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__	:  Optional[int]						=  None


lowerCAmelCase__	:  int						=  logging.get_logger(__name__)

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

lowerCAmelCase__	:  Dict						=  {
    '''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__	:  Union[str, Any]						=  {
    '''facebook/mbart-large-50-one-to-many-mmt''': 10_24,
}

# fmt: off
lowerCAmelCase__	:  Dict						=  ['''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 					__snake_case    (     _lowerCamelCase      ):
					__lowerCamelCase			   =  VOCAB_FILES_NAMES
					__lowerCamelCase			   =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					__lowerCamelCase			   =  PRETRAINED_VOCAB_FILES_MAP
					__lowerCamelCase			   =  ["input_ids", "attention_mask"]
					__lowerCamelCase			   =  MBartaaTokenizer

					__lowerCamelCase			   =  []
					__lowerCamelCase			   =  []




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



									'''simple docstring'''







									snake_case__   :      List[str]   				= AddedToken(__UpperCamelCase       ,			lstrip=__UpperCamelCase       ,			rstrip=__UpperCamelCase						) if isinstance(__UpperCamelCase       ,			__UpperCamelCase						) else mask_token

									snake_case__   :      Union[str, Any]   				= 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__(
									    __UpperCamelCase       ,			src_lang=__UpperCamelCase       ,			tgt_lang=__UpperCamelCase       ,			tokenizer_file=__UpperCamelCase       ,			eos_token=__UpperCamelCase       ,			sep_token=__UpperCamelCase       ,			cls_token=__UpperCamelCase       ,			unk_token=__UpperCamelCase       ,			pad_token=__UpperCamelCase       ,			mask_token=__UpperCamelCase       ,			**__UpperCamelCase       ,			)

									snake_case__   :      Tuple   				= vocab_file
									snake_case__   :      str   				= False if not self.vocab_file else True

									snake_case__   :      Union[str, Any]   				= {
									    lang_code: self.convert_tokens_to_ids(__UpperCamelCase						) for lang_code in FAIRSEQ_LANGUAGE_CODES
									}

									snake_case__   :      Union[str, Any]   				= src_lang if src_lang is not None else '''en_XX'''
									snake_case__   :      Tuple   				= tgt_lang
									snake_case__   :      Any   				= self.lang_code_to_id[self._src_lang]
									self.set_src_lang_special_tokens(self._src_lang						)




					@property
					def      __a     (  self						)	->    List[Any]:



									'''simple docstring'''







									return self._src_lang




					@src_lang.setter
					def      __a     (  self       ,			__UpperCamelCase						)	->    List[Any]:



									'''simple docstring'''







									snake_case__   :      str   				= new_src_lang
									self.set_src_lang_special_tokens(self._src_lang						)




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase = None						)	->    str:



									'''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       ,			__UpperCamelCase       ,			__UpperCamelCase = "en_XX"       ,			__UpperCamelCase = None       ,			__UpperCamelCase = "ro_RO"       ,			**__UpperCamelCase       ,			)	->    Any:



									'''simple docstring'''







									snake_case__   :      int   				= src_lang
									snake_case__   :      List[str]   				= tgt_lang
									return super().prepare_seqaseq_batch(__UpperCamelCase       ,			__UpperCamelCase       ,			**__UpperCamelCase						)




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



									'''simple docstring'''







									return self.set_src_lang_special_tokens(self.src_lang						)




					def      __a     (  self						)	->    int:



									'''simple docstring'''







									return self.set_tgt_lang_special_tokens(self.tgt_lang						)




					def      __a     (  self       ,			__UpperCamelCase						)	->    Any:



									'''simple docstring'''







									snake_case__   :      List[str]   				= self.convert_tokens_to_ids(__UpperCamelCase						)
									snake_case__   :      str   				= [self.cur_lang_code_id]
									snake_case__   :      List[Any]   				= [self.eos_token_id]

									snake_case__   :      int   				= self.convert_ids_to_tokens(self.prefix_tokens						)
									snake_case__   :      Dict   				= self.convert_ids_to_tokens(self.suffix_tokens						)

									snake_case__   :      List[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       ,			__UpperCamelCase						)	->    Any:



									'''simple docstring'''







									snake_case__   :      List[str]   				= self.convert_tokens_to_ids(__UpperCamelCase						)
									snake_case__   :      Tuple   				= [self.cur_lang_code_id]
									snake_case__   :      int   				= [self.eos_token_id]

									snake_case__   :      Tuple   				= self.convert_ids_to_tokens(self.prefix_tokens						)
									snake_case__   :      List[Any]   				= self.convert_ids_to_tokens(self.suffix_tokens						)

									snake_case__   :      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       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			**__UpperCamelCase						)	->    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'						)
									snake_case__   :      Union[str, Any]   				= src_lang
									snake_case__   :      Dict   				= self(__UpperCamelCase       ,			add_special_tokens=__UpperCamelCase       ,			return_tensors=__UpperCamelCase       ,			**__UpperCamelCase						)
									snake_case__   :      Tuple   				= self.convert_tokens_to_ids(__UpperCamelCase						)
									snake_case__   :      List[str]   				= tgt_lang_id
									return inputs




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase = None						)	->    Tuple:



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

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

									return (out_vocab_file,)



'''simple docstring'''
from typing import TYPE_CHECKING

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


_A		:      Optional[Any]						   ={
    '''configuration_mobilevitv2''': [
        '''MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''MobileViTV2Config''',
        '''MobileViTV2OnnxConfig''',
    ],
}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _A		:      Union[str, Any]						   =[
     '''MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''MobileViTV2ForImageClassification''',
     '''MobileViTV2ForSemanticSegmentation''',
     '''MobileViTV2Model''',
     '''MobileViTV2PreTrainedModel''',
 ]

if TYPE_CHECKING:
 from .configuration_mobilevitva import (
     MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
     MobileViTVaConfig,
     MobileViTVaOnnxConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mobilevitva import (
      MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
      MobileViTVaForImageClassification,
      MobileViTVaForSemanticSegmentation,
      MobileViTVaModel,
      MobileViTVaPreTrainedModel,
  )

else:
 import sys

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