

"""simple docstring"""






import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_lowerCAmelCase						=		get_tests_dir("""fixtures/test_sentencepiece_no_bos.model""")







@require_sentencepiece
@require_tokenizers
class 	__UpperCamelCase    (    a__							,							unittest.TestCase						):
  _UpperCAmelCase          =		PegasusTokenizer
  _UpperCAmelCase          =		PegasusTokenizerFast
  _UpperCAmelCase          =		True
  _UpperCAmelCase          =		True

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      super().setUp()

      # We have a SentencePiece fixture for testing
      _lowerCAmelCase       :   str  =    PegasusTokenizer(_A      )
      tokenizer.save_pretrained(self.tmpdirname      )

  @cached_property
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return PegasusTokenizer.from_pretrained('google/pegasus-large'      )

  def __lowerCamelCase		(		self    ,**_A      ):
      '''simple docstring'''

      return PegasusTokenizer.from_pretrained(self.tmpdirname    ,**_A      )

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      return ("This is a test", "This is a test")

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Dict  =    '</s>'
      _lowerCAmelCase       :   str  =    1

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

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[Any]  =    list(self.get_tokenizer().get_vocab().keys()      )

      self.assertEqual(vocab_keys[0]    ,'<pad>'      )
      self.assertEqual(vocab_keys[1]    ,'</s>'      )
      self.assertEqual(vocab_keys[-1]    ,'v'      )
      self.assertEqual(len(_A      )    ,1103      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      self.assertEqual(self.get_tokenizer().vocab_size    ,1103      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   str  =    self.rust_tokenizer_class.from_pretrained(self.tmpdirname      )
      _lowerCAmelCase       :   Any  =    self.tokenizer_class.from_pretrained(self.tmpdirname      )
      _lowerCAmelCase       :   int  =    (
          'Let\'s see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important'
          ' </s> <pad> <pad> <pad>'
      )
      _lowerCAmelCase       :   str  =    rust_tokenizer([raw_input_str]    ,return_tensors=_A    ,add_special_tokens=_A      ).input_ids[0]
      _lowerCAmelCase       :   List[Any]  =    py_tokenizer([raw_input_str]    ,return_tensors=_A    ,add_special_tokens=_A      ).input_ids[0]
      self.assertListEqual(_A    ,_A      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   str  =    self._large_tokenizer
      # <mask_1> masks whole sentence while <mask_2> masks single word
      _lowerCAmelCase       :   Optional[Any]  =    '<mask_1> To ensure a <mask_2> flow of bank resolutions.'
      _lowerCAmelCase       :   str  =    [2, 413, 615, 114, 3, 1971, 113, 1679, 1_0710, 107, 1]
      _lowerCAmelCase       :   Union[str, Any]  =    tokenizer([raw_input_str]    ,return_tensors=_A      ).input_ids[0]
      self.assertListEqual(_A    ,_A      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Tuple  =    self._large_tokenizer
      # The tracebacks for the following asserts are **better** without messages or self.assertEqual
      assert tokenizer.vocab_size == 9_6103
      assert tokenizer.pad_token_id == 0
      assert tokenizer.eos_token_id == 1
      assert tokenizer.offset == 103
      assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
      assert tokenizer.unk_token == "<unk>"
      assert tokenizer.model_max_length == 1024
      _lowerCAmelCase       :   Dict  =    'To ensure a smooth flow of bank resolutions.'
      _lowerCAmelCase       :   Dict  =    [413, 615, 114, 2291, 1971, 113, 1679, 1_0710, 107, 1]
      _lowerCAmelCase       :   str  =    tokenizer([raw_input_str]    ,return_tensors=_A      ).input_ids[0]
      self.assertListEqual(_A    ,_A      )
      assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]      ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]

  @require_torch
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[Any]  =    ['This is going to be way too long.' * 150, 'short example']
      _lowerCAmelCase       :   List[Any]  =    ['not super long but more than 5 tokens', 'tiny']
      _lowerCAmelCase       :   List[Any]  =    self._large_tokenizer(_A    ,padding=_A    ,truncation=_A    ,return_tensors='pt'      )
      _lowerCAmelCase       :   str  =    self._large_tokenizer(
          text_target=_A    ,max_length=5    ,padding=_A    ,truncation=_A    ,return_tensors='pt'      )

      assert batch.input_ids.shape == (2, 1024)
      assert batch.attention_mask.shape == (2, 1024)
      assert targets["input_ids"].shape == (2, 5)
      assert len(_A      ) == 2  # input_ids, attention_mask.







