


import math
from collections.abc import Callable






def 							__lowercase   ( __lowerCAmelCase							:				Callable[[float], float]					,    __lowerCAmelCase							:				float					,    __lowerCAmelCase							:				float   ):
							a__             =     xa
							a__             =     xa
							while True:
														if x_n == x_na or function(__lowerCAmelCase   ) == function(__lowerCAmelCase   ):
																					raise ZeroDivisionError('float division by zero, could not find root'   )
														a__             =     x_na - (
														    function(__lowerCAmelCase   ) / ((function(__lowerCAmelCase   ) - function(__lowerCAmelCase   )) / (x_na - x_n))
														)
														if abs(x_na - x_na   ) < 1_0**-5:
																					return x_na
														a__             =     x_na
														a__             =     x_na






def 							__lowercase   ( __lowerCAmelCase							:				float   ):
							return math.pow(__lowerCAmelCase					,    3   ) - (2 * x) - 5


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








from typing import TYPE_CHECKING

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


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

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

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							:       Optional[int]     			=      [
						    '''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

						snake_case							:       int     			=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




