






from multiprocessing import Lock, Pipe, Process

# lock used to ensure that two processes do not access a pipe at the same time
lowerCAmelCase_           = Lock()







def      snake_case		(							UpperCAmelCase	:       Dict,						UpperCAmelCase	:       int,						UpperCAmelCase	:       Any,						UpperCAmelCase	:       Dict,						UpperCAmelCase	:       Optional[int],						UpperCAmelCase	:       Tuple,						UpperCAmelCase	:       Union[str, Any]   ):
				global process_lock

				# we perform n swaps since after n swaps we know we are sorted
				# we *could* stop early if we are sorted already, but it takes as long to
				# find out we are sorted as it does to sort the list with this algorithm
				for i in range(0,						10   ):
								if (i + position) % 2 == 0 and r_send is not None:
												# send your value to your right neighbor
												process_lock.acquire()
												r_send[1].send(_UpperCamelCase   )
												process_lock.release()

												# receive your right neighbor's value
												process_lock.acquire()
												A	 =      rr_cv[0].recv()
												process_lock.release()

												# take the lower value since you are on the left
												A	 =      min(_UpperCamelCase,						_UpperCamelCase   )
								elif (i + position) % 2 != 0 and l_send is not None:
												# send your value to your left neighbor
												process_lock.acquire()
												l_send[1].send(_UpperCamelCase   )
												process_lock.release()

												# receive your left neighbor's value
												process_lock.acquire()
												A	 =      lr_cv[0].recv()
												process_lock.release()

												# take the higher value since you are on the right
												A	 =      max(_UpperCamelCase,						_UpperCamelCase   )
    # after all swaps are performed, send the values back to main
				result_pipe[1].send(_UpperCamelCase   )







def      snake_case		(							UpperCAmelCase	:       int   ):
				A	 =      []
				A	 =      []
				# initialize the list of pipes where the values will be retrieved
				for _ in arr:
								result_pipe.append(Pipe()   )
				# creates the processes
				# the first and last process only have one neighbor so they are made outside
				# of the loop
				A	 =      Pipe()
				A	 =      Pipe()
				process_array_.append(
				    Process(
				        target=_UpperCamelCase,						args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0]),						)   )
				A	 =      temp_rs
				A	 =      temp_rr

				for i in range(1,						len(_UpperCamelCase   ) - 1   ):
								A	 =      Pipe()
								A	 =      Pipe()
								process_array_.append(
								    Process(
								        target=_UpperCamelCase,						args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i]),						)   )
								A	 =      temp_rs
								A	 =      temp_rr

				process_array_.append(
				    Process(
				        target=_UpperCamelCase,						args=(
				            len(_UpperCamelCase   ) - 1,
				            arr[len(_UpperCamelCase   ) - 1],
				            temp_ls,
				            None,
				            temp_lr,
				            None,
				            result_pipe[len(_UpperCamelCase   ) - 1],
				        ),						)   )

				# start the processes
				for p in process_array_:
								p.start()

				# wait for the processes to end and write their values to the list
				for p in range(0,						len(_UpperCamelCase   )   ):
								A	 =      result_pipe[p][0].recv()
								process_array_[p].join()
				return arr







def      snake_case		(							):
				A	 =      list(range(10,						0,						-1   )   )
				print('Initial List'   )
				print(*_UpperCamelCase   )
				A	 =      odd_even_transposition(_UpperCamelCase   )
				print('Sorted List\n'   )
				print(*_UpperCamelCase   )


if __name__ == "__main__":
					main()













import unittest

from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class      UpperCamelCase (				unittest.TestCase ):







		"""simple docstring"""



