"""simple docstring"""




import unittest

from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch

class       a_      (     unittest.TestCase			):
					UpperCamelCase_ :	Optional[Any]			=    JukeboxTokenizer
					UpperCamelCase_ :	List[Any]			=    {
					    "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 						_SCREAMING_SNAKE_CASE						(	self						:     Any     ):
							import torch

							lowerCAmelCase__        =   JukeboxTokenizer.from_pretrained("""openai/jukebox-1b-lyrics"""     )
							lowerCAmelCase__        =   tokenizer(**self.metas     )["""input_ids"""]
							# fmt: off
							lowerCAmelCase__        =   [
							    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 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]     ):
							import torch

							lowerCAmelCase__        =   JukeboxTokenizer.from_pretrained("""openai/jukebox-5b-lyrics"""     )
							lowerCAmelCase__        =   tokenizer(**self.metas     )["""input_ids"""]
							# fmt: off
							lowerCAmelCase__        =   [
							    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 unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__lowerCAmelCase    :							Optional[Any]													=						get_tests_dir("fixtures/test_sentencepiece_no_bos.model")

@require_sentencepiece
@require_tokenizers
class       a_      (     __UpperCamelCase ,		unittest.TestCase			):
					UpperCamelCase_ :	Tuple			=    PegasusTokenizer
					UpperCamelCase_ :	Any			=    PegasusTokenizerFast
					UpperCamelCase_ :	int			=    True
					UpperCamelCase_ :	Any			=    True




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple     ):
							super().setUp()

							# We have a SentencePiece fixture for testing
							lowerCAmelCase__        =   PegasusTokenizer(snake_case__     )
							tokenizer.save_pretrained(self.tmpdirname     )




					@cached_property
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]     ):
							return PegasusTokenizer.from_pretrained("""google/pegasus-large"""     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]  ,						**snake_case__						:     Optional[Any]     ):
							return PegasusTokenizer.from_pretrained(self.tmpdirname  ,						**snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple  ,						snake_case__						:     Optional[Any]     ):
							return ("This is a test", "This is a test")




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]     ):
							lowerCAmelCase__        =   """</s>"""
							lowerCAmelCase__        =   1

							self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case__     )  ,						snake_case__     )
							self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case__     )  ,						snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]     ):
							lowerCAmelCase__        =   list(self.get_tokenizer().get_vocab().keys()     )

							self.assertEqual(vocab_keys[0]  ,						"""<pad>"""     )
							self.assertEqual(vocab_keys[1]  ,						"""</s>"""     )
							self.assertEqual(vocab_keys[-1]  ,						"""v"""     )
							self.assertEqual(len(snake_case__     )  ,						1103     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any     ):
							self.assertEqual(self.get_tokenizer().vocab_size  ,						1103     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict     ):
							lowerCAmelCase__        =   self.rust_tokenizer_class.from_pretrained(self.tmpdirname     )
							lowerCAmelCase__        =   self.tokenizer_class.from_pretrained(self.tmpdirname     )
							lowerCAmelCase__        =   (
							    """Let's see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important"""
							    """ </s> <pad> <pad> <pad>"""
							)
							lowerCAmelCase__        =   rust_tokenizer([raw_input_str]  ,						return_tensors=snake_case__  ,						add_special_tokens=snake_case__     ).input_ids[0]
							lowerCAmelCase__        =   py_tokenizer([raw_input_str]  ,						return_tensors=snake_case__  ,						add_special_tokens=snake_case__     ).input_ids[0]
							self.assertListEqual(snake_case__  ,						snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict     ):
							lowerCAmelCase__        =   self._large_tokenizer
							# <mask_1> masks whole sentence while <mask_2> masks single word
							lowerCAmelCase__        =   """<mask_1> To ensure a <mask_2> flow of bank resolutions."""
							lowerCAmelCase__        =   [2, 413, 615, 114, 3, 1971, 113, 1679, 10710, 107, 1]
							lowerCAmelCase__        =   tokenizer([raw_input_str]  ,						return_tensors=snake_case__     ).input_ids[0]
							self.assertListEqual(snake_case__  ,						snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int     ):
							lowerCAmelCase__        =   self._large_tokenizer
							# The tracebacks for the following asserts are **better** without messages or self.assertEqual
							assert tokenizer.vocab_size == 96103
							assert tokenizer.pad_token_id == 0
							assert tokenizer.eos_token_id == 1
							assert tokenizer.offset == 103
							assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
							assert tokenizer.unk_token == "<unk>"
							assert tokenizer.model_max_length == 1024
							lowerCAmelCase__        =   """To ensure a smooth flow of bank resolutions."""
							lowerCAmelCase__        =   [413, 615, 114, 2291, 1971, 113, 1679, 10710, 107, 1]
							lowerCAmelCase__        =   tokenizer([raw_input_str]  ,						return_tensors=snake_case__     ).input_ids[0]
							self.assertListEqual(snake_case__  ,						snake_case__     )
							assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]     ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]




					@require_torch
					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]     ):
							lowerCAmelCase__        =   ["""This is going to be way too long.""" * 150, """short example"""]
							lowerCAmelCase__        =   ["""not super long but more than 5 tokens""", """tiny"""]
							lowerCAmelCase__        =   self._large_tokenizer(snake_case__  ,						padding=snake_case__  ,						truncation=snake_case__  ,						return_tensors="""pt"""     )
							lowerCAmelCase__        =   self._large_tokenizer(
							    text_target=snake_case__  ,						max_length=5  ,						padding=snake_case__  ,						truncation=snake_case__  ,						return_tensors="""pt"""     )

							assert batch.input_ids.shape == (2, 1024)
							assert batch.attention_mask.shape == (2, 1024)
							assert targets["input_ids"].shape == (2, 5)
							assert len(snake_case__     ) == 2  # input_ids, attention_mask.




