





'''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


_A:					List[str]							=				logging.get_logger(__name__)

_A:					Dict							=				{"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

_A:					int							=				{
    """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"""
        ),
    },
}
_A:					int							=				{
    """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"""
        ),
    },
}
_A:					Dict							=				{
    """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"""
        ),
    },
}

_A:					Any							=				{
    """facebook/dpr-ctx_encoder-single-nq-base""": 512,
    """facebook/dpr-ctx_encoder-multiset-base""": 512,
}
_A:					Tuple							=				{
    """facebook/dpr-question_encoder-single-nq-base""": 512,
    """facebook/dpr-question_encoder-multiset-base""": 512,
}
_A:					int							=				{
    """facebook/dpr-reader-single-nq-base""": 512,
    """facebook/dpr-reader-multiset-base""": 512,
}


_A:					Union[str, Any]							=				{
    """facebook/dpr-ctx_encoder-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-ctx_encoder-multiset-base""": {"""do_lower_case""": True},
}
_A:					List[Any]							=				{
    """facebook/dpr-question_encoder-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-question_encoder-multiset-base""": {"""do_lower_case""": True},
}
_A:					Dict							=				{
    """facebook/dpr-reader-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-reader-multiset-base""": {"""do_lower_case""": True},
}






class UpperCAmelCase							(		UpperCAmelCase_							):
    _A :			Tuple						   =       VOCAB_FILES_NAMES
    _A :			Dict						   =       CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
    _A :			Any						   =       CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    _A :			Optional[Any]						   =       CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION






class UpperCAmelCase							(		UpperCAmelCase_							):
    _A :			List[str]						   =       VOCAB_FILES_NAMES
    _A :			str						   =       QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
    _A :			Union[str, Any]						   =       QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    _A :			int						   =       QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION



_A:					Any							=				collections.namedtuple(
    """DPRSpanPrediction""", ["""span_score""", """relevance_score""", """doc_id""", """start_index""", """end_index""", """text"""]
)

_A:					Tuple							=				collections.namedtuple("""DPRReaderOutput""", ["""start_logits""", """end_logits""", """relevance_logits"""])


_A:					Optional[Any]							=				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(UpperCAmelCase_							)
class UpperCAmelCase							:



    def __call__(  self     ,			__A     ,			__A = None     ,			__A = None     ,			__A = False     ,			__A = False     ,			__A = None     ,			__A = None     ,			__A = None     ,			**__A     ,			):
     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					=   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					=   titles if not isinstance(__A     ,			__A					) else [titles]
     __UpperCAmelCase					=   texts if not isinstance(__A     ,			__A					) else [texts]
     __UpperCAmelCase					=   len(__A					)
     __UpperCAmelCase					=   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					=   super().__call__(__A     ,			__A     ,			padding=__A     ,			truncation=__A					)['input_ids']
     __UpperCAmelCase					=   super().__call__(__A     ,			add_special_tokens=__A     ,			padding=__A     ,			truncation=__A					)['input_ids']
     __UpperCAmelCase					=   {
         '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					=   []
      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					=   attention_mask
     return self.pad(__A     ,			padding=__A     ,			max_length=__A     ,			return_tensors=__A					)



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A = 16     ,			__A = 64     ,			__A = 4     ,			):
     __UpperCAmelCase					=   reader_input['input_ids']
     __UpperCAmelCase					,    __UpperCAmelCase					,    __UpperCAmelCase					=   reader_output[:3]
     __UpperCAmelCase					=   len(__A					)
     __UpperCAmelCase					=   sorted(range(__A					)     ,			reverse=__A     ,			key=relevance_logits.__getitem__					)
     __UpperCAmelCase					=   []
     for doc_id in sorted_docs:
      __UpperCAmelCase					=   list(input_ids[doc_id]					)
      # assuming question & title information is at the beginning of the sequence
      __UpperCAmelCase					=   sequence_ids.index(self.sep_token_id     ,			2					) + 1  # second sep id
      if sequence_ids[-1] == self.pad_token_id:
       __UpperCAmelCase					=   sequence_ids.index(self.pad_token_id					)
      else:
       __UpperCAmelCase					=   len(__A					)

