'''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:
    _A      :				Optional[Any] 				=    None


_A      :				Any 				=    logging.get_logger(__name__)

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

_A      :				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'''
        ),
    },
}

_A      :				Optional[int] 				=    {
    '''facebook/mbart-large-50-one-to-many-mmt''': 1024,
}

# fmt: off
_A      :				Union[str, 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     _lowercase					(    UpperCAmelCase__      ):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE					:	List[Any]				     = VOCAB_FILES_NAMES
 _SCREAMING_SNAKE_CASE					:	int				     = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _SCREAMING_SNAKE_CASE					:	Optional[int]				     = PRETRAINED_VOCAB_FILES_MAP
 _SCREAMING_SNAKE_CASE					:	str				     = ["""input_ids""", """attention_mask"""]
 _SCREAMING_SNAKE_CASE					:	Optional[Any]				     = MBartaaTokenizer

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

  __lowerCAmelCase    							=						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__(
      SCREAMING_SNAKE_CASE__				,	src_lang=SCREAMING_SNAKE_CASE__				,	tgt_lang=SCREAMING_SNAKE_CASE__				,	tokenizer_file=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__				,	)

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

  __lowerCAmelCase    							=						{
      lang_code: self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__							) for lang_code in FAIRSEQ_LANGUAGE_CODES
  }

  __lowerCAmelCase    							=						src_lang if src_lang is not None else """en_XX"""
  __lowerCAmelCase    							=						tgt_lang
  __lowerCAmelCase    							=						self.lang_code_to_id[self._src_lang]
  self.set_src_lang_special_tokens(self._src_lang							)
 @property
 def a (				self       :     List[Any]							)  ->   str:
  return self._src_lang
 @src_lang.setter
 def a (				self       :     Dict				,	SCREAMING_SNAKE_CASE__       :     str							)  ->   None:
  __lowerCAmelCase    							=						new_src_lang
  self.set_src_lang_special_tokens(self._src_lang							)
 def a (				self       :     Union[str, Any]				,	SCREAMING_SNAKE_CASE__       :     List[int]				,	SCREAMING_SNAKE_CASE__       :     Optional[List[int]] = None							)  ->   List[int]:
  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 a (				self       :     str				,	SCREAMING_SNAKE_CASE__       :     List[str]				,	SCREAMING_SNAKE_CASE__       :     str = "en_XX"				,	SCREAMING_SNAKE_CASE__       :     Optional[List[str]] = None				,	SCREAMING_SNAKE_CASE__       :     str = "ro_RO"				,	**SCREAMING_SNAKE_CASE__       :     int				,	)  ->   BatchEncoding:
  __lowerCAmelCase    							=						src_lang
  __lowerCAmelCase    							=						tgt_lang
  return super().prepare_seqaseq_batch(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__				,	**SCREAMING_SNAKE_CASE__							)
 def a (				self       :     Optional[int]							)  ->   str:
  return self.set_src_lang_special_tokens(self.src_lang							)
 def a (				self       :     int							)  ->   Tuple:
  return self.set_tgt_lang_special_tokens(self.tgt_lang							)
 def a (				self       :     Optional[int]				,	SCREAMING_SNAKE_CASE__       :     str							)  ->   None:
  __lowerCAmelCase    							=						self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase    							=						[self.cur_lang_code_id]
  __lowerCAmelCase    							=						[self.eos_token_id]

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

  __lowerCAmelCase    							=						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 a (				self       :     Tuple				,	SCREAMING_SNAKE_CASE__       :     str							)  ->   None:
  __lowerCAmelCase    							=						self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase    							=						[self.cur_lang_code_id]
  __lowerCAmelCase    							=						[self.eos_token_id]

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

  __lowerCAmelCase    							=						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 a (				self       :     str				,	SCREAMING_SNAKE_CASE__       :     str				,	SCREAMING_SNAKE_CASE__       :     str				,	SCREAMING_SNAKE_CASE__       :     Optional[str]				,	SCREAMING_SNAKE_CASE__       :     Optional[str]				,	**SCREAMING_SNAKE_CASE__       :     Dict							)  ->   List[Any]:
  if src_lang is None or tgt_lang is None:
   raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""							)
  __lowerCAmelCase    							=						src_lang
  __lowerCAmelCase    							=						self(SCREAMING_SNAKE_CASE__				,	add_special_tokens=SCREAMING_SNAKE_CASE__				,	return_tensors=SCREAMING_SNAKE_CASE__				,	**SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase    							=						self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase    							=						tgt_lang_id
  return inputs





 def a (				self       :     Optional[int]				,	SCREAMING_SNAKE_CASE__       :     str				,	SCREAMING_SNAKE_CASE__       :     Optional[str] = None							)  ->   Tuple[str]:
  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
  __lowerCAmelCase    							=						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,)







'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


_A      :				Optional[int] 				=    {
    '''configuration_speech_to_text_2''': ['''SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Speech2Text2Config'''],
    '''processing_speech_to_text_2''': ['''Speech2Text2Processor'''],
    '''tokenization_speech_to_text_2''': ['''Speech2Text2Tokenizer'''],
}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _A      :				Any 				=    [
     '''SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''Speech2Text2ForCausalLM''',
     '''Speech2Text2PreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_speech_to_text_a import SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextaConfig
 from .processing_speech_to_text_a import SpeechaTextaProcessor
 from .tokenization_speech_to_text_a import SpeechaTextaTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_speech_to_text_a import (
      SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST,
      SpeechaTextaForCausalLM,
      SpeechaTextaPreTrainedModel,
  )

else:
 import sys

 _A      :				Tuple 				=    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






