



'''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 __snake_case( __A						,				unittest.TestCase   ):


						'''simple docstring'''



						UpperCAmelCase				:       Any 	=						"hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"


						def 							__snake_case       (   self      ,			A_=0 )      ->      Dict:
							lowerCAmelCase       =      floats_tensor((1, 3, 128, 128)      ,			rng=random.Random(A_ ) )
							lowerCAmelCase       =      np.random.RandomState(A_ )
							lowerCAmelCase       =      {
							    "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 							__snake_case       (   self )      ->      Any:
							lowerCAmelCase       =      OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      ,			provider="""CPUExecutionProvider""" )
							pipe.set_progress_bar_config(disable=A_ )

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

							assert image.shape == (1, 128, 128, 3)
							lowerCAmelCase       =      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 							__snake_case       (   self )      ->      List[Any]:
							lowerCAmelCase       =      OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      ,			provider="""CPUExecutionProvider""" )
							lowerCAmelCase       =      PNDMScheduler.from_config(pipe.scheduler.config      ,			skip_prk_steps=A_ )
							pipe.set_progress_bar_config(disable=A_ )

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

							assert image.shape == (1, 128, 128, 3)
							lowerCAmelCase       =      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 							__snake_case       (   self )      ->      Union[str, Any]:
							lowerCAmelCase       =      OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      ,			provider="""CPUExecutionProvider""" )
							lowerCAmelCase       =      LMSDiscreteScheduler.from_config(pipe.scheduler.config )
							pipe.set_progress_bar_config(disable=A_ )

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

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

							assert image.shape == (1, 128, 128, 3)
							lowerCAmelCase       =      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 							__snake_case       (   self )      ->      List[Any]:
							lowerCAmelCase       =      OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      ,			provider="""CPUExecutionProvider""" )
							lowerCAmelCase       =      EulerDiscreteScheduler.from_config(pipe.scheduler.config )
							pipe.set_progress_bar_config(disable=A_ )

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

							assert image.shape == (1, 128, 128, 3)
							lowerCAmelCase       =      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 							__snake_case       (   self )      ->      Dict:
							lowerCAmelCase       =      OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      ,			provider="""CPUExecutionProvider""" )
							lowerCAmelCase       =      EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
							pipe.set_progress_bar_config(disable=A_ )

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

							assert image.shape == (1, 128, 128, 3)
							lowerCAmelCase       =      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 							__snake_case       (   self )      ->      List[str]:
							lowerCAmelCase       =      OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      ,			provider="""CPUExecutionProvider""" )
							lowerCAmelCase       =      DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
							pipe.set_progress_bar_config(disable=A_ )

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

							assert image.shape == (1, 128, 128, 3)
							lowerCAmelCase       =      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 __snake_case( unittest.TestCase   ):


						'''simple docstring'''



						@property
						def 							__snake_case       (   self )      ->      Optional[int]:
							return (
							    "CUDAExecutionProvider",
							    {
							        "gpu_mem_limit": "15000000000",  # 15GB
							        "arena_extend_strategy": "kSameAsRequested",
							    },
							)


						@property
						def 							__snake_case       (   self )      ->      Dict:
							lowerCAmelCase       =      ort.SessionOptions()
							lowerCAmelCase       =      False
							return options


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

							lowerCAmelCase       =      "A fantasy landscape, trending on artstation"

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

							assert images.shape == (1, 512, 768, 3)
							lowerCAmelCase       =      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 							__snake_case       (   self )      ->      List[str]:
							lowerCAmelCase       =      load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
							    """/img2img/sketch-mountains-input.jpg""" )
							lowerCAmelCase       =      init_image.resize((768, 512) )
							lowerCAmelCase       =      LMSDiscreteScheduler.from_pretrained(
							    """runwayml/stable-diffusion-v1-5"""      ,			subfolder="""scheduler"""      ,			revision="""onnx""" )
							lowerCAmelCase       =      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_ )

							lowerCAmelCase       =      "A fantasy landscape, trending on artstation"

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

							assert images.shape == (1, 512, 768, 3)
							lowerCAmelCase       =      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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_      =     {
    'configuration_swinv2': ['SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Swinv2Config'],
}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_      =     [
			    'SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'Swinv2ForImageClassification',
			    'Swinv2ForMaskedImageModeling',
			    'Swinv2Model',
			    'Swinv2PreTrainedModel',
			]


if TYPE_CHECKING:
			from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_swinva import (
						    SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    SwinvaForImageClassification,
						    SwinvaForMaskedImageModeling,
						    SwinvaModel,
						    SwinvaPreTrainedModel,
						)


else:
			import sys

			a_      =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)