      __UpperCAmelCase					=   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 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A     ,			__A     ,			):
     __UpperCAmelCase					=   []
     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					=   sorted(__A     ,			key=lambda __A					: x[1]     ,			reverse=__A					)
     __UpperCAmelCase					=   []
     for (start_index, end_index), score in scores:
      if start_index > end_index:
       raise ValueError(f'Wrong span indices: [{start_index}:{end_index}]'					)
      __UpperCAmelCase					=   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(UpperCAmelCase_							)
class UpperCAmelCase							(		UpperCAmelCase_					,			UpperCAmelCase_							):
    _A :			Optional[int]						   =       VOCAB_FILES_NAMES
    _A :			Optional[int]						   =       READER_PRETRAINED_VOCAB_FILES_MAP
    _A :			Dict						   =       READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    _A :			Union[str, Any]						   =       READER_PRETRAINED_INIT_CONFIGURATION
    _A :			List[Any]						   =       ["""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 UpperCAmelCase							(		UpperCAmelCase_							):



    def __init__(  self     ,			__A     ,			__A=13     ,			__A=7     ,			__A=True     ,			__A=True     ,			__A=True     ,			__A=True     ,			__A=99     ,			__A=32     ,			__A=5     ,			__A=4     ,			__A=37     ,			__A="gelu"     ,			__A=0.1     ,			__A=0.1     ,			__A=512     ,			__A=16     ,			__A=2     ,			__A=0.0_2     ,			__A=False     ,			__A=True     ,			__A="None"     ,			__A=3     ,			__A=4     ,			__A=None     ,			):
     __UpperCAmelCase					=   parent
     __UpperCAmelCase					=   batch_size
     __UpperCAmelCase					=   seq_length
     __UpperCAmelCase					=   is_training
     __UpperCAmelCase					=   use_input_mask
     __UpperCAmelCase					=   use_token_type_ids
     __UpperCAmelCase					=   use_labels
     __UpperCAmelCase					=   vocab_size
     __UpperCAmelCase					=   hidden_size
     __UpperCAmelCase					=   num_hidden_layers
     __UpperCAmelCase					=   num_attention_heads
     __UpperCAmelCase					=   intermediate_size
     __UpperCAmelCase					=   hidden_act
     __UpperCAmelCase					=   hidden_dropout_prob
     __UpperCAmelCase					=   attention_probs_dropout_prob
     __UpperCAmelCase					=   max_position_embeddings
     __UpperCAmelCase					=   type_vocab_size
     __UpperCAmelCase					=   type_sequence_label_size
     __UpperCAmelCase					=   initializer_range
     __UpperCAmelCase					=   num_labels
     __UpperCAmelCase					=   num_choices
     __UpperCAmelCase					=   relative_attention
     __UpperCAmelCase					=   position_biased_input
     __UpperCAmelCase					=   pos_att_type
     __UpperCAmelCase					=   scope



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   ids_tensor([self.batch_size, self.seq_length]     ,			self.vocab_size					)

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

     __UpperCAmelCase					=   None
     if self.use_token_type_ids:
      __UpperCAmelCase					=   ids_tensor([self.batch_size, self.seq_length]     ,			self.type_vocab_size					)

     __UpperCAmelCase					=   None
     __UpperCAmelCase					=   None
     __UpperCAmelCase					=   None
     if self.use_labels:
      __UpperCAmelCase					=   ids_tensor([self.batch_size]     ,			self.type_sequence_label_size					)
      __UpperCAmelCase					=   ids_tensor([self.batch_size, self.seq_length]     ,			self.num_labels					)
      __UpperCAmelCase					=   ids_tensor([self.batch_size]     ,			self.num_choices					)

     __UpperCAmelCase					=   self.get_config()

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



    def 					__lowerCamelCase						(  self					):
     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 					__lowerCamelCase						(  self     ,			__A					):
     self.parent.assertListEqual(list(result.loss.size()					)     ,			[]					)



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A					):
     __UpperCAmelCase					=   DebertaVaModel(config=__A					)
     model.to(__A					)
     model.eval()
     __UpperCAmelCase					=   model(__A     ,			attention_mask=__A     ,			token_type_ids=__A					)[0]
     __UpperCAmelCase					=   model(__A     ,			token_type_ids=__A					)[0]
     __UpperCAmelCase					=   model(__A					)[0]

