



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 				snake_case__(_UpperCamelCase    ,      unittest.TestCase      ):

					"""simple docstring"""






					lowercase_								=		SpeechTaTokenizer
					lowercase_								=		False
					lowercase_								=		True







					def 			snake_case					(							self :     Union[str, Any]					):
									super().setUp()

									# We have a SentencePiece fixture for testing
									lowercase__					:   str													=					SpeechTaTokenizer(SCREAMING_SNAKE_CASE					)

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

									tokenizer.save_pretrained(self.tmpdirname					)







					def 			snake_case					(							self :     str				,       SCREAMING_SNAKE_CASE :     Optional[int]					):
									lowercase__					:   Optional[Any]													=					"this is a test"
									lowercase__					:   Optional[int]													=					"this is a test"
									return input_text, output_text







					def 			snake_case					(							self :     str				,       SCREAMING_SNAKE_CASE :     str				,       SCREAMING_SNAKE_CASE :     Dict=False				,       SCREAMING_SNAKE_CASE :     Optional[Any]=20				,       SCREAMING_SNAKE_CASE :     Tuple=5					):
									lowercase__							,							lowercase__					:   Dict													=					self.get_input_output_texts(SCREAMING_SNAKE_CASE					)
									lowercase__					:   List[Any]													=					tokenizer.encode(SCREAMING_SNAKE_CASE				,       add_special_tokens=SCREAMING_SNAKE_CASE					)
									lowercase__					:   Union[str, Any]													=					tokenizer.decode(SCREAMING_SNAKE_CASE				,       clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE					)
									return text, ids







					def 			snake_case					(							self :     str					):
									lowercase__					:   Any													=					"<pad>"
									lowercase__					:   Union[str, Any]													=					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 			snake_case					(							self :     Optional[Any]					):
									lowercase__					:   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 			snake_case					(							self :     str					):
									self.assertEqual(self.get_tokenizer().vocab_size				,       79					)







					def 			snake_case					(							self :     int					):
									lowercase__					:   Optional[int]													=					self.get_tokenizers(do_lower_case=SCREAMING_SNAKE_CASE					)
									for tokenizer in tokenizers:
													with self.subTest(f"""{tokenizer.__class__.__name__}"""					):
																	lowercase__					:   Union[str, Any]													=					tokenizer.vocab_size
																	lowercase__					:   str													=					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)

																	lowercase__					:   List[Any]													=					["aaaaa bbbbbb", "cccccccccdddddddd"]
																	lowercase__					:   int													=					tokenizer.add_tokens(SCREAMING_SNAKE_CASE					)
																	lowercase__					:   Any													=					tokenizer.vocab_size
																	lowercase__					:   Optional[Any]													=					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					)					)

																	lowercase__					:   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					)

																	lowercase__					:   List[str]													=					{"eos_token": ">>>>|||<||<<|<<", "pad_token": "<<<<<|||>|>>>>|>"}
																	lowercase__					:   Optional[Any]													=					tokenizer.add_special_tokens(SCREAMING_SNAKE_CASE					)
																	lowercase__					:   List[str]													=					tokenizer.vocab_size
																	lowercase__					:   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					)					)

																	lowercase__					:   Any													=					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 			snake_case					(							self :     Optional[Any]					):
									pass







					def 			snake_case					(							self :     Union[str, Any]					):
									pass







					def 			snake_case					(							self :     Union[str, Any]					):
									lowercase__					:   Optional[Any]													=					self.get_tokenizer()

									lowercase__					:   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]				,       )

									lowercase__					:   Optional[Any]													=					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", "."]					)

									lowercase__					:   str													=					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

									lowercase__					:   Tuple													=					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 			snake_case					(							self :     Any					):
									# Use custom sequence because this tokenizer does not handle numbers.
									lowercase__					:   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
									lowercase__					:   Dict													=					{
									    "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=SCREAMING_SNAKE_CASE				,       model_name="microsoft/speecht5_asr"				,       revision="c5ef64c71905caeccde0e4462ef3f9077224c524"				,       sequences=SCREAMING_SNAKE_CASE				,       )









import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser


def  __lowerCamelCase (       lowerCamelCase__						):

				"""simple docstring"""


				lowercase__					:   Optional[Any]													=					{}
				lowercase__					:   Tuple													=					tokenizer(example["content"]					,			truncation=lowerCamelCase__						)["input_ids"]
				lowercase__					:   Optional[int]													=					len(example["content"]						) / len(output["input_ids"]						)
				return output


lowerCAmelCase__					  =  HfArgumentParser(PretokenizationArguments)
lowerCAmelCase__					  =  parser.parse_args()
if args.num_workers is None:
				lowerCAmelCase__					  =  multiprocessing.cpu_count()
lowerCAmelCase__					  =  AutoTokenizer.from_pretrained(args.tokenizer_dir)

lowerCAmelCase__					  =  time.time()
lowerCAmelCase__					  =  load_dataset(args.dataset_name, split='''train''')
print(f'''Dataset loaded in {time.time()-t_start:.2f}s''')

lowerCAmelCase__					  =  time.time()
lowerCAmelCase__					  =  ds.map(
    tokenize,
    num_proc=args.num_workers,
    remove_columns=[
        '''repo_name''',
        '''path''',
        '''copies''',
        '''size''',
        '''content''',
        '''license''',
        '''hash''',
        '''line_mean''',
        '''line_max''',
        '''alpha_frac''',
        '''autogenerated''',
    ],
)
print(f'''Dataset tokenized in {time.time()-t_start:.2f}s''')

lowerCAmelCase__					  =  time.time()
ds.push_to_hub(args.tokenized_data_repo)
print(f'''Data pushed to the hub in {time.time()-t_start:.2f}s''')




