






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							:   int							=  get_tests_dir("fixtures/test_sentencepiece_no_bos.model")


@require_sentencepiece
@require_tokenizers
class 					_snake_case						(			_A       ,    unittest.TestCase ):
	_A          =   PegasusTokenizer
	_A          =   PegasusTokenizerFast
	_A          =   True
	_A          =   True






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

					# We have a SentencePiece fixture for testing
					snake_case__      :List[Any]         =   PegasusTokenizer(UpperCamelCase       )
					tokenizer.save_pretrained(self.tmpdirname       )






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






	def    lowerCAmelCase_							(		self					,**UpperCamelCase       )						->    PegasusTokenizer:
					return PegasusTokenizer.from_pretrained(self.tmpdirname					,**UpperCamelCase       )






	def    lowerCAmelCase_							(		self					,UpperCamelCase       )						->    Optional[Any]:
					return ("This is a test", "This is a test")






	def    lowerCAmelCase_							(		self       )						->    int:
					snake_case__      :int         =   "</s>"
					snake_case__      :str         =   1

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






	def    lowerCAmelCase_							(		self       )						->    List[str]:
					snake_case__      :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(UpperCamelCase       )					,1_103       )






	def    lowerCAmelCase_							(		self       )						->    Optional[Any]:
					self.assertEqual(self.get_tokenizer().vocab_size					,1_103       )






	def    lowerCAmelCase_							(		self       )						->    Any:
					snake_case__      :List[Any]         =   self.rust_tokenizer_class.from_pretrained(self.tmpdirname       )
					snake_case__      :Dict         =   self.tokenizer_class.from_pretrained(self.tmpdirname       )
					snake_case__      :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>"
					)
					snake_case__      :List[str]         =   rust_tokenizer([raw_input_str]					,return_tensors=UpperCamelCase					,add_special_tokens=UpperCamelCase       ).input_ids[0]
					snake_case__      :Tuple         =   py_tokenizer([raw_input_str]					,return_tensors=UpperCamelCase					,add_special_tokens=UpperCamelCase       ).input_ids[0]
					self.assertListEqual(UpperCamelCase					,UpperCamelCase       )






	def    lowerCAmelCase_							(		self       )						->    Optional[Any]:
					snake_case__      :Optional[int]         =   self._large_tokenizer
					# <mask_1> masks whole sentence while <mask_2> masks single word
					snake_case__      :Optional[int]         =   "<mask_1> To ensure a <mask_2> flow of bank resolutions."
					snake_case__      :Optional[Any]         =   [2, 413, 615, 114, 3, 1_971, 113, 1_679, 10_710, 107, 1]
					snake_case__      :Optional[Any]         =   tokenizer([raw_input_str]					,return_tensors=UpperCamelCase       ).input_ids[0]
					self.assertListEqual(UpperCamelCase					,UpperCamelCase       )






	def    lowerCAmelCase_							(		self       )						->    Any:
					snake_case__      :Optional[int]         =   self._large_tokenizer
					# The tracebacks for the following asserts are **better** without messages or self.assertEqual
					assert tokenizer.vocab_size == 96_103
					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 == 1_024
					snake_case__      :List[Any]         =   "To ensure a smooth flow of bank resolutions."
					snake_case__      :int         =   [413, 615, 114, 2_291, 1_971, 113, 1_679, 10_710, 107, 1]
					snake_case__      :Optional[Any]         =   tokenizer([raw_input_str]					,return_tensors=UpperCamelCase       ).input_ids[0]
					self.assertListEqual(UpperCamelCase					,UpperCamelCase       )
					assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]       ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]






	@require_torch
	def    lowerCAmelCase_							(		self       )						->    List[str]:
					snake_case__      :List[str]         =   ["This is going to be way too long." * 150, "short example"]
					snake_case__      :Dict         =   ["not super long but more than 5 tokens", "tiny"]
					snake_case__      :Tuple         =   self._large_tokenizer(UpperCamelCase					,padding=UpperCamelCase					,truncation=UpperCamelCase					,return_tensors="pt"       )
					snake_case__      :Any         =   self._large_tokenizer(
					    text_target=UpperCamelCase					,max_length=5					,padding=UpperCamelCase					,truncation=UpperCamelCase					,return_tensors="pt"       )

					assert batch.input_ids.shape == (2, 1_024)
					assert batch.attention_mask.shape == (2, 1_024)
					assert targets["input_ids"].shape == (2, 5)
					assert len(UpperCamelCase       ) == 2  # input_ids, attention_mask.






