



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


__snake_case	:				List[Any]  					=			logging.get_logger(__name__)

__snake_case	:				Any  					=			{
    'source_spm': 'source.spm',
    'target_spm': 'target.spm',
    'vocab': 'vocab.json',
    'target_vocab_file': 'target_vocab.json',
    'tokenizer_config_file': 'tokenizer_config.json',
}

__snake_case	:				Optional[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'
        )
    },
}

__snake_case	:				Any  					=			{'Helsinki-NLP/opus-mt-en-de': 512}
__snake_case	:				Optional[Any]  					=			{}

class       UpperCamelCase  (					_SCREAMING_SNAKE_CASE				):





     """simple docstring"""






     _lowerCamelCase					: Tuple				   =VOCAB_FILES_NAMES
     _lowerCamelCase					: Optional[Any]				   =PRETRAINED_VOCAB_FILES_MAP
     _lowerCamelCase					: Any				   =PRETRAINED_INIT_CONFIGURATION
     _lowerCamelCase					: Any				   =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _lowerCamelCase					: Optional[Any]				   =['input_ids', 'attention_mask']
     _lowerCamelCase					: Optional[Any]				   =re.compile(">>.+<<"				)  # type: re.Pattern





     def __init__(					self :							Optional[int]	,      _lowerCamelCase :							List[Any]	,      _lowerCamelCase :							List[str]	,      _lowerCamelCase :							Tuple	,      _lowerCamelCase :							str=None	,      _lowerCamelCase :							Optional[Any]=None	,      _lowerCamelCase :							str=None	,      _lowerCamelCase :							Any="<unk>"	,      _lowerCamelCase :							List[str]="</s>"	,      _lowerCamelCase :							List[Any]="<pad>"	,      _lowerCamelCase :							Union[str, Any]=5_1_2	,      _lowerCamelCase :							Optional[Dict[str, Any]] = None	,      _lowerCamelCase :							str=False	,      **_lowerCamelCase :							Tuple	,      ):
          A__				=   {} 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=UpperCamelCase__	,      target_lang=UpperCamelCase__	,      unk_token=UpperCamelCase__	,      eos_token=UpperCamelCase__	,      pad_token=UpperCamelCase__	,      model_max_length=UpperCamelCase__	,      sp_model_kwargs=self.sp_model_kwargs	,      target_vocab_file=UpperCamelCase__	,      separate_vocabs=UpperCamelCase__	,      **UpperCamelCase__	,      )
          assert Path(UpperCamelCase__ ).exists(), F'''cannot find spm source {source_spm}'''

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

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

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

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

          self._setup_normalizer()





     def 						A__  (					self :							str ):
          try:
               from sacremoses import MosesPunctNormalizer

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





     def 						A__  (					self :							Dict	,      _lowerCamelCase :							str ):
          return self.punc_normalizer(UpperCamelCase__ ) if x else ""





     def 						A__  (					self :							List[Any]	,      _lowerCamelCase :							Optional[int] ):
          return self.current_encoder.get(UpperCamelCase__	,      self.current_encoder[self.unk_token] )





     def 						A__  (					self :							Optional[Any]	,      _lowerCamelCase :							str ):
          A__				=   self.language_code_re.match(UpperCamelCase__ )
          A__				=   [match.group(0 )] if match else []
          return code, self.language_code_re.sub(''''''	,      UpperCamelCase__ )





     def 						A__  (					self :							List[str]	,      _lowerCamelCase :							str ):
          A__	,     A__				=   self.remove_language_code(UpperCamelCase__ )
          A__				=   self.current_spm.encode(UpperCamelCase__	,      out_type=UpperCamelCase__ )
          return code + pieces





     def 						A__  (					self :							Any	,      _lowerCamelCase :							int ):
          return self.decoder.get(UpperCamelCase__	,      self.unk_token )





     def 						A__  (					self :							Optional[Any]	,      _lowerCamelCase :							int	,      **_lowerCamelCase :							Tuple ):
          return super().batch_decode(UpperCamelCase__	,      **UpperCamelCase__ )





     def 						A__  (					self :							Optional[Any]	,      _lowerCamelCase :							List[Any]	,      **_lowerCamelCase :							Dict ):
          return super().decode(UpperCamelCase__	,      **UpperCamelCase__ )





