
import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest



class       _snake_case   (	UpperCAmelCase_							):
 __lowerCAmelCase  :     int        =			(DDPMParallelScheduler,)


 def      lowercase__     (					self					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__  :   List[Any]    		=      {
       """num_train_timesteps""": 10_00,
       """beta_start""": 0.0_0_0_1,
       """beta_end""": 0.0_2,
       """beta_schedule""": """linear""",
       """variance_type""": """fixed_small""",
       """clip_sample""": True,
   }

   config.update(**SCREAMING_SNAKE_CASE_)
   return config


 def      lowercase__     (					self):


   '''simple docstring'''




   for timesteps in [1, 5, 1_00, 10_00]:
     self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_)


 def      lowercase__     (					self):


   '''simple docstring'''




   for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]					,  [0.0_0_2, 0.0_2, 0.2, 2]):
     self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE_					,  beta_end=SCREAMING_SNAKE_CASE_)


 def      lowercase__     (					self):


   '''simple docstring'''




   for schedule in ["linear", "squaredcos_cap_v2"]:
     self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE_)


 def      lowercase__     (					self):


   '''simple docstring'''




   for variance in ["fixed_small", "fixed_large", "other"]:
     self.check_over_configs(variance_type=SCREAMING_SNAKE_CASE_)


 def      lowercase__     (					self):


   '''simple docstring'''




   for clip_sample in [True, False]:
     self.check_over_configs(clip_sample=SCREAMING_SNAKE_CASE_)


 def      lowercase__     (					self):


   '''simple docstring'''




   self.check_over_configs(thresholding=SCREAMING_SNAKE_CASE_)
   for threshold in [0.5, 1.0, 2.0]:
     for prediction_type in ["epsilon", "sample", "v_prediction"]:
       self.check_over_configs(
           thresholding=SCREAMING_SNAKE_CASE_					,  prediction_type=SCREAMING_SNAKE_CASE_					,  sample_max_value=SCREAMING_SNAKE_CASE_					,  )


 def      lowercase__     (					self):


   '''simple docstring'''




   for prediction_type in ["epsilon", "sample", "v_prediction"]:
     self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE_)


 def      lowercase__     (					self):


   '''simple docstring'''




   for t in [0, 5_00, 9_99]:
     self.check_over_forward(time_step=SCREAMING_SNAKE_CASE_)


 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   int    		=      self.scheduler_classes[0]
   lowercase__  :   Union[str, Any]    		=      self.get_scheduler_config()
   lowercase__  :   Union[str, Any]    		=      scheduler_class(**SCREAMING_SNAKE_CASE_)

   assert torch.sum(torch.abs(scheduler._get_variance(0) - 0.0)) < 1E-5
   assert torch.sum(torch.abs(scheduler._get_variance(4_87) - 0.0_0_9_7_9)) < 1E-5
   assert torch.sum(torch.abs(scheduler._get_variance(9_99) - 0.0_2)) < 1E-5


 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   List[Any]    		=      self.scheduler_classes[0]
   lowercase__  :   List[Any]    		=      self.get_scheduler_config()
   lowercase__  :   Optional[Any]    		=      scheduler_class(**SCREAMING_SNAKE_CASE_)

   lowercase__  :   Any    		=      len(SCREAMING_SNAKE_CASE_)

   lowercase__  :   List[str]    		=      self.dummy_model()
   lowercase__  :   Union[str, Any]    		=      self.dummy_sample_deter
   lowercase__  :   int    		=      self.dummy_sample_deter + 0.1
   lowercase__  :   List[Any]    		=      self.dummy_sample_deter - 0.1

   lowercase__  :   List[str]    		=      samplea.shape[0]
   lowercase__  :   Any    		=      torch.stack([samplea, samplea, samplea]					,  dim=0)
   lowercase__  :   str    		=      torch.arange(SCREAMING_SNAKE_CASE_)[0:3, None].repeat(1					,  SCREAMING_SNAKE_CASE_)

   lowercase__  :   Optional[Any]    		=      model(samples.flatten(0					,  1)					,  timesteps.flatten(0					,  1))
   lowercase__  :   List[Any]    		=      scheduler.batch_step_no_noise(SCREAMING_SNAKE_CASE_					,  timesteps.flatten(0					,  1)					,  samples.flatten(0					,  1))

   lowercase__  :   str    		=      torch.sum(torch.abs(SCREAMING_SNAKE_CASE_))
   lowercase__  :   Optional[Any]    		=      torch.mean(torch.abs(SCREAMING_SNAKE_CASE_))

   assert abs(result_sum.item() - 1_1_5_3.1_8_3_3) < 1E-2
   assert abs(result_mean.item() - 0.5_0_0_5) < 1E-3


