





'''simple docstring'''






import unittest

from transformers import DebertaConfig, 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 (
          DebertaForMaskedLM,
          DebertaForQuestionAnswering,
          DebertaForSequenceClassification,
          DebertaForTokenClassification,
          DebertaModel,
      )
      from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST




class 			UpperCAmelCase					(      snake_case_       ):




      def __init__(      self      :			List[str]      ,      __snake_case      :			str      ,      __snake_case      :			Optional[Any]=13      ,      __snake_case      :			str=7      ,      __snake_case      :			List[str]=True      ,      __snake_case      :			List[str]=True      ,      __snake_case      :			Dict=True      ,      __snake_case      :			Tuple=True      ,      __snake_case      :			str=99      ,      __snake_case      :			Optional[int]=32      ,      __snake_case      :			int=5      ,      __snake_case      :			Any=4      ,      __snake_case      :			Any=37      ,      __snake_case      :			Union[str, Any]="gelu"      ,      __snake_case      :			List[str]=0.1      ,      __snake_case      :			Dict=0.1      ,      __snake_case      :			Tuple=5_12      ,      __snake_case      :			Any=16      ,      __snake_case      :			Union[str, Any]=2      ,      __snake_case      :			int=0.02      ,      __snake_case      :			Dict=False      ,      __snake_case      :			Any=True      ,      __snake_case      :			List[str]="None"      ,      __snake_case      :			Optional[int]=3      ,      __snake_case      :			Union[str, Any]=4      ,      __snake_case      :			Dict=None      ,      )    ->				List[Any]:
            _lowerCAmelCase												=							parent
            _lowerCAmelCase												=							batch_size
            _lowerCAmelCase												=							seq_length
            _lowerCAmelCase												=							is_training
            _lowerCAmelCase												=							use_input_mask
            _lowerCAmelCase												=							use_token_type_ids
            _lowerCAmelCase												=							use_labels
            _lowerCAmelCase												=							vocab_size
            _lowerCAmelCase												=							hidden_size
            _lowerCAmelCase												=							num_hidden_layers
            _lowerCAmelCase												=							num_attention_heads
            _lowerCAmelCase												=							intermediate_size
            _lowerCAmelCase												=							hidden_act
            _lowerCAmelCase												=							hidden_dropout_prob
            _lowerCAmelCase												=							attention_probs_dropout_prob
            _lowerCAmelCase												=							max_position_embeddings
            _lowerCAmelCase												=							type_vocab_size
            _lowerCAmelCase												=							type_sequence_label_size
            _lowerCAmelCase												=							initializer_range
            _lowerCAmelCase												=							num_labels
            _lowerCAmelCase												=							num_choices
            _lowerCAmelCase												=							relative_attention
            _lowerCAmelCase												=							position_biased_input
            _lowerCAmelCase												=							pos_att_type
            _lowerCAmelCase												=							scope




      def     lowercase__   (      self      :			Tuple  )    ->				Dict:
            _lowerCAmelCase												=							ids_tensor([self.batch_size, self.seq_length]      ,      self.vocab_size  )

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

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

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

            _lowerCAmelCase												=							self.get_config()

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




      def     lowercase__   (      self      :			Union[str, Any]  )    ->				Optional[int]:
            return DebertaConfig(
                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      :			List[Any]  )    ->				Optional[int]:
            _lowerCAmelCase												=							self.get_config()
            _lowerCAmelCase												=							3_00
            return config




      def     lowercase__   (      self      :			Optional[Any]      ,      __snake_case      :			Tuple  )    ->				Optional[Any]:
            self.parent.assertListEqual(list(result.loss.size()  )      ,      []  )




      def     lowercase__   (      self      :			List[str]      ,      __snake_case      :			Optional[int]      ,      __snake_case      :			Optional[int]      ,      __snake_case      :			List[Any]      ,      __snake_case      :			str      ,      __snake_case      :			List[Any]      ,      __snake_case      :			Optional[Any]      ,      __snake_case      :			Any  )    ->				Union[str, Any]:
            _lowerCAmelCase												=							DebertaModel(config=__snake_case  )
            model.to(__snake_case  )
            model.eval()
            _lowerCAmelCase												=							model(__snake_case      ,      attention_mask=__snake_case      ,      token_type_ids=__snake_case  )[0]
            _lowerCAmelCase												=							model(__snake_case      ,      token_type_ids=__snake_case  )[0]
            _lowerCAmelCase												=							model(__snake_case  )[0]

