






import unittest

from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch


class 							__a							(  unittest.TestCase						):





		'''simple docstring'''



		UpperCAmelCase__      :				Any				    =						JukeboxTokenizer
		UpperCAmelCase__      :				Dict				    =						{
		    """artist""": """Zac Brown Band""",
		    """genres""": """Country""",
		    """lyrics""": """I met a traveller from an antique land,
        Who said \"Two vast and trunkless legs of stone
        Stand in the desert. . . . Near them, on the sand,
        Half sunk a shattered visage lies, whose frown,
        And wrinkled lip, and sneer of cold command,
        Tell that its sculptor well those passions read
        Which yet survive, stamped on these lifeless things,
        The hand that mocked them, and the heart that fed;
        And on the pedestal, these words appear:
        My name is Ozymandias, King of Kings;
        Look on my Works, ye Mighty, and despair!
        Nothing beside remains. Round the decay
        Of that colossal Wreck, boundless and bare
        The lone and level sands stretch far away
        """,
		}


		@require_torch
		def   __snake_case			(						self       ):
						import torch

						SCREAMING_SNAKE_CASE_   :	str													=   JukeboxTokenizer.from_pretrained('openai/jukebox-1b-lyrics'       )
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   tokenizer(**self.metas       )['input_ids']
						# fmt: off
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   [
						    torch.tensor([[
						        0, 0, 0, 7169, 507, 9, 76, 39, 31, 46, 76, 27,
						        76, 46, 44, 27, 48, 31, 38, 38, 31, 44, 76, 32,
						        44, 41, 39, 76, 27, 40, 76, 27, 40, 46, 35, 43,
						        47, 31, 76, 38, 27, 40, 30, 64, 78, 76, 76, 76,
						        76, 76, 76, 76, 76, 23, 34, 41, 76, 45, 27, 35,
						        30, 76, 71, 20, 49, 41, 76, 48, 27, 45, 46, 76,
						        27, 40, 30, 76, 46, 44, 47, 40, 37, 38, 31, 45,
						        45, 76, 38, 31, 33, 45, 76, 41, 32, 76, 45, 46,
						        41, 40, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
						        19, 46, 27, 40, 30, 76, 35, 40, 76, 46, 34, 31,
						        76, 30, 31, 45, 31, 44, 46, 63, 76, 63, 76, 63,
						        76, 63, 76, 14, 31, 27, 44, 76, 46, 34, 31, 39,
						        64, 76, 41, 40, 76, 46, 34, 31, 76, 45, 27, 40,
						        30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76, 8,
						        27, 38, 32, 76, 45, 47, 40, 37, 76, 27, 76, 45,
						        34, 27, 46, 46, 31, 44, 31, 30, 76, 48, 35, 45,
						        27, 33, 31, 76, 38, 35, 31, 45, 64, 76, 49, 34,
						        41, 45, 31, 76, 32, 44, 41, 49, 40, 64, 78, 76,
						        76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76, 49,
						        44, 35, 40, 37, 38, 31, 30, 76, 38, 35, 42, 64,
						        76, 27, 40, 30, 76, 45, 40, 31, 31, 44, 76, 41,
						        32, 76, 29, 41, 38, 30, 76, 29, 41, 39, 39, 27,
