

# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput



class 					lowercase		( _UpperCamelCase      ):



 '''simple docstring'''


 def __init__(self , __a , __a				)				->      str:






  """simple docstring"""





  super().__init__()
  self.register_modules(unet=__a , scheduler=__a				)




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






  """simple docstring"""





  UpperCAmelCase__           =       torch.randn(
      (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) , generator=__a , )
  UpperCAmelCase__           =       image.to(self.device				)

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

  for t in self.progress_bar(self.scheduler.timesteps				):
   # 1. predict noise model_output
   UpperCAmelCase__           =       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
   UpperCAmelCase__           =       self.scheduler.step(__a , __a , __a				).prev_sample

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

  if not return_dict:
   return (image,), "This is a local test"

  return ImagePipelineOutput(images=__a				), "This is a local test"






from collections import OrderedDict
from dataclasses import fields
from typing import Any, Tuple

import numpy as np

from .import_utils import is_torch_available
def 		UpperCamelCase_(    snake_case__:						Optional[int]     ) ->				Tuple:
 if is_torch_available():
  import torch

  if isinstance(snake_case__						,					torch.Tensor     ):
   return True

 return isinstance(snake_case__						,					np.ndarray     )







class 					lowercase		( _UpperCamelCase      ):



 '''simple docstring'''


 def 						UpperCamelCase__							(self				)				->      int:






  """simple docstring"""





  UpperCAmelCase__           =       fields(self				)

  # Safety and consistency checks
  if not len(__a				):
   raise ValueError(F"{self.__class__.__name__} has no fields."				)

  UpperCAmelCase__           =       getattr(self , class_fields[0].name				)
  UpperCAmelCase__           =       all(getattr(self , field.name				) is None for field in class_fields[1:]				)

  if other_fields_are_none and isinstance(__a , __a				):
   for key, value in first_field.items():
    UpperCAmelCase__           =       value
  else:
   for field in class_fields:
    UpperCAmelCase__           =       getattr(self , field.name				)
    if v is not None:
     UpperCAmelCase__           =       v




 def __delitem__(self , *__a , **__a				)				->      Optional[int]:






  """simple docstring"""





  raise Exception(F"You cannot use ``__delitem__`` on a {self.__class__.__name__} instance."				)




 def 						UpperCamelCase__							(self , *__a , **__a				)				->      str:






  """simple docstring"""





  raise Exception(F"You cannot use ``setdefault`` on a {self.__class__.__name__} instance."				)




 def 						UpperCamelCase__							(self , *__a , **__a				)				->      Optional[int]:






  """simple docstring"""





  raise Exception(F"You cannot use ``pop`` on a {self.__class__.__name__} instance."				)




 def 						UpperCamelCase__							(self , *__a , **__a				)				->      Tuple:






  """simple docstring"""





  raise Exception(F"You cannot use ``update`` on a {self.__class__.__name__} instance."				)




 def __getitem__(self , __a				)				->      Tuple:






  """simple docstring"""





  if isinstance(__a , __a				):
   UpperCAmelCase__           =       dict(self.items()				)
   return inner_dict[k]
  else:
   return self.to_tuple()[k]




 def __setattr__(self , __a , __a				)				->      Any:






  """simple docstring"""





  if name in self.keys() and value is not None:
   # Don't call self.__setitem__ to avoid recursion errors
   super().__setitem__(__a , __a				)
  super().__setattr__(__a , __a				)




 def __setitem__(self , __a , __a				)				->      Union[str, Any]:






  """simple docstring"""





  super().__setitem__(__a , __a				)
  # Don't call self.__setattr__ to avoid recursion errors
  super().__setattr__(__a , __a				)




 def 						UpperCamelCase__							(self				)				->      Tuple[Any]:






  """simple docstring"""





  return tuple(self[k] for k in self.keys()				)



