



'''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


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


A__    :   Optional[Any]           =					'''▁'''

A__    :   str           =					{'''vocab_file''': '''spiece.model'''}

A__    :   int           =					{
    '''vocab_file''': {
        '''google/reformer-crime-and-punishment''': (
            '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'''
        )
    }
}

A__    :   int           =					{
    '''google/reformer-crime-and-punishment''': 5_2_4_2_8_8,
}



class  snake_case__						(    SCREAMING_SNAKE_CASE_				):
 A__				= VOCAB_FILES_NAMES
 A__				= PRETRAINED_VOCAB_FILES_MAP
 A__				= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 A__				= ['''input_ids''', '''attention_mask''']
 def __init__(   self						:							int      ,     __a						:							Dict      ,     __a						:							int="</s>"      ,     __a						:							List[str]="<unk>"      ,     __a						:							int=[]      ,     __a						:							Optional[Dict[str, Any]] = None      ,     **__a						:							Optional[Any]      ,     )      ->   None:







       '''simple docstring'''
       __snake_case :   Any													=    {} if sp_model_kwargs is None else sp_model_kwargs

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

       __snake_case :   List[Any]													=    vocab_file
       __snake_case :   Dict													=    spm.SentencePieceProcessor(**self.sp_model_kwargs  )
       self.sp_model.Load(__a  )
 @property
 def A_   (   self						:							str  )      ->   Optional[Any]:







       '''simple docstring'''
       return self.sp_model.get_piece_size()
 def A_   (   self						:							Optional[Any]  )      ->   Dict[str, int]:







       '''simple docstring'''
       __snake_case :   List[str]													=    {self.convert_ids_to_tokens(__a  ): i for i in range(self.vocab_size  )}
       vocab.update(self.added_tokens_encoder  )
       return vocab
 def __getstate__(   self						:							str  )      ->   Tuple:







       '''simple docstring'''
       __snake_case :   List[str]													=    self.__dict__.copy()
       __snake_case :   str													=    None
       return state
 def __setstate__(   self						:							Dict      ,     __a						:							str  )      ->   Tuple:







       '''simple docstring'''
       __snake_case :   str													=    d

       # for backward compatibility
       if not hasattr(self      ,     'sp_model_kwargs'  ):
             __snake_case :   Optional[int]													=    {}

       __snake_case :   List[Any]													=    spm.SentencePieceProcessor(**self.sp_model_kwargs  )
       self.sp_model.Load(self.vocab_file  )
 def A_   (   self						:							Optional[int]      ,     __a						:							str  )      ->   List[str]:







       '''simple docstring'''
       return self.sp_model.encode(__a      ,     out_type=__a  )
 def A_   (   self						:							Optional[Any]      ,     __a						:							List[str]  )      ->   Optional[int]:







       '''simple docstring'''
       return self.sp_model.piece_to_id(__a  )
 def A_   (   self						:							Union[str, Any]      ,     __a						:							Any  )      ->   List[Any]:







       '''simple docstring'''
       if index < self.sp_model.get_piece_size():
             __snake_case :   List[str]													=    self.sp_model.IdToPiece(__a  )
       return token
 def A_   (   self						:							List[str]      ,     __a						:							Union[str, Any]  )      ->   List[str]:







       '''simple docstring'''
       __snake_case :   Tuple													=    []
       __snake_case :   List[str]													=    ''
       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(__a  ) + token
                   __snake_case :   Tuple													=    []
             else:
                   current_sub_tokens.append(__a  )
       out_string += self.sp_model.decode(__a  )
       return out_string.strip()





 def A_   (   self						:							Tuple      ,     __a						:							str      ,     __a						:							Optional[str] = None  )      ->   Tuple[str]:







       '''simple docstring'''
       if not os.path.isdir(__a  ):
             logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''  )
             return
       __snake_case :   List[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:
                   __snake_case :   List[str]													=    self.sp_model.serialized_model_proto()
                   fi.write(__a  )

       return (out_vocab_file,)








'''simple docstring'''





import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING


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



class  snake_case__						(    SCREAMING_SNAKE_CASE_				):
 A__				= '''upernet'''
 def __init__(   self						:							Optional[int]      ,     __a						:							Dict=None      ,     __a						:							List[str]=512      ,     __a						:							int=0.0_2      ,     __a						:							Optional[Any]=[1, 2, 3, 6]      ,     __a						:							Union[str, Any]=True      ,     __a						:							Any=0.4      ,     __a						:							int=384      ,     __a						:							int=256      ,     __a						:							str=1      ,     __a						:							List[Any]=False      ,     __a						:							Dict=255      ,     **__a						:							Dict      ,     )      ->   Optional[int]:







       '''simple docstring'''
       super().__init__(**__a  )

       if backbone_config is None:
             logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'  )
             __snake_case :   Optional[int]													=    CONFIG_MAPPING['resnet'](out_features=['stage1', 'stage2', 'stage3', 'stage4']  )
       elif isinstance(__a      ,     __a  ):
             __snake_case :   List[Any]													=    backbone_config.get('model_type'  )
             __snake_case :   List[str]													=    CONFIG_MAPPING[backbone_model_type]
             __snake_case :   int													=    config_class.from_dict(__a  )

       __snake_case :   Any													=    backbone_config
       __snake_case :   Tuple													=    hidden_size
       __snake_case :   str													=    initializer_range
       __snake_case :   Dict													=    pool_scales
       __snake_case :   Optional[Any]													=    use_auxiliary_head
       __snake_case :   Optional[Any]													=    auxiliary_loss_weight
       __snake_case :   Optional[int]													=    auxiliary_in_channels
       __snake_case :   Dict													=    auxiliary_channels
       __snake_case :   Any													=    auxiliary_num_convs
       __snake_case :   Optional[Any]													=    auxiliary_concat_input
       __snake_case :   List[str]													=    loss_ignore_index





 def A_   (   self						:							List[Any]  )      ->   Union[str, Any]:







       '''simple docstring'''
       __snake_case :   Union[str, Any]													=    copy.deepcopy(self.__dict__  )
       __snake_case :   str													=    self.backbone_config.to_dict()
       __snake_case :   Optional[int]													=    self.__class__.model_type
       return output



