"""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			   = get_tests_dir("fixtures/test_sentencepiece_bpe_char.model")


@require_sentencepiece
@require_tokenizers
class     UpperCAmelCase						(_UpperCAmelCase    ,unittest.TestCase  ):



    """simple docstring"""

    _UpperCAmelCase	:Union[str, Any]						    = SpeechTaTokenizer
    _UpperCAmelCase	:int						    = False
    _UpperCAmelCase	:Optional[Any]						    = True






    def _snake_case			(   self							):
       super().setUp()

       # We have a SentencePiece fixture for testing
       lowercase__:   Optional[int]					=      SpeechTaTokenizer(__SCREAMING_SNAKE_CASE							)

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

       tokenizer.save_pretrained(self.tmpdirname							)






    def _snake_case			(   self    ,			_UpperCAmelCase							):
       lowercase__:   str					=      '''this is a test'''
       lowercase__:   List[str]					=      '''this is a test'''
       return input_text, output_text






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase=False    ,			_UpperCAmelCase=20    ,			_UpperCAmelCase=5							):
       lowercase__, lowercase__:   str					=      self.get_input_output_texts(__SCREAMING_SNAKE_CASE							)
       lowercase__:   str					=      tokenizer.encode(__SCREAMING_SNAKE_CASE    ,			add_special_tokens=__SCREAMING_SNAKE_CASE							)
       lowercase__:   str					=      tokenizer.decode(__SCREAMING_SNAKE_CASE    ,			clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE							)
       return text, ids






    def _snake_case			(   self							):
       lowercase__:   Any					=      '''<pad>'''
       lowercase__:   Tuple					=      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							):
       lowercase__:   List[str]					=      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							):
       self.assertEqual(self.get_tokenizer().vocab_size    ,			79							)






    def _snake_case			(   self							):
       lowercase__:   Optional[Any]					=      self.get_tokenizers(do_lower_case=__SCREAMING_SNAKE_CASE							)
       for tokenizer in tokenizers:
          with self.subTest(F"""{tokenizer.__class__.__name__}"""							):
             lowercase__:   List[str]					=      tokenizer.vocab_size
             lowercase__:   List[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__:   Dict					=      ['''aaaaa bbbbbb''', '''cccccccccdddddddd''']
             lowercase__:   str					=      tokenizer.add_tokens(__SCREAMING_SNAKE_CASE							)
             lowercase__:   List[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__:   Dict					=      {'''eos_token''': '''>>>>|||<||<<|<<''', '''pad_token''': '''<<<<<|||>|>>>>|>'''}
             lowercase__:   Optional[int]					=      tokenizer.add_special_tokens(__SCREAMING_SNAKE_CASE							)
             lowercase__:   Tuple					=      tokenizer.vocab_size
             lowercase__:   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_a + len(__SCREAMING_SNAKE_CASE							)							)

             lowercase__:   Tuple					=      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							):
       pass






