

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

from tokenizers import processors

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


if is_sentencepiece_available():
 from .tokenization_mbartaa import MBartaaTokenizer
else:
 _UpperCamelCase     =    None


_UpperCamelCase     =    logging.get_logger(__name__)

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

_UpperCamelCase     =    {
    '''vocab_file''': {
        '''facebook/mbart-large-50-one-to-many-mmt''': (
            '''https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/mbart-large-50-one-to-many-mmt''': (
            '''https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/tokenizer.json'''
        ),
    },
}

_UpperCamelCase     =    {
    '''facebook/mbart-large-50-one-to-many-mmt''': 1024,
}

# fmt: off
_UpperCamelCase     =    ['''ar_AR''', '''cs_CZ''', '''de_DE''', '''en_XX''', '''es_XX''', '''et_EE''', '''fi_FI''', '''fr_XX''', '''gu_IN''', '''hi_IN''', '''it_IT''', '''ja_XX''', '''kk_KZ''', '''ko_KR''', '''lt_LT''', '''lv_LV''', '''my_MM''', '''ne_NP''', '''nl_XX''', '''ro_RO''', '''ru_RU''', '''si_LK''', '''tr_TR''', '''vi_VN''', '''zh_CN''', '''af_ZA''', '''az_AZ''', '''bn_IN''', '''fa_IR''', '''he_IL''', '''hr_HR''', '''id_ID''', '''ka_GE''', '''km_KH''', '''mk_MK''', '''ml_IN''', '''mn_MN''', '''mr_IN''', '''pl_PL''', '''ps_AF''', '''pt_XX''', '''sv_SE''', '''sw_KE''', '''ta_IN''', '''te_IN''', '''th_TH''', '''tl_XX''', '''uk_UA''', '''ur_PK''', '''xh_ZA''', '''gl_ES''', '''sl_SI''']



class 					lowercase		( _UpperCamelCase      ):



 '''simple docstring'''


 __SCREAMING_SNAKE_CASE      							=       VOCAB_FILES_NAMES
 __SCREAMING_SNAKE_CASE      							=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __SCREAMING_SNAKE_CASE      							=       PRETRAINED_VOCAB_FILES_MAP
 __SCREAMING_SNAKE_CASE      							=       ["""input_ids""", """attention_mask"""]
 __SCREAMING_SNAKE_CASE      							=       MBartaaTokenizer

 __SCREAMING_SNAKE_CASE      							=       []
 __SCREAMING_SNAKE_CASE      							=       []




 def __init__(self , __a=None , __a=None , __a=None , __a=None , __a="</s>" , __a="</s>" , __a="<s>" , __a="<unk>" , __a="<pad>" , __a="<mask>" , **__a , )				->      Dict:






  """simple docstring"""





  UpperCAmelCase__           =       AddedToken(__a , lstrip=__a , rstrip=__a				) if isinstance(__a , __a				) else mask_token

  UpperCAmelCase__           =       kwargs.get('additional_special_tokens' , []				)
  kwargs["additional_special_tokens"] += [
      code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
  ]

  super().__init__(
      __a , src_lang=__a , tgt_lang=__a , tokenizer_file=__a , eos_token=__a , sep_token=__a , cls_token=__a , unk_token=__a , pad_token=__a , mask_token=__a , **__a , )

  UpperCAmelCase__           =       vocab_file
  UpperCAmelCase__           =       False if not self.vocab_file else True

  UpperCAmelCase__           =       {
      lang_code: self.convert_tokens_to_ids(__a				) for lang_code in FAIRSEQ_LANGUAGE_CODES
  }

  UpperCAmelCase__           =       src_lang if src_lang is not None else 'en_XX'
  UpperCAmelCase__           =       tgt_lang
  UpperCAmelCase__           =       self.lang_code_to_id[self._src_lang]
  self.set_src_lang_special_tokens(self._src_lang				)




 @property
 def 						UpperCamelCase__							(self				)				->      str:






