


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  _a							(    _lowercase					,						unittest.TestCase):
	_a  :  Optional[int]				   =							PegasusTokenizer
	_a  :  Any				   =							PegasusTokenizerFast
	_a  :  Optional[Any]				   =							True
	_a  :  Optional[Any]				   =							True




	def 							UpperCAmelCase__( self						:		List[Any]		)->	Optional[Any]:
			super().setUp()

			# We have a SentencePiece fixture for testing
			lowerCAmelCase__       :      Union[str, Any]   					= PegasusTokenizer(_SCREAMING_SNAKE_CASE		)
			tokenizer.save_pretrained(self.tmpdirname		)




	@cached_property
	def 							UpperCAmelCase__( self						:		int		)->	List[Any]:
			return PegasusTokenizer.from_pretrained('''google/pegasus-large'''		)




	def 							UpperCAmelCase__( self						:		List[Any]		, **_SCREAMING_SNAKE_CASE						:		List[str]		)->	PegasusTokenizer:
			return PegasusTokenizer.from_pretrained(self.tmpdirname		, **_SCREAMING_SNAKE_CASE		)




	def 							UpperCAmelCase__( self						:		List[str]		, _SCREAMING_SNAKE_CASE						:		Any		)->	str:
			return ("This is a test", "This is a test")




	def 							UpperCAmelCase__( self						:		Tuple		)->	Union[str, Any]:
			lowerCAmelCase__       :      List[Any]   					= '''</s>'''
			lowerCAmelCase__       :      Optional[int]   					= 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 							UpperCAmelCase__( self						:		Optional[int]		)->	Optional[int]:
			lowerCAmelCase__       :      Tuple   					= 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 							UpperCAmelCase__( self						:		Tuple		)->	Optional[int]:
			self.assertEqual(self.get_tokenizer().vocab_size		, 1103		)




	def 							UpperCAmelCase__( self						:		Optional[int]		)->	List[str]:
			lowerCAmelCase__       :      Dict   					= self.rust_tokenizer_class.from_pretrained(self.tmpdirname		)
			lowerCAmelCase__       :      Union[str, 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__       :      Any   					= rust_tokenizer([raw_input_str]		, return_tensors=_SCREAMING_SNAKE_CASE		, add_special_tokens=_SCREAMING_SNAKE_CASE		).input_ids[0]
			lowerCAmelCase__       :      Tuple   					= 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 							UpperCAmelCase__( self						:		str		)->	Dict:
			lowerCAmelCase__       :      Optional[int]   					= self._large_tokenizer
			# <mask_1> masks whole sentence while <mask_2> masks single word
			lowerCAmelCase__       :      Any   					= '''<mask_1> To ensure a <mask_2> flow of bank resolutions.'''
			lowerCAmelCase__       :      Optional[int]   					= [2, 413, 615, 114, 3, 1971, 113, 1679, 1_0710, 107, 1]
			lowerCAmelCase__       :      Tuple   					= tokenizer([raw_input_str]		, return_tensors=_SCREAMING_SNAKE_CASE		).input_ids[0]
			self.assertListEqual(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)




	def 							UpperCAmelCase__( self						:		Dict		)->	int:
			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__       :      str   					= '''To ensure a smooth flow of bank resolutions.'''
			lowerCAmelCase__       :      Union[str, Any]   					= [413, 615, 114, 2291, 1971, 113, 1679, 1_0710, 107, 1]
			lowerCAmelCase__       :      List[str]   					= 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 							UpperCAmelCase__( self						:		Any		)->	Tuple:
			lowerCAmelCase__       :      Tuple   					= ['''This is going to be way too long.''' * 150, '''short example''']
			lowerCAmelCase__       :      Tuple   					= ['''not super long but more than 5 tokens''', '''tiny''']
			lowerCAmelCase__       :      Dict   					= self._large_tokenizer(_SCREAMING_SNAKE_CASE		, padding=_SCREAMING_SNAKE_CASE		, truncation=_SCREAMING_SNAKE_CASE		, return_tensors='''pt'''		)
			lowerCAmelCase__       :      List[str]   					= 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 							UpperCAmelCase__( self						:		Any		)->	Any:
			# fmt: off
			lowerCAmelCase__       :      int   					= {'''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=_SCREAMING_SNAKE_CASE		, model_name='''google/bigbird-pegasus-large-arxiv'''		, revision='''ba85d0851d708441f91440d509690f1ab6353415'''		, )





