



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__					:   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],
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									    ]
									}
									# fmt: on

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









import os
from argparse import ArgumentParser, Namespace

from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand


if not is_tf_available() and not is_torch_available():
				raise RuntimeError('''At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training''')

# TF training parameters
lowerCAmelCase__					  =  False
lowerCAmelCase__					  =  False


def  __lowerCamelCase (       lowerCamelCase__						):

				"""simple docstring"""


				return TrainCommand(lowerCamelCase__						)

class 				snake_case__(_UpperCamelCase      ):

					"""simple docstring"""






					@staticmethod
					def 			snake_case					(							SCREAMING_SNAKE_CASE :     ArgumentParser					):
									lowercase__					:   Optional[int]													=					parser.add_parser("train"				,       help="CLI tool to train a model on a task."					)

									train_parser.add_argument(
									    "--train_data"				,       type=SCREAMING_SNAKE_CASE				,       required=SCREAMING_SNAKE_CASE				,       help="path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences."				,       )
									train_parser.add_argument(
									    "--column_label"				,       type=SCREAMING_SNAKE_CASE				,       default=0				,       help="Column of the dataset csv file with example labels."					)
									train_parser.add_argument(
									    "--column_text"				,       type=SCREAMING_SNAKE_CASE				,       default=1				,       help="Column of the dataset csv file with example texts."					)
									train_parser.add_argument(
									    "--column_id"				,       type=SCREAMING_SNAKE_CASE				,       default=2				,       help="Column of the dataset csv file with example ids."					)
									train_parser.add_argument(
									    "--skip_first_row"				,       action="store_true"				,       help="Skip the first row of the csv file (headers)."					)

									train_parser.add_argument("--validation_data"				,       type=SCREAMING_SNAKE_CASE				,       default=""				,       help="path to validation dataset."					)
									train_parser.add_argument(
									    "--validation_split"				,       type=SCREAMING_SNAKE_CASE				,       default=0.1				,       help="if validation dataset is not provided, fraction of train dataset to use as validation dataset."				,       )

									train_parser.add_argument("--output"				,       type=SCREAMING_SNAKE_CASE				,       default="./"				,       help="path to saved the trained model."					)

									train_parser.add_argument(
									    "--task"				,       type=SCREAMING_SNAKE_CASE				,       default="text_classification"				,       help="Task to train the model on."					)
									train_parser.add_argument(
									    "--model"				,       type=SCREAMING_SNAKE_CASE				,       default="bert-base-uncased"				,       help="Model's name or path to stored model."					)
									train_parser.add_argument("--train_batch_size"				,       type=SCREAMING_SNAKE_CASE				,       default=32				,       help="Batch size for training."					)
									train_parser.add_argument("--valid_batch_size"				,       type=SCREAMING_SNAKE_CASE				,       default=64				,       help="Batch size for validation."					)
									train_parser.add_argument("--learning_rate"				,       type=SCREAMING_SNAKE_CASE				,       default=3E-5				,       help="Learning rate."					)
									train_parser.add_argument("--adam_epsilon"				,       type=SCREAMING_SNAKE_CASE				,       default=1E-0_8				,       help="Epsilon for Adam optimizer."					)
									train_parser.set_defaults(func=SCREAMING_SNAKE_CASE					)







					def __init__(							self :     int				,       SCREAMING_SNAKE_CASE :     Namespace					):
									lowercase__					:   int													=					logging.get_logger("transformers-cli/training"					)

									lowercase__					:   List[Any]													=					"tf" if is_tf_available() else "torch"

									os.makedirs(args.output				,       exist_ok=SCREAMING_SNAKE_CASE					)
									lowercase__					:   Optional[Any]													=					args.output

									lowercase__					:   Union[str, Any]													=					args.column_label
									lowercase__					:   Optional[int]													=					args.column_text
									lowercase__					:   Optional[int]													=					args.column_id

									self.logger.info(f"""Loading {args.task} pipeline for {args.model}"""					)
									if args.task == "text_classification":
													lowercase__					:   int													=					TextClassificationPipeline.from_pretrained(args.model					)
									elif args.task == "token_classification":
													raise NotImplementedError
									elif args.task == "question_answering":
													raise NotImplementedError

									self.logger.info(f"""Loading dataset from {args.train_data}"""					)
									lowercase__					:   List[str]													=					Processor.create_from_csv(
									    args.train_data				,       column_label=args.column_label				,       column_text=args.column_text				,       column_id=args.column_id				,       skip_first_row=args.skip_first_row				,       )
									lowercase__					:   Union[str, Any]													=					None
									if args.validation_data:
													self.logger.info(f"""Loading validation dataset from {args.validation_data}"""					)
													lowercase__					:   Optional[int]													=					Processor.create_from_csv(
													    args.validation_data				,       column_label=args.column_label				,       column_text=args.column_text				,       column_id=args.column_id				,       skip_first_row=args.skip_first_row				,       )

									lowercase__					:   Dict													=					args.validation_split
									lowercase__					:   List[str]													=					args.train_batch_size
									lowercase__					:   Any													=					args.valid_batch_size
									lowercase__					:   Optional[int]													=					args.learning_rate
									lowercase__					:   int													=					args.adam_epsilon







					def 			snake_case					(							self :     Dict					):
									if self.framework == "tf":
													return self.run_tf()
									return self.run_torch()







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







					def 			snake_case					(							self :     Union[str, Any]					):
									self.pipeline.fit(
									    self.train_dataset				,       validation_data=self.valid_dataset				,       validation_split=self.validation_split				,       learning_rate=self.learning_rate				,       adam_epsilon=self.adam_epsilon				,       train_batch_size=self.train_batch_size				,       valid_batch_size=self.valid_batch_size				,       )

									# Save trained pipeline
									self.pipeline.save_pretrained(self.output					)