						        40, 30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76,
						        20, 31, 38, 38, 76, 46, 34, 27, 46, 76, 35, 46,
						        45, 76, 45, 29, 47, 38, 42, 46, 41, 44, 76, 49,
						        31, 38, 38, 76, 46, 34, 41, 45, 31, 76, 42, 27,
						        45, 45, 35, 41, 40, 45, 76, 44, 31, 27, 30, 78,
						        76, 76, 76, 76, 76, 76, 76, 76, 23, 34, 35, 29,
						        34, 76, 51, 31, 46, 76, 45, 47, 44, 48, 35, 48,
						        31, 64, 76, 45, 46, 27, 39, 42, 31, 30, 76, 41,
						        40, 76, 46, 34, 31, 45, 31, 76, 38, 35, 32, 31,
						        38, 31, 45, 45, 76, 46, 34, 35, 40, 33, 45, 64,
						        78, 76, 76, 76, 76, 76, 76, 76, 76, 20, 34, 31,
						        76, 34, 27, 40, 30, 76, 46, 34, 27, 46, 76, 39,
						        41, 29, 37, 31, 30, 76, 46, 34, 31, 39, 64, 76,
						        27, 40, 30, 76, 46, 34, 31, 76, 34, 31, 27, 44,
						        46, 76, 46, 34, 27, 46, 76, 32, 31, 30, 66, 78,
						        76, 76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76,
						        41, 40, 76, 46, 34, 31, 76, 42, 31, 30, 31, 45,
						        46, 27, 38, 64, 76, 46, 34, 31, 45, 31, 76, 49,
						        41, 44, 30, 45, 76, 27, 42, 42, 31, 27, 44, 65,
						        78, 76, 76, 76, 76, 76, 76, 76, 76, 13, 51, 76,
						        40, 27, 39, 31, 76, 35, 45, 76, 15, 52, 51, 39,
						        27, 40, 30, 35, 27, 45, 64, 76, 11, 35, 40, 33,
						        76, 41, 32, 76, 11, 35, 40, 33, 45, 66, 78, 76,
						        76, 76, 76, 76, 76, 76, 76, 12, 41, 41, 37, 76,
						        41, 40, 76, 39, 51, 76, 23, 41, 44, 37, 45, 64,
						        76, 51, 31, 76, 13, 35, 33, 34, 46, 51, 64, 76,
						        27, 40, 30, 76, 30, 31, 45, 42, 27, 35, 44, 67,
						        78, 76, 76, 76, 76, 76, 76, 76, 76, 14, 41, 46,
						        34, 35, 40, 33, 76, 28, 31, 45, 35, 30, 31, 76,
						        44, 31, 39, 27, 35, 40, 45, 63, 76, 18, 41, 47,
						        40, 30, 76, 46, 34, 31, 76, 30, 31, 29, 27, 51,
						        78, 76, 76, 76, 76, 76, 76, 76, 76, 15, 32, 76,
						        46, 34, 27, 46, 76, 29, 41, 38, 41, 45, 45, 27,
						        38, 76, 23, 44, 31, 29, 37, 64, 76, 28, 41, 47,
						        40, 30, 38, 31, 45, 45, 76, 27, 40, 30, 76, 28,
						        27, 44, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
						        20, 34, 31, 76, 38, 41, 40, 31, 76, 27, 40, 30,
						        76, 38, 31, 48, 31, 38, 76, 45, 27, 40, 30, 45,
						        76, 45, 46, 44, 31, 46, 29, 34, 76, 32, 27, 44,
						        76, 27, 49, 27, 51, 78, 76, 76, 76, 76, 76, 76,
						        76, 76]]       ),
						    torch.tensor([[0, 0, 0, 1069, 11]]       ),
						    torch.tensor([[0, 0, 0, 1069, 11]]       ),
						]
						# fmt: on
						self.assertTrue(torch.allclose(tokens[0] ,    EXPECTED_OUTPUT[0]       )       )
						self.assertTrue(torch.allclose(tokens[1] ,    EXPECTED_OUTPUT[1]       )       )
						self.assertTrue(torch.allclose(tokens[2] ,    EXPECTED_OUTPUT[2]       )       )






