


UpperCAmelCase__        =				{
    '''0000''': '''0''',
    '''0001''': '''1''',
    '''0010''': '''2''',
    '''0011''': '''3''',
    '''0100''': '''4''',
    '''0101''': '''5''',
    '''0110''': '''6''',
    '''0111''': '''7''',
    '''1000''': '''8''',
    '''1001''': '''9''',
    '''1010''': '''a''',
    '''1011''': '''b''',
    '''1100''': '''c''',
    '''1101''': '''d''',
    '''1110''': '''e''',
    '''1111''': '''f''',
}
def 							UpperCAmelCase_   (				__snake_case		)	->     str:







 """simple docstring"""


 _lowercase				 =str(__snake_case		).strip()

 # Exceptions
 if not binary_str:
  raise ValueError('''Empty string was passed to the function'''		)
 _lowercase				 =binary_str[0] == '''-'''
 _lowercase				 =binary_str[1:] if is_negative else binary_str
 if not all(char in '''01''' for char in binary_str		):
  raise ValueError('''Non-binary value was passed to the function'''		)

 _lowercase				 =(
     '''0''' * (4 * (divmod(len(__snake_case		)	,		4		)[0] + 1) - len(__snake_case		)) + binary_str
 )

 _lowercase				 =[]
 for x in range(0	,		len(__snake_case		)	,		4		):
  hexadecimal.append(BITS_TO_HEX[binary_str[x : x + 4]]		)
 _lowercase				 ='''0x''' + ''''''.join(__snake_case		)

 return "-" + hexadecimal_str if is_negative else hexadecimal_str


if __name__ == "__main__":
  from doctest import testmod

  testmod()





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  lowerCamelCase__  (			lowerCAmelCase):
     SCREAMING_SNAKE_CASE__      =     42
     SCREAMING_SNAKE_CASE__      =     42





     def __init__(self						,	UpperCAmelCase						,	UpperCAmelCase			)	->  Tuple:
      super().__init__()

      _lowercase				 =PNDMScheduler.from_config(scheduler.config			)

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





     @torch.no_grad()
     def __call__(self						,	UpperCAmelCase = 1						,	UpperCAmelCase = 5_0						,	UpperCAmelCase = None						,	UpperCAmelCase = "pil"						,	UpperCAmelCase = True						,	**UpperCAmelCase						,	)	->  Union[ImagePipelineOutput, Tuple]:
      _lowercase				 =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			):
       _lowercase				 =self.unet(UpperCAmelCase						,	UpperCAmelCase			).sample

       _lowercase				 =self.scheduler.step(UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase			).prev_sample

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

      if not return_dict:
       return (image,)

      return ImagePipelineOutput(images=UpperCAmelCase			)

