
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


_UpperCamelCase:					Dict     =logging.get_logger(__name__)

_UpperCamelCase:					Optional[int]     ={'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

_UpperCamelCase:					List[Any]     ={
    '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'
        ),
    },
}

_UpperCamelCase:					List[Any]     ={
    '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,
}

_UpperCamelCase:					Union[str, Any]     ={
    'microsoft/deberta-base': {'do_lower_case': False},
    'microsoft/deberta-large': {'do_lower_case': False},
}





class 						__lowercase(     SCREAMING_SNAKE_CASE   ):


       """simple docstring"""







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



       def __init__(     self					:			Union[str, Any]  ,      _lowerCAmelCase					:			str=None  ,      _lowerCAmelCase					:			List[Any]=None  ,      _lowerCAmelCase					:			Dict=None  ,      _lowerCAmelCase					:			Tuple="replace"  ,      _lowerCAmelCase					:			Dict="[CLS]"  ,      _lowerCAmelCase					:			Dict="[SEP]"  ,      _lowerCAmelCase					:			Optional[Any]="[SEP]"  ,      _lowerCAmelCase					:			int="[CLS]"  ,      _lowerCAmelCase					:			Tuple="[UNK]"  ,      _lowerCAmelCase					:			List[str]="[PAD]"  ,      _lowerCAmelCase					:			str="[MASK]"  ,      _lowerCAmelCase					:			List[str]=False  ,      **_lowerCAmelCase					:			Dict  ,      )					->   List[str]:
         super().__init__(
             _lowerCAmelCase  ,      _lowerCAmelCase  ,      tokenizer_file=_lowerCAmelCase  ,      errors=_lowerCAmelCase  ,      bos_token=_lowerCAmelCase  ,      eos_token=_lowerCAmelCase  ,      unk_token=_lowerCAmelCase  ,      sep_token=_lowerCAmelCase  ,      cls_token=_lowerCAmelCase  ,      pad_token=_lowerCAmelCase  ,      mask_token=_lowerCAmelCase  ,      add_prefix_space=_lowerCAmelCase  ,      **_lowerCAmelCase  ,      )
         _lowerCAmelCase						=		kwargs.pop('add_bos_token'  ,      _lowerCAmelCase       )

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

         _lowerCAmelCase						=		add_prefix_space



       @property
       def SCREAMING_SNAKE_CASE_				(     self					:			Tuple       )					->   str:
         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 SCREAMING_SNAKE_CASE_				(     self					:			Tuple  ,      _lowerCAmelCase					:			str       )					->   Tuple:
         _lowerCAmelCase						=		AddedToken(_lowerCAmelCase  ,      lstrip=_lowerCAmelCase  ,      rstrip=_lowerCAmelCase       ) if isinstance(_lowerCAmelCase  ,      _lowerCAmelCase       ) else value
         _lowerCAmelCase						=		value



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



       def SCREAMING_SNAKE_CASE_				(     self					:			List[Any]  ,      _lowerCAmelCase					:			List[int]  ,      _lowerCAmelCase					:			Optional[List[int]] = None       )					->   List[int]:
         _lowerCAmelCase						=		[self.sep_token_id]
         _lowerCAmelCase						=		[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					:			List[Any]  ,      *_lowerCAmelCase					:			Tuple  ,      **_lowerCAmelCase					:			int       )					->   BatchEncoding:
         _lowerCAmelCase						=		kwargs.get('is_split_into_words'  ,      _lowerCAmelCase       )
         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(*_lowerCAmelCase  ,      **_lowerCAmelCase       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Any  ,      *_lowerCAmelCase					:			Optional[int]  ,      **_lowerCAmelCase					:			Optional[Any]       )					->   BatchEncoding:
         _lowerCAmelCase						=		kwargs.get('is_split_into_words'  ,      _lowerCAmelCase       )

         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(*_lowerCAmelCase  ,      **_lowerCAmelCase       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Dict  ,      _lowerCAmelCase					:			str  ,      _lowerCAmelCase					:			Optional[str] = None       )					->   Tuple[str]:
         _lowerCAmelCase						=		self._tokenizer.model.save(_lowerCAmelCase  ,      name=_lowerCAmelCase       )
         return tuple(_lowerCAmelCase       )






       def SCREAMING_SNAKE_CASE_				(     self					:			List[str]  ,      _lowerCAmelCase					:			"Conversation"       )					->   List[int]:
         _lowerCAmelCase						=		[]
         for is_user, text in conversation.iter_texts():
           input_ids.extend(self.encode(_lowerCAmelCase  ,      add_special_tokens=_lowerCAmelCase       ) + [self.eos_token_id]       )

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


import abc
import copy
import dataclasses
from dataclasses import dataclass
from typing import ClassVar, Dict, Type, TypeVar

from ..features import Features


_UpperCamelCase:					List[Any]     =TypeVar('T', bound='TaskTemplate')





@dataclass(frozen=SCREAMING_SNAKE_CASE   )
class 						__lowercase(     abc.ABC   ):


       """simple docstring"""







       UpperCamelCase_				 =							42
       UpperCamelCase_				 =							42
       UpperCamelCase_				 =							42



       def SCREAMING_SNAKE_CASE_				(     self					:			T  ,      _lowerCAmelCase					:			Features       )					->   T:
         return copy.deepcopy(self       )



       @property
       def SCREAMING_SNAKE_CASE_				(     self					:			str       )					->   Features:
         return Features(**self.input_schema  ,      **self.label_schema       )



       @property
       @abc.abstractmethod
       def SCREAMING_SNAKE_CASE_				(     self					:			Dict       )					->   Dict[str, str]:
         raise NotImplementedError






       @classmethod
       def SCREAMING_SNAKE_CASE_				(     cls					:			Type[T]  ,      _lowerCAmelCase					:			dict       )					->   T:
         _lowerCAmelCase						=		{f.name for f in dataclasses.fields(cls       )}
         return cls(**{k: v for k, v in template_dict.items() if k in field_names}       )