		@require_torch
		def   __snake_case			(						self       ):
						import torch

						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   JukeboxTokenizer.from_pretrained('openai/jukebox-5b-lyrics'       )
						SCREAMING_SNAKE_CASE_   :	Any													=   tokenizer(**self.metas       )['input_ids']
						# fmt: off
						SCREAMING_SNAKE_CASE_   :	str													=   [
						    torch.tensor([[
						        0, 0, 0, 1069, 11, -1, -1, -1, -1, 9, 77, 39,
						        31, 46, 77, 27, 77, 46, 44, 27, 48, 31, 38, 38,
						        31, 44, 77, 32, 44, 41, 39, 77, 27, 40, 77, 27,
						        40, 46, 35, 43, 47, 31, 77, 38, 27, 40, 30, 64,
						        79, 77, 77, 77, 77, 77, 77, 77, 77, 23, 34, 41,
						        77, 45, 27, 35, 30, 77, 72, 20, 49, 41, 77, 48,
						        27, 45, 46, 77, 27, 40, 30, 77, 46, 44, 47, 40,
						        37, 38, 31, 45, 45, 77, 38, 31, 33, 45, 77, 41,
						        32, 77, 45, 46, 41, 40, 31, 79, 77, 77, 77, 77,
						        77, 77, 77, 77, 19, 46, 27, 40, 30, 77, 35, 40,
						        77, 46, 34, 31, 77, 30, 31, 45, 31, 44, 46, 63,
						        77, 63, 77, 63, 77, 63, 77, 14, 31, 27, 44, 77,
						        46, 34, 31, 39, 64, 77, 41, 40, 77, 46, 34, 31,
						        77, 45, 27, 40, 30, 64, 79, 77, 77, 77, 77, 77,
						        77, 77, 77, 8, 27, 38, 32, 77, 45, 47, 40, 37,
						        77, 27, 77, 45, 34, 27, 46, 46, 31, 44, 31, 30,
						        77, 48, 35, 45, 27, 33, 31, 77, 38, 35, 31, 45,
						        64, 77, 49, 34, 41, 45, 31, 77, 32, 44, 41, 49,
						        40, 64, 79, 77, 77, 77, 77, 77, 77, 77, 77, 1,
						        40, 30, 77, 49, 44, 35, 40, 37, 38, 31, 30, 77,
						        38, 35, 42, 64, 77, 27, 40, 30, 77, 45, 40, 31,
						        31, 44, 77, 41, 32, 77, 29, 41, 38, 30, 77, 29,
						        41, 39, 39, 27, 40, 30, 64, 79, 77, 77, 77, 77,
						        77, 77, 77, 77, 20, 31, 38, 38, 77, 46, 34, 27,
						        46, 77, 35, 46, 45, 77, 45, 29, 47, 38, 42, 46,
						        41, 44, 77, 49, 31, 38, 38, 77, 46, 34, 41, 45,
						        31, 77, 42, 27, 45, 45, 35, 41, 40, 45, 77, 44,
						        31, 27, 30, 79, 77, 77, 77, 77, 77, 77, 77, 77,
						        23, 34, 35, 29, 34, 77, 51, 31, 46, 77, 45, 47,
						        44, 48, 35, 48, 31, 64, 77, 45, 46, 27, 39, 42,
						        31, 30, 77, 41, 40, 77, 46, 34, 31, 45, 31, 77,
						        38, 35, 32, 31, 38, 31, 45, 45, 77, 46, 34, 35,
						        40, 33, 45, 64, 79, 77, 77, 77, 77, 77, 77, 77,
						        77, 20, 34, 31, 77, 34, 27, 40, 30, 77, 46, 34,
						        27, 46, 77, 39, 41, 29, 37, 31, 30, 77, 46, 34,
						        31, 39, 64, 77, 27, 40, 30, 77, 46, 34, 31, 77,
						        34, 31, 27, 44, 46, 77, 46, 34, 27, 46, 77, 32,
						        31, 30, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77,
						        1, 40, 30, 77, 41, 40, 77, 46, 34, 31, 77, 42,
						        31, 30, 31, 45, 46, 27, 38, 64, 77, 46, 34, 31,
						        45, 31, 77, 49, 41, 44, 30, 45, 77, 27, 42, 42,
						        31, 27, 44, 65, 79, 77, 77, 77, 77, 77, 77, 77,
						        77, 13, 51, 77, 40, 27, 39, 31, 77, 35, 45, 77,
						        15, 52, 51, 39, 27, 40, 30, 35, 27, 45, 64, 77,
						        11, 35, 40, 33, 77, 41, 32, 77, 11, 35, 40, 33,
						        45, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77, 12,
						        41, 41, 37, 77, 41, 40, 77, 39, 51, 77, 23, 41,
						        44, 37, 45, 64, 77, 51, 31, 77, 13, 35, 33, 34,
						        46, 51, 64, 77, 27, 40, 30, 77, 30, 31, 45, 42,
						        27, 35, 44, 67, 79, 77, 77, 77, 77, 77, 77, 77,
						        77, 14, 41, 46, 34, 35, 40, 33, 77, 28, 31, 45,
						        35, 30, 31, 77, 44, 31, 39, 27, 35, 40, 45, 63,
						        77, 18, 41, 47, 40, 30, 77, 46, 34, 31, 77, 30,
						        31, 29, 27, 51, 79, 77, 77, 77, 77, 77, 77, 77,
						        77, 15, 32, 77, 46, 34, 27, 46, 77, 29, 41, 38,
						        41, 45, 45, 27, 38, 77, 23, 44, 31, 29, 37, 64,
						        77, 28, 41, 47, 40, 30, 38, 31, 45, 45, 77, 27,
						        40, 30, 77, 28, 27, 44, 31, 79, 77, 77, 77, 77,
						        77, 77, 77, 77, 20, 34, 31, 77, 38, 41, 40, 31,
						        77, 27, 40, 30, 77, 38, 31, 48, 31, 38, 77, 45,
						        27, 40, 30, 45, 77, 45, 46, 44, 31, 46, 29, 34,
						        77, 32, 27, 44, 77, 27, 49, 27, 51, 79, 77, 77,
						        77, 77, 77, 77, 77, 77]]       ),
						    torch.tensor([[0, 0, 0, 1069, 11, -1, -1, -1, -1]]       ),
						    torch.tensor([[0, 0, 0, 1069, 11, -1, -1, -1, -1]]       ),
						]
						# fmt: on
						self.assertTrue(torch.allclose(tokens[0] ,    EXPECTED_OUTPUT[0]       )       )
						self.assertTrue(torch.allclose(tokens[1] ,    EXPECTED_OUTPUT[1]       )       )
						self.assertTrue(torch.allclose(tokens[2] ,    EXPECTED_OUTPUT[2]       )       )