    def _snake_case			(   self							):
       pass






    def _snake_case			(   self							):
       lowercase__:   Union[str, 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__:   int					=      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__:   Any					=      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__:   List[str]					=      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							):
       lowercase__:   Optional[Any]					=      [
           '''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__:   Any					=      {
           '''input_ids''': [
               [4, 32, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 64, 19, 8, 13, 18, 5, 13, 15, 22, 4, 28, 9, 8, 20, 9, 4, 7, 12, 4, 24, 22, 6, 8, 13, 17, 11, 39, 6, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 7, 9, 14, 4, 24, 22, 6, 8, 13, 17, 11, 39, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 39, 25, 5, 13, 6, 63, 4, 24, 13, 8, 27, 10, 14, 5, 12, 4, 21, 5, 9, 5, 13, 7, 15, 39, 24, 16, 13, 24, 8, 12, 5, 4, 7, 13, 17, 11, 10, 6, 5, 17, 6, 16, 13, 5, 12, 4, 64, 40, 47, 54, 32, 23, 4, 53, 49, 32, 23, 4, 54, 8, 40, 47, 54, 32, 7, 23, 4, 69, 52, 43, 23, 4, 51, 10, 12, 6, 10, 15, 40, 5, 13, 6, 23, 4, 69, 52, 48, 5, 6, 26, 26, 26, 63, 4, 19, 8, 13, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 61, 9, 14, 5, 13, 12, 6, 7, 9, 14, 10, 9, 21, 4, 64, 48, 52, 61, 63, 4, 7, 9, 14, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 53, 5, 9, 5, 13, 7, 6, 10, 8, 9, 4, 64, 48, 52, 53, 63, 4, 20, 10, 6, 11, 4, 8, 27, 5, 13, 4, 6, 11, 10, 13, 6, 22, 39, 6, 20, 8, 4, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 4, 18, 8, 14, 5, 15, 12, 4, 10, 9, 4, 8, 9, 5, 4, 11, 16, 9, 14, 13, 5, 14, 4, 24, 15, 16, 12, 4, 15, 7, 9, 21, 16, 7, 21, 5, 12, 4, 7, 9, 14, 4, 14, 5, 5, 24, 4, 10, 9, 6, 5, 13, 8, 24, 5, 13, 7, 25, 10, 15, 10, 6, 22, 4, 25, 5, 6, 20, 5, 5, 9, 4, 58, 7, 37, 23, 4, 49, 22, 32, 8, 13, 17, 11, 4, 7, 9, 14, 4, 32, 5, 9, 12, 8, 13, 55, 15, 8, 20, 26, 2],
               [4, 40, 47, 54, 32, 4, 10, 12, 4, 14, 5, 12, 10, 21, 9, 5, 14, 4, 6, 8, 4, 24, 13, 5, 39, 6, 13, 7, 10, 9, 4, 14, 5, 5, 24, 4, 25, 10, 14, 10, 13, 5, 17, 6, 10, 8, 9, 7, 15, 4, 13, 5, 24, 13, 5, 12, 5, 9, 6, 7, 6, 10, 8, 9, 12, 4, 19, 13, 8, 18, 4, 16, 9, 15, 7, 25, 5, 15, 5, 14, 4, 6, 5, 37, 6, 4, 25, 22, 4, 46, 8, 10, 9, 6, 15, 22, 4, 17, 8, 9, 14, 10, 6, 10, 8, 9, 10, 9, 21, 4, 8, 9, 4, 25, 8, 6, 11, 4, 15, 5, 19, 6, 4, 7, 9, 14, 4, 13, 10, 21, 11, 6, 4, 17, 8, 9, 6, 5, 37, 6, 4, 10, 9, 4, 7, 15, 15, 4, 15, 7, 22, 5, 13, 12, 26, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
               [4, 32, 11, 5, 4, 45, 16, 10, 17, 28, 4, 25, 13, 8, 20, 9, 4, 19, 8, 37, 4, 46, 16, 18, 24, 12, 4, 8, 27, 5, 13, 4, 6, 11, 5, 4, 15, 7, 57, 22, 4, 14, 8, 21, 26, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
           ],
           '''attention_mask''': [
               [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
               [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
               [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
           ]
       }
       # fmt: on

       self.tokenizer_integration_test_util(
           expected_encoding=__SCREAMING_SNAKE_CASE    ,			model_name='''microsoft/speecht5_asr'''    ,			revision='''c5ef64c71905caeccde0e4462ef3f9077224c524'''    ,			sequences=__SCREAMING_SNAKE_CASE    ,			)



'''simple docstring'''



from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


UpperCAmelCase :       Optional[int]			   =     logging.get_logger(__name__)

UpperCAmelCase :       str			   =     {
    'facebook/convnextv2-tiny-1k-224': 'https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json',
}







class 						lowerCAmelCase__   (      a ,					a					):



 """simple docstring"""



 lowerCAmelCase__  					=      "convnextv2"
 def __init__(		self					:       Any					,							__SCREAMING_SNAKE_CASE					:       int=3					,							__SCREAMING_SNAKE_CASE					:       Dict=4					,							__SCREAMING_SNAKE_CASE					:       List[Any]=4					,							__SCREAMING_SNAKE_CASE					:       Optional[int]=None					,							__SCREAMING_SNAKE_CASE					:       Dict=None					,							__SCREAMING_SNAKE_CASE					:       Optional[int]="gelu"					,							__SCREAMING_SNAKE_CASE					:       Union[str, Any]=0.02					,							__SCREAMING_SNAKE_CASE					:       Dict=1E-12					,							__SCREAMING_SNAKE_CASE					:       List[str]=0.0					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]=224					,							__SCREAMING_SNAKE_CASE					:       Tuple=None					,							__SCREAMING_SNAKE_CASE					:       List[str]=None					,							**__SCREAMING_SNAKE_CASE					:       Union[str, Any]					,							)  -> Union[str, Any]:


      """simple docstring"""




      super().__init__(**__SCREAMING_SNAKE_CASE			)

      __SCREAMING_SNAKE_CASE     =      num_channels
      __SCREAMING_SNAKE_CASE     =      patch_size
      __SCREAMING_SNAKE_CASE     =      num_stages
      __SCREAMING_SNAKE_CASE     =      [96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
      __SCREAMING_SNAKE_CASE     =      [3, 3, 9, 3] if depths is None else depths
      __SCREAMING_SNAKE_CASE     =      hidden_act
      __SCREAMING_SNAKE_CASE     =      initializer_range
      __SCREAMING_SNAKE_CASE     =      layer_norm_eps
      __SCREAMING_SNAKE_CASE     =      drop_path_rate
      __SCREAMING_SNAKE_CASE     =      image_size
      __SCREAMING_SNAKE_CASE     =      ["""stem"""] + [f'stage{idx}' for idx in range(1					,							len(self.depths			) + 1			)]
      __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      get_aligned_output_features_output_indices(
          out_features=__SCREAMING_SNAKE_CASE					,							out_indices=__SCREAMING_SNAKE_CASE					,							stage_names=self.stage_names			)






