





'''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       (      lowerCAmelCase__ ,      unittest.TestCase						):







	'''simple docstring'''

	a    :				int											=       "hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase=0 )							->							List[str]:







				'''simple docstring'''





				__lowercase										=							floats_tensor((1, 3, 128, 128)     ,rng=random.Random(__UpperCamelCase ) )
				__lowercase										=							np.random.RandomState(__UpperCamelCase )
				__lowercase										=							{
				    '''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 						_UpperCAmelCase     (self )							->							List[Any]:







				'''simple docstring'''





				__lowercase										=							OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint     ,provider='''CPUExecutionProvider''' )
				pipe.set_progress_bar_config(disable=__UpperCamelCase )

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

				assert image.shape == (1, 128, 128, 3)
				__lowercase										=							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 						_UpperCAmelCase     (self )							->							int:







				'''simple docstring'''





				__lowercase										=							OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint     ,provider='''CPUExecutionProvider''' )
				__lowercase										=							PNDMScheduler.from_config(pipe.scheduler.config     ,skip_prk_steps=__UpperCamelCase )
				pipe.set_progress_bar_config(disable=__UpperCamelCase )

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

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







				'''simple docstring'''





				__lowercase										=							OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint     ,provider='''CPUExecutionProvider''' )
				__lowercase										=							LMSDiscreteScheduler.from_config(pipe.scheduler.config )
				pipe.set_progress_bar_config(disable=__UpperCamelCase )

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

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

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







				'''simple docstring'''





				__lowercase										=							OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint     ,provider='''CPUExecutionProvider''' )
				__lowercase										=							EulerDiscreteScheduler.from_config(pipe.scheduler.config )
				pipe.set_progress_bar_config(disable=__UpperCamelCase )

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

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







				'''simple docstring'''





				__lowercase										=							OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint     ,provider='''CPUExecutionProvider''' )
				__lowercase										=							EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
				pipe.set_progress_bar_config(disable=__UpperCamelCase )

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

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







				'''simple docstring'''





				__lowercase										=							OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint     ,provider='''CPUExecutionProvider''' )
				__lowercase										=							DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
				pipe.set_progress_bar_config(disable=__UpperCamelCase )

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

				assert image.shape == (1, 128, 128, 3)
				__lowercase										=							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 						_UpperCAmelCase     (self )							->							Dict:







				'''simple docstring'''





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


	@property
	def 						_UpperCAmelCase     (self )							->							Union[str, Any]:







				'''simple docstring'''





				__lowercase										=							ort.SessionOptions()
				__lowercase										=							False
				return options


	def 						_UpperCAmelCase     (self )							->							Union[str, Any]:







				'''simple docstring'''





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

				__lowercase										=							'''A fantasy landscape, trending on artstation'''

				__lowercase										=							np.random.RandomState(0 )
				__lowercase										=							pipe(
				    prompt=__UpperCamelCase     ,image=__UpperCamelCase     ,strength=0.7_5     ,guidance_scale=7.5     ,num_inference_steps=10     ,generator=__UpperCamelCase     ,output_type='''np'''     ,)
				__lowercase										=							output.images
				__lowercase										=							images[0, 255:258, 383:386, -1]

				assert images.shape == (1, 512, 768, 3)
				__lowercase										=							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 						_UpperCAmelCase     (self )							->							Dict:







				'''simple docstring'''





				__lowercase										=							load_image(
				    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
				    '''/img2img/sketch-mountains-input.jpg''' )
				__lowercase										=							init_image.resize((768, 512) )
				__lowercase										=							LMSDiscreteScheduler.from_pretrained(
				    '''runwayml/stable-diffusion-v1-5'''     ,subfolder='''scheduler'''     ,revision='''onnx''' )
				__lowercase										=							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 )

				__lowercase										=							'''A fantasy landscape, trending on artstation'''

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

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












'''simple docstring'''



from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_SCREAMING_SNAKE_CASE					=    {'''configuration_van''': ['''VAN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VanConfig''']}


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE					=    [
	    '''VAN_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''VanForImageClassification''',
	    '''VanModel''',
	    '''VanPreTrainedModel''',
	]

if TYPE_CHECKING:
	from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_van import (
		    VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
		    VanForImageClassification,
		    VanModel,
		    VanPreTrainedModel,
		)

else:
	import sys

	_SCREAMING_SNAKE_CASE					=    _LazyModule(__name__, globals()['''__file__'''], _import_structure)





