



'''simple docstring'''






lowerCAmelCase:      Tuple		 =		'0.18.2'

from .configuration_utils import ConfigMixin
from .utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_onnx_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    logging,
)


try:
     if not is_onnx_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from .utils.dummy_onnx_objects import *  # noqa F403
else:
     from .pipelines import OnnxRuntimeModel

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from .utils.dummy_pt_objects import *  # noqa F403
else:
     from .models import (
         AutoencoderKL,
         ControlNetModel,
         ModelMixin,
         PriorTransformer,
         TaFilmDecoder,
         TransformeraDModel,
         UNetaDModel,
         UNetaDConditionModel,
         UNetaDModel,
         UNetaDConditionModel,
         VQModel,
     )
     from .optimization import (
         get_constant_schedule,
         get_constant_schedule_with_warmup,
         get_cosine_schedule_with_warmup,
         get_cosine_with_hard_restarts_schedule_with_warmup,
         get_linear_schedule_with_warmup,
         get_polynomial_decay_schedule_with_warmup,
         get_scheduler,
     )
     from .pipelines import (
         AudioPipelineOutput,
         ConsistencyModelPipeline,
         DanceDiffusionPipeline,
         DDIMPipeline,
         DDPMPipeline,
         DiffusionPipeline,
         DiTPipeline,
         ImagePipelineOutput,
         KarrasVePipeline,
         LDMPipeline,
         LDMSuperResolutionPipeline,
         PNDMPipeline,
         RePaintPipeline,
         ScoreSdeVePipeline,
     )
     from .schedulers import (
         CMStochasticIterativeScheduler,
         DDIMInverseScheduler,
         DDIMParallelScheduler,
         DDIMScheduler,
         DDPMParallelScheduler,
         DDPMScheduler,
         DEISMultistepScheduler,
         DPMSolverMultistepInverseScheduler,
         DPMSolverMultistepScheduler,
         DPMSolverSinglestepScheduler,
         EulerAncestralDiscreteScheduler,
         EulerDiscreteScheduler,
         HeunDiscreteScheduler,
         IPNDMScheduler,
         KarrasVeScheduler,
         KDPMaAncestralDiscreteScheduler,
         KDPMaDiscreteScheduler,
         PNDMScheduler,
         RePaintScheduler,
         SchedulerMixin,
         ScoreSdeVeScheduler,
         UnCLIPScheduler,
         UniPCMultistepScheduler,
         VQDiffusionScheduler,
     )
     from .training_utils import EMAModel

try:
     if not (is_torch_available() and is_scipy_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from .utils.dummy_torch_and_scipy_objects import *  # noqa F403
else:
     from .schedulers import LMSDiscreteScheduler

try:
     if not (is_torch_available() and is_torchsde_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from .utils.dummy_torch_and_torchsde_objects import *  # noqa F403
else:
     from .schedulers import DPMSolverSDEScheduler

try:
     if not (is_torch_available() and is_transformers_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from .utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
     from .pipelines import (
         AltDiffusionImgaImgPipeline,
         AltDiffusionPipeline,
         AudioLDMPipeline,
         CycleDiffusionPipeline,
         IFImgaImgPipeline,
         IFImgaImgSuperResolutionPipeline,
         IFInpaintingPipeline,
         IFInpaintingSuperResolutionPipeline,
         IFPipeline,
         IFSuperResolutionPipeline,
         ImageTextPipelineOutput,
         KandinskyImgaImgPipeline,
         KandinskyInpaintPipeline,
         KandinskyPipeline,
         KandinskyPriorPipeline,
         KandinskyVaaControlnetImgaImgPipeline,
         KandinskyVaaControlnetPipeline,
         KandinskyVaaImgaImgPipeline,
         KandinskyVaaInpaintPipeline,
         KandinskyVaaPipeline,
         KandinskyVaaPriorEmbaEmbPipeline,
         KandinskyVaaPriorPipeline,
         LDMTextToImagePipeline,
         PaintByExamplePipeline,
         SemanticStableDiffusionPipeline,
         ShapEImgaImgPipeline,
         ShapEPipeline,
         StableDiffusionAttendAndExcitePipeline,
         StableDiffusionControlNetImgaImgPipeline,
         StableDiffusionControlNetInpaintPipeline,
         StableDiffusionControlNetPipeline,
         StableDiffusionDepthaImgPipeline,
         StableDiffusionDiffEditPipeline,
         StableDiffusionImageVariationPipeline,
         StableDiffusionImgaImgPipeline,
         StableDiffusionInpaintPipeline,
         StableDiffusionInpaintPipelineLegacy,
         StableDiffusionInstructPixaPixPipeline,
         StableDiffusionLatentUpscalePipeline,
         StableDiffusionLDMaDPipeline,
         StableDiffusionModelEditingPipeline,
         StableDiffusionPanoramaPipeline,
         StableDiffusionParadigmsPipeline,
         StableDiffusionPipeline,
         StableDiffusionPipelineSafe,
         StableDiffusionPixaPixZeroPipeline,
         StableDiffusionSAGPipeline,
         StableDiffusionUpscalePipeline,
         StableUnCLIPImgaImgPipeline,
         StableUnCLIPPipeline,
         TextToVideoSDPipeline,
         TextToVideoZeroPipeline,
         UnCLIPImageVariationPipeline,
         UnCLIPPipeline,
         UniDiffuserModel,
         UniDiffuserPipeline,
         UniDiffuserTextDecoder,
         VersatileDiffusionDualGuidedPipeline,
         VersatileDiffusionImageVariationPipeline,
         VersatileDiffusionPipeline,
         VersatileDiffusionTextToImagePipeline,
         VideoToVideoSDPipeline,
         VQDiffusionPipeline,
     )

