


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		,					UpperCAmelCase		,					UpperCAmelCase=1_3		,					UpperCAmelCase=7		,					UpperCAmelCase=True		,					UpperCAmelCase=True		,					UpperCAmelCase=True		,					UpperCAmelCase=True		,					UpperCAmelCase=9_9		,					UpperCAmelCase=3_2		,					UpperCAmelCase=2		,					UpperCAmelCase=4		,					UpperCAmelCase=3_7		,					UpperCAmelCase="gelu"		,					UpperCAmelCase=0.1		,					UpperCAmelCase=0.1		,					UpperCAmelCase=5_1_2		,					UpperCAmelCase=1_6		,					UpperCAmelCase=2		,					UpperCAmelCase=0.02		,					UpperCAmelCase=3		,					UpperCAmelCase=4		,					UpperCAmelCase=None		,					)					->       List[str]:
      _lowercase											=parent
      _lowercase											=1_3
      _lowercase											=7
      _lowercase											=True
      _lowercase											=True
      _lowercase											=True
      _lowercase											=True
      _lowercase											=9_9
      _lowercase											=3_2
      _lowercase											=2
      _lowercase											=4
      _lowercase											=3_7
      _lowercase											='''gelu'''
      _lowercase											=0.1
      _lowercase											=0.1
      _lowercase											=5_1_2
      _lowercase											=1_6
      _lowercase											=False
      _lowercase											=-1
      _lowercase											=True
      _lowercase											=2
      _lowercase											=0.02
      _lowercase											=3
      _lowercase											=4
      _lowercase											=None





     def 					__A (self	)					->       Optional[int]:
      _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=UpperCAmelCase		,					)

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





     def 					__A (self		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase	)					->       Union[str, Any]:
      _lowercase											=TFDebertaModel(config=UpperCAmelCase	)
      _lowercase											={'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

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

      _lowercase											=model(UpperCAmelCase	)

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





     def 					__A (self		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase	)					->       Optional[Any]:
      _lowercase											=TFDebertaForMaskedLM(config=UpperCAmelCase	)
      _lowercase											={
          '''input_ids''': input_ids,
          '''attention_mask''': input_mask,
          '''token_type_ids''': token_type_ids,
      }
      _lowercase											=model(UpperCAmelCase	)
      self.parent.assertEqual(result.logits.shape		,					(self.batch_size, self.seq_length, self.vocab_size)	)





     def 					__A (self		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase	)					->       str:
      _lowercase											=self.num_labels
      _lowercase											=TFDebertaForSequenceClassification(config=UpperCAmelCase	)
      _lowercase											={
          '''input_ids''': input_ids,
          '''attention_mask''': input_mask,
          '''token_type_ids''': token_type_ids,
      }

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





     def 					__A (self		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase	)					->       Tuple:
      _lowercase											=self.num_labels
      _lowercase											=TFDebertaForTokenClassification(config=UpperCAmelCase	)
      _lowercase											={
          '''input_ids''': input_ids,
          '''attention_mask''': input_mask,
          '''token_type_ids''': token_type_ids,
      }
      _lowercase											=model(UpperCAmelCase	)
      self.parent.assertEqual(result.logits.shape		,					(self.batch_size, self.seq_length, self.num_labels)	)





     def 					__A (self		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase	)					->       Any:
      _lowercase											=TFDebertaForQuestionAnswering(config=UpperCAmelCase	)
      _lowercase											={
          '''input_ids''': input_ids,
          '''attention_mask''': input_mask,
          '''token_type_ids''': token_type_ids,
      }

      _lowercase											=model(UpperCAmelCase	)
      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	)					->       Optional[int]:
      _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__       (	lowerCAmelCase       ,							lowerCAmelCase       ,							unittest.TestCase):
     SCREAMING_SNAKE_CASE__				      =      (
         (
             TFDebertaModel,
             TFDebertaForMaskedLM,
             TFDebertaForQuestionAnswering,
             TFDebertaForSequenceClassification,
             TFDebertaForTokenClassification,
         )
         if is_tf_available()
         else ()
     )
     SCREAMING_SNAKE_CASE__				      =      (
         {
             '''feature-extraction''': TFDebertaModel,
             '''fill-mask''': TFDebertaForMaskedLM,
             '''question-answering''': TFDebertaForQuestionAnswering,
             '''text-classification''': TFDebertaForSequenceClassification,
             '''token-classification''': TFDebertaForTokenClassification,
             '''zero-shot''': TFDebertaForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )

     SCREAMING_SNAKE_CASE__				      =      False
     SCREAMING_SNAKE_CASE__				      =      False





     def 					__A (self	)					->       Tuple:
      _lowercase											=TFDebertaModelTester(self	)
      _lowercase											=ConfigTester(self		,					config_class=UpperCAmelCase		,					hidden_size=3_7	)





     def 					__A (self	)					->       Any:
      self.config_tester.run_common_tests()





     def 					__A (self	)					->       List[Any]:
      _lowercase											=self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*UpperCAmelCase	)





     def 					__A (self	)					->       List[str]:
      _lowercase											=self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase	)





     def 					__A (self	)					->       List[Any]:
      _lowercase											=self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase	)





     def 					__A (self	)					->       Optional[int]:
      _lowercase											=self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*UpperCAmelCase	)





     def 					__A (self	)					->       int:
      _lowercase											=self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase	)





     @slow
     def 					__A (self	)					->       Any:
      _lowercase											=TFDebertaModel.from_pretrained('''kamalkraj/deberta-base'''	)
      self.assertIsNotNone(UpperCAmelCase	)







@require_tf
class 				lowerCamelCase__       (	unittest.TestCase):





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





     @slow
     def 					__A (self	)					->       int:
      _lowercase											=TFDebertaModel.from_pretrained('''kamalkraj/deberta-base'''	)
      _lowercase											=tf.constant([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]	)
      _lowercase											=tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]	)
      _lowercase											=model(UpperCAmelCase		,					attention_mask=UpperCAmelCase	)[0]

      _lowercase											=tf.constant(
          [
              [
                  [-0.5985_5896, -0.8055_2566, -0.846_2135],
                  [1.448_4025, -0.9348_3794, -0.8059_3085],
                  [0.312_2741, 0.0031_6059, -1.413_1377],
              ]
          ]	)
      tf.debugging.assert_near(output[:, 1:4, 1:4]		,					UpperCAmelCase		,					atol=1e-4	)





def     UpperCAmelCase_ (							__snake_case						)					->      str:







 """simple docstring"""


 return " ".join(sorted(set(sentence.split()						)						)						)


if __name__ == "__main__":
  import doctest

  doctest.testmod()

