





"""simple docstring"""




def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       float   )					->float:






	'''simple docstring'''






	return 10 - x * x






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       float       ,			_lowercase		:       float   )					->float:






	'''simple docstring'''






	if equation(_lowercase   ) * equation(_lowercase   ) >= 0:
		raise ValueError("Wrong space!"   )

	a :						List[str]      =							a
	while (b - a) >= 0.01:
		# Find middle point
		a :						Optional[int]      =							(a + b) / 2
		# Check if middle point is root
		if equation(_lowercase   ) == 0.0:
			break
		# Decide the side to repeat the steps
		if equation(_lowercase   ) * equation(_lowercase   ) < 0:
			a :						List[Any]      =							c
		else:
			a :						Dict      =							c
	return c


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		print(bisection(-2, 5))
		print(bisection(0, 6))









"""simple docstring"""




import unittest

import numpy as np

from transformers import RoFormerConfig, 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():
		import jax.numpy as jnp

		from transformers.models.roformer.modeling_flax_roformer import (
		    FlaxRoFormerForMaskedLM,
		    FlaxRoFormerForMultipleChoice,
		    FlaxRoFormerForQuestionAnswering,
		    FlaxRoFormerForSequenceClassification,
		    FlaxRoFormerForTokenClassification,
		    FlaxRoFormerModel,
		)


class 			__UpperCamelCase							(		unittest.TestCase		):






							def __init__(  self ,   lowerCAmelCase__ ,   lowerCAmelCase__=13 ,   lowerCAmelCase__=7 ,   lowerCAmelCase__=True ,   lowerCAmelCase__=True ,   lowerCAmelCase__=True ,   lowerCAmelCase__=True ,   lowerCAmelCase__=99 ,   lowerCAmelCase__=32 ,   lowerCAmelCase__=5 ,   lowerCAmelCase__=4 ,   lowerCAmelCase__=37 ,   lowerCAmelCase__="gelu" ,   lowerCAmelCase__=0.1 ,   lowerCAmelCase__=0.1 ,   lowerCAmelCase__=512 ,   lowerCAmelCase__=16 ,   lowerCAmelCase__=2 ,   lowerCAmelCase__=0.02 ,   lowerCAmelCase__=4 ,   )      ->					List[str]:
								a :						Any      =							parent
								a :						str      =							batch_size
								a :						str      =							seq_length
								a :						List[str]      =							is_training
								a :						Optional[int]      =							use_attention_mask
								a :						Optional[int]      =							use_token_type_ids
								a :						Union[str, Any]      =							use_labels
								a :						Optional[int]      =							vocab_size
								a :						Optional[Any]      =							hidden_size
								a :						List[Any]      =							num_hidden_layers
								a :						Optional[int]      =							num_attention_heads
								a :						Optional[int]      =							intermediate_size
								a :						Tuple      =							hidden_act
								a :						List[str]      =							hidden_dropout_prob
								a :						Optional[Any]      =							attention_probs_dropout_prob
								a :						List[Any]      =							max_position_embeddings
								a :						Optional[int]      =							type_vocab_size
								a :						Dict      =							type_sequence_label_size
								a :						int      =							initializer_range
								a :						Tuple      =							num_choices






							def        __a  (  self )      ->					List[Any]:
								a :						Dict      =							ids_tensor([self.batch_size, self.seq_length] ,   self.vocab_size )

								a :						List[str]      =							None
								if self.use_attention_mask:
									a :						Optional[Any]      =							random_attention_mask([self.batch_size, self.seq_length] )

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

								a :						Tuple      =							RoFormerConfig(
								    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 ,   is_decoder=lowerCAmelCase__ ,   initializer_range=self.initializer_range ,   )

								return config, input_ids, token_type_ids, attention_mask






							def        __a  (  self )      ->					List[str]:
								a :						Dict      =							self.prepare_config_and_inputs()
								a,			a,			a,			a :						int      =							config_and_inputs
								a :						Dict      =							{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
								return config, inputs_dict


@require_flax
class 			__UpperCamelCase							(		a__						,			unittest.TestCase		):
							lowerCamelCase  :   Union[str, Any]    =True

							lowerCamelCase  :   Union[str, Any]    =(
							    (
							        FlaxRoFormerModel,
							        FlaxRoFormerForMaskedLM,
							        FlaxRoFormerForSequenceClassification,
							        FlaxRoFormerForTokenClassification,
							        FlaxRoFormerForMultipleChoice,
							        FlaxRoFormerForQuestionAnswering,
							    )
							    if is_flax_available()
							    else ()
							)






							def        __a  (  self )      ->					List[Any]:
								a :						Dict      =							FlaxRoFormerModelTester(self )






							@slow
							def        __a  (  self )      ->					List[str]:
								for model_class_name in self.all_model_classes:
									a :						Any      =							model_class_name.from_pretrained("junnyu/roformer_chinese_small" ,   from_pt=lowerCAmelCase__ )
									a :						Optional[int]      =							model(np.ones((1, 1) ) )
									self.assertIsNotNone(lowerCAmelCase__ )


@require_flax
class 			__UpperCamelCase							(		unittest.TestCase		):






							@slow
							def        __a  (  self )      ->					List[str]:
								a :						str      =							FlaxRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base" )
								a :						List[Any]      =							jnp.array([[0, 1, 2, 3, 4, 5]] )
								a :						Any      =							model(lowerCAmelCase__ )[0]

								a :						int      =							5_0000

								a :						Any      =							(1, 6, vocab_size)
								self.assertEqual(output.shape ,   lowerCAmelCase__ )

								a :						List[Any]      =							jnp.array(
								    [[[-0.1_205, -1.0_265, 0.2_922], [-1.5_134, 0.1_974, 0.1_519], [-5.0_135, -3.9_003, -0.8_404]]] )

								self.assertTrue(jnp.allclose(output[:, :3, :3] ,   lowerCAmelCase__ ,   atol=1E-4 ) )


