






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]       )       )







from __future__ import annotations

from collections import Counter
from random import random


class 							__a							:





		'''simple docstring'''



		def __init__(						self       ):
						SCREAMING_SNAKE_CASE_   :	List[str]													=   {}


		def   __snake_case			(						self ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	int													=   {}


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       ):
						if nodea not in self.connections:
										self.add_node(UpperCamelCase__       )
						if nodea not in self.connections:
										self.add_node(UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Tuple													=   probability


		def   __snake_case			(						self       ):
						return list(self.connections       )






		def   __snake_case			(						self ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	Dict													=   0
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   random()

						for dest in self.connections[node]:
										current_probability += self.connections[node][dest]
										if current_probability > random_value:
														return dest
						return ""






def 					_lowerCamelCase(			lowerCAmelCase__				:		str    ,    lowerCAmelCase__				:		list[tuple[str, str, float]]    ,    lowerCAmelCase__				:		int      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   MarkovChainGraphUndirectedUnweighted()

				for nodea, nodea, probability in transitions:
								graph.add_transition_probability(lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__      )

				SCREAMING_SNAKE_CASE_   :	List[str]													=   Counter(graph.get_nodes()      )
				SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   start

				for _ in range(lowerCAmelCase__      ):
								SCREAMING_SNAKE_CASE_   :	Dict													=   graph.transition(lowerCAmelCase__      )
								visited[node] += 1

				return visited


if __name__ == "__main__":
				import doctest

				doctest.testmod()