





"""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  __lowercase	(	_UpperCamelCase      ,					unittest.TestCase							):







					'''simple docstring'''




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







					def        _lowerCamelCase						(  self		,	_UpperCAmelCase=0   ):
									__a       :  str					=     floats_tensor((1, 3, 128, 128)		,	rng=random.Random(_UpperCAmelCase   )   )
									__a       :  List[str]					=     np.random.RandomState(_UpperCAmelCase   )
									__a       :  str					=     {
									    '''prompt''': '''A painting of a squirrel eating a burger''',
									    '''image''': image,
									    '''generator''': generator,
									    '''num_inference_steps''': 3,
									    '''strength''': 0.7_5,
									    '''guidance_scale''': 7.5,
									    '''output_type''': '''numpy''',
									}
									return inputs







					def        _lowerCamelCase						(  self   ):
									__a       :  str					=     OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,	provider='''CPUExecutionProvider'''   )
									pipe.set_progress_bar_config(disable=_UpperCAmelCase   )

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

									assert image.shape == (1, 128, 128, 3)
									__a       :  Dict					=     np.array([0.6_9_6_4_3, 0.5_8_4_8_4, 0.5_0_3_1_4, 0.5_8_7_6_0, 0.5_5_3_6_8, 0.5_9_6_4_3, 0.5_1_5_2_9, 0.4_1_2_1_7, 0.4_9_0_8_7]   )
									assert np.abs(image_slice - expected_slice   ).max() < 1e-1







					def        _lowerCamelCase						(  self   ):
									__a       :  str					=     OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,	provider='''CPUExecutionProvider'''   )
									__a       :  Optional[int]					=     PNDMScheduler.from_config(pipe.scheduler.config		,	skip_prk_steps=_UpperCAmelCase   )
									pipe.set_progress_bar_config(disable=_UpperCAmelCase   )

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

									assert image.shape == (1, 128, 128, 3)
									__a       :  int					=     np.array([0.6_1_7_3_7, 0.5_4_6_4_2, 0.5_3_1_8_3, 0.5_4_4_6_5, 0.5_2_7_4_2, 0.6_0_5_2_5, 0.4_9_9_6_9, 0.4_0_6_5_5, 0.4_8_1_5_4]   )

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







					def        _lowerCamelCase						(  self   ):
									__a       :  int					=     OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,	provider='''CPUExecutionProvider'''   )
									__a       :  str					=     LMSDiscreteScheduler.from_config(pipe.scheduler.config   )
									pipe.set_progress_bar_config(disable=_UpperCAmelCase   )

									# warmup pass to apply optimizations
									__a       :  Any					=     pipe(**self.get_dummy_inputs()   )

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

									assert image.shape == (1, 128, 128, 3)
									__a       :  List[Any]					=     np.array([0.5_2_7_6_1, 0.5_9_9_7_7, 0.4_9_0_3_3, 0.4_9_6_1_9, 0.5_4_2_8_2, 0.5_0_3_1_1, 0.4_7_6_0_0, 0.4_0_9_1_8, 0.4_5_2_0_3]   )

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







					def        _lowerCamelCase						(  self   ):
									__a       :  Optional[int]					=     OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,	provider='''CPUExecutionProvider'''   )
									__a       :  List[Any]					=     EulerDiscreteScheduler.from_config(pipe.scheduler.config   )
									pipe.set_progress_bar_config(disable=_UpperCAmelCase   )

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

									assert image.shape == (1, 128, 128, 3)
									__a       :  Optional[Any]					=     np.array([0.5_2_9_1_1, 0.6_0_0_0_4, 0.4_9_2_2_9, 0.4_9_8_0_5, 0.5_4_5_0_2, 0.5_0_6_8_0, 0.4_7_7_7_7, 0.4_1_0_2_8, 0.4_5_3_0_4]   )

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







					def        _lowerCamelCase						(  self   ):
									__a       :  Union[str, Any]					=     OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,	provider='''CPUExecutionProvider'''   )
									__a       :  Tuple					=     EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config   )
									pipe.set_progress_bar_config(disable=_UpperCAmelCase   )

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

									assert image.shape == (1, 128, 128, 3)
									__a       :  Dict					=     np.array([0.5_2_9_1_1, 0.6_0_0_0_4, 0.4_9_2_2_9, 0.4_9_8_0_5, 0.5_4_5_0_2, 0.5_0_6_8_0, 0.4_7_7_7_7, 0.4_1_0_2_8, 0.4_5_3_0_4]   )

