
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


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


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



  def __UpperCamelCase				(				self )						->      Union[str, Any]:
    """simple docstring"""
    super().setUp()

    # We have a SentencePiece fixture for testing
    UpperCamelCase						=  SpeechTaTokenizer(A_ )

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

    tokenizer.save_pretrained(self.tmpdirname )



  def __UpperCamelCase				(				self       ,   A_ )						->      List[str]:
    """simple docstring"""
    UpperCamelCase						=  'this is a test'
    UpperCamelCase						=  'this is a test'
    return input_text, output_text



  def __UpperCamelCase				(				self       ,   A_       ,   A_=False       ,   A_=20       ,   A_=5 )						->      Optional[Any]:
    """simple docstring"""
    UpperCamelCase			,						UpperCamelCase						=  self.get_input_output_texts(A_ )
    UpperCamelCase						=  tokenizer.encode(A_       ,   add_special_tokens=A_ )
    UpperCamelCase						=  tokenizer.decode(A_       ,   clean_up_tokenization_spaces=A_ )
    return text, ids



  def __UpperCamelCase				(				self )						->      Any:
    """simple docstring"""
    UpperCamelCase						=  '<pad>'
    UpperCamelCase						=  1

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



  def __UpperCamelCase				(				self )						->      Tuple:
    """simple docstring"""
    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(A_ )       ,   81 )



  def __UpperCamelCase				(				self )						->      Dict:
    """simple docstring"""
    self.assertEqual(self.get_tokenizer().vocab_size       ,   79 )



  def __UpperCamelCase				(				self )						->      Optional[int]:
    """simple docstring"""
    UpperCamelCase						=  self.get_tokenizers(do_lower_case=A_ )
    for tokenizer in tokenizers:
      with self.subTest(F'''{tokenizer.__class__.__name__}''' ):
        UpperCamelCase						=  tokenizer.vocab_size
        UpperCamelCase						=  len(A_ )

        self.assertNotEqual(A_       ,   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(A_ )
        UpperCamelCase						=  tokenizer.vocab_size
        UpperCamelCase						=  len(A_ )

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

        UpperCamelCase						=  tokenizer.encode('aaaaa bbbbbb low cccccccccdddddddd l'       ,   add_special_tokens=A_ )

        self.assertGreaterEqual(len(A_ )       ,   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(A_ )
        UpperCamelCase						=  tokenizer.vocab_size
        UpperCamelCase						=  len(A_ )

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

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

        self.assertGreaterEqual(len(A_ )       ,   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 )						->      Dict:
    """simple docstring"""
    pass



  def __UpperCamelCase				(				self )						->      Tuple:
    """simple docstring"""
    pass



  def __UpperCamelCase				(				self )						->      Optional[int]:
    """simple docstring"""
    UpperCamelCase						=  self.get_tokenizer()

    UpperCamelCase						=  tokenizer.tokenize('This is a test' )
    # fmt: off
    self.assertListEqual(A_       ,   [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(A_ )       ,   [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(
        A_       ,   [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(A_ )
    # fmt: off
    self.assertListEqual(A_       ,   [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(A_ )
    self.assertListEqual(
        A_       ,   [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 )						->      List[str]:
    """simple docstring"""
    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=A_       ,   model_name='microsoft/speecht5_asr'       ,   revision='c5ef64c71905caeccde0e4462ef3f9077224c524'       ,   sequences=A_       ,   )


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

import torch
from transformers import CLIPTextModel, CLIPTokenizer

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils import logging
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


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


class        lowercase      (	_SCREAMING_SNAKE_CASE						,     _SCREAMING_SNAKE_CASE     ):



		@register_to_config
		def __init__(				self       ,   A_       ,   A_ = None       ,   A_ = None )						->      Any:
				"""simple docstring"""
				super().__init__()

				UpperCamelCase						=  learnable

				if self.learnable:
						assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
						assert length is not None, "learnable=True requires `length` to be set"

						UpperCamelCase						=  torch.zeros(A_       ,   A_ )
				else:
						UpperCamelCase						=  None

