
"""simple docstring"""


import math as m






def    lowercase	(  lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__=0.001	):
			return (f(a + h	) - f(a - h	)) / (2 * h)






def    lowercase	(  lowerCAmelCase__  ,lowerCAmelCase__=0  ,lowerCAmelCase__=100  ,lowerCAmelCase__=0.0_001  ,lowerCAmelCase__=1E-6  ,lowerCAmelCase__=False	):
			lowerCamelCase_ 		=     xa  # set the initial guess
			lowerCamelCase_ 		=     [a]
			lowerCamelCase_ 		=     abs(f(lowerCAmelCase__	)	)
			lowerCamelCase_ 		=     lambda lowerCAmelCase__	: calc_derivative(lowerCAmelCase__  ,lowerCAmelCase__  ,h=lowerCAmelCase__	)  # noqa: E731  Derivative of f(x)
			for _ in range(lowerCAmelCase__	):
						if fa(lowerCAmelCase__	) == 0:
									raise ValueError('''No converging solution found'''	)
						lowerCamelCase_ 		=     a - f(lowerCAmelCase__	) / fa(lowerCAmelCase__	)  # Calculate the next estimate
						if logsteps:
									steps.append(lowerCAmelCase__	)
						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

						A_						       =				lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
						A_       ,      A_       ,      A_						       =				newton_raphson(
						    f, xa=10, maxiter=1000, 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}}}")





"""simple docstring"""


from typing import TYPE_CHECKING

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


A_						       =				{
    """configuration_flava""": [
        """FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """FlavaConfig""",
        """FlavaImageCodebookConfig""",
        """FlavaImageConfig""",
        """FlavaMultimodalConfig""",
        """FlavaTextConfig""",
    ],
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_						       =				["""FlavaFeatureExtractor"""]
						A_						       =				["""FlavaImageProcessor"""]
						A_						       =				["""FlavaProcessor"""]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_						       =				[
						    """FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """FlavaForPreTraining""",
						    """FlavaImageCodebook""",
						    """FlavaImageModel""",
						    """FlavaModel""",
						    """FlavaMultimodalModel""",
						    """FlavaPreTrainedModel""",
						    """FlavaTextModel""",
						]

if TYPE_CHECKING:
						from .configuration_flava import (
						    FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    FlavaConfig,
						    FlavaImageCodebookConfig,
						    FlavaImageConfig,
						    FlavaMultimodalConfig,
						    FlavaTextConfig,
						)

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_flava import FlavaFeatureExtractor
												from .image_processing_flava import FlavaImageProcessor
												from .processing_flava import FlavaProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flava import (
												    FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST,
												    FlavaForPreTraining,
												    FlavaImageCodebook,
												    FlavaImageModel,
												    FlavaModel,
												    FlavaMultimodalModel,
												    FlavaPreTrainedModel,
												    FlavaTextModel,
												)

else:
						import sys

						A_						       =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



