"""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


lowerCAmelCase__  :  int						     =	get_tests_dir('fixtures/test_sentencepiece_bpe_char.model')
@require_sentencepiece
@require_tokenizers
class 				snake_case       ( __lowerCamelCase      ,    unittest.TestCase				):




					"""simple docstring"""

					snake_case__				    = SpeechTaTokenizer
					snake_case__				    = False
					snake_case__				    = True







					def 		__lowerCAmelCase						(				self   :	Optional[Any]			):
									super().setUp()

									# We have a SentencePiece fixture for testing
									UpperCAmelCase__             =							SpeechTaTokenizer(lowerCamelCase__			)

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

									tokenizer.save_pretrained(self.tmpdirname			)







					def 		__lowerCAmelCase						(				self   :	List[str]    ,lowerCamelCase__   :	int			):
									UpperCAmelCase__             =							'''this is a test'''
									UpperCAmelCase__             =							'''this is a test'''
									return input_text, output_text







					def 		__lowerCAmelCase						(				self   :	int    ,lowerCamelCase__   :	Tuple    ,lowerCamelCase__   :	List[Any]=False    ,lowerCamelCase__   :	List[Any]=20    ,lowerCamelCase__   :	str=5			):
									UpperCAmelCase__             =							self.get_input_output_texts(lowerCamelCase__			)
									UpperCAmelCase__             =							tokenizer.encode(lowerCamelCase__    ,add_special_tokens=lowerCamelCase__			)
									UpperCAmelCase__             =							tokenizer.decode(lowerCamelCase__    ,clean_up_tokenization_spaces=lowerCamelCase__			)
									return text, ids







					def 		__lowerCAmelCase						(				self   :	Dict			):
									UpperCAmelCase__             =							'''<pad>'''
									UpperCAmelCase__             =							1

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







					def 		__lowerCAmelCase						(				self   :	List[Any]			):
									UpperCAmelCase__             =							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(lowerCamelCase__			)    ,81			)







					def 		__lowerCAmelCase						(				self   :	str			):
									self.assertEqual(self.get_tokenizer().vocab_size    ,79			)







					def 		__lowerCAmelCase						(				self   :	Optional[Any]			):
									UpperCAmelCase__             =							self.get_tokenizers(do_lower_case=lowerCamelCase__			)
									for tokenizer in tokenizers:
													with self.subTest(f'''{tokenizer.__class__.__name__}'''			):
																	UpperCAmelCase__             =							tokenizer.vocab_size
																	UpperCAmelCase__             =							len(lowerCamelCase__			)

																	self.assertNotEqual(lowerCamelCase__    ,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)

																	UpperCAmelCase__             =							['''aaaaa bbbbbb''', '''cccccccccdddddddd''']
																	UpperCAmelCase__             =							tokenizer.add_tokens(lowerCamelCase__			)
																	UpperCAmelCase__             =							tokenizer.vocab_size
																	UpperCAmelCase__             =							len(lowerCamelCase__			)

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

																	UpperCAmelCase__             =							tokenizer.encode('aaaaa bbbbbb low cccccccccdddddddd l'    ,add_special_tokens=lowerCamelCase__			)

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

																	UpperCAmelCase__             =							{'''eos_token''': '''>>>>|||<||<<|<<''', '''pad_token''': '''<<<<<|||>|>>>>|>'''}
																	UpperCAmelCase__             =							tokenizer.add_special_tokens(lowerCamelCase__			)
																	UpperCAmelCase__             =							tokenizer.vocab_size
																	UpperCAmelCase__             =							len(lowerCamelCase__			)

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

																	UpperCAmelCase__             =							tokenizer.encode(
																	    '>>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l'    ,add_special_tokens=lowerCamelCase__			)

																	self.assertGreaterEqual(len(lowerCamelCase__			)    ,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 		__lowerCAmelCase						(				self   :	int			):
									pass







					def 		__lowerCAmelCase						(				self   :	List[Any]			):
									pass







					def 		__lowerCAmelCase						(				self   :	Optional[int]			):
									UpperCAmelCase__             =							self.get_tokenizer()

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

									UpperCAmelCase__             =							tokenizer.tokenize('I was born in 92000, and this is falsé.'			)
									self.assertListEqual(
									    lowerCamelCase__    ,[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', '.']			)

									UpperCAmelCase__             =							tokenizer.convert_tokens_to_ids(lowerCamelCase__			)
									# fmt: off
									self.assertListEqual(lowerCamelCase__    ,[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

									UpperCAmelCase__             =							tokenizer.convert_ids_to_tokens(lowerCamelCase__			)
									self.assertListEqual(
									    lowerCamelCase__    ,[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 		__lowerCAmelCase						(				self   :	Union[str, Any]			):
									UpperCAmelCase__             =							[
									    '''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
									UpperCAmelCase__             =							{
									    '''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=lowerCamelCase__    ,model_name='microsoft/speecht5_asr'    ,revision='c5ef64c71905caeccde0e4462ef3f9077224c524'    ,sequences=lowerCamelCase__    ,)










import math
from typing import Any, Callable, List, Optional, Tuple, Union

import numpy as np
import torch

from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor


if is_onnx_available():
  from ..onnx_utils import OnnxRuntimeModel

from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
from .continous_encoder import SpectrogramContEncoder
from .notes_encoder import SpectrogramNotesEncoder


UpperCAmelCase_     							= logging.get_logger(__name__)  # pylint: disable=invalid-name

UpperCAmelCase_     							= 256






class     lowercase__       (	__lowerCamelCase				):

   '''simple docstring'''


   a						:							List[str]   		=		["melgan"]




   def __init__(    self,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__,		) ->      None:






       """simple docstring"""
       super().__init__()

       # From MELGAN
       UpperCamelCase__							:				Optional[int]          =      math.log(1E-5							)  # Matches MelGAN training.
       UpperCamelCase__							:				int          =      4.0  # Largest value for most examples
       UpperCamelCase__							:				Optional[int]          =      128

       self.register_modules(
           notes_encoder=__magic_name__,		continuous_encoder=__magic_name__,		decoder=__magic_name__,		scheduler=__magic_name__,		melgan=__magic_name__,		)




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__=(-1.0, 1.0),		__magic_name__=False							) ->      Any:






       """simple docstring"""
       UpperCamelCase__   ,UpperCamelCase__							:				str          =      output_range
       if clip:
           UpperCamelCase__							:				Union[str, Any]          =      torch.clip(__magic_name__,		self.min_value,		self.max_value							)
       # Scale to [0, 1].
       UpperCamelCase__							:				List[str]          =      (features - self.min_value) / (self.max_value - self.min_value)
       # Scale to [min_out, max_out].
       return zero_one * (max_out - min_out) + min_out




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__=(-1.0, 1.0),		__magic_name__=False							) ->      Optional[int]:






       """simple docstring"""
       UpperCamelCase__   ,UpperCamelCase__							:				List[str]          =      input_range
       UpperCamelCase__							:				Any          =      torch.clip(__magic_name__,		__magic_name__,		__magic_name__							) if clip else outputs
       # Scale to [0, 1].
       UpperCamelCase__							:				Any          =      (outputs - min_out) / (max_out - min_out)
       # Scale to [self.min_value, self.max_value].
       return zero_one * (self.max_value - self.min_value) + self.min_value




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__							) ->      List[str]:






       """simple docstring"""
       UpperCamelCase__							:				Union[str, Any]          =      input_tokens > 0
       UpperCamelCase__   ,UpperCamelCase__							:				Any          =      self.notes_encoder(
           encoder_input_tokens=__magic_name__,		encoder_inputs_mask=__magic_name__							)

       UpperCamelCase__   ,UpperCamelCase__							:				Optional[Any]          =      self.continuous_encoder(
           encoder_inputs=__magic_name__,		encoder_inputs_mask=__magic_name__							)

       return [(tokens_encoded, tokens_mask), (continuous_encoded, continuous_mask)]




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__							) ->      str:






       """simple docstring"""
       UpperCamelCase__							:				Any          =      noise_time
       if not torch.is_tensor(__magic_name__							):
           UpperCamelCase__							:				Tuple          =      torch.tensor([timesteps],		dtype=torch.long,		device=input_tokens.device							)
       elif torch.is_tensor(__magic_name__							) and len(timesteps.shape							) == 0:
           UpperCamelCase__							:				Union[str, Any]          =      timesteps[None].to(input_tokens.device							)

       # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
       UpperCamelCase__							:				Dict          =      timesteps * torch.ones(input_tokens.shape[0],		dtype=timesteps.dtype,		device=timesteps.device							)

       UpperCamelCase__							:				List[str]          =      self.decoder(
           encodings_and_masks=__magic_name__,		decoder_input_tokens=__magic_name__,		decoder_noise_time=__magic_name__							)
       return logits




