

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


lowerCAmelCase__		      =       get_tests_dir("""fixtures/test_sentencepiece_bpe_char.model""")

@require_sentencepiece
@require_tokenizers
class     a__  (    snake_case							,		unittest.TestCase	):
			"""simple docstring"""







			__lowerCamelCase       				=				SpeechTaTokenizer
			__lowerCamelCase       				=				False
			__lowerCamelCase       				=				True


			def    UpperCamelCase						(			self				)     ->  Any:
							'''simple docstring'''






							super().setUp()

							# We have a SentencePiece fixture for testing
							A__						=	SpeechTaTokenizer(lowercase				)

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

							tokenizer.save_pretrained(self.tmpdirname				)


			def    UpperCamelCase						(			self					,					lowercase				)     ->  Union[str, Any]:
							'''simple docstring'''






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


			def    UpperCamelCase						(			self					,					lowercase					,					lowercase=False					,					lowercase=20					,					lowercase=5				)     ->  Optional[Any]:
							'''simple docstring'''






							A__			, A__						=	self.get_input_output_texts(lowercase				)
							A__						=	tokenizer.encode(lowercase					,					add_special_tokens=lowercase				)
							A__						=	tokenizer.decode(lowercase					,					clean_up_tokenization_spaces=lowercase				)
							return text, ids


			def    UpperCamelCase						(			self				)     ->  Union[str, Any]:
							'''simple docstring'''






							A__						=	"<pad>"
							A__						=	1

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


			def    UpperCamelCase						(			self				)     ->  List[str]:
							'''simple docstring'''






							A__						=	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(lowercase				)					,					81				)


			def    UpperCamelCase						(			self				)     ->  Dict:
							'''simple docstring'''






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


			def    UpperCamelCase						(			self				)     ->  Optional[int]:
							'''simple docstring'''






							A__						=	self.get_tokenizers(do_lower_case=lowercase				)
							for tokenizer in tokenizers:
											with self.subTest(F'{tokenizer.__class__.__name__}'				):
															A__						=	tokenizer.vocab_size
															A__						=	len(lowercase				)

															self.assertNotEqual(lowercase					,					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__						=	["aaaaa bbbbbb", "cccccccccdddddddd"]
															A__						=	tokenizer.add_tokens(lowercase				)
															A__						=	tokenizer.vocab_size
															A__						=	len(lowercase				)

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

															A__						=	tokenizer.encode("aaaaa bbbbbb low cccccccccdddddddd l"					,					add_special_tokens=lowercase				)

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

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

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

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

															self.assertGreaterEqual(len(lowercase				)					,					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				)     ->  Tuple:
							'''simple docstring'''






							pass


			def    UpperCamelCase						(			self				)     ->  Any:
							'''simple docstring'''






							pass


			def    UpperCamelCase						(			self				)     ->  List[Any]:
							'''simple docstring'''






							A__						=	self.get_tokenizer()

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

							A__						=	tokenizer.tokenize("I was born in 92000, and this is falsé."				)
							self.assertListEqual(
							    lowercase					,					[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__						=	tokenizer.convert_tokens_to_ids(lowercase				)
							# fmt: off
							self.assertListEqual(lowercase					,					[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__						=	tokenizer.convert_ids_to_tokens(lowercase				)
							self.assertListEqual(
							    lowercase					,					[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				)     ->  int:
							'''simple docstring'''






							A__						=	[
							    "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__						=	{
							    "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=lowercase					,					model_name="microsoft/speecht5_asr"					,					revision="c5ef64c71905caeccde0e4462ef3f9077224c524"					,					sequences=lowercase					,					)





"""simple docstring"""







import unittest

from transformers import BigBirdTokenizer, BigBirdTokenizerFast
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__      				=   '''▁'''

lowerCAmelCase__      				=   get_tests_dir('''fixtures/test_sentencepiece.model''')


@require_sentencepiece
@require_tokenizers
class       SCREAMING_SNAKE_CASE__				(		lowercase   ,    unittest.TestCase				):
       """simple docstring"""


       a  :				str   =BigBirdTokenizer
       a  :				Union[str, Any]   =BigBirdTokenizerFast
       a  :				Tuple   =True
       a  :				Any   =True

       def        lowercase__							(						self						):






