code: 
  
  from __future__ import annotations
  
  import sys
  
  
  
  
  
  
  class    __A				:
  
  
  
  
  
  
      def __init__(	self			:    List[Any]    ,				UpperCAmelCase_			:    str    ,				UpperCAmelCase_			:    int    ):
          lowerCAmelCase				:			str    			=      letter
          lowerCAmelCase				:			int    			=      freq
          lowerCAmelCase				:			dict[str, str]    			=      {}
  
  
  
  
  
  
      def __repr__(	self			:    str    ):
          return f"{self.letter}:{self.freq}"
  
  
  
  
  
  
  class    __A				:
  
  
  
  
  
  
      def __init__(	self			:    Dict    ,				UpperCAmelCase_			:    int    ,				UpperCAmelCase_			:    Letter | TreeNode    ,				UpperCAmelCase_			:    Letter | TreeNode    ):
          lowerCAmelCase				:			int    			=      freq
          lowerCAmelCase				:			Letter | TreeNode    			=      left
          lowerCAmelCase				:			Letter | TreeNode    			=      right
  
  
  
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase				)					-> list[Letter]:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      lowerCAmelCase				:			dict[str, int]    			=      {}
      with open(_UpperCAmelCase				) as f:
          while True:
              lowerCAmelCase				:			Optional[int]    			=      f.read(1				)
              if not c:
                  break
              lowerCAmelCase				:			Union[str, Any]    			=      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 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase				)					-> Letter | TreeNode:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      lowerCAmelCase				:			list[Letter | TreeNode]    			=      letters  # type: ignore
      while len(_UpperCAmelCase				) > 1:
          lowerCAmelCase				:			Dict    			=      response.pop(0				)
          lowerCAmelCase				:			List[Any]    			=      response.pop(0				)
          lowerCAmelCase				:			Optional[Any]    			=      left.freq + right.freq
          lowerCAmelCase				:			List[str]    			=      TreeNode(_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase				)
          response.append(_UpperCAmelCase				)
          response.sort(key=lambda _UpperCAmelCase				: x.freq				)
      return response[0]
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase				)					-> list[Letter]:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      if isinstance(_UpperCAmelCase,      _UpperCAmelCase				):
          lowerCAmelCase				:			Dict    			=      bitstring
          return [root]
      lowerCAmelCase				:			TreeNode    			=      root  # type: ignore
      lowerCAmelCase				:			List[str]    			=      []
      letters += traverse_tree(treenode.left,      bitstring + '0'				)
      letters += traverse_tree(treenode.right,      bitstring + '1'				)
      return letters
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase				)					-> None:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      lowerCAmelCase				:			int    			=      parse_file(_UpperCAmelCase				)
      lowerCAmelCase				:			Union[str, Any]    			=      build_tree(_UpperCAmelCase				)
      lowerCAmelCase				:			str    			=      {
          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:
              lowerCAmelCase				:			Tuple    			=      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])
  
  
  
  
  
  
style_context: 
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
  
  
  __A   :     List[Any]      =			{'''configuration_umt5''': ['''UMT5Config''', '''UMT5OnnxConfig''']}
  
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   __A   :     Tuple      =			[
       '''UMT5EncoderModel''',
       '''UMT5ForConditionalGeneration''',
       '''UMT5ForQuestionAnswering''',
       '''UMT5Model''',
       '''UMT5PreTrainedModel''',
   ]
  
  if TYPE_CHECKING:
   from .configuration_umta import UMTaConfig, UMTaOnnxConfig
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_umta import (
        UMTaEncoderModel,
        UMTaForConditionalGeneration,
        UMTaForQuestionAnswering,
        UMTaModel,
        UMTaPreTrainedModel,
    )
  else:
   import sys
  
   __A   :     Any      =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  
  
  
  
  