


"""simple docstring"""







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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
       from .tokenization_rembert import RemBertTokenizer
else:
       lowerCamelCase_       :       Optional[int]      						=  None

lowerCamelCase_       :       Union[str, Any]      						=  logging.get_logger(__name__)
lowerCamelCase_       :       str      						=  {"""vocab_file""": """sentencepiece.model""", """tokenizer_file""": """tokenizer.json"""}

lowerCamelCase_       :       int      						=  {
    """vocab_file""": {
        """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/sentencepiece.model""",
    },
    """tokenizer_file""": {
        """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/tokenizer.json""",
    },
}

lowerCamelCase_       :       Optional[Any]      						=  {
    """google/rembert""": 2_5_6,
}

lowerCamelCase_       :       Optional[Any]      						=  """▁"""






class 				__A    (	_SCREAMING_SNAKE_CASE      ):






 """simple docstring"""




 __lowerCAmelCase			 =  VOCAB_FILES_NAMES
 __lowerCAmelCase			 =  PRETRAINED_VOCAB_FILES_MAP
 __lowerCAmelCase			 =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowerCAmelCase			 =  RemBertTokenizer


 def __init__(		self			,   __A=None			,   __A=None			,   __A=True			,   __A=True			,   __A=False			,   __A="[CLS]"			,   __A="[SEP]"			,   __A="<unk>"			,   __A="[SEP]"			,   __A="<pad>"			,   __A="[CLS]"			,   __A="[MASK]"			,   **__A			,   ) ->					Dict:
     # Mask token behave like a normal word, i.e. include the space before it
     a					=AddedToken(__A			,   lstrip=__A			,   rstrip=__A						) if isinstance(__A			,   __A						) else mask_token

     super().__init__(
         __A			,   tokenizer_file=__A			,   do_lower_case=__A			,   remove_space=__A			,   keep_accents=__A			,   bos_token=__A			,   eos_token=__A			,   unk_token=__A			,   sep_token=__A			,   pad_token=__A			,   cls_token=__A			,   mask_token=__A			,   **__A			,   )

     a					=do_lower_case
     a					=remove_space
     a					=keep_accents
     a					=vocab_file
     a					=False if not self.vocab_file else True


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A = None						) ->					List[int]:
     a					=[self.sep_token_id]
     a					=[self.cls_token_id]
     if token_ids_a is None:
         return cls + token_ids_a + sep
     return cls + token_ids_a + sep + token_ids_a + sep


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A = None			,   __A = False						) ->					List[int]:

     if already_has_special_tokens:
         if token_ids_a is not None:
             raise ValueError(
                 '''You should not supply a second sequence if the provided sequence of '''
                 '''ids is already formatted with special tokens for the model.'''						)
         return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

     if token_ids_a is not None:
         return [1] + ([0] * len(__A						)) + [1] + ([0] * len(__A						)) + [1]
     return [1] + ([0] * len(__A						)) + [1]


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A = None						) ->					List[int]:
     a					=[self.sep_token_id]
     a					=[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						) * [0] + len(token_ids_a + sep						) * [1]





 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A = None						) ->					Tuple[str]:
     if not os.path.isdir(__A						):
         logger.error('''Vocabulary path ({}) should be a directory'''.format(__A						)						)
         return
     a					=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						):
         copyfile(self.vocab_file			,   __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


lowerCamelCase_       :       int      						=  logging.get_logger(__name__)






class 				__A    (	_SCREAMING_SNAKE_CASE      ):






 """simple docstring"""




 __lowerCAmelCase			 =  "upernet"


 def __init__(		self			,   __A=None			,   __A=512			,   __A=0.02			,   __A=[1, 2, 3, 6]			,   __A=True			,   __A=0.4			,   __A=384			,   __A=256			,   __A=1			,   __A=False			,   __A=255			,   **__A			,   ) ->					Tuple:
     super().__init__(**__A						)

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

     a					=backbone_config
     a					=hidden_size
     a					=initializer_range
     a					=pool_scales
     a					=use_auxiliary_head
     a					=auxiliary_loss_weight
     a					=auxiliary_in_channels
     a					=auxiliary_channels
     a					=auxiliary_num_convs
     a					=auxiliary_concat_input
     a					=loss_ignore_index





 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Dict:
     a					=copy.deepcopy(self.__dict__						)
     a					=self.backbone_config.to_dict()
     a					=self.__class__.model_type
     return output