         """simple docstring"""





         super().setUp()

         lowerCAmelCase     :     str								=							self.tokenizer_class(snake_case__		,     keep_accents=snake_case__						)
         tokenizer.save_pretrained(self.tmpdirname						)

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     str								=							"<s>"
         lowerCAmelCase     :     Optional[int]								=							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        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Dict								=							list(self.get_tokenizer().get_vocab().keys()						)

         self.assertEqual(vocab_keys[0]		,     "<unk>"						)
         self.assertEqual(vocab_keys[1]		,     "<s>"						)
         self.assertEqual(vocab_keys[-1]		,     "[MASK]"						)
         self.assertEqual(len(snake_case__						)		,     1_004						)

       def        lowercase__							(						self						):






         """simple docstring"""





         self.assertEqual(self.get_tokenizer().vocab_size		,     1_000						)

       def        lowercase__							(						self						):






         """simple docstring"""





         if not self.test_rust_tokenizer:
           return

         lowerCAmelCase     :     Tuple								=							self.get_tokenizer()
         lowerCAmelCase     :     Optional[int]								=							self.get_rust_tokenizer()

         lowerCAmelCase     :     Tuple								=							"I was born in 92000, and this is falsé."

         lowerCAmelCase     :     Optional[int]								=							tokenizer.tokenize(snake_case__						)
         lowerCAmelCase     :     int								=							rust_tokenizer.tokenize(snake_case__						)
         self.assertListEqual(snake_case__		,     snake_case__						)

         lowerCAmelCase     :     Tuple								=							tokenizer.encode(snake_case__		,     add_special_tokens=snake_case__						)
         lowerCAmelCase     :     int								=							rust_tokenizer.encode(snake_case__		,     add_special_tokens=snake_case__						)
         self.assertListEqual(snake_case__		,     snake_case__						)

         lowerCAmelCase     :     List[Any]								=							self.get_rust_tokenizer()
         lowerCAmelCase     :     Tuple								=							tokenizer.encode(snake_case__						)
         lowerCAmelCase     :     List[Any]								=							rust_tokenizer.encode(snake_case__						)
         self.assertListEqual(snake_case__		,     snake_case__						)

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Any								=							BigBirdTokenizer(snake_case__		,     keep_accents=snake_case__						)

         lowerCAmelCase     :     Union[str, Any]								=							tokenizer.tokenize("This is a test"						)
         self.assertListEqual(snake_case__		,     ["▁This", "▁is", "▁a", "▁t", "est"]						)

         self.assertListEqual(
             tokenizer.convert_tokens_to_ids(snake_case__						)		,     [285, 46, 10, 170, 382]		,     )

         lowerCAmelCase     :     Any								=							tokenizer.tokenize("I was born in 92000, and this is falsé."						)
         self.assertListEqual(
             snake_case__		,     [
                 SPIECE_UNDERLINE + "I",
                 SPIECE_UNDERLINE + "was",
                 SPIECE_UNDERLINE + "b",
                 "or",
                 "n",
                 SPIECE_UNDERLINE + "in",
                 SPIECE_UNDERLINE + "",
                 "9",
                 "2",
                 "0",
                 "0",
                 "0",
                 ",",
                 SPIECE_UNDERLINE + "and",
                 SPIECE_UNDERLINE + "this",
                 SPIECE_UNDERLINE + "is",
                 SPIECE_UNDERLINE + "f",
                 "al",
                 "s",
                 "é",
                 ".",
             ]		,     )
         lowerCAmelCase     :     str								=							tokenizer.convert_tokens_to_ids(snake_case__						)
         self.assertListEqual(
             snake_case__		,     [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]		,     )

         lowerCAmelCase     :     List[str]								=							tokenizer.convert_ids_to_tokens(snake_case__						)
         self.assertListEqual(
             snake_case__		,     [
                 SPIECE_UNDERLINE + "I",
                 SPIECE_UNDERLINE + "was",
                 SPIECE_UNDERLINE + "b",
                 "or",
                 "n",
                 SPIECE_UNDERLINE + "in",
                 SPIECE_UNDERLINE + "",
                 "<unk>",
                 "2",
                 "0",
                 "0",
                 "0",
                 ",",
                 SPIECE_UNDERLINE + "and",
                 SPIECE_UNDERLINE + "this",
                 SPIECE_UNDERLINE + "is",
                 SPIECE_UNDERLINE + "f",
                 "al",
                 "s",
                 "<unk>",
                 ".",
             ]		,     )

