
"""simple docstring"""




import unittest

from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase__      :    Any    							= get_tests_dir('fixtures/test_sentencepiece_bpe_char.model')



@require_sentencepiece
@require_tokenizers
class 	lowerCAmelCase_						(a__ ,					unittest.TestCase  ):


				"""simple docstring"""



				__UpperCamelCase      :						Dict					       =   SpeechTaTokenizer
				__UpperCamelCase      :						Optional[Any]					       =   False
				__UpperCamelCase      :						Any					       =   True



				def 	__magic_name__    (self    )					->		Dict:



											"""simple docstring"""




											super().setUp()

											# We have a SentencePiece fixture for testing
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   SpeechTaTokenizer(SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   AddedToken("""<mask>"""   ,       lstrip=SCREAMING_SNAKE_CASE__   ,       rstrip=SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       List[str]           =   mask_token
											tokenizer.add_special_tokens({"""mask_token""": mask_token}    )
											tokenizer.add_tokens(["""<ctc_blank>"""]    )

											tokenizer.save_pretrained(self.tmpdirname    )



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		Any:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[str]           =   """this is a test"""
											SCREAMING_SNAKE_CASE__						:       List[str]           =   """this is a test"""
											return input_text, output_text



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__=False   ,       SCREAMING_SNAKE_CASE__=20   ,       SCREAMING_SNAKE_CASE__=5    )					->		List[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       str           =   self.get_input_output_texts(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Any           =   tokenizer.encode(SCREAMING_SNAKE_CASE__   ,       add_special_tokens=SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       List[str]           =   tokenizer.decode(SCREAMING_SNAKE_CASE__   ,       clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__    )
											return text, ids



				def 	__magic_name__    (self    )					->		str:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   """<pad>"""
											SCREAMING_SNAKE_CASE__						:       str           =   1

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



				def 	__magic_name__    (self    )					->		str:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   list(self.get_tokenizer().get_vocab().keys()    )

											self.assertEqual(vocab_keys[0]   ,       """<s>"""    )
											self.assertEqual(vocab_keys[1]   ,       """<pad>"""    )
											self.assertEqual(vocab_keys[-4]   ,       """œ"""    )
											self.assertEqual(vocab_keys[-2]   ,       """<mask>"""    )
											self.assertEqual(vocab_keys[-1]   ,       """<ctc_blank>"""    )
											self.assertEqual(len(SCREAMING_SNAKE_CASE__    )   ,       81    )



				def 	__magic_name__    (self    )					->		List[Any]:



											"""simple docstring"""




											self.assertEqual(self.get_tokenizer().vocab_size   ,       79    )



				def 	__magic_name__    (self    )					->		Union[str, Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       int           =   self.get_tokenizers(do_lower_case=SCREAMING_SNAKE_CASE__    )
											for tokenizer in tokenizers:
																		with self.subTest(F'''{tokenizer.__class__.__name__}'''    ):
																									SCREAMING_SNAKE_CASE__						:       Dict           =   tokenizer.vocab_size
																									SCREAMING_SNAKE_CASE__						:       Tuple           =   len(SCREAMING_SNAKE_CASE__    )

																									self.assertNotEqual(SCREAMING_SNAKE_CASE__   ,       0    )

																									# We usually have added tokens from the start in tests because our vocab fixtures are
																									# smaller than the original vocabs - let's not assert this
																									# self.assertEqual(vocab_size, all_size)

																									SCREAMING_SNAKE_CASE__						:       Optional[int]           =   ["""aaaaa bbbbbb""", """cccccccccdddddddd"""]
																									SCREAMING_SNAKE_CASE__						:       Any           =   tokenizer.add_tokens(SCREAMING_SNAKE_CASE__    )
																									SCREAMING_SNAKE_CASE__						:       Tuple           =   tokenizer.vocab_size
																									SCREAMING_SNAKE_CASE__						:       int           =   len(SCREAMING_SNAKE_CASE__    )

																									self.assertNotEqual(SCREAMING_SNAKE_CASE__   ,       0    )
																									self.assertEqual(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )
																									self.assertEqual(SCREAMING_SNAKE_CASE__   ,       len(SCREAMING_SNAKE_CASE__    )    )
																									self.assertEqual(SCREAMING_SNAKE_CASE__   ,       all_size + len(SCREAMING_SNAKE_CASE__    )    )

																									SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   tokenizer.encode("""aaaaa bbbbbb low cccccccccdddddddd l"""   ,       add_special_tokens=SCREAMING_SNAKE_CASE__    )

																									self.assertGreaterEqual(len(SCREAMING_SNAKE_CASE__    )   ,       4    )
																									self.assertGreater(tokens[0]   ,       tokenizer.vocab_size - 1    )
																									self.assertGreater(tokens[-3]   ,       tokenizer.vocab_size - 1    )

