



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:
						_snake_case	=						None

_snake_case	=						logging.get_logger(__name__)
_snake_case	=						{"""vocab_file""": """sentencepiece.model""", """tokenizer_file""": """tokenizer.json"""}

_snake_case	=						{
    """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""",
    },
}

_snake_case	=						{
    """google/rembert""": 256,
}

_snake_case	=						"""▁"""



class 						UpperCAmelCase_					( __snake_case):
							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,    ):

														'''simple docstring'''

														_lowerCAmelCase  :							List[Any]				     =					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_,    )

														_lowerCAmelCase  :							str				     =					do_lower_case
														_lowerCAmelCase  :							List[str]				     =					remove_space
														_lowerCAmelCase  :							int				     =					keep_accents
														_lowerCAmelCase  :							List[Any]				     =					vocab_file
														_lowerCAmelCase  :							List[Any]				     =					False if not self.vocab_file else True







							def      snake_case__   (     self,    __a,    __a = None):

														'''simple docstring'''

														_lowerCAmelCase  :							Any				     =					[self.sep_token_id]
														_lowerCAmelCase  :							Optional[Any]				     =					[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      snake_case__   (     self,    __a,    __a = None,    __a = False):

														'''simple docstring'''

														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      snake_case__   (     self,    __a,    __a = None):

														'''simple docstring'''

														_lowerCAmelCase  :							Optional[int]				     =					[self.sep_token_id]
														_lowerCAmelCase  :							int				     =					[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      snake_case__   (     self,    __a,    __a = None):

														'''simple docstring'''

														if not os.path.isdir(A_):
																					logger.error("Vocabulary path ({}) should be a directory".format(A_))
																					return
														_lowerCAmelCase  :							Dict				     =					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,)






import copy

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


_snake_case	=						logging.get_logger(__name__)



class 						UpperCAmelCase_					( a):
							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,    ):

														'''simple docstring'''

														super().__init__(**__a)

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

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







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Any				     =					copy.deepcopy(self.__dict__)
														_lowerCAmelCase  :							List[Any]				     =					self.backbone_config.to_dict()
														_lowerCAmelCase  :							Optional[Any]				     =					self.__class__.model_type
														return output