	@slow
	def    lowerCAmelCase_							(		self       )						->    str:
					# fmt: off
					snake_case__      :Union[str, Any]         =   {"input_ids": [[38_979, 143, 18_485, 606, 130, 26_669, 87_686, 121, 54_189, 1_129, 111, 26_669, 87_686, 121, 9_114, 14_787, 121, 13_249, 158, 592, 956, 121, 14_621, 31_576, 143, 62_613, 108, 9_688, 930, 43_430, 11_562, 62_613, 304, 108, 11_443, 897, 108, 9_314, 17_415, 63_399, 108, 11_443, 7_614, 18_316, 118, 4_284, 7_148, 12_430, 143, 1_400, 25_703, 158, 111, 4_284, 7_148, 11_772, 143, 21_297, 1_064, 158, 122, 204, 3_506, 1_754, 1_133, 14_787, 1_581, 115, 33_224, 4_482, 111, 1_355, 110, 29_173, 317, 50_833, 108, 20_147, 94_665, 111, 77_198, 107, 1], [110, 62_613, 117, 638, 112, 1_133, 121, 20_098, 1_355, 79_050, 13_872, 135, 1_596, 53_541, 1_352, 141, 13_039, 5_542, 124, 302, 518, 111, 268, 2_956, 115, 149, 4_427, 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, 1_235, 2_799, 18_289, 17_780, 204, 109, 9_474, 1_296, 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=UpperCamelCase					,model_name="google/bigbird-pegasus-large-arxiv"					,revision="ba85d0851d708441f91440d509690f1ab6353415"					,)




@require_sentencepiece
@require_tokenizers
class 					_snake_case						(			_A       ,    unittest.TestCase ):
	_A          =   PegasusTokenizer
	_A          =   PegasusTokenizerFast
	_A          =   True
	_A          =   True






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

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






	@cached_property
	def    lowerCAmelCase_							(		self       )						->    Union[str, Any]:
					return PegasusTokenizer.from_pretrained("google/bigbird-pegasus-large-arxiv"       )






	def    lowerCAmelCase_							(		self					,**UpperCamelCase       )						->    PegasusTokenizer:
					return PegasusTokenizer.from_pretrained(self.tmpdirname					,**UpperCamelCase       )






	def    lowerCAmelCase_							(		self					,UpperCamelCase       )						->    str:
					return ("This is a test", "This is a test")






	def    lowerCAmelCase_							(		self       )						->    Any:
					snake_case__      :Optional[Any]         =   self.rust_tokenizer_class.from_pretrained(self.tmpdirname       )
					snake_case__      :int         =   self.tokenizer_class.from_pretrained(self.tmpdirname       )
					snake_case__      :Optional[int]         =   (
					    "Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"
					    " <pad> <pad> <pad>"
					)
					snake_case__      :str         =   rust_tokenizer([raw_input_str]					,return_tensors=UpperCamelCase					,add_special_tokens=UpperCamelCase       ).input_ids[0]
					snake_case__      :int         =   py_tokenizer([raw_input_str]					,return_tensors=UpperCamelCase					,add_special_tokens=UpperCamelCase       ).input_ids[0]
					self.assertListEqual(UpperCamelCase					,UpperCamelCase       )






	@require_torch
	def    lowerCAmelCase_							(		self       )						->    Dict:
					snake_case__      :List[str]         =   ["This is going to be way too long." * 1_000, "short example"]
					snake_case__      :List[str]         =   ["not super long but more than 5 tokens", "tiny"]
					snake_case__      :Dict         =   self._large_tokenizer(UpperCamelCase					,padding=UpperCamelCase					,truncation=UpperCamelCase					,return_tensors="pt"       )
					snake_case__      :Dict         =   self._large_tokenizer(
					    text_target=UpperCamelCase					,max_length=5					,padding=UpperCamelCase					,truncation=UpperCamelCase					,return_tensors="pt"       )

					assert batch.input_ids.shape == (2, 4_096)
					assert batch.attention_mask.shape == (2, 4_096)
					assert targets["input_ids"].shape == (2, 5)
					assert len(UpperCamelCase       ) == 2  # input_ids, attention_mask.






	def    lowerCAmelCase_							(		self       )						->    int:
					snake_case__      :Optional[int]         =   (
					    "This is an example string that is used to test the original TF implementation against the HF"
					    " implementation"
					)

					snake_case__      :Union[str, Any]         =   self._large_tokenizer(UpperCamelCase       ).input_ids

					self.assertListEqual(
					    UpperCamelCase					,[182, 117, 142, 587, 4_211, 120, 117, 263, 112, 804, 109, 856, 25_016, 3_137, 464, 109, 26_955, 3_137, 1]					,)







def 					lowercase_ (    __snake_case     :							list      )     -> list:





				'''simple docstring'''


				if any(not isinstance(__snake_case	,	__snake_case      ) or x < 0 for x in sequence      ):
								raise TypeError("Sequence must be list of non-negative integers"      )
				for _ in range(len(__snake_case      )      ):
								for i, (rod_upper, rod_lower) in enumerate(zip(__snake_case	,	sequence[1:]      )      ):
												if rod_upper > rod_lower:
																sequence[i] -= rod_upper - rod_lower
																sequence[i + 1] += rod_upper - rod_lower
				return sequence


if __name__ == "__main__":
	assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
	assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]