       @cached_property
       def        lowercase__							(						self						):






         """simple docstring"""





         return BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base"						)

       @slow
       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     List[Any]								=							"Hello World!"
         lowerCAmelCase     :     Any								=							[65, 18_536, 2_260, 101, 66]

         self.assertListEqual(snake_case__		,     self.big_tokenizer.encode(snake_case__						)						)

       @slow
       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Union[str, Any]								=							(
             "This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
             " add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"
         )
         # fmt: off
         lowerCAmelCase     :     List[str]								=							[65, 871, 419, 358, 946, 991, 2_521, 452, 358, 1_357, 387, 7_751, 3_536, 112, 985, 456, 126, 865, 938, 5_400, 5_734, 458, 1_368, 467, 786, 2_462, 5_246, 1_159, 633, 865, 4_519, 457, 582, 852, 2_557, 427, 916, 508, 405, 34_324, 497, 391, 408, 11_342, 1_244, 385, 100, 938, 985, 456, 574, 362, 12_597, 3_200, 3_129, 1_172, 66]  # noqa: E231
         # fmt: on
         self.assertListEqual(snake_case__		,     self.big_tokenizer.encode(snake_case__						)						)

       @require_torch
       @slow
       def        lowercase__							(						self						):






         """simple docstring"""





         import torch

         from transformers import BigBirdConfig, BigBirdModel

         # Build sequence
         lowerCAmelCase     :     Dict								=							list(self.big_tokenizer.get_vocab().keys()						)[:10]
         lowerCAmelCase     :     int								=							" ".join(snake_case__						)
         lowerCAmelCase     :     Dict								=							self.big_tokenizer.encode_plus(snake_case__		,     return_tensors="pt"		,     return_token_type_ids=snake_case__						)
         lowerCAmelCase     :     Any								=							self.big_tokenizer.batch_encode_plus(
             [sequence + " " + sequence]		,     return_tensors="pt"		,     return_token_type_ids=snake_case__						)

         lowerCAmelCase     :     str								=							BigBirdConfig(attention_type="original_full"						)
         lowerCAmelCase     :     Any								=							BigBirdModel(snake_case__						)

         assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

         with torch.no_grad():
           model(**snake_case__						)
           model(**snake_case__						)

       @slow
       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     List[str]								=							BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base"						)
         lowerCAmelCase     :     Union[str, Any]								=							tokenizer.decode(tokenizer("Paris is the [MASK]."						).input_ids						)

         self.assertTrue(decoded_text == "[CLS] Paris is the[MASK].[SEP]"						)







       @slow
       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Any								=							{"input_ids": [[65, 39_286, 458, 36_335, 2_001, 456, 13_073, 13_266, 455, 113, 7_746, 1_741, 11_157, 391, 13_073, 13_266, 455, 113, 3_967, 35_412, 113, 4_936, 109, 3_870, 2_377, 113, 30_084, 45_720, 458, 134, 17_496, 112, 503, 11_672, 113, 118, 112, 5_665, 13_347, 38_687, 112, 1_496, 31_389, 112, 3_268, 47_264, 134, 962, 112, 16_377, 8_035, 23_130, 430, 12_169, 15_518, 28_592, 458, 146, 41_697, 109, 391, 12_169, 15_518, 16_689, 458, 146, 41_358, 109, 452, 726, 4_034, 111, 763, 35_412, 5_082, 388, 1_903, 111, 9_051, 391, 2_870, 48_918, 1_900, 1_123, 550, 998, 112, 9_586, 15_985, 455, 391, 410, 22_955, 37_636, 114, 66], [65, 448, 17_496, 419, 3_663, 385, 763, 113, 27_533, 2_870, 3_283, 13_043, 1_639, 24_713, 523, 656, 24_013, 18_550, 2_521, 517, 27_014, 21_244, 420, 1_212, 1_465, 391, 927, 4_833, 388, 578, 11_786, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [65, 484, 2_169, 7_687, 21_932, 18_146, 726, 363, 17_032, 3_391, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 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], [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]]}  # noqa: E501
         # fmt: on

         self.tokenizer_integration_test_util(
             expected_encoding=snake_case__		,     model_name="google/bigbird-roberta-base"		,     revision="215c99f1600e06f83acce68422f2035b2b5c3510"		,     )





