'''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


__lowercase        =    get_tests_dir('''fixtures/test_sentencepiece_bpe_char.model''')






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


			'''simple docstring'''






			UpperCAmelCase_     :	Any						=			SpeechTaTokenizer
			UpperCAmelCase_     :	List[Any]						=			False
			UpperCAmelCase_     :	Tuple						=			True



			def 				a_			(  self):




					"""simple docstring"""


					super().setUp()

					# We have a SentencePiece fixture for testing
					lowerCAmelCase     =    SpeechTaTokenizer(__lowerCAmelCase)

					lowerCAmelCase     =    AddedToken("""<mask>"""			,       lstrip=__lowerCAmelCase			,       rstrip=__lowerCAmelCase)
					lowerCAmelCase     =    mask_token
					tokenizer.add_special_tokens({"""mask_token""": mask_token})
					tokenizer.add_tokens(["""<ctc_blank>"""])

					tokenizer.save_pretrained(self.tmpdirname)



			def 				a_			(  self			,       __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase     =    """this is a test"""
					lowerCAmelCase     =    """this is a test"""
					return input_text, output_text



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase=False			,       __lowerCAmelCase=20			,       __lowerCAmelCase=5):




					"""simple docstring"""


					lowerCAmelCase   ,	lowerCAmelCase     =    self.get_input_output_texts(__lowerCAmelCase)
					lowerCAmelCase     =    tokenizer.encode(__lowerCAmelCase			,       add_special_tokens=__lowerCAmelCase)
					lowerCAmelCase     =    tokenizer.decode(__lowerCAmelCase			,       clean_up_tokenization_spaces=__lowerCAmelCase)
					return text, ids



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    """<pad>"""
					lowerCAmelCase     =    1

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



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    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(__lowerCAmelCase)			,       81)



			def 				a_			(  self):




					"""simple docstring"""


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



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    self.get_tokenizers(do_lower_case=__lowerCAmelCase)
					for tokenizer in tokenizers:
							with self.subTest(f"{tokenizer.__class__.__name__}"):
									lowerCAmelCase     =    tokenizer.vocab_size
									lowerCAmelCase     =    len(__lowerCAmelCase)

									self.assertNotEqual(__lowerCAmelCase			,       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)

									lowerCAmelCase     =    ["""aaaaa bbbbbb""", """cccccccccdddddddd"""]
									lowerCAmelCase     =    tokenizer.add_tokens(__lowerCAmelCase)
									lowerCAmelCase     =    tokenizer.vocab_size
									lowerCAmelCase     =    len(__lowerCAmelCase)

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

									lowerCAmelCase     =    tokenizer.encode("""aaaaa bbbbbb low cccccccccdddddddd l"""			,       add_special_tokens=__lowerCAmelCase)

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

									lowerCAmelCase     =    {"""eos_token""": """>>>>|||<||<<|<<""", """pad_token""": """<<<<<|||>|>>>>|>"""}
									lowerCAmelCase     =    tokenizer.add_special_tokens(__lowerCAmelCase)
									lowerCAmelCase     =    tokenizer.vocab_size
									lowerCAmelCase     =    len(__lowerCAmelCase)

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

									lowerCAmelCase     =    tokenizer.encode(
									    """>>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l"""			,       add_special_tokens=__lowerCAmelCase)

									self.assertGreaterEqual(len(__lowerCAmelCase)			,       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 				a_			(  self):




					"""simple docstring"""


					pass



			def 				a_			(  self):




					"""simple docstring"""


					pass



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    self.get_tokenizer()

					lowerCAmelCase     =    tokenizer.tokenize("""This is a test""")
					# fmt: off
					self.assertListEqual(__lowerCAmelCase			,       [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(__lowerCAmelCase)			,       [4, 32, 11, 10, 12, 4, 10, 12, 4, 7, 4, 6, 5, 12, 6]			,       )

					lowerCAmelCase     =    tokenizer.tokenize("""I was born in 92000, and this is falsé.""")
					self.assertListEqual(
					    __lowerCAmelCase			,       [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""", """."""])

					lowerCAmelCase     =    tokenizer.convert_tokens_to_ids(__lowerCAmelCase)
					# fmt: off
					self.assertListEqual(__lowerCAmelCase			,       [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

					lowerCAmelCase     =    tokenizer.convert_ids_to_tokens(__lowerCAmelCase)
					self.assertListEqual(
					    __lowerCAmelCase			,       [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 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    [
					    """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
					lowerCAmelCase     =    {
					    """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],
					        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
					    ]
					}
					# fmt: on

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





'''simple docstring'''




import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, Features, Value
from .base import TaskTemplate






@dataclass(frozen=lowerCAmelCase__      )
class 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






			UpperCAmelCase_     :	str						=			field(default='''automatic-speech-recognition'''					,		metadata={'''include_in_asdict_even_if_is_default''': True}      )
			UpperCAmelCase_     :	ClassVar[Features]						=			Features({'''audio''': Audio()}      )
			UpperCAmelCase_     :	ClassVar[Features]						=			Features({'''transcription''': Value('''string'''      )}      )
			UpperCAmelCase_     :	str						=			"audio"
			UpperCAmelCase_     :	str						=			"transcription"



			def 				a_			(  self			,       __lowerCAmelCase):




					"""simple docstring"""


					if self.audio_column not in features:
							raise ValueError(f"Column {self.audio_column} is not present in features.")
					if not isinstance(features[self.audio_column]			,       __lowerCAmelCase):
							raise ValueError(f"Column {self.audio_column} is not an Audio type.")
					lowerCAmelCase     =    copy.deepcopy(self)
					lowerCAmelCase     =    self.input_schema.copy()
					lowerCAmelCase     =    features[self.audio_column]
					lowerCAmelCase     =    input_schema
					return task_template







			@property
			def 				a_			(  self):




					"""simple docstring"""


					return {self.audio_column: "audio", self.transcription_column: "transcription"}




