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 						__snake_case						(			lowerCAmelCase__		):





      '''simple docstring'''






      _snake_case     		=							42
      _snake_case     		=							42



      def __init__(       self  :					Union[str, Any]     ,	_UpperCamelCase  :					UNetaDModel     ,	_UpperCamelCase  :					PNDMScheduler)					->str:


          """simple docstring"""


          super().__init__()

          _lowerCamelCase  :		List[str]			    =    PNDMScheduler.from_config(scheduler.config)

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




      @torch.no_grad()
      def __call__(       self  :					Optional[Any]     ,	_UpperCamelCase  :					int = 1     ,	_UpperCamelCase  :					int = 50     ,	_UpperCamelCase  :					Optional[Union[torch.Generator, List[torch.Generator]]] = None     ,	_UpperCamelCase  :					Optional[str] = "pil"     ,	_UpperCamelCase  :					bool = True     ,	**_UpperCamelCase  :					str     ,	)					->str:


          """simple docstring"""


          _lowerCamelCase  :		Union[str, Any]			    =    randn_tensor(
              (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)     ,	generator=_lowercase     ,	device=self.device     ,	)

          self.scheduler.set_timesteps(_lowercase)
          for t in self.progress_bar(self.scheduler.timesteps):
              _lowerCamelCase  :		Tuple			    =    self.unet(_lowercase     ,	_lowercase).sample

              _lowerCamelCase  :		List[str]			    =    self.scheduler.step(_lowercase     ,	_lowercase     ,	_lowercase).prev_sample

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

          if not return_dict:
              return (image,)

          return ImagePipelineOutput(images=_lowercase)










def      UpperCAmelCase  (						UpperCamelCase__   )     ->     int:







				'''simple docstring'''


				barcode //= 10  # exclude the last digit
				__lowerCAmelCase					=  False
				__lowerCAmelCase					=  0

				# extract and check each digit
				while barcode != 0:
								__lowerCAmelCase					=  1 if checker else 3
								s += mult * (barcode % 10)
								barcode //= 10
								__lowerCAmelCase					=  not checker

				return (10 - (s % 10)) % 10





def      UpperCAmelCase  (						UpperCamelCase__   )     ->     bool:







				'''simple docstring'''


				return len(str(UpperCamelCase__   )   ) == 13 and get_check_digit(UpperCamelCase__   ) == barcode % 10





def      UpperCAmelCase  (						UpperCamelCase__   )     ->     int:







				'''simple docstring'''


				if str(UpperCamelCase__   ).isalpha():
								__lowerCAmelCase					=  F'''Barcode \'{barcode}\' has alphabetic characters.'''
								raise ValueError(UpperCamelCase__   )
				elif int(UpperCamelCase__   ) < 0:
								raise ValueError("""The entered barcode has a negative value. Try again."""   )
				else:
								return int(UpperCamelCase__   )


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	__A						:		Tuple          =						get_barcode(input("Barcode: ").strip())

	if is_valid(barcode):
		print(f"""'{barcode}' is a valid barcode.""")
	else:
		print(f"""'{barcode}' is NOT a valid barcode.""")





