

from typing import List, Optional, Tuple, Union

import torch

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






class 							A_			(		snake_case__ ):
     _lowercase      :					UNetaDModel
     _lowercase      :					PNDMScheduler


     def __init__(							self      :		List[str]      ,  UpperCAmelCase      :		UNetaDModel      ,  UpperCAmelCase      :		PNDMScheduler )							->     Tuple:
      super().__init__()

      __lowerCAmelCase:       Tuple            =				PNDMScheduler.from_config(scheduler.config )

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


     @torch.no_grad()
     def __call__(							self      :		List[str]      ,  UpperCAmelCase      :		int = 1      ,  UpperCAmelCase      :		int = 5_0      ,  UpperCAmelCase      :		Optional[Union[torch.Generator, List[torch.Generator]]] = None      ,  UpperCAmelCase      :		Optional[str] = "pil"      ,  UpperCAmelCase      :		bool = True      ,  **UpperCAmelCase      :		Tuple      ,  )							->     Union[ImagePipelineOutput, Tuple]:
      __lowerCAmelCase:       Optional[int]            =				randn_tensor(
          (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)      ,  generator=UpperCAmelCase      ,  device=self.device      ,  )

      self.scheduler.set_timesteps(UpperCAmelCase )
      for t in self.progress_bar(self.scheduler.timesteps ):
       __lowerCAmelCase:       List[Any]            =				self.unet(UpperCAmelCase      ,  UpperCAmelCase ).sample

       __lowerCAmelCase:       int            =				self.scheduler.step(UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase ).prev_sample

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

      if not return_dict:
       return (image,)

      return ImagePipelineOutput(images=UpperCAmelCase )








from __future__ import annotations







def _a			(	SCREAMING_SNAKE_CASE		:      list[int]		)   ->   list[int]:

 """simple docstring"""






 if not list_of_ints:
  return []

 if min(SCREAMING_SNAKE_CASE		) < 0:
  raise ValueError('All numbers must be positive'		)

 __lowerCAmelCase:       Tuple            =				max(len(bin(SCREAMING_SNAKE_CASE		)[2:]		) for x in list_of_ints		)
 return _msd_radix_sort(SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE		)







def _a			(	SCREAMING_SNAKE_CASE		:      list[int]   , SCREAMING_SNAKE_CASE		:      int		)   ->   list[int]:

 """simple docstring"""






 if bit_position == 0 or len(SCREAMING_SNAKE_CASE		) in [0, 1]:
  return list_of_ints

 __lowerCAmelCase:       List[str]            =				[]
 __lowerCAmelCase:       Any            =				[]
 # Split numbers based on bit at bit_position from the right
 for number in list_of_ints:
  if (number >> (bit_position - 1)) & 1:
   # number has a one at bit bit_position
   ones.append(SCREAMING_SNAKE_CASE		)
  else:
   # number has a zero at bit bit_position
   zeros.append(SCREAMING_SNAKE_CASE		)

    # recursively split both lists further
 __lowerCAmelCase:       Dict            =				_msd_radix_sort(SCREAMING_SNAKE_CASE   , bit_position - 1		)
 __lowerCAmelCase:       Union[str, Any]            =				_msd_radix_sort(SCREAMING_SNAKE_CASE   , bit_position - 1		)

 # recombine lists
 __lowerCAmelCase:       List[str]            =				zeros
 res.extend(SCREAMING_SNAKE_CASE		)

 return res







def _a			(	SCREAMING_SNAKE_CASE		:      list[int]		)   ->   List[str]:

 """simple docstring"""






 __lowerCAmelCase:       Optional[int]            =				len(SCREAMING_SNAKE_CASE		)
 if not list_of_ints or length == 1:
  return

 if min(SCREAMING_SNAKE_CASE		) < 0:
  raise ValueError('All numbers must be positive'		)

 __lowerCAmelCase:       Optional[int]            =				max(len(bin(SCREAMING_SNAKE_CASE		)[2:]		) for x in list_of_ints		)
 _msd_radix_sort_inplace(SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , 0   , SCREAMING_SNAKE_CASE		)







def _a			(	SCREAMING_SNAKE_CASE		:      list[int]   , SCREAMING_SNAKE_CASE		:      int   , SCREAMING_SNAKE_CASE		:      int   , SCREAMING_SNAKE_CASE		:      int		)   ->   str:

 """simple docstring"""






 if bit_position == 0 or end_index - begin_index <= 1:
  return

 bit_position -= 1

 __lowerCAmelCase:       Optional[int]            =				begin_index
 __lowerCAmelCase:       Optional[Any]            =				end_index - 1
 while i <= j:
  __lowerCAmelCase:       Any            =				False
  if not (list_of_ints[i] >> bit_position) & 1:
   # found zero at the beginning
   i += 1
   __lowerCAmelCase:       Optional[Any]            =				True
  if (list_of_ints[j] >> bit_position) & 1:
   # found one at the end
   j -= 1
   __lowerCAmelCase:       List[str]            =				True

  if changed:
   continue

  __lowerCAmelCase     ,							__lowerCAmelCase:       Union[str, Any]            =				list_of_ints[j], list_of_ints[i]
  j -= 1
  if j != i:
   i += 1

 _msd_radix_sort_inplace(SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE		)
 _msd_radix_sort_inplace(SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE		)


if __name__ == "__main__":
  import doctest

  doctest.testmod()





