






'''simple docstring'''







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 					a__    :
     '''simple docstring'''







     def __init__(self    :	Any				,				lowercase__    :	Optional[int]				,				lowercase__    :	Optional[int]=13				,				lowercase__    :	Optional[Any]=7				,				lowercase__    :	Optional[int]=True				,				lowercase__    :	Optional[int]=True				,				lowercase__    :	Dict=True				,				lowercase__    :	Any=True				,				lowercase__    :	Tuple=99				,				lowercase__    :	int=32				,				lowercase__    :	Any=2				,				lowercase__    :	Optional[Any]=4				,				lowercase__    :	int=37				,				lowercase__    :	int="gelu"				,				lowercase__    :	str=0.1				,				lowercase__    :	int=0.1				,				lowercase__    :	str=512				,				lowercase__    :	List[Any]=16				,				lowercase__    :	int=2				,				lowercase__    :	str=0.02				,				lowercase__    :	Optional[int]=3				,				lowercase__    :	str=4				,				lowercase__    :	List[str]=None				,				)  ->       List[Any]:
           __A		   =      parent
           __A		   =      13
           __A		   =      7
           __A		   =      True
           __A		   =      True
           __A		   =      True
           __A		   =      True
           __A		   =      99
           __A		   =      32
           __A		   =      2
           __A		   =      4
           __A		   =      37
           __A		   =      "gelu"
           __A		   =      0.1
           __A		   =      0.1
           __A		   =      512
           __A		   =      16
           __A		   =      False
           __A		   =      -1
           __A		   =      True
           __A		   =      2
           __A		   =      0.02
           __A		   =      3
           __A		   =      4
           __A		   =      None






     def     _UpperCamelCase   (self    :	List[Any]					)  ->       List[str]:
           __A		   =      ids_tensor([self.batch_size, self.seq_length]				,				self.vocab_size					)

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

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

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

           __A		   =      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=lowercase__				,				)

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






     def     _UpperCamelCase   (self    :	Optional[int]				,				lowercase__    :	Dict				,				lowercase__    :	List[Any]				,				lowercase__    :	List[Any]				,				lowercase__    :	List[Any]				,				lowercase__    :	Dict				,				lowercase__    :	str				,				lowercase__    :	List[Any]					)  ->       Optional[Any]:
           __A		   =      TFDebertaModel(config=lowercase__					)
           __A		   =      {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

           __A		   =      [input_ids, input_mask]
           __A		   =      model(lowercase__					)

           __A		   =      model(lowercase__					)

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






     def     _UpperCamelCase   (self    :	List[Any]				,				lowercase__    :	Any				,				lowercase__    :	Optional[Any]				,				lowercase__    :	Any				,				lowercase__    :	List[str]				,				lowercase__    :	Union[str, Any]				,				lowercase__    :	Optional[Any]				,				lowercase__    :	List[str]					)  ->       List[Any]:
           __A		   =      TFDebertaForMaskedLM(config=lowercase__					)
           __A		   =      {
               "input_ids": input_ids,
               "attention_mask": input_mask,
               "token_type_ids": token_type_ids,
           }
           __A		   =      model(lowercase__					)
           self.parent.assertEqual(result.logits.shape				,				(self.batch_size, self.seq_length, self.vocab_size)					)






     def     _UpperCamelCase   (self    :	Tuple				,				lowercase__    :	List[str]				,				lowercase__    :	Optional[Any]				,				lowercase__    :	str				,				lowercase__    :	List[str]				,				lowercase__    :	int				,				lowercase__    :	Union[str, Any]				,				lowercase__    :	List[str]					)  ->       List[Any]:
           __A		   =      self.num_labels
           __A		   =      TFDebertaForSequenceClassification(config=lowercase__					)
           __A		   =      {
               "input_ids": input_ids,
               "attention_mask": input_mask,
               "token_type_ids": token_type_ids,
           }

           __A		   =      model(lowercase__					)
           self.parent.assertEqual(result.logits.shape				,				(self.batch_size, self.num_labels)					)






     def     _UpperCamelCase   (self    :	str				,				lowercase__    :	Dict				,				lowercase__    :	Tuple				,				lowercase__    :	int				,				lowercase__    :	Optional[int]				,				lowercase__    :	Dict				,				lowercase__    :	Union[str, Any]				,				lowercase__    :	str					)  ->       int:
           __A		   =      self.num_labels
           __A		   =      TFDebertaForTokenClassification(config=lowercase__					)
           __A		   =      {
               "input_ids": input_ids,
               "attention_mask": input_mask,
               "token_type_ids": token_type_ids,
           }
           __A		   =      model(lowercase__					)
           self.parent.assertEqual(result.logits.shape				,				(self.batch_size, self.seq_length, self.num_labels)					)






     def     _UpperCamelCase   (self    :	Tuple				,				lowercase__    :	Any				,				lowercase__    :	Union[str, Any]				,				lowercase__    :	Optional[Any]				,				lowercase__    :	Optional[Any]				,				lowercase__    :	List[Any]				,				lowercase__    :	Optional[Any]				,				lowercase__    :	Tuple					)  ->       Tuple:
           __A		   =      TFDebertaForQuestionAnswering(config=lowercase__					)
           __A		   =      {
               "input_ids": input_ids,
               "attention_mask": input_mask,
               "token_type_ids": token_type_ids,
           }

