from typing import List, Optional, Tuple, Union

import torch

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



class     snake_case_			(						a__			):






   def __init__( self							:	Optional[int]      ,		lowercase_							:	Dict      ,		lowercase_							:	Optional[int]    )   ->		int:
       super().__init__()
       self.register_modules(unet=lowercase_      ,		scheduler=lowercase_    )







   @torch.no_grad()
   def __call__( self							:	List[str]      ,		lowercase_							:	Dict = 1      ,		lowercase_							:	Optional[int] = None      ,		lowercase_							:	int = 10_00      ,		lowercase_							:	int = "pil"      ,		lowercase_							:	Optional[int] = True      ,		)   ->		int:
       # Sample gaussian noise to begin loop
       if isinstance(self.unet.config.sample_size      ,		lowercase_    ):
           lowercase__					:  Optional[Any]          =							(
               batch_size,
               self.unet.config.in_channels,
               self.unet.config.sample_size,
               self.unet.config.sample_size,
           )
       else:
           lowercase__					:  List[str]          =							(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

       if self.device.type == "mps":
           # randn does not work reproducibly on mps
           lowercase__					:  Tuple          =							randn_tensor(lowercase_      ,		generator=lowercase_    )
           lowercase__					:  Dict          =							image.to(self.device    )
       else:
           lowercase__					:  Tuple          =							randn_tensor(lowercase_      ,		generator=lowercase_      ,		device=self.device    )

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

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

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

       lowercase__					:  str          =							(image / 2 + 0.5).clamp(0      ,		1    )
       lowercase__					:  List[str]          =							image.cpu().permute(0      ,		2      ,		3      ,		1    ).numpy()
       if output_type == "pil":
           lowercase__					:  List[Any]          =							self.numpy_to_pil(lowercase_    )

       if not return_dict:
           return (image,)

       return ImagePipelineOutput(images=lowercase_    )







'''simple docstring'''







from __future__ import annotations

from enum import Enum, unique
from typing import TypeVar

# Create a generic variable that can be 'Enum', or any subclass.
a__    :   Optional[Any]								=			TypeVar('T', bound='Enum')
@unique
class    lowercase_	(       a__  ):
    __UpperCAmelCase       =      80
    __UpperCAmelCase       =      70
    __UpperCAmelCase       =      60
    __UpperCAmelCase       =      50
    __UpperCAmelCase       =      40
    __UpperCAmelCase       =      30
    __UpperCAmelCase       =      20
    __UpperCAmelCase       =      10

@unique
class    lowercase_	(       a__  ):
    __UpperCAmelCase       =      24
    __UpperCAmelCase       =      21
    __UpperCAmelCase       =      18
    __UpperCAmelCase       =      15
    __UpperCAmelCase       =      12
    __UpperCAmelCase       =      9
    __UpperCAmelCase       =      6
    __UpperCAmelCase       =      3
    __UpperCAmelCase       =      2
    __UpperCAmelCase       =      1
    __UpperCAmelCase       =      -1
    __UpperCAmelCase       =      -2
    __UpperCAmelCase       =      -3
    __UpperCAmelCase       =      -6
    __UpperCAmelCase       =      -9
    __UpperCAmelCase       =      -12
    __UpperCAmelCase       =      -15
    __UpperCAmelCase       =      -18
    __UpperCAmelCase       =      -21
    __UpperCAmelCase       =      -24


    @classmethod
    def   __a				(	cls       ):
          return {unit.name: unit.value for unit in cls if unit.value > 0}


    @classmethod
    def   __a				(	cls       ):
          return {unit.name: unit.value for unit in cls if unit.value < 0}







def _UpperCamelCase			(	__A       )				->						str:


      '''simple docstring'''



      UpperCamelCase__      =							SIUnit.get_positive() if value > 0 else SIUnit.get_negative()
      for name_prefix, value_prefix in prefixes.items():
            UpperCamelCase__      =							value / (10**value_prefix)
            if numerical_part > 1:
                  return F'''{numerical_part!s} {name_prefix}'''
      return str(__A       )







def _UpperCamelCase			(	__A       )				->						str:


      '''simple docstring'''



      for prefix in BinaryUnit:
            UpperCamelCase__      =							value / (2**prefix.value)
            if numerical_part > 1:
                  return F'''{numerical_part!s} {prefix.name}'''
      return str(__A       )


if __name__ == "__main__":
  import doctest

  doctest.testmod()





