



"""simple docstring"""

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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowercase__					:						Dict												=						logging.get_logger(__name__)


lowercase__					:						List[str]												=						'''▁'''

lowercase__					:						Optional[Any]												=						{'''vocab_file''': '''spiece.model'''}

lowercase__					:						List[str]												=						{
    '''vocab_file''': {
        '''google/reformer-crime-and-punishment''': (
            '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'''
        )
    }
}

lowercase__					:						List[Any]												=						{
    '''google/reformer-crime-and-punishment''': 52_42_88,
}




class  _UpperCAmelCase (    lowerCAmelCase__):
 _lowerCAmelCase     :    Optional[Any]	      =	VOCAB_FILES_NAMES
 _lowerCAmelCase     :    List[Any]	      =	PRETRAINED_VOCAB_FILES_MAP
 _lowerCAmelCase     :    int	      =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowerCAmelCase     :    str	      =	["""input_ids""", """attention_mask"""]

 def __init__(  self	: Optional[int]						,  lowercase_	: int						,  lowercase_	: Union[str, Any]="</s>"						,  lowercase_	: Any="<unk>"						,  lowercase_	: Tuple=[]						,  lowercase_	: Optional[Dict[str, Any]] = None						,  **lowercase_	: int						,  ):
     snake_case_				:  Union[str, Any]       =			{} if sp_model_kwargs is None else sp_model_kwargs

     super().__init__(
         eos_token=lowercase_						,  unk_token=lowercase_						,  additional_special_tokens=lowercase_						,  sp_model_kwargs=self.sp_model_kwargs						,  **lowercase_						,  )

     snake_case_				:  List[Any]       =			vocab_file
     snake_case_				:  Union[str, Any]       =			spm.SentencePieceProcessor(**self.sp_model_kwargs       )
     self.sp_model.Load(lowercase_       )

 @property
 def 		_snake_case      (  self	: List[Any]       ):
     return self.sp_model.get_piece_size()

 def 		_snake_case      (  self	: List[Any]       ):
     snake_case_				:  Tuple       =			{self.convert_ids_to_tokens(lowercase_       ): i for i in range(self.vocab_size       )}
     vocab.update(self.added_tokens_encoder       )
     return vocab

 def __getstate__(  self	: Dict       ):
     snake_case_				:  Union[str, Any]       =			self.__dict__.copy()
     snake_case_				:  Optional[Any]       =			None
     return state

 def __setstate__(  self	: Union[str, Any]						,  lowercase_	: List[Any]       ):
     snake_case_				:  Tuple       =			d

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

     snake_case_				:  List[str]       =			spm.SentencePieceProcessor(**self.sp_model_kwargs       )
     self.sp_model.Load(self.vocab_file       )

 def 		_snake_case      (  self	: str						,  lowercase_	: str       ):
     return self.sp_model.encode(lowercase_						,  out_type=lowercase_       )

 def 		_snake_case      (  self	: Dict						,  lowercase_	: Optional[Any]       ):
     return self.sp_model.piece_to_id(lowercase_       )

 def 		_snake_case      (  self	: Optional[Any]						,  lowercase_	: str       ):
     if index < self.sp_model.get_piece_size():
         snake_case_				:  Optional[Any]       =			self.sp_model.IdToPiece(lowercase_       )
     return token

 def 		_snake_case      (  self	: Dict						,  lowercase_	: Optional[Any]       ):
     snake_case_				:  Tuple       =			[]
     snake_case_				:  Optional[Any]       =			''''''
     for token in tokens:
         # make sure that special tokens are not decoded using sentencepiece model
         if token in self.all_special_tokens:
             out_string += self.sp_model.decode(lowercase_       ) + token
             snake_case_				:  Union[str, Any]       =			[]
         else:
             current_sub_tokens.append(lowercase_       )
     out_string += self.sp_model.decode(lowercase_       )
     return out_string.strip()






 def 		_snake_case      (  self	: Dict						,  lowercase_	: str						,  lowercase_	: Optional[str] = None       ):
     if not os.path.isdir(lowercase_       ):
         logger.error(f"Vocabulary path ({save_directory}) should be a directory"       )
         return
     snake_case_				:  Tuple       =			os.path.join(
         lowercase_						,  (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']       )

     if os.path.abspath(self.vocab_file       ) != os.path.abspath(lowercase_       ) and os.path.isfile(self.vocab_file       ):
         copyfile(self.vocab_file						,  lowercase_       )
     elif not os.path.isfile(self.vocab_file       ):
         with open(lowercase_						,  '''wb'''       ) as fi:
             snake_case_				:  Union[str, Any]       =			self.sp_model.serialized_model_proto()
             fi.write(lowercase_       )

     return (out_vocab_file,)










"""simple docstring"""

from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available




@dataclass
class  _UpperCAmelCase (    lowerCAmelCase__):
 _lowerCAmelCase     :    Union[List[PIL.Image.Image], np.ndarray]
 _lowerCAmelCase     :    Optional[List[bool]]


if is_transformers_available() and is_torch_available():
     from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline





