


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




@require_sentencepiece
@require_tokenizers
class       lowercase_					(lowercase__      ,					unittest.TestCase   ):
				snake_case	    =SpeechTaTokenizer
				snake_case	    =False
				snake_case	    =True





				def 						__UpperCamelCase    (				self)  -> Tuple:
											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_)  -> str:
											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)  -> str:
											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)  -> str:
											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)  -> str:
											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)  -> Optional[int]:
											self.assertEqual(self.get_tokenizer().vocab_size       ,  79)





				def 						__UpperCamelCase    (				self)  -> str:
											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)  -> Union[str, Any]:
											pass





				def 						__UpperCamelCase    (				self)  -> Optional[Any]:
											pass





				def 						__UpperCamelCase    (				self)  -> Union[str, Any]:
											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)  -> Optional[int]:
											# Use custom sequence because this tokenizer does not handle numbers.
											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 argparse
import os
from pathlib import Path

import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download

from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
    BarkCoarseConfig,
    BarkConfig,
    BarkFineConfig,
    BarkSemanticConfig,
)
from transformers.models.bark.generation_configuration_bark import (
    BarkCoarseGenerationConfig,
    BarkFineGenerationConfig,
    BarkGenerationConfig,
    BarkSemanticGenerationConfig,
)
from transformers.models.bark.modeling_bark import BarkCoarseModel, BarkFineModel, BarkModel, BarkSemanticModel
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCAmelCase            =	logging.get_logger(__name__)

set_seed(7_70)


_lowerCAmelCase            =	{
    'c_attn': 'att_proj',
    'c_proj': 'out_proj',
    'c_fc': 'in_proj',
    'transformer.': '',
    'h.': 'layers.',
    'ln_1': 'layernorm_1',
    'ln_2': 'layernorm_2',
    'ln_f': 'layernorm_final',
    'wpe': 'position_embeds_layer',
    'wte': 'input_embeds_layer',
}


_lowerCAmelCase            =	{
    'text_small': {
        'repo_id': 'suno/bark',
        'file_name': 'text.pt',
    },
    'coarse_small': {
        'repo_id': 'suno/bark',
        'file_name': 'coarse.pt',
    },
    'fine_small': {
        'repo_id': 'suno/bark',
        'file_name': 'fine.pt',
    },
    'text': {
        'repo_id': 'suno/bark',
        'file_name': 'text_2.pt',
    },
    'coarse': {
        'repo_id': 'suno/bark',
        'file_name': 'coarse_2.pt',
    },
    'fine': {
        'repo_id': 'suno/bark',
        'file_name': 'fine_2.pt',
    },
}

_lowerCAmelCase            =	os.path.dirname(os.path.abspath(__file__))
_lowerCAmelCase            =	os.path.join(os.path.expanduser('~'), '.cache')
_lowerCAmelCase            =	os.path.join(os.getenv('XDG_CACHE_HOME', default_cache_dir), 'suno', 'bark_v0')


def      UpperCamelCase       ( _A  ,				_A=False )     ->       Dict:
				lowercase    :      Union[str, Any]       = model_type
				if use_small:
								key += "_small"
				return os.path.join(_A  ,				REMOTE_MODEL_PATHS[key]["""file_name"""] )


def      UpperCamelCase       ( _A  ,				_A )     ->       List[Any]:
				os.makedirs(_A  ,				exist_ok=_A )
				hf_hub_download(repo_id=_A  ,				filename=_A  ,				local_dir=_A )


