



"""simple docstring"""
import inspect
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput



class       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__ ):

   def __init__(   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)  ->      Optional[Any]:





    '''simple docstring'''




    super().__init__()
    self.register_modules(vqvae=_SCREAMING_SNAKE_CASE      ,     unet=_SCREAMING_SNAKE_CASE      ,     scheduler=_SCREAMING_SNAKE_CASE						)




   @torch.no_grad()
   def __call__(   self      ,     _SCREAMING_SNAKE_CASE = 1      ,     _SCREAMING_SNAKE_CASE = None      ,     _SCREAMING_SNAKE_CASE = 0.0      ,     _SCREAMING_SNAKE_CASE = 50      ,     _SCREAMING_SNAKE_CASE = "pil"      ,     _SCREAMING_SNAKE_CASE = True      ,     **_SCREAMING_SNAKE_CASE      ,     )  ->      Union[Tuple, ImagePipelineOutput]:





    '''simple docstring'''




    UpperCAmelCase	:					Optional[Any]	=	randn_tensor(
        (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)      ,     generator=_SCREAMING_SNAKE_CASE      ,     )
    UpperCAmelCase	:					Optional[Any]	=	latents.to(self.device						)

    # scale the initial noise by the standard deviation required by the scheduler
    UpperCAmelCase	:					Optional[int]	=	latents * self.scheduler.init_noise_sigma

    self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE						)

    # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
    UpperCAmelCase	:					List[Any]	=	"""eta""" in set(inspect.signature(self.scheduler.step						).parameters.keys()						)

    UpperCAmelCase	:					str	=	{}
    if accepts_eta:
     UpperCAmelCase	:					Any	=	eta

    for t in self.progress_bar(self.scheduler.timesteps						):
     UpperCAmelCase	:					Tuple	=	self.scheduler.scale_model_input(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)
     # predict the noise residual
     UpperCAmelCase	:					Tuple	=	self.unet(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						).sample
     # compute the previous noisy sample x_t -> x_t-1
     UpperCAmelCase	:					Any	=	self.scheduler.step(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     **_SCREAMING_SNAKE_CASE						).prev_sample

    # decode the image latents with the VAE
    UpperCAmelCase	:					int	=	self.vqvae.decode(_SCREAMING_SNAKE_CASE						).sample

    UpperCAmelCase	:					Optional[Any]	=	(image / 2 + 0.5).clamp(0      ,     1						)
    UpperCAmelCase	:					Optional[Any]	=	image.cpu().permute(0      ,     2      ,     3      ,     1						).numpy()
    if output_type == "pil":
     UpperCAmelCase	:					Dict	=	self.numpy_to_pil(_SCREAMING_SNAKE_CASE						)

    if not return_dict:
     return (image,)

    return ImagePipelineOutput(images=_SCREAMING_SNAKE_CASE						)





"""simple docstring"""
from typing import TYPE_CHECKING

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


A:   List[str]								=		{"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   Tuple								=		["DPTFeatureExtractor"]
      A:   int								=		["DPTImageProcessor"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   Optional[Any]								=		[
          "DPT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "DPTForDepthEstimation",
          "DPTForSemanticSegmentation",
          "DPTModel",
          "DPTPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_dpt import DPTFeatureExtractor
            from .image_processing_dpt import DPTImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_dpt import (
                DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
                DPTForDepthEstimation,
                DPTForSemanticSegmentation,
                DPTModel,
                DPTPreTrainedModel,
            )


else:
      import sys

      A:   str								=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