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


						'''simple docstring'''
						import torch

						A	 =      JukeboxTokenizer.from_pretrained('openai/jukebox-1b-lyrics'		)
						A	 =      tokenizer(**self.metas		)['input_ids']
						# fmt: off
						A	 =      [
						    torch.tensor([[
						        0, 0, 0, 7_1_6_9, 5_0_7, 9, 7_6, 3_9, 3_1, 4_6, 7_6, 2_7,
						        7_6, 4_6, 4_4, 2_7, 4_8, 3_1, 3_8, 3_8, 3_1, 4_4, 7_6, 3_2,
						        4_4, 4_1, 3_9, 7_6, 2_7, 4_0, 7_6, 2_7, 4_0, 4_6, 3_5, 4_3,
						        4_7, 3_1, 7_6, 3_8, 2_7, 4_0, 3_0, 6_4, 7_8, 7_6, 7_6, 7_6,
						        7_6, 7_6, 7_6, 7_6, 7_6, 2_3, 3_4, 4_1, 7_6, 4_5, 2_7, 3_5,
						        3_0, 7_6, 7_1, 2_0, 4_9, 4_1, 7_6, 4_8, 2_7, 4_5, 4_6, 7_6,
						        2_7, 4_0, 3_0, 7_6, 4_6, 4_4, 4_7, 4_0, 3_7, 3_8, 3_1, 4_5,
						        4_5, 7_6, 3_8, 3_1, 3_3, 4_5, 7_6, 4_1, 3_2, 7_6, 4_5, 4_6,
						        4_1, 4_0, 3_1, 7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6,
						        1_9, 4_6, 2_7, 4_0, 3_0, 7_6, 3_5, 4_0, 7_6, 4_6, 3_4, 3_1,
						        7_6, 3_0, 3_1, 4_5, 3_1, 4_4, 4_6, 6_3, 7_6, 6_3, 7_6, 6_3,
						        7_6, 6_3, 7_6, 1_4, 3_1, 2_7, 4_4, 7_6, 4_6, 3_4, 3_1, 3_9,
						        6_4, 7_6, 4_1, 4_0, 7_6, 4_6, 3_4, 3_1, 7_6, 4_5, 2_7, 4_0,
						        3_0, 6_4, 7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 8,
						        2_7, 3_8, 3_2, 7_6, 4_5, 4_7, 4_0, 3_7, 7_6, 2_7, 7_6, 4_5,
						        3_4, 2_7, 4_6, 4_6, 3_1, 4_4, 3_1, 3_0, 7_6, 4_8, 3_5, 4_5,
						        2_7, 3_3, 3_1, 7_6, 3_8, 3_5, 3_1, 4_5, 6_4, 7_6, 4_9, 3_4,
						        4_1, 4_5, 3_1, 7_6, 3_2, 4_4, 4_1, 4_9, 4_0, 6_4, 7_8, 7_6,
						        7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 1, 4_0, 3_0, 7_6, 4_9,
						        4_4, 3_5, 4_0, 3_7, 3_8, 3_1, 3_0, 7_6, 3_8, 3_5, 4_2, 6_4,
						        7_6, 2_7, 4_0, 3_0, 7_6, 4_5, 4_0, 3_1, 3_1, 4_4, 7_6, 4_1,
						        3_2, 7_6, 2_9, 4_1, 3_8, 3_0, 7_6, 2_9, 4_1, 3_9, 3_9, 2_7,
						        4_0, 3_0, 6_4, 7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6,
						        2_0, 3_1, 3_8, 3_8, 7_6, 4_6, 3_4, 2_7, 4_6, 7_6, 3_5, 4_6,
						        4_5, 7_6, 4_5, 2_9, 4_7, 3_8, 4_2, 4_6, 4_1, 4_4, 7_6, 4_9,
						        3_1, 3_8, 3_8, 7_6, 4_6, 3_4, 4_1, 4_5, 3_1, 7_6, 4_2, 2_7,
						        4_5, 4_5, 3_5, 4_1, 4_0, 4_5, 7_6, 4_4, 3_1, 2_7, 3_0, 7_8,
						        7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 2_3, 3_4, 3_5, 2_9,
						        3_4, 7_6, 5_1, 3_1, 4_6, 7_6, 4_5, 4_7, 4_4, 4_8, 3_5, 4_8,
						        3_1, 6_4, 7_6, 4_5, 4_6, 2_7, 3_9, 4_2, 3_1, 3_0, 7_6, 4_1,