  @slow
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Tuple  =    {'input_ids': [[3_8979, 143, 1_8485, 606, 130, 2_6669, 8_7686, 121, 5_4189, 1129, 111, 2_6669, 8_7686, 121, 9114, 1_4787, 121, 1_3249, 158, 592, 956, 121, 1_4621, 3_1576, 143, 6_2613, 108, 9688, 930, 4_3430, 1_1562, 6_2613, 304, 108, 1_1443, 897, 108, 9314, 1_7415, 6_3399, 108, 1_1443, 7614, 1_8316, 118, 4284, 7148, 1_2430, 143, 1400, 2_5703, 158, 111, 4284, 7148, 1_1772, 143, 2_1297, 1064, 158, 122, 204, 3506, 1754, 1133, 1_4787, 1581, 115, 3_3224, 4482, 111, 1355, 110, 2_9173, 317, 5_0833, 108, 2_0147, 9_4665, 111, 7_7198, 107, 1], [110, 6_2613, 117, 638, 112, 1133, 121, 2_0098, 1355, 7_9050, 1_3872, 135, 1596, 5_3541, 1352, 141, 1_3039, 5542, 124, 302, 518, 111, 268, 2956, 115, 149, 4427, 107, 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], [139, 1235, 2799, 1_8289, 1_7780, 204, 109, 9474, 1296, 107, 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]], '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
      # fmt: on

      self.tokenizer_integration_test_util(
          expected_encoding=_A    ,model_name='google/bigbird-pegasus-large-arxiv'    ,revision='ba85d0851d708441f91440d509690f1ab6353415'    ,)







@require_sentencepiece
@require_tokenizers
class 	__UpperCamelCase    (    a__							,							unittest.TestCase						):
  _UpperCAmelCase          =		PegasusTokenizer
  _UpperCAmelCase          =		PegasusTokenizerFast
  _UpperCAmelCase          =		True
  _UpperCAmelCase          =		True

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      super().setUp()

      # We have a SentencePiece fixture for testing
      _lowerCAmelCase       :   Optional[Any]  =    PegasusTokenizer(_A    ,offset=0    ,mask_token_sent=_A    ,mask_token='[MASK]'      )
      tokenizer.save_pretrained(self.tmpdirname      )

  @cached_property
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return PegasusTokenizer.from_pretrained('google/bigbird-pegasus-large-arxiv'      )

  def __lowerCamelCase		(		self    ,**_A      ):
      '''simple docstring'''

      return PegasusTokenizer.from_pretrained(self.tmpdirname    ,**_A      )

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      return ("This is a test", "This is a test")

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   str  =    self.rust_tokenizer_class.from_pretrained(self.tmpdirname      )
      _lowerCAmelCase       :   Optional[Any]  =    self.tokenizer_class.from_pretrained(self.tmpdirname      )
      _lowerCAmelCase       :   List[str]  =    (
          'Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'
          ' <pad> <pad> <pad>'
      )
      _lowerCAmelCase       :   List[str]  =    rust_tokenizer([raw_input_str]    ,return_tensors=_A    ,add_special_tokens=_A      ).input_ids[0]
      _lowerCAmelCase       :   Tuple  =    py_tokenizer([raw_input_str]    ,return_tensors=_A    ,add_special_tokens=_A      ).input_ids[0]
      self.assertListEqual(_A    ,_A      )

  @require_torch
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[Any]  =    ['This is going to be way too long.' * 1000, 'short example']
      _lowerCAmelCase       :   Dict  =    ['not super long but more than 5 tokens', 'tiny']
      _lowerCAmelCase       :   List[Any]  =    self._large_tokenizer(_A    ,padding=_A    ,truncation=_A    ,return_tensors='pt'      )
      _lowerCAmelCase       :   int  =    self._large_tokenizer(
          text_target=_A    ,max_length=5    ,padding=_A    ,truncation=_A    ,return_tensors='pt'      )

      assert batch.input_ids.shape == (2, 4096)
      assert batch.attention_mask.shape == (2, 4096)
      assert targets["input_ids"].shape == (2, 5)
      assert len(_A      ) == 2  # input_ids, attention_mask.







  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Tuple  =    (
          'This is an example string that is used to test the original TF implementation against the HF'
          ' implementation'
      )

      _lowerCAmelCase       :   int  =    self._large_tokenizer(_A      ).input_ids

      self.assertListEqual(
          _A    ,[182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 2_5016, 3137, 464, 109, 2_6955, 3137, 1]    ,)








"""simple docstring"""






import baseaa

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    return baseaa.aaaencode(string.encode('utf-8'   )   )

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    return baseaa.aaadecode(_lowerCamelCase   ).decode('utf-8'   )


if __name__ == "__main__":
 import doctest

 doctest.testmod()





