




"""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


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






@require_sentencepiece
@require_tokenizers
class 							__SCREAMING_SNAKE_CASE						(				_a       ,		unittest.TestCase		):
							snake_case_     =							SpeechTaTokenizer
							snake_case_     =							False
							snake_case_     =							True


							def 						_UpperCamelCase       (			self      :						int							):
													'''simple docstring'''





													super().setUp()

													# We have a SentencePiece fixture for testing
													A__						:  Union[str, Any]		  =  SpeechTaTokenizer(_A							)

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

													tokenizer.save_pretrained(self.tmpdirname							)


							def 						_UpperCamelCase       (			self      :						List[str]			,    snake_case      :						Dict							):
													'''simple docstring'''





													A__						:  List[Any]		  =  """this is a test"""
													A__						:  Optional[Any]		  =  """this is a test"""
													return input_text, output_text


							def 						_UpperCamelCase       (			self      :						Dict			,    snake_case      :						int			,    snake_case      :						List[Any]=False			,    snake_case      :						List[str]=20			,    snake_case      :						Dict=5							):
													'''simple docstring'''





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


							def 						_UpperCamelCase       (			self      :						Optional[int]							):
													'''simple docstring'''





													A__						:  Any		  =  """<pad>"""
													A__						:  Union[str, Any]		  =  1

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


							def 						_UpperCamelCase       (			self      :						int							):
													'''simple docstring'''





													A__						:  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(_A							)			,    81							)


							def 						_UpperCamelCase       (			self      :						Optional[int]							):
													'''simple docstring'''





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


							def 						_UpperCamelCase       (			self      :						Union[str, Any]							):
													'''simple docstring'''





													A__						:  Optional[int]		  =  self.get_tokenizers(do_lower_case=_A							)
													for tokenizer in tokenizers:
																			with self.subTest(F'{tokenizer.__class__.__name__}'							):
																									A__						:  Dict		  =  tokenizer.vocab_size
																									A__						:  str		  =  len(_A							)

																									self.assertNotEqual(_A			,    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)

																									A__						:  List[str]		  =  ["""aaaaa bbbbbb""", """cccccccccdddddddd"""]
																									A__						:  str		  =  tokenizer.add_tokens(_A							)
																									A__						:  Optional[Any]		  =  tokenizer.vocab_size
																									A__						:  int		  =  len(_A							)

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

																									A__						:  Dict		  =  tokenizer.encode("""aaaaa bbbbbb low cccccccccdddddddd l"""			,    add_special_tokens=_A							)

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

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

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

																									A__						:  str		  =  tokenizer.encode(
																									    """>>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l"""			,    add_special_tokens=_A							)

																									self.assertGreaterEqual(len(_A							)			,    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 						_UpperCamelCase       (			self      :						Optional[Any]							):
													'''simple docstring'''





													pass


							def 						_UpperCamelCase       (			self      :						str							):
													'''simple docstring'''





													pass


							def 						_UpperCamelCase       (			self      :						int							):
													'''simple docstring'''





													A__						:  Optional[int]		  =  self.get_tokenizer()

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

													A__						:  Tuple		  =  tokenizer.tokenize("""I was born in 92000, and this is falsé."""							)
													self.assertListEqual(
													    _A			,    [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""", """."""]							)

													A__						:  Union[str, Any]		  =  tokenizer.convert_tokens_to_ids(_A							)
													# fmt: off
													self.assertListEqual(_A			,    [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

													A__						:  Tuple		  =  tokenizer.convert_ids_to_tokens(_A							)
													self.assertListEqual(
													    _A			,    [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 						_UpperCamelCase       (			self      :						str							):
													'''simple docstring'''





													A__						:  str		  =  [
													    """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
													A__						:  List[str]		  =  {
													    """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],
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													    ]
													}
													# fmt: on

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














"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A_    =							{'''configuration_sew''': ['''SEW_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWConfig''']}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_    =							[
						    '''SEW_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''SEWForCTC''',
						    '''SEWForSequenceClassification''',
						    '''SEWModel''',
						    '''SEWPreTrainedModel''',
						]

if TYPE_CHECKING:
						from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_sew import (
												    SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
												    SEWForCTC,
												    SEWForSequenceClassification,
												    SEWModel,
												    SEWPreTrainedModel,
												)


else:
						import sys

						A_    =							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






