






def 				snake_case__							(							UpperCAmelCase							:  float       )     ->   Optional[Any]:
    return 1_0 - x * x


def 				snake_case__							(							UpperCAmelCase							:  float    ,			UpperCAmelCase							:  float       )     ->   Optional[Any]:
    # Bolzano theory in order to find if there is a root between a and b
    if equation(UpperCAmelCase       ) * equation(UpperCAmelCase       ) >= 0:
        raise ValueError("Wrong space!"       )

    lowerCAmelCase__   :Optional[int]      							=				a
    while (b - a) >= 0.01:
        # Find middle point
        lowerCAmelCase__   :Any      							=				(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:
            lowerCAmelCase__   :Tuple      							=				c
        else:
            lowerCAmelCase__   :Tuple      							=				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


_a    :   str        =			{"""configuration_wavlm""": ["""WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """WavLMConfig"""]}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _a    :   Optional[Any]        =			[
     """WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """WavLMForAudioFrameClassification""",
     """WavLMForCTC""",
     """WavLMForSequenceClassification""",
     """WavLMForXVector""",
     """WavLMModel""",
     """WavLMPreTrainedModel""",
 ]

if TYPE_CHECKING:
 from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_wavlm import (
      WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
      WavLMForAudioFrameClassification,
      WavLMForCTC,
      WavLMForSequenceClassification,
      WavLMForXVector,
      WavLMModel,
      WavLMPreTrainedModel,
  )

else:
 import sys

 _a    :   str        =			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