def      UpperCamelCase       ( _A  ,				_A  ,				_A=False  ,				_A="text" )     ->       List[Any]:
				if model_type == "text":
								lowercase    :      Any       = BarkSemanticModel
								lowercase    :      List[Any]       = BarkSemanticConfig
								lowercase    :      List[Any]       = BarkSemanticGenerationConfig
				elif model_type == "coarse":
								lowercase    :      Tuple       = BarkCoarseModel
								lowercase    :      str       = BarkCoarseConfig
								lowercase    :      Optional[int]       = BarkCoarseGenerationConfig
				elif model_type == "fine":
								lowercase    :      str       = BarkFineModel
								lowercase    :      Optional[Any]       = BarkFineConfig
								lowercase    :      Optional[int]       = BarkFineGenerationConfig
				else:
								raise NotImplementedError()
				lowercase    :      Optional[int]       = F"""{model_type}_small""" if use_small else model_type
				lowercase    :      str       = REMOTE_MODEL_PATHS[model_key]
				if not os.path.exists(_A ):
								logger.info(F"""{model_type} model not found, downloading into `{CACHE_DIR}`.""" )
								_download(model_info["""repo_id"""]  ,				model_info["""file_name"""] )
				lowercase    :      Dict       = torch.load(_A  ,				map_location=_A )
				# this is a hack
				lowercase    :      int       = checkpoint["""model_args"""]
				if "input_vocab_size" not in model_args:
								lowercase    :      str       = model_args["""vocab_size"""]
								lowercase    :      Optional[Any]       = model_args["""vocab_size"""]
								del model_args["vocab_size"]

				# convert Bark model arguments to HF Bark model arguments
				lowercase    :      Optional[int]       = model_args.pop("""n_head""" )
				lowercase    :      Union[str, Any]       = model_args.pop("""n_embd""" )
				lowercase    :      Dict       = model_args.pop("""n_layer""" )

				lowercase    :      List[str]       = ConfigClass(**checkpoint["""model_args"""] )
				lowercase    :      Dict       = ModelClass(config=_A )
				lowercase    :      List[str]       = GenerationConfigClass()

				lowercase    :      int       = model_generation_config
				lowercase    :      List[Any]       = checkpoint["""model"""]
				# fixup checkpoint
				lowercase    :      Union[str, Any]       = """_orig_mod."""
				for k, v in list(state_dict.items() ):
								if k.startswith(_A ):
												# replace part of the key with corresponding layer name in HF implementation
												lowercase    :      List[str]       = k[len(_A ) :]
												for old_layer_name in new_layer_name_dict:
																lowercase    :      int       = new_k.replace(_A  ,				new_layer_name_dict[old_layer_name] )

												lowercase    :      List[Any]       = state_dict.pop(_A )

				lowercase    :      Dict       = set(state_dict.keys() ) - set(model.state_dict().keys() )
				lowercase    :      Dict       = {k for k in extra_keys if not k.endswith(""".attn.bias""" )}
				lowercase    :      Optional[int]       = set(model.state_dict().keys() ) - set(state_dict.keys() )
				lowercase    :      Dict       = {k for k in missing_keys if not k.endswith(""".attn.bias""" )}
				if len(_A ) != 0:
								raise ValueError(F"""extra keys found: {extra_keys}""" )
				if len(_A ) != 0:
								raise ValueError(F"""missing keys: {missing_keys}""" )
				model.load_state_dict(_A  ,				strict=_A )
				lowercase    :      Union[str, Any]       = model.num_parameters(exclude_embeddings=_A )
				lowercase    :      Union[str, Any]       = checkpoint["""best_val_loss"""].item()
				logger.info(F"""model loaded: {round(n_params/1e6  ,				1 )}M params, {round(_A  ,				3 )} loss""" )
				model.eval()
				model.to(_A )
				del checkpoint, state_dict

				return model


def      UpperCamelCase       ( _A  ,				_A=False  ,				_A="text" )     ->       Dict:
				if model_type not in ("text", "coarse", "fine"):
								raise NotImplementedError()

				lowercase    :      Optional[Any]       = """cpu"""  # do conversion on cpu

				lowercase    :      Dict       = _get_ckpt_path(_A  ,				use_small=_A )
				lowercase    :      Any       = _load_model(_A  ,				_A  ,				model_type=_A  ,				use_small=_A )

