






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])













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__)





