





from sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403





def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case = "x" ,__snake_case = 10**-10 ,__snake_case = 1 ,)       ->   complex:







	'''simple docstring'''







	lowerCamelCase__              =	symbols(__snake_case							)
	lowerCamelCase__              =	lambdify(__snake_case ,__snake_case							)
	lowerCamelCase__              =	lambdify(__snake_case ,diff(__snake_case ,__snake_case							)							)

	lowerCamelCase__              =	starting_point

	while True:
		if diff_function(__snake_case							) != 0:
			lowerCamelCase__              =	prev_guess - multiplicity * func(__snake_case							) / diff_function(
			    __snake_case							)
		else:
			raise ZeroDivisionError('''Could not find root'''							) from None

		# Precision is checked by comparing the difference of consecutive guesses
		if abs(next_guess - prev_guess							) < precision:
			return next_guess

		lowerCamelCase__              =	next_guess


# Let's Execute
if __name__ == "__main__":
			# Find root of trigonometric function
			# Find value of pi
			print(f"""The root of sin(x) = 0 is {newton_raphson('sin(x)', 2)}""")

			# Find root of polynomial
			# Find fourth Root of 5
			print(f"""The root of x**4 - 5 = 0 is {newton_raphson('x**4 -5', 0.4 +5j)}""")

			# Find value of e
			print(
			    "The root of log(y) - 1 = 0 is ",
			    f"""{newton_raphson('log(y) - 1', 2, variable='y')}""",
			)

			# Exponential Roots
			print(
			    "The root of exp(x) - 1 = 0 is",
			    f"""{newton_raphson('exp(x) - 1', 10, precision=0.005)}""",
			)

			# Find root of cos(x)
			print(f"""The root of cos(x) = 0 is {newton_raphson('cos(x)', 0)}""")










from typing import TYPE_CHECKING

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


_a						=   {
    "configuration_funnel": ["FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP", "FunnelConfig"],
    "convert_funnel_original_tf_checkpoint_to_pytorch": [],
    "tokenization_funnel": ["FunnelTokenizer"],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_a						=   ["FunnelTokenizerFast"]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_a						=   [
			    "FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "FunnelBaseModel",
			    "FunnelForMaskedLM",
			    "FunnelForMultipleChoice",
			    "FunnelForPreTraining",
			    "FunnelForQuestionAnswering",
			    "FunnelForSequenceClassification",
			    "FunnelForTokenClassification",
			    "FunnelModel",
			    "FunnelPreTrainedModel",
			    "load_tf_weights_in_funnel",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_a						=   [
			    "TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "TFFunnelBaseModel",
			    "TFFunnelForMaskedLM",
			    "TFFunnelForMultipleChoice",
			    "TFFunnelForPreTraining",
			    "TFFunnelForQuestionAnswering",
			    "TFFunnelForSequenceClassification",
			    "TFFunnelForTokenClassification",
			    "TFFunnelModel",
			    "TFFunnelPreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
			from .tokenization_funnel import FunnelTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_funnel_fast import FunnelTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_funnel import (
						    FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
						    FunnelBaseModel,
						    FunnelForMaskedLM,
						    FunnelForMultipleChoice,
						    FunnelForPreTraining,
						    FunnelForQuestionAnswering,
						    FunnelForSequenceClassification,
						    FunnelForTokenClassification,
						    FunnelModel,
						    FunnelPreTrainedModel,
						    load_tf_weights_in_funnel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_funnel import (
						    TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFFunnelBaseModel,
						    TFFunnelForMaskedLM,
						    TFFunnelForMultipleChoice,
						    TFFunnelForPreTraining,
						    TFFunnelForQuestionAnswering,
						    TFFunnelForSequenceClassification,
						    TFFunnelForTokenClassification,
						    TFFunnelModel,
						    TFFunnelPreTrainedModel,
						)

else:
			import sys

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



