"""simple docstring"""




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


_a   :	Optional[int] 					=     logging.get_logger(__name__)

_a   :	Any 					=     '▁'

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

_a   :	int 					=     {
    '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',
    },
}

_a   :	Dict 					=     {'vinai/bartpho-syllable': 1_024}
class 		__A							(				SCREAMING_SNAKE_CASE_	):
     _UpperCamelCase							:    List[Any]	      =	VOCAB_FILES_NAMES
     _UpperCamelCase							:    Any	      =	PRETRAINED_VOCAB_FILES_MAP
     _UpperCamelCase							:    List[Any]	      =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _UpperCamelCase							:    List[str]	      =	["input_ids", "attention_mask"]






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

       _lowerCAmelCase		: List[Any]						    =  {} if sp_model_kwargs is None else sp_model_kwargs

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

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

       # Load the reduced vocab

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

       _lowerCAmelCase		: Optional[Any]						    =  {v: k for k, v in self.fairseq_tokens_to_ids.items()}






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






     def __setstate__(	self       ,  a__						):
       _lowerCAmelCase		: str						    =  d

       # for backward compatibility
       if not hasattr(self       ,  """sp_model_kwargs"""						):
         _lowerCAmelCase		: Tuple						    =  {}

       _lowerCAmelCase		: List[str]						    =  spm.SentencePieceProcessor(**self.sp_model_kwargs						)
       self.sp_model.LoadFromSerializedProto(self.sp_model_proto						)






     def 					__A						(	self       ,  a__       ,  a__ = None						):

       if token_ids_a is None:
         return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       _lowerCAmelCase		: Union[str, Any]						    =  [self.cls_token_id]
       _lowerCAmelCase		: str						    =  [self.sep_token_id]
       return cls + token_ids_a + sep + sep + token_ids_a + sep






     def 					__A						(	self       ,  a__       ,  a__ = None       ,  a__ = False						):

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

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






     def 					__A						(	self       ,  a__       ,  a__ = None						):
       _lowerCAmelCase		: str						    =  [self.sep_token_id]
       _lowerCAmelCase		: List[str]						    =  [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						):
       return len(self.fairseq_ids_to_tokens						)






     def 					__A						(	self						):
       _lowerCAmelCase		: Union[str, Any]						    =  {self.convert_ids_to_tokens(a__						): i for i in range(self.vocab_size						)}
       vocab.update(self.added_tokens_encoder						)
       return vocab






     def 					__A						(	self       ,  a__						):
       return self.sp_model.encode(a__       ,  out_type=a__						)






     def 					__A						(	self       ,  a__						):
       if token in self.fairseq_tokens_to_ids:
         return self.fairseq_tokens_to_ids[token]
       else:
         return self.unk_token_id






     def 					__A						(	self       ,  a__						):
       return self.fairseq_ids_to_tokens[index]






     def 					__A						(	self       ,  a__						):
       _lowerCAmelCase		: Dict						    =  """""".join(a__						).replace(a__       ,  """ """						).strip()
       return out_string






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

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

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

       return out_vocab_file, out_monolingual_vocab_file





"""simple docstring"""




from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
     if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
     from .pipeline_unclip import UnCLIPPipeline
     from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
     from .text_proj import UnCLIPTextProjModel




