
"""simple docstring"""


import os
from collections.abc import Iterator






def    _snake_case					(		snake_case__ :  str = "."  ):
	for dir_path, dir_names, filenames in os.walk(snake_case__  ):
		A     = [d for d in dir_names if d != 'scripts' and d[0] not in '._']
		for filename in filenames:
			if filename == "__init__.py":
				continue
			if os.path.splitext(snake_case__  )[1] in (".py", ".ipynb"):
				yield os.path.join(snake_case__						,   snake_case__  ).lstrip('./'  )






def    _snake_case					(		snake_case__ :  str  ):
	return F'{i * "  "}*' if i else "\n##"






def    _snake_case					(		snake_case__ :  str						,   snake_case__ :  str  ):
	A     = old_path.split(os.sep  )
	for i, new_part in enumerate(new_path.split(os.sep  )  ):
		if (i + 1 > len(snake_case__  ) or old_parts[i] != new_part) and new_part:
			print(F'{md_prefix(snake_case__  )} {new_part.replace("_"						,   " "  ).title()}'  )
	return new_path






def    _snake_case					(		snake_case__ :  str = "."  ):
	A     = ''
	for filepath in sorted(good_file_paths(snake_case__  )  ):
		A ,   A     = os.path.split(snake_case__  )
		if filepath != old_path:
			A     = print_path(snake_case__						,   snake_case__  )
		A     = (filepath.count(os.sep  ) + 1) if filepath else 0
		A     = F'{filepath}/{filename}'.replace(' '						,   '%20'  )
		A     = os.path.splitext(filename.replace('_'						,   ' '  ).title()  )[0]
		print(F'{md_prefix(snake_case__  )} [{filename}]({url})'  )


if __name__ == "__main__":
		print_directory_md('''.''')

"""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 					lowerCAmelCase_					(   _lowercase		,       unittest.TestCase     ):



						'''simple docstring'''


						_lowerCamelCase:     Dict   =     '''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''


						def     _SCREAMING_SNAKE_CASE   (			self  :   Any	,A_  :   List[str]=0   )  -> str:
							A     = floats_tensor((1, 3, 128, 128)	,rng=random.Random(A_   )   )
							A     = np.random.RandomState(A_   )
							A     = {
							    '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  :   Dict   )  -> Tuple:
							A     = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint	,provider='CPUExecutionProvider'   )
							pipe.set_progress_bar_config(disable=A_   )

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

							assert image.shape == (1, 128, 128, 3)
							A     = np.array([0.6_96_43, 0.5_84_84, 0.5_03_14, 0.5_87_60, 0.5_53_68, 0.5_96_43, 0.5_15_29, 0.4_12_17, 0.4_90_87]   )
							assert np.abs(image_slice - expected_slice   ).max() < 1e-1


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]   )  -> Any:
							A     = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint	,provider='CPUExecutionProvider'   )
							A     = PNDMScheduler.from_config(pipe.scheduler.config	,skip_prk_steps=A_   )
							pipe.set_progress_bar_config(disable=A_   )

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

							assert image.shape == (1, 128, 128, 3)
							A     = np.array([0.6_17_37, 0.5_46_42, 0.5_31_83, 0.5_44_65, 0.5_27_42, 0.6_05_25, 0.4_99_69, 0.4_06_55, 0.4_81_54]   )

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


						def     _SCREAMING_SNAKE_CASE   (			self  :   Union[str, Any]   )  -> int:
							A     = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint	,provider='CPUExecutionProvider'   )
							A     = LMSDiscreteScheduler.from_config(pipe.scheduler.config   )
							pipe.set_progress_bar_config(disable=A_   )

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

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

							assert image.shape == (1, 128, 128, 3)
							A     = np.array([0.5_27_61, 0.5_99_77, 0.4_90_33, 0.4_96_19, 0.5_42_82, 0.5_03_11, 0.4_76_00, 0.4_09_18, 0.4_52_03]   )

