"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCamelCase_							:							Optional[Any]				 =		{
    '''configuration_flaubert''': ['''FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FlaubertConfig''', '''FlaubertOnnxConfig'''],
    '''tokenization_flaubert''': ['''FlaubertTokenizer'''],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase_							:							Dict				 =		[
						    '''FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''FlaubertForMultipleChoice''',
						    '''FlaubertForQuestionAnswering''',
						    '''FlaubertForQuestionAnsweringSimple''',
						    '''FlaubertForSequenceClassification''',
						    '''FlaubertForTokenClassification''',
						    '''FlaubertModel''',
						    '''FlaubertWithLMHeadModel''',
						    '''FlaubertPreTrainedModel''',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase_							:							str				 =		[
						    '''TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TFFlaubertForMultipleChoice''',
						    '''TFFlaubertForQuestionAnsweringSimple''',
						    '''TFFlaubertForSequenceClassification''',
						    '''TFFlaubertForTokenClassification''',
						    '''TFFlaubertModel''',
						    '''TFFlaubertPreTrainedModel''',
						    '''TFFlaubertWithLMHeadModel''',
						]


if TYPE_CHECKING:
						from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
						from .tokenization_flaubert import FlaubertTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flaubert import (
												    FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    FlaubertForMultipleChoice,
												    FlaubertForQuestionAnswering,
												    FlaubertForQuestionAnsweringSimple,
												    FlaubertForSequenceClassification,
												    FlaubertForTokenClassification,
												    FlaubertModel,
												    FlaubertPreTrainedModel,
												    FlaubertWithLMHeadModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_flaubert import (
												    TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFFlaubertForMultipleChoice,
												    TFFlaubertForQuestionAnsweringSimple,
												    TFFlaubertForSequenceClassification,
												    TFFlaubertForTokenClassification,
												    TFFlaubertModel,
												    TFFlaubertPreTrainedModel,
												    TFFlaubertWithLMHeadModel,
												)

else:
						import sys

						UpperCamelCase_							:							Union[str, Any]				 =		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)









"""simple docstring"""



from math import exp




def       snake_case__		(   _SCREAMING_SNAKE_CASE			)    ->float:
 return 8 * x - 2 * exp(-x			)




def       snake_case__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)    ->float:
 UpperCAmelCase__ 						=				lower_bound
 UpperCAmelCase__ 						=				upper_bound
 for _ in range(0       ,	_SCREAMING_SNAKE_CASE			):
  UpperCAmelCase__						,	UpperCAmelCase__ 						=				xa, xa - (f(_SCREAMING_SNAKE_CASE			) * (xa - xa)) / (f(_SCREAMING_SNAKE_CASE			) - f(_SCREAMING_SNAKE_CASE			))
 return xa


if __name__ == "__main__":
 print(F'''Example: {secant_method(1, 3, 2)}''')