   @torch.no_grad()
   def __call__(    self,		__magic_name__,		__magic_name__ = None,		__magic_name__ = 100,		__magic_name__ = True,		__magic_name__ = "numpy",		__magic_name__ = None,		__magic_name__ = 1,		) ->      Union[AudioPipelineOutput, Tuple]:






       """simple docstring"""
       if (callback_steps is None) or (
           callback_steps is not None and (not isinstance(__magic_name__,		__magic_name__							) or callback_steps <= 0)
       ):
           raise ValueError(
               f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
               f" {type(__magic_name__							)}."							)

       UpperCamelCase__							:				Dict          =      np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims],		dtype=np.floataa							)
       UpperCamelCase__							:				Tuple          =      np.zeros([1, 0, self.n_dims],		np.floataa							)
       UpperCamelCase__							:				List[Any]          =      torch.ones((1, TARGET_FEATURE_LENGTH),		dtype=__magic_name__,		device=self.device							)

       for i, encoder_input_tokens in enumerate(__magic_name__							):
           if i == 0:
               UpperCamelCase__							:				str          =      torch.from_numpy(pred_mel[:1].copy()							).to(
                   device=self.device,		dtype=self.decoder.dtype							)
               # The first chunk has no previous context.
               UpperCamelCase__							:				Any          =      torch.zeros((1, TARGET_FEATURE_LENGTH),		dtype=__magic_name__,		device=self.device							)
           else:
               # The full song pipeline does not feed in a context feature, so the mask
               # will be all 0s after the feature converter. Because we know we're
               # feeding in a full context chunk from the previous prediction, set it
               # to all 1s.
               UpperCamelCase__							:				List[str]          =      ones

           UpperCamelCase__							:				int          =      self.scale_features(
               __magic_name__,		output_range=[-1.0, 1.0],		clip=__magic_name__							)

           UpperCamelCase__							:				Union[str, Any]          =      self.encode(
               input_tokens=torch.IntTensor([encoder_input_tokens]							).to(device=self.device							),		continuous_inputs=__magic_name__,		continuous_mask=__magic_name__,		)

           # Sample encoder_continuous_inputs shaped gaussian noise to begin loop
           UpperCamelCase__							:				Optional[int]          =      randn_tensor(
               shape=encoder_continuous_inputs.shape,		generator=__magic_name__,		device=self.device,		dtype=self.decoder.dtype,		)

           # set step values
           self.scheduler.set_timesteps(__magic_name__							)

           # Denoising diffusion loop
           for j, t in enumerate(self.progress_bar(self.scheduler.timesteps							)							):
               UpperCamelCase__							:				Union[str, Any]          =      self.decode(
                   encodings_and_masks=__magic_name__,		input_tokens=__magic_name__,		noise_time=t / self.scheduler.config.num_train_timesteps,		)

               # Compute previous output: x_t -> x_t-1
               UpperCamelCase__							:				List[Any]          =      self.scheduler.step(__magic_name__,		__magic_name__,		__magic_name__,		generator=__magic_name__							).prev_sample

           UpperCamelCase__							:				List[Any]          =      self.scale_to_features(__magic_name__,		input_range=[-1.0, 1.0]							)
           UpperCamelCase__							:				List[Any]          =      mel[:1]
           UpperCamelCase__							:				int          =      mel.cpu().float().numpy()

           UpperCamelCase__							:				Union[str, Any]          =      np.concatenate([full_pred_mel, pred_mel[:1]],		axis=1							)

           # call the callback, if provided
           if callback is not None and i % callback_steps == 0:
               callback(__magic_name__,		__magic_name__							)

           logger.info('''Generated segment''',		__magic_name__							)

       if output_type == "numpy" and not is_onnx_available():
           raise ValueError(
               '''Cannot return output in \'np\' format if ONNX is not available. Make sure to have ONNX installed or set \'output_type\' to \'mel\'.'''							)
       elif output_type == "numpy" and self.melgan is None:
           raise ValueError(
               '''Cannot return output in \'np\' format if melgan component is not defined. Make sure to define `self.melgan` or set \'output_type\' to \'mel\'.'''							)

       if output_type == "numpy":
           UpperCamelCase__							:				Optional[int]          =      self.melgan(input_features=full_pred_mel.astype(np.floataa							)							)
       else:
           UpperCamelCase__							:				Any          =      full_pred_mel

       if not return_dict:
           return (output,)

       return AudioPipelineOutput(audios=__magic_name__							)