 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   Tuple    		=      self.scheduler_classes[0]
   lowercase__  :   Optional[int]    		=      self.get_scheduler_config()
   lowercase__  :   Tuple    		=      scheduler_class(**SCREAMING_SNAKE_CASE_)

   lowercase__  :   Union[str, Any]    		=      len(SCREAMING_SNAKE_CASE_)

   lowercase__  :   Union[str, Any]    		=      self.dummy_model()
   lowercase__  :   List[Any]    		=      self.dummy_sample_deter
   lowercase__  :   Optional[int]    		=      torch.manual_seed(0)

   for t in reversed(range(SCREAMING_SNAKE_CASE_)):
     # 1. predict noise residual
     lowercase__  :   Union[str, Any]    		=      model(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)

     # 2. predict previous mean of sample x_t-1
     lowercase__  :   Optional[int]    		=      scheduler.step(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  generator=SCREAMING_SNAKE_CASE_).prev_sample

     lowercase__  :   Optional[int]    		=      pred_prev_sample

   lowercase__  :   Optional[Any]    		=      torch.sum(torch.abs(SCREAMING_SNAKE_CASE_))
   lowercase__  :   Union[str, Any]    		=      torch.mean(torch.abs(SCREAMING_SNAKE_CASE_))

   assert abs(result_sum.item() - 2_5_8.9_6_0_6) < 1E-2
   assert abs(result_mean.item() - 0.3_3_7_2) < 1E-3


 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   Dict    		=      self.scheduler_classes[0]
   lowercase__  :   str    		=      self.get_scheduler_config(prediction_type="""v_prediction""")
   lowercase__  :   List[Any]    		=      scheduler_class(**SCREAMING_SNAKE_CASE_)

   lowercase__  :   Optional[Any]    		=      len(SCREAMING_SNAKE_CASE_)

   lowercase__  :   str    		=      self.dummy_model()
   lowercase__  :   Any    		=      self.dummy_sample_deter
   lowercase__  :   Union[str, Any]    		=      torch.manual_seed(0)

   for t in reversed(range(SCREAMING_SNAKE_CASE_)):
     # 1. predict noise residual
     lowercase__  :   List[Any]    		=      model(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)

     # 2. predict previous mean of sample x_t-1
     lowercase__  :   Any    		=      scheduler.step(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  generator=SCREAMING_SNAKE_CASE_).prev_sample

     lowercase__  :   Optional[int]    		=      pred_prev_sample

   lowercase__  :   Optional[int]    		=      torch.sum(torch.abs(SCREAMING_SNAKE_CASE_))
   lowercase__  :   Optional[int]    		=      torch.mean(torch.abs(SCREAMING_SNAKE_CASE_))

   assert abs(result_sum.item() - 2_0_2.0_2_9_6) < 1E-2
   assert abs(result_mean.item() - 0.2_6_3_1) < 1E-3


 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   Optional[int]    		=      self.scheduler_classes[0]
   lowercase__  :   Dict    		=      self.get_scheduler_config()
   lowercase__  :   Tuple    		=      scheduler_class(**SCREAMING_SNAKE_CASE_)

   lowercase__  :   Optional[int]    		=      [1_00, 87, 50, 1, 0]

   scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE_)

   lowercase__  :   Optional[int]    		=      scheduler.timesteps

   for i, timestep in enumerate(SCREAMING_SNAKE_CASE_):
     if i == len(SCREAMING_SNAKE_CASE_) - 1:
       lowercase__  :   Optional[Any]    		=      -1
     else:
       lowercase__  :   str    		=      timesteps[i + 1]

     lowercase__  :   Dict    		=      scheduler.previous_timestep(SCREAMING_SNAKE_CASE_)
     lowercase__  :   str    		=      prev_t.item()

     self.assertEqual(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)


 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   Any    		=      self.scheduler_classes[0]
   lowercase__  :   Any    		=      self.get_scheduler_config()
   lowercase__  :   str    		=      scheduler_class(**SCREAMING_SNAKE_CASE_)

   lowercase__  :   List[Any]    		=      [1_00, 87, 50, 51, 0]

   with self.assertRaises(SCREAMING_SNAKE_CASE_					,  msg="""`custom_timesteps` must be in descending order."""):
     scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE_)


 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   Union[str, Any]    		=      self.scheduler_classes[0]
   lowercase__  :   Optional[Any]    		=      self.get_scheduler_config()
   lowercase__  :   int    		=      scheduler_class(**SCREAMING_SNAKE_CASE_)

   lowercase__  :   List[Any]    		=      [1_00, 87, 50, 1, 0]
   lowercase__  :   Tuple    		=      len(SCREAMING_SNAKE_CASE_)

   with self.assertRaises(SCREAMING_SNAKE_CASE_					,  msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`."""):
     scheduler.set_timesteps(num_inference_steps=SCREAMING_SNAKE_CASE_					,  timesteps=SCREAMING_SNAKE_CASE_)



 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   Optional[Any]    		=      self.scheduler_classes[0]
   lowercase__  :   Any    		=      self.get_scheduler_config()
   lowercase__  :   int    		=      scheduler_class(**SCREAMING_SNAKE_CASE_)

   lowercase__  :   List[Any]    		=      [scheduler.config.num_train_timesteps]

   with self.assertRaises(
       SCREAMING_SNAKE_CASE_					,  msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""					,  ):
     scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE_)








lowerCamelCase__    :		Any          =   """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






