

import safs





class 			__lowerCAmelCase					( safs.SaFileSystem							):
		pass





from __future__ import annotations

import unittest

from transformers import DebertaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers import (
      TFDebertaForMaskedLM,
      TFDebertaForQuestionAnswering,
      TFDebertaForSequenceClassification,
      TFDebertaForTokenClassification,
      TFDebertaModel,
  )





class 			__lowerCAmelCase					:




  def __init__(    self							,       lowerCAmelCase							,       lowerCAmelCase=13							,       lowerCAmelCase=7							,       lowerCAmelCase=True							,       lowerCAmelCase=True							,       lowerCAmelCase=True							,       lowerCAmelCase=True							,       lowerCAmelCase=99							,       lowerCAmelCase=32							,       lowerCAmelCase=2							,       lowerCAmelCase=4							,       lowerCAmelCase=37							,       lowerCAmelCase="gelu"							,       lowerCAmelCase=0.1							,       lowerCAmelCase=0.1							,       lowerCAmelCase=512							,       lowerCAmelCase=16							,       lowerCAmelCase=2							,       lowerCAmelCase=0.02							,       lowerCAmelCase=3							,       lowerCAmelCase=4							,       lowerCAmelCase=None							,       )  ->			Tuple:
        '''simple docstring'''






        _lowercase     	=parent
        _lowercase     	=13
        _lowercase     	=7
        _lowercase     	=True
        _lowercase     	=True
        _lowercase     	=True
        _lowercase     	=True
        _lowercase     	=99
        _lowercase     	=32
        _lowercase     	=2
        _lowercase     	=4
        _lowercase     	=37
        _lowercase     	='gelu'
        _lowercase     	=0.1
        _lowercase     	=0.1
        _lowercase     	=512
        _lowercase     	=16
        _lowercase     	=False
        _lowercase     	=-1
        _lowercase     	=True
        _lowercase     	=2
        _lowercase     	=0.02
        _lowercase     	=3
        _lowercase     	=4
        _lowercase     	=None




  def   A__					(    self )  ->			Tuple:
        '''simple docstring'''






        _lowercase     	=ids_tensor([self.batch_size, self.seq_length]							,       self.vocab_size )

        _lowercase     	=None
        if self.use_input_mask:
              _lowercase     	=random_attention_mask([self.batch_size, self.seq_length] )

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

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

        _lowercase     	=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							,       relative_attention=self.relative_attention							,       max_relative_positions=self.max_relative_positions							,       position_biased_input=self.position_biased_input							,       initializer_range=self.initializer_range							,       return_dict=lowerCAmelCase							,       )

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




  def   A__					(    self							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase )  ->			List[Any]:
        '''simple docstring'''






        _lowercase     	=TFDebertaModel(config=lowerCAmelCase )
        _lowercase     	={'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

        _lowercase     	=[input_ids, input_mask]
        _lowercase     	=model(lowerCAmelCase )

        _lowercase     	=model(lowerCAmelCase )

        self.parent.assertEqual(result.last_hidden_state.shape							,       (self.batch_size, self.seq_length, self.hidden_size) )




  def   A__					(    self							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase )  ->			Optional[Any]:
        '''simple docstring'''






        _lowercase     	=TFDebertaForMaskedLM(config=lowerCAmelCase )
        _lowercase     	={
            'input_ids': input_ids,
            'attention_mask': input_mask,
            'token_type_ids': token_type_ids,
        }
        _lowercase     	=model(lowerCAmelCase )
        self.parent.assertEqual(result.logits.shape							,       (self.batch_size, self.seq_length, self.vocab_size) )




  def   A__					(    self							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase )  ->			Union[str, Any]:
        '''simple docstring'''






        _lowercase     	=self.num_labels
        _lowercase     	=TFDebertaForSequenceClassification(config=lowerCAmelCase )
        _lowercase     	={
            'input_ids': input_ids,
            'attention_mask': input_mask,
            'token_type_ids': token_type_ids,
        }

        _lowercase     	=model(lowerCAmelCase )
        self.parent.assertEqual(result.logits.shape							,       (self.batch_size, self.num_labels) )




  def   A__					(    self							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase )  ->			Dict:
        '''simple docstring'''






