

import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
     from transformers.pipelines.conversational import Conversation


lowercase_									=    logging.get_logger(__name__)

lowercase_									=    {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

lowercase_									=    {
    "vocab_file": {
        "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json",
        "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json",
        "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json",
        "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json",
        "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json",
        "microsoft/deberta-xlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt",
        "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt",
        "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt",
        "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt",
        "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt",
        "microsoft/deberta-xlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt"
        ),
    },
}

lowercase_									=    {
    "microsoft/deberta-base": 512,
    "microsoft/deberta-large": 512,
    "microsoft/deberta-xlarge": 512,
    "microsoft/deberta-base-mnli": 512,
    "microsoft/deberta-large-mnli": 512,
    "microsoft/deberta-xlarge-mnli": 512,
}

lowercase_									=    {
    "microsoft/deberta-base": {"do_lower_case": False},
    "microsoft/deberta-large": {"do_lower_case": False},
}


class 					A		(				__SCREAMING_SNAKE_CASE					):







    """simple docstring"""





    lowerCamelCase						      =				VOCAB_FILES_NAMES
    lowerCamelCase						      =				PRETRAINED_VOCAB_FILES_MAP
    lowerCamelCase						      =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCamelCase						      =				['input_ids', 'attention_mask', 'token_type_ids']
    lowerCamelCase						      =				DebertaTokenizer


    def __init__( self					:			Dict,lowercase_					:			Optional[Any]=None,lowercase_					:			List[Any]=None,lowercase_					:			List[Any]=None,lowercase_					:			Tuple="replace",lowercase_					:			str="[CLS]",lowercase_					:			List[Any]="[SEP]",lowercase_					:			Optional[int]="[SEP]",lowercase_					:			Any="[CLS]",lowercase_					:			Dict="[UNK]",lowercase_					:			List[Any]="[PAD]",lowercase_					:			Dict="[MASK]",lowercase_					:			List[str]=False,**lowercase_					:			List[str],)->       List[Any]:





        '''simple docstring'''







        super().__init__(
            __UpperCAmelCase,__UpperCAmelCase,tokenizer_file=__UpperCAmelCase,errors=__UpperCAmelCase,bos_token=__UpperCAmelCase,eos_token=__UpperCAmelCase,unk_token=__UpperCAmelCase,sep_token=__UpperCAmelCase,cls_token=__UpperCAmelCase,pad_token=__UpperCAmelCase,mask_token=__UpperCAmelCase,add_prefix_space=__UpperCAmelCase,**__UpperCAmelCase,)
        A__        =  kwargs.pop('add_bos_token',__UpperCAmelCase						)

        A__        =  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()						)
        if pre_tok_state.get('add_prefix_space',__UpperCAmelCase						) != add_prefix_space:
            A__        =  getattr(__UpperCAmelCase,pre_tok_state.pop('type'						)						)
            A__        =  add_prefix_space
            A__        =  pre_tok_class(**__UpperCAmelCase						)

        A__        =  add_prefix_space


    @property
    def 		snake_case__	( self					:			Optional[int]						)->       str:





        '''simple docstring'''







        if self._mask_token is None:
            if self.verbose:
                logger.error('Using mask_token, but it is not set yet.'						)
            return None
        return str(self._mask_token						)


    @mask_token.setter
    def 		snake_case__	( self					:			str,lowercase_					:			str						)->       Union[str, Any]:





        '''simple docstring'''







        A__        =  AddedToken(__UpperCAmelCase,lstrip=__UpperCAmelCase,rstrip=__UpperCAmelCase						) if isinstance(__UpperCAmelCase,__UpperCAmelCase						) else value
        A__        =  value


    def 		snake_case__	( self					:			List[str],lowercase_					:			Optional[int],lowercase_					:			Tuple = None						)->       Dict:





        '''simple docstring'''







        if token_ids_a is None:
            return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        A__        =  [self.cls_token_id]
        A__        =  [self.sep_token_id]
        return cls + token_ids_a + sep + token_ids_a + sep


    def 		snake_case__	( self					:			List[Any],lowercase_					:			Any,lowercase_					:			int = None						)->       List[str]:





        '''simple docstring'''







        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 		snake_case__	( self					:			List[str],*lowercase_					:			Optional[Any],**lowercase_					:			List[str]						)->       Optional[int]:





        '''simple docstring'''







        A__        =  kwargs.get('is_split_into_words',__UpperCAmelCase						)
        assert self.add_prefix_space or not is_split_into_words, (
            F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
            "to use it with pretokenized inputs."
        )

        return super()._batch_encode_plus(*__UpperCAmelCase,**__UpperCAmelCase						)


    def 		snake_case__	( self					:			Any,*lowercase_					:			List[Any],**lowercase_					:			List[str]						)->       str:





        '''simple docstring'''







        A__        =  kwargs.get('is_split_into_words',__UpperCAmelCase						)

        assert self.add_prefix_space or not is_split_into_words, (
            F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
            "to use it with pretokenized inputs."
        )

        return super()._encode_plus(*__UpperCAmelCase,**__UpperCAmelCase						)


    def 		snake_case__	( self					:			List[Any],lowercase_					:			int,lowercase_					:			List[str] = None						)->       str:





        '''simple docstring'''







        A__        =  self._tokenizer.model.save(__UpperCAmelCase,name=__UpperCAmelCase						)
        return tuple(__UpperCAmelCase						)


    def 		snake_case__	( self					:			Optional[Any],lowercase_					:			Optional[Any]						)->       List[Any]:





        '''simple docstring'''







        A__        =  []
        for is_user, text in conversation.iter_texts():
            input_ids.extend(self.encode(__UpperCAmelCase,add_special_tokens=__UpperCAmelCase						) + [self.eos_token_id]						)

        if len(__UpperCAmelCase						) > self.model_max_length:
            A__        =  input_ids[-self.model_max_length :]
        return input_ids



"""simple docstring"""

from sklearn.neural_network import MLPClassifier

UpperCamelCase      :							Any          =					[[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
UpperCamelCase      :							Any          =					[0, 1, 0, 0]


UpperCamelCase      :							Optional[Any]          =					MLPClassifier(
    solver="lbfgs", alpha=1E-5, hidden_layer_sizes=(5, 2), random_state=1
)

clf.fit(X, y)


UpperCamelCase      :							List[Any]          =					[[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
UpperCamelCase      :							Optional[Any]          =					clf.predict(test)






def 				A		(							snake_case :int						) ->      int:
 return list(snake_case						)


if __name__ == "__main__":
      import doctest

      doctest.testmod()



