


'''simple docstring'''
import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer


UpperCamelCase__    					=      logging.get_logger(__name__)

UpperCamelCase__    					=      {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCamelCase__    					=      {
    '''vocab_file''': {
        '''facebook/dpr-ctx_encoder-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt'''
        ),
        '''facebook/dpr-ctx_encoder-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/dpr-ctx_encoder-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json'''
        ),
        '''facebook/dpr-ctx_encoder-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json'''
        ),
    },
}
UpperCamelCase__    					=      {
    '''vocab_file''': {
        '''facebook/dpr-question_encoder-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt'''
        ),
        '''facebook/dpr-question_encoder-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/dpr-question_encoder-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json'''
        ),
        '''facebook/dpr-question_encoder-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json'''
        ),
    },
}
UpperCamelCase__    					=      {
    '''vocab_file''': {
        '''facebook/dpr-reader-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt'''
        ),
        '''facebook/dpr-reader-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/dpr-reader-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json'''
        ),
        '''facebook/dpr-reader-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json'''
        ),
    },
}

UpperCamelCase__    					=      {
    '''facebook/dpr-ctx_encoder-single-nq-base''': 5_1_2,
    '''facebook/dpr-ctx_encoder-multiset-base''': 5_1_2,
}
UpperCamelCase__    					=      {
    '''facebook/dpr-question_encoder-single-nq-base''': 5_1_2,
    '''facebook/dpr-question_encoder-multiset-base''': 5_1_2,
}
UpperCamelCase__    					=      {
    '''facebook/dpr-reader-single-nq-base''': 5_1_2,
    '''facebook/dpr-reader-multiset-base''': 5_1_2,
}


UpperCamelCase__    					=      {
    '''facebook/dpr-ctx_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-ctx_encoder-multiset-base''': {'''do_lower_case''': True},
}
UpperCamelCase__    					=      {
    '''facebook/dpr-question_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-question_encoder-multiset-base''': {'''do_lower_case''': True},
}
UpperCamelCase__    					=      {
    '''facebook/dpr-reader-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-reader-multiset-base''': {'''do_lower_case''': True},
}




class 			lowerCamelCase_   (	__a      ):
      lowerCAmelCase__		     =     VOCAB_FILES_NAMES
      lowerCAmelCase__		     =     CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
      lowerCAmelCase__		     =     CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      lowerCAmelCase__		     =     CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION




class 			lowerCamelCase_   (	__a      ):
      lowerCAmelCase__		     =     VOCAB_FILES_NAMES
      lowerCAmelCase__		     =     QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
      lowerCAmelCase__		     =     QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      lowerCAmelCase__		     =     QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION

UpperCamelCase__    					=      collections.namedtuple(
    '''DPRSpanPrediction''', ['''span_score''', '''relevance_score''', '''doc_id''', '''start_index''', '''end_index''', '''text''']
)

UpperCamelCase__    					=      collections.namedtuple('''DPRReaderOutput''', ['''start_logits''', '''end_logits''', '''relevance_logits'''])


UpperCamelCase__    					=      R'''
    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.
    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),
    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`
    with the format:

    ```
    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>
    ```

    Args:
        questions (`str` or `List[str]`):
            The questions to be encoded. You can specify one question for many passages. In this case, the question
            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in
            `titles` or `texts`.
        titles (`str` or `List[str]`):
            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.
        texts (`str` or `List[str]`):
            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.
        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
            Activates and controls padding. Accepts the following values:

            - `True` or `\'longest\'`: Pad to the longest sequence in the batch (or no padding if only a single sequence
              if provided).
            - `\'max_length\'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided.
            - `False` or `\'do_not_pad\'` (default): No padding (i.e., can output a batch with sequences of different
              lengths).
        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
            Activates and controls truncation. Accepts the following values:

            - `True` or `\'longest_first\'`: Truncate to a maximum length specified with the argument `max_length` or to
              the maximum acceptable input length for the model if that argument is not provided. This will truncate
              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch
              of pairs) is provided.
            - `\'only_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the first
              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `\'only_second\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the
              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths
              greater than the model maximum admissible input size).
        max_length (`int`, *optional*):
                Controls the maximum length to use by one of the truncation/padding parameters.