																									SCREAMING_SNAKE_CASE__						:       List[Any]           =   {"""eos_token""": """>>>>|||<||<<|<<""", """pad_token""": """<<<<<|||>|>>>>|>"""}
																									SCREAMING_SNAKE_CASE__						:       Optional[int]           =   tokenizer.add_special_tokens(SCREAMING_SNAKE_CASE__    )
																									SCREAMING_SNAKE_CASE__						:       Dict           =   tokenizer.vocab_size
																									SCREAMING_SNAKE_CASE__						:       str           =   len(SCREAMING_SNAKE_CASE__    )

																									self.assertNotEqual(SCREAMING_SNAKE_CASE__   ,       0    )
																									self.assertEqual(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )
																									self.assertEqual(SCREAMING_SNAKE_CASE__   ,       len(SCREAMING_SNAKE_CASE__    )    )
																									self.assertEqual(SCREAMING_SNAKE_CASE__   ,       all_size_a + len(SCREAMING_SNAKE_CASE__    )    )

																									SCREAMING_SNAKE_CASE__						:       int           =   tokenizer.encode(
																									    """>>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l"""   ,       add_special_tokens=SCREAMING_SNAKE_CASE__    )

																									self.assertGreaterEqual(len(SCREAMING_SNAKE_CASE__    )   ,       6    )
																									self.assertGreater(tokens[0]   ,       tokenizer.vocab_size - 1    )
																									self.assertGreater(tokens[0]   ,       tokens[1]    )
																									self.assertGreater(tokens[-3]   ,       tokenizer.vocab_size - 1    )
																									self.assertGreater(tokens[-3]   ,       tokens[-4]    )
																									self.assertEqual(tokens[0]   ,       tokenizer.eos_token_id    )
																									self.assertEqual(tokens[-3]   ,       tokenizer.pad_token_id    )



				def 	__magic_name__    (self    )					->		Dict:



											"""simple docstring"""




											pass



				def 	__magic_name__    (self    )					->		Tuple:



											"""simple docstring"""




											pass



				def 	__magic_name__    (self    )					->		List[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   self.get_tokenizer()

											SCREAMING_SNAKE_CASE__						:       Tuple           =   tokenizer.tokenize("""This is a test"""    )
											# fmt: off
											self.assertListEqual(SCREAMING_SNAKE_CASE__   ,       [SPIECE_UNDERLINE, """T""", """h""", """i""", """s""", SPIECE_UNDERLINE, """i""", """s""", SPIECE_UNDERLINE, """a""", SPIECE_UNDERLINE, """t""", """e""", """s""", """t"""]    )
											# fmt: on

											self.assertListEqual(
											    tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__    )   ,       [4, 32, 11, 10, 12, 4, 10, 12, 4, 7, 4, 6, 5, 12, 6]   ,       )

											SCREAMING_SNAKE_CASE__						:       str           =   tokenizer.tokenize("""I was born in 92000, and this is falsé."""    )
											self.assertListEqual(
											    SCREAMING_SNAKE_CASE__   ,       [SPIECE_UNDERLINE, """I""", SPIECE_UNDERLINE, """w""", """a""", """s""", SPIECE_UNDERLINE, """b""", """o""", """r""", """n""", SPIECE_UNDERLINE, """i""", """n""", SPIECE_UNDERLINE, """92000""", """,""", SPIECE_UNDERLINE, """a""", """n""", """d""", SPIECE_UNDERLINE, """t""", """h""", """i""", """s""", SPIECE_UNDERLINE, """i""", """s""", SPIECE_UNDERLINE, """f""", """a""", """l""", """s""", """é""", """."""]    )

											SCREAMING_SNAKE_CASE__						:       Tuple           =   tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__    )
											# fmt: off
											self.assertListEqual(SCREAMING_SNAKE_CASE__   ,       [4, 30, 4, 20, 7, 12, 4, 25, 8, 13, 9, 4, 10, 9, 4, 3, 23, 4, 7, 9, 14, 4, 6, 11, 10, 12, 4, 10, 12, 4, 19, 7, 15, 12, 73, 26]    )
											# fmt: on

											SCREAMING_SNAKE_CASE__						:       Any           =   tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__    )
											self.assertListEqual(
											    SCREAMING_SNAKE_CASE__   ,       [SPIECE_UNDERLINE, """I""", SPIECE_UNDERLINE, """w""", """a""", """s""", SPIECE_UNDERLINE, """b""", """o""", """r""", """n""", SPIECE_UNDERLINE, """i""", """n""", SPIECE_UNDERLINE, """<unk>""", """,""", SPIECE_UNDERLINE, """a""", """n""", """d""", SPIECE_UNDERLINE, """t""", """h""", """i""", """s""", SPIECE_UNDERLINE, """i""", """s""", SPIECE_UNDERLINE, """f""", """a""", """l""", """s""", """é""", """."""]    )



				@slow
				def 	__magic_name__    (self    )					->		Dict:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   [
											    """Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides """
											    """general-purpose architectures (BERT, GPT, RoBERTa, XLM, DistilBert, XLNet...) for Natural """
											    """Language Understanding (NLU) and Natural Language Generation (NLG) with over thirty-two pretrained """
											    """models in one hundred plus languages and deep interoperability between Jax, PyTorch and TensorFlow.""",
											    """BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly """
											    """conditioning on both left and right context in all layers.""",
											    """The quick brown fox jumps over the lazy dog.""",
											]

