"""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   :	List[str] 					=     logging.get_logger(__name__)

_a   :	Union[str, Any] 					=     {'vocab_file': 'sentencepiece.bpe.model'}

_a   :	Any 					=     {
    'vocab_file': {
        'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez-orangesum-title': (
            'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'
        ),
    },
}

_a   :	Union[str, Any] 					=     {
    'moussaKam/mbarthez': 1_024,
    'moussaKam/barthez': 1_024,
    'moussaKam/barthez-orangesum-title': 1_024,
}

_a   :	Union[str, Any] 					=     '▁'
class 		__A							(				SCREAMING_SNAKE_CASE_	):
     _UpperCamelCase							:    List[Any]	      =	VOCAB_FILES_NAMES
     _UpperCamelCase							:    Tuple	      =	PRETRAINED_VOCAB_FILES_MAP
     _UpperCamelCase							:    Dict	      =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _UpperCamelCase							:    Optional[int]	      =	["input_ids", "attention_mask"]






     def __init__(	self       ,  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		: Union[str, Any]						    =  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		: Optional[Any]						    =  vocab_file
       _lowerCAmelCase		: List[str]						    =  spm.SentencePieceProcessor(**self.sp_model_kwargs						)
       self.sp_model.Load(str(a__						)						)

       _lowerCAmelCase		: Any						    =  {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

       _lowerCAmelCase		: List[str]						    =  len(self.sp_model						) - 1
       _lowerCAmelCase		: str						    =  {v: k for k, v in self.fairseq_tokens_to_ids.items()}






     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		: Dict						    =  [self.cls_token_id]
       _lowerCAmelCase		: Any						    =  [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		: Union[str, Any]						    =  [self.sep_token_id]
       _lowerCAmelCase		: 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 					__A						(	self						):
       return len(self.sp_model						)






     def 					__A						(	self						):
       _lowerCAmelCase		: List[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]
       _lowerCAmelCase		: Optional[int]						    =  self.sp_model.PieceToId(a__						)

       return spm_id if spm_id else self.unk_token_id






     def 					__A						(	self       ,  a__						):
       if index in self.fairseq_ids_to_tokens:
         return self.fairseq_ids_to_tokens[index]
       return self.sp_model.IdToPiece(a__						)






     def 					__A						(	self       ,  a__						):
       _lowerCAmelCase		: Union[str, Any]						    =  []
       _lowerCAmelCase		: int						    =  """"""
       _lowerCAmelCase		: Dict						    =  False
       for token in tokens:
         # make sure that special tokens are not decoded using sentencepiece model
         if token in self.all_special_tokens:
           if not prev_is_special:
             out_string += " "
           out_string += self.sp_model.decode(a__						) + token
           _lowerCAmelCase		: int						    =  True
           _lowerCAmelCase		: Any						    =  []
         else:
           current_sub_tokens.append(a__						)
           _lowerCAmelCase		: Any						    =  False
       out_string += self.sp_model.decode(a__						)
       return out_string.strip()






     def __getstate__(	self						):
       _lowerCAmelCase		: int						    =  self.__dict__.copy()
       _lowerCAmelCase		: Any						    =  None
       return state






     def __setstate__(	self       ,  a__						):
       _lowerCAmelCase		: Optional[Any]						    =  d

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

       _lowerCAmelCase		: int						    =  spm.SentencePieceProcessor(**self.sp_model_kwargs						)
       self.sp_model.Load(self.vocab_file						)






     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[Any]						    =  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__						) 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__						)

       return (out_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




