





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 						UpperCamelCase__					( lowerCAmelCase_  ):



     '''simple docstring'''

     __snake_case       :      UNetaDModel
     __snake_case       :      PNDMScheduler






     def __init__(				self		:       List[Any]		,lowerCamelCase__		:       UNetaDModel		,lowerCamelCase__		:       PNDMScheduler			)	->						Optional[int]:




           '''simple docstring'''


           super().__init__()

           SCREAMING_SNAKE_CASE						=		PNDMScheduler.from_config(scheduler.config			)

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






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		randn_tensor(
               (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)		,generator=lowerCamelCase__		,device=self.device		,)

           self.scheduler.set_timesteps(lowerCamelCase__			)
           for t in self.progress_bar(self.scheduler.timesteps			):
                 SCREAMING_SNAKE_CASE						=		self.unet(lowerCamelCase__		,lowerCamelCase__			).sample

                 SCREAMING_SNAKE_CASE						=		self.scheduler.step(lowerCamelCase__		,lowerCamelCase__		,lowerCamelCase__			).prev_sample

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

           if not return_dict:
                 return (image,)

           return ImagePipelineOutput(images=lowerCamelCase__			)









import os
from string import ascii_letters

SCREAMING_SNAKE_CASE_		  =			ascii_letters + """ \t\n"""



def   __lowercase  ( )					->  dict[str, None]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE						=		os.path.split(os.path.realpath(_SCREAMING_SNAKE_CASE      )      )
      SCREAMING_SNAKE_CASE						=		{}
      with open(path[0] + """/dictionary.txt"""      ) as dictionary_file:
            for word in dictionary_file.read().split("""\n"""      ):
                  SCREAMING_SNAKE_CASE						=		None
      return english_words


SCREAMING_SNAKE_CASE_		  =			load_dictionary()



def   __lowercase  ( _SCREAMING_SNAKE_CASE      )					->  float:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE						=		message.upper()
      SCREAMING_SNAKE_CASE						=		remove_non_letters(_SCREAMING_SNAKE_CASE      )
      SCREAMING_SNAKE_CASE						=		message.split()
      SCREAMING_SNAKE_CASE						=		len([word for word in possible_words if word in ENGLISH_WORDS]      )
      return float(_SCREAMING_SNAKE_CASE      ) / len(_SCREAMING_SNAKE_CASE      )



def   __lowercase  ( _SCREAMING_SNAKE_CASE      )					->  str:


      '''simple docstring'''


      return "".join(symbol for symbol in message if symbol in LETTERS_AND_SPACE      )



def   __lowercase  ( _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE = 20					,		_SCREAMING_SNAKE_CASE = 85      )					->  bool:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE						=		get_english_count(_SCREAMING_SNAKE_CASE      ) * 1_00 >= word_percentage
      SCREAMING_SNAKE_CASE						=		len(remove_non_letters(_SCREAMING_SNAKE_CASE      )      )
      SCREAMING_SNAKE_CASE						=		(float(_SCREAMING_SNAKE_CASE      ) / len(_SCREAMING_SNAKE_CASE      )) * 1_00
      SCREAMING_SNAKE_CASE						=		message_letters_percentage >= letter_percentage
      return words_match and letters_match


if __name__ == "__main__":
      import doctest

      doctest.testmod()


