"""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_mbart import MBartTokenizer
else:
     _a   :	str 					=     None


_a   :	List[str] 					=     logging.get_logger(__name__)


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

_a   :	Optional[int] 					=     {
    'vocab_file': {
        'facebook/mbart-large-en-ro': (
            'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'
        ),
        'facebook/mbart-large-cc25': (
            'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'facebook/mbart-large-en-ro': 'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json',
        'facebook/mbart-large-cc25': 'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json',
    },
}

_a   :	Union[str, Any] 					=     {
    'facebook/mbart-large-en-ro': 1_024,
    'facebook/mbart-large-cc25': 1_024,
}

# fmt: off
_a   :	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']
class 		__A							(				SCREAMING_SNAKE_CASE_	):
     _UpperCamelCase							:    List[str]	      =	VOCAB_FILES_NAMES
     _UpperCamelCase							:    Tuple	      =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _UpperCamelCase							:    Tuple	      =	PRETRAINED_VOCAB_FILES_MAP
     _UpperCamelCase							:    int	      =	["input_ids", "attention_mask"]
     _UpperCamelCase							:    Tuple	      =	MBartTokenizer

     _UpperCamelCase							:    List[int]	      =	[]
     _UpperCamelCase							:    List[int]	      =	[]






     def __init__(	self       ,  a__=None       ,  a__=None       ,  a__="<s>"       ,  a__="</s>"       ,  a__="</s>"       ,  a__="<s>"       ,  a__="<unk>"       ,  a__="<pad>"       ,  a__="<mask>"       ,  a__=None       ,  a__=None       ,  a__=None       ,  **a__       ,  ):
       # Mask token behave like a normal word, i.e. include the space before it
       _lowerCAmelCase		: Dict						    =  AddedToken(a__       ,  lstrip=a__       ,  rstrip=a__						) if isinstance(a__       ,  a__						) else mask_token

       super().__init__(
           vocab_file=a__       ,  tokenizer_file=a__       ,  bos_token=a__       ,  eos_token=a__       ,  sep_token=a__       ,  cls_token=a__       ,  unk_token=a__       ,  pad_token=a__       ,  mask_token=a__       ,  src_lang=a__       ,  tgt_lang=a__       ,  additional_special_tokens=a__       ,  **a__       ,  )

       _lowerCAmelCase		: Optional[int]						    =  vocab_file
       _lowerCAmelCase		: Dict						    =  False if not self.vocab_file else True

       _lowerCAmelCase		: List[Any]						    =  FAIRSEQ_LANGUAGE_CODES.copy()

       if additional_special_tokens is not None:
         # Only add those special tokens if they are not already there.
         _additional_special_tokens.extend(
             [t for t in additional_special_tokens if t not in _additional_special_tokens]						)

       self.add_special_tokens({"""additional_special_tokens""": _additional_special_tokens}						)
       _lowerCAmelCase		: str						    =  {
           lang_code: self.convert_tokens_to_ids(a__						) for lang_code in FAIRSEQ_LANGUAGE_CODES
       }

       _lowerCAmelCase		: Optional[int]						    =  src_lang if src_lang is not None else """en_XX"""
       _lowerCAmelCase		: Optional[Any]						    =  self.convert_tokens_to_ids(self._src_lang						)
       _lowerCAmelCase		: Optional[Any]						    =  tgt_lang
       self.set_src_lang_special_tokens(self._src_lang						)






     @property
     def 					__A						(	self						):
       return self._src_lang






     @src_lang.setter
     def 					__A						(	self       ,  a__						):
       _lowerCAmelCase		: Dict						    =  new_src_lang
       self.set_src_lang_special_tokens(self._src_lang						)






     def 					__A						(	self       ,  a__       ,  a__ = None						):
       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       ,  a__       ,  a__ = None						):
       _lowerCAmelCase		: Any						    =  [self.sep_token_id]
       _lowerCAmelCase		: Optional[int]						    =  [self.cls_token_id]

       if token_ids_a is None:
         return len(cls + token_ids_a + sep						) * [0]
       return len(cls + token_ids_a + sep + sep + token_ids_a + sep						) * [0]






     def 					__A						(	self       ,  a__       ,  a__       ,  a__       ,  a__       ,  **a__						):
       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		: Optional[int]						    =  src_lang
       _lowerCAmelCase		: int						    =  self(a__       ,  add_special_tokens=a__       ,  return_tensors=a__       ,  **a__						)
       _lowerCAmelCase		: Dict						    =  self.convert_tokens_to_ids(a__						)
       _lowerCAmelCase		: Optional[int]						    =  tgt_lang_id
       return inputs






     def 					__A						(	self       ,  a__       ,  a__ = "en_XX"       ,  a__ = None       ,  a__ = "ro_RO"       ,  **a__       ,  ):
       _lowerCAmelCase		: Dict						    =  src_lang
       _lowerCAmelCase		: Optional[int]						    =  tgt_lang
       return super().prepare_seqaseq_batch(a__       ,  a__       ,  **a__						)






     def 					__A						(	self						):
       return self.set_src_lang_special_tokens(self.src_lang						)






     def 					__A						(	self						):
       return self.set_tgt_lang_special_tokens(self.tgt_lang						)






     def 					__A						(	self       ,  a__						):
       _lowerCAmelCase		: Optional[Any]						    =  self.convert_tokens_to_ids(a__						)
       _lowerCAmelCase		: Tuple						    =  []
       _lowerCAmelCase		: Dict						    =  [self.eos_token_id, self.cur_lang_code]

       _lowerCAmelCase		: Optional[int]						    =  self.convert_ids_to_tokens(self.prefix_tokens						)
       _lowerCAmelCase		: Dict						    =  self.convert_ids_to_tokens(self.suffix_tokens						)

       _lowerCAmelCase		: Any						    =  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       ,  a__						):
       _lowerCAmelCase		: Any						    =  self.convert_tokens_to_ids(a__						)
       _lowerCAmelCase		: Tuple						    =  []
       _lowerCAmelCase		: List[Any]						    =  [self.eos_token_id, self.cur_lang_code]

       _lowerCAmelCase		: Optional[Any]						    =  self.convert_ids_to_tokens(self.prefix_tokens						)
       _lowerCAmelCase		: List[str]						    =  self.convert_ids_to_tokens(self.suffix_tokens						)

       _lowerCAmelCase		: str						    =  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       ,  a__       ,  a__ = None						):
       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
       _lowerCAmelCase		: Tuple						    =  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,)


"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_a   :	Tuple 					=     {'configuration_wavlm': ['WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'WavLMConfig']}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _a   :	str 					=     [
         'WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST',
         'WavLMForAudioFrameClassification',
         'WavLMForCTC',
         'WavLMForSequenceClassification',
         'WavLMForXVector',
         'WavLMModel',
         'WavLMPreTrainedModel',
     ]

if TYPE_CHECKING:
     from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_wavlm import (
              WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
              WavLMForAudioFrameClassification,
              WavLMForCTC,
              WavLMForSequenceClassification,
              WavLMForXVector,
              WavLMModel,
              WavLMPreTrainedModel,
          )

else:
     import sys

     _a   :	Any 					=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