											# fmt: off
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   {
											    """input_ids""": [
											        [4, 32, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 64, 19, 8, 13, 18, 5, 13, 15, 22, 4, 28, 9, 8, 20, 9, 4, 7, 12, 4, 24, 22, 6, 8, 13, 17, 11, 39, 6, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 7, 9, 14, 4, 24, 22, 6, 8, 13, 17, 11, 39, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 39, 25, 5, 13, 6, 63, 4, 24, 13, 8, 27, 10, 14, 5, 12, 4, 21, 5, 9, 5, 13, 7, 15, 39, 24, 16, 13, 24, 8, 12, 5, 4, 7, 13, 17, 11, 10, 6, 5, 17, 6, 16, 13, 5, 12, 4, 64, 40, 47, 54, 32, 23, 4, 53, 49, 32, 23, 4, 54, 8, 40, 47, 54, 32, 7, 23, 4, 69, 52, 43, 23, 4, 51, 10, 12, 6, 10, 15, 40, 5, 13, 6, 23, 4, 69, 52, 48, 5, 6, 26, 26, 26, 63, 4, 19, 8, 13, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 61, 9, 14, 5, 13, 12, 6, 7, 9, 14, 10, 9, 21, 4, 64, 48, 52, 61, 63, 4, 7, 9, 14, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 53, 5, 9, 5, 13, 7, 6, 10, 8, 9, 4, 64, 48, 52, 53, 63, 4, 20, 10, 6, 11, 4, 8, 27, 5, 13, 4, 6, 11, 10, 13, 6, 22, 39, 6, 20, 8, 4, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 4, 18, 8, 14, 5, 15, 12, 4, 10, 9, 4, 8, 9, 5, 4, 11, 16, 9, 14, 13, 5, 14, 4, 24, 15, 16, 12, 4, 15, 7, 9, 21, 16, 7, 21, 5, 12, 4, 7, 9, 14, 4, 14, 5, 5, 24, 4, 10, 9, 6, 5, 13, 8, 24, 5, 13, 7, 25, 10, 15, 10, 6, 22, 4, 25, 5, 6, 20, 5, 5, 9, 4, 58, 7, 37, 23, 4, 49, 22, 32, 8, 13, 17, 11, 4, 7, 9, 14, 4, 32, 5, 9, 12, 8, 13, 55, 15, 8, 20, 26, 2],
											        [4, 40, 47, 54, 32, 4, 10, 12, 4, 14, 5, 12, 10, 21, 9, 5, 14, 4, 6, 8, 4, 24, 13, 5, 39, 6, 13, 7, 10, 9, 4, 14, 5, 5, 24, 4, 25, 10, 14, 10, 13, 5, 17, 6, 10, 8, 9, 7, 15, 4, 13, 5, 24, 13, 5, 12, 5, 9, 6, 7, 6, 10, 8, 9, 12, 4, 19, 13, 8, 18, 4, 16, 9, 15, 7, 25, 5, 15, 5, 14, 4, 6, 5, 37, 6, 4, 25, 22, 4, 46, 8, 10, 9, 6, 15, 22, 4, 17, 8, 9, 14, 10, 6, 10, 8, 9, 10, 9, 21, 4, 8, 9, 4, 25, 8, 6, 11, 4, 15, 5, 19, 6, 4, 7, 9, 14, 4, 13, 10, 21, 11, 6, 4, 17, 8, 9, 6, 5, 37, 6, 4, 10, 9, 4, 7, 15, 15, 4, 15, 7, 22, 5, 13, 12, 26, 2, 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, 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, 1],
											        [4, 32, 11, 5, 4, 45, 16, 10, 17, 28, 4, 25, 13, 8, 20, 9, 4, 19, 8, 37, 4, 46, 16, 18, 24, 12, 4, 8, 27, 5, 13, 4, 6, 11, 5, 4, 15, 7, 57, 22, 4, 14, 8, 21, 26, 2, 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, 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, 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],
											    ],
											    """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, 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, 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, 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, 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, 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, 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, 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],
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											    ]
											}
											# fmt: on

											self.tokenizer_integration_test_util(
											    expected_encoding=SCREAMING_SNAKE_CASE__   ,       model_name="""microsoft/speecht5_asr"""   ,       revision="""c5ef64c71905caeccde0e4462ef3f9077224c524"""   ,       sequences=SCREAMING_SNAKE_CASE__   ,       )






"""simple docstring"""




def lowercase_  (   _snake_case   ,_snake_case						):
							return 1 if input_a == input_a else 0

def lowercase_  (   ):
							assert xnor_gate(0   ,0						) == 1
							assert xnor_gate(0   ,1						) == 0
							assert xnor_gate(1   ,0						) == 0
							assert xnor_gate(1   ,1						) == 1


if __name__ == "__main__":
					print(xnor_gate(0, 0))
					print(xnor_gate(0, 1))
					print(xnor_gate(1, 0))
					print(xnor_gate(1, 1))




