
"""simple docstring"""






from __future__ import annotations

from decimal import Decimal
from math import *  # noqa: F403

from sympy import diff





def __A						(   a_ :							str		,		a_ :							float | Decimal		,		a_ :							float = 10**-10     )->							float:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Tuple				= a
		while True:
				SCREAMING_SNAKE_CASE       :     Tuple				= Decimal(a_     ) - (
				    Decimal(eval(a_     )     ) / Decimal(eval(str(diff(a_     )     )     )     )  # noqa: S307
				)
				# This number dictates the accuracy of the answer
				if abs(eval(a_     )     ) < precision:  # noqa: S307
						return float(a_     )


# 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
					print(f'''The root of x**2 - 5*x + 2 = 0 is {newton_raphson("x**2 - 5*x + 2", 0.4)}''')
					# Find Square Root of 5
					print(f'''The root of log(x) - 1 = 0 is {newton_raphson("log(x) - 1", 2)}''')
					# Exponential Roots
					print(f'''The root of exp(x) - 1 = 0 is {newton_raphson("exp(x) - 1", 0)}''')







"""simple docstring"""






from typing import TYPE_CHECKING

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


lowerCamelCase__ :       Optional[Any]						     =			{
    "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:
					lowerCamelCase__ :       Optional[Any]						     =			["FunnelTokenizerFast"]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase__ :       Dict						     =			[
					    "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:
					lowerCamelCase__ :       Tuple						     =			[
					    "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

					lowerCamelCase__ :       Optional[int]						     =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





