

'''simple docstring'''






from __future__ import annotations

import sys
class 	SCREAMING_SNAKE_CASE__			:




    def __init__(					self				,     lowercase__				,     lowercase__			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			str     =       letter
          SCREAMING_SNAKE_CASE_      :			int     =       freq
          SCREAMING_SNAKE_CASE_      :			dict[str, str]     =       {}







    def __repr__(					self			):






          """simple docstring"""






          return F"{self.letter}:{self.freq}"






class 	SCREAMING_SNAKE_CASE__			:




    def __init__(					self				,     lowercase__				,     lowercase__				,     lowercase__			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			int     =       freq
          SCREAMING_SNAKE_CASE_      :			Letter | TreeNode     =       left
          SCREAMING_SNAKE_CASE_      :			Letter | TreeNode     =       right






def 					__lowerCamelCase			(      SCREAMING_SNAKE_CASE_     :	str    )							->   List[str]:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_      :			dict[str, int]     =       {}
      with open(SCREAMING_SNAKE_CASE_    ) as f:
            while True:
                  SCREAMING_SNAKE_CASE_      :			List[Any]     =       f.read(1    )
                  if not c:
                        break
                  SCREAMING_SNAKE_CASE_      :			Tuple     =       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 					__lowerCamelCase			(      SCREAMING_SNAKE_CASE_     :	list[Letter]    )							->   Union[str, Any]:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_      :			list[Letter | TreeNode]     =       letters  # type: ignore
      while len(SCREAMING_SNAKE_CASE_    ) > 1:
            SCREAMING_SNAKE_CASE_      :			List[str]     =       response.pop(0    )
            SCREAMING_SNAKE_CASE_      :			str     =       response.pop(0    )
            SCREAMING_SNAKE_CASE_      :			Dict     =       left.freq + right.freq
            SCREAMING_SNAKE_CASE_      :			List[str]     =       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 					__lowerCamelCase			(      SCREAMING_SNAKE_CASE_     :	Letter | TreeNode  ,  SCREAMING_SNAKE_CASE_     :	str    )							->   List[Any]:
      """simple docstring"""

      if isinstance(SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_    ):
            SCREAMING_SNAKE_CASE_      :			Any     =       bitstring
            return [root]
      SCREAMING_SNAKE_CASE_      :			TreeNode     =       root  # type: ignore
      SCREAMING_SNAKE_CASE_      :			List[Any]     =       []
      letters += traverse_tree(treenode.left  ,  bitstring + "0"    )
      letters += traverse_tree(treenode.right  ,  bitstring + "1"    )
      return letters


def 					__lowerCamelCase			(      SCREAMING_SNAKE_CASE_     :	str    )							->   Dict:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_      :			List[str]     =       parse_file(SCREAMING_SNAKE_CASE_    )
      SCREAMING_SNAKE_CASE_      :			str     =       build_tree(SCREAMING_SNAKE_CASE_    )
      SCREAMING_SNAKE_CASE_      :			Tuple     =       {
          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:
                  SCREAMING_SNAKE_CASE_      :			Any     =       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])





'''simple docstring'''







import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
   from .tokenization_xlm_roberta import XLMRobertaTokenizer
else:
   A      :     Optional[int]										= None


A      :     Optional[Any]										= logging.get_logger(__name__)

A      :     Dict										= {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

A      :     Optional[int]										= {
    '''vocab_file''': {
        '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large-finetuned-conll02-dutch''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll02-spanish''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-english''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-german''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json''',
        '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json''',
        '''xlm-roberta-large-finetuned-conll02-dutch''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll02-spanish''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll03-english''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll03-german''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json'''
        ),
    },
}