				UpperCamelCase						=  torch.nn.Parameter(A_ )






class        lowercase      (	_SCREAMING_SNAKE_CASE     ):
		__lowercase : VQModel
		__lowercase : CLIPTextModel
		__lowercase : CLIPTokenizer
		__lowercase : TransformeraDModel
		__lowercase : LearnedClassifierFreeSamplingEmbeddings
		__lowercase : VQDiffusionScheduler



		def __init__(				self       ,   A_       ,   A_       ,   A_       ,   A_       ,   A_       ,   A_       ,   )						->      Optional[Any]:
				"""simple docstring"""
				super().__init__()

				self.register_modules(
				    vqvae=A_       ,   transformer=A_       ,   text_encoder=A_       ,   tokenizer=A_       ,   scheduler=A_       ,   learned_classifier_free_sampling_embeddings=A_       ,   )



		def __UpperCamelCase				(				self       ,   A_       ,   A_       ,   A_ )						->      Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase						=  len(A_ ) if isinstance(A_       ,   A_ ) else 1

				# get prompt text embeddings
				UpperCamelCase						=  self.tokenizer(
				    A_       ,   padding='max_length'       ,   max_length=self.tokenizer.model_max_length       ,   return_tensors='pt'       ,   )
				UpperCamelCase						=  text_inputs.input_ids

				if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
						UpperCamelCase						=  self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :] )
						logger.warning(
						    'The following part of your input was truncated because CLIP can only handle sequences up to'
						    F''' {self.tokenizer.model_max_length} tokens: {removed_text}''' )
						UpperCamelCase						=  text_input_ids[:, : self.tokenizer.model_max_length]
				UpperCamelCase						=  self.text_encoder(text_input_ids.to(self.device ) )[0]

				# NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
				# While CLIP does normalize the pooled output of the text transformer when combining
				# the image and text embeddings, CLIP does not directly normalize the last hidden state.
				#
				# CLIP normalizing the pooled output.
				# https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
				UpperCamelCase						=  prompt_embeds / prompt_embeds.norm(dim=-1       ,   keepdim=A_ )

				# duplicate text embeddings for each generation per prompt
				UpperCamelCase						=  prompt_embeds.repeat_interleave(A_       ,   dim=0 )

				if do_classifier_free_guidance:
						if self.learned_classifier_free_sampling_embeddings.learnable:
								UpperCamelCase						=  self.learned_classifier_free_sampling_embeddings.embeddings
								UpperCamelCase						=  negative_prompt_embeds.unsqueeze(0 ).repeat(A_       ,   1       ,   1 )
						else:
								UpperCamelCase						=  [''] * batch_size

								UpperCamelCase						=  text_input_ids.shape[-1]
								UpperCamelCase						=  self.tokenizer(
								    A_       ,   padding='max_length'       ,   max_length=A_       ,   truncation=A_       ,   return_tensors='pt'       ,   )
								UpperCamelCase						=  self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
								# See comment for normalizing text embeddings
								UpperCamelCase						=  negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1       ,   keepdim=A_ )

						# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
						UpperCamelCase						=  negative_prompt_embeds.shape[1]
						UpperCamelCase						=  negative_prompt_embeds.repeat(1       ,   A_       ,   1 )
						UpperCamelCase						=  negative_prompt_embeds.view(batch_size * num_images_per_prompt       ,   A_       ,   -1 )

						# For classifier free guidance, we need to do two forward passes.
						# Here we concatenate the unconditional and text embeddings into a single batch
						# to avoid doing two forward passes
						UpperCamelCase						=  torch.cat([negative_prompt_embeds, prompt_embeds] )

				return prompt_embeds



		@torch.no_grad()
		def __call__(				self       ,   A_       ,   A_ = 100       ,   A_ = 5.0       ,   A_ = 1.0       ,   A_ = 1       ,   A_ = None       ,   A_ = None       ,   A_ = "pil"       ,   A_ = True       ,   A_ = None       ,   A_ = 1       ,   )						->      Union[ImagePipelineOutput, Tuple]:
				"""simple docstring"""
				if isinstance(A_       ,   A_ ):
						UpperCamelCase						=  1
				elif isinstance(A_       ,   A_ ):
						UpperCamelCase						=  len(A_ )
				else:
						raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(A_ )}''' )

