


"""simple docstring"""






import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_lowerCamelCase     =  get_tests_dir('fixtures/test_sentencepiece.model')


@require_sentencepiece
@require_tokenizers
class 			lowerCamelCase_		(			lowercase       ,  unittest.TestCase   ):





				"""simple docstring"""







				_lowerCAmelCase      :      List[Any]      							=					XLMRobertaTokenizer
				_lowerCAmelCase      :      Tuple      							=					XLMRobertaTokenizerFast
				_lowerCAmelCase      :      int      							=					True
				_lowerCAmelCase      :      Tuple      							=					True



				def   lowerCAmelCase__		(						self     ):
								super().setUp()

								# We have a SentencePiece fixture for testing
								SCREAMING_SNAKE_CASE__     							=				XLMRobertaTokenizer(UpperCAmelCase__       ,		keep_accents=UpperCAmelCase__     )
								tokenizer.save_pretrained(self.tmpdirname     )



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				"<pad>"
								SCREAMING_SNAKE_CASE__     							=				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     ):
								SCREAMING_SNAKE_CASE__     							=				list(self.get_tokenizer().get_vocab().keys()     )

								self.assertEqual(vocab_keys[0]       ,		"<s>"     )
								self.assertEqual(vocab_keys[1]       ,		"<pad>"     )
								self.assertEqual(vocab_keys[-1]       ,		"<mask>"     )
								self.assertEqual(len(UpperCAmelCase__     )       ,		1002     )



				def   lowerCAmelCase__		(						self     ):
								self.assertEqual(self.get_tokenizer().vocab_size       ,		1002     )



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				XLMRobertaTokenizer(UpperCAmelCase__       ,		keep_accents=UpperCAmelCase__     )

								SCREAMING_SNAKE_CASE__     							=				tokenizer.tokenize("This is a test"     )
								self.assertListEqual(UpperCAmelCase__       ,		["▁This", "▁is", "▁a", "▁t", "est"]     )

								self.assertListEqual(
								    tokenizer.convert_tokens_to_ids(UpperCAmelCase__     )       ,		[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]       ,		)

								SCREAMING_SNAKE_CASE__     							=				tokenizer.tokenize("I was born in 92000, and this is falsé."     )
								self.assertListEqual(
								    UpperCAmelCase__       ,		[
								        SPIECE_UNDERLINE + "I",
								        SPIECE_UNDERLINE + "was",
								        SPIECE_UNDERLINE + "b",
								        "or",
								        "n",
								        SPIECE_UNDERLINE + "in",
								        SPIECE_UNDERLINE + "",
								        "9",
								        "2",
								        "0",
								        "0",
								        "0",
								        ",",
								        SPIECE_UNDERLINE + "and",
								        SPIECE_UNDERLINE + "this",
								        SPIECE_UNDERLINE + "is",
								        SPIECE_UNDERLINE + "f",
								        "al",
								        "s",
								        "é",
								        ".",
								    ]       ,		)
								SCREAMING_SNAKE_CASE__     							=				tokenizer.convert_tokens_to_ids(UpperCAmelCase__     )
								self.assertListEqual(
								    UpperCAmelCase__       ,		[
								        value + tokenizer.fairseq_offset
								        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
								        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
								    ]       ,		)

								SCREAMING_SNAKE_CASE__     							=				tokenizer.convert_ids_to_tokens(UpperCAmelCase__     )
								self.assertListEqual(
								    UpperCAmelCase__       ,		[
								        SPIECE_UNDERLINE + "I",
								        SPIECE_UNDERLINE + "was",
								        SPIECE_UNDERLINE + "b",
								        "or",
								        "n",
								        SPIECE_UNDERLINE + "in",
								        SPIECE_UNDERLINE + "",
								        "<unk>",
								        "2",
								        "0",
								        "0",
								        "0",
								        ",",
								        SPIECE_UNDERLINE + "and",
								        SPIECE_UNDERLINE + "this",
								        SPIECE_UNDERLINE + "is",
								        SPIECE_UNDERLINE + "f",
								        "al",
								        "s",
								        "<unk>",
								        ".",
								    ]       ,		)



				def   lowerCAmelCase__		(						self     ):
								if not self.test_slow_tokenizer:
												# as we don't have a slow version, we can't compare the outputs between slow and fast versions
												return

