import torch

from transformers import AutoTokenizer




class  __lowerCAmelCase	(							a_    ):



 '''simple docstring'''

 def __init__(      self							:		Optional[int]							,      lowerCamelCase							:		Any	)			->				str:
        _UpperCamelCase  :							List[str]	    =	AutoTokenizer.from_pretrained(lowerCamelCase	)

 def        __snake_case       (      self							:		Optional[int]							,      lowerCamelCase							:		List[str]	)			->				str:

        if isinstance(lowerCamelCase							,      lowerCamelCase	) and all(isinstance(_x							,      lowerCamelCase	) for _x in x	):
               _UpperCamelCase  :							Any	    =	None
               for l in x:
                      _UpperCamelCase  :							List[str]	    =	self.tokenizer(
                          lowerCamelCase							,      padding='max_length'							,      max_length=3_8_4							,      truncation=lowerCamelCase							,      return_tensors='pt'							,      )
                      _UpperCamelCase  :							Optional[Any]	    =	torch.tensor([len(lowerCamelCase	)]	)
                      if d is not None:
                             for k in d.keys():
                                    _UpperCamelCase  :							Dict	    =	torch.cat((d[k], t[k])							,      0	)
                      else:
                             _UpperCamelCase  :							Optional[int]	    =	t

               _UpperCamelCase  :							Optional[int]	    =	torch.tensor(self.tokenizer.convert_tokens_to_ids('[E]'	)	)
               _UpperCamelCase  :							Dict	    =	torch.tensor(self.tokenizer.convert_tokens_to_ids('[/E]'	)	)

        elif isinstance(lowerCamelCase							,      lowerCamelCase	) and all(isinstance(_x							,      lowerCamelCase	) for _x in x	):
               _UpperCamelCase  :							int	    =	self.tokenizer(
                   lowerCamelCase							,      padding='max_length'							,      max_length=3_8_4							,      truncation=lowerCamelCase							,      return_tensors='pt'							,      )

        else:
               raise Exception(
                   'Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of'
                   ' strings` for supports are supported.'	)

        return d

 def        __snake_case       (      self							:		List[Any]							,      lowerCamelCase							:		Tuple							,      lowerCamelCase							:		Tuple							,      lowerCamelCase							:		List[str]							,      lowerCamelCase							:		List[str]							,      lowerCamelCase							:		List[str]=0.7_0	)			->				Optional[int]:
        _UpperCamelCase  :							Union[str, Any]	    =	[]
        for idx in range(len(W_query['input_ids']	)	):
               _UpperCamelCase  :							List[Any]	    =	range(p_start.shape[1]	)

               _UpperCamelCase  :							List[str]	    =	[]
               for start_id in start_indexes:
                      for end_id in end_indexes:
                             if start_id < end_id:
                                    output.append(
                                        (
                                            start_id,
                                            end_id,
                                            p_start[idx][start_id].item(),
                                            p_end[idx][end_id].item(),
                                        )	)

               output.sort(key=lambda lowerCamelCase	: (tup[2] * tup[3])							,      reverse=lowerCamelCase	)
               _UpperCamelCase  :							Dict	    =	[]
               for k in range(len(lowerCamelCase	)	):
                      if output[k][2] * output[k][3] >= thresh:
                             _UpperCamelCase    ,   _UpperCamelCase  :							int	    =	output[k][0], output[k][1]
                             _UpperCamelCase  :							Optional[Any]	    =	self.tokenizer.decode(W_query['input_ids'][idx][c_start_pos:c_end_pos]	)
                             temp.append((decoded, output[k][2] * output[k][3])	)

               final_outputs.append(lowerCamelCase	)

        return final_outputs





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


__UpperCAmelCase   						=     logging.get_logger(__name__)

__UpperCAmelCase   						=     {
    "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 				UpperCamelCase__		(	__SCREAMING_SNAKE_CASE	):






       """simple docstring"""


       UpperCAmelCase_				       ="deberta"



       def __init__(							self ,     _A=50265 ,     _A=768 ,     _A=12 ,     _A=12 ,     _A=3072 ,     _A="gelu" ,     _A=0.1 ,     _A=0.1 ,     _A=512 ,     _A=0 ,     _A=0.02 ,     _A=1E-7 ,     _A=False ,     _A=-1 ,     _A=0 ,     _A=True ,     _A=None ,     _A=0 ,     _A="gelu" ,     **_A ,     )				->				Optional[int]:
          super().__init__(**_A	)

          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_	=      max_position_embeddings
          SCREAMING_SNAKE_CASE_	=      type_vocab_size
          SCREAMING_SNAKE_CASE_	=      initializer_range
          SCREAMING_SNAKE_CASE_	=      relative_attention
          SCREAMING_SNAKE_CASE_	=      max_relative_positions
          SCREAMING_SNAKE_CASE_	=      pad_token_id
          SCREAMING_SNAKE_CASE_	=      position_biased_input

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

          SCREAMING_SNAKE_CASE_	=      pos_att_type
          SCREAMING_SNAKE_CASE_	=      vocab_size
          SCREAMING_SNAKE_CASE_	=      layer_norm_eps

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







class 				UpperCamelCase__		(	__SCREAMING_SNAKE_CASE	):






       """simple docstring"""


       @property
       def     _UpperCamelCase	(							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'''}
          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     _UpperCamelCase	(							self	)				->				int:
          return 12




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


