




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_		:					int          =  get_tests_dir("fixtures/test_sentencepiece_bpe_char.model")




@require_sentencepiece
@require_tokenizers
class 		__A   (      UpperCamelCase__       ,    unittest.TestCase      ):
						UpperCamelCase      =       SpeechTaTokenizer
						UpperCamelCase      =       False
						UpperCamelCase      =       True

						def      A__    (		self :Dict			):
										'''simple docstring'''

										super().setUp()

										# We have a SentencePiece fixture for testing
										__magic_name__						:						int										=SpeechTaTokenizer(__snake_case			)

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

										tokenizer.save_pretrained(self.tmpdirname			)

						def      A__    (		self :List[Any]     ,			__snake_case :List[Any]			):
										'''simple docstring'''

										__magic_name__						:						Union[str, Any]										="""this is a test"""
										__magic_name__						:						Tuple										="""this is a test"""
										return input_text, output_text

						def      A__    (		self :str     ,			__snake_case :Tuple     ,			__snake_case :Union[str, Any]=False     ,			__snake_case :List[str]=20     ,			__snake_case :str=5			):
										'''simple docstring'''

										__magic_name__					,			__magic_name__						:						List[str]										=self.get_input_output_texts(__snake_case			)
										__magic_name__						:						Tuple										=tokenizer.encode(__snake_case     ,			add_special_tokens=__snake_case			)
										__magic_name__						:						Optional[int]										=tokenizer.decode(__snake_case     ,			clean_up_tokenization_spaces=__snake_case			)
										return text, ids

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

										__magic_name__						:						int										="""<pad>"""
										__magic_name__						:						Union[str, Any]										=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      A__    (		self :int			):
										'''simple docstring'''

										__magic_name__						:						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(__snake_case			)     ,			81			)

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

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

						def      A__    (		self :List[str]			):
										'''simple docstring'''

										__magic_name__						:						List[Any]										=self.get_tokenizers(do_lower_case=__snake_case			)
										for tokenizer in tokenizers:
														with self.subTest(f"{tokenizer.__class__.__name__}"			):
																		__magic_name__						:						Tuple										=tokenizer.vocab_size
																		__magic_name__						:						Optional[Any]										=len(__snake_case			)

																		self.assertNotEqual(__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)

																		__magic_name__						:						Optional[Any]										=["""aaaaa bbbbbb""", """cccccccccdddddddd"""]
																		__magic_name__						:						Dict										=tokenizer.add_tokens(__snake_case			)
																		__magic_name__						:						List[str]										=tokenizer.vocab_size
																		__magic_name__						:						Tuple										=len(__snake_case			)

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

																		__magic_name__						:						Any										=tokenizer.encode("""aaaaa bbbbbb low cccccccccdddddddd l"""     ,			add_special_tokens=__snake_case			)

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

																		__magic_name__						:						Any										={"""eos_token""": """>>>>|||<||<<|<<""", """pad_token""": """<<<<<|||>|>>>>|>"""}
																		__magic_name__						:						Any										=tokenizer.add_special_tokens(__snake_case			)
																		__magic_name__						:						Dict										=tokenizer.vocab_size
																		__magic_name__						:						str										=len(__snake_case			)

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

																		__magic_name__						:						Optional[int]										=tokenizer.encode(
																		    """>>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l"""     ,			add_special_tokens=__snake_case			)

																		self.assertGreaterEqual(len(__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      A__    (		self :Optional[int]			):
										'''simple docstring'''

										pass

						def      A__    (		self :List[str]			):
										'''simple docstring'''

										pass

						def      A__    (		self :Dict			):
										'''simple docstring'''

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

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

										__magic_name__						:						int										=tokenizer.tokenize("""I was born in 92000, and this is falsé."""			)
										self.assertListEqual(
										    __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""", """."""]			)

										__magic_name__						:						Any										=tokenizer.convert_tokens_to_ids(__snake_case			)
										# fmt: off
										self.assertListEqual(__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

										__magic_name__						:						List[str]										=tokenizer.convert_ids_to_tokens(__snake_case			)
										self.assertListEqual(
										    __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      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										__magic_name__						:						Dict										=[
										    """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
										__magic_name__						:						Tuple										={
										    """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""": [
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										    ]
										}
										# fmt: on

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






'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


lowerCAmelCase					:    Optional[Any]           = {
    'configuration_falcon': ['FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FalconConfig'],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase					:    str           = [
						    'FALCON_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'FalconForCausalLM',
						    'FalconModel',
						    'FalconPreTrainedModel',
						    'FalconForSequenceClassification',
						    'FalconForTokenClassification',
						    'FalconForQuestionAnswering',
						]


if TYPE_CHECKING:
						from .configuration_falcon import FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP, FalconConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_falcon import (
												    FALCON_PRETRAINED_MODEL_ARCHIVE_LIST,
												    FalconForCausalLM,
												    FalconForQuestionAnswering,
												    FalconForSequenceClassification,
												    FalconForTokenClassification,
												    FalconModel,
												    FalconPreTrainedModel,
												)


else:
						import sys

						lowerCAmelCase					:    Tuple           = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