				# load bark initial model
				lowercase    :      Tuple       = _bark_load_model(_A  ,				"""cpu"""  ,				model_type=_A  ,				use_small=_A )

				if model_type == "text":
								lowercase    :      str       = bark_model["""model"""]

				if model.num_parameters(exclude_embeddings=_A ) != bark_model.get_num_params():
								raise ValueError("""initial and new models don't have the same number of parameters""" )

				# check if same output as the bark model
				lowercase    :      Union[str, Any]       = 5
				lowercase    :      Tuple       = 10

				if model_type in ["text", "coarse"]:
								lowercase    :      Any       = torch.randint(256  ,				(batch_size, sequence_length)  ,				dtype=torch.int )
								lowercase    :      Optional[Any]       = bark_model(_A )[0]

								lowercase    :      Tuple       = model(_A )

								# take last logits
								lowercase    :      int       = output_new_model_total.logits[:, [-1], :]

				else:
								lowercase    :      Dict       = 3
								lowercase    :      Any       = 8
								lowercase    :      str       = torch.randint(256  ,				(batch_size, sequence_length, n_codes_total)  ,				dtype=torch.int )

								lowercase    :      str       = model(_A  ,				_A )
								lowercase    :      List[Any]       = bark_model(_A  ,				_A )

								lowercase    :      Any       = output_new_model_total.logits

				# output difference should come from the difference of self-attention implementation design
				if output_new_model.shape != output_old_model.shape:
								raise ValueError("""initial and new outputs don't have the same shape""" )
				if (output_new_model - output_old_model).abs().max().item() > 1e-3:
								raise ValueError("""initial and new outputs are not equal""" )

				Path(_A ).mkdir(exist_ok=_A )
				model.save_pretrained(_A )


def      UpperCamelCase       ( _A  ,				_A  ,				_A  ,				_A  ,				_A  ,				_A  ,				)     ->       Optional[int]:
				lowercase    :      str       = os.path.join(_A  ,				_A )

				lowercase    :      Dict       = BarkSemanticConfig.from_pretrained(os.path.join(_A  ,				"""config.json""" ) )
				lowercase    :      List[Any]       = BarkCoarseConfig.from_pretrained(os.path.join(_A  ,				"""config.json""" ) )
				lowercase    :      List[Any]       = BarkFineConfig.from_pretrained(os.path.join(_A  ,				"""config.json""" ) )
				lowercase    :      List[Any]       = EncodecConfig.from_pretrained("""facebook/encodec_24khz""" )

				lowercase    :      Any       = BarkSemanticModel.from_pretrained(_A )
				lowercase    :      str       = BarkCoarseModel.from_pretrained(_A )
				lowercase    :      int       = BarkFineModel.from_pretrained(_A )
				lowercase    :      Optional[Any]       = EncodecModel.from_pretrained("""facebook/encodec_24khz""" )

				lowercase    :      int       = BarkConfig.from_sub_model_configs(
				    _A  ,				_A  ,				_A  ,				_A )

				lowercase    :      Dict       = BarkGenerationConfig.from_sub_model_configs(
				    semantic.generation_config  ,				coarseAcoustic.generation_config  ,				fineAcoustic.generation_config )

				lowercase    :      str       = BarkModel(_A )

				lowercase    :      int       = semantic
				lowercase    :      List[Any]       = coarseAcoustic
				lowercase    :      Union[str, Any]       = fineAcoustic
				lowercase    :      Any       = codec

				lowercase    :      int       = bark_generation_config

				Path(_A ).mkdir(exist_ok=_A )
				bark.save_pretrained(_A  ,				repo_id=_A  ,				push_to_hub=_A )


if __name__ == "__main__":
				_lowerCAmelCase            =	argparse.ArgumentParser()
				# Required parameters

				parser.add_argument('model_type', type=str, help='text, coarse or fine.')
				parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
				parser.add_argument('--is_small', action='store_true', help='convert the small version instead of the large.')

				_lowerCAmelCase            =	parser.parse_args()

				load_model(args.pytorch_dump_folder_path, model_type=args.model_type, use_small=args.is_small)