@require_sentencepiece
@require_tokenizers
class  _a							(    _lowercase					,						unittest.TestCase):
	_a  :  int				   =							PegasusTokenizer
	_a  :  List[str]				   =							PegasusTokenizerFast
	_a  :  Union[str, Any]				   =							True
	_a  :  Tuple				   =							True




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

			# We have a SentencePiece fixture for testing
			lowerCAmelCase__       :      List[str]   					= PegasusTokenizer(_SCREAMING_SNAKE_CASE		, offset=0		, mask_token_sent=_SCREAMING_SNAKE_CASE		, mask_token='''[MASK]'''		)
			tokenizer.save_pretrained(self.tmpdirname		)




	@cached_property
	def 							UpperCAmelCase__( self						:		str		)->	Dict:
			return PegasusTokenizer.from_pretrained('''google/bigbird-pegasus-large-arxiv'''		)




	def 							UpperCAmelCase__( self						:		Optional[Any]		, **_SCREAMING_SNAKE_CASE						:		str		)->	PegasusTokenizer:
			return PegasusTokenizer.from_pretrained(self.tmpdirname		, **_SCREAMING_SNAKE_CASE		)




	def 							UpperCAmelCase__( self						:		Dict		, _SCREAMING_SNAKE_CASE						:		Dict		)->	Dict:
			return ("This is a test", "This is a test")




	def 							UpperCAmelCase__( self						:		str		)->	Tuple:
			lowerCAmelCase__       :      Optional[int]   					= self.rust_tokenizer_class.from_pretrained(self.tmpdirname		)
			lowerCAmelCase__       :      Dict   					= self.tokenizer_class.from_pretrained(self.tmpdirname		)
			lowerCAmelCase__       :      Any   					= (
			    '''Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'''
			    ''' <pad> <pad> <pad>'''
			)
			lowerCAmelCase__       :      Dict   					= rust_tokenizer([raw_input_str]		, return_tensors=_SCREAMING_SNAKE_CASE		, add_special_tokens=_SCREAMING_SNAKE_CASE		).input_ids[0]
			lowerCAmelCase__       :      Tuple   					= 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 							UpperCAmelCase__( self						:		List[Any]		)->	int:
			lowerCAmelCase__       :      str   					= ['''This is going to be way too long.''' * 1000, '''short example''']
			lowerCAmelCase__       :      Any   					= ['''not super long but more than 5 tokens''', '''tiny''']
			lowerCAmelCase__       :      Union[str, Any]   					= self._large_tokenizer(_SCREAMING_SNAKE_CASE		, padding=_SCREAMING_SNAKE_CASE		, truncation=_SCREAMING_SNAKE_CASE		, return_tensors='''pt'''		)
			lowerCAmelCase__       :      List[str]   					= 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 							UpperCAmelCase__( self						:		Optional[int]		)->	Union[str, Any]:
			lowerCAmelCase__       :      List[Any]   					= (
			    '''This is an example string that is used to test the original TF implementation against the HF'''
			    ''' implementation'''
			)

			lowerCAmelCase__       :      str   					= 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, 2_5016, 3137, 464, 109, 2_6955, 3137, 1]		, )








def 					lowerCamelCase_   (	_a = 4_000_000	):




		"""simple docstring"""






		lowerCAmelCase__       :      str   					= []
		lowerCAmelCase__					,     lowerCAmelCase__       :      Optional[Any]   					= 0, 1
		while b <= n:
				if b % 2 == 0:
						even_fibs.append(_a	)
				lowerCAmelCase__					,     lowerCAmelCase__       :      Union[str, Any]   					= b, a + b
		return sum(_a	)


if __name__ == "__main__":
		print(f'''{solution() = }''')




