
"""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		:       Optional[Any]     						=       get_tests_dir('fixtures/test_sentencepiece_bpe_char.model')







@require_sentencepiece
@require_tokenizers
class     lowercase(_lowercase    ,						unittest.TestCase       ):
     __snake_case:	Union[str, Any]						=     SpeechTaTokenizer
     __snake_case:	Any						=     False
     __snake_case:	List[Any]						=     True



     def 			lowercase__  (							self   )		->     Tuple:







         """simple docstring"""



         super().setUp()

         # We have a SentencePiece fixture for testing
         a__		=			SpeechTaTokenizer(__SCREAMING_SNAKE_CASE   )

         a__		=			AddedToken('<mask>'     ,				lstrip=__SCREAMING_SNAKE_CASE     ,				rstrip=__SCREAMING_SNAKE_CASE   )
         a__		=			mask_token
         tokenizer.add_special_tokens({'mask_token': mask_token}   )
         tokenizer.add_tokens(['<ctc_blank>']   )

         tokenizer.save_pretrained(self.tmpdirname   )



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE   )		->     int:







         """simple docstring"""



         a__		=			'this is a test'
         a__		=			'this is a test'
         return input_text, output_text



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE=False     ,				__SCREAMING_SNAKE_CASE=2_0     ,				__SCREAMING_SNAKE_CASE=5   )		->     Optional[Any]:







         """simple docstring"""



         a__	,     a__		=			self.get_input_output_texts(__SCREAMING_SNAKE_CASE   )
         a__		=			tokenizer.encode(__SCREAMING_SNAKE_CASE     ,				add_special_tokens=__SCREAMING_SNAKE_CASE   )
         a__		=			tokenizer.decode(__SCREAMING_SNAKE_CASE     ,				clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE   )
         return text, ids



     def 			lowercase__  (							self   )		->     Optional[int]:







         """simple docstring"""



         a__		=			'<pad>'
         a__		=			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 			lowercase__  (							self   )		->     int:







         """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(__SCREAMING_SNAKE_CASE   )     ,				8_1   )



     def 			lowercase__  (							self   )		->     Optional[Any]:







         """simple docstring"""



         self.assertEqual(self.get_tokenizer().vocab_size     ,				7_9   )



     def 			lowercase__  (							self   )		->     Dict:







         """simple docstring"""



         a__		=			self.get_tokenizers(do_lower_case=__SCREAMING_SNAKE_CASE   )
         for tokenizer in tokenizers:
             with self.subTest(f'{tokenizer.__class__.__name__}'   ):
                 a__		=			tokenizer.vocab_size
                 a__		=			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)

                 a__		=			['aaaaa bbbbbb', 'cccccccccdddddddd']
                 a__		=			tokenizer.add_tokens(__SCREAMING_SNAKE_CASE   )
                 a__		=			tokenizer.vocab_size
                 a__		=			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   )   )

                 a__		=			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   )

                 a__		=			{'eos_token': '>>>>|||<||<<|<<', 'pad_token': '<<<<<|||>|>>>>|>'}
                 a__		=			tokenizer.add_special_tokens(__SCREAMING_SNAKE_CASE   )
                 a__		=			tokenizer.vocab_size
                 a__		=			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   )   )

                 a__		=			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 			lowercase__  (							self   )		->     Optional[Any]:







         """simple docstring"""



         pass



     def 			lowercase__  (							self   )		->     List[Any]:







         """simple docstring"""



         pass



     def 			lowercase__  (							self   )		->     Dict:







         """simple docstring"""



         a__		=			self.get_tokenizer()

         a__		=			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, 3_2, 1_1, 1_0, 1_2, 4, 1_0, 1_2, 4, 7, 4, 6, 5, 1_2, 6]     ,				)

         a__		=			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', '.']   )

         a__		=			tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE   )
         # fmt: off
         self.assertListEqual(__SCREAMING_SNAKE_CASE     ,				[4, 3_0, 4, 2_0, 7, 1_2, 4, 2_5, 8, 1_3, 9, 4, 1_0, 9, 4, 3, 2_3, 4, 7, 9, 1_4, 4, 6, 1_1, 1_0, 1_2, 4, 1_0, 1_2, 4, 1_9, 7, 1_5, 1_2, 7_3, 2_6]   )
         # fmt: on

         a__		=			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 			lowercase__  (							self   )		->     Dict:







