
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 							lowercase   :



		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_=3							, A_=4							, A_=None							, )    ->   Dict:
				"""simple docstring"""
				UpperCamelCase            =	parent
				UpperCamelCase            =	13
				UpperCamelCase            =	7
				UpperCamelCase            =	True
				UpperCamelCase            =	True
				UpperCamelCase            =	True
				UpperCamelCase            =	True
				UpperCamelCase            =	99
				UpperCamelCase            =	32
				UpperCamelCase            =	2
				UpperCamelCase            =	4
				UpperCamelCase            =	37
				UpperCamelCase            =	'''gelu'''
				UpperCamelCase            =	0.1
				UpperCamelCase            =	0.1
				UpperCamelCase            =	512
				UpperCamelCase            =	16
				UpperCamelCase            =	False
				UpperCamelCase            =	-1
				UpperCamelCase            =	True
				UpperCamelCase            =	2
				UpperCamelCase            =	0.02
				UpperCamelCase            =	3
				UpperCamelCase            =	4
				UpperCamelCase            =	None



		def __UpperCamelCase					(					self			)    ->   str:
				"""simple docstring"""
				UpperCamelCase            =	ids_tensor([self.batch_size, self.seq_length]							, self.vocab_size			)

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

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

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

				UpperCamelCase            =	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 __UpperCamelCase					(					self							, A_							, A_							, A_							, A_							, A_							, A_							, A_			)    ->   Optional[int]:
				"""simple docstring"""
				UpperCamelCase            =	TFDebertaModel(config=__UpperCAmelCase			)
				UpperCamelCase            =	{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

				UpperCamelCase            =	[input_ids, input_mask]
				UpperCamelCase            =	model(__UpperCAmelCase			)

				UpperCamelCase            =	model(__UpperCAmelCase			)

				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_			)    ->   Any:
				"""simple docstring"""
				UpperCamelCase            =	TFDebertaForMaskedLM(config=__UpperCAmelCase			)
				UpperCamelCase            =	{
				    '''input_ids''': input_ids,
				    '''attention_mask''': input_mask,
				    '''token_type_ids''': token_type_ids,
				}
				UpperCamelCase            =	model(__UpperCAmelCase			)
				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_			)    ->   Optional[int]:
				"""simple docstring"""
				UpperCamelCase            =	self.num_labels
				UpperCamelCase            =	TFDebertaForSequenceClassification(config=__UpperCAmelCase			)
				UpperCamelCase            =	{
				    '''input_ids''': input_ids,
				    '''attention_mask''': input_mask,
				    '''token_type_ids''': token_type_ids,
				}

				UpperCamelCase            =	model(__UpperCAmelCase			)
				self.parent.assertEqual(result.logits.shape							, (self.batch_size, self.num_labels)			)



		def __UpperCamelCase					(					self							, A_							, A_							, A_							, A_							, A_							, A_							, A_			)    ->   Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase            =	self.num_labels
				UpperCamelCase            =	TFDebertaForTokenClassification(config=__UpperCAmelCase			)
				UpperCamelCase            =	{
				    '''input_ids''': input_ids,
				    '''attention_mask''': input_mask,
				    '''token_type_ids''': token_type_ids,
				}
				UpperCamelCase            =	model(__UpperCAmelCase			)
				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_			)    ->   List[str]:
				"""simple docstring"""
				UpperCamelCase            =	TFDebertaForQuestionAnswering(config=__UpperCAmelCase			)
				UpperCamelCase            =	{
				    '''input_ids''': input_ids,
				    '''attention_mask''': input_mask,
				    '''token_type_ids''': token_type_ids,
				}

				UpperCamelCase            =	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 __UpperCamelCase					(					self			)    ->   Optional[int]:
				"""simple docstring"""
				UpperCamelCase            =	self.prepare_config_and_inputs()
				(
				    UpperCamelCase						
				)            =	config_and_inputs
				UpperCamelCase            =	{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
				return config, inputs_dict






