

def    __lowerCamelCase      (					UpperCamelCase__ ):







							'''simple docstring'''






							return 10 - x * x





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__ ):







							'''simple docstring'''






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

							snake_case_     							=       a
							while (b - a) >= 0.01:
														# Find middle point
														snake_case_     							=       (a + b) / 2
														# Check if middle point is root
														if equation(UpperCamelCase__ ) == 0.0:
																					break
														# Decide the side to repeat the steps
														if equation(UpperCamelCase__ ) * equation(UpperCamelCase__ ) < 0:
																					snake_case_     							=       c
														else:
																					snake_case_     							=       c
							return c


if __name__ == "__main__":
					import doctest

					doctest.testmod()

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







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCAmelCase						:  int									=			{
    """configuration_lilt""": ["""LILT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LiltConfig"""],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase						:  str									=			[
					    """LILT_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """LiltForQuestionAnswering""",
					    """LiltForSequenceClassification""",
					    """LiltForTokenClassification""",
					    """LiltModel""",
					    """LiltPreTrainedModel""",
					]

if TYPE_CHECKING:
					from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_lilt import (
										    LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    LiltForQuestionAnswering,
										    LiltForSequenceClassification,
										    LiltForTokenClassification,
										    LiltModel,
										    LiltPreTrainedModel,
										)

else:
					import sys

					_UpperCAmelCase						:  Dict									=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




