake_case   :			Union[str, Any]						    = pipe(
									    prompt=_lowercase   ,	image=_lowercase   ,	strength=0.75   ,	guidance_scale=7.5   ,	num_inference_steps=20   ,	generator=_lowercase   ,	output_type="np"   ,	)
									snake_case   :			Dict						    = output.images
									snake_case   :			Optional[Any]						    = images[0, 255:258, 383:386, -1]

									assert images.shape == (1, 512, 768, 3)
									snake_case   :			Optional[Any]						    = 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  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							int     ,		lowerCamelCase_:							int ):
						"""simple docstring"""






						return int((input_a, input_a).count(0 ) != 0 )

def  __SCREAMING_SNAKE_CASE		(			):
						"""simple docstring"""






						assert nand_gate(0     ,		0 ) == 1
						assert nand_gate(0     ,		1 ) == 1
						assert nand_gate(1     ,		0 ) == 1
						assert nand_gate(1     ,		1 ) == 0


if __name__ == "__main__":
			print(nand_gate(0, 0))
			print(nand_gate(0, 1))
			print(nand_gate(1, 0))
			print(nand_gate(1, 1))