								SCREAMING_SNAKE_CASE__     							=				(self.rust_tokenizer_class, "hf-internal-testing/tiny-xlm-roberta", {})
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
												with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''     ):
																SCREAMING_SNAKE_CASE__     							=				self.rust_tokenizer_class.from_pretrained(UpperCAmelCase__       ,		**UpperCAmelCase__     )
																SCREAMING_SNAKE_CASE__     							=				self.tokenizer_class.from_pretrained(UpperCAmelCase__       ,		**UpperCAmelCase__     )

																SCREAMING_SNAKE_CASE__     							=				tempfile.mkdtemp()

																SCREAMING_SNAKE_CASE__     							=				tokenizer_r.save_pretrained(UpperCAmelCase__     )
																SCREAMING_SNAKE_CASE__     							=				tokenizer_p.save_pretrained(UpperCAmelCase__     )

																# Checks it save with the same files + the tokenizer.json file for the fast one
																self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files     )     )
																SCREAMING_SNAKE_CASE__     							=				tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f     )
																self.assertSequenceEqual(UpperCAmelCase__       ,		UpperCAmelCase__     )

																# Checks everything loads correctly in the same way
																SCREAMING_SNAKE_CASE__     							=				tokenizer_r.from_pretrained(UpperCAmelCase__     )
																SCREAMING_SNAKE_CASE__     							=				tokenizer_p.from_pretrained(UpperCAmelCase__     )

																# Check special tokens are set accordingly on Rust and Python
																for key in tokenizer_pp.special_tokens_map:
																				self.assertTrue(hasattr(UpperCAmelCase__       ,		UpperCAmelCase__     )     )
																				# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
																				# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

																shutil.rmtree(UpperCAmelCase__     )

																# Save tokenizer rust, legacy_format=True
																SCREAMING_SNAKE_CASE__     							=				tempfile.mkdtemp()

																SCREAMING_SNAKE_CASE__     							=				tokenizer_r.save_pretrained(UpperCAmelCase__       ,		legacy_format=UpperCAmelCase__     )
																SCREAMING_SNAKE_CASE__     							=				tokenizer_p.save_pretrained(UpperCAmelCase__     )

																# Checks it save with the same files
																self.assertSequenceEqual(UpperCAmelCase__       ,		UpperCAmelCase__     )

																# Checks everything loads correctly in the same way
																SCREAMING_SNAKE_CASE__     							=				tokenizer_r.from_pretrained(UpperCAmelCase__     )
																SCREAMING_SNAKE_CASE__     							=				tokenizer_p.from_pretrained(UpperCAmelCase__     )

																# Check special tokens are set accordingly on Rust and Python
																for key in tokenizer_pp.special_tokens_map:
																				self.assertTrue(hasattr(UpperCAmelCase__       ,		UpperCAmelCase__     )     )

																shutil.rmtree(UpperCAmelCase__     )

																# Save tokenizer rust, legacy_format=False
																SCREAMING_SNAKE_CASE__     							=				tempfile.mkdtemp()

																SCREAMING_SNAKE_CASE__     							=				tokenizer_r.save_pretrained(UpperCAmelCase__       ,		legacy_format=UpperCAmelCase__     )
																SCREAMING_SNAKE_CASE__     							=				tokenizer_p.save_pretrained(UpperCAmelCase__     )

																# Checks it saved the tokenizer.json file
																self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files     )     )

																# Checks everything loads correctly in the same way
																SCREAMING_SNAKE_CASE__     							=				tokenizer_r.from_pretrained(UpperCAmelCase__     )
																SCREAMING_SNAKE_CASE__     							=				tokenizer_p.from_pretrained(UpperCAmelCase__     )

