





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


_UpperCAmelCase        =				get_tests_dir('fixtures/test_sentencepiece_no_bos.model')



@require_sentencepiece
@require_tokenizers
class 							_UpperCamelCase					(	lowerCAmelCase_  ,						unittest.TestCase     ):
       _UpperCamelCase				:					Tuple         =   PegasusTokenizer
       _UpperCamelCase				:					List[Any]         =   PegasusTokenizerFast
       _UpperCamelCase				:					List[str]         =   True
       _UpperCamelCase				:					List[str]         =   True

       def lowercase (		self: Optional[int]		)       -> List[str]:




              """simple docstring"""


              super().setUp()

              # We have a SentencePiece fixture for testing
              UpperCamelCase_          =  PegasusTokenizer(_SCREAMING_SNAKE_CASE		)
              tokenizer.save_pretrained(self.tmpdirname		)

       @cached_property
       def lowercase (		self: Any		)       -> Union[str, Any]:




              """simple docstring"""


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

       def lowercase (		self: Tuple					,   **_SCREAMING_SNAKE_CASE: Dict		)       -> PegasusTokenizer:




              """simple docstring"""


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

       def lowercase (		self: str					,   _SCREAMING_SNAKE_CASE: Dict		)       -> str:




              """simple docstring"""


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

       def lowercase (		self: Any		)       -> str:




              """simple docstring"""


              UpperCamelCase_          =  "</s>"
              UpperCamelCase_          =  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 lowercase (		self: Dict		)       -> List[Any]:




              """simple docstring"""


              UpperCamelCase_          =  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(_SCREAMING_SNAKE_CASE		)					,   1103		)

       def lowercase (		self: int		)       -> Optional[int]:




              """simple docstring"""


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

       def lowercase (		self: List[Any]		)       -> Optional[int]:




              """simple docstring"""


              UpperCamelCase_          =  self.rust_tokenizer_class.from_pretrained(self.tmpdirname		)
              UpperCamelCase_          =  self.tokenizer_class.from_pretrained(self.tmpdirname		)
              UpperCamelCase_          =  (
                  "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>"
              )
              UpperCamelCase_          =  rust_tokenizer([raw_input_str]					,   return_tensors=_SCREAMING_SNAKE_CASE					,   add_special_tokens=_SCREAMING_SNAKE_CASE		).input_ids[0]
              UpperCamelCase_          =  py_tokenizer([raw_input_str]					,   return_tensors=_SCREAMING_SNAKE_CASE					,   add_special_tokens=_SCREAMING_SNAKE_CASE		).input_ids[0]
              self.assertListEqual(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)

       def lowercase (		self: int		)       -> List[Any]:




              """simple docstring"""


              UpperCamelCase_          =  self._large_tokenizer
              # <mask_1> masks whole sentence while <mask_2> masks single word
              UpperCamelCase_          =  "<mask_1> To ensure a <mask_2> flow of bank resolutions."
              UpperCamelCase_          =  [2, 413, 615, 114, 3, 1971, 113, 1679, 10710, 107, 1]
              UpperCamelCase_          =  tokenizer([raw_input_str]					,   return_tensors=_SCREAMING_SNAKE_CASE		).input_ids[0]
              self.assertListEqual(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)

       def lowercase (		self: Optional[int]		)       -> Any:




              """simple docstring"""


              UpperCamelCase_          =  self._large_tokenizer
              # The tracebacks for the following asserts are **better** without messages or self.assertEqual
              assert tokenizer.vocab_size == 96103
              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
              UpperCamelCase_          =  "To ensure a smooth flow of bank resolutions."
              UpperCamelCase_          =  [413, 615, 114, 2291, 1971, 113, 1679, 10710, 107, 1]
              UpperCamelCase_          =  tokenizer([raw_input_str]					,   return_tensors=_SCREAMING_SNAKE_CASE		).input_ids[0]
              self.assertListEqual(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)
              assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]		) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]