				UpperCamelCase						=  batch_size * num_images_per_prompt

				UpperCamelCase						=  guidance_scale > 1.0

				UpperCamelCase						=  self._encode_prompt(A_       ,   A_       ,   A_ )

				if (callback_steps is None) or (
				    callback_steps is not None and (not isinstance(A_       ,   A_ ) or callback_steps <= 0)
				):
						raise ValueError(
						    F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
						    F''' {type(A_ )}.''' )

				# get the initial completely masked latents unless the user supplied it

				UpperCamelCase						=  (batch_size, self.transformer.num_latent_pixels)
				if latents is None:
						UpperCamelCase						=  self.transformer.num_vector_embeds - 1
						UpperCamelCase						=  torch.full(A_       ,   A_ ).to(self.device )
				else:
						if latents.shape != latents_shape:
								raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}''' )
						if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
								raise ValueError(
								    'Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,'
								    F''' {self.transformer.num_vector_embeds - 1} (inclusive).''' )
						UpperCamelCase						=  latents.to(self.device )

				# set timesteps
				self.scheduler.set_timesteps(A_       ,   device=self.device )

				UpperCamelCase						=  self.scheduler.timesteps.to(self.device )

				UpperCamelCase						=  latents

				for i, t in enumerate(self.progress_bar(A_ ) ):
						# expand the sample if we are doing classifier free guidance
						UpperCamelCase						=  torch.cat([sample] * 2 ) if do_classifier_free_guidance else sample

						# predict the un-noised image
						# model_output == `log_p_x_0`
						UpperCamelCase						=  self.transformer(A_       ,   encoder_hidden_states=A_       ,   timestep=A_ ).sample

						if do_classifier_free_guidance:
								UpperCamelCase			,						UpperCamelCase						=  model_output.chunk(2 )
								UpperCamelCase						=  model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
								model_output -= torch.logsumexp(A_       ,   dim=1       ,   keepdim=A_ )

						UpperCamelCase						=  self.truncate(A_       ,   A_ )

						# remove `log(0)`'s (`-inf`s)
						UpperCamelCase						=  model_output.clamp(-70 )

						# compute the previous noisy sample x_t -> x_t-1
						UpperCamelCase						=  self.scheduler.step(A_       ,   timestep=A_       ,   sample=A_       ,   generator=A_ ).prev_sample

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

				UpperCamelCase						=  self.vqvae.config.vq_embed_dim
				UpperCamelCase						=  (batch_size, self.transformer.height, self.transformer.width, embedding_channels)
				UpperCamelCase						=  self.vqvae.quantize.get_codebook_entry(A_       ,   shape=A_ )
				UpperCamelCase						=  self.vqvae.decode(A_       ,   force_not_quantize=A_ ).sample

				UpperCamelCase						=  (image / 2 + 0.5).clamp(0       ,   1 )
				UpperCamelCase						=  image.cpu().permute(0       ,   2       ,   3       ,   1 ).numpy()

				if output_type == "pil":
						UpperCamelCase						=  self.numpy_to_pil(A_ )

				if not return_dict:
						return (image,)

				return ImagePipelineOutput(images=A_ )



		def __UpperCamelCase				(				self       ,   A_       ,   A_ )						->      torch.FloatTensor:
				"""simple docstring"""
				UpperCamelCase			,						UpperCamelCase						=  torch.sort(A_       ,   1       ,   descending=A_ )
				UpperCamelCase						=  torch.exp(A_ )
				UpperCamelCase						=  sorted_p_x_0.cumsum(dim=1 ) < truncation_rate

				# Ensure that at least the largest probability is not zeroed out
				UpperCamelCase						=  torch.full_like(keep_mask[:, 0:1, :]       ,   A_ )
				UpperCamelCase						=  torch.cat((all_true, keep_mask)       ,   dim=1 )
				UpperCamelCase						=  keep_mask[:, :-1, :]

				UpperCamelCase						=  keep_mask.gather(1       ,   indices.argsort(1 ) )

				UpperCamelCase						=  log_p_x_0.clone()

				UpperCamelCase						=  -torch.inf  # -inf = log(0)

				return rv