					@slow
					def 						_SCREAMING_SNAKE_CASE						(	self						:     str     ):
							# fmt: off
							lowerCAmelCase__        =   {"""input_ids""": [[38979, 143, 18485, 606, 130, 26669, 87686, 121, 54189, 1129, 111, 26669, 87686, 121, 9114, 14787, 121, 13249, 158, 592, 956, 121, 14621, 31576, 143, 62613, 108, 9688, 930, 43430, 11562, 62613, 304, 108, 11443, 897, 108, 9314, 17415, 63399, 108, 11443, 7614, 18316, 118, 4284, 7148, 12430, 143, 1400, 25703, 158, 111, 4284, 7148, 11772, 143, 21297, 1064, 158, 122, 204, 3506, 1754, 1133, 14787, 1581, 115, 33224, 4482, 111, 1355, 110, 29173, 317, 50833, 108, 20147, 94665, 111, 77198, 107, 1], [110, 62613, 117, 638, 112, 1133, 121, 20098, 1355, 79050, 13872, 135, 1596, 53541, 1352, 141, 13039, 5542, 124, 302, 518, 111, 268, 2956, 115, 149, 4427, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [139, 1235, 2799, 18289, 17780, 204, 109, 9474, 1296, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
							# fmt: on

							self.tokenizer_integration_test_util(
							    expected_encoding=snake_case__  ,						model_name="""google/bigbird-pegasus-large-arxiv"""  ,						revision="""ba85d0851d708441f91440d509690f1ab6353415"""  ,						)

@require_sentencepiece
@require_tokenizers
class       a_      (     __UpperCamelCase ,		unittest.TestCase			):
					UpperCamelCase_ :	str			=    PegasusTokenizer
					UpperCamelCase_ :	Optional[int]			=    PegasusTokenizerFast
					UpperCamelCase_ :	Union[str, Any]			=    True
					UpperCamelCase_ :	Optional[int]			=    True




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]     ):
							super().setUp()

							# We have a SentencePiece fixture for testing
							lowerCAmelCase__        =   PegasusTokenizer(snake_case__  ,						offset=0  ,						mask_token_sent=snake_case__  ,						mask_token="""[MASK]"""     )
							tokenizer.save_pretrained(self.tmpdirname     )




					@cached_property
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict     ):
							return PegasusTokenizer.from_pretrained("""google/bigbird-pegasus-large-arxiv"""     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]  ,						**snake_case__						:     List[Any]     ):
							return PegasusTokenizer.from_pretrained(self.tmpdirname  ,						**snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     str  ,						snake_case__						:     Dict     ):
							return ("This is a test", "This is a test")




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]     ):
							lowerCAmelCase__        =   self.rust_tokenizer_class.from_pretrained(self.tmpdirname     )
							lowerCAmelCase__        =   self.tokenizer_class.from_pretrained(self.tmpdirname     )
							lowerCAmelCase__        =   (
							    """Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"""
							    """ <pad> <pad> <pad>"""
							)
							lowerCAmelCase__        =   rust_tokenizer([raw_input_str]  ,						return_tensors=snake_case__  ,						add_special_tokens=snake_case__     ).input_ids[0]
							lowerCAmelCase__        =   py_tokenizer([raw_input_str]  ,						return_tensors=snake_case__  ,						add_special_tokens=snake_case__     ).input_ids[0]
							self.assertListEqual(snake_case__  ,						snake_case__     )




					@require_torch
					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]     ):
							lowerCAmelCase__        =   ["""This is going to be way too long.""" * 1000, """short example"""]
							lowerCAmelCase__        =   ["""not super long but more than 5 tokens""", """tiny"""]
							lowerCAmelCase__        =   self._large_tokenizer(snake_case__  ,						padding=snake_case__  ,						truncation=snake_case__  ,						return_tensors="""pt"""     )
							lowerCAmelCase__        =   self._large_tokenizer(
							    text_target=snake_case__  ,						max_length=5  ,						padding=snake_case__  ,						truncation=snake_case__  ,						return_tensors="""pt"""     )

							assert batch.input_ids.shape == (2, 4096)
							assert batch.attention_mask.shape == (2, 4096)
							assert targets["input_ids"].shape == (2, 5)
							assert len(snake_case__     ) == 2  # input_ids, attention_mask.




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]     ):
							lowerCAmelCase__        =   (
							    """This is an example string that is used to test the original TF implementation against the HF"""
							    """ implementation"""
							)

							lowerCAmelCase__        =   self._large_tokenizer(snake_case__     ).input_ids

							self.assertListEqual(
							    snake_case__  ,						[182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 25016, 3137, 464, 109, 26955, 3137, 1]  ,						)