						        4_0, 7_6, 4_6, 3_4, 3_1, 4_5, 3_1, 7_6, 3_8, 3_5, 3_2, 3_1,
						        3_8, 3_1, 4_5, 4_5, 7_6, 4_6, 3_4, 3_5, 4_0, 3_3, 4_5, 6_4,
						        7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 2_0, 3_4, 3_1,
						        7_6, 3_4, 2_7, 4_0, 3_0, 7_6, 4_6, 3_4, 2_7, 4_6, 7_6, 3_9,
						        4_1, 2_9, 3_7, 3_1, 3_0, 7_6, 4_6, 3_4, 3_1, 3_9, 6_4, 7_6,
						        2_7, 4_0, 3_0, 7_6, 4_6, 3_4, 3_1, 7_6, 3_4, 3_1, 2_7, 4_4,
						        4_6, 7_6, 4_6, 3_4, 2_7, 4_6, 7_6, 3_2, 3_1, 3_0, 6_6, 7_8,
						        7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 1, 4_0, 3_0, 7_6,
						        4_1, 4_0, 7_6, 4_6, 3_4, 3_1, 7_6, 4_2, 3_1, 3_0, 3_1, 4_5,
						        4_6, 2_7, 3_8, 6_4, 7_6, 4_6, 3_4, 3_1, 4_5, 3_1, 7_6, 4_9,
						        4_1, 4_4, 3_0, 4_5, 7_6, 2_7, 4_2, 4_2, 3_1, 2_7, 4_4, 6_5,
						        7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 1_3, 5_1, 7_6,
						        4_0, 2_7, 3_9, 3_1, 7_6, 3_5, 4_5, 7_6, 1_5, 5_2, 5_1, 3_9,
						        2_7, 4_0, 3_0, 3_5, 2_7, 4_5, 6_4, 7_6, 1_1, 3_5, 4_0, 3_3,
						        7_6, 4_1, 3_2, 7_6, 1_1, 3_5, 4_0, 3_3, 4_5, 6_6, 7_8, 7_6,
						        7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 1_2, 4_1, 4_1, 3_7, 7_6,
						        4_1, 4_0, 7_6, 3_9, 5_1, 7_6, 2_3, 4_1, 4_4, 3_7, 4_5, 6_4,
						        7_6, 5_1, 3_1, 7_6, 1_3, 3_5, 3_3, 3_4, 4_6, 5_1, 6_4, 7_6,
						        2_7, 4_0, 3_0, 7_6, 3_0, 3_1, 4_5, 4_2, 2_7, 3_5, 4_4, 6_7,
						        7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 1_4, 4_1, 4_6,
						        3_4, 3_5, 4_0, 3_3, 7_6, 2_8, 3_1, 4_5, 3_5, 3_0, 3_1, 7_6,
						        4_4, 3_1, 3_9, 2_7, 3_5, 4_0, 4_5, 6_3, 7_6, 1_8, 4_1, 4_7,
						        4_0, 3_0, 7_6, 4_6, 3_4, 3_1, 7_6, 3_0, 3_1, 2_9, 2_7, 5_1,
						        7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 1_5, 3_2, 7_6,
						        4_6, 3_4, 2_7, 4_6, 7_6, 2_9, 4_1, 3_8, 4_1, 4_5, 4_5, 2_7,
						        3_8, 7_6, 2_3, 4_4, 3_1, 2_9, 3_7, 6_4, 7_6, 2_8, 4_1, 4_7,
						        4_0, 3_0, 3_8, 3_1, 4_5, 4_5, 7_6, 2_7, 4_0, 3_0, 7_6, 2_8,
						        2_7, 4_4, 3_1, 7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6,
						        2_0, 3_4, 3_1, 7_6, 3_8, 4_1, 4_0, 3_1, 7_6, 2_7, 4_0, 3_0,
						        7_6, 3_8, 3_1, 4_8, 3_1, 3_8, 7_6, 4_5, 2_7, 4_0, 3_0, 4_5,
						        7_6, 4_5, 4_6, 4_4, 3_1, 4_6, 2_9, 3_4, 7_6, 3_2, 2_7, 4_4,
						        7_6, 2_7, 4_9, 2_7, 5_1, 7_8, 7_6, 7_6, 7_6, 7_6, 7_6, 7_6,
						        7_6, 7_6]]		),
						    torch.tensor([[0, 0, 0, 1_0_6_9, 1_1]]		),
						    torch.tensor([[0, 0, 0, 1_0_6_9, 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]		)		)







