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       : Optional[Any]      					=		get_tests_dir('''fixtures/test_sentencepiece_no_bos.model''')


@require_sentencepiece
@require_tokenizers
class     lowercase     (  _a					,							unittest.TestCase  ):
							lowercase__    :							List[str]										=				PegasusTokenizer
							lowercase__    :							Optional[Any]										=				PegasusTokenizerFast
							lowercase__    :							int										=				True
							lowercase__    :							Dict										=				True




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



												'''simple docstring'''

												super().setUp()

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




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



												'''simple docstring'''

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




							def 						__snake_case(    self :       Dict						,				**_UpperCamelCase :       Optional[int]   )       ->					PegasusTokenizer:



												'''simple docstring'''

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




							def 						__snake_case(    self :       Optional[int]						,				_UpperCamelCase :       int   )       ->					List[Any]:



												'''simple docstring'''

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




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							"</s>"
												SCREAMING_SNAKE_CASE           =							1

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




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							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(lowerCAmelCase_   )						,				1_103   )




							def 						__snake_case(    self :       int   )       ->					Tuple:



												'''simple docstring'''

												self.assertEqual(self.get_tokenizer().vocab_size						,				1_103   )




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



												'''simple docstring'''

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




							def 						__snake_case(    self :       Dict   )       ->					List[str]:



												'''simple docstring'''

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




							def 						__snake_case(    self :       str   )       ->					Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							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
												SCREAMING_SNAKE_CASE           =							"To ensure a smooth flow of bank resolutions."
												SCREAMING_SNAKE_CASE           =							[413, 615, 114, 2_291, 1_971, 113, 1_679, 10_710, 107, 1]
												SCREAMING_SNAKE_CASE           =							tokenizer([raw_input_str]						,				return_tensors=lowerCAmelCase_   ).input_ids[0]
												self.assertListEqual(lowerCAmelCase_						,				lowerCAmelCase_   )
												assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]   ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]




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



												'''simple docstring'''

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





							@slow
							def 						__snake_case(    self :       Optional[Any]   )       ->					List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							{"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=lowerCAmelCase_						,				model_name="google/bigbird-pegasus-large-arxiv"						,				revision="ba85d0851d708441f91440d509690f1ab6353415"						,				)



@require_sentencepiece
@require_tokenizers
class     lowercase     (  _a					,							unittest.TestCase  ):
							lowercase__    :							Tuple										=				PegasusTokenizer
							lowercase__    :							Dict										=				PegasusTokenizerFast
							lowercase__    :							Tuple										=				True
							lowercase__    :							Tuple										=				True




							def 						__snake_case(    self :       Any   )       ->					str:



												'''simple docstring'''

												super().setUp()

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




							@cached_property
							def 						__snake_case(    self :       Tuple   )       ->					str:



												'''simple docstring'''

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




							def 						__snake_case(    self :       Tuple						,				**_UpperCamelCase :       List[str]   )       ->					PegasusTokenizer:



												'''simple docstring'''

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




							def 						__snake_case(    self :       Optional[Any]						,				_UpperCamelCase :       Tuple   )       ->					Dict:



												'''simple docstring'''

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




							def 						__snake_case(    self :       str   )       ->					List[str]:



												'''simple docstring'''

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




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							["This is going to be way too long." * 1_000, "short example"]
												SCREAMING_SNAKE_CASE           =							["not super long but more than 5 tokens", "tiny"]
												SCREAMING_SNAKE_CASE           =							self._large_tokenizer(lowerCAmelCase_						,				padding=lowerCAmelCase_						,				truncation=lowerCAmelCase_						,				return_tensors="pt"   )
												SCREAMING_SNAKE_CASE           =							self._large_tokenizer(
												    text_target=lowerCAmelCase_						,				max_length=5						,				padding=lowerCAmelCase_						,				truncation=lowerCAmelCase_						,				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(lowerCAmelCase_   ) == 2  # input_ids, attention_mask.





							def 						__snake_case(    self :       int   )       ->					int:



												'''simple docstring'''

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

												SCREAMING_SNAKE_CASE           =							self._large_tokenizer(lowerCAmelCase_   ).input_ids

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










'''simple docstring'''


from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass

@dataclass
class      A       :
							lowercase_							     =							42
							lowercase_							     =							42



class      A       :







							def __init__(    self			:    Optional[Any]				,     lowerCAmelCase_			:    int   )     ->				str:


														"""simple docstring"""


														_a      	=							[[] for _ in range(lowerCAmelCase_   )]
														_a      	=							size







							def __getitem__(    self			:    Any				,     lowerCAmelCase_			:    int   )     ->				Iterator[Edge]:


														"""simple docstring"""


														return iter(self._graph[vertex]   )







							@property
							def   __lowerCAmelCase     (    self			:    str   )     ->				Tuple:


														"""simple docstring"""


														return self._size







							def   __lowerCAmelCase     (    self			:    Union[str, Any]				,     lowerCAmelCase_			:    int				,     lowerCAmelCase_			:    int				,     lowerCAmelCase_			:    int   )     ->				Dict:


														"""simple docstring"""


														if weight not in (0, 1):
																					raise ValueError('''Edge weight must be either 0 or 1.'''   )

														if to_vertex < 0 or to_vertex >= self.size:
																					raise ValueError('''Vertex indexes must be in [0; size).'''   )

														self._graph[from_vertex].append(Edge(lowerCAmelCase_				,     lowerCAmelCase_   )   )







							def   __lowerCAmelCase     (    self			:    Tuple				,     lowerCAmelCase_			:    int				,     lowerCAmelCase_			:    int   )     ->				int | None:


														"""simple docstring"""


														_a      	=							deque([start_vertex]   )
														_a      	=							[None] * self.size
														_a      	=							0

														while queue:
																					_a      	=							queue.popleft()
																					_a      	=							distances[current_vertex]
																					if current_distance is None:
																												continue

																					for edge in self[current_vertex]:
																												_a      	=							current_distance + edge.weight
																												_a      	=							distances[edge.destination_vertex]
																												if (
																												    isinstance(lowerCAmelCase_				,     lowerCAmelCase_   )
																												    and new_distance >= dest_vertex_distance
																												):
																																			continue
																												_a      	=							new_distance
																												if edge.weight == 0:
																																			queue.appendleft(edge.destination_vertex   )
																												else:
																																			queue.append(edge.destination_vertex   )

														if distances[finish_vertex] is None:
																					raise ValueError('''No path from start_vertex to finish_vertex.'''   )

														return distances[finish_vertex]


if __name__ == "__main__":
				import doctest

				doctest.testmod()

