"""simple docstring"""




import unittest

import numpy as np

from transformers import ElectraConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
					from transformers.models.electra.modeling_flax_electra import (
					    FlaxElectraForCausalLM,
					    FlaxElectraForMaskedLM,
					    FlaxElectraForMultipleChoice,
					    FlaxElectraForPreTraining,
					    FlaxElectraForQuestionAnswering,
					    FlaxElectraForSequenceClassification,
					    FlaxElectraForTokenClassification,
					    FlaxElectraModel,
					)

class        __SCREAMING_SNAKE_CASE     (   unittest.TestCase							):







				def __init__(    self    :str      ,__UpperCAmelCase    :List[str]      ,__UpperCAmelCase    :Tuple=13      ,__UpperCAmelCase    :List[Any]=7      ,__UpperCAmelCase    :Union[str, Any]=True      ,__UpperCAmelCase    :str=True      ,__UpperCAmelCase    :Tuple=True      ,__UpperCAmelCase    :List[str]=True      ,__UpperCAmelCase    :List[Any]=99      ,__UpperCAmelCase    :Any=24      ,__UpperCAmelCase    :Any=32      ,__UpperCAmelCase    :Optional[Any]=5      ,__UpperCAmelCase    :List[str]=4      ,__UpperCAmelCase    :Any=37      ,__UpperCAmelCase    :List[str]="gelu"      ,__UpperCAmelCase    :Union[str, Any]=0.1      ,__UpperCAmelCase    :Optional[Any]=0.1      ,__UpperCAmelCase    :str=5_12      ,__UpperCAmelCase    :Tuple=16      ,__UpperCAmelCase    :str=2      ,__UpperCAmelCase    :List[Any]=0.02      ,__UpperCAmelCase    :int=4      ,)      ->					Dict:




									"""simple docstring"""



									lowerCamelCase__		:   Optional[Any]  				=					parent
									lowerCamelCase__		:   Tuple  				=					batch_size
									lowerCamelCase__		:   Union[str, Any]  				=					seq_length
									lowerCamelCase__		:   Any  				=					is_training
									lowerCamelCase__		:   List[Any]  				=					use_attention_mask
									lowerCamelCase__		:   str  				=					use_token_type_ids
									lowerCamelCase__		:   Optional[int]  				=					use_labels
									lowerCamelCase__		:   Tuple  				=					vocab_size
									lowerCamelCase__		:   int  				=					hidden_size
									lowerCamelCase__		:   List[str]  				=					embedding_size
									lowerCamelCase__		:   Any  				=					num_hidden_layers
									lowerCamelCase__		:   Tuple  				=					num_attention_heads
									lowerCamelCase__		:   int  				=					intermediate_size
									lowerCamelCase__		:   str  				=					hidden_act
									lowerCamelCase__		:   List[Any]  				=					hidden_dropout_prob
									lowerCamelCase__		:   List[Any]  				=					attention_probs_dropout_prob
									lowerCamelCase__		:   Union[str, Any]  				=					max_position_embeddings
									lowerCamelCase__		:   Any  				=					type_vocab_size
									lowerCamelCase__		:   Optional[Any]  				=					type_sequence_label_size
									lowerCamelCase__		:   List[Any]  				=					initializer_range
									lowerCamelCase__		:   Union[str, Any]  				=					num_choices







				def   lowercase_		(    self    :List[Any]  )      ->					Optional[int]:




									"""simple docstring"""



									lowerCamelCase__		:   int  				=					ids_tensor([self.batch_size, self.seq_length]      ,self.vocab_size  )

									lowerCamelCase__		:   Any  				=					None
									if self.use_attention_mask:
														lowerCamelCase__		:   Union[str, Any]  				=					random_attention_mask([self.batch_size, self.seq_length]  )

									lowerCamelCase__		:   Dict  				=					None
									if self.use_token_type_ids:
														lowerCamelCase__		:   List[str]  				=					ids_tensor([self.batch_size, self.seq_length]      ,self.type_vocab_size  )

									lowerCamelCase__		:   int  				=					ElectraConfig(
									    vocab_size=self.vocab_size      ,hidden_size=self.hidden_size      ,embedding_size=self.embedding_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      ,initializer_range=self.initializer_range      ,)

									return config, input_ids, token_type_ids, attention_mask







				def   lowercase_		(    self    :str  )      ->					List[Any]:




									"""simple docstring"""



									lowerCamelCase__		:   Optional[Any]  				=					self.prepare_config_and_inputs()
									lowerCamelCase__				,						lowerCamelCase__				,						lowerCamelCase__				,						lowerCamelCase__		:   Tuple  				=					config_and_inputs
									lowerCamelCase__		:   List[str]  				=					{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
									return config, inputs_dict





@require_flax
class        __SCREAMING_SNAKE_CASE     (   _lowerCAmelCase						,       unittest.TestCase							):
				UpperCAmelCase	=      True

				UpperCAmelCase	=      (
				    (
				        FlaxElectraModel,
				        FlaxElectraForCausalLM,
				        FlaxElectraForMaskedLM,
				        FlaxElectraForPreTraining,
				        FlaxElectraForTokenClassification,
				        FlaxElectraForQuestionAnswering,
				        FlaxElectraForMultipleChoice,
				        FlaxElectraForSequenceClassification,
				    )
				    if is_flax_available()
				    else ()
				)







				def   lowercase_		(    self    :List[Any]  )      ->					Tuple:




									"""simple docstring"""



									lowerCamelCase__		:   str  				=					FlaxElectraModelTester(self  )







				@slow
				def   lowercase_		(    self    :List[Any]  )      ->					Optional[Any]:




									"""simple docstring"""



									for model_class_name in self.all_model_classes:
														if model_class_name == FlaxElectraForMaskedLM:
																			lowerCamelCase__		:   Union[str, Any]  				=					model_class_name.from_pretrained('''google/electra-small-generator'''  )
														else:
																			lowerCamelCase__		:   Dict  				=					model_class_name.from_pretrained('''google/electra-small-discriminator'''  )
														lowerCamelCase__		:   List[Any]  				=					model(np.ones((1, 1)  )  )
														self.assertIsNotNone(__UpperCAmelCase  )







"""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))