       @require_torch
       def lowercase (		self: int		)       -> Union[str, Any]:




              """simple docstring"""


              UpperCamelCase_          =  ["This is going to be way too long." * 150, "short example"]
              UpperCamelCase_          =  ["not super long but more than 5 tokens", "tiny"]
              UpperCamelCase_          =  self._large_tokenizer(_SCREAMING_SNAKE_CASE					,   padding=_SCREAMING_SNAKE_CASE					,   truncation=_SCREAMING_SNAKE_CASE					,   return_tensors="pt"		)
              UpperCamelCase_          =  self._large_tokenizer(
                  text_target=_SCREAMING_SNAKE_CASE					,   max_length=5					,   padding=_SCREAMING_SNAKE_CASE					,   truncation=_SCREAMING_SNAKE_CASE					,   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(_SCREAMING_SNAKE_CASE		) == 2  # input_ids, attention_mask.



       @slow
       def lowercase (		self: int		)       -> Optional[int]:




              """simple docstring"""


              UpperCamelCase_          =  {"input_ids": [[38979, 143, 18485, 606, 130, 26669, 87686, 121, 54189, 1129, 111, 26669, 87686, 121, 9114, 14787, 121, 13249, 158, 592, 956, 121, 14621, 31576, 143, 62613, 108, 9688, 930, 43430, 11562, 62613, 304, 108, 11443, 897, 108, 9314, 17415, 63399, 108, 11443, 7614, 18316, 118, 4284, 7148, 12430, 143, 1400, 25703, 158, 111, 4284, 7148, 11772, 143, 21297, 1064, 158, 122, 204, 3506, 1754, 1133, 14787, 1581, 115, 33224, 4482, 111, 1355, 110, 29173, 317, 50833, 108, 20147, 94665, 111, 77198, 107, 1], [110, 62613, 117, 638, 112, 1133, 121, 20098, 1355, 79050, 13872, 135, 1596, 53541, 1352, 141, 13039, 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, 18289, 17780, 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=_SCREAMING_SNAKE_CASE					,   model_name="google/bigbird-pegasus-large-arxiv"					,   revision="ba85d0851d708441f91440d509690f1ab6353415"					,   )



@require_sentencepiece
@require_tokenizers
class 							_UpperCamelCase					(	lowerCAmelCase_  ,						unittest.TestCase     ):
       _UpperCamelCase				:					Dict         =   PegasusTokenizer
       _UpperCamelCase				:					Dict         =   PegasusTokenizerFast
       _UpperCamelCase				:					Optional[int]         =   True
       _UpperCamelCase				:					Tuple         =   True

       def lowercase (		self: Optional[Any]		)       -> Any:




              """simple docstring"""


              super().setUp()

              # We have a SentencePiece fixture for testing
              UpperCamelCase_          =  PegasusTokenizer(_SCREAMING_SNAKE_CASE					,   offset=0					,   mask_token_sent=_SCREAMING_SNAKE_CASE					,   mask_token="[MASK]"		)
              tokenizer.save_pretrained(self.tmpdirname		)

       @cached_property
       def lowercase (		self: Optional[int]		)       -> int:




              """simple docstring"""


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

       def lowercase (		self: List[str]					,   **_SCREAMING_SNAKE_CASE: Dict		)       -> PegasusTokenizer:




              """simple docstring"""


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

       def lowercase (		self: Optional[Any]					,   _SCREAMING_SNAKE_CASE: Any		)       -> Dict:




              """simple docstring"""


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

       def lowercase (		self: List[Any]		)       -> str:




              """simple docstring"""


