'''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:
 snake_case  =			None


snake_case  =			logging.get_logger(__name__)

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

snake_case  =			{
    '''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'''
        ),
    },
}

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

# fmt: off
snake_case  =			['''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        SCREAMING_SNAKE_CASE   (				__a ):
  """simple docstring"""







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

  __A     =		[]
  __A     =		[]



  def __init__(  self   :					Any					,					__lowerCAmelCase   :					Dict=None					,					__lowerCAmelCase   :					List[str]=None					,					__lowerCAmelCase   :					Union[str, Any]=None					,					__lowerCAmelCase   :					Optional[Any]=None					,					__lowerCAmelCase   :					int="</s>"					,					__lowerCAmelCase   :					int="</s>"					,					__lowerCAmelCase   :					Dict="<s>"					,					__lowerCAmelCase   :					str="<unk>"					,					__lowerCAmelCase   :					Tuple="<pad>"					,					__lowerCAmelCase   :					str="<mask>"					,					**__lowerCAmelCase   :					Tuple					,					):


        """simple docstring"""



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

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

        _lowerCAmelCase		   =   {
            lang_code: self.convert_tokens_to_ids(__lowerCAmelCase					) 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   :					int					):


        """simple docstring"""



        return self._src_lang



  @src_lang.setter
  def   a   (  self   :					Optional[Any]					,					__lowerCAmelCase   :					str					):


        """simple docstring"""



        _lowerCAmelCase		   =   new_src_lang
        self.set_src_lang_special_tokens(self._src_lang					)



  def   a   (  self   :					Any					,					__lowerCAmelCase   :					List[int]					,					__lowerCAmelCase   :					Optional[List[int]] = None					):


        """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   a   (  self   :					Any					,					__lowerCAmelCase   :					List[str]					,					__lowerCAmelCase   :					str = "en_XX"					,					__lowerCAmelCase   :					Optional[List[str]] = None					,					__lowerCAmelCase   :					str = "ro_RO"					,					**__lowerCAmelCase   :					Dict					,					):


        """simple docstring"""



        _lowerCAmelCase		   =   src_lang
        _lowerCAmelCase		   =   tgt_lang
        return super().prepare_seqaseq_batch(__lowerCAmelCase					,					__lowerCAmelCase					,					**__lowerCAmelCase					)



  def   a   (  self   :					Union[str, Any]					):


        """simple docstring"""



        return self.set_src_lang_special_tokens(self.src_lang					)



  def   a   (  self   :					Optional[int]					):


        """simple docstring"""



        return self.set_tgt_lang_special_tokens(self.tgt_lang					)



  def   a   (  self   :					List[Any]					,					__lowerCAmelCase   :					str					):


        """simple docstring"""



        _lowerCAmelCase		   =   self.convert_tokens_to_ids(__lowerCAmelCase					)
        _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   :					int					,					__lowerCAmelCase   :					str					):


        """simple docstring"""



        _lowerCAmelCase		   =   self.convert_tokens_to_ids(__lowerCAmelCase					)
        _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					,					__lowerCAmelCase   :					List[Any]					,					__lowerCAmelCase   :					str					,					__lowerCAmelCase   :					Optional[str]					,					__lowerCAmelCase   :					Optional[str]					,					**__lowerCAmelCase   :					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'					)
        _lowerCAmelCase		   =   src_lang
        _lowerCAmelCase		   =   self(__lowerCAmelCase					,					add_special_tokens=__lowerCAmelCase					,					return_tensors=__lowerCAmelCase					,					**__lowerCAmelCase					)
        _lowerCAmelCase		   =   self.convert_tokens_to_ids(__lowerCAmelCase					)
        _lowerCAmelCase		   =   tgt_lang_id
        return inputs



  def   a   (  self   :					Optional[Any]					,					__lowerCAmelCase   :					str					,					__lowerCAmelCase   :					Optional[str] = None					):


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

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

        return (out_vocab_file,)






'''simple docstring'''




from typing import TYPE_CHECKING

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


snake_case  =			{
    '''configuration_align''': [
        '''ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''AlignConfig''',
        '''AlignTextConfig''',
        '''AlignVisionConfig''',
    ],
    '''processing_align''': ['''AlignProcessor'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case  =			[
     '''ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''AlignModel''',
     '''AlignPreTrainedModel''',
     '''AlignTextModel''',
     '''AlignVisionModel''',
 ]

if TYPE_CHECKING:
 from .configuration_align import (
     ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP,
     AlignConfig,
     AlignTextConfig,
     AlignVisionConfig,
 )
 from .processing_align import AlignProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_align import (
      ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST,
      AlignModel,
      AlignPreTrainedModel,
      AlignTextModel,
      AlignVisionModel,
  )

else:
 import sys

 snake_case  =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





