



import json
import os
import unittest

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin

class 	lowerCamelCase_    ( _A						,unittest.TestCase						):






						'''simple docstring'''
						a__   =  BioGptTokenizer
						a__   =  False







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				List[Any]:
									super().setUp()

									# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
									A							:					Optional[Any]              =						[
									    "l",
									    "o",
									    "w",
									    "e",
									    "r",
									    "s",
									    "t",
									    "i",
									    "d",
									    "n",
									    "w</w>",
									    "r</w>",
									    "t</w>",
									    "lo",
									    "low",
									    "er</w>",
									    "low</w>",
									    "lowest</w>",
									    "newer</w>",
									    "wider</w>",
									    "<unk>",
									]
									A							:					Optional[int]              =						dict(zip(__lowerCamelCase	,	range(len(__lowerCamelCase ) ) ) )
									A							:					str              =						["l o 123", "lo w 1456", "e r</w> 1789", ""]

									A							:					Tuple              =						os.path.join(self.tmpdirname	,	VOCAB_FILES_NAMES["vocab_file"] )
									A							:					List[str]              =						os.path.join(self.tmpdirname	,	VOCAB_FILES_NAMES["merges_file"] )
									with open(self.vocab_file	,	"w" ) as fp:
												fp.write(json.dumps(__lowerCamelCase ) )
									with open(self.merges_file	,	"w" ) as fp:
												fp.write("\n".join(__lowerCamelCase ) )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any]	,	__lowerCamelCase      :			Optional[Any] )      ->				str:
									A							:					Optional[Any]              =						"lower newer"
									A							:					Union[str, Any]              =						"lower newer"
									return input_text, output_text







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int] )      ->				Dict:
									A							:					Optional[int]              =						BioGptTokenizer(self.vocab_file	,	self.merges_file )

									A							:					str              =						"lower"
									A							:					int              =						["low", "er</w>"]
									A							:					Any              =						tokenizer.tokenize(__lowerCamelCase )
									self.assertListEqual(__lowerCamelCase	,	__lowerCamelCase )

									A							:					List[Any]              =						tokens + ["<unk>"]
									A							:					Tuple              =						[14, 15, 20]
									self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase )	,	__lowerCamelCase )







						@slow
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any] )      ->				int:
									A							:					Optional[int]              =						BioGptTokenizer.from_pretrained("microsoft/biogpt" )

									A							:					Dict              =						tokenizer.encode("sequence builders"	,	add_special_tokens=__lowerCamelCase )
									A							:					Optional[Any]              =						tokenizer.encode("multi-sequence build"	,	add_special_tokens=__lowerCamelCase )

									A							:					str              =						tokenizer.build_inputs_with_special_tokens(__lowerCamelCase )
									A							:					Union[str, Any]              =						tokenizer.build_inputs_with_special_tokens(__lowerCamelCase	,	__lowerCamelCase )

									self.assertTrue(encoded_sentence == [2] + text )
									self.assertTrue(encoded_pair == [2] + text + [2] + text_a )




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




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase = "x" ,    _lowerCamelCase = 10**-10 ,    _lowerCamelCase = 1 ,    ):
			A							:					str              =						symbols(_lowerCamelCase    )
			A							:					int              =						lambdify(_lowerCamelCase ,    _lowerCamelCase    )
			A							:					List[str]              =						lambdify(_lowerCamelCase ,    diff(_lowerCamelCase ,    _lowerCamelCase    )    )

			A							:					Optional[int]              =						starting_point

			while True:
						if diff_function(_lowerCamelCase    ) != 0:
									A							:					Optional[Any]              =						prev_guess - multiplicity * func(_lowerCamelCase    ) / diff_function(
									    _lowerCamelCase    )
						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

						A							:					int              =						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)}""")