




import json
import os
import re
import warnings
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__SCREAMING_SNAKE_CASE			:      Union[str, Any]      			=logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE			:      Dict      			={
    """source_spm""": """source.spm""",
    """target_spm""": """target.spm""",
    """vocab""": """vocab.json""",
    """target_vocab_file""": """target_vocab.json""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

__SCREAMING_SNAKE_CASE			:      Any      			={
    """source_spm""": {
        """Helsinki-NLP/opus-mt-en-de""": """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/source.spm"""
    },
    """target_spm""": {
        """Helsinki-NLP/opus-mt-en-de""": """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/target.spm"""
    },
    """vocab""": {
        """Helsinki-NLP/opus-mt-en-de""": """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/vocab.json"""
    },
    """tokenizer_config_file""": {
        """Helsinki-NLP/opus-mt-en-de""": (
            """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/tokenizer_config.json"""
        )
    },
}

__SCREAMING_SNAKE_CASE			:      Union[str, Any]      			={"""Helsinki-NLP/opus-mt-en-de""": 512}
__SCREAMING_SNAKE_CASE			:      Union[str, Any]      			={}






class 	A	(						_lowerCAmelCase						):
     """simple docstring"""

     _snake_case:		Union[str, Any]   =VOCAB_FILES_NAMES
     _snake_case:		str   =PRETRAINED_VOCAB_FILES_MAP
     _snake_case:		List[Any]   =PRETRAINED_INIT_CONFIGURATION
     _snake_case:		str   =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _snake_case:		List[str]   =["input_ids", "attention_mask"]
     _snake_case:		Dict   =re.compile(">>.+<<"						)  # type: re.Pattern




     def __init__(			self     :      Optional[int]		,     A__     :      Optional[Any]		,     A__     :      Optional[int]		,     A__     :      Dict		,     A__     :      int=None		,     A__     :      Union[str, Any]=None		,     A__     :      Tuple=None		,     A__     :      int="<unk>"		,     A__     :      Optional[int]="</s>"		,     A__     :      Any="<pad>"		,     A__     :      Tuple=5_1_2		,     A__     :      Optional[Dict[str, Any]] = None		,     A__     :      Tuple=False		,     **A__     :      int		,     ) ->				None:
           lowerCamelCase__					     = {} if sp_model_kwargs is None else sp_model_kwargs

           super().__init__(
               # bos_token=bos_token,  unused. Start decoding with config.decoder_start_token_id
               source_lang=A__		,     target_lang=A__		,     unk_token=A__		,     eos_token=A__		,     pad_token=A__		,     model_max_length=A__		,     sp_model_kwargs=self.sp_model_kwargs		,     target_vocab_file=A__		,     separate_vocabs=A__		,     **A__		,     )
           assert Path(A__	).exists(), F"""cannot find spm source {source_spm}"""

           lowerCamelCase__					     = separate_vocabs
           lowerCamelCase__					     = load_json(A__	)
           if self.unk_token not in self.encoder:
                 raise KeyError('<unk> token must be in vocab'	)
           assert self.pad_token in self.encoder

           if separate_vocabs:
                 lowerCamelCase__					     = load_json(A__	)
                 lowerCamelCase__					     = {v: k for k, v in self.target_encoder.items()}
                 lowerCamelCase__					     = []
           else:
                 lowerCamelCase__					     = {v: k for k, v in self.encoder.items()}
                 lowerCamelCase__					     = [k for k in self.encoder if k.startswith('>>'	) and k.endswith('<<'	)]

           lowerCamelCase__					     = source_lang
           lowerCamelCase__					     = target_lang
           lowerCamelCase__					     = [source_spm, target_spm]

           # load SentencePiece model for pre-processing
           lowerCamelCase__					     = load_spm(A__		,     self.sp_model_kwargs	)
           lowerCamelCase__					     = load_spm(A__		,     self.sp_model_kwargs	)
           lowerCamelCase__					     = self.spm_source
           lowerCamelCase__					     = self.encoder

           # Multilingual target side: default to using first supported language code.

           self._setup_normalizer()




     def        __snake_case      (			self     :      int	) ->				Any:
           try:
                 from sacremoses import MosesPunctNormalizer

                 lowerCamelCase__					     = MosesPunctNormalizer(self.source_lang	).normalize
           except (ImportError, FileNotFoundError):
                 warnings.warn('Recommended: pip install sacremoses.'	)
                 lowerCamelCase__					     = lambda A__	: x




     def        __snake_case      (			self     :      Tuple		,     A__     :      str	) ->				str:
           return self.punc_normalizer(A__	) if x else ""




     def        __snake_case      (			self     :      Union[str, Any]		,     A__     :      Tuple	) ->				Tuple:
           return self.current_encoder.get(A__		,     self.current_encoder[self.unk_token]	)