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



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A					):
     __UpperCAmelCase					=   DebertaVaForMaskedLM(config=__A					)
     model.to(__A					)
     model.eval()
     __UpperCAmelCase					=   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 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A					):
     __UpperCAmelCase					=   self.num_labels
     __UpperCAmelCase					=   DebertaVaForSequenceClassification(__A					)
     model.to(__A					)
     model.eval()
     __UpperCAmelCase					=   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 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A					):
     __UpperCAmelCase					=   self.num_labels
     __UpperCAmelCase					=   DebertaVaForTokenClassification(config=__A					)
     model.to(__A					)
     model.eval()
     __UpperCAmelCase					=   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 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A					):
     __UpperCAmelCase					=   DebertaVaForQuestionAnswering(config=__A					)
     model.to(__A					)
     model.eval()
     __UpperCAmelCase					=   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 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A					):
     __UpperCAmelCase					=   DebertaVaForMultipleChoice(config=__A					)
     model.to(__A					)
     model.eval()
     __UpperCAmelCase					=   input_ids.unsqueeze(1					).expand(-1     ,			self.num_choices     ,			-1					).contiguous()
     __UpperCAmelCase					=   token_type_ids.unsqueeze(1					).expand(-1     ,			self.num_choices     ,			-1					).contiguous()
     __UpperCAmelCase					=   input_mask.unsqueeze(1					).expand(-1     ,			self.num_choices     ,			-1					).contiguous()
     __UpperCAmelCase					=   model(
         __A     ,			attention_mask=__A     ,			token_type_ids=__A     ,			labels=__A     ,			)
     self.parent.assertEqual(result.logits.shape     ,			(self.batch_size, self.num_choices)					)




    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.prepare_config_and_inputs()
     (
         (
         __UpperCAmelCase
     )					,    (
         __UpperCAmelCase
     )					,    (
         __UpperCAmelCase
     )					,    (
         __UpperCAmelCase
     )					,    (
         __UpperCAmelCase
     )					,    (
         __UpperCAmelCase
     )					,    (
         __UpperCAmelCase
     )					,    
     )					=   config_and_inputs
     __UpperCAmelCase					=   {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
     return config, inputs_dict






@require_torch
class UpperCAmelCase							(		UpperCAmelCase_					,			UpperCAmelCase_					,			unittest.TestCase							):
    _A :			Optional[Any]						   =       (
        (
            DebertaVaModel,
            DebertaVaForMaskedLM,
            DebertaVaForSequenceClassification,
            DebertaVaForTokenClassification,
            DebertaVaForQuestionAnswering,
            DebertaVaForMultipleChoice,
        )
        if is_torch_available()
        else ()
    )
    _A :			int						   =       (
        {
            """feature-extraction""": DebertaVaModel,
            """fill-mask""": DebertaVaForMaskedLM,
            """question-answering""": DebertaVaForQuestionAnswering,
            """text-classification""": DebertaVaForSequenceClassification,
            """token-classification""": DebertaVaForTokenClassification,
            """zero-shot""": DebertaVaForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )

    _A :			List[str]						   =       True
    _A :			Union[str, Any]						   =       False
    _A :			int						   =       False
    _A :			Dict						   =       False
    _A :			int						   =       False



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   DebertaVaModelTester(self					)
     __UpperCAmelCase					=   ConfigTester(self     ,			config_class=__A     ,			hidden_size=37					)



    def 					__lowerCamelCase						(  self					):
     self.config_tester.run_common_tests()



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_deberta_model(*__A					)



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_deberta_for_sequence_classification(*__A					)



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_deberta_for_masked_lm(*__A					)



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_deberta_for_question_answering(*__A					)



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_deberta_for_token_classification(*__A					)



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_deberta_for_multiple_choice(*__A					)




    @slow
    def 					__lowerCamelCase						(  self					):
     for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
      __UpperCAmelCase					=   DebertaVaModel.from_pretrained(__A					)
      self.assertIsNotNone(__A					)






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



    @unittest.skip(reason='Model not available yet'					)
    def 					__lowerCamelCase						(  self					):
     pass




    @slow
    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   DebertaVaModel.from_pretrained('microsoft/deberta-v2-xlarge'					)

     __UpperCAmelCase					=   torch.tensor([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]					)
     __UpperCAmelCase					=   torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]					)
     with torch.no_grad():
      __UpperCAmelCase					=   model(__A     ,			attention_mask=__A					)[0]
     # compare the actual values for a slice.
     __UpperCAmelCase					=   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]}'					)