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




      def     lowercase__   (      self      :			int      ,      __snake_case      :			Optional[Any]      ,      __snake_case      :			Any      ,      __snake_case      :			Union[str, Any]      ,      __snake_case      :			Dict      ,      __snake_case      :			List[str]      ,      __snake_case      :			List[str]      ,      __snake_case      :			Optional[int]  )    ->				List[str]:
            _lowerCAmelCase												=							DebertaForMaskedLM(config=__snake_case  )
            model.to(__snake_case  )
            model.eval()
            _lowerCAmelCase												=							model(__snake_case      ,      attention_mask=__snake_case      ,      token_type_ids=__snake_case      ,      labels=__snake_case  )

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




      def     lowercase__   (      self      :			int      ,      __snake_case      :			Dict      ,      __snake_case      :			List[Any]      ,      __snake_case      :			Tuple      ,      __snake_case      :			Optional[Any]      ,      __snake_case      :			Tuple      ,      __snake_case      :			Optional[Any]      ,      __snake_case      :			str  )    ->				Optional[int]:
            _lowerCAmelCase												=							self.num_labels
            _lowerCAmelCase												=							DebertaForSequenceClassification(__snake_case  )
            model.to(__snake_case  )
            model.eval()
            _lowerCAmelCase												=							model(__snake_case      ,      attention_mask=__snake_case      ,      token_type_ids=__snake_case      ,      labels=__snake_case  )
            self.parent.assertListEqual(list(result.logits.size()  )      ,      [self.batch_size, self.num_labels]  )
            self.check_loss_output(__snake_case  )




      def     lowercase__   (      self      :			Union[str, Any]      ,      __snake_case      :			str      ,      __snake_case      :			str      ,      __snake_case      :			Tuple      ,      __snake_case      :			List[Any]      ,      __snake_case      :			Dict      ,      __snake_case      :			Optional[int]      ,      __snake_case      :			int  )    ->				int:
            _lowerCAmelCase												=							self.num_labels
            _lowerCAmelCase												=							DebertaForTokenClassification(config=__snake_case  )
            model.to(__snake_case  )
            model.eval()
            _lowerCAmelCase												=							model(__snake_case      ,      attention_mask=__snake_case      ,      token_type_ids=__snake_case      ,      labels=__snake_case  )
            self.parent.assertEqual(result.logits.shape      ,      (self.batch_size, self.seq_length, self.num_labels)  )




      def     lowercase__   (      self      :			Dict      ,      __snake_case      :			Any      ,      __snake_case      :			List[str]      ,      __snake_case      :			Tuple      ,      __snake_case      :			Any      ,      __snake_case      :			Any      ,      __snake_case      :			Tuple      ,      __snake_case      :			List[Any]  )    ->				List[str]:
            _lowerCAmelCase												=							DebertaForQuestionAnswering(config=__snake_case  )
            model.to(__snake_case  )
            model.eval()
            _lowerCAmelCase												=							model(
                __snake_case      ,      attention_mask=__snake_case      ,      token_type_ids=__snake_case      ,      start_positions=__snake_case      ,      end_positions=__snake_case      ,      )
            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      :			Union[str, Any]  )    ->				List[str]:
            _lowerCAmelCase												=							self.prepare_config_and_inputs()
            (
                (
                _lowerCAmelCase
            )					,							(
                _lowerCAmelCase
            )					,							(
                _lowerCAmelCase
            )					,							(
                _lowerCAmelCase
            )					,							(
                _lowerCAmelCase
            )					,							(
                _lowerCAmelCase
            )					,							(
                _lowerCAmelCase
            )					,							
            )												=							config_and_inputs
            _lowerCAmelCase												=							{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
            return config, inputs_dict




