import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__magic_name__								=logging.get_logger(__name__)

__magic_name__								='''▁'''

__magic_name__								={'''vocab_file''': '''sentencepiece.bpe.model''', '''monolingual_vocab_file''': '''dict.txt'''}

__magic_name__								={
    '''vocab_file''': {
        '''vinai/bartpho-syllable''': '''https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model''',
    },
    '''monolingual_vocab_file''': {
        '''vinai/bartpho-syllable''': '''https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt''',
    },
}

__magic_name__								={'''vinai/bartpho-syllable''': 1024}



class     _A   (    __UpperCamelCase							):
  SCREAMING_SNAKE_CASE_	:	Any						=VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE_	:	Optional[int]						=PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE_	:	Tuple						=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE_	:	Optional[Any]						=["""input_ids""", """attention_mask"""]






  def __init__(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_="<s>"   ,				SCREAMING_SNAKE_CASE_="</s>"   ,				SCREAMING_SNAKE_CASE_="</s>"   ,				SCREAMING_SNAKE_CASE_="<s>"   ,				SCREAMING_SNAKE_CASE_="<unk>"   ,				SCREAMING_SNAKE_CASE_="<pad>"   ,				SCREAMING_SNAKE_CASE_="<mask>"   ,				SCREAMING_SNAKE_CASE_ = None   ,				**SCREAMING_SNAKE_CASE_   ,				)		->      Union[str, Any]:





       '''simple docstring'''





       UpperCamelCase__										=      AddedToken(SCREAMING_SNAKE_CASE_   ,				lstrip=SCREAMING_SNAKE_CASE_   ,				rstrip=SCREAMING_SNAKE_CASE_	) if isinstance(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	) else mask_token

       UpperCamelCase__										=      {} if sp_model_kwargs is None else sp_model_kwargs

       super().__init__(
           bos_token=SCREAMING_SNAKE_CASE_   ,				eos_token=SCREAMING_SNAKE_CASE_   ,				unk_token=SCREAMING_SNAKE_CASE_   ,				sep_token=SCREAMING_SNAKE_CASE_   ,				cls_token=SCREAMING_SNAKE_CASE_   ,				pad_token=SCREAMING_SNAKE_CASE_   ,				mask_token=SCREAMING_SNAKE_CASE_   ,				sp_model_kwargs=self.sp_model_kwargs   ,				**SCREAMING_SNAKE_CASE_   ,				)

       UpperCamelCase__										=      vocab_file
       UpperCamelCase__										=      monolingual_vocab_file
       UpperCamelCase__										=      spm.SentencePieceProcessor(**self.sp_model_kwargs	)
       self.sp_model.Load(str(SCREAMING_SNAKE_CASE_	)	)

       # Load the reduced vocab

       # Keep order of special tokens for backward compatibility
       UpperCamelCase__										=      {}
       UpperCamelCase__										=      0
       for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
            if str(SCREAMING_SNAKE_CASE_	) not in self.fairseq_tokens_to_ids:
                 UpperCamelCase__										=      cnt
                 cnt += 1
       with open(SCREAMING_SNAKE_CASE_   ,				'''r'''   ,				encoding='''utf-8'''	) as f:
            for line in f.readlines():
                 UpperCamelCase__										=      line.strip().split()[0]
                 UpperCamelCase__										=      len(self.fairseq_tokens_to_ids	)
       if str(SCREAMING_SNAKE_CASE_	) not in self.fairseq_tokens_to_ids:
            UpperCamelCase__										=      len(self.fairseq_tokens_to_ids	)

       UpperCamelCase__										=      {v: k for k, v in self.fairseq_tokens_to_ids.items()}






  def __getstate__(self	)		->      List[str]:





       '''simple docstring'''





       UpperCamelCase__										=      self.__dict__.copy()
       UpperCamelCase__										=      None
       UpperCamelCase__										=      self.sp_model.serialized_model_proto()
       return state






  def __setstate__(self   ,				SCREAMING_SNAKE_CASE_	)		->      List[Any]:





       '''simple docstring'''





       UpperCamelCase__										=      d

       # for backward compatibility
       if not hasattr(self   ,				'''sp_model_kwargs'''	):
            UpperCamelCase__										=      {}

       UpperCamelCase__										=      spm.SentencePieceProcessor(**self.sp_model_kwargs	)
       self.sp_model.LoadFromSerializedProto(self.sp_model_proto	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = None	)		->      Tuple:





       '''simple docstring'''





       if token_ids_a is None:
            return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       UpperCamelCase__										=      [self.cls_token_id]
       UpperCamelCase__										=      [self.sep_token_id]
       return cls + token_ids_a + sep + sep + token_ids_a + sep






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = False	)		->      int:





       '''simple docstring'''





       if already_has_special_tokens:
            return super().get_special_tokens_mask(
                token_ids_a=SCREAMING_SNAKE_CASE_   ,				token_ids_a=SCREAMING_SNAKE_CASE_   ,				already_has_special_tokens=SCREAMING_SNAKE_CASE_	)

       if token_ids_a is None:
            return [1] + ([0] * len(SCREAMING_SNAKE_CASE_	)) + [1]
       return [1] + ([0] * len(SCREAMING_SNAKE_CASE_	)) + [1, 1] + ([0] * len(SCREAMING_SNAKE_CASE_	)) + [1]






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = None	)		->      Any:





       '''simple docstring'''





       UpperCamelCase__										=      [self.sep_token_id]
       UpperCamelCase__										=      [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]