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


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]   )  -> Tuple:
							A     = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint	,provider='CPUExecutionProvider'   )
							A     = EulerDiscreteScheduler.from_config(pipe.scheduler.config   )
							pipe.set_progress_bar_config(disable=A_   )

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

							assert image.shape == (1, 128, 128, 3)
							A     = np.array([0.5_29_11, 0.6_00_04, 0.4_92_29, 0.4_98_05, 0.5_45_02, 0.5_06_80, 0.4_77_77, 0.4_10_28, 0.4_53_04]   )

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


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[Any]   )  -> str:
							A     = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint	,provider='CPUExecutionProvider'   )
							A     = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config   )
							pipe.set_progress_bar_config(disable=A_   )

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

							assert image.shape == (1, 128, 128, 3)
							A     = np.array([0.5_29_11, 0.6_00_04, 0.4_92_29, 0.4_98_05, 0.5_45_02, 0.5_06_80, 0.4_77_77, 0.4_10_28, 0.4_53_04]   )

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




						def     _SCREAMING_SNAKE_CASE   (			self  :   int   )  -> Tuple:
							A     = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint	,provider='CPUExecutionProvider'   )
							A     = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config   )
							pipe.set_progress_bar_config(disable=A_   )

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

							assert image.shape == (1, 128, 128, 3)
							A     = np.array([0.6_53_31, 0.5_82_77, 0.4_82_04, 0.5_60_59, 0.5_36_65, 0.5_62_35, 0.5_09_69, 0.4_00_09, 0.4_65_52]   )

							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     _SCREAMING_SNAKE_CASE   (			self  :   int   )  -> Union[str, Any]:
							return (
							    "CUDAExecutionProvider",
							    {
							        "gpu_mem_limit": "15000000000",  # 15GB
							        "arena_extend_strategy": "kSameAsRequested",
							    },
							)


						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   str   )  -> List[Any]:
							A     = ort.SessionOptions()
							A     = False
							return options


						def     _SCREAMING_SNAKE_CASE   (			self  :   Tuple   )  -> str:
							A     = load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    '/img2img/sketch-mountains-input.jpg'   )
							A     = init_image.resize((768, 512)   )
							# using the PNDM scheduler by default
							A     = 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_   )

							A     = 'A fantasy landscape, trending on artstation'

							A     = np.random.RandomState(0   )
							A     = pipe(
							    prompt=A_	,image=A_	,strength=0.75	,guidance_scale=7.5	,num_inference_steps=10	,generator=A_	,output_type='np'	,)
							A     = output.images
							A     = images[0, 255:258, 383:386, -1]

							assert images.shape == (1, 512, 768, 3)
							A     = np.array([0.49_09, 0.50_59, 0.53_72, 0.46_23, 0.48_76, 0.50_49, 0.48_20, 0.49_56, 0.50_19]   )
							# 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  :   Optional[int]   )  -> Optional[Any]:
							A     = load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    '/img2img/sketch-mountains-input.jpg'   )
							A     = init_image.resize((768, 512)   )
							A     = LMSDiscreteScheduler.from_pretrained(
							    'runwayml/stable-diffusion-v1-5'	,subfolder='scheduler'	,revision='onnx'   )
							A     = 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_   )

							A     = 'A fantasy landscape, trending on artstation'

							A     = np.random.RandomState(0   )
							A     = pipe(
							    prompt=A_	,image=A_	,strength=0.75	,guidance_scale=7.5	,num_inference_steps=20	,generator=A_	,output_type='np'	,)
							A     = output.images
							A     = images[0, 255:258, 383:386, -1]

							assert images.shape == (1, 512, 768, 3)
							A     = np.array([0.80_43, 0.9_26, 0.95_81, 0.81_19, 0.89_54, 0.9_13, 0.72_09, 0.74_63, 0.74_31]   )
							# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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