





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 		lowerCAmelCase     ( __a      ,       unittest.TestCase			):

					'''simple docstring'''

					_A						:			Union[str, Any]									=      '''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''



					def       lowerCAmelCase				(  self				:	int		,     __a				:	List[Any]=0							)			->  str:


												"""simple docstring"""

												__lowercase      :							str				=     floats_tensor((1, 3, 128, 128)		,     rng=random.Random(__a							)							)
												__lowercase      :							int				=     np.random.RandomState(__a							)
												__lowercase      :							str				=     {
												    """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       lowerCAmelCase				(  self				:	List[str]							)			->  Tuple:


												"""simple docstring"""

												__lowercase      :							Dict				=     OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,     provider="""CPUExecutionProvider"""							)
												pipe.set_progress_bar_config(disable=__a							)

												__lowercase      :							List[str]				=     self.get_dummy_inputs()
												__lowercase      :							Optional[Any]				=     pipe(**__a							).images
												__lowercase      :							List[str]				=     image[0, -3:, -3:, -1].flatten()

												assert image.shape == (1, 128, 128, 3)
												__lowercase      :							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       lowerCAmelCase				(  self				:	str							)			->  Any:


												"""simple docstring"""

												__lowercase      :							int				=     OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,     provider="""CPUExecutionProvider"""							)
												__lowercase      :							Optional[Any]				=     PNDMScheduler.from_config(pipe.scheduler.config		,     skip_prk_steps=__a							)
												pipe.set_progress_bar_config(disable=__a							)

												__lowercase      :							Optional[int]				=     self.get_dummy_inputs()
												__lowercase      :							Union[str, Any]				=     pipe(**__a							).images
												__lowercase      :							Optional[int]				=     image[0, -3:, -3:, -1]

												assert image.shape == (1, 128, 128, 3)
												__lowercase      :							str				=     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       lowerCAmelCase				(  self				:	List[Any]							)			->  Union[str, Any]:


												"""simple docstring"""

												__lowercase      :							Dict				=     OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,     provider="""CPUExecutionProvider"""							)
												__lowercase      :							List[Any]				=     LMSDiscreteScheduler.from_config(pipe.scheduler.config							)
												pipe.set_progress_bar_config(disable=__a							)

												# warmup pass to apply optimizations
												__lowercase      :							Optional[int]				=     pipe(**self.get_dummy_inputs()							)

												__lowercase      :							Dict				=     self.get_dummy_inputs()
												__lowercase      :							Any				=     pipe(**__a							).images
												__lowercase      :							Tuple				=     image[0, -3:, -3:, -1]

												assert image.shape == (1, 128, 128, 3)
												__lowercase      :							Union[str, 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       lowerCAmelCase				(  self				:	List[str]							)			->  Optional[int]:


												"""simple docstring"""

												__lowercase      :							str				=     OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,     provider="""CPUExecutionProvider"""							)
												__lowercase      :							Any				=     EulerDiscreteScheduler.from_config(pipe.scheduler.config							)
												pipe.set_progress_bar_config(disable=__a							)

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

												assert image.shape == (1, 128, 128, 3)
												__lowercase      :							List[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       lowerCAmelCase				(  self				:	int							)			->  Dict:


												"""simple docstring"""

												__lowercase      :							Tuple				=     OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,     provider="""CPUExecutionProvider"""							)
												__lowercase      :							int				=     EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config							)
												pipe.set_progress_bar_config(disable=__a							)

												__lowercase      :							Optional[int]				=     self.get_dummy_inputs()
												__lowercase      :							Optional[int]				=     pipe(**__a							).images
												__lowercase      :							List[str]				=     image[0, -3:, -3:, -1]

												assert image.shape == (1, 128, 128, 3)
												__lowercase      :							Optional[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       lowerCAmelCase				(  self				:	List[str]							)			->  Optional[Any]:


												"""simple docstring"""

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

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

												assert image.shape == (1, 128, 128, 3)
												__lowercase      :							List[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 		lowerCAmelCase     ( unittest.TestCase			):

					'''simple docstring'''

					@property
					def       lowerCAmelCase				(  self				:	Tuple							)			->  Any:


												"""simple docstring"""

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



					@property
					def       lowerCAmelCase				(  self				:	Optional[int]							)			->  int:


												"""simple docstring"""

												__lowercase      :							List[Any]				=     ort.SessionOptions()
												__lowercase      :							Tuple				=     False
												return options



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


												"""simple docstring"""

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

												__lowercase      :							str				=     """A fantasy landscape, trending on artstation"""

												__lowercase      :							Tuple				=     np.random.RandomState(0							)
												__lowercase      :							Any				=     pipe(
												    prompt=__a		,     image=__a		,     strength=0.75		,     guidance_scale=7.5		,     num_inference_steps=10		,     generator=__a		,     output_type="""np"""		,     )
												__lowercase      :							int				=     output.images
												__lowercase      :							Tuple				=     images[0, 255:258, 383:386, -1]

												assert images.shape == (1, 512, 768, 3)
												__lowercase      :							int				=     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       lowerCAmelCase				(  self				:	int							)			->  Any:


												"""simple docstring"""

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

												__lowercase      :							Any				=     """A fantasy landscape, trending on artstation"""

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

												assert images.shape == (1, 512, 768, 3)
												__lowercase      :							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







def       snake_case_		(     lowerCAmelCase_       :						int       ,       lowerCAmelCase_       :						int      ):
							return int((input_a, input_a).count(1      ) != 0      )
def       snake_case_		(     ):
							assert or_gate(0       ,       0      ) == 0
							assert or_gate(0       ,       1      ) == 1
							assert or_gate(1       ,       0      ) == 1
							assert or_gate(1       ,       1      ) == 1


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