


import math as m
def 							_UpperCAmelCase       (UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						int       ,							UpperCamelCase__  :						Union[str, Any]=0.0_01		):
       return (f(a + h		) - f(a - h		)) / (2 * h)
def 							_UpperCAmelCase       (UpperCamelCase__  :						int       ,							UpperCamelCase__  :						Dict=0       ,							UpperCamelCase__  :						List[Any]=100       ,							UpperCamelCase__  :						str=0.00_01       ,							UpperCamelCase__  :						Optional[int]=1E-6       ,							UpperCamelCase__  :						Any=False		):
       _A   : int       					=				xa  # set the initial guess
       _A   : List[str]       					=				[a]
       _A   : Any       					=				abs(f(UpperCamelCase__		)		)
       _A   : List[str]       					=				lambda UpperCamelCase__		: calc_derivative(UpperCamelCase__       ,							UpperCamelCase__       ,							h=UpperCamelCase__		)  # noqa: E731  Derivative of f(x)
       for _ in range(UpperCamelCase__		):
              if fa(UpperCamelCase__		) == 0:
                     raise ValueError("No converging solution found"		)
              _A   : Any       					=				a - f(UpperCamelCase__		) / fa(UpperCamelCase__		)  # Calculate the next estimate
              if logsteps:
                     steps.append(UpperCamelCase__		)
              if error < maxerror:
                     break
       else:
              raise ValueError("Iteration limit reached, no converging solution found"		)
       if logsteps:
              # If logstep is true, then log intermediate steps
              return a, error, steps
       return a, error


if __name__ == "__main__":
    from matplotlib import pyplot as plt

    lowerCAmelCase__ 	=				lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
    lowerCAmelCase__ 	=				newton_raphson(
        f, xa=10, maxiter=10_00, step=1E-6, logsteps=True
    )
    plt.plot([abs(f(x)) for x in steps])
    plt.xlabel('step')
    plt.ylabel('error')
    plt.show()
    print(f"solution = {{{solution:f}}}, error = {{{error:f}}}")




from typing import TYPE_CHECKING

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


lowerCAmelCase__   	=				{
    'configuration_segformer': ['SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SegformerConfig', 'SegformerOnnxConfig']
}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__   	=				['SegformerFeatureExtractor']
    lowerCAmelCase__   	=				['SegformerImageProcessor']

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

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__   	=				[
        '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

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