






'''simple docstring'''







import math
from collections.abc import Callable
def     A__      (					__lowerCAmelCase		:      Callable[[float], float] ,     __lowerCAmelCase		:      float ,     __lowerCAmelCase		:      float    ):
						lowerCamelCase__								=	xa
						lowerCamelCase__								=	xa
						while True:
												if x_n == x_na or function(__lowerCAmelCase    ) == function(__lowerCAmelCase    ):
																		raise ZeroDivisionError("""float division by zero, could not find root"""    )
												lowerCamelCase__								=	x_na - (
												    function(__lowerCAmelCase    ) / ((function(__lowerCAmelCase    ) - function(__lowerCAmelCase    )) / (x_na - x_n))
												)
												if abs(x_na - x_na    ) < 10**-5:
																		return x_na
												lowerCamelCase__								=	x_na
												lowerCamelCase__								=	x_na
def     A__      (					__lowerCAmelCase		:      float    ):
						return math.pow(__lowerCAmelCase ,     3    ) - (2 * x) - 5


if __name__ == "__main__":
							print(intersection(f, 3, 3.5))



"""simple docstring"""
from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE_					     =	{
    '''configuration_rag''': ['''RagConfig'''],
    '''retrieval_rag''': ['''RagRetriever'''],
    '''tokenization_rag''': ['''RagTokenizer'''],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE_					     =	[
			    '''RagModel''',
			    '''RagPreTrainedModel''',
			    '''RagSequenceForGeneration''',
			    '''RagTokenForGeneration''',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE_					     =	[
			    '''TFRagModel''',
			    '''TFRagPreTrainedModel''',
			    '''TFRagSequenceForGeneration''',
			    '''TFRagTokenForGeneration''',
			]


if TYPE_CHECKING:
			from .configuration_rag import RagConfig
			from .retrieval_rag import RagRetriever
			from .tokenization_rag import RagTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_rag import (
						    TFRagModel,
						    TFRagPreTrainedModel,
						    TFRagSequenceForGeneration,
						    TFRagTokenForGeneration,
						)

else:
			import sys

			SCREAMING_SNAKE_CASE_					     =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
