
import math as m






def        lowerCamelCase__    (    __lowerCAmelCase  :  Optional[int]   ,							__lowerCAmelCase  :  Union[str, Any]   ,							__lowerCAmelCase  :  Tuple=0.001    ):




			"""simple docstring"""


			return (f(a + h    ) - f(a - h    )) / (2 * h)






def        lowerCamelCase__    (    __lowerCAmelCase  :  Optional[int]   ,							__lowerCAmelCase  :  Dict=0   ,							__lowerCAmelCase  :  Tuple=100   ,							__lowerCAmelCase  :  Tuple=0.0_001   ,							__lowerCAmelCase  :  int=1e-6   ,							__lowerCAmelCase  :  Optional[int]=False    ):




			"""simple docstring"""


			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=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_sentencepiece_available, is_torch_available


_A       =					{
    "configuration_xlm_prophetnet": ["XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMProphetNetConfig"],
}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A       =					["XLMProphetNetTokenizer"]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A       =					[
			    "XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "XLMProphetNetDecoder",
			    "XLMProphetNetEncoder",
			    "XLMProphetNetForCausalLM",
			    "XLMProphetNetForConditionalGeneration",
			    "XLMProphetNetModel",
			    "XLMProphetNetPreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_xlm_prophetnet import (
						    XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
						    XLMProphetNetDecoder,
						    XLMProphetNetEncoder,
						    XLMProphetNetForCausalLM,
						    XLMProphetNetForConditionalGeneration,
						    XLMProphetNetModel,
						    XLMProphetNetPreTrainedModel,
						)

else:
			import sys

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



