





'''simple docstring'''

from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowercase_         =			logging.get_logger(__name__)


class  a_					(    lowerCamelCase__	):

    '''simple docstring'''
    UpperCamelCase			   =			['input_ids', 'attention_mask']


    def __init__(		self			,		A="[PAD]"			,		A="[BOS]"			,		A="[EOS]"			,		A="[MASK]"			,		A="[CLS]"			,		A="[SEP]"			,		A=2048			,		**A			,		)		->       None:
        _SCREAMING_SNAKE_CASE     =      AddedToken(lowercase_			,		lstrip=lowercase_			,		rstrip=lowercase_	) if isinstance(lowercase_			,		lowercase_	) else pad_token
        _SCREAMING_SNAKE_CASE     =      AddedToken(lowercase_			,		lstrip=lowercase_			,		rstrip=lowercase_	) if isinstance(lowercase_			,		lowercase_	) else bos_token
        _SCREAMING_SNAKE_CASE     =      AddedToken(lowercase_			,		lstrip=lowercase_			,		rstrip=lowercase_	) if isinstance(lowercase_			,		lowercase_	) else eos_token
        _SCREAMING_SNAKE_CASE     =      AddedToken(lowercase_			,		lstrip=lowercase_			,		rstrip=lowercase_	) if isinstance(lowercase_			,		lowercase_	) else mask_token
        _SCREAMING_SNAKE_CASE     =      AddedToken(lowercase_			,		lstrip=lowercase_			,		rstrip=lowercase_	) if isinstance(lowercase_			,		lowercase_	) else cls_token
        _SCREAMING_SNAKE_CASE     =      AddedToken(lowercase_			,		lstrip=lowercase_			,		rstrip=lowercase_	) if isinstance(lowercase_			,		lowercase_	) else sep_token

        super().__init__(
            pad_token=lowercase_			,		bos_token=lowercase_			,		eos_token=lowercase_			,		mask_token=lowercase_			,		cls_token=lowercase_			,		sep_token=lowercase_			,		model_max_length=lowercase_			,		**lowercase_			,		)

        _SCREAMING_SNAKE_CASE     =      2**8  # utf is 8 bits

        # define special tokens dict
        _SCREAMING_SNAKE_CASE     =      {
            self.pad_token: 0,
            self.bos_token: 1,
            self.eos_token: 2,
            self.mask_token: 3,
            self.cls_token: 4,
            self.sep_token: 5,
        }
        _SCREAMING_SNAKE_CASE     =      len(self.special_tokens_encoder	)
        _SCREAMING_SNAKE_CASE     =      {v: k for k, v in self.special_tokens_encoder.items()}


    def       snake_case_(		self	)		->       Dict[str, int]:
        _SCREAMING_SNAKE_CASE     =      self.special_tokens_encoder.copy()
        vocab.update(self.added_tokens_encoder	)
        for i in range(self._utf_vocab_size	):
            _SCREAMING_SNAKE_CASE     =      chr(lowercase_	)
            _SCREAMING_SNAKE_CASE     =      i + len(self.special_tokens_encoder	)
        return vocab


    @property
    def       snake_case_(		self	)		->       Any:
        return self._utf_vocab_size + self._num_special_tokens


    def       snake_case_(		self			,		A			,		A = None			,		A = False	)		->       List[int]:






        if already_has_special_tokens:
            return super().get_special_tokens_mask(
                token_ids_a=lowercase_			,		token_ids_a=lowercase_			,		already_has_special_tokens=lowercase_	)

        # normal case: some special tokens
        if token_ids_a is None:
            return [1] + [0] * len(lowercase_	) + [1]
        return [1] + ([0] * len(lowercase_	)) + [1] + ([0] * len(lowercase_	)) + [1]


    def       snake_case_(		self			,		A			,		A = None	)		->       List[int]:






        if token_ids_a is None:
            return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        else:
            return [self.cls_token_id] + token_ids_a + [self.sep_token_id] + token_ids_a + [self.sep_token_id]


    def       snake_case_(		self			,		A	)		->       List[str]:
        _SCREAMING_SNAKE_CASE     =      [chr(lowercase_	) for i in text.encode("""utf-8"""	)]
        return tokens


    def       snake_case_(		self			,		A	)		->       Tuple:






        if token in self.special_tokens_encoder:
            _SCREAMING_SNAKE_CASE     =      self.special_tokens_encoder[token]
        elif token in self.added_tokens_encoder:
            _SCREAMING_SNAKE_CASE     =      self.added_tokens_encoder[token]
        elif len(lowercase_	) != 1:
            _SCREAMING_SNAKE_CASE     =      self.unk_token_id
        else:
            _SCREAMING_SNAKE_CASE     =      ord(lowercase_	) + self._num_special_tokens
        return token_id


    def       snake_case_(		self			,		A	)		->       Dict:






        if index in self.special_tokens_decoder:
            _SCREAMING_SNAKE_CASE     =      self.special_tokens_decoder[index]
        elif index in self.added_tokens_decoder:
            _SCREAMING_SNAKE_CASE     =      self.added_tokens_decoder[index]
        else:
            _SCREAMING_SNAKE_CASE     =      chr(index - self._num_special_tokens	)
        return token


    def       snake_case_(		self			,		A	)		->       Dict:
        _SCREAMING_SNAKE_CASE     =      b""""""
        for token in tokens:
            if token in self.special_tokens_decoder:
                _SCREAMING_SNAKE_CASE     =      self.special_tokens_decoder[token].encode("""utf-8"""	)
            elif token in self.added_tokens_decoder:
                _SCREAMING_SNAKE_CASE     =      self.special_tokens_decoder[token].encode("""utf-8"""	)
            elif token in self.special_tokens_encoder:
                _SCREAMING_SNAKE_CASE     =      token.encode("""utf-8"""	)
            elif token in self.added_tokens_encoder:
                _SCREAMING_SNAKE_CASE     =      token.encode("""utf-8"""	)
            else:
                _SCREAMING_SNAKE_CASE     =      bytes([ord(lowercase_	)]	)
            bstring += tok_string
        _SCREAMING_SNAKE_CASE     =      bstring.decode("""utf-8"""			,		errors="""replace"""	)
        return string





    def       snake_case_(		self			,		A			,		A = None	)		->       Tuple[str]:
        return ()










'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase_         =			logging.get_logger(__name__)

lowercase_         =			{
    """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/config.json""",
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}


class  a_					(    snake_case_	):

    '''simple docstring'''
    UpperCamelCase			   =			'''rembert'''


    def __init__(		self			,		A=25_0300			,		A=1152			,		A=32			,		A=18			,		A=256			,		A=1664			,		A=4608			,		A="gelu"			,		A=0.0			,		A=0.0			,		A=0.1			,		A=512			,		A=2			,		A=0.02			,		A=1e-12			,		A=True			,		A=0			,		A=312			,		A=313			,		**A			,		)		->       Dict:
        super().__init__(pad_token_id=A			,		bos_token_id=A			,		eos_token_id=A			,		**A	)

        _SCREAMING_SNAKE_CASE     =      vocab_size
        _SCREAMING_SNAKE_CASE     =      input_embedding_size
        _SCREAMING_SNAKE_CASE     =      output_embedding_size
        _SCREAMING_SNAKE_CASE     =      max_position_embeddings
        _SCREAMING_SNAKE_CASE     =      hidden_size
        _SCREAMING_SNAKE_CASE     =      num_hidden_layers
        _SCREAMING_SNAKE_CASE     =      num_attention_heads
        _SCREAMING_SNAKE_CASE     =      intermediate_size
        _SCREAMING_SNAKE_CASE     =      hidden_act
        _SCREAMING_SNAKE_CASE     =      hidden_dropout_prob
        _SCREAMING_SNAKE_CASE     =      attention_probs_dropout_prob
        _SCREAMING_SNAKE_CASE     =      classifier_dropout_prob
        _SCREAMING_SNAKE_CASE     =      initializer_range
        _SCREAMING_SNAKE_CASE     =      type_vocab_size
        _SCREAMING_SNAKE_CASE     =      layer_norm_eps
        _SCREAMING_SNAKE_CASE     =      use_cache
        _SCREAMING_SNAKE_CASE     =      False




class  a_					(    snake_case_	):

    '''simple docstring'''


    @property
    def       snake_case_(		self	)		->       Mapping[str, Mapping[int, str]]:
        if self.task == "multiple-choice":
            _SCREAMING_SNAKE_CASE     =      {0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
            _SCREAMING_SNAKE_CASE     =      {0: """batch""", 1: """sequence"""}
        return OrderedDict(
            [
                ("""input_ids""", dynamic_axis),
                ("""attention_mask""", dynamic_axis),
                ("""token_type_ids""", dynamic_axis),
            ]	)





    @property
    def       snake_case_(		self	)		->       float:
        return 1e-4



