
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


_snake_case		  =   logging.get_logger(__name__)

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

_snake_case		  =   {
    "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"
        ),
    },
}

_snake_case		  =   {
    "microsoft/deberta-base": 5_12,
    "microsoft/deberta-large": 5_12,
    "microsoft/deberta-xlarge": 5_12,
    "microsoft/deberta-base-mnli": 5_12,
    "microsoft/deberta-large-mnli": 5_12,
    "microsoft/deberta-xlarge-mnli": 5_12,
}

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


class 			UpperCamelCase_				(  A     ):




      '''simple docstring'''







      a		:List[Any]						 =      VOCAB_FILES_NAMES
      a		:Dict						 =      PRETRAINED_VOCAB_FILES_MAP
      a		:Any						 =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      a		:Dict						 =      ['input_ids', 'attention_mask', 'token_type_ids']
      a		:Dict						 =      DebertaTokenizer







      def __init__( self					,					_UpperCAmelCase=None					,					_UpperCAmelCase=None					,					_UpperCAmelCase=None					,					_UpperCAmelCase="replace"					,					_UpperCAmelCase="[CLS]"					,					_UpperCAmelCase="[SEP]"					,					_UpperCAmelCase="[SEP]"					,					_UpperCAmelCase="[CLS]"					,					_UpperCAmelCase="[UNK]"					,					_UpperCAmelCase="[PAD]"					,					_UpperCAmelCase="[MASK]"					,					_UpperCAmelCase=False					,					**_UpperCAmelCase					,					):
            super().__init__(
                SCREAMING_SNAKE_CASE_					,					SCREAMING_SNAKE_CASE_					,					tokenizer_file=SCREAMING_SNAKE_CASE_					,					errors=SCREAMING_SNAKE_CASE_					,					bos_token=SCREAMING_SNAKE_CASE_					,					eos_token=SCREAMING_SNAKE_CASE_					,					unk_token=SCREAMING_SNAKE_CASE_					,					sep_token=SCREAMING_SNAKE_CASE_					,					cls_token=SCREAMING_SNAKE_CASE_					,					pad_token=SCREAMING_SNAKE_CASE_					,					mask_token=SCREAMING_SNAKE_CASE_					,					add_prefix_space=SCREAMING_SNAKE_CASE_					,					**SCREAMING_SNAKE_CASE_					,					)
            lowerCAmelCase_						=      kwargs.pop('''add_bos_token'''					,					SCREAMING_SNAKE_CASE_)

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

            lowerCAmelCase_						=      add_prefix_space







      @property
      def lowercase__		( self):
            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 lowercase__		( self					,					_UpperCAmelCase):
            lowerCAmelCase_						=      AddedToken(SCREAMING_SNAKE_CASE_					,					lstrip=SCREAMING_SNAKE_CASE_					,					rstrip=SCREAMING_SNAKE_CASE_) if isinstance(SCREAMING_SNAKE_CASE_					,					SCREAMING_SNAKE_CASE_) else value
            lowerCAmelCase_						=      value







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase = None):
            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 lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase = None):
            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 lowercase__		( self					,					*_UpperCAmelCase					,					**_UpperCAmelCase):
            lowerCAmelCase_						=      kwargs.get('''is_split_into_words'''					,					SCREAMING_SNAKE_CASE_)
            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(*SCREAMING_SNAKE_CASE_					,					**SCREAMING_SNAKE_CASE_)







      def lowercase__		( self					,					*_UpperCAmelCase					,					**_UpperCAmelCase):
            lowerCAmelCase_						=      kwargs.get('''is_split_into_words'''					,					SCREAMING_SNAKE_CASE_)

            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(*SCREAMING_SNAKE_CASE_					,					**SCREAMING_SNAKE_CASE_)







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase = None):
            lowerCAmelCase_						=      self._tokenizer.model.save(SCREAMING_SNAKE_CASE_					,					name=SCREAMING_SNAKE_CASE_)
            return tuple(SCREAMING_SNAKE_CASE_)







      def lowercase__		( self					,					_UpperCAmelCase):
            lowerCAmelCase_						=      []
            for is_user, text in conversation.iter_texts():
                  input_ids.extend(self.encode(SCREAMING_SNAKE_CASE_					,					add_special_tokens=SCREAMING_SNAKE_CASE_) + [self.eos_token_id])