     def        __snake_case      (			self     :      Union[str, Any]		,     A__     :      str	) ->				Any:
           lowerCamelCase__					     = self.language_code_re.match(A__	)
           lowerCamelCase__					     = [match.group(0	)] if match else []
           return code, self.language_code_re.sub(''		,     A__	)




     def        __snake_case      (			self     :      int		,     A__     :      str	) ->				List[str]:
           lowerCamelCase__     ,						lowerCamelCase__					     = self.remove_language_code(A__	)
           lowerCamelCase__					     = self.current_spm.encode(A__		,     out_type=A__	)
           return code + pieces




     def        __snake_case      (			self     :      Any		,     A__     :      int	) ->				str:
           return self.decoder.get(A__		,     self.unk_token	)




     def        __snake_case      (			self     :      Dict		,     A__     :      Optional[Any]		,     **A__     :      Optional[Any]	) ->				int:
           return super().batch_decode(A__		,     **A__	)




     def        __snake_case      (			self     :      List[Any]		,     A__     :      int		,     **A__     :      str	) ->				Optional[Any]:
           return super().decode(A__		,     **A__	)




     def        __snake_case      (			self     :      Union[str, Any]		,     A__     :      List[str]	) ->				str:
           lowerCamelCase__					     = self.spm_source if self._decode_use_source_tokenizer else self.spm_target
           lowerCamelCase__					     = []
           lowerCamelCase__					     = ''
           for token in tokens:
                 # make sure that special tokens are not decoded using sentencepiece model
                 if token in self.all_special_tokens:
                       out_string += sp_model.decode_pieces(A__	) + token + " "
                       lowerCamelCase__					     = []
                 else:
                       current_sub_tokens.append(A__	)
           out_string += sp_model.decode_pieces(A__	)
           return out_string.strip()




     def        __snake_case      (			self     :      int		,     A__     :      Optional[int]		,     A__     :      str=None	) ->				List[int]:
           if token_ids_a is None:
                 return token_ids_a + [self.eos_token_id]
           # We don't expect to process pairs, but leave the pair logic for API consistency
           return token_ids_a + token_ids_a + [self.eos_token_id]




     def        __snake_case      (			self     :      Optional[Any]	) ->				Tuple:
           lowerCamelCase__					     = self.spm_source
           lowerCamelCase__					     = self.encoder




     def        __snake_case      (			self     :      Dict	) ->				List[str]:
           lowerCamelCase__					     = self.spm_target
           if self.separate_vocabs:
                 lowerCamelCase__					     = self.target_encoder




     @property
     def        __snake_case      (			self     :      int	) ->				int:
           return len(self.encoder	)




     def        __snake_case      (			self     :      int		,     A__     :      str		,     A__     :      Optional[str] = None	) ->				Tuple[str]:
           if not os.path.isdir(A__	):
                 logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""	)
                 return
           lowerCamelCase__					     = []

           if self.separate_vocabs:
                 lowerCamelCase__					     = os.path.join(
                     A__		,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab']		,     )
                 lowerCamelCase__					     = os.path.join(
                     A__		,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['target_vocab_file']		,     )
                 save_json(self.encoder		,     A__	)
                 save_json(self.target_encoder		,     A__	)
                 saved_files.append(A__	)
                 saved_files.append(A__	)
           else:
                 lowerCamelCase__					     = os.path.join(
                     A__		,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab']	)
                 save_json(self.encoder		,     A__	)
                 saved_files.append(A__	)

           for spm_save_filename, spm_orig_path, spm_model in zip(
               [VOCAB_FILES_NAMES['source_spm'], VOCAB_FILES_NAMES['target_spm']]		,     self.spm_files		,     [self.spm_source, self.spm_target]		,     ):
                 lowerCamelCase__					     = os.path.join(
                     A__		,     (filename_prefix + '-' if filename_prefix else '') + spm_save_filename	)
                 if os.path.abspath(A__	) != os.path.abspath(A__	) and os.path.isfile(A__	):
                       copyfile(A__		,     A__	)
                       saved_files.append(A__	)
                 elif not os.path.isfile(A__	):
                       with open(A__		,     'wb'	) as fi:
                             lowerCamelCase__					     = spm_model.serialized_model_proto()
                             fi.write(A__	)
                       saved_files.append(A__	)

           return tuple(A__	)




     def        __snake_case      (			self     :      Any	) ->				Dict:
           return self.get_src_vocab()




     def        __snake_case      (			self     :      Optional[int]	) ->				List[Any]:
           return dict(self.encoder		,     **self.added_tokens_encoder	)




     def        __snake_case      (			self     :      Optional[Any]	) ->				Optional[int]:
           return dict(self.target_encoder		,     **self.added_tokens_decoder	)




     def __getstate__(			self     :      List[Any]	) ->				Dict:
           lowerCamelCase__					     = self.__dict__.copy()
           state.update(
               {k: None for k in ['spm_source', 'spm_target', 'current_spm', 'punc_normalizer', 'target_vocab_file']}	)
           return state




     def __setstate__(			self     :      List[Any]		,     A__     :      Dict	) ->				None:
           lowerCamelCase__					     = d