         """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, 3_2, 1_3, 7, 9, 1_2, 1_9, 8, 1_3, 1_8, 5, 1_3, 1_2, 4, 6_4, 1_9, 8, 1_3, 1_8, 5, 1_3, 1_5, 2_2, 4, 2_8, 9, 8, 2_0, 9, 4, 7, 1_2, 4, 2_4, 2_2, 6, 8, 1_3, 1_7, 1_1, 3_9, 6, 1_3, 7, 9, 1_2, 1_9, 8, 1_3, 1_8, 5, 1_3, 1_2, 4, 7, 9, 1_4, 4, 2_4, 2_2, 6, 8, 1_3, 1_7, 1_1, 3_9, 2_4, 1_3, 5, 6, 1_3, 7, 1_0, 9, 5, 1_4, 3_9, 2_5, 5, 1_3, 6, 6_3, 4, 2_4, 1_3, 8, 2_7, 1_0, 1_4, 5, 1_2, 4, 2_1, 5, 9, 5, 1_3, 7, 1_5, 3_9, 2_4, 1_6, 1_3, 2_4, 8, 1_2, 5, 4, 7, 1_3, 1_7, 1_1, 1_0, 6, 5, 1_7, 6, 1_6, 1_3, 5, 1_2, 4, 6_4, 4_0, 4_7, 5_4, 3_2, 2_3, 4, 5_3, 4_9, 3_2, 2_3, 4, 5_4, 8, 4_0, 4_7, 5_4, 3_2, 7, 2_3, 4, 6_9, 5_2, 4_3, 2_3, 4, 5_1, 1_0, 1_2, 6, 1_0, 1_5, 4_0, 5, 1_3, 6, 2_3, 4, 6_9, 5_2, 4_8, 5, 6, 2_6, 2_6, 2_6, 6_3, 4, 1_9, 8, 1_3, 4, 4_8, 7, 6, 1_6, 1_3, 7, 1_5, 4, 5_2, 7, 9, 2_1, 1_6, 7, 2_1, 5, 4, 6_1, 9, 1_4, 5, 1_3, 1_2, 6, 7, 9, 1_4, 1_0, 9, 2_1, 4, 6_4, 4_8, 5_2, 6_1, 6_3, 4, 7, 9, 1_4, 4, 4_8, 7, 6, 1_6, 1_3, 7, 1_5, 4, 5_2, 7, 9, 2_1, 1_6, 7, 2_1, 5, 4, 5_3, 5, 9, 5, 1_3, 7, 6, 1_0, 8, 9, 4, 6_4, 4_8, 5_2, 5_3, 6_3, 4, 2_0, 1_0, 6, 1_1, 4, 8, 2_7, 5, 1_3, 4, 6, 1_1, 1_0, 1_3, 6, 2_2, 3_9, 6, 2_0, 8, 4, 2_4, 1_3, 5, 6, 1_3, 7, 1_0, 9, 5, 1_4, 4, 1_8, 8, 1_4, 5, 1_5, 1_2, 4, 1_0, 9, 4, 8, 9, 5, 4, 1_1, 1_6, 9, 1_4, 1_3, 5, 1_4, 4, 2_4, 1_5, 1_6, 1_2, 4, 1_5, 7, 9, 2_1, 1_6, 7, 2_1, 5, 1_2, 4, 7, 9, 1_4, 4, 1_4, 5, 5, 2_4, 4, 1_0, 9, 6, 5, 1_3, 8, 2_4, 5, 1_3, 7, 2_5, 1_0, 1_5, 1_0, 6, 2_2, 4, 2_5, 5, 6, 2_0, 5, 5, 9, 4, 5_8, 7, 3_7, 2_3, 4, 4_9, 2_2, 3_2, 8, 1_3, 1_7, 1_1, 4, 7, 9, 1_4, 4, 3_2, 5, 9, 1_2, 8, 1_3, 5_5, 1_5, 8, 2_0, 2_6, 2],
                 [4, 4_0, 4_7, 5_4, 3_2, 4, 1_0, 1_2, 4, 1_4, 5, 1_2, 1_0, 2_1, 9, 5, 1_4, 4, 6, 8, 4, 2_4, 1_3, 5, 3_9, 6, 1_3, 7, 1_0, 9, 4, 1_4, 5, 5, 2_4, 4, 2_5, 1_0, 1_4, 1_0, 1_3, 5, 1_7, 6, 1_0, 8, 9, 7, 1_5, 4, 1_3, 5, 2_4, 1_3, 5, 1_2, 5, 9, 6, 7, 6, 1_0, 8, 9, 1_2, 4, 1_9, 1_3, 8, 1_8, 4, 1_6, 9, 1_5, 7, 2_5, 5, 1_5, 5, 1_4, 4, 6, 5, 3_7, 6, 4, 2_5, 2_2, 4, 4_6, 8, 1_0, 9, 6, 1_5, 2_2, 4, 1_7, 8, 9, 1_4, 1_0, 6, 1_0, 8, 9, 1_0, 9, 2_1, 4, 8, 9, 4, 2_5, 8, 6, 1_1, 4, 1_5, 5, 1_9, 6, 4, 7, 9, 1_4, 4, 1_3, 1_0, 2_1, 1_1, 6, 4, 1_7, 8, 9, 6, 5, 3_7, 6, 4, 1_0, 9, 4, 7, 1_5, 1_5, 4, 1_5, 7, 2_2, 5, 1_3, 1_2, 2_6, 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, 3_2, 1_1, 5, 4, 4_5, 1_6, 1_0, 1_7, 2_8, 4, 2_5, 1_3, 8, 2_0, 9, 4, 1_9, 8, 3_7, 4, 4_6, 1_6, 1_8, 2_4, 1_2, 4, 8, 2_7, 5, 1_3, 4, 6, 1_1, 5, 4, 1_5, 7, 5_7, 2_2, 4, 1_4, 8, 2_1, 2_6, 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     ,				)