        _lowercase     	=self.num_labels
        _lowercase     	=TFDebertaForTokenClassification(config=lowerCAmelCase )
        _lowercase     	={
            'input_ids': input_ids,
            'attention_mask': input_mask,
            'token_type_ids': token_type_ids,
        }
        _lowercase     	=model(lowerCAmelCase )
        self.parent.assertEqual(result.logits.shape							,       (self.batch_size, self.seq_length, self.num_labels) )




  def   A__					(    self							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase )  ->			List[Any]:
        '''simple docstring'''






        _lowercase     	=TFDebertaForQuestionAnswering(config=lowerCAmelCase )
        _lowercase     	={
            'input_ids': input_ids,
            'attention_mask': input_mask,
            'token_type_ids': token_type_ids,
        }

        _lowercase     	=model(lowerCAmelCase )
        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   A__					(    self )  ->			str:
        '''simple docstring'''






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





@require_tf
class 			__lowerCAmelCase					( SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      unittest.TestCase							):
  _a						      =  (
      (
          TFDebertaModel,
          TFDebertaForMaskedLM,
          TFDebertaForQuestionAnswering,
          TFDebertaForSequenceClassification,
          TFDebertaForTokenClassification,
      )
      if is_tf_available()
      else ()
  )
  _a						      =  (
      {
          """feature-extraction""": TFDebertaModel,
          """fill-mask""": TFDebertaForMaskedLM,
          """question-answering""": TFDebertaForQuestionAnswering,
          """text-classification""": TFDebertaForSequenceClassification,
          """token-classification""": TFDebertaForTokenClassification,
          """zero-shot""": TFDebertaForSequenceClassification,
      }
      if is_tf_available()
      else {}
  )

  _a						      =  False
  _a						      =  False




  def   A__					(    self )  ->			Optional[int]:
        '''simple docstring'''






        _lowercase     	=TFDebertaModelTester(self )
        _lowercase     	=ConfigTester(self							,       config_class=lowerCAmelCase							,       hidden_size=37 )




  def   A__					(    self )  ->			Any:
        '''simple docstring'''






        self.config_tester.run_common_tests()




  def   A__					(    self )  ->			int:
        '''simple docstring'''






        _lowercase     	=self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCAmelCase )




  def   A__					(    self )  ->			Dict:
        '''simple docstring'''






        _lowercase     	=self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*lowerCAmelCase )




  def   A__					(    self )  ->			List[Any]:
        '''simple docstring'''






        _lowercase     	=self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase )




  def   A__					(    self )  ->			Union[str, Any]:
        '''simple docstring'''






        _lowercase     	=self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase )




  def   A__					(    self )  ->			Optional[int]:
        '''simple docstring'''






        _lowercase     	=self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase )




  @slow
  def   A__					(    self )  ->			Optional[Any]:
        '''simple docstring'''






        _lowercase     	=TFDebertaModel.from_pretrained('kamalkraj/deberta-base' )
        self.assertIsNotNone(lowerCAmelCase )





@require_tf
class 			__lowerCAmelCase					( unittest.TestCase							):




  @unittest.skip(reason='Model not available yet' )
  def   A__					(    self )  ->			Optional[int]:
        '''simple docstring'''






        pass




  @slow
  def   A__					(    self )  ->			Dict:
        '''simple docstring'''






        _lowercase     	=TFDebertaModel.from_pretrained('kamalkraj/deberta-base' )
        _lowercase     	=tf.constant([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]] )
        _lowercase     	=tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
        _lowercase     	=model(lowerCAmelCase							,       attention_mask=lowerCAmelCase )[0]

        _lowercase     	=tf.constant(
            [
                [
                    [-0.59855896, -0.80552566, -0.8462135],
                    [1.4484025, -0.93483794, -0.80593085],
                    [0.3122741, 0.00316059, -1.4131377],
                ]
            ] )
        tf.debugging.assert_near(output[:, 1:4, 1:4]							,       lowerCAmelCase							,       atol=1e-4 )


