




"""simple docstring"""







import math
from collections.abc import Callable




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    xa
					__SCREAMING_SNAKE_CASE						       =    xa
					while True:
										if x_n == x_na or function(__UpperCAmelCase       ) == function(__UpperCAmelCase       ):
															raise ZeroDivisionError("float division by zero, could not find root"       )
										__SCREAMING_SNAKE_CASE						       =    x_na - (
										    function(__UpperCAmelCase       ) / ((function(__UpperCAmelCase       ) - function(__UpperCAmelCase       )) / (x_na - x_n))
										)
										if abs(x_na - x_na       ) < 10**-5:
															return x_na
										__SCREAMING_SNAKE_CASE						       =    x_na
										__SCREAMING_SNAKE_CASE						       =    x_na




def        UpperCAmelCase__	(lowerCAmelCase_       ):




					'''simple docstring'''


					return math.pow(__UpperCAmelCase  ,						3       ) - (2 * x) - 5


if __name__ == "__main__":
		print(intersection(f, 3, 3.5))




"""simple docstring"""







from typing import TYPE_CHECKING

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


a__    :							List[str]            =					{
    '''configuration_biogpt''': ['''BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BioGptConfig'''],
    '''tokenization_biogpt''': ['''BioGptTokenizer'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__    :							Union[str, Any]            =					[
		    '''BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''BioGptForCausalLM''',
		    '''BioGptForTokenClassification''',
		    '''BioGptForSequenceClassification''',
		    '''BioGptModel''',
		    '''BioGptPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
		from .tokenization_biogpt import BioGptTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_biogpt import (
				    BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BioGptForCausalLM,
				    BioGptForSequenceClassification,
				    BioGptForTokenClassification,
				    BioGptModel,
				    BioGptPreTrainedModel,
				)


else:
		import sys

		a__    :							Optional[int]            =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
