






'''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()


