






def        _a  (     UpperCamelCase_					:				float   ) ->   Tuple:
      """simple docstring"""







      return 10 - x * x


def        _a  (     UpperCamelCase_					:				float    ,   UpperCamelCase_					:				float   ) ->   str:
      """simple docstring"""







      if equation(__lowerCAmelCase   ) * equation(__lowerCAmelCase   ) >= 0:
            raise ValueError("Wrong space!"   )

      lowerCAmelCase__       =						a
      while (b - a) >= 0.01:
            # Find middle point
            lowerCAmelCase__       =						(a + b) / 2
            # Check if middle point is root
            if equation(__lowerCAmelCase   ) == 0.0:
                  break
            # Decide the side to repeat the steps
            if equation(__lowerCAmelCase   ) * equation(__lowerCAmelCase   ) < 0:
                  lowerCAmelCase__       =						c
            else:
                  lowerCAmelCase__       =						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_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


a_							=    {'''configuration_plbart''': ['''PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PLBartConfig''']}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a_							=    ['''PLBartTokenizer''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a_							=    [
	    '''PLBART_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''PLBartForCausalLM''',
	    '''PLBartForConditionalGeneration''',
	    '''PLBartForSequenceClassification''',
	    '''PLBartModel''',
	    '''PLBartPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_plbart import PLBartTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_plbart import (
		    PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
		    PLBartForCausalLM,
		    PLBartForConditionalGeneration,
		    PLBartForSequenceClassification,
		    PLBartModel,
		    PLBartPreTrainedModel,
		)


else:
	import sys

	a_							=    _LazyModule(__name__, globals()['''__file__'''], _import_structure)






