




from typing import List, Optional, Tuple, Union

import torch

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






class 	A	(						_lowerCAmelCase						):
     """simple docstring"""

     def __init__(			self     :      Union[str, Any]		,     A__     :      Dict		,     A__     :      int	) ->				Union[str, Any]:
           super().__init__()
           self.register_modules(unet=A__		,     scheduler=A__	)




     @torch.no_grad()
     def __call__(			self     :      int		,     A__     :      int = 1		,     A__     :      Optional[Union[torch.Generator, List[torch.Generator]]] = None		,     A__     :      int = 1_0_0_0		,     A__     :      Optional[str] = "pil"		,     A__     :      bool = True		,     ) ->				Union[ImagePipelineOutput, Tuple]:
           # Sample gaussian noise to begin loop
           if isinstance(self.unet.config.sample_size		,     A__	):
                 lowerCamelCase__					     = (
                     batch_size,
                     self.unet.config.in_channels,
                     self.unet.config.sample_size,
                     self.unet.config.sample_size,
                 )
           else:
                 lowerCamelCase__					     = (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

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

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

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

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

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

           if not return_dict:
                 return (image,)

           return ImagePipelineOutput(images=A__	)





from __future__ import annotations

import sys






class 	A	:
     """simple docstring"""

     def __init__(			self     :      str		,     A__     :      str		,     A__     :      int	) ->				str:
           lowerCamelCase__					     = letter
           lowerCamelCase__					     = freq
           lowerCamelCase__					     = {}




     def __repr__(			self     :      Any	) ->				str:
           return F"""{self.letter}:{self.freq}"""






class 	A	:
     """simple docstring"""

     def __init__(			self     :      Optional[Any]		,     A__     :      int		,     A__     :      Letter | TreeNode		,     A__     :      Letter | TreeNode	) ->				Any:
           lowerCamelCase__					     = freq
           lowerCamelCase__					     = left
           lowerCamelCase__					     = right

def 	lowerCAmelCase_	(     lowerCamelCase:				str  )    ->	list[Letter]:


      '''simple docstring'''




      lowerCamelCase__					     = {}
      with open(lowerCamelCase  ) as f:
            while True:
                  lowerCamelCase__					     = f.read(1  )
                  if not c:
                        break
                  lowerCamelCase__					     = chars[c] + 1 if c in chars else 1
      return sorted((Letter(lowerCamelCase   , lowerCamelCase  ) for c, f in chars.items())   , key=lambda lowerCamelCase  : x.freq  )

def 	lowerCAmelCase_	(     lowerCamelCase:				list[Letter]  )    ->	Letter | TreeNode:


      '''simple docstring'''




      lowerCamelCase__					     = letters  # type: ignore
      while len(lowerCamelCase  ) > 1:
            lowerCamelCase__					     = response.pop(0  )
            lowerCamelCase__					     = response.pop(0  )
            lowerCamelCase__					     = left.freq + right.freq
            lowerCamelCase__					     = TreeNode(lowerCamelCase   , lowerCamelCase   , lowerCamelCase  )
            response.append(lowerCamelCase  )
            response.sort(key=lambda lowerCamelCase  : x.freq  )
      return response[0]

def 	lowerCAmelCase_	(     lowerCamelCase:				Letter | TreeNode   , lowerCamelCase:				str  )    ->	list[Letter]:


      '''simple docstring'''




      if isinstance(lowerCamelCase   , lowerCamelCase  ):
            lowerCamelCase__					     = bitstring
            return [root]
      lowerCamelCase__					     = root  # type: ignore
      lowerCamelCase__					     = []
      letters += traverse_tree(treenode.left   , bitstring + '0'  )
      letters += traverse_tree(treenode.right   , bitstring + '1'  )
      return letters

def 	lowerCAmelCase_	(     lowerCamelCase:				str  )    ->	None:


      '''simple docstring'''




      lowerCamelCase__					     = parse_file(lowerCamelCase  )
      lowerCamelCase__					     = build_tree(lowerCamelCase  )
      lowerCamelCase__					     = {
          k: v for letter in traverse_tree(lowerCamelCase   , ''  ) for k, v in letter.bitstring.items()
      }
      print(f"""Huffman Coding  of {file_path}: """  )
      with open(lowerCamelCase  ) as f:
            while True:
                  lowerCamelCase__					     = f.read(1  )
                  if not c:
                        break
                  print(letters[c]   , end=' '  )
      print()


if __name__ == "__main__":
      # pass the file path to the huffman function
      huffman(sys.argv[1])