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





from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

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


if TYPE_CHECKING:
 from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


lowerCamelCase_				:							Any       =    logging.get_logger(__name__)

lowerCamelCase_				:							Optional[Any]       =    {
    "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/config.json",
    "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/config.json",
    "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/config.json",
    "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/config.json",
    "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/config.json",
    "microsoft/deberta-xlarge-mnli": "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/config.json",
}







class  _lowerCamelCase			(lowerCamelCase					):
     lowercase__				  =			"""deberta"""

     def __init__(  self							,       SCREAMING_SNAKE_CASE_=50_265							,       SCREAMING_SNAKE_CASE_=768							,       SCREAMING_SNAKE_CASE_=12							,       SCREAMING_SNAKE_CASE_=12							,       SCREAMING_SNAKE_CASE_=3_072							,       SCREAMING_SNAKE_CASE_="gelu"							,       SCREAMING_SNAKE_CASE_=0.1							,       SCREAMING_SNAKE_CASE_=0.1							,       SCREAMING_SNAKE_CASE_=512							,       SCREAMING_SNAKE_CASE_=0							,       SCREAMING_SNAKE_CASE_=0.0_2							,       SCREAMING_SNAKE_CASE_=1E-7							,       SCREAMING_SNAKE_CASE_=False							,       SCREAMING_SNAKE_CASE_=-1							,       SCREAMING_SNAKE_CASE_=0							,       SCREAMING_SNAKE_CASE_=True							,       SCREAMING_SNAKE_CASE_=None							,       SCREAMING_SNAKE_CASE_=0							,       SCREAMING_SNAKE_CASE_="gelu"							,       **SCREAMING_SNAKE_CASE_							,       ):
            super().__init__(**SCREAMING_SNAKE_CASE_    )

            __snake_case						       =   hidden_size
            __snake_case						       =   num_hidden_layers
            __snake_case						       =   num_attention_heads
            __snake_case						       =   intermediate_size
            __snake_case						       =   hidden_act
            __snake_case						       =   hidden_dropout_prob
            __snake_case						       =   attention_probs_dropout_prob
            __snake_case						       =   max_position_embeddings
            __snake_case						       =   type_vocab_size
            __snake_case						       =   initializer_range
            __snake_case						       =   relative_attention
            __snake_case						       =   max_relative_positions
            __snake_case						       =   pad_token_id
            __snake_case						       =   position_biased_input

            # Backwards compatibility
            if type(SCREAMING_SNAKE_CASE_    ) == str:
                   __snake_case						       =   [x.strip() for x in pos_att_type.lower().split('|'    )]

            __snake_case						       =   pos_att_type
            __snake_case						       =   vocab_size
            __snake_case						       =   layer_norm_eps

            __snake_case						       =   kwargs.get('pooler_hidden_size'							,       SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   pooler_dropout
            __snake_case						       =   pooler_hidden_act







class  _lowerCamelCase			(lowerCamelCase					):

     @property
     def 						__lowerCamelCase				(  self    ):
            if self.task == "multiple-choice":
                   __snake_case						       =   {0: 'batch', 1: 'choice', 2: 'sequence'}
            else:
                   __snake_case						       =   {0: 'batch', 1: 'sequence'}
            if self._config.type_vocab_size > 0:
                   return OrderedDict(
                       [('input_ids', dynamic_axis), ('attention_mask', dynamic_axis), ('token_type_ids', dynamic_axis)]    )
            else:
                   return OrderedDict([('input_ids', dynamic_axis), ('attention_mask', dynamic_axis)]    )

     @property
     def 						__lowerCamelCase				(  self    ):
            return 12

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_ = -1							,       SCREAMING_SNAKE_CASE_ = -1							,       SCREAMING_SNAKE_CASE_ = -1							,       SCREAMING_SNAKE_CASE_ = False							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = 3							,       SCREAMING_SNAKE_CASE_ = 40							,       SCREAMING_SNAKE_CASE_ = 40							,       SCREAMING_SNAKE_CASE_ = None							,       ):
            __snake_case						       =   super().generate_dummy_inputs(preprocessor=SCREAMING_SNAKE_CASE_							,       framework=SCREAMING_SNAKE_CASE_    )
            if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
                   del dummy_inputs["token_type_ids"]
            return dummy_inputs
