

from collections.abc import Callable






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Callable[[float], float]							,       UpperCAmelCase_							:     float							,       UpperCAmelCase_							:     float	)						->				float:




	'''simple docstring'''




	_UpperCamelCase   :		float			=						a
	_UpperCamelCase   :		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:
		_UpperCamelCase   :		float			=						start + (end - start) / 2.0
		while abs(start - mid	) > 1_0**-7:  # until precisely equals to 10^-7
			if function(UpperCAmelCase_	) == 0:
				return mid
			elif function(UpperCAmelCase_	) * function(UpperCAmelCase_	) < 0:
				_UpperCamelCase   :		Dict			=						mid
			else:
				_UpperCamelCase   :		Optional[Any]			=						mid
			_UpperCamelCase   :		Any			=						start + (end - start) / 2.0
		return mid






def 					lowerCamelCase_     (							UpperCAmelCase_							:     float	)						->				float:




	'''simple docstring'''




	return x**3 - 2 * x - 5


if __name__ == "__main__":
							print(bisection(f, 1, 1_0_0_0))

							import doctest

							doctest.testmod()







from typing import TYPE_CHECKING

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


lowerCAmelCase__								=		{
    """configuration_canine""": ["""CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CanineConfig"""],
    """tokenization_canine""": ["""CanineTokenizer"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase__								=		[
							    """CANINE_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """CanineForMultipleChoice""",
							    """CanineForQuestionAnswering""",
							    """CanineForSequenceClassification""",
							    """CanineForTokenClassification""",
							    """CanineLayer""",
							    """CanineModel""",
							    """CaninePreTrainedModel""",
							    """load_tf_weights_in_canine""",
							]


if TYPE_CHECKING:
							from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
							from .tokenization_canine import CanineTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_canine import (
														    CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
														    CanineForMultipleChoice,
														    CanineForQuestionAnswering,
														    CanineForSequenceClassification,
														    CanineForTokenClassification,
														    CanineLayer,
														    CanineModel,
														    CaninePreTrainedModel,
														    load_tf_weights_in_canine,
														)


else:
							import sys

							lowerCAmelCase__								=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