		@require_torch
		def       A(  self		:       Tuple		)    ->   List[Any]:


						'''simple docstring'''
						import torch

						A	 =      JukeboxTokenizer.from_pretrained('openai/jukebox-5b-lyrics'		)
						A	 =      tokenizer(**self.metas		)['input_ids']
						# fmt: off
						A	 =      [
						    torch.tensor([[
						        0, 0, 0, 1_0_6_9, 1_1, -1, -1, -1, -1, 9, 7_7, 3_9,
						        3_1, 4_6, 7_7, 2_7, 7_7, 4_6, 4_4, 2_7, 4_8, 3_1, 3_8, 3_8,
						        3_1, 4_4, 7_7, 3_2, 4_4, 4_1, 3_9, 7_7, 2_7, 4_0, 7_7, 2_7,
						        4_0, 4_6, 3_5, 4_3, 4_7, 3_1, 7_7, 3_8, 2_7, 4_0, 3_0, 6_4,
						        7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 2_3, 3_4, 4_1,
						        7_7, 4_5, 2_7, 3_5, 3_0, 7_7, 7_2, 2_0, 4_9, 4_1, 7_7, 4_8,
						        2_7, 4_5, 4_6, 7_7, 2_7, 4_0, 3_0, 7_7, 4_6, 4_4, 4_7, 4_0,
						        3_7, 3_8, 3_1, 4_5, 4_5, 7_7, 3_8, 3_1, 3_3, 4_5, 7_7, 4_1,
						        3_2, 7_7, 4_5, 4_6, 4_1, 4_0, 3_1, 7_9, 7_7, 7_7, 7_7, 7_7,
						        7_7, 7_7, 7_7, 7_7, 1_9, 4_6, 2_7, 4_0, 3_0, 7_7, 3_5, 4_0,
						        7_7, 4_6, 3_4, 3_1, 7_7, 3_0, 3_1, 4_5, 3_1, 4_4, 4_6, 6_3,
						        7_7, 6_3, 7_7, 6_3, 7_7, 6_3, 7_7, 1_4, 3_1, 2_7, 4_4, 7_7,
						        4_6, 3_4, 3_1, 3_9, 6_4, 7_7, 4_1, 4_0, 7_7, 4_6, 3_4, 3_1,
						        7_7, 4_5, 2_7, 4_0, 3_0, 6_4, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7,
						        7_7, 7_7, 7_7, 8, 2_7, 3_8, 3_2, 7_7, 4_5, 4_7, 4_0, 3_7,
						        7_7, 2_7, 7_7, 4_5, 3_4, 2_7, 4_6, 4_6, 3_1, 4_4, 3_1, 3_0,
						        7_7, 4_8, 3_5, 4_5, 2_7, 3_3, 3_1, 7_7, 3_8, 3_5, 3_1, 4_5,
						        6_4, 7_7, 4_9, 3_4, 4_1, 4_5, 3_1, 7_7, 3_2, 4_4, 4_1, 4_9,
						        4_0, 6_4, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 1,
						        4_0, 3_0, 7_7, 4_9, 4_4, 3_5, 4_0, 3_7, 3_8, 3_1, 3_0, 7_7,
						        3_8, 3_5, 4_2, 6_4, 7_7, 2_7, 4_0, 3_0, 7_7, 4_5, 4_0, 3_1,
						        3_1, 4_4, 7_7, 4_1, 3_2, 7_7, 2_9, 4_1, 3_8, 3_0, 7_7, 2_9,
						        4_1, 3_9, 3_9, 2_7, 4_0, 3_0, 6_4, 7_9, 7_7, 7_7, 7_7, 7_7,
						        7_7, 7_7, 7_7, 7_7, 2_0, 3_1, 3_8, 3_8, 7_7, 4_6, 3_4, 2_7,
						        4_6, 7_7, 3_5, 4_6, 4_5, 7_7, 4_5, 2_9, 4_7, 3_8, 4_2, 4_6,
						        4_1, 4_4, 7_7, 4_9, 3_1, 3_8, 3_8, 7_7, 4_6, 3_4, 4_1, 4_5,
						        3_1, 7_7, 4_2, 2_7, 4_5, 4_5, 3_5, 4_1, 4_0, 4_5, 7_7, 4_4,
						        3_1, 2_7, 3_0, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7,
						        2_3, 3_4, 3_5, 2_9, 3_4, 7_7, 5_1, 3_1, 4_6, 7_7, 4_5, 4_7,
						        4_4, 4_8, 3_5, 4_8, 3_1, 6_4, 7_7, 4_5, 4_6, 2_7, 3_9, 4_2,
						        3_1, 3_0, 7_7, 4_1, 4_0, 7_7, 4_6, 3_4, 3_1, 4_5, 3_1, 7_7,
