


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        snake_case_   (lowerCamelCase_	,						unittest.TestCase      ):
					UpperCAmelCase__					:    Any          =			'''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''
					def  lowerCamelCase__(      self   :Dict			,__snake_case   :Union[str, Any]=0      )					->    Tuple:
												a__             =     floats_tensor((1, 3, 1_28, 1_28)			,rng=random.Random(__snake_case      )      )
												a__             =     np.random.RandomState(__snake_case      )
												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  lowerCamelCase__(      self   :Optional[int]      )					->    List[Any]:
												a__             =     OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint			,provider='CPUExecutionProvider'      )
												pipe.set_progress_bar_config(disable=__snake_case      )

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

												assert image.shape == (1, 1_28, 1_28, 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  lowerCamelCase__(      self   :Tuple      )					->    Dict:
												a__             =     OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint			,provider='CPUExecutionProvider'      )
												a__             =     PNDMScheduler.from_config(pipe.scheduler.config			,skip_prk_steps=__snake_case      )
												pipe.set_progress_bar_config(disable=__snake_case      )

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

												assert image.shape == (1, 1_28, 1_28, 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  lowerCamelCase__(      self   :int      )					->    Union[str, Any]:
												a__             =     OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint			,provider='CPUExecutionProvider'      )
												a__             =     LMSDiscreteScheduler.from_config(pipe.scheduler.config      )
												pipe.set_progress_bar_config(disable=__snake_case      )

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

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

												assert image.shape == (1, 1_28, 1_28, 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  lowerCamelCase__(      self   :Optional[int]      )					->    Any:
												a__             =     OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint			,provider='CPUExecutionProvider'      )
												a__             =     EulerDiscreteScheduler.from_config(pipe.scheduler.config      )
												pipe.set_progress_bar_config(disable=__snake_case      )

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

												assert image.shape == (1, 1_28, 1_28, 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  lowerCamelCase__(      self   :int      )					->    int:
												a__             =     OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint			,provider='CPUExecutionProvider'      )
												a__             =     EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config      )
												pipe.set_progress_bar_config(disable=__snake_case      )

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

												assert image.shape == (1, 1_28, 1_28, 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  lowerCamelCase__(      self   :List[Any]      )					->    int:
												a__             =     OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint			,provider='CPUExecutionProvider'      )
												a__             =     DPMSolverMultistepScheduler.from_config(pipe.scheduler.config      )
												pipe.set_progress_bar_config(disable=__snake_case      )

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

												assert image.shape == (1, 1_28, 1_28, 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        snake_case_   (unittest.TestCase      ):
					@property
					def  lowerCamelCase__(      self   :str      )					->    str:
												return (
												    "CUDAExecutionProvider",
												    {
												        "gpu_mem_limit": "15000000000",  # 15GB
												        "arena_extend_strategy": "kSameAsRequested",
												    },
												)
					@property
					def  lowerCamelCase__(      self   :List[Any]      )					->    Optional[Any]:
												a__             =     ort.SessionOptions()
												a__             =     False
												return options
					def  lowerCamelCase__(      self   :Optional[Any]      )					->    Optional[int]:
												a__             =     load_image(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
												    '/img2img/sketch-mountains-input.jpg'      )
												a__             =     init_image.resize((7_68, 5_12)      )
												# using the PNDM scheduler by default
												a__             =     OnnxStableDiffusionImgaImgPipeline.from_pretrained(
												    'CompVis/stable-diffusion-v1-4'			,revision='onnx'			,safety_checker=__snake_case			,feature_extractor=__snake_case			,provider=self.gpu_provider			,sess_options=self.gpu_options			,)
												pipe.set_progress_bar_config(disable=__snake_case      )

												a__             =     'A fantasy landscape, trending on artstation'

												a__             =     np.random.RandomState(0      )
												a__             =     pipe(
												    prompt=__snake_case			,image=__snake_case			,strength=0.75			,guidance_scale=7.5			,num_inference_steps=10			,generator=__snake_case			,output_type='np'			,)
												a__             =     output.images
												a__             =     images[0, 2_55:2_58, 3_83:3_86, -1]

												assert images.shape == (1, 5_12, 7_68, 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  lowerCamelCase__(      self   :Tuple      )					->    Any:
												a__             =     load_image(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
												    '/img2img/sketch-mountains-input.jpg'      )
												a__             =     init_image.resize((7_68, 5_12)      )
												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=__snake_case			,safety_checker=__snake_case			,feature_extractor=__snake_case			,provider=self.gpu_provider			,sess_options=self.gpu_options			,)
												pipe.set_progress_bar_config(disable=__snake_case      )

												a__             =     'A fantasy landscape, trending on artstation'

												a__             =     np.random.RandomState(0      )
												a__             =     pipe(
												    prompt=__snake_case			,image=__snake_case			,strength=0.75			,guidance_scale=7.5			,num_inference_steps=20			,generator=__snake_case			,output_type='np'			,)
												a__             =     output.images
												a__             =     images[0, 2_55:2_58, 3_83:3_86, -1]

												assert images.shape == (1, 5_12, 7_68, 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








import argparse


snake_case							:       int     			=      '''docs/source/_static/js/custom.js'''






def 							__lowercase   ( __lowerCAmelCase							:				Optional[Any]   ):
							with open(__lowerCAmelCase					,    encoding='utf-8'					,    newline='\n'   ) as f:
														a__             =     f.readlines()
							a__             =     0

							# First let's put the right version
							while not lines[index].startswith('const stableVersion ='   ):
														index += 1
							a__             =     F'const stableVersion = "v{version}"\n'

							# Then update the dictionary
							while not lines[index].startswith('const versionMapping = {'   ):
														index += 1

							# We go until the end
							while not lines[index].startswith('}'   ):
														index += 1
							# We add the new version at the end
							lines[index - 1] += F'    "v{version}": "v{version}",\n'

							with open(__lowerCAmelCase					,    'w'					,    encoding='utf-8'					,    newline='\n'   ) as f:
														f.writelines(__lowerCAmelCase   )


if __name__ == "__main__":
						snake_case							:       Optional[Any]     			=      argparse.ArgumentParser()
						parser.add_argument('''--version''', help='''Release version.''')
						snake_case							:       Optional[int]     			=      parser.parse_args()
						update_custom_js(args.version)




