


'''simple docstring'''
import unittest

import numpy as np
import torch

from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class      lowercase			(							unittest.TestCase      ):

		"""simple docstring"""

		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Any							      =				10




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Optional[Any]							      =				[1, 2, 3, 4]
							UpperCamelCase__     :int							      =				[1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
							self.assertEqual(truncate_or_pad(a_					,     self.block_size					,     0							)					,     a_							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Dict							      =				[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
							UpperCamelCase__     :Any							      =				[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
							self.assertEqual(truncate_or_pad(a_					,     self.block_size					,     0							)					,     a_							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :List[str]							      =				[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
							UpperCamelCase__     :str							      =				[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
							self.assertEqual(truncate_or_pad(a_					,     self.block_size					,     0							)					,     a_							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Tuple							      =				"It was the year of Our Lord one thousand seven hundred and\n        seventy-five.\n\nSpiritual revelations were conceded to England at that\n        favoured period, as at this."
							UpperCamelCase__     :Optional[Any]							      =				process_story(a_							)
							self.assertEqual(a_					,     []							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :List[Any]							      =				""
							UpperCamelCase__     :Optional[int]							      =				process_story(a_							)
							self.assertEqual(a_					,     []							)
							self.assertEqual(a_					,     []							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Optional[Any]							      =				(
							    "It was the year of Our Lord one thousand seven hundred and "
							    "seventy-five\n\nSpiritual revelations were conceded to England "
							    "at that favoured period, as at this.\n@highlight\n\nIt was the best of times"
							)
							UpperCamelCase__     :Tuple							      =				process_story(a_							)

							UpperCamelCase__     :List[Any]							      =				[
							    "It was the year of Our Lord one thousand seven hundred and seventy-five.",
							    "Spiritual revelations were conceded to England at that favoured period, as at this.",
							]
							self.assertEqual(a_					,     a_							)

							UpperCamelCase__     :Dict							      =				["It was the best of times."]
							self.assertEqual(a_					,     a_							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Optional[int]							      =				torch.tensor([1, 2, 3, 4]							)
							UpperCamelCase__     :Any							      =				torch.tensor([1, 1, 1, 1]							)
							np.testing.assert_array_equal(build_mask(a_					,     0							).numpy()					,     expected.numpy()							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :List[str]							      =				torch.tensor([1, 2, 3, 4, 23, 23, 23]							)
							UpperCamelCase__     :Any							      =				torch.tensor([1, 1, 1, 1, 0, 0, 0]							)
							np.testing.assert_array_equal(build_mask(a_					,     23							).numpy()					,     expected.numpy()							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :List[Any]							      =				torch.tensor([8, 2, 3, 4, 1, 1, 1]							)
							UpperCamelCase__     :Dict							      =				torch.tensor([1, 1, 1, 1, 0, 0, 0]							)
							np.testing.assert_array_equal(build_mask(a_					,     1							).numpy()					,     expected.numpy()							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Union[str, Any]							      =				101
							UpperCamelCase__     :List[str]							      =				torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 101, 5, 6], [1, 101, 3, 4, 101, 6]]							)
							UpperCamelCase__     :List[str]							      =				torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]]							)

							UpperCamelCase__     :Optional[Any]							      =				compute_token_type_ids(a_					,     a_							)
							np.testing.assert_array_equal(a_					,     a_							)



'''simple docstring'''
from __future__ import annotations

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

from sympy import diff


def     a   (    __a				,      __a				,      __a = 10**-10     )						->			float:



					'''simple docstring'''






					UpperCamelCase__     :Tuple							      =				a
					while True:
										UpperCamelCase__     :Dict							      =				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)}""")