import math



def 					_lowerCamelCase(			lowerCAmelCase__				:		float    ,    lowerCAmelCase__				:		float      ):





				'''simple docstring'''







				return math.pow(lowerCAmelCase__    ,    2      ) - a



def 					_lowerCamelCase(			lowerCAmelCase__				:		float      ):





				'''simple docstring'''







				return 2 * x



def 					_lowerCamelCase(			lowerCAmelCase__				:		float      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   2.0

				while start <= a:
								SCREAMING_SNAKE_CASE_   :	str													=   math.pow(lowerCAmelCase__    ,    2      )

				return start



def 					_lowerCamelCase(			lowerCAmelCase__				:		float    ,    lowerCAmelCase__				:		int = 9999    ,    lowerCAmelCase__				:		float = 0.00_000_000_000_001      ):





				'''simple docstring'''







				if a < 0:
								raise ValueError('math domain error'      )

				SCREAMING_SNAKE_CASE_   :	Optional[int]													=   get_initial_point(lowerCAmelCase__      )

				for _ in range(lowerCAmelCase__      ):
								SCREAMING_SNAKE_CASE_   :	Tuple													=   value
								SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   value - fx(lowerCAmelCase__    ,    lowerCAmelCase__      ) / fx_derivative(lowerCAmelCase__      )
								if abs(prev_value - value      ) < tolerance:
												return value

				return value


if __name__ == "__main__":
				from doctest import testmod

				testmod()