     def 						A__  (					self :							Any	,      _lowerCamelCase :							List[str] ):
          A__				=   self.spm_source if self._decode_use_source_tokenizer else self.spm_target
          A__				=   []
          A__				=   ''''''
          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(UpperCamelCase__ ) + token + " "
                    A__				=   []
               else:
                    current_sub_tokens.append(UpperCamelCase__ )
          out_string += sp_model.decode_pieces(UpperCamelCase__ )
          return out_string.strip()





     def 						A__  (					self :							Any	,      _lowerCamelCase :							Dict	,      _lowerCamelCase :							List[Any]=None ):
          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 						A__  (					self :							Dict ):
          A__				=   self.spm_source
          A__				=   self.encoder





     def 						A__  (					self :							Dict ):
          A__				=   self.spm_target
          if self.separate_vocabs:
               A__				=   self.target_encoder





     @property
     def 						A__  (					self :							Any ):
          return len(self.encoder )





     def 						A__  (					self :							Tuple	,      _lowerCamelCase :							str	,      _lowerCamelCase :							Optional[str] = None ):
          if not os.path.isdir(UpperCamelCase__ ):
               logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
               return
          A__				=   []

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

          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]	,      ):
               A__				=   os.path.join(
                   UpperCamelCase__	,      (filename_prefix + '''-''' if filename_prefix else '''''') + spm_save_filename )
               if os.path.abspath(UpperCamelCase__ ) != os.path.abspath(UpperCamelCase__ ) and os.path.isfile(UpperCamelCase__ ):
                    copyfile(UpperCamelCase__	,      UpperCamelCase__ )
                    saved_files.append(UpperCamelCase__ )
               elif not os.path.isfile(UpperCamelCase__ ):
                    with open(UpperCamelCase__	,      '''wb''' ) as fi:
                         A__				=   spm_model.serialized_model_proto()
                         fi.write(UpperCamelCase__ )
                    saved_files.append(UpperCamelCase__ )

          return tuple(UpperCamelCase__ )





     def 						A__  (					self :							Optional[Any] ):
          return self.get_src_vocab()





     def 						A__  (					self :							List[Any] ):
          return dict(self.encoder	,      **self.added_tokens_encoder )





     def 						A__  (					self :							int ):
          return dict(self.target_encoder	,      **self.added_tokens_decoder )





     def __getstate__(					self :							Union[str, Any] ):
          A__				=   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[str]	,      _lowerCamelCase :							Dict ):
          A__				=   d

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

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





     def 						A__  (					self :							List[Any]	,      *_lowerCamelCase :							List[Any]	,      **_lowerCamelCase :							List[Any] ):
          return 1





     def 						A__  (					self :							List[Any]	,      _lowerCamelCase :							List[str] ):
          A__				=   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 						A__  (					self :							str	,      _lowerCamelCase :							List	,      _lowerCamelCase :							Optional[List] = None	,      _lowerCamelCase :							bool = False ):
          if already_has_special_tokens:
               return self._special_token_mask(UpperCamelCase__ )
          elif token_ids_a is None:
               return self._special_token_mask(UpperCamelCase__ ) + [1]
          else:
               return self._special_token_mask(token_ids_a + token_ids_a ) + [1]



def   a_	(			__a		,							__a      ):
     A__				=   sentencepiece.SentencePieceProcessor(**__a      )
     spm.Load(__a      )
     return spm



def   a_	(			__a		,							__a      ):







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



def   a_	(			__a      ):







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








"""simple docstring"""







def   a_	(			__a      ):
     if not isinstance(__a		,							__a      ):
          raise TypeError('''recursive_lucas_number accepts only integer arguments.'''      )
     if n_th_number == 0:
          return 2
     if n_th_number == 1:
          return 1

     return recursive_lucas_number(n_th_number - 1      ) + recursive_lucas_number(
         n_th_number - 2      )



def   a_	(			__a      ):
     if not isinstance(__a		,							__a      ):
          raise TypeError('''dynamic_lucas_number accepts only integer arguments.'''      )
     A__	,     A__				=   2, 1
     for _ in range(__a      ):
          A__	,     A__				=   b, a + b
     return a


if __name__ == "__main__":
 from doctest import testmod

 testmod()
 __snake_case	:				Dict  					=			int(input('Enter the number of terms in lucas series:\n').strip())
 print('Using recursive function to calculate lucas series:')
 print(' '.join(str(recursive_lucas_number(i)) for i in range(n)))
 print('\nUsing dynamic function to calculate lucas series:')
 print(' '.join(str(dynamic_lucas_number(i)) for i in range(n)))



