


from typing import List, Optional, Tuple, Union

import torch

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

class    __magic_name__						(  __lowerCAmelCase):


     def __init__(   self				:  List[str]				,						lowerCamelCase__				:  int				,						lowerCamelCase__				:  int      )	->   int:





      '''simple docstring'''






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


     @torch.no_grad()
     def __call__(   self				:  Optional[int]				,						lowerCamelCase__				:  int = 1				,						lowerCamelCase__				:  Optional[Union[torch.Generator, List[torch.Generator]]] = None				,						lowerCamelCase__				:  int = 1000				,						lowerCamelCase__				:  Optional[str] = "pil"				,						lowerCamelCase__				:  bool = True				,						)	->   Union[ImagePipelineOutput, Tuple]:





      '''simple docstring'''






      if isinstance(self.unet.config.sample_size				,						lowerCamelCase__      ):
       UpperCamelCase__       :     Any 							= (
           batch_size,
           self.unet.config.in_channels,
           self.unet.config.sample_size,
           self.unet.config.sample_size,
       )
      else:
       UpperCamelCase__       :     int 							= (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

      if self.device.type == "mps":
       # randn does not work reproducibly on mps
       UpperCamelCase__       :     Optional[Any] 							= randn_tensor(lowerCamelCase__				,						generator=lowerCamelCase__      )
       UpperCamelCase__       :     List[str] 							= image.to(self.device      )
      else:
       UpperCamelCase__       :     Any 							= randn_tensor(lowerCamelCase__				,						generator=lowerCamelCase__				,						device=self.device      )

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

      for t in self.progress_bar(self.scheduler.timesteps      ):
       # 1. predict noise model_output
       UpperCamelCase__       :     Union[str, Any] 							= self.unet(lowerCamelCase__				,						lowerCamelCase__      ).sample

       # 2. compute previous image: x_t -> x_t-1
       UpperCamelCase__       :     int 							= self.scheduler.step(lowerCamelCase__				,						lowerCamelCase__				,						lowerCamelCase__				,						generator=lowerCamelCase__      ).prev_sample

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

      if not return_dict:
       return (image,)

      return ImagePipelineOutput(images=lowerCamelCase__      )






from hashlib import mda, shaaaa

__UpperCamelCase							:							Dict    							=					(shaaaa, mda)

class    __magic_name__						:


     def __init__(   self				:  Optional[Any]				,						lowerCamelCase__				:  int = 8      )	->   None:





      '''simple docstring'''






      UpperCamelCase__       :     int 							= 0b0
      UpperCamelCase__       :     Optional[int] 							= size


     def   UpperCAmelCase__						(   self				:  Tuple				,						lowerCamelCase__				:  str      )	->   None:





      '''simple docstring'''






      UpperCamelCase__       :     Union[str, Any] 							= self.hash_(lowerCamelCase__      )
      self.bitarray |= h


     def   UpperCAmelCase__						(   self				:  Any				,						lowerCamelCase__				:  str      )	->   bool:





      '''simple docstring'''






      UpperCamelCase__       :     Any 							= self.hash_(lowerCamelCase__      )
      return (h & self.bitarray) == h


     def __contains__(   self				:  Any				,						lowerCamelCase__				:  str      )	->   bool:





      '''simple docstring'''






      return self.exists(lowerCamelCase__      )


     def   UpperCAmelCase__						(   self				:  Union[str, Any]				,						lowerCamelCase__				:  int      )	->   str:





      '''simple docstring'''






      UpperCamelCase__       :     Optional[int] 							= bin(lowerCamelCase__      )[2:]
      return res.zfill(self.size      )


     @property
     def   UpperCAmelCase__						(   self				:  Tuple      )	->   str:





      '''simple docstring'''






      return self.format_bin(self.bitarray      )


     def   UpperCAmelCase__						(   self				:  int				,						lowerCamelCase__				:  str      )	->   int:





      '''simple docstring'''






      UpperCamelCase__       :     Union[str, Any] 							= 0b0
      for func in HASH_FUNCTIONS:
       UpperCamelCase__       :     Optional[Any] 							= (
           int.from_bytes(func(value.encode()      ).digest()				,						'''little'''      ) % self.size
       )
       res |= 2**position
      return res


     def   UpperCAmelCase__						(   self				:  Union[str, Any]				,						lowerCamelCase__				:  str      )	->   str:





      '''simple docstring'''






      return self.format_bin(self.hash_(lowerCamelCase__      )      )


     @property
     def   UpperCAmelCase__						(   self				:  str      )	->   float:





      '''simple docstring'''






      UpperCamelCase__       :     Tuple 							= bin(self.bitarray      ).count('''1'''      )
      return (n_ones / self.size) ** len(lowerCamelCase__      )


