"""simple docstring"""







from collections.abc import Callable


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    float							=							a
	_lowercase :    float							=							b
	if function(__UpperCAmelCase			) == 0:  # one of the a or b is a root for the function
		return a
	elif function(__UpperCAmelCase			) == 0:
		return b
	elif (
	    function(__UpperCAmelCase			) * function(__UpperCAmelCase			) > 0
	):  # if none of these are root and they are both positive or negative,
		# then this algorithm can't find the root
		raise ValueError('could not find root in given interval.'			)
	else:
		_lowercase :    float							=							start + (end - start) / 2.0
		while abs(start - mid			) > 10**-7:  # until precisely equals to 10^-7
			if function(__UpperCAmelCase			) == 0:
				return mid
			elif function(__UpperCAmelCase			) * function(__UpperCAmelCase			) < 0:
				_lowercase :    Union[str, Any]							=							mid
			else:
				_lowercase :    Any							=							mid
			_lowercase :    Dict							=							start + (end - start) / 2.0
		return mid


def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	return x**3 - 2 * x - 5


if __name__ == "__main__":
						print(bisection(f, 1, 1000))

						import doctest

						doctest.testmod()




"""simple docstring"""







from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE									=						{
    'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
    'tokenization_biogpt': ['BioGptTokenizer'],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						SCREAMING_SNAKE_CASE									=						[
						    '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

						SCREAMING_SNAKE_CASE									=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



