

def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							float					)	->					float:




				'''simple docstring'''





				return 1_0 - x * x



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							float		,				SCREAMING_SNAKE_CASE_:							float					)	->					float:




				'''simple docstring'''





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

				A__						=	a
				while (b - a) >= 0.01:
								# Find middle point
								A__						=	(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:
												A__						=	c
								else:
												A__						=	c
				return c


if __name__ == "__main__":
	import doctest

	doctest.testmod()

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





"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase_			     =  {"configuration_xglm": ["XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XGLMConfig"]}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			     =  ["XGLMTokenizer"]

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			     =  ["XGLMTokenizerFast"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			     =  [
				    "XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "XGLMForCausalLM",
				    "XGLMModel",
				    "XGLMPreTrainedModel",
				]


try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			     =  [
				    "FlaxXGLMForCausalLM",
				    "FlaxXGLMModel",
				    "FlaxXGLMPreTrainedModel",
				]


try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			     =  [
				    "TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "TFXGLMForCausalLM",
				    "TFXGLMModel",
				    "TFXGLMPreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_xglm import XGLMTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_xglm_fast import XGLMTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_xglm import (
								    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFXGLMForCausalLM,
								    TFXGLMModel,
								    TFXGLMPreTrainedModel,
								)


else:
				import sys

				lowercase_			     =  _LazyModule(__name__, globals()["__file__"], _import_structure)