                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length
                is required by one of the truncation/padding parameters. If the model has no specific maximum input
                length (like XLNet) truncation/padding to a maximum length will be deactivated.
        return_tensors (`str` or [`~utils.TensorType`], *optional*):
                If set, will return tensors instead of list of python integers. Acceptable values are:

                - `\'tf\'`: Return TensorFlow `tf.constant` objects.
                - `\'pt\'`: Return PyTorch `torch.Tensor` objects.
                - `\'np\'`: Return Numpy `np.ndarray` objects.
        return_attention_mask (`bool`, *optional*):
            Whether or not to return the attention mask. If not set, will return the attention mask according to the
            specific tokenizer\'s default, defined by the `return_outputs` attribute.

            [What are attention masks?](../glossary#attention-mask)

    Returns:
        `Dict[str, List[List[int]]]`: A dictionary with the following keys:

        - `input_ids`: List of token ids to be fed to a model.
        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.
    '''




@add_start_docstrings(__a      )
class 			lowerCamelCase_   :






      def __call__(					self				:    Any						,						_A				:    Optional[int]						,						_A				:    Optional[str] = None						,						_A				:    Optional[str] = None						,						_A				:    Union[bool, str] = False						,						_A				:    Union[bool, str] = False						,						_A				:    Optional[int] = None						,						_A				:    Optional[Union[str, TensorType]] = None						,						_A				:    Optional[bool] = None						,						**_A				:    Dict						,						):


          '''simple docstring'''


          if titles is None and texts is None:
              return super().__call__(
                  _A						,						padding=_A						,						truncation=_A						,						max_length=_A						,						return_tensors=_A						,						return_attention_mask=_A						,						**_A						,						)
          elif titles is None or texts is None:
              UpperCAmelCase__			:		int													=				titles if texts is None else texts
              return super().__call__(
                  _A						,						_A						,						padding=_A						,						truncation=_A						,						max_length=_A						,						return_tensors=_A						,						return_attention_mask=_A						,						**_A						,						)
          UpperCAmelCase__			:		Optional[Any]													=				titles if not isinstance(_A						,						_A ) else [titles]
          UpperCAmelCase__			:		str													=				texts if not isinstance(_A						,						_A ) else [texts]
          UpperCAmelCase__			:		int													=				len(_A )
          UpperCAmelCase__			:		Optional[Any]													=				questions if not isinstance(_A						,						_A ) else [questions] * n_passages
          if len(_A ) != len(_A ):
              raise ValueError(
                  f"""There should be as many titles than texts but got {len(_A )} titles and {len(_A )} texts.""" )
          UpperCAmelCase__			:		int													=				super().__call__(_A						,						_A						,						padding=_A						,						truncation=_A )['''input_ids''']
          UpperCAmelCase__			:		List[Any]													=				super().__call__(_A						,						add_special_tokens=_A						,						padding=_A						,						truncation=_A )['''input_ids''']
          UpperCAmelCase__			:		List[str]													=				{
              '''input_ids''': [
                  (encoded_question_and_title + encoded_text)[:max_length]
                  if max_length is not None and truncation
                  else encoded_question_and_title + encoded_text
                  for encoded_question_and_title, encoded_text in zip(_A						,						_A )
              ]
          }
          if return_attention_mask is not False:
              UpperCAmelCase__			:		int													=				[]
              for input_ids in encoded_inputs["input_ids"]:
                  attention_mask.append([int(input_id != self.pad_token_id ) for input_id in input_ids] )
              UpperCAmelCase__			:		List[Any]													=				attention_mask
          return self.pad(_A						,						padding=_A						,						max_length=_A						,						return_tensors=_A )






      def lowercase_							(					self				:    List[str]						,						_A				:    BatchEncoding						,						_A				:    DPRReaderOutput						,						_A				:    int = 16						,						_A				:    int = 64						,						_A				:    int = 4						,						):


          '''simple docstring'''


          UpperCAmelCase__			:		List[Any]													=				reader_input['''input_ids''']
          UpperCAmelCase__						,						UpperCAmelCase__						,						UpperCAmelCase__			:		Tuple													=				reader_output[:3]
          UpperCAmelCase__			:		int													=				len(_A )
          UpperCAmelCase__			:		Tuple													=				sorted(range(_A )						,						reverse=_A						,						key=relevance_logits.__getitem__ )
          UpperCAmelCase__			:		List[DPRReaderOutput]													=				[]
          for doc_id in sorted_docs:
              UpperCAmelCase__			:		Tuple													=				list(input_ids[doc_id] )
              # assuming question & title information is at the beginning of the sequence
              UpperCAmelCase__			:		str													=				sequence_ids.index(self.sep_token_id						,						2 ) + 1  # second sep id
              if sequence_ids[-1] == self.pad_token_id:
                  UpperCAmelCase__			:		Optional[Any]													=				sequence_ids.index(self.pad_token_id )
              else:
                  UpperCAmelCase__			:		Tuple													=				len(_A )

