




def 					snake_case_     (		SCREAMING_SNAKE_CASE_				)			-> List[Any]:
							if not isinstance(SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				):
														raise ValueError("double_factorial() only accepts integral values"				)
							if n < 0:
														raise ValueError("double_factorial() not defined for negative values"				)
							return 1 if n <= 1 else n * double_factorial(n - 2				)


if __name__ == "__main__":
						import doctest

						doctest.testmod()







"""simple docstring"""







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:
 lowerCAmelCase_		:			int				   =					None


lowerCAmelCase_		:			Tuple				   =					logging.get_logger(__name__)

lowerCAmelCase_		:			List[Any]				   =					{'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCAmelCase_		:			List[Any]				   =					{
    '''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'''
        ),
    },
}

lowerCAmelCase_		:			int				   =					{
    '''facebook/mbart-large-50-one-to-many-mmt''': 1_0_2_4,
}

# fmt: off
lowerCAmelCase_		:			List[Any]				   =					['''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      UpperCamelCase_       (				a_     ):
  _A				:    Union[str, Any] =		VOCAB_FILES_NAMES
  _A				:    Optional[int] =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _A				:    Tuple =		PRETRAINED_VOCAB_FILES_MAP
  _A				:    Dict =		['input_ids', 'attention_mask']
  _A				:    Any =		MBartaaTokenizer

  _A				:    List[int] =		[]
  _A				:    List[int] =		[]



  def __init__(					self       ,						snake_case__=None       ,						snake_case__=None       ,						snake_case__=None       ,						snake_case__=None       ,						snake_case__="</s>"       ,						snake_case__="</s>"       ,						snake_case__="<s>"       ,						snake_case__="<unk>"       ,						snake_case__="<pad>"       ,						snake_case__="<mask>"       ,						**snake_case__       ,						)	->	Optional[Any]:



         """simple docstring"""





         UpperCAmelCase					=	AddedToken(snake_case__       ,						lstrip=snake_case__       ,						rstrip=snake_case__ ) if isinstance(snake_case__       ,						snake_case__ ) 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__(
             snake_case__       ,						src_lang=snake_case__       ,						tgt_lang=snake_case__       ,						tokenizer_file=snake_case__       ,						eos_token=snake_case__       ,						sep_token=snake_case__       ,						cls_token=snake_case__       ,						unk_token=snake_case__       ,						pad_token=snake_case__       ,						mask_token=snake_case__       ,						**snake_case__       ,						)

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

         UpperCAmelCase					=	{
             lang_code: self.convert_tokens_to_ids(snake_case__ ) 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       ,						snake_case__ )	->	None:



         """simple docstring"""





         UpperCAmelCase					=	new_src_lang
         self.set_src_lang_special_tokens(self._src_lang )



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__ = 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       ,						snake_case__       ,						snake_case__ = "en_XX"       ,						snake_case__ = None       ,						snake_case__ = "ro_RO"       ,						**snake_case__       ,						)	->	BatchEncoding:



         """simple docstring"""





         UpperCAmelCase					=	src_lang
         UpperCAmelCase					=	tgt_lang
         return super().prepare_seqaseq_batch(snake_case__       ,						snake_case__       ,						**snake_case__ )



  def 						UpperCamelCase_ (					self )	->	int:



         """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       ,						snake_case__ )	->	None:



         """simple docstring"""





         UpperCAmelCase					=	self.convert_tokens_to_ids(snake_case__ )
         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       ,						snake_case__ )	->	None:



         """simple docstring"""





         UpperCAmelCase					=	self.convert_tokens_to_ids(snake_case__ )
         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       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						**snake_case__ )	->	Dict:



         """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(snake_case__       ,						add_special_tokens=snake_case__       ,						return_tensors=snake_case__       ,						**snake_case__ )
         UpperCAmelCase					=	self.convert_tokens_to_ids(snake_case__ )
         UpperCAmelCase					=	tgt_lang_id
         return inputs



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__ = 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(snake_case__ ):
                logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
                return
         UpperCAmelCase					=	os.path.join(
             snake_case__       ,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )

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

         return (out_vocab_file,)
