
"""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


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




@require_sentencepiece
@require_tokenizers
class 			_SCREAMING_SNAKE_CASE					(			A__	, unittest.TestCase							):
			UpperCAmelCase_  :Tuple				 =						PegasusTokenizer
			UpperCAmelCase_  :List[Any]				 =						PegasusTokenizerFast
			UpperCAmelCase_  :Optional[int]				 =						True
			UpperCAmelCase_  :Union[str, Any]				 =						True






			def  __lowerCAmelCase (					self       )			->     Tuple:
						super().setUp()

						# We have a SentencePiece fixture for testing
						lowerCAmelCase_    :Optional[int]			     =  PegasusTokenizer(__A       )
						tokenizer.save_pretrained(self.tmpdirname       )






			@cached_property
			def  __lowerCAmelCase (					self       )			->     int:
						return PegasusTokenizer.from_pretrained("""google/pegasus-large"""       )






			def  __lowerCAmelCase (					self       ,   **__A       )			->     PegasusTokenizer:
						return PegasusTokenizer.from_pretrained(self.tmpdirname       ,   **__A       )






			def  __lowerCAmelCase (					self       ,   __A       )			->     List[Any]:
						return ("This is a test", "This is a test")






			def  __lowerCAmelCase (					self       )			->     int:
						lowerCAmelCase_    :Any			     =  """</s>"""
						lowerCAmelCase_    :Optional[int]			     =  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       )			->     int:
						lowerCAmelCase_    :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       )			->     Optional[Any]:
						self.assertEqual(self.get_tokenizer().vocab_size       ,   1103       )






			def  __lowerCAmelCase (					self       )			->     Optional[Any]:
						lowerCAmelCase_    :Dict			     =  self.rust_tokenizer_class.from_pretrained(self.tmpdirname       )
						lowerCAmelCase_    :Optional[Any]			     =  self.tokenizer_class.from_pretrained(self.tmpdirname       )
						lowerCAmelCase_    :Optional[Any]			     =  (
						    """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_    :List[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       )			->     Optional[int]:
						lowerCAmelCase_    :Dict			     =  self._large_tokenizer
						# <mask_1> masks whole sentence while <mask_2> masks single word
						lowerCAmelCase_    :int			     =  """<mask_1> To ensure a <mask_2> flow of bank resolutions."""
						lowerCAmelCase_    :Any			     =  [2, 413, 615, 114, 3, 1971, 113, 1679, 1_0710, 107, 1]
						lowerCAmelCase_    :Dict			     =  tokenizer([raw_input_str]       ,   return_tensors=__A       ).input_ids[0]
						self.assertListEqual(__A       ,   __A       )






			def  __lowerCAmelCase (					self       )			->     str:
						lowerCAmelCase_    :int			     =  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_    :List[str]			     =  [413, 615, 114, 2291, 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       )
						assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]       ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]






			@require_torch
			def  __lowerCAmelCase (					self       )			->     Tuple:
						lowerCAmelCase_    :Dict			     =  ["""This is going to be way too long.""" * 150, """short example"""]
						lowerCAmelCase_    :int			     =  ["""not super long but more than 5 tokens""", """tiny"""]
						lowerCAmelCase_    :str			     =  self._large_tokenizer(__A       ,   padding=__A       ,   truncation=__A       ,   return_tensors="""pt"""       )
						lowerCAmelCase_    :Dict			     =  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       )			->     List[Any]:
						# fmt: off
						lowerCAmelCase_    :Dict			     =  {"""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 			_SCREAMING_SNAKE_CASE					(			A__	, unittest.TestCase							):
			UpperCAmelCase_  :Optional[Any]				 =						PegasusTokenizer
			UpperCAmelCase_  :Any				 =						PegasusTokenizerFast
			UpperCAmelCase_  :Optional[Any]				 =						True
			UpperCAmelCase_  :List[Any]				 =						True






			def  __lowerCAmelCase (					self       )			->     List[str]:
						super().setUp()

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






			@cached_property
			def  __lowerCAmelCase (					self       )			->     int:
						return PegasusTokenizer.from_pretrained("""google/bigbird-pegasus-large-arxiv"""       )






			def  __lowerCAmelCase (					self       ,   **__A       )			->     PegasusTokenizer:
						return PegasusTokenizer.from_pretrained(self.tmpdirname       ,   **__A       )






			def  __lowerCAmelCase (					self       ,   __A       )			->     Tuple:
						return ("This is a test", "This is a test")






			def  __lowerCAmelCase (					self       )			->     Union[str, Any]:
						lowerCAmelCase_    :Any			     =  self.rust_tokenizer_class.from_pretrained(self.tmpdirname       )
						lowerCAmelCase_    :Optional[Any]			     =  self.tokenizer_class.from_pretrained(self.tmpdirname       )
						lowerCAmelCase_    :int			     =  (
						    """Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"""
						    """ <pad> <pad> <pad>"""
						)
						lowerCAmelCase_    :int			     =  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       )






			@require_torch
			def  __lowerCAmelCase (					self       )			->     Optional[Any]:
						lowerCAmelCase_    :Optional[Any]			     =  ["""This is going to be way too long.""" * 1000, """short example"""]
						lowerCAmelCase_    :List[str]			     =  ["""not super long but more than 5 tokens""", """tiny"""]
						lowerCAmelCase_    :Any			     =  self._large_tokenizer(__A       ,   padding=__A       ,   truncation=__A       ,   return_tensors="""pt"""       )
						lowerCAmelCase_    :Any			     =  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       )			->     List[str]:
						lowerCAmelCase_    :Dict			     =  (
						    """This is an example string that is used to test the original TF implementation against the HF"""
						    """ implementation"""
						)

						lowerCAmelCase_    :Any			     =  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"""



__A		:       Any  				=		{
    '''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''',
}