              UpperCAmelCase__			:		Optional[Any]													=				self._get_best_spans(
                  start_logits=start_logits[doc_id][passage_offset:sequence_len]						,						end_logits=end_logits[doc_id][passage_offset:sequence_len]						,						max_answer_length=_A						,						top_spans=_A						,						)
              for start_index, end_index in best_spans:
                  start_index += passage_offset
                  end_index += passage_offset
                  nbest_spans_predictions.append(
                      DPRSpanPrediction(
                          span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index]						,						relevance_score=relevance_logits[doc_id]						,						doc_id=_A						,						start_index=_A						,						end_index=_A						,						text=self.decode(sequence_ids[start_index : end_index + 1] )						,						) )
              if len(_A ) >= num_spans:
                  break
          return nbest_spans_predictions[:num_spans]






      def lowercase_							(					self				:    Any						,						_A				:    List[int]						,						_A				:    List[int]						,						_A				:    int						,						_A				:    int						,						):


          '''simple docstring'''


          UpperCAmelCase__			:		str													=				[]
          for start_index, start_score in enumerate(_A ):
              for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length] ):
                  scores.append(((start_index, start_index + answer_length), start_score + end_score) )
          UpperCAmelCase__			:		Tuple													=				sorted(_A						,						key=lambda _A : x[1]						,						reverse=_A )
          UpperCAmelCase__			:		List[Any]													=				[]
          for (start_index, end_index), score in scores:
              if start_index > end_index:
                  raise ValueError(f"""Wrong span indices: [{start_index}:{end_index}]""" )
              UpperCAmelCase__			:		Tuple													=				end_index - start_index + 1
              if length > max_answer_length:
                  raise ValueError(f"""Span is too long: {length} > {max_answer_length}""" )
              if any(
                  start_index <= prev_start_index <= prev_end_index <= end_index
                  or prev_start_index <= start_index <= end_index <= prev_end_index
                  for (prev_start_index, prev_end_index) in chosen_span_intervals ):
                  continue
              chosen_span_intervals.append((start_index, end_index) )

              if len(_A ) == top_spans:
                  break
          return chosen_span_intervals




@add_end_docstrings(__a      )
class 			lowerCamelCase_   (	__a     ,       __a      ):
      lowerCAmelCase__		     =     VOCAB_FILES_NAMES
      lowerCAmelCase__		     =     READER_PRETRAINED_VOCAB_FILES_MAP
      lowerCAmelCase__		     =     READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      lowerCAmelCase__		     =     READER_PRETRAINED_INIT_CONFIGURATION
      lowerCAmelCase__		     =     ['input_ids', 'attention_mask']




'''simple docstring'''
import unittest

from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import (
      DebertaVaForMaskedLM,
      DebertaVaForMultipleChoice,
      DebertaVaForQuestionAnswering,
      DebertaVaForSequenceClassification,
      DebertaVaForTokenClassification,
      DebertaVaModel,
  )
  from transformers.models.deberta_va.modeling_deberta_va import DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST




class 			lowerCamelCase_   (	__a      ):






      def __init__(					self				:    Optional[Any]						,						_A				:    Optional[Any]						,						_A				:    List[str]=13						,						_A				:    Any=7						,						_A				:    str=True						,						_A				:    Any=True						,						_A				:    Any=True						,						_A				:    Optional[int]=True						,						_A				:    int=99						,						_A				:    Optional[int]=32						,						_A				:    List[Any]=5						,						_A				:    Optional[Any]=4						,						_A				:    Dict=37						,						_A				:    Any="gelu"						,						_A				:    str=0.1						,						_A				:    int=0.1						,						_A				:    Optional[Any]=512						,						_A				:    Optional[Any]=16						,						_A				:    List[Any]=2						,						_A				:    str=0.0_2						,						_A				:    Optional[Any]=False						,						_A				:    Any=True						,						_A				:    Dict="None"						,						_A				:    List[str]=3						,						_A				:    List[str]=4						,						_A				:    Tuple=None						,						):