A      :     List[str]										= {
    '''xlm-roberta-base''': 512,
    '''xlm-roberta-large''': 512,
    '''xlm-roberta-large-finetuned-conll02-dutch''': 512,
    '''xlm-roberta-large-finetuned-conll02-spanish''': 512,
    '''xlm-roberta-large-finetuned-conll03-english''': 512,
    '''xlm-roberta-large-finetuned-conll03-german''': 512,
}


class  __lowerCamelCase (       a_       ):
    """simple docstring"""
    a													=    VOCAB_FILES_NAMES
    a													=    PRETRAINED_VOCAB_FILES_MAP
    a													=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    a													=    ["input_ids", "attention_mask"]
    a													=    XLMRobertaTokenizer


    def __init__(  self			:							Union[str, Any]      ,							SCREAMING_SNAKE_CASE			:							Dict=None      ,							SCREAMING_SNAKE_CASE			:							List[Any]=None      ,							SCREAMING_SNAKE_CASE			:							Any="<s>"      ,							SCREAMING_SNAKE_CASE			:							Tuple="</s>"      ,							SCREAMING_SNAKE_CASE			:							str="</s>"      ,							SCREAMING_SNAKE_CASE			:							Optional[Any]="<s>"      ,							SCREAMING_SNAKE_CASE			:							Any="<unk>"      ,							SCREAMING_SNAKE_CASE			:							Union[str, Any]="<pad>"      ,							SCREAMING_SNAKE_CASE			:							List[Any]="<mask>"      ,							**SCREAMING_SNAKE_CASE			:							int      ,							):
           # Mask token behave like a normal word, i.e. include the space before it
           _A     :	str      =					AddedToken(SCREAMING_SNAKE_CASE      ,							lstrip=SCREAMING_SNAKE_CASE      ,							rstrip=SCREAMING_SNAKE_CASE) if isinstance(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE) else mask_token

           super().__init__(
               SCREAMING_SNAKE_CASE      ,							tokenizer_file=SCREAMING_SNAKE_CASE      ,							bos_token=SCREAMING_SNAKE_CASE      ,							eos_token=SCREAMING_SNAKE_CASE      ,							sep_token=SCREAMING_SNAKE_CASE      ,							cls_token=SCREAMING_SNAKE_CASE      ,							unk_token=SCREAMING_SNAKE_CASE      ,							pad_token=SCREAMING_SNAKE_CASE      ,							mask_token=SCREAMING_SNAKE_CASE      ,							**SCREAMING_SNAKE_CASE      ,							)

           _A     :	Dict      =					vocab_file
           _A     :	Optional[Any]      =					False if not self.vocab_file else True


    def    A    (  self			:							int      ,							SCREAMING_SNAKE_CASE			:							List[int]      ,							SCREAMING_SNAKE_CASE			:							Optional[List[int]] = None):

           if token_ids_a is None:
                  return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
           _A     :	Optional[Any]      =					[self.cls_token_id]
           _A     :	Optional[int]      =					[self.sep_token_id]
           return cls + token_ids_a + sep + sep + token_ids_a + sep


    def    A    (  self			:							List[str]      ,							SCREAMING_SNAKE_CASE			:							List[int]      ,							SCREAMING_SNAKE_CASE			:							Optional[List[int]] = None):
           _A     :	Any      =					[self.sep_token_id]
           _A     :	Optional[Any]      =					[self.cls_token_id]

           if token_ids_a is None:
                  return len(cls + token_ids_a + sep) * [0]
           return len(cls + token_ids_a + sep + sep + token_ids_a + sep) * [0]


    def    A    (  self			:							Dict      ,							SCREAMING_SNAKE_CASE			:							str      ,							SCREAMING_SNAKE_CASE			:							Optional[str] = None):
           if not self.can_save_slow_tokenizer:
                  raise ValueError(
                      'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
                      'tokenizer.')

           if not os.path.isdir(SCREAMING_SNAKE_CASE):
                  logger.error(F'Vocabulary path ({save_directory}) should be a directory.')
                  return
           _A     :	Any      =					os.path.join(
               SCREAMING_SNAKE_CASE      ,							(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])

           if os.path.abspath(self.vocab_file) != os.path.abspath(SCREAMING_SNAKE_CASE):
                  copyfile(self.vocab_file      ,							SCREAMING_SNAKE_CASE)

           return (out_vocab_file,)






