




"""simple docstring"""






import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
			from transformers.models.roberta.modeling_flax_roberta import (
			    FlaxRobertaForCausalLM,
			    FlaxRobertaForMaskedLM,
			    FlaxRobertaForMultipleChoice,
			    FlaxRobertaForQuestionAnswering,
			    FlaxRobertaForSequenceClassification,
			    FlaxRobertaForTokenClassification,
			    FlaxRobertaModel,
			)






class lowerCAmelCase__	(     unittest.TestCase						):






			'''simple docstring'''



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

							_lowerCamelCase     :			Optional[int]    =    None
							if self.use_attention_mask:
											_lowerCamelCase     :			int    =    random_attention_mask([self.batch_size, self.seq_length]  )

							_lowerCamelCase     :			Optional[Any]    =    None
							if self.use_token_type_ids:
											_lowerCamelCase     :			List[Any]    =    ids_tensor([self.batch_size, self.seq_length]  ,      self.type_vocab_size  )

							_lowerCamelCase     :			List[Any]    =    RobertaConfig(
							    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=lowercase  ,      initializer_range=self.initializer_range  ,      )

							return config, input_ids, token_type_ids, attention_mask
			def 					A_    (  self  ):
							_lowerCamelCase     :			Tuple    =    self.prepare_config_and_inputs()
							_lowerCamelCase, _lowerCamelCase, _lowerCamelCase, _lowerCamelCase     :			List[str]    =    config_and_inputs
							_lowerCamelCase     :			str    =    {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
							return config, inputs_dict






			def 					A_    (  self  ):
							_lowerCamelCase     :			Dict    =    self.prepare_config_and_inputs()
							_lowerCamelCase, _lowerCamelCase, _lowerCamelCase, _lowerCamelCase     :			Dict    =    config_and_inputs

							_lowerCamelCase     :			Dict    =    True
							_lowerCamelCase     :			List[str]    =    floats_tensor([self.batch_size, self.seq_length, self.hidden_size]  )
							_lowerCamelCase     :			Dict    =    ids_tensor([self.batch_size, self.seq_length]  ,      vocab_size=2  )

							return (
							    config,
							    input_ids,
							    token_type_ids,
							    encoder_hidden_states,
							    encoder_attention_mask,
							)






@require_flax
class lowerCAmelCase__	(     lowercase,	unittest.TestCase						):






			'''simple docstring'''



			lowerCamelCase__      =			True

			lowerCamelCase__      =			(
			    (
			        FlaxRobertaModel,
			        FlaxRobertaForCausalLM,
			        FlaxRobertaForMaskedLM,
			        FlaxRobertaForSequenceClassification,
			        FlaxRobertaForTokenClassification,
			        FlaxRobertaForMultipleChoice,
			        FlaxRobertaForQuestionAnswering,
			    )
			    if is_flax_available()
			    else ()
			)
			def 					A_    (  self  ):
							_lowerCamelCase     :			Union[str, Any]    =    FlaxRobertaModelTester(self  )






			@slow
			def 					A_    (  self  ):
							for model_class_name in self.all_model_classes:
											_lowerCamelCase     :			Any    =    model_class_name.from_pretrained('roberta-base'  ,      from_pt=lowercase  )
											_lowerCamelCase     :			List[Any]    =    model(np.ones((1, 1)  )  )
											self.assertIsNotNone(lowercase  )


def    __UpperCAmelCase (	__a      :			float	)			->     float:

					"""simple docstring"""

					return 10 - x * x



def    __UpperCAmelCase (	__a      :			float		,__a      :			float	)			->     float:

					"""simple docstring"""

					if equation(__a	) * equation(__a	) >= 0:
										raise ValueError('''Wrong space!'''	)

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


if __name__ == "__main__":
			import doctest

			doctest.testmod()

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