@require_torch
class 			UpperCAmelCase					(      snake_case_							,		snake_case_							,		unittest.TestCase       ):
      _lowercase:      int											=       (
          (
              DebertaModel,
              DebertaForMaskedLM,
              DebertaForSequenceClassification,
              DebertaForTokenClassification,
              DebertaForQuestionAnswering,
          )
          if is_torch_available()
          else ()
      )
      _lowercase:      Optional[Any]											=       (
          {
              '''feature-extraction''': DebertaModel,
              '''fill-mask''': DebertaForMaskedLM,
              '''question-answering''': DebertaForQuestionAnswering,
              '''text-classification''': DebertaForSequenceClassification,
              '''token-classification''': DebertaForTokenClassification,
              '''zero-shot''': DebertaForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )

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




      def     lowercase__   (      self      :			Optional[Any]  )    ->				str:
            _lowerCAmelCase												=							DebertaModelTester(self  )
            _lowerCAmelCase												=							ConfigTester(self      ,      config_class=__snake_case      ,      hidden_size=37  )




      def     lowercase__   (      self      :			int  )    ->				List[Any]:
            self.config_tester.run_common_tests()




      def     lowercase__   (      self      :			Dict  )    ->				Optional[int]:
            _lowerCAmelCase												=							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_deberta_model(*__snake_case  )




      def     lowercase__   (      self      :			List[Any]  )    ->				List[str]:
            _lowerCAmelCase												=							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_deberta_for_sequence_classification(*__snake_case  )




      def     lowercase__   (      self      :			Any  )    ->				Optional[Any]:
            _lowerCAmelCase												=							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_deberta_for_masked_lm(*__snake_case  )




      def     lowercase__   (      self      :			str  )    ->				Union[str, Any]:
            _lowerCAmelCase												=							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_deberta_for_question_answering(*__snake_case  )




      def     lowercase__   (      self      :			Any  )    ->				Optional[int]:
            _lowerCAmelCase												=							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_deberta_for_token_classification(*__snake_case  )




      @slow
      def     lowercase__   (      self      :			Union[str, Any]  )    ->				Optional[int]:
            for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  _lowerCAmelCase												=							DebertaModel.from_pretrained(__snake_case  )
                  self.assertIsNotNone(__snake_case  )




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




      @unittest.skip(reason="""Model not available yet"""  )
      def     lowercase__   (      self      :			List[str]  )    ->				List[Any]:
            pass




      @slow
      def     lowercase__   (      self      :			Optional[int]  )    ->				Optional[Any]:
            _lowerCAmelCase												=							DebertaModel.from_pretrained("""microsoft/deberta-base"""  )

            _lowerCAmelCase												=							torch.tensor([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]  )
            _lowerCAmelCase												=							torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]  )
            with torch.no_grad():
                  _lowerCAmelCase												=							model(__snake_case      ,      attention_mask=__snake_case  )[0]
            # compare the actual values for a slice.
            _lowerCAmelCase												=							torch.tensor(
                [[[-0.59_86, -0.80_55, -0.84_62], [1.44_84, -0.93_48, -0.80_59], [0.31_23, 0.00_32, -1.41_31]]]  )
            self.assertTrue(torch.allclose(output[:, 1:4, 1:4]      ,      __snake_case      ,      atol=1E-4  )      ,      f"{output[:, 1:4, 1:4]}"  )













'''simple docstring'''






def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase  ):





      """simple docstring"""


      if len(lowerCAmelCase  ) != len(lowerCAmelCase  ):
            raise ValueError("""String lengths must match!"""  )

      _lowerCAmelCase												=							0

      for chara, chara in zip(lowerCAmelCase							,     lowerCAmelCase  ):
            if chara != chara:
                  count += 1

      return count


if __name__ == "__main__":
      import doctest

      doctest.testmod()






