"""simple docstring"""





import unittest

from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class  _snake_case     (					unittest.TestCase		):
						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       lowerCamelCase__     (       self      :			Optional[int]    ):
													import torch

													__lowerCamelCase					:		List[str]		   =     JukeboxTokenizer.from_pretrained("openai/jukebox-1b-lyrics"    )
													__lowerCamelCase					:		List[Any]		   =     tokenizer(**self.metas    )['''input_ids''']
													# fmt: off
													__lowerCamelCase					:		List[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       lowerCamelCase__     (       self      :			str    ):
													import torch

													__lowerCamelCase					:		Optional[Any]		   =     JukeboxTokenizer.from_pretrained("openai/jukebox-5b-lyrics"    )
													__lowerCamelCase					:		Any		   =     tokenizer(**self.metas    )['''input_ids''']
													# fmt: off
													__lowerCamelCase					:		Optional[int]		   =     [
													    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]    )    )






"""simple docstring"""





import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
				import torch


if is_vision_available():
				from PIL import Image

				from transformers import MgpstrProcessor, ViTImageProcessor
@require_torch
@require_vision
class  _snake_case     (					unittest.TestCase		):
						snake_case__			  =			ViTImageProcessor if is_vision_available() else None






						@property
						def       lowerCamelCase__     (       self      :			List[Any]    ):
													return self.image_processor_tester.prepare_image_processor_dict()






						def       lowerCamelCase__     (       self      :			Tuple    ):
													__lowerCamelCase					:		str		   =     (3, 32, 128)
													__lowerCamelCase					:		Optional[int]		   =     tempfile.mkdtemp()

													# fmt: off
													__lowerCamelCase					:		Tuple		   =     ["[GO]", "[s]", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z"]
													# fmt: on
													__lowerCamelCase					:		Dict		   =     dict(zip(UpperCAmelCase							,					range(len(UpperCAmelCase    )    )    )    )

													__lowerCamelCase					:		List[Any]		   =     os.path.join(self.tmpdirname							,					VOCAB_FILES_NAMES["vocab_file"]    )
													with open(self.vocab_file							,					"w"							,					encoding="utf-8"    ) as fp:
																				fp.write(json.dumps(UpperCAmelCase    ) + "\n"    )

													__lowerCamelCase					:		int		   =     {
													    "do_normalize": False,
													    "do_resize": True,
													    "image_processor_type": "ViTImageProcessor",
													    "resample": 3,
													    "size": {"height": 32, "width": 128},
													}
													__lowerCamelCase					:		Dict		   =     os.path.join(self.tmpdirname							,					UpperCAmelCase    )
													with open(self.image_processor_file							,					"w"							,					encoding="utf-8"    ) as fp:
																				json.dump(UpperCAmelCase							,					UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			Tuple							,					**UpperCAmelCase      :			Optional[int]    ):
													return MgpstrTokenizer.from_pretrained(self.tmpdirname							,					**UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			List[str]							,					**UpperCAmelCase      :			List[str]    ):
													return ViTImageProcessor.from_pretrained(self.tmpdirname							,					**UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			Tuple    ):
													shutil.rmtree(self.tmpdirname    )






						def       lowerCamelCase__     (       self      :			str    ):
													__lowerCamelCase					:		Any		   =     np.random.randint(255							,					size=(3, 30, 400)							,					dtype=np.uinta    )

													__lowerCamelCase					:		Union[str, Any]		   =     Image.fromarray(np.moveaxis(UpperCAmelCase							,					0							,					-1    )    )

													return image_input






						def       lowerCamelCase__     (       self      :			List[str]    ):
													__lowerCamelCase					:		Union[str, Any]		   =     self.get_tokenizer()
													__lowerCamelCase					:		Any		   =     self.get_image_processor()

													__lowerCamelCase					:		List[Any]		   =     MgpstrProcessor(tokenizer=UpperCAmelCase							,					image_processor=UpperCAmelCase    )
													processor.save_pretrained(self.tmpdirname    )
													__lowerCamelCase					:		str		   =     MgpstrProcessor.from_pretrained(self.tmpdirname							,					use_fast=UpperCAmelCase    )

													self.assertEqual(processor.char_tokenizer.get_vocab()							,					tokenizer.get_vocab()    )
													self.assertIsInstance(processor.char_tokenizer							,					UpperCAmelCase    )

													self.assertEqual(processor.image_processor.to_json_string()							,					image_processor.to_json_string()    )
													self.assertIsInstance(processor.image_processor							,					UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			Optional[int]    ):
													__lowerCamelCase					:		Any		   =     self.get_tokenizer()
													__lowerCamelCase					:		Dict		   =     self.get_image_processor()

													__lowerCamelCase					:		Optional[int]		   =     MgpstrProcessor(tokenizer=UpperCAmelCase							,					image_processor=UpperCAmelCase    )
													processor.save_pretrained(self.tmpdirname    )

													__lowerCamelCase					:		Dict		   =     self.get_tokenizer(bos_token="(BOS)"							,					eos_token="(EOS)"    )
													__lowerCamelCase					:		Any		   =     self.get_image_processor(do_normalize=UpperCAmelCase							,					padding_value=1.0    )