          '''simple docstring'''


          UpperCAmelCase__			:		Tuple													=				parent
          UpperCAmelCase__			:		Union[str, Any]													=				batch_size
          UpperCAmelCase__			:		Dict													=				seq_length
          UpperCAmelCase__			:		Dict													=				is_training
          UpperCAmelCase__			:		Optional[Any]													=				use_input_mask
          UpperCAmelCase__			:		Optional[Any]													=				use_token_type_ids
          UpperCAmelCase__			:		Union[str, Any]													=				use_labels
          UpperCAmelCase__			:		Tuple													=				vocab_size
          UpperCAmelCase__			:		Tuple													=				hidden_size
          UpperCAmelCase__			:		Any													=				num_hidden_layers
          UpperCAmelCase__			:		Tuple													=				num_attention_heads
          UpperCAmelCase__			:		List[Any]													=				intermediate_size
          UpperCAmelCase__			:		Union[str, Any]													=				hidden_act
          UpperCAmelCase__			:		Any													=				hidden_dropout_prob
          UpperCAmelCase__			:		Any													=				attention_probs_dropout_prob
          UpperCAmelCase__			:		int													=				max_position_embeddings
          UpperCAmelCase__			:		Optional[int]													=				type_vocab_size
          UpperCAmelCase__			:		Union[str, Any]													=				type_sequence_label_size
          UpperCAmelCase__			:		int													=				initializer_range
          UpperCAmelCase__			:		Any													=				num_labels
          UpperCAmelCase__			:		Optional[Any]													=				num_choices
          UpperCAmelCase__			:		List[Any]													=				relative_attention
          UpperCAmelCase__			:		int													=				position_biased_input
          UpperCAmelCase__			:		str													=				pos_att_type
          UpperCAmelCase__			:		Union[str, Any]													=				scope






      def lowercase_							(					self				:    Tuple ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[Any]													=				ids_tensor([self.batch_size, self.seq_length]						,						self.vocab_size )

          UpperCAmelCase__			:		Any													=				None
          if self.use_input_mask:
              UpperCAmelCase__			:		Tuple													=				ids_tensor([self.batch_size, self.seq_length]						,						vocab_size=2 )

          UpperCAmelCase__			:		Optional[Any]													=				None
          if self.use_token_type_ids:
              UpperCAmelCase__			:		List[str]													=				ids_tensor([self.batch_size, self.seq_length]						,						self.type_vocab_size )

          UpperCAmelCase__			:		Dict													=				None
          UpperCAmelCase__			:		Any													=				None
          UpperCAmelCase__			:		Dict													=				None
          if self.use_labels:
              UpperCAmelCase__			:		int													=				ids_tensor([self.batch_size]						,						self.type_sequence_label_size )
              UpperCAmelCase__			:		List[str]													=				ids_tensor([self.batch_size, self.seq_length]						,						self.num_labels )
              UpperCAmelCase__			:		Dict													=				ids_tensor([self.batch_size]						,						self.num_choices )

          UpperCAmelCase__			:		Optional[Any]													=				self.get_config()

          return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels






      def lowercase_							(					self				:    List[str] ):


          '''simple docstring'''


          return DebertaVaConfig(
              vocab_size=self.vocab_size						,						hidden_size=self.hidden_size						,						num_hidden_layers=self.num_hidden_layers						,						num_attention_heads=self.num_attention_heads						,						intermediate_size=self.intermediate_size						,						hidden_act=self.hidden_act						,						hidden_dropout_prob=self.hidden_dropout_prob						,						attention_probs_dropout_prob=self.attention_probs_dropout_prob						,						max_position_embeddings=self.max_position_embeddings						,						type_vocab_size=self.type_vocab_size						,						initializer_range=self.initializer_range						,						relative_attention=self.relative_attention						,						position_biased_input=self.position_biased_input						,						pos_att_type=self.pos_att_type						,						)






      def lowercase_							(					self				:    Dict						,						_A				:    Optional[int] ):