  @property
  def 		_a					(self	)		->      str:





       '''simple docstring'''





       return len(self.fairseq_ids_to_tokens	)






  def 		_a					(self	)		->      Optional[Any]:





       '''simple docstring'''





       UpperCamelCase__										=      {self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_	): i for i in range(self.vocab_size	)}
       vocab.update(self.added_tokens_encoder	)
       return vocab






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      List[str]:





       '''simple docstring'''





       return self.sp_model.encode(SCREAMING_SNAKE_CASE_   ,				out_type=SCREAMING_SNAKE_CASE_	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      Optional[Any]:





       '''simple docstring'''





       if token in self.fairseq_tokens_to_ids:
            return self.fairseq_tokens_to_ids[token]
       else:
            return self.unk_token_id






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      Any:





       '''simple docstring'''





       return self.fairseq_ids_to_tokens[index]






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      List[Any]:





       '''simple docstring'''





       UpperCamelCase__										=      ''''''.join(SCREAMING_SNAKE_CASE_	).replace(SCREAMING_SNAKE_CASE_   ,				''' '''	).strip()
       return out_string






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = None	)		->      Tuple:





       '''simple docstring'''





       if not os.path.isdir(SCREAMING_SNAKE_CASE_	):
            logger.error(F"Vocabulary path ({save_directory}) should be a directory"	)
            return
       UpperCamelCase__										=      os.path.join(
           SCREAMING_SNAKE_CASE_   ,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']	)
       UpperCamelCase__										=      os.path.join(
           SCREAMING_SNAKE_CASE_   ,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''monolingual_vocab_file''']   ,				)

       if os.path.abspath(self.vocab_file	) != os.path.abspath(SCREAMING_SNAKE_CASE_	) and os.path.isfile(self.vocab_file	):
            copyfile(self.vocab_file   ,				SCREAMING_SNAKE_CASE_	)
       elif not os.path.isfile(self.vocab_file	):
            with open(SCREAMING_SNAKE_CASE_   ,				'''wb'''	) as fi:
                 UpperCamelCase__										=      self.sp_model.serialized_model_proto()
                 fi.write(SCREAMING_SNAKE_CASE_	)

       if os.path.abspath(self.monolingual_vocab_file	) != os.path.abspath(
           SCREAMING_SNAKE_CASE_	) and os.path.isfile(self.monolingual_vocab_file	):
            copyfile(self.monolingual_vocab_file   ,				SCREAMING_SNAKE_CASE_	)
       elif not os.path.isfile(self.monolingual_vocab_file	):
            with open(SCREAMING_SNAKE_CASE_   ,				'''w'''   ,				encoding='''utf-8'''	) as fp:
                 for token in self.fairseq_tokens_to_ids:
                      if token not in self.all_special_tokens:
                           fp.write(F"{str(SCREAMING_SNAKE_CASE_	)} \n"	)

       return out_vocab_file, out_monolingual_vocab_file




from ...configuration_utils import PretrainedConfig
from ...utils import logging


__magic_name__								=logging.get_logger(__name__)

__magic_name__								={
    '''facebook/vit-mae-base''': '''https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json''',
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}



class     _A   (    __UpperCamelCase							):
  SCREAMING_SNAKE_CASE_	:	Dict						="vit_mae"






  def __init__(self   ,				SCREAMING_SNAKE_CASE_=768   ,				SCREAMING_SNAKE_CASE_=12   ,				SCREAMING_SNAKE_CASE_=12   ,				SCREAMING_SNAKE_CASE_=3072   ,				SCREAMING_SNAKE_CASE_="gelu"   ,				SCREAMING_SNAKE_CASE_=0.0   ,				SCREAMING_SNAKE_CASE_=0.0   ,				SCREAMING_SNAKE_CASE_=0.02   ,				SCREAMING_SNAKE_CASE_=1E-12   ,				SCREAMING_SNAKE_CASE_=224   ,				SCREAMING_SNAKE_CASE_=16   ,				SCREAMING_SNAKE_CASE_=3   ,				SCREAMING_SNAKE_CASE_=True   ,				SCREAMING_SNAKE_CASE_=16   ,				SCREAMING_SNAKE_CASE_=512   ,				SCREAMING_SNAKE_CASE_=8   ,				SCREAMING_SNAKE_CASE_=2048   ,				SCREAMING_SNAKE_CASE_=0.75   ,				SCREAMING_SNAKE_CASE_=False   ,				**SCREAMING_SNAKE_CASE_   ,				)		->      Optional[Any]:





       '''simple docstring'''





       super().__init__(**SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      hidden_size
       UpperCamelCase__										=      num_hidden_layers
       UpperCamelCase__										=      num_attention_heads
       UpperCamelCase__										=      intermediate_size
       UpperCamelCase__										=      hidden_act
       UpperCamelCase__										=      hidden_dropout_prob
       UpperCamelCase__										=      attention_probs_dropout_prob
       UpperCamelCase__										=      initializer_range
       UpperCamelCase__										=      layer_norm_eps
       UpperCamelCase__										=      image_size
       UpperCamelCase__										=      patch_size
       UpperCamelCase__										=      num_channels
       UpperCamelCase__										=      qkv_bias
       UpperCamelCase__										=      decoder_num_attention_heads
       UpperCamelCase__										=      decoder_hidden_size
       UpperCamelCase__										=      decoder_num_hidden_layers
       UpperCamelCase__										=      decoder_intermediate_size
       UpperCamelCase__										=      mask_ratio
       UpperCamelCase__										=      norm_pix_loss