           # for backward compatibility
           if not hasattr(self		,     'sp_model_kwargs'	):
                 lowerCamelCase__					     = {}

           lowerCamelCase__     ,						lowerCamelCase__					     = (load_spm(A__		,     self.sp_model_kwargs	) for f in self.spm_files)
           lowerCamelCase__					     = self.spm_source
           self._setup_normalizer()




     def        __snake_case      (			self     :      Tuple		,     *A__     :      Optional[Any]		,     **A__     :      Tuple	) ->				Any:
           return 1




     def        __snake_case      (			self     :      Optional[Any]		,     A__     :      Union[str, Any]	) ->				int:
           lowerCamelCase__					     = set(self.all_special_ids	)  # call it once instead of inside list comp
           all_special_ids.remove(self.unk_token_id	)  # <unk> is only sometimes special
           return [1 if x in all_special_ids else 0 for x in seq]




     def        __snake_case      (			self     :      str		,     A__     :      List		,     A__     :      Optional[List] = None		,     A__     :      bool = False	) ->				List[int]:
           if already_has_special_tokens:
                 return self._special_token_mask(A__	)
           elif token_ids_a is None:
                 return self._special_token_mask(A__	) + [1]
           else:
                 return self._special_token_mask(token_ids_a + token_ids_a	) + [1]

def 	lowerCAmelCase_	(     lowerCamelCase:				str   , lowerCamelCase:				Dict[str, Any]  )    ->	sentencepiece.SentencePieceProcessor:


      '''simple docstring'''




      lowerCamelCase__					     = sentencepiece.SentencePieceProcessor(**lowerCamelCase  )
      spm.Load(lowerCamelCase  )
      return spm

def 	lowerCAmelCase_	(     lowerCamelCase:				List[str]   , lowerCamelCase:				str  )    ->	None:


      '''simple docstring'''




      with open(lowerCamelCase   , 'w'  ) as f:
            json.dump(lowerCamelCase   , lowerCamelCase   , indent=2  )


def 	lowerCAmelCase_	(     lowerCamelCase:				str  )    ->	Union[Dict, List]:


      '''simple docstring'''




      with open(lowerCamelCase   , 'r'  ) as f:
            return json.load(lowerCamelCase  )





from __future__ import annotations

import sys






class 	A	:
     """simple docstring"""

     def __init__(			self     :      str		,     A__     :      str		,     A__     :      int	) ->				str:
           lowerCamelCase__					     = letter
           lowerCamelCase__					     = freq
           lowerCamelCase__					     = {}




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






class 	A	:
     """simple docstring"""

     def __init__(			self     :      Optional[Any]		,     A__     :      int		,     A__     :      Letter | TreeNode		,     A__     :      Letter | TreeNode	) ->				Any:
           lowerCamelCase__					     = freq
           lowerCamelCase__					     = left
           lowerCamelCase__					     = right

def 	lowerCAmelCase_	(     lowerCamelCase:				str  )    ->	list[Letter]:


      '''simple docstring'''




      lowerCamelCase__					     = {}
      with open(lowerCamelCase  ) as f:
            while True:
                  lowerCamelCase__					     = f.read(1  )
                  if not c:
                        break
                  lowerCamelCase__					     = chars[c] + 1 if c in chars else 1
      return sorted((Letter(lowerCamelCase   , lowerCamelCase  ) for c, f in chars.items())   , key=lambda lowerCamelCase  : x.freq  )

def 	lowerCAmelCase_	(     lowerCamelCase:				list[Letter]  )    ->	Letter | TreeNode:


      '''simple docstring'''




      lowerCamelCase__					     = letters  # type: ignore
      while len(lowerCamelCase  ) > 1:
            lowerCamelCase__					     = response.pop(0  )
            lowerCamelCase__					     = response.pop(0  )
            lowerCamelCase__					     = left.freq + right.freq
            lowerCamelCase__					     = TreeNode(lowerCamelCase   , lowerCamelCase   , lowerCamelCase  )
            response.append(lowerCamelCase  )
            response.sort(key=lambda lowerCamelCase  : x.freq  )
      return response[0]

def 	lowerCAmelCase_	(     lowerCamelCase:				Letter | TreeNode   , lowerCamelCase:				str  )    ->	list[Letter]:


      '''simple docstring'''




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

def 	lowerCAmelCase_	(     lowerCamelCase:				str  )    ->	None:


      '''simple docstring'''




      lowerCamelCase__					     = parse_file(lowerCamelCase  )
      lowerCamelCase__					     = build_tree(lowerCamelCase  )
      lowerCamelCase__					     = {
          k: v for letter in traverse_tree(lowerCamelCase   , ''  ) for k, v in letter.bitstring.items()
      }
      print(f"""Huffman Coding  of {file_path}: """  )
      with open(lowerCamelCase  ) as f:
            while True:
                  lowerCamelCase__					     = 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])