try:
     if not (is_torch_available() and is_transformers_available() and is_invisible_watermark_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from .utils.dummy_torch_and_transformers_and_invisible_watermark_objects import *  # noqa F403
else:
     from .pipelines import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline

try:
     if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from .utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
     from .pipelines import StableDiffusionKDiffusionPipeline

try:
     if not (is_torch_available() and is_transformers_available() and is_onnx_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from .utils.dummy_torch_and_transformers_and_onnx_objects import *  # noqa F403
else:
     from .pipelines import (
         OnnxStableDiffusionImgaImgPipeline,
         OnnxStableDiffusionInpaintPipeline,
         OnnxStableDiffusionInpaintPipelineLegacy,
         OnnxStableDiffusionPipeline,
         OnnxStableDiffusionUpscalePipeline,
         StableDiffusionOnnxPipeline,
     )

try:
     if not (is_torch_available() and is_librosa_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from .utils.dummy_torch_and_librosa_objects import *  # noqa F403
else:
     from .pipelines import AudioDiffusionPipeline, Mel

try:
     if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from .utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
     from .pipelines import SpectrogramDiffusionPipeline

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from .utils.dummy_flax_objects import *  # noqa F403
else:
     from .models.controlnet_flax import FlaxControlNetModel
     from .models.modeling_flax_utils import FlaxModelMixin
     from .models.unet_ad_condition_flax import FlaxUNetaDConditionModel
     from .models.vae_flax import FlaxAutoencoderKL
     from .pipelines import FlaxDiffusionPipeline
     from .schedulers import (
         FlaxDDIMScheduler,
         FlaxDDPMScheduler,
         FlaxDPMSolverMultistepScheduler,
         FlaxKarrasVeScheduler,
         FlaxLMSDiscreteScheduler,
         FlaxPNDMScheduler,
         FlaxSchedulerMixin,
         FlaxScoreSdeVeScheduler,
     )


try:
     if not (is_flax_available() and is_transformers_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from .utils.dummy_flax_and_transformers_objects import *  # noqa F403
else:
     from .pipelines import (
         FlaxStableDiffusionControlNetPipeline,
         FlaxStableDiffusionImgaImgPipeline,
         FlaxStableDiffusionInpaintPipeline,
         FlaxStableDiffusionPipeline,
     )

try:
     if not (is_note_seq_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from .utils.dummy_note_seq_objects import *  # noqa F403
else:
     from .pipelines import MidiProcessor




'''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 							a__(      lowerCamelCase__	,				unittest.TestCase			):
    lowercase__				=					"""hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"""







    def     lowercase_ (   self				:						Dict		,    __snake_case				:						Union[str, Any]=0			):
      a		:      List[str]       =  floats_tensor((1, 3, 1_28, 1_28)		,    rng=random.Random(__snake_case			)			)
      a		:      Optional[Any]       =  np.random.RandomState(__snake_case			)
      a		:      List[str]       =  {
          '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     lowercase_ (   self				:						Union[str, Any]			):
      a		:      Dict       =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,    provider='CPUExecutionProvider'			)
      pipe.set_progress_bar_config(disable=__snake_case			)

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

      assert image.shape == (1, 1_28, 1_28, 3)
      a		:      Dict       =  np.array([0.69643, 0.58484, 0.50314, 0.58760, 0.55368, 0.59643, 0.51529, 0.41217, 0.49087]			)
      assert np.abs(image_slice - expected_slice			).max() < 1e-1







    def     lowercase_ (   self				:						str			):
      a		:      List[Any]       =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,    provider='CPUExecutionProvider'			)
      a		:      Dict       =  PNDMScheduler.from_config(pipe.scheduler.config		,    skip_prk_steps=__snake_case			)
      pipe.set_progress_bar_config(disable=__snake_case			)

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

      assert image.shape == (1, 1_28, 1_28, 3)
      a		:      int       =  np.array([0.61737, 0.54642, 0.53183, 0.54465, 0.52742, 0.60525, 0.49969, 0.40655, 0.48154]			)

