




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


_UpperCAmelCase	:						Union[str, Any]			=     logging.get_logger(__name__)

_UpperCAmelCase	:						int			=     "▁"

_UpperCAmelCase	:						str			=     {"vocab_file": "sentencepiece.bpe.model", "monolingual_vocab_file": "dict.txt"}

_UpperCAmelCase	:						str			=     {
    "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",
    },
}

_UpperCAmelCase	:						Any			=     {"vinai/bartpho-syllable": 1024}




class    __lowerCAmelCase       (							lowerCAmelCase):
       _a 					=						VOCAB_FILES_NAMES
       _a 					=						PRETRAINED_VOCAB_FILES_MAP
       _a 					=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       _a 					=						['''input_ids''', '''attention_mask''']






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

         lowercase    :Tuple      	=						{} if sp_model_kwargs is None else sp_model_kwargs

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

         lowercase    :Union[str, Any]      	=						vocab_file
         lowercase    :Optional[Any]      	=						monolingual_vocab_file
         lowercase    :Tuple      	=						spm.SentencePieceProcessor(**self.sp_model_kwargs				)
         self.sp_model.Load(str(_lowerCAmelCase				)				)

         # Load the reduced vocab

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

         lowercase    :Union[str, Any]      	=						{v: k for k, v in self.fairseq_tokens_to_ids.items()}






       def __getstate__(   self:					List[Any]				):
         lowercase    :Dict      	=						self.__dict__.copy()
         lowercase    :Tuple      	=						None
         lowercase    :Optional[int]      	=						self.sp_model.serialized_model_proto()
         return state






       def __setstate__(   self:					Any       ,					_lowerCAmelCase:					Dict				):
         lowercase    :int      	=						d

         # for backward compatibility
         if not hasattr(self       ,					"sp_model_kwargs"				):
           lowercase    :Optional[Any]      	=						{}

         lowercase    :str      	=						spm.SentencePieceProcessor(**self.sp_model_kwargs				)
         self.sp_model.LoadFromSerializedProto(self.sp_model_proto				)






       def 					SCREAMING_SNAKE_CASE    (   self:					List[str]       ,					_lowerCAmelCase:					List[int]       ,					_lowerCAmelCase:					Optional[List[int]] = None				):

         if token_ids_a is None:
           return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         lowercase    :Optional[Any]      	=						[self.cls_token_id]
         lowercase    :List[str]      	=						[self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep






       def 					SCREAMING_SNAKE_CASE    (   self:					Any       ,					_lowerCAmelCase:					List[int]       ,					_lowerCAmelCase:					Optional[List[int]] = None       ,					_lowerCAmelCase:					bool = False				):

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

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






       def 					SCREAMING_SNAKE_CASE    (   self:					Any       ,					_lowerCAmelCase:					List[int]       ,					_lowerCAmelCase:					Optional[List[int]] = None				):
         lowercase    :List[Any]      	=						[self.sep_token_id]
         lowercase    :Any      	=						[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 					SCREAMING_SNAKE_CASE    (   self:					Tuple				):
         return len(self.fairseq_ids_to_tokens				)






       def 					SCREAMING_SNAKE_CASE    (   self:					Any				):
         lowercase    :List[str]      	=						{self.convert_ids_to_tokens(_lowerCAmelCase				): i for i in range(self.vocab_size				)}
         vocab.update(self.added_tokens_encoder				)
         return vocab






       def 					SCREAMING_SNAKE_CASE    (   self:					Any       ,					_lowerCAmelCase:					str				):
         return self.sp_model.encode(_lowerCAmelCase       ,					out_type=_lowerCAmelCase				)






       def 					SCREAMING_SNAKE_CASE    (   self:					List[Any]       ,					_lowerCAmelCase:					Any				):
         if token in self.fairseq_tokens_to_ids:
           return self.fairseq_tokens_to_ids[token]
         else:
           return self.unk_token_id






       def 					SCREAMING_SNAKE_CASE    (   self:					List[Any]       ,					_lowerCAmelCase:					Dict				):
         return self.fairseq_ids_to_tokens[index]






       def 					SCREAMING_SNAKE_CASE    (   self:					List[Any]       ,					_lowerCAmelCase:					Optional[int]				):
         lowercase    :str      	=						"".join(_lowerCAmelCase				).replace(_lowerCAmelCase       ,					" "				).strip()
         return out_string






       def 					SCREAMING_SNAKE_CASE    (   self:					int       ,					_lowerCAmelCase:					str       ,					_lowerCAmelCase:					Optional[str] = None				):
         if not os.path.isdir(_lowerCAmelCase				):
           logger.error(F"Vocabulary path ({save_directory}) should be a directory"				)
           return
         lowercase    :Optional[int]      	=						os.path.join(
             _lowerCAmelCase       ,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]				)
         lowercase    :Optional[int]      	=						os.path.join(
             _lowerCAmelCase       ,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["monolingual_vocab_file"]       ,					)

         if os.path.abspath(self.vocab_file				) != os.path.abspath(_lowerCAmelCase				) and os.path.isfile(self.vocab_file				):
           copyfile(self.vocab_file       ,					_lowerCAmelCase				)
         elif not os.path.isfile(self.vocab_file				):
           with open(_lowerCAmelCase       ,					"wb"				) as fi:
             lowercase    :int      	=						self.sp_model.serialized_model_proto()
             fi.write(_lowerCAmelCase				)

         if os.path.abspath(self.monolingual_vocab_file				) != os.path.abspath(
             _lowerCAmelCase				) and os.path.isfile(self.monolingual_vocab_file				):
           copyfile(self.monolingual_vocab_file       ,					_lowerCAmelCase				)
         elif not os.path.isfile(self.monolingual_vocab_file				):
           with open(_lowerCAmelCase       ,					"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(_lowerCAmelCase				)} \n"				)

         return out_vocab_file, out_monolingual_vocab_file









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCAmelCase	:						List[Any]			=     {
    "configuration_swinv2": ["SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Swinv2Config"],
}


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase	:						Union[str, Any]			=     [
         "SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST",
         "Swinv2ForImageClassification",
         "Swinv2ForMaskedImageModeling",
         "Swinv2Model",
         "Swinv2PreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_swinva import (
              SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
              SwinvaForImageClassification,
              SwinvaForMaskedImageModeling,
              SwinvaModel,
              SwinvaPreTrainedModel,
          )


else:
     import sys

     _UpperCAmelCase	:						Tuple			=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



