code: 
  
  
  
  
  
  
  from __future__ import annotations
  
  import sys
  
  
  
  
  
  
  
  class     UpperCAmelCase_		:
  
  
  
        """simple docstring"""
        def __init__(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)       ->   Union[str, Any]:
            UpperCamelCase :str										=     letter
            UpperCamelCase :int										=     freq
            UpperCamelCase :dict[str, str]										=     {}
  
  
  
  
        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_				)       ->   int:
            UpperCamelCase :int										=     freq
            UpperCamelCase :Letter | TreeNode										=     left
            UpperCamelCase :Letter | TreeNode										=     right
  
  
  
  
  
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			str ):
      UpperCamelCase :dict[str, int]										=     {}
      with open(SCREAMING_SNAKE_CASE__ ) as f:
          while True:
              UpperCamelCase :Union[str, Any]										=     f.read(1 )
              if not c:
                  break
              UpperCamelCase :int										=     chars[c] + 1 if c in chars else 1
      return sorted((Letter(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__ ) for c, f in chars.items())  ,						key=lambda SCREAMING_SNAKE_CASE__ : x.freq )
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			list[Letter] ):
      UpperCamelCase :list[Letter | TreeNode]										=     letters  # type: ignore
      while len(SCREAMING_SNAKE_CASE__ ) > 1:
          UpperCamelCase :int										=     response.pop(0 )
          UpperCamelCase :Optional[int]										=     response.pop(0 )
          UpperCamelCase :Tuple										=     left.freq + right.freq
          UpperCamelCase :Optional[Any]										=     TreeNode(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__ )
          response.append(SCREAMING_SNAKE_CASE__ )
          response.sort(key=lambda SCREAMING_SNAKE_CASE__ : x.freq )
      return response[0]
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			Letter | TreeNode  ,						SCREAMING_SNAKE_CASE__						:			str ):
      if isinstance(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__ ):
          UpperCamelCase :List[str]										=     bitstring
          return [root]
      UpperCamelCase :TreeNode										=     root  # type: ignore
      UpperCamelCase :Any										=     []
      letters += traverse_tree(treenode.left  ,						bitstring + '''0''' )
      letters += traverse_tree(treenode.right  ,						bitstring + '''1''' )
      return letters
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			str ):
      UpperCamelCase :Tuple										=     parse_file(SCREAMING_SNAKE_CASE__ )
      UpperCamelCase :List[str]										=     build_tree(SCREAMING_SNAKE_CASE__ )
      UpperCamelCase :Any										=     {
          k: v for letter in traverse_tree(SCREAMING_SNAKE_CASE__  ,						'''''' ) for k, v in letter.bitstring.items()
      }
      print(F'''Huffman Coding  of {file_path}: ''' )
      with open(SCREAMING_SNAKE_CASE__ ) as f:
          while True:
              UpperCamelCase :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_sentencepiece_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  __snake_case							      =						{
      """configuration_open_llama""": ["""OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OpenLlamaConfig"""],
  }
  
  try:
   if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   __snake_case							      =						["""LlamaTokenizer"""]
  
  try:
   if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   __snake_case							      =						["""LlamaTokenizerFast"""]
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   __snake_case							      =						[
       """OpenLlamaForCausalLM""",
       """OpenLlamaModel""",
       """OpenLlamaPreTrainedModel""",
       """OpenLlamaForSequenceClassification""",
   ]
  
  
  if TYPE_CHECKING:
   from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig
  
   try:
    if not is_sentencepiece_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from transformers import LlamaTokenizer
  
   try:
    if not is_tokenizers_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from transformers import LlamaTokenizerFast
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_open_llama import (
        OpenLlamaForCausalLM,
        OpenLlamaForSequenceClassification,
        OpenLlamaModel,
        OpenLlamaPreTrainedModel,
    )
  
  
  else:
   import sys
  
   __snake_case							      =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  
  
  
  
  