              UpperCamelCase_          =  self.rust_tokenizer_class.from_pretrained(self.tmpdirname		)
              UpperCamelCase_          =  self.tokenizer_class.from_pretrained(self.tmpdirname		)
              UpperCamelCase_          =  (
                  "Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"
                  " <pad> <pad> <pad>"
              )
              UpperCamelCase_          =  rust_tokenizer([raw_input_str]					,   return_tensors=_SCREAMING_SNAKE_CASE					,   add_special_tokens=_SCREAMING_SNAKE_CASE		).input_ids[0]
              UpperCamelCase_          =  py_tokenizer([raw_input_str]					,   return_tensors=_SCREAMING_SNAKE_CASE					,   add_special_tokens=_SCREAMING_SNAKE_CASE		).input_ids[0]
              self.assertListEqual(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)

       @require_torch
       def lowercase (		self: Dict		)       -> List[Any]:




              """simple docstring"""


              UpperCamelCase_          =  ["This is going to be way too long." * 1000, "short example"]
              UpperCamelCase_          =  ["not super long but more than 5 tokens", "tiny"]
              UpperCamelCase_          =  self._large_tokenizer(_SCREAMING_SNAKE_CASE					,   padding=_SCREAMING_SNAKE_CASE					,   truncation=_SCREAMING_SNAKE_CASE					,   return_tensors="pt"		)
              UpperCamelCase_          =  self._large_tokenizer(
                  text_target=_SCREAMING_SNAKE_CASE					,   max_length=5					,   padding=_SCREAMING_SNAKE_CASE					,   truncation=_SCREAMING_SNAKE_CASE					,   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(_SCREAMING_SNAKE_CASE		) == 2  # input_ids, attention_mask.



       def lowercase (		self: Union[str, Any]		)       -> Optional[int]:




              """simple docstring"""


              UpperCamelCase_          =  (
                  "This is an example string that is used to test the original TF implementation against the HF"
                  " implementation"
              )

              UpperCamelCase_          =  self._large_tokenizer(_SCREAMING_SNAKE_CASE		).input_ids

              self.assertListEqual(
                  _SCREAMING_SNAKE_CASE					,   [182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 25016, 3137, 464, 109, 26955, 3137, 1]					,   )








_UpperCAmelCase        =				{
    'Pillow': 'Pillow',
    'accelerate': 'accelerate>=0.11.0',
    'compel': 'compel==0.1.8',
    'black': 'black~=23.1',
    'datasets': 'datasets',
    'filelock': 'filelock',
    'flax': 'flax>=0.4.1',
    'hf-doc-builder': 'hf-doc-builder>=0.3.0',
    'huggingface-hub': 'huggingface-hub>=0.13.2',
    'requests-mock': 'requests-mock==1.10.0',
    'importlib_metadata': 'importlib_metadata',
    'invisible-watermark': 'invisible-watermark',
    'isort': 'isort>=5.5.4',
    'jax': 'jax>=0.2.8,!=0.3.2',
    'jaxlib': 'jaxlib>=0.1.65',
    'Jinja2': 'Jinja2',
    'k-diffusion': 'k-diffusion>=0.0.12',
    'torchsde': 'torchsde',
    'note_seq': 'note_seq',
    'librosa': 'librosa',
    'numpy': 'numpy',
    'omegaconf': 'omegaconf',
    'parameterized': 'parameterized',
    'protobuf': 'protobuf>=3.20.3,<4',
    'pytest': 'pytest',
    'pytest-timeout': 'pytest-timeout',
    'pytest-xdist': 'pytest-xdist',
    'ruff': 'ruff>=0.0.241',
    'safetensors': 'safetensors',
    'sentencepiece': 'sentencepiece>=0.1.91,!=0.1.92',
    'scipy': 'scipy',
    'onnx': 'onnx',
    'regex': 'regex!=2019.12.17',
    'requests': 'requests',
    'tensorboard': 'tensorboard',
    'torch': 'torch>=1.4',
    'torchvision': 'torchvision',
    'transformers': 'transformers>=4.25.1',
    'urllib3': 'urllib3<=2.0.0',
}