						        3_8, 3_5, 3_2, 3_1, 3_8, 3_1, 4_5, 4_5, 7_7, 4_6, 3_4, 3_5,
						        4_0, 3_3, 4_5, 6_4, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7,
						        7_7, 2_0, 3_4, 3_1, 7_7, 3_4, 2_7, 4_0, 3_0, 7_7, 4_6, 3_4,
						        2_7, 4_6, 7_7, 3_9, 4_1, 2_9, 3_7, 3_1, 3_0, 7_7, 4_6, 3_4,
						        3_1, 3_9, 6_4, 7_7, 2_7, 4_0, 3_0, 7_7, 4_6, 3_4, 3_1, 7_7,
						        3_4, 3_1, 2_7, 4_4, 4_6, 7_7, 4_6, 3_4, 2_7, 4_6, 7_7, 3_2,
						        3_1, 3_0, 6_6, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7,
						        1, 4_0, 3_0, 7_7, 4_1, 4_0, 7_7, 4_6, 3_4, 3_1, 7_7, 4_2,
						        3_1, 3_0, 3_1, 4_5, 4_6, 2_7, 3_8, 6_4, 7_7, 4_6, 3_4, 3_1,
						        4_5, 3_1, 7_7, 4_9, 4_1, 4_4, 3_0, 4_5, 7_7, 2_7, 4_2, 4_2,
						        3_1, 2_7, 4_4, 6_5, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7,
						        7_7, 1_3, 5_1, 7_7, 4_0, 2_7, 3_9, 3_1, 7_7, 3_5, 4_5, 7_7,
						        1_5, 5_2, 5_1, 3_9, 2_7, 4_0, 3_0, 3_5, 2_7, 4_5, 6_4, 7_7,
						        1_1, 3_5, 4_0, 3_3, 7_7, 4_1, 3_2, 7_7, 1_1, 3_5, 4_0, 3_3,
						        4_5, 6_6, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 1_2,
						        4_1, 4_1, 3_7, 7_7, 4_1, 4_0, 7_7, 3_9, 5_1, 7_7, 2_3, 4_1,
						        4_4, 3_7, 4_5, 6_4, 7_7, 5_1, 3_1, 7_7, 1_3, 3_5, 3_3, 3_4,
						        4_6, 5_1, 6_4, 7_7, 2_7, 4_0, 3_0, 7_7, 3_0, 3_1, 4_5, 4_2,
						        2_7, 3_5, 4_4, 6_7, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7,
						        7_7, 1_4, 4_1, 4_6, 3_4, 3_5, 4_0, 3_3, 7_7, 2_8, 3_1, 4_5,
						        3_5, 3_0, 3_1, 7_7, 4_4, 3_1, 3_9, 2_7, 3_5, 4_0, 4_5, 6_3,
						        7_7, 1_8, 4_1, 4_7, 4_0, 3_0, 7_7, 4_6, 3_4, 3_1, 7_7, 3_0,
						        3_1, 2_9, 2_7, 5_1, 7_9, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7, 7_7,
						        7_7, 1_5, 3_2, 7_7, 4_6, 3_4, 2_7, 4_6, 7_7, 2_9, 4_1, 3_8,
						        4_1, 4_5, 4_5, 2_7, 3_8, 7_7, 2_3, 4_4, 3_1, 2_9, 3_7, 6_4,
						        7_7, 2_8, 4_1, 4_7, 4_0, 3_0, 3_8, 3_1, 4_5, 4_5, 7_7, 2_7,
						        4_0, 3_0, 7_7, 2_8, 2_7, 4_4, 3_1, 7_9, 7_7, 7_7, 7_7, 7_7,
						        7_7, 7_7, 7_7, 7_7, 2_0, 3_4, 3_1, 7_7, 3_8, 4_1, 4_0, 3_1,
						        7_7, 2_7, 4_0, 3_0, 7_7, 3_8, 3_1, 4_8, 3_1, 3_8, 7_7, 4_5,
						        2_7, 4_0, 3_0, 4_5, 7_7, 4_5, 4_6, 4_4, 3_1, 4_6, 2_9, 3_4,
						        7_7, 3_2, 2_7, 4_4, 7_7, 2_7, 4_9, 2_7, 5_1, 7_9, 7_7, 7_7,
						        7_7, 7_7, 7_7, 7_7, 7_7, 7_7]]		),
						    torch.tensor([[0, 0, 0, 1_0_6_9, 1_1, -1, -1, -1, -1]]		),
						    torch.tensor([[0, 0, 0, 1_0_6_9, 1_1, -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]		)		)





