


_snake_case             =     '''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 unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class UpperCAmelCase__	( unittest.TestCase	):





  """simple docstring"""

  @property
  def   __lowercase		(		self      :     List[str]       ):


     '''simple docstring'''






     torch.manual_seed(0       )
     _a				:	int			=   UNetaDModel(
         block_out_channels=(32, 64)							,layers_per_block=2							,sample_size=32							,in_channels=3							,out_channels=3							,down_block_types=('DownBlock2D', 'AttnDownBlock2D')							,up_block_types=('AttnUpBlock2D', 'UpBlock2D')							,)
     return model


  def   __lowercase		(		self      :     Optional[Any]       ):


     '''simple docstring'''






     _a				:	str			=   self.dummy_uncond_unet
     _a				:	int			=   PNDMScheduler()

     _a				:	str			=   PNDMPipeline(unet=_a							,scheduler=_a       )
     pndm.to(_a       )
     pndm.set_progress_bar_config(disable=_a       )

     _a				:	Optional[int]			=   torch.manual_seed(0       )
     _a				:	Optional[Any]			=   pndm(generator=_a							,num_inference_steps=20							,output_type='numpy'       ).images

     _a				:	List[str]			=   torch.manual_seed(0       )
     _a				:	Any			=   pndm(generator=_a							,num_inference_steps=20							,output_type='numpy'							,return_dict=_a       )[0]

     _a				:	List[Any]			=   image[0, -3:, -3:, -1]
     _a				:	Any			=   image_from_tuple[0, -3:, -3:, -1]

     assert image.shape == (1, 32, 32, 3)
     _a				:	List[Any]			=   np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]       )

     assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2
     assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 1E-2







@slow
@require_torch
class UpperCAmelCase__	( unittest.TestCase	):





  """simple docstring"""

  def   __lowercase		(		self      :     Tuple       ):


     '''simple docstring'''






     _a				:	List[str]			=   'google/ddpm-cifar10-32'

     _a				:	str			=   UNetaDModel.from_pretrained(_a       )
     _a				:	Union[str, Any]			=   PNDMScheduler()

     _a				:	Tuple			=   PNDMPipeline(unet=_a							,scheduler=_a       )
     pndm.to(_a       )
     pndm.set_progress_bar_config(disable=_a       )
     _a				:	str			=   torch.manual_seed(0       )
     _a				:	Optional[Any]			=   pndm(generator=_a							,output_type='numpy'       ).images

     _a				:	Dict			=   image[0, -3:, -3:, -1]

     assert image.shape == (1, 32, 32, 3)
     _a				:	Tuple			=   np.array([0.1564, 0.1_4645, 0.1406, 0.1_4715, 0.1_2425, 0.1_4045, 0.1_3115, 0.1_2175, 0.125]       )

     assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2



