'''simple docstring'''




from __future__ import annotations

import sys




class 	__UpperCAmelCase				:





       '''simple docstring'''



       def __init__(      self    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE )		->     str:
           A_     =   letter
           A_     =   freq
           A_     =   {}

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




class 	__UpperCAmelCase				:





       '''simple docstring'''



       def __init__(      self    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE )		->     Any:
           A_     =   freq
           A_     =   left
           A_     =   right





def      _UpperCAmelCase    (							_UpperCamelCase    :	str   )	->      list[Letter]:
    A_     =   {}
    with open(_UpperCamelCase   ) as f:
        while True:
            A_     =   f.read(1   )
            if not c:
                break
            A_     =   chars[c] + 1 if c in chars else 1
    return sorted((Letter(_UpperCamelCase,   _UpperCamelCase   ) for c, f in chars.items()),   key=lambda _UpperCamelCase   : x.freq   )





def      _UpperCAmelCase    (							_UpperCamelCase    :	list[Letter]   )	->      Letter | TreeNode:
    A_     =   letters  # type: ignore
    while len(_UpperCamelCase   ) > 1:
        A_     =   response.pop(0   )
        A_     =   response.pop(0   )
        A_     =   left.freq + right.freq
        A_     =   TreeNode(_UpperCamelCase,   _UpperCamelCase,   _UpperCamelCase   )
        response.append(_UpperCamelCase   )
        response.sort(key=lambda _UpperCamelCase   : x.freq   )
    return response[0]





def      _UpperCAmelCase    (							_UpperCamelCase    :	Letter | TreeNode,   _UpperCamelCase    :	str   )	->      list[Letter]:




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





def      _UpperCAmelCase    (							_UpperCamelCase    :	str   )	->      None:
    A_     =   parse_file(_UpperCamelCase   )
    A_     =   build_tree(_UpperCamelCase   )
    A_     =   {
        k: v for letter in traverse_tree(_UpperCamelCase,   ''''''   ) for k, v in letter.bitstring.items()
    }
    print(F'''Huffman Coding  of {file_path}: '''   )
    with open(_UpperCamelCase   ) as f:
        while True:
            A_     =   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])












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__	)