          '''simple docstring'''


          self.parent.assertListEqual(list(result.loss.size() )						,						[] )






      def lowercase_							(					self				:    int						,						_A				:    int						,						_A				:    Any						,						_A				:    Tuple						,						_A				:    List[Any]						,						_A				:    str						,						_A				:    Union[str, Any]						,						_A				:    Any ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[Any]													=				DebertaVaModel(config=_A )
          model.to(_A )
          model.eval()
          UpperCAmelCase__			:		str													=				model(_A						,						attention_mask=_A						,						token_type_ids=_A )[0]
          UpperCAmelCase__			:		List[str]													=				model(_A						,						token_type_ids=_A )[0]
          UpperCAmelCase__			:		List[Any]													=				model(_A )[0]

          self.parent.assertListEqual(list(sequence_output.size() )						,						[self.batch_size, self.seq_length, self.hidden_size] )






      def lowercase_							(					self				:    Optional[Any]						,						_A				:    Tuple						,						_A				:    List[Any]						,						_A				:    Optional[Any]						,						_A				:    int						,						_A				:    List[Any]						,						_A				:    Optional[int]						,						_A				:    str ):


          '''simple docstring'''


          UpperCAmelCase__			:		Dict													=				DebertaVaForMaskedLM(config=_A )
          model.to(_A )
          model.eval()
          UpperCAmelCase__			:		Any													=				model(_A						,						attention_mask=_A						,						token_type_ids=_A						,						labels=_A )

          self.parent.assertEqual(result.logits.shape						,						(self.batch_size, self.seq_length, self.vocab_size) )






      def lowercase_							(					self				:    str						,						_A				:    str						,						_A				:    Any						,						_A				:    Any						,						_A				:    List[Any]						,						_A				:    Dict						,						_A				:    Tuple						,						_A				:    Tuple ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[Any]													=				self.num_labels
          UpperCAmelCase__			:		Union[str, Any]													=				DebertaVaForSequenceClassification(_A )
          model.to(_A )
          model.eval()
          UpperCAmelCase__			:		str													=				model(_A						,						attention_mask=_A						,						token_type_ids=_A						,						labels=_A )
          self.parent.assertListEqual(list(result.logits.size() )						,						[self.batch_size, self.num_labels] )
          self.check_loss_output(_A )






      def lowercase_							(					self				:    Any						,						_A				:    List[str]						,						_A				:    List[str]						,						_A				:    Optional[int]						,						_A				:    Tuple						,						_A				:    Dict						,						_A				:    List[str]						,						_A				:    Optional[int] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[Any]													=				self.num_labels
          UpperCAmelCase__			:		int													=				DebertaVaForTokenClassification(config=_A )
          model.to(_A )
          model.eval()
          UpperCAmelCase__			:		List[Any]													=				model(_A						,						attention_mask=_A						,						token_type_ids=_A						,						labels=_A )
          self.parent.assertEqual(result.logits.shape						,						(self.batch_size, self.seq_length, self.num_labels) )






      def lowercase_							(					self				:    str						,						_A				:    List[str]						,						_A				:    str						,						_A				:    Optional[int]						,						_A				:    Optional[int]						,						_A				:    Union[str, Any]						,						_A				:    Dict						,						_A				:    Optional[int] ):


          '''simple docstring'''


          UpperCAmelCase__			:		str													=				DebertaVaForQuestionAnswering(config=_A )
          model.to(_A )
          model.eval()
          UpperCAmelCase__			:		int													=				model(
              _A						,						attention_mask=_A						,						token_type_ids=_A						,						start_positions=_A						,						end_positions=_A						,						)
          self.parent.assertEqual(result.start_logits.shape						,						(self.batch_size, self.seq_length) )
          self.parent.assertEqual(result.end_logits.shape						,						(self.batch_size, self.seq_length) )






      def lowercase_							(					self				:    Any						,						_A				:    Tuple						,						_A				:    Optional[int]						,						_A				:    Optional[int]						,						_A				:    str						,						_A				:    List[str]						,						_A				:    Any						,						_A				:    int ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[Any]													=				DebertaVaForMultipleChoice(config=_A )
          model.to(_A )
          model.eval()
          UpperCAmelCase__			:		Optional[Any]													=				input_ids.unsqueeze(1 ).expand(-1						,						self.num_choices						,						-1 ).contiguous()
          UpperCAmelCase__			:		str													=				token_type_ids.unsqueeze(1 ).expand(-1						,						self.num_choices						,						-1 ).contiguous()
          UpperCAmelCase__			:		Dict													=				input_mask.unsqueeze(1 ).expand(-1						,						self.num_choices						,						-1 ).contiguous()
          UpperCAmelCase__			:		List[str]													=				model(
              _A						,						attention_mask=_A						,						token_type_ids=_A						,						labels=_A						,						)
          self.parent.assertEqual(result.logits.shape						,						(self.batch_size, self.num_choices) )






      def lowercase_							(					self				:    Optional[int] ):


