

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


lowercase     :       Optional[Any]        =       {
    "configuration_segformer": ["SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "SegformerConfig", "SegformerOnnxConfig"]
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase     :       Tuple        =       ["SegformerFeatureExtractor"]
					lowercase     :       List[str]        =       ["SegformerImageProcessor"]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase     :       Tuple        =       [
					    "SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "SegformerDecodeHead",
					    "SegformerForImageClassification",
					    "SegformerForSemanticSegmentation",
					    "SegformerLayer",
					    "SegformerModel",
					    "SegformerPreTrainedModel",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase     :       Optional[int]        =       [
					    "TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TFSegformerDecodeHead",
					    "TFSegformerForImageClassification",
					    "TFSegformerForSemanticSegmentation",
					    "TFSegformerModel",
					    "TFSegformerPreTrainedModel",
					]


if TYPE_CHECKING:
					from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_segformer import SegformerFeatureExtractor
										from .image_processing_segformer import SegformerImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_segformer import (
										    SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    SegformerDecodeHead,
										    SegformerForImageClassification,
										    SegformerForSemanticSegmentation,
										    SegformerLayer,
										    SegformerModel,
										    SegformerPreTrainedModel,
										)
					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_segformer import (
										    TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFSegformerDecodeHead,
										    TFSegformerForImageClassification,
										    TFSegformerForSemanticSegmentation,
										    TFSegformerModel,
										    TFSegformerPreTrainedModel,
										)

else:
					import sys

					lowercase     :       Tuple        =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


from collections.abc import Callable

lowercase     :       Optional[Any]        =       Callable[[float], float]  # type alias for a real -> real function







def  UpperCAmelCase_   (_lowerCAmelCase :		RealFunc     ,			_lowerCAmelCase :		RealFunc     ,			_lowerCAmelCase :		int     ,			):
			__UpperCamelCase    :    Any								=      float(_lowerCAmelCase		)
			while True:
						try:
									__UpperCamelCase    :    List[Any]								=      prev_guess - function(_lowerCAmelCase		) / derivative(_lowerCAmelCase		)
						except ZeroDivisionError:
									raise ZeroDivisionError("Could not find root"		) from None
						if abs(prev_guess - next_guess		) < 10**-5:
									return next_guess
						__UpperCamelCase    :    Tuple								=      next_guess







def  UpperCAmelCase_   (_lowerCAmelCase :		float		):
			return (x**3) - (2 * x) - 5







def  UpperCAmelCase_   (_lowerCAmelCase :		float		):
			return 3 * (x**2) - 2


if __name__ == "__main__":
					print(newton(f, fa, 3))