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







					def        _lowerCamelCase						(  self   ):
									__a       :  Dict					=     OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,	provider='''CPUExecutionProvider'''   )
									__a       :  Optional[Any]					=     DPMSolverMultistepScheduler.from_config(pipe.scheduler.config   )
									pipe.set_progress_bar_config(disable=_UpperCAmelCase   )

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

									assert image.shape == (1, 128, 128, 3)
									__a       :  Optional[Any]					=     np.array([0.6_5_3_3_1, 0.5_8_2_7_7, 0.4_8_2_0_4, 0.5_6_0_5_9, 0.5_3_6_6_5, 0.5_6_2_3_5, 0.5_0_9_6_9, 0.4_0_0_0_9, 0.4_6_5_5_2]   )

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






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







					'''simple docstring'''




					@property
					def        _lowerCamelCase						(  self   ):
									return (
									    "CUDAExecutionProvider",
									    {
									        "gpu_mem_limit": "15000000000",  # 15GB
									        "arena_extend_strategy": "kSameAsRequested",
									    },
									)







					@property
					def        _lowerCamelCase						(  self   ):
									__a       :  Optional[int]					=     ort.SessionOptions()
									__a       :  Any					=     False
									return options







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

									__a       :  int					=     '''A fantasy landscape, trending on artstation'''

									__a       :  Union[str, Any]					=     np.random.RandomState(0   )
									__a       :  Tuple					=     pipe(
									    prompt=_UpperCAmelCase		,	image=_UpperCAmelCase		,	strength=0.7_5		,	guidance_scale=7.5		,	num_inference_steps=10		,	generator=_UpperCAmelCase		,	output_type='''np'''		,	)
									__a       :  Optional[int]					=     output.images
									__a       :  Tuple					=     images[0, 255:258, 383:386, -1]

									assert images.shape == (1, 512, 768, 3)
									__a       :  str					=     np.array([0.4_9_0_9, 0.5_0_5_9, 0.5_3_7_2, 0.4_6_2_3, 0.4_8_7_6, 0.5_0_4_9, 0.4_8_2_0, 0.4_9_5_6, 0.5_0_1_9]   )
									# 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   ):
									__a       :  List[Any]					=     load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
									    '''/img2img/sketch-mountains-input.jpg'''   )
									__a       :  Any					=     init_image.resize((768, 512)   )
									__a       :  str					=     LMSDiscreteScheduler.from_pretrained(
									    '''runwayml/stable-diffusion-v1-5'''		,	subfolder='''scheduler'''		,	revision='''onnx'''   )
									__a       :  str					=     OnnxStableDiffusionImgaImgPipeline.from_pretrained(
									    '''runwayml/stable-diffusion-v1-5'''		,	revision='''onnx'''		,	scheduler=_UpperCAmelCase		,	safety_checker=_UpperCAmelCase		,	feature_extractor=_UpperCAmelCase		,	provider=self.gpu_provider		,	sess_options=self.gpu_options		,	)
									pipe.set_progress_bar_config(disable=_UpperCAmelCase   )

									__a       :  Union[str, Any]					=     '''A fantasy landscape, trending on artstation'''

									__a       :  Tuple					=     np.random.RandomState(0   )
									__a       :  Dict					=     pipe(
									    prompt=_UpperCAmelCase		,	image=_UpperCAmelCase		,	strength=0.7_5		,	guidance_scale=7.5		,	num_inference_steps=20		,	generator=_UpperCAmelCase		,	output_type='''np'''		,	)
									__a       :  Tuple					=     output.images
									__a       :  Tuple					=     images[0, 255:258, 383:386, -1]

									assert images.shape == (1, 512, 768, 3)
									__a       :  List[str]					=     np.array([0.8_0_4_3, 0.9_2_6, 0.9_5_8_1, 0.8_1_1_9, 0.8_9_5_4, 0.9_1_3, 0.7_2_0_9, 0.7_4_6_3, 0.7_4_3_1]   )
									# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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





from __future__ import annotations

from collections.abc import Iterator

class 		__lowerCAmelCase	:
							def __init__(						self						:Optional[Any]		,      __magic_name__						:int				):







								'''simple docstring'''







								a										=							value
								a										=							None
								a										=							None







class 		__lowerCAmelCase	:
							def __init__(						self						:str		,      __magic_name__						:Node				):







								'''simple docstring'''







								a										=							tree
							def       lowerCamelCase__							(						self						:str		,      __magic_name__						:Node | None				):







								'''simple docstring'''







								if node is None:
									return 0
								return node.value + (
								    self.depth_first_search(node.left				) + self.depth_first_search(node.right				)
								)





							def __iter__(						self						:Tuple				):







								'''simple docstring'''







								yield self.depth_first_search(self.tree				)


if __name__ == "__main__":
		import doctest

		doctest.testmod()

