






"""simple docstring"""






from __future__ import annotations

import unittest

from transformers import DebertaVaConfig, 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 (
         TFDebertaVaForMaskedLM,
         TFDebertaVaForQuestionAnswering,
         TFDebertaVaForSequenceClassification,
         TFDebertaVaForTokenClassification,
         TFDebertaVaModel,
     )





class       snake_case_       :



 """simple docstring"""

 def __init__(      self  ,					__a  ,					__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=2  ,					__a=0.02  ,					__a=False  ,					__a=True  ,					__a="None"  ,					__a=3  ,					__a=4  ,					__a=None  ,					):


     """simple docstring"""

     A__							 =       parent
     A__							 =       batch_size
     A__							 =       seq_length
     A__							 =       is_training
     A__							 =       use_input_mask
     A__							 =       use_token_type_ids
     A__							 =       use_labels
     A__							 =       vocab_size
     A__							 =       hidden_size
     A__							 =       num_hidden_layers
     A__							 =       num_attention_heads
     A__							 =       intermediate_size
     A__							 =       hidden_act
     A__							 =       hidden_dropout_prob
     A__							 =       attention_probs_dropout_prob
     A__							 =       max_position_embeddings
     A__							 =       type_vocab_size
     A__							 =       type_sequence_label_size
     A__							 =       initializer_range
     A__							 =       num_labels
     A__							 =       num_choices
     A__							 =       relative_attention
     A__							 =       position_biased_input
     A__							 =       pos_att_type
     A__							 =       scope


 def       _UpperCAmelCase						(      self						):


     """simple docstring"""

     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__							 =       DebertaVaConfig(
         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  ,					position_biased_input=self.position_biased_input  ,					initializer_range=self.initializer_range  ,					return_dict=__a  ,					)

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


 def       _UpperCAmelCase						(      self  ,					__a  ,					__a  ,					__a  ,					__a  ,					__a  ,					__a  ,					__a						):


     """simple docstring"""

     A__							 =       TFDebertaVaModel(config=__a						)
     A__							 =       {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

     A__							 =       [input_ids, input_mask]
     A__							 =       model(__a						)

     A__							 =       model(__a						)

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


 def       _UpperCAmelCase						(      self  ,					__a  ,					__a  ,					__a  ,					__a  ,					__a  ,					__a  ,					__a						):


     """simple docstring"""

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


 def       _UpperCAmelCase						(      self  ,					__a  ,					__a  ,					__a  ,					__a  ,					__a  ,					__a  ,					__a						):


     """simple docstring"""

     A__							 =       self.num_labels
     A__							 =       TFDebertaVaForSequenceClassification(config=__a						)
     A__							 =       {
         'input_ids': input_ids,
         'attention_mask': input_mask,
         'token_type_ids': token_type_ids,
     }

     A__							 =       model(__a						)
     self.parent.assertEqual(result.logits.shape  ,					(self.batch_size, self.num_labels)						)


 def       _UpperCAmelCase						(      self  ,					__a  ,					__a  ,					__a  ,					__a  ,					__a  ,					__a  ,					__a						):


     """simple docstring"""

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


 def       _UpperCAmelCase						(      self  ,					__a  ,					__a  ,					__a  ,					__a  ,					__a  ,					__a  ,					__a						):


     """simple docstring"""

     A__							 =       TFDebertaVaForQuestionAnswering(config=__a						)
     A__							 =       {
         'input_ids': input_ids,
         'attention_mask': input_mask,
         'token_type_ids': token_type_ids,
     }

     A__							 =       model(__a						)
     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						):


     """simple docstring"""

     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       snake_case_       (    _lowerCamelCase    ,							_lowerCamelCase    ,							unittest.TestCase       ):



 """simple docstring"""