  """simple docstring"""





  return self._src_lang




 @src_lang.setter
 def 						UpperCamelCase__							(self , __a				)				->      None:






  """simple docstring"""





  UpperCAmelCase__           =       new_src_lang
  self.set_src_lang_special_tokens(self._src_lang				)




 def 						UpperCamelCase__							(self , __a , __a = None				)				->      List[int]:






  """simple docstring"""





  if token_ids_a is None:
   return self.prefix_tokens + token_ids_a + self.suffix_tokens
  # We don't expect to process pairs, but leave the pair logic for API consistency
  return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens




 def 						UpperCamelCase__							(self , __a , __a = "en_XX" , __a = None , __a = "ro_RO" , **__a , )				->      BatchEncoding:






  """simple docstring"""





  UpperCAmelCase__           =       src_lang
  UpperCAmelCase__           =       tgt_lang
  return super().prepare_seqaseq_batch(__a , __a , **__a				)




 def 						UpperCamelCase__							(self				)				->      Dict:






  """simple docstring"""





  return self.set_src_lang_special_tokens(self.src_lang				)




 def 						UpperCamelCase__							(self				)				->      Optional[int]:






  """simple docstring"""





  return self.set_tgt_lang_special_tokens(self.tgt_lang				)




 def 						UpperCamelCase__							(self , __a				)				->      None:






  """simple docstring"""





  UpperCAmelCase__           =       self.convert_tokens_to_ids(__a				)
  UpperCAmelCase__           =       [self.cur_lang_code_id]
  UpperCAmelCase__           =       [self.eos_token_id]

  UpperCAmelCase__           =       self.convert_ids_to_tokens(self.prefix_tokens				)
  UpperCAmelCase__           =       self.convert_ids_to_tokens(self.suffix_tokens				)

  UpperCAmelCase__           =       processors.TemplateProcessing(
      single=prefix_tokens_str + ['$A'] + suffix_tokens_str , pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens				)				) , )




 def 						UpperCamelCase__							(self , __a				)				->      None:






  """simple docstring"""





  UpperCAmelCase__           =       self.convert_tokens_to_ids(__a				)
  UpperCAmelCase__           =       [self.cur_lang_code_id]
  UpperCAmelCase__           =       [self.eos_token_id]

  UpperCAmelCase__           =       self.convert_ids_to_tokens(self.prefix_tokens				)
  UpperCAmelCase__           =       self.convert_ids_to_tokens(self.suffix_tokens				)

  UpperCAmelCase__           =       processors.TemplateProcessing(
      single=prefix_tokens_str + ['$A'] + suffix_tokens_str , pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens				)				) , )




 def 						UpperCamelCase__							(self , __a , __a , __a , __a , **__a				)				->      int:






  """simple docstring"""





  if src_lang is None or tgt_lang is None:
   raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'				)
  UpperCAmelCase__           =       src_lang
  UpperCAmelCase__           =       self(__a , add_special_tokens=__a , return_tensors=__a , **__a				)
  UpperCAmelCase__           =       self.convert_tokens_to_ids(__a				)
  UpperCAmelCase__           =       tgt_lang_id
  return inputs




 def 						UpperCamelCase__							(self , __a , __a = None				)				->      Tuple[str]:






  """simple docstring"""





  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(__a				):
   logger.error(F"Vocabulary path ({save_directory}) should be a directory"				)
   return
  UpperCAmelCase__           =       os.path.join(
      __a , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']				)

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

  return (out_vocab_file,)






def 		UpperCamelCase_(    snake_case__:						int     ) ->				int:
 if not isinstance(snake_case__						,					snake_case__     ):
  UpperCAmelCase__           =       f"Input value of [number={number}] must be an integer"
  raise TypeError(snake_case__     )
 if number < 1:
  raise ValueError('Input must be a positive integer'     )
 return number * (2 * number - 1)


if __name__ == "__main__":
 import doctest

 doctest.testmod()