@require_tf
class 							lowercase   (						_lowerCAmelCase    ,  _lowerCAmelCase    ,  unittest.TestCase  ):
		__lowercase			:			Tuple      	=							(
		    (
		        TFDebertaModel,
		        TFDebertaForMaskedLM,
		        TFDebertaForQuestionAnswering,
		        TFDebertaForSequenceClassification,
		        TFDebertaForTokenClassification,
		    )
		    if is_tf_available()
		    else ()
		)
		__lowercase			:			Union[str, Any]      	=							(
		    {
		        "feature-extraction": TFDebertaModel,
		        "fill-mask": TFDebertaForMaskedLM,
		        "question-answering": TFDebertaForQuestionAnswering,
		        "text-classification": TFDebertaForSequenceClassification,
		        "token-classification": TFDebertaForTokenClassification,
		        "zero-shot": TFDebertaForSequenceClassification,
		    }
		    if is_tf_available()
		    else {}
		)

		__lowercase			:			Optional[int]      	=							False
		__lowercase			:			Any      	=							False



		def __UpperCamelCase					(					self			)    ->   List[Any]:
				"""simple docstring"""
				UpperCamelCase            =	TFDebertaModelTester(self			)
				UpperCamelCase            =	ConfigTester(self							, config_class=__UpperCAmelCase							, hidden_size=37			)



		def __UpperCamelCase					(					self			)    ->   Dict:
				"""simple docstring"""
				self.config_tester.run_common_tests()



		def __UpperCamelCase					(					self			)    ->   List[Any]:
				"""simple docstring"""
				UpperCamelCase            =	self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*__UpperCAmelCase			)



		def __UpperCamelCase					(					self			)    ->   Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase            =	self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_masked_lm(*__UpperCAmelCase			)



		def __UpperCamelCase					(					self			)    ->   Dict:
				"""simple docstring"""
				UpperCamelCase            =	self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_question_answering(*__UpperCAmelCase			)



		def __UpperCamelCase					(					self			)    ->   List[Any]:
				"""simple docstring"""
				UpperCamelCase            =	self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_sequence_classification(*__UpperCAmelCase			)



		def __UpperCamelCase					(					self			)    ->   int:
				"""simple docstring"""
				UpperCamelCase            =	self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_token_classification(*__UpperCAmelCase			)



		@slow
		def __UpperCamelCase					(					self			)    ->   List[Any]:
				"""simple docstring"""
				UpperCamelCase            =	TFDebertaModel.from_pretrained('kamalkraj/deberta-base'			)
				self.assertIsNotNone(__UpperCAmelCase			)






@require_tf
class 							lowercase   (						unittest.TestCase  ):



		@unittest.skip(reason='Model not available yet'			)
		def __UpperCamelCase					(					self			)    ->   int:
				"""simple docstring"""
				pass



		@slow
		def __UpperCamelCase					(					self			)    ->   Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase            =	TFDebertaModel.from_pretrained('kamalkraj/deberta-base'			)
				UpperCamelCase            =	tf.constant([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]			)
				UpperCamelCase            =	tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]			)
				UpperCamelCase            =	model(__UpperCAmelCase							, attention_mask=__UpperCAmelCase			)[0]

				UpperCamelCase            =	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			)

"""simple docstring"""




from collections.abc import Callable

UpperCAmelCase       :    Optional[int]									=			Callable[[float], float]  # type alias for a real -> real function



def 				__a       (			_lowercase							, _lowercase							, _lowercase							, ):







					"""simple docstring"""





					lowerCamelCase__		:   Tuple  				=					float(_lowercase						)
					while True:
										try:
															lowerCamelCase__		:   str  				=					prev_guess - function(_lowercase						) / derivative(_lowercase						)
										except ZeroDivisionError:
															raise ZeroDivisionError('''Could not find root'''						) from None
										if abs(prev_guess - next_guess						) < 10**-5:
															return next_guess
										lowerCamelCase__		:   List[str]  				=					next_guess



def 				__a       (			_lowercase						):







					"""simple docstring"""





					return (x**3) - (2 * x) - 5



def 				__a       (			_lowercase						):







					"""simple docstring"""





					return 3 * (x**2) - 2


if __name__ == "__main__":
					print(newton(f, fa, 3))