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







    def     lowercase_ (   self				:						List[str]			):
      a		:      Optional[int]       =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,    provider='CPUExecutionProvider'			)
      a		:      List[Any]       =  LMSDiscreteScheduler.from_config(pipe.scheduler.config			)
      pipe.set_progress_bar_config(disable=__snake_case			)

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

      a		:      Optional[int]       =  self.get_dummy_inputs()
      a		:      Optional[int]       =  pipe(**__snake_case			).images
      a		:      Any       =  image[0, -3:, -3:, -1]

      assert image.shape == (1, 1_28, 1_28, 3)
      a		:      Any       =  np.array([0.52761, 0.59977, 0.49033, 0.49619, 0.54282, 0.50311, 0.47600, 0.40918, 0.45203]			)

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







    def     lowercase_ (   self				:						List[Any]			):
      a		:      Dict       =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,    provider='CPUExecutionProvider'			)
      a		:      Any       =  EulerDiscreteScheduler.from_config(pipe.scheduler.config			)
      pipe.set_progress_bar_config(disable=__snake_case			)

      a		:      List[str]       =  self.get_dummy_inputs()
      a		:      Optional[int]       =  pipe(**__snake_case			).images
      a		:      Any       =  image[0, -3:, -3:, -1]

      assert image.shape == (1, 1_28, 1_28, 3)
      a		:      Dict       =  np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304]			)

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







    def     lowercase_ (   self				:						Tuple			):
      a		:      Any       =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,    provider='CPUExecutionProvider'			)
      a		:      Optional[Any]       =  EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config			)
      pipe.set_progress_bar_config(disable=__snake_case			)

      a		:      Dict       =  self.get_dummy_inputs()
      a		:      Union[str, Any]       =  pipe(**__snake_case			).images
      a		:      int       =  image[0, -3:, -3:, -1]

      assert image.shape == (1, 1_28, 1_28, 3)
      a		:      int       =  np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304]			)

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







    def     lowercase_ (   self				:						Optional[Any]			):
      a		:      Optional[int]       =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,    provider='CPUExecutionProvider'			)
      a		:      Optional[int]       =  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config			)
      pipe.set_progress_bar_config(disable=__snake_case			)

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

      assert image.shape == (1, 1_28, 1_28, 3)
      a		:      Optional[int]       =  np.array([0.65331, 0.58277, 0.48204, 0.56059, 0.53665, 0.56235, 0.50969, 0.40009, 0.46552]			)

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

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







    @property
    def     lowercase_ (   self				:						List[Any]			):
      return (
          "CUDAExecutionProvider",
          {
              "gpu_mem_limit": "15000000000",  # 15GB
              "arena_extend_strategy": "kSameAsRequested",
          },
      )







    @property
    def     lowercase_ (   self				:						Dict			):
      a		:      Tuple       =  ort.SessionOptions()
      a		:      Optional[Any]       =  False
      return options







    def     lowercase_ (   self				:						List[str]			):
      a		:      Dict       =  load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/img2img/sketch-mountains-input.jpg'			)
      a		:      Optional[int]       =  init_image.resize((7_68, 5_12)			)
      # using the PNDM scheduler by default
      a		:      Any       =  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		:      Union[str, Any]       =  'A fantasy landscape, trending on artstation'

      a		:      Dict       =  np.random.RandomState(0			)
      a		:      Optional[int]       =  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		:      str       =  output.images
      a		:      Optional[int]       =  images[0, 2_55:2_58, 3_83:3_86, -1]

      assert images.shape == (1, 5_12, 7_68, 3)
      a		:      List[str]       =  np.array([0.4909, 0.5059, 0.5372, 0.4623, 0.4876, 0.5049, 0.4820, 0.4956, 0.5019]			)
      # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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







    def     lowercase_ (   self				:						str			):
      a		:      Optional[Any]       =  load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/img2img/sketch-mountains-input.jpg'			)
      a		:      List[str]       =  init_image.resize((7_68, 5_12)			)
      a		:      Union[str, Any]       =  LMSDiscreteScheduler.from_pretrained(
          'runwayml/stable-diffusion-v1-5'		,    subfolder='scheduler'		,    revision='onnx'			)
      a		:      Any       =  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		:      Optional[int]       =  'A fantasy landscape, trending on artstation'

      a		:      str       =  np.random.RandomState(0			)
      a		:      List[str]       =  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		:      str       =  output.images
      a		:      Tuple       =  images[0, 2_55:2_58, 3_83:3_86, -1]

      assert images.shape == (1, 5_12, 7_68, 3)
      a		:      Union[str, Any]       =  np.array([0.8043, 0.926, 0.9581, 0.8119, 0.8954, 0.913, 0.7209, 0.7463, 0.7431]			)
      # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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