




'''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:
 _lowerCAmelCase										=     None


_lowerCAmelCase										=     logging.get_logger(__name__)


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

_lowerCAmelCase										=     {
    '''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''',
    },
}

_lowerCAmelCase										=     {
    '''facebook/mbart-large-en-ro''': 1024,
    '''facebook/mbart-large-cc25''': 1024,
}

# fmt: off
_lowerCAmelCase										=     ['''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 	lowerCAmelCase_(    A__					):
  '''simple docstring'''




  __lowercase							:				List[str]           =				VOCAB_FILES_NAMES
  __lowercase							:				Tuple           =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __lowercase							:				Any           =				PRETRAINED_VOCAB_FILES_MAP
  __lowercase							:				str           =				["input_ids", "attention_mask"]
  __lowercase							:				str           =				MBartTokenizer

  __lowercase							:				List[int]           =				[]
  __lowercase							:				List[int]           =				[]




  def __init__(   self  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,__UpperCAmelCase="<s>"  ,__UpperCAmelCase="</s>"  ,__UpperCAmelCase="</s>"  ,__UpperCAmelCase="<s>"  ,__UpperCAmelCase="<unk>"  ,__UpperCAmelCase="<pad>"  ,__UpperCAmelCase="<mask>"  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,**__UpperCAmelCase  ,)   ->		int:
      # Mask token behave like a normal word, i.e. include the space before it
      lowerCAmelCase__			:  List[str]											= 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__			:  Any											= vocab_file
      lowerCAmelCase__			:  Any											= 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__			:  Union[str, Any]											= {
          lang_code: self.convert_tokens_to_ids(__A				) for lang_code in FAIRSEQ_LANGUAGE_CODES
      }

      lowerCAmelCase__			:  List[str]											= src_lang if src_lang is not None else """en_XX"""
      lowerCAmelCase__			:  Tuple											= self.convert_tokens_to_ids(self._src_lang				)
      lowerCAmelCase__			:  List[Any]											= tgt_lang
      self.set_src_lang_special_tokens(self._src_lang				)




  @property
  def 							UpperCAmelCase_     (   self				)   ->		str:
      return self._src_lang




  @src_lang.setter
  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase				)   ->		None:
      lowerCAmelCase__			:  int											= new_src_lang
      self.set_src_lang_special_tokens(self._src_lang				)




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase = 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 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase = None				)   ->		List[int]:
      lowerCAmelCase__			:  Any											= [self.sep_token_id]
      lowerCAmelCase__			:  Tuple											= [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 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,**__UpperCAmelCase				)   ->		Tuple:
      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__			:  Any											= src_lang
      lowerCAmelCase__			:  Dict											= self(__A  ,add_special_tokens=__A  ,return_tensors=__A  ,**__A				)
      lowerCAmelCase__			:  List[Any]											= self.convert_tokens_to_ids(__A				)
      lowerCAmelCase__			:  str											= tgt_lang_id
      return inputs




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase = "en_XX"  ,__UpperCAmelCase = None  ,__UpperCAmelCase = "ro_RO"  ,**__UpperCAmelCase  ,)   ->		BatchEncoding:
      lowerCAmelCase__			:  str											= src_lang
      lowerCAmelCase__			:  str											= tgt_lang
      return super().prepare_seqaseq_batch(__A  ,__A  ,**__A				)




  def 							UpperCAmelCase_     (   self				)   ->		Any:
      return self.set_src_lang_special_tokens(self.src_lang				)




  def 							UpperCAmelCase_     (   self				)   ->		List[str]:
      return self.set_tgt_lang_special_tokens(self.tgt_lang				)




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase				)   ->		None:
      lowerCAmelCase__			:  Optional[Any]											= self.convert_tokens_to_ids(__A				)
      lowerCAmelCase__			:  Any											= []
      lowerCAmelCase__			:  Union[str, Any]											= [self.eos_token_id, self.cur_lang_code]

      lowerCAmelCase__			:  Dict											= self.convert_ids_to_tokens(self.prefix_tokens				)
      lowerCAmelCase__			:  Optional[Any]											= self.convert_ids_to_tokens(self.suffix_tokens				)

      lowerCAmelCase__			:  int											= 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  ,__UpperCAmelCase				)   ->		None:
      lowerCAmelCase__			:  Dict											= self.convert_tokens_to_ids(__A				)
      lowerCAmelCase__			:  Dict											= []
      lowerCAmelCase__			:  str											= [self.eos_token_id, self.cur_lang_code]

      lowerCAmelCase__			:  str											= self.convert_ids_to_tokens(self.prefix_tokens				)
      lowerCAmelCase__			:  int											= self.convert_ids_to_tokens(self.suffix_tokens				)

      lowerCAmelCase__			:  Optional[int]											= 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  ,__UpperCAmelCase  ,__UpperCAmelCase = 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(__A				):
          logger.error(F"""Vocabulary path ({save_directory}) should be a directory."""				)
          return
      lowerCAmelCase__			:  int											= 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 ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_lowerCAmelCase										=     logging.get_logger(__name__)

_lowerCAmelCase										=     {
    '''facebook/convnextv2-tiny-1k-224''': '''https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json''',
}






class 	lowerCAmelCase_(    SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_					):
  '''simple docstring'''




  __lowercase							:				Tuple           =				'''convnextv2'''




  def __init__(   self  ,__UpperCAmelCase=3  ,__UpperCAmelCase=4  ,__UpperCAmelCase=4  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,__UpperCAmelCase="gelu"  ,__UpperCAmelCase=0.0_2  ,__UpperCAmelCase=1E-12  ,__UpperCAmelCase=0.0  ,__UpperCAmelCase=224  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,**__UpperCAmelCase  ,)   ->		Union[str, Any]:
      super().__init__(**__UpperCAmelCase				)

      lowerCAmelCase__			:  int											= num_channels
      lowerCAmelCase__			:  List[Any]											= patch_size
      lowerCAmelCase__			:  Union[str, Any]											= num_stages
      lowerCAmelCase__			:  Tuple											= [96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
      lowerCAmelCase__			:  str											= [3, 3, 9, 3] if depths is None else depths
      lowerCAmelCase__			:  Optional[Any]											= hidden_act
      lowerCAmelCase__			:  str											= initializer_range
      lowerCAmelCase__			:  List[str]											= layer_norm_eps
      lowerCAmelCase__			:  Dict											= drop_path_rate
      lowerCAmelCase__			:  int											= image_size
      lowerCAmelCase__			:  int											= ["""stem"""] + [F"""stage{idx}""" for idx in range(1  ,len(self.depths				) + 1				)]
      lowerCAmelCase__				,       lowerCAmelCase__			:  Tuple											= get_aligned_output_features_output_indices(
          out_features=__UpperCAmelCase  ,out_indices=__UpperCAmelCase  ,stage_names=self.stage_names				)