 SCREAMING_SNAKE_CASE_:							List[Any]   			=					(
     (
         TFDebertaVaModel,
         TFDebertaVaForMaskedLM,
         TFDebertaVaForQuestionAnswering,
         TFDebertaVaForSequenceClassification,
         TFDebertaVaForTokenClassification,
     )
     if is_tf_available()
     else ()
 )
 SCREAMING_SNAKE_CASE_:							Any   			=					(
     {
         """feature-extraction""": TFDebertaVaModel,
         """fill-mask""": TFDebertaVaForMaskedLM,
         """question-answering""": TFDebertaVaForQuestionAnswering,
         """text-classification""": TFDebertaVaForSequenceClassification,
         """token-classification""": TFDebertaVaForTokenClassification,
         """zero-shot""": TFDebertaVaForSequenceClassification,
     }
     if is_tf_available()
     else {}
 )

 SCREAMING_SNAKE_CASE_:							Optional[Any]   			=					False
 SCREAMING_SNAKE_CASE_:							List[str]   			=					False


 def       _UpperCAmelCase						(      self						):


     """simple docstring"""

     A__							 =       TFDebertaVaModelTester(self						)
     A__							 =       ConfigTester(self  ,					config_class=__a  ,					hidden_size=37						)


 def       _UpperCAmelCase						(      self						):


     """simple docstring"""

     self.config_tester.run_common_tests()


 def       _UpperCAmelCase						(      self						):


     """simple docstring"""

     A__							 =       self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*__a						)


 def       _UpperCAmelCase						(      self						):


     """simple docstring"""

     A__							 =       self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_lm(*__a						)


 def       _UpperCAmelCase						(      self						):


     """simple docstring"""

     A__							 =       self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_question_answering(*__a						)


 def       _UpperCAmelCase						(      self						):


     """simple docstring"""

     A__							 =       self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_sequence_classification(*__a						)


 def       _UpperCAmelCase						(      self						):


     """simple docstring"""

     A__							 =       self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_token_classification(*__a						)






 @slow
 def       _UpperCAmelCase						(      self						):


     """simple docstring"""

     A__							 =       TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge'						)
     self.assertIsNotNone(__a						)





@require_tf
class       snake_case_       (    unittest.TestCase       ):



 """simple docstring"""

 @unittest.skip(reason='Model not available yet'						)
 def       _UpperCAmelCase						(      self						):


     """simple docstring"""

     pass






 @slow
 def       _UpperCAmelCase						(      self						):


     """simple docstring"""

     A__							 =       TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge'						)
     A__							 =       tf.constant([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]						)
     A__							 =       tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]						)
     A__							 =       model(__a  ,					attention_mask=__a						)[0]

     A__							 =       tf.constant(
         [[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]]						)
     tf.debugging.assert_near(output[:, 1:4, 1:4]  ,					__a  ,					atol=1E-4						)










"""simple docstring"""






from math import factorial, pi



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__ = 30				):

				if not isinstance(lowerCAmelCase__       ,(int, float)				):
								raise ValueError('maclaurin_sin() requires either an int or float for theta'				)

				if not isinstance(lowerCAmelCase__       ,lowerCAmelCase__				) or accuracy <= 0:
								raise ValueError('maclaurin_sin() requires a positive int for accuracy'				)

				A__							 =       float(lowerCAmelCase__				)
				A__							 =       theta // (2 * pi)
				theta -= 2 * div * pi
				return sum(
				    (-1) ** r * theta ** (2 * r + 1) / factorial(2 * r + 1				) for r in range(lowerCAmelCase__				)				)



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__ = 30				):

				if not isinstance(lowerCAmelCase__       ,(int, float)				):
								raise ValueError('maclaurin_cos() requires either an int or float for theta'				)

				if not isinstance(lowerCAmelCase__       ,lowerCAmelCase__				) or accuracy <= 0:
								raise ValueError('maclaurin_cos() requires a positive int for accuracy'				)

				A__							 =       float(lowerCAmelCase__				)
				A__							 =       theta // (2 * pi)
				theta -= 2 * div * pi
				return sum((-1) ** r * theta ** (2 * r) / factorial(2 * r				) for r in range(lowerCAmelCase__				)				)


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					print(maclaurin_sin(10))
					print(maclaurin_sin(-10))
					print(maclaurin_sin(10, 15))
					print(maclaurin_sin(-10, 15))

					print(maclaurin_cos(5))
					print(maclaurin_cos(-5))
					print(maclaurin_cos(10, 15))
					print(maclaurin_cos(-10, 15))