          '''simple docstring'''


          UpperCAmelCase__			:		int													=				self.prepare_config_and_inputs()
          (
              (
              UpperCAmelCase__
          )						,						(
              UpperCAmelCase__
          )						,						(
              UpperCAmelCase__
          )						,						(
              UpperCAmelCase__
          )						,						(
              UpperCAmelCase__
          )						,						(
              UpperCAmelCase__
          )						,						(
              UpperCAmelCase__
          )						,						
          )			:		Any													=				config_and_inputs
          UpperCAmelCase__			:		Optional[int]													=				{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
          return config, inputs_dict




@require_torch
class 			lowerCamelCase_   (	__a     ,       __a     ,       unittest.TestCase      ):
      lowerCAmelCase__		     =     (
          (
              DebertaVaModel,
              DebertaVaForMaskedLM,
              DebertaVaForSequenceClassification,
              DebertaVaForTokenClassification,
              DebertaVaForQuestionAnswering,
              DebertaVaForMultipleChoice,
          )
          if is_torch_available()
          else ()
      )
      lowerCAmelCase__		     =     (
          {
              'feature-extraction': DebertaVaModel,
              'fill-mask': DebertaVaForMaskedLM,
              'question-answering': DebertaVaForQuestionAnswering,
              'text-classification': DebertaVaForSequenceClassification,
              'token-classification': DebertaVaForTokenClassification,
              'zero-shot': DebertaVaForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )

      lowerCAmelCase__		     =     True
      lowerCAmelCase__		     =     False
      lowerCAmelCase__		     =     False
      lowerCAmelCase__		     =     False
      lowerCAmelCase__		     =     False






      def lowercase_							(					self				:    Optional[Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[Any]													=				DebertaVaModelTester(self )
          UpperCAmelCase__			:		Tuple													=				ConfigTester(self						,						config_class=_A						,						hidden_size=37 )






      def lowercase_							(					self				:    str ):


          '''simple docstring'''


          self.config_tester.run_common_tests()






      def lowercase_							(					self				:    Optional[int] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Any													=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_model(*_A )






      def lowercase_							(					self				:    List[Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[Any]													=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_for_sequence_classification(*_A )






      def lowercase_							(					self				:    List[str] ):


          '''simple docstring'''


          UpperCAmelCase__			:		int													=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_for_masked_lm(*_A )






      def lowercase_							(					self				:    List[str] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[int]													=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_for_question_answering(*_A )






      def lowercase_							(					self				:    Any ):


          '''simple docstring'''


          UpperCAmelCase__			:		int													=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_for_token_classification(*_A )






      def lowercase_							(					self				:    Tuple ):


          '''simple docstring'''


          UpperCAmelCase__			:		Union[str, Any]													=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_for_multiple_choice(*_A )






      @slow
      def lowercase_							(					self				:    List[Any] ):


          '''simple docstring'''


          for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              UpperCAmelCase__			:		List[str]													=				DebertaVaModel.from_pretrained(_A )
              self.assertIsNotNone(_A )




@require_torch
@require_sentencepiece
@require_tokenizers
class 			lowerCamelCase_   (	unittest.TestCase      ):






      @unittest.skip(reason='''Model not available yet''' )
      def lowercase_							(					self				:    Optional[Any] ):


          '''simple docstring'''


          pass






      @slow
      def lowercase_							(					self				:    List[Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Tuple													=				DebertaVaModel.from_pretrained('''microsoft/deberta-v2-xlarge''' )

          UpperCAmelCase__			:		List[Any]													=				torch.tensor([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]] )
          UpperCAmelCase__			:		List[str]													=				torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
          with torch.no_grad():
              UpperCAmelCase__			:		List[str]													=				model(_A						,						attention_mask=_A )[0]
          # compare the actual values for a slice.
          UpperCAmelCase__			:		str													=				torch.tensor(
              [[[0.2_3_5_6, 0.1_9_4_8, 0.0_3_6_9], [-0.1_0_6_3, 0.3_5_8_6, -0.5_1_5_2], [-0.6_3_9_9, -0.0_2_5_9, -0.2_5_2_5]]] )
          self.assertTrue(torch.allclose(output[:, 1:4, 1:4]						,						_A						,						atol=1e-4 )						,						f"""{output[:, 1:4, 1:4]}""" )