"""simple docstring"""




import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


a		:       List[str]     						=       16
a		:       str     						=       32



def     __magic_name__		(					UpperCamelCase     :		Accelerator							,		UpperCamelCase     :		int = 16     )     ->						Dict:
    a__		=			AutoTokenizer.from_pretrained('bert-base-cased'     )
    a__		=			load_dataset('glue'							,		'mrpc'     )

    def tokenize_function(UpperCamelCase     :		Optional[int]     ):
        # max_length=None => use the model max length (it's actually the default)
        a__		=			tokenizer(examples['sentence1']							,		examples['sentence2']							,		truncation=UpperCamelCase							,		max_length=UpperCamelCase     )
        return outputs

    # Apply the method we just defined to all the examples in all the splits of the dataset
    # starting with the main process first:
    with accelerator.main_process_first():
        a__		=			datasets.map(
            UpperCamelCase							,		batched=UpperCamelCase							,		remove_columns=['idx', 'sentence1', 'sentence2']							,		)

    # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
    # transformers library
    a__		=			tokenized_datasets.rename_column('label'							,		'labels'     )

    def collate_fn(UpperCamelCase     :		List[str]     ):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        a__		=			128 if accelerator.distributed_type == DistributedType.TPU else None
        # When using mixed precision we want round multiples of 8/16
        if accelerator.mixed_precision == "fp8":
            a__		=			16
        elif accelerator.mixed_precision != "no":
            a__		=			8
        else:
            a__		=			None

        return tokenizer.pad(
            UpperCamelCase							,		padding='longest'							,		max_length=UpperCamelCase							,		pad_to_multiple_of=UpperCamelCase							,		return_tensors='pt'							,		)

    # Instantiate dataloaders.
    a__		=			DataLoader(
        tokenized_datasets['train']							,		shuffle=UpperCamelCase							,		collate_fn=UpperCamelCase							,		batch_size=UpperCamelCase     )
    a__		=			DataLoader(
        tokenized_datasets['validation']							,		shuffle=UpperCamelCase							,		collate_fn=UpperCamelCase							,		batch_size=UpperCamelCase     )

    return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
    from accelerate.test_utils.training import mocked_dataloaders