													__lowerCamelCase					:		List[str]		   =     MgpstrProcessor.from_pretrained(
													    self.tmpdirname							,					bos_token="(BOS)"							,					eos_token="(EOS)"							,					do_normalize=UpperCAmelCase							,					padding_value=1.0    )

													self.assertEqual(processor.char_tokenizer.get_vocab()							,					tokenizer_add_kwargs.get_vocab()    )
													self.assertIsInstance(processor.char_tokenizer							,					UpperCAmelCase    )

													self.assertEqual(processor.image_processor.to_json_string()							,					image_processor_add_kwargs.to_json_string()    )
													self.assertIsInstance(processor.image_processor							,					UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			Optional[Any]    ):
													__lowerCamelCase					:		List[str]		   =     self.get_image_processor()
													__lowerCamelCase					:		Dict		   =     self.get_tokenizer()

													__lowerCamelCase					:		Optional[int]		   =     MgpstrProcessor(tokenizer=UpperCAmelCase							,					image_processor=UpperCAmelCase    )

													__lowerCamelCase					:		Optional[int]		   =     self.prepare_image_inputs()

													__lowerCamelCase					:		Optional[int]		   =     image_processor(UpperCAmelCase							,					return_tensors="np"    )
													__lowerCamelCase					:		Tuple		   =     processor(images=UpperCAmelCase							,					return_tensors="np"    )

													for key in input_image_proc.keys():
																				self.assertAlmostEqual(input_image_proc[key].sum()							,					input_processor[key].sum()							,					delta=1E-2    )






						def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
													__lowerCamelCase					:		int		   =     self.get_image_processor()
													__lowerCamelCase					:		Any		   =     self.get_tokenizer()

													__lowerCamelCase					:		int		   =     MgpstrProcessor(tokenizer=UpperCAmelCase							,					image_processor=UpperCAmelCase    )

													__lowerCamelCase					:		Optional[Any]		   =     "test"

													__lowerCamelCase					:		List[Any]		   =     processor(text=UpperCAmelCase    )

													__lowerCamelCase					:		Optional[int]		   =     tokenizer(UpperCAmelCase    )
													for key in encoded_tok.keys():
																				self.assertListEqual(encoded_tok[key]							,					encoded_processor[key]    )






						def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
													__lowerCamelCase					:		Union[str, Any]		   =     self.get_image_processor()
													__lowerCamelCase					:		Optional[Any]		   =     self.get_tokenizer()

													__lowerCamelCase					:		Optional[int]		   =     MgpstrProcessor(tokenizer=UpperCAmelCase							,					image_processor=UpperCAmelCase    )

													__lowerCamelCase					:		str		   =     "test"
													__lowerCamelCase					:		Union[str, Any]		   =     self.prepare_image_inputs()

													__lowerCamelCase					:		Optional[Any]		   =     processor(text=UpperCAmelCase							,					images=UpperCAmelCase    )

													self.assertListEqual(list(inputs.keys()    )							,					["pixel_values", "labels"]    )

													# test if it raises when no input is passed
													with pytest.raises(UpperCAmelCase    ):
																				processor()






						def       lowerCamelCase__     (       self      :			Optional[Any]    ):
													__lowerCamelCase					:		Any		   =     self.get_image_processor()
													__lowerCamelCase					:		Optional[Any]		   =     self.get_tokenizer()

													__lowerCamelCase					:		Dict		   =     MgpstrProcessor(tokenizer=UpperCAmelCase							,					image_processor=UpperCAmelCase    )

													__lowerCamelCase					:		Optional[Any]		   =     [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

													__lowerCamelCase					:		List[Any]		   =     processor.char_decode(UpperCAmelCase    )
													__lowerCamelCase					:		Dict		   =     tokenizer.batch_decode(UpperCAmelCase    )
													__lowerCamelCase					:		Any		   =     [seq.replace(" "							,					""    ) for seq in decoded_tok]

													self.assertListEqual(UpperCAmelCase							,					UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			Any    ):
													__lowerCamelCase					:		str		   =     self.get_image_processor()
													__lowerCamelCase					:		str		   =     self.get_tokenizer()

													__lowerCamelCase					:		Union[str, Any]		   =     MgpstrProcessor(tokenizer=UpperCAmelCase							,					image_processor=UpperCAmelCase    )

													__lowerCamelCase					:		Tuple		   =     None
													__lowerCamelCase					:		int		   =     self.prepare_image_inputs()

													__lowerCamelCase					:		List[str]		   =     processor(text=UpperCAmelCase							,					images=UpperCAmelCase    )

													self.assertListEqual(list(inputs.keys()    )							,					processor.model_input_names    )







						def       lowerCamelCase__     (       self      :			Optional[Any]    ):
													__lowerCamelCase					:		Optional[Any]		   =     self.get_image_processor()
													__lowerCamelCase					:		Tuple		   =     self.get_tokenizer()

													__lowerCamelCase					:		List[Any]		   =     MgpstrProcessor(tokenizer=UpperCAmelCase							,					image_processor=UpperCAmelCase    )

													__lowerCamelCase					:		Optional[int]		   =     torch.randn(1							,					27							,					38    )
													__lowerCamelCase					:		List[str]		   =     torch.randn(1							,					27							,					50257    )
													__lowerCamelCase					:		Union[str, Any]		   =     torch.randn(1							,					27							,					30522    )

													__lowerCamelCase					:		str		   =     processor.batch_decode([char_input, bpe_input, wp_input]    )

													self.assertListEqual(list(results.keys()    )							,					["generated_text", "scores", "char_preds", "bpe_preds", "wp_preds"]    )