           __A		   =      model(lowercase__					)
           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     _UpperCamelCase   (self    :	List[Any]					)  ->       Any:
           __A		   =      self.prepare_config_and_inputs()
           (
               (
               __A
           )   ,				(
               __A
           )   ,				(
               __A
           )   ,				(
               __A
           )   ,				(
               __A
           )   ,				(
               __A
           )   ,				(
               __A
           )   ,				
           )		   =      config_and_inputs
           __A		   =      {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
           return config, inputs_dict







@require_tf
class 					a__    (			lowerCamelCase ,						lowerCamelCase ,						unittest.TestCase						):
     '''simple docstring'''







     _snake_case             =  (
         (
             TFDebertaModel,
             TFDebertaForMaskedLM,
             TFDebertaForQuestionAnswering,
             TFDebertaForSequenceClassification,
             TFDebertaForTokenClassification,
         )
         if is_tf_available()
         else ()
     )
     _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 {}
     )

     _snake_case             =  False
     _snake_case             =  False






     def     _UpperCamelCase   (self    :	Any					)  ->       Optional[Any]:
           __A		   =      TFDebertaModelTester(self					)
           __A		   =      ConfigTester(self				,				config_class=lowercase__				,				hidden_size=37					)






     def     _UpperCamelCase   (self    :	Union[str, Any]					)  ->       Tuple:
           self.config_tester.run_common_tests()






     def     _UpperCamelCase   (self    :	Any					)  ->       Union[str, Any]:
           __A		   =      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*lowercase__					)






     def     _UpperCamelCase   (self    :	List[str]					)  ->       Dict:
           __A		   =      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_lm(*lowercase__					)






     def     _UpperCamelCase   (self    :	Dict					)  ->       Tuple:
           __A		   =      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_question_answering(*lowercase__					)






     def     _UpperCamelCase   (self    :	Union[str, Any]					)  ->       str:
           __A		   =      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_sequence_classification(*lowercase__					)






     def     _UpperCamelCase   (self    :	int					)  ->       Tuple:
           __A		   =      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_token_classification(*lowercase__					)






     @slow
     def     _UpperCamelCase   (self    :	Optional[Any]					)  ->       str:
           __A		   =      TFDebertaModel.from_pretrained("kamalkraj/deberta-base"					)
           self.assertIsNotNone(lowercase__					)







@require_tf
class 					a__    (			unittest.TestCase						):
     '''simple docstring'''







     @unittest.skip(reason="Model not available yet"					)
     def     _UpperCamelCase   (self    :	Union[str, Any]					)  ->       Tuple:
           pass






     @slow
     def     _UpperCamelCase   (self    :	List[str]					)  ->       Tuple:
           __A		   =      TFDebertaModel.from_pretrained("kamalkraj/deberta-base"					)
           __A		   =      tf.constant([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]					)
           __A		   =      tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]					)
           __A		   =      model(lowercase__				,				attention_mask=lowercase__					)[0]

           __A		   =      tf.constant(
               [
                   [
                       [-0.59_85_58_96, -0.80_55_25_66, -0.8_46_21_35],
                       [1.4_48_40_25, -0.93_48_37_94, -0.80_59_30_85],
                       [0.3_12_27_41, 0.00_31_60_59, -1.4_13_13_77],
                   ]
               ]					)
           tf.debugging.assert_near(output[:, 1:4, 1:4]				,				lowercase__				,				atol=1e-4					)










'''simple docstring'''







def 						_UpperCamelCase							(     snake_case__							,			snake_case__							):
      if n < 0:
            return 0
      # table[i] represents the number of ways to get to amount i
      __A		   =      [0] * (n + 1)

      # There is exactly 1 way to get to zero(You pick no coins).
      __A		   =      1

      # Pick all coins one by one and update table[] values
      # after the index greater than or equal to the value of the
      # picked coin
      for coin_val in s:
            for j in range(snake_case__							,			n + 1							):
                  table[j] += table[j - coin_val]

      return table[n]


if __name__ == "__main__":
       import doctest

       doctest.testmod()