    a		:       Union[str, Any]     						=       mocked_dataloaders  # noqa: F811



def     __magic_name__		(					UpperCamelCase     :		List[Any]							,		UpperCamelCase     :		Optional[int]     )     ->						Optional[Any]:
    # For testing only
    if os.environ.get('TESTING_MOCKED_DATALOADERS'							,		UpperCamelCase     ) == "1":
        a__		=			2
    # New Code #
    a__		=			int(args.gradient_accumulation_steps     )
    a__		=			int(args.local_sgd_steps     )
    # Initialize accelerator
    a__		=			Accelerator(
        cpu=args.cpu							,		mixed_precision=args.mixed_precision							,		gradient_accumulation_steps=UpperCamelCase     )
    if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
        raise NotImplementedError('LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'     )
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    a__		=			config['lr']
    a__		=			int(config['num_epochs']     )
    a__		=			int(config['seed']     )
    a__		=			int(config['batch_size']     )

    a__		=			evaluate.load('glue'							,		'mrpc'     )

    set_seed(UpperCamelCase     )
    a__	,     a__		=			get_dataloaders(UpperCamelCase							,		UpperCamelCase     )
    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    a__		=			AutoModelForSequenceClassification.from_pretrained('bert-base-cased'							,		return_dict=UpperCamelCase     )

    # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
    # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
    # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
    a__		=			model.to(accelerator.device     )

    # Instantiate optimizer
    a__		=			AdamW(params=model.parameters()							,		lr=UpperCamelCase     )

    # Instantiate scheduler
    a__		=			get_linear_schedule_with_warmup(
        optimizer=UpperCamelCase							,		num_warmup_steps=100							,		num_training_steps=(len(UpperCamelCase     ) * num_epochs)							,		)

    # Prepare everything
    # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
    # prepare method.
    a__	,     a__	,     a__	,     a__	,     a__		=			accelerator.prepare(
        UpperCamelCase							,		UpperCamelCase							,		UpperCamelCase							,		UpperCamelCase							,		UpperCamelCase     )

    # Now we train the model
    for epoch in range(UpperCamelCase     ):
        model.train()
        with LocalSGD(
            accelerator=UpperCamelCase							,		model=UpperCamelCase							,		local_sgd_steps=UpperCamelCase							,		enabled=local_sgd_steps is not None     ) as local_sgd:
            for step, batch in enumerate(UpperCamelCase     ):
                # We could avoid this line since we set the accelerator with `device_placement=True`.
                batch.to(accelerator.device     )
                # New code #
                # We use the new `accumulate` context manager to perform gradient accumulation
                # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
                with accelerator.accumulate(UpperCamelCase     ):
                    a__		=			model(**UpperCamelCase     )
                    a__		=			output.loss
                    accelerator.backward(UpperCamelCase     )
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()
                    # LocalSGD-specific line
                    local_sgd.step()

        model.eval()
        for step, batch in enumerate(UpperCamelCase     ):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device     )
            with torch.no_grad():
                a__		=			model(**UpperCamelCase     )
            a__		=			outputs.logits.argmax(dim=-1     )
            a__	,     a__		=			accelerator.gather_for_metrics((predictions, batch['labels'])     )
            metric.add_batch(
                predictions=UpperCamelCase							,		references=UpperCamelCase							,		)

        a__		=			metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(f'epoch {epoch}:'							,		UpperCamelCase     )



def     __magic_name__		(					)     ->						Any:
    a__		=			argparse.ArgumentParser(description='Simple example of training script.'     )
    parser.add_argument(
        '--mixed_precision'							,		type=UpperCamelCase							,		default=UpperCamelCase							,		choices=['no', 'fp16', 'bf16', 'fp8']							,		help='Whether to use mixed precision. Choose'
        'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
        'and an Nvidia Ampere GPU.'							,		)
    # New Code #
    parser.add_argument(
        '--gradient_accumulation_steps'							,		type=UpperCamelCase							,		default=1							,		help='The number of minibatches to be ran before gradients are accumulated.'							,		)
    parser.add_argument(
        '--local_sgd_steps'							,		type=UpperCamelCase							,		default=8							,		help='Number of local SGD steps or None to disable local SGD'     )
    parser.add_argument('--cpu'							,		action='store_true'							,		help='If passed, will train on the CPU.'     )
    a__		=			parser.parse_args()
    a__		=			{'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
    training_function(UpperCamelCase							,		UpperCamelCase     )


if __name__ == "__main__":
    main()


