

'''simple docstring'''




from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class   _SCREAMING_SNAKE_CASE   (   __a     ):



 def __init__(   self		:   Optional[int]			,				a__		:   Union[str, Any]			,				a__		:   int      ):
      super().__init__()

      # make sure scheduler can always be converted to DDIM
      __magic_name__						       =    DDIMScheduler.from_config(scheduler.config      )

      self.register_modules(unet=a__			,				scheduler=a__      )



 @torch.no_grad()
 def __call__(   self		:   List[Any]			,				a__		:   int = 1			,				a__		:   Optional[Union[torch.Generator, List[torch.Generator]]] = None			,				a__		:   float = 0.0			,				a__		:   int = 50			,				a__		:   Optional[bool] = None			,				a__		:   Optional[str] = "pil"			,				a__		:   bool = True			,				):

      # Sample gaussian noise to begin loop
      if isinstance(self.unet.config.sample_size			,				a__      ):
           __magic_name__						       =    (
               batch_size,
               self.unet.config.in_channels,
               self.unet.config.sample_size,
               self.unet.config.sample_size,
           )
      else:
           __magic_name__						       =    (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

      if isinstance(a__			,				a__      ) and len(a__      ) != batch_size:
           raise ValueError(
               F'''You have passed a list of generators of length {len(a__      )}, but requested an effective batch'''
               F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''      )

      __magic_name__						       =    randn_tensor(a__			,				generator=a__			,				device=self.device			,				dtype=self.unet.dtype      )

      # set step values
      self.scheduler.set_timesteps(a__      )

      for t in self.progress_bar(self.scheduler.timesteps      ):
           # 1. predict noise model_output
           __magic_name__						       =    self.unet(a__			,				a__      ).sample

           # 2. predict previous mean of image x_t-1 and add variance depending on eta
           # eta corresponds to η in paper and should be between [0, 1]
           # do x_t -> x_t-1
           __magic_name__						       =    self.scheduler.step(
               a__			,				a__			,				a__			,				eta=a__			,				use_clipped_model_output=a__			,				generator=a__      ).prev_sample

      __magic_name__						       =    (image / 2 + 0.5).clamp(0			,				1      )
      __magic_name__						       =    image.cpu().permute(0			,				2			,				3			,				1      ).numpy()
      if output_type == "pil":
           __magic_name__						       =    self.numpy_to_pil(a__      )

      if not return_dict:
           return (image,)

      return ImagePipelineOutput(images=a__      )









'''simple docstring'''




from typing import TYPE_CHECKING

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


_lowerCAmelCase         =						{"configuration_glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"]}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCAmelCase         =						["GLPNFeatureExtractor"]
      _lowerCAmelCase         =						["GLPNImageProcessor"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCAmelCase         =						[
          "GLPN_PRETRAINED_MODEL_ARCHIVE_LIST",
          "GLPNForDepthEstimation",
          "GLPNLayer",
          "GLPNModel",
          "GLPNPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_glpn import GLPNFeatureExtractor
            from .image_processing_glpn import GLPNImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_glpn import (
                GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
                GLPNForDepthEstimation,
                GLPNLayer,
                GLPNModel,
                GLPNPreTrainedModel,
            )


else:
      import sys

      _lowerCAmelCase         =						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