																# Check special tokens are set accordingly on Rust and Python
																for key in tokenizer_pp.special_tokens_map:
																				self.assertTrue(hasattr(UpperCAmelCase__       ,		UpperCAmelCase__     )     )

																shutil.rmtree(UpperCAmelCase__     )



				@cached_property
				def   lowerCAmelCase__		(						self     ):
								return XLMRobertaTokenizer.from_pretrained("xlm-roberta-base"     )



				def   lowerCAmelCase__		(						self     ):
								with tempfile.NamedTemporaryFile() as f:
												shutil.copyfile(UpperCAmelCase__       ,		f.name     )
												SCREAMING_SNAKE_CASE__     							=				XLMRobertaTokenizer(f.name       ,		keep_accents=UpperCAmelCase__     )
												SCREAMING_SNAKE_CASE__     							=				pickle.dumps(UpperCAmelCase__     )
								pickle.loads(UpperCAmelCase__     )



				def   lowerCAmelCase__		(						self     ):
								if not self.test_rust_tokenizer:
												return

								SCREAMING_SNAKE_CASE__     							=				self.get_tokenizer()
								SCREAMING_SNAKE_CASE__     							=				self.get_rust_tokenizer()

								SCREAMING_SNAKE_CASE__     							=				"I was born in 92000, and this is falsé."

								SCREAMING_SNAKE_CASE__     							=				tokenizer.tokenize(UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				rust_tokenizer.tokenize(UpperCAmelCase__     )
								self.assertListEqual(UpperCAmelCase__       ,		UpperCAmelCase__     )

								SCREAMING_SNAKE_CASE__     							=				tokenizer.encode(UpperCAmelCase__       ,		add_special_tokens=UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				rust_tokenizer.encode(UpperCAmelCase__       ,		add_special_tokens=UpperCAmelCase__     )
								self.assertListEqual(UpperCAmelCase__       ,		UpperCAmelCase__     )

								SCREAMING_SNAKE_CASE__     							=				self.get_rust_tokenizer()
								SCREAMING_SNAKE_CASE__     							=				tokenizer.encode(UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				rust_tokenizer.encode(UpperCAmelCase__     )
								self.assertListEqual(UpperCAmelCase__       ,		UpperCAmelCase__     )



				@slow
				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				"Hello World!"
								SCREAMING_SNAKE_CASE__     							=				[0, 3_5378, 6661, 38, 2]
								# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
								# xlmr.eval()
								# xlmr.encode(symbols)

								self.assertListEqual(UpperCAmelCase__       ,		self.big_tokenizer.encode(UpperCAmelCase__     )     )



				@slow
				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				(
								    "This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
								    " add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"
								)
								SCREAMING_SNAKE_CASE__     							=				[
								    0,
								    3293,
								    83,
								    10,
								    4552,
								    4989,
								    7986,
								    678,
								    10,
								    5915,
								    111,
								    17_9459,
								    12_4850,
								    4,
								    6044,
								    237,
								    12,
								    6,
								    5,
								    6,
								    4,
								    6780,
								    705,
								    15,
								    1388,
								    44,
								    378,
								    1_0114,
								    711,
								    152,
								    20,
								    6,
								    5,
								    2_2376,
								    642,
								    1221,
								    1_5190,
								    3_4153,
								    450,
								    5608,
								    959,
								    1119,
								    5_7702,
								    136,
								    186,
								    47,
								    1098,
								    2_9367,
								    47,
								    # 4426, # What fairseq tokenizes from "<unk>": "_<"
								    # 3678, # What fairseq tokenizes from "<unk>": "unk"
								    # 2740, # What fairseq tokenizes from "<unk>": ">"
								    3,  # What we tokenize from "<unk>": "<unk>"
								    6,  # Residue from the tokenization: an extra sentencepiece underline
								    4,
								    6044,
								    237,
								    6284,
								    5_0901,
								    528,
								    31,
								    90,
								    34,
								    927,
								    2,
								]
								# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
								# xlmr.eval()
								# xlmr.encode(symbols)

								self.assertListEqual(UpperCAmelCase__       ,		self.big_tokenizer.encode(UpperCAmelCase__     )     )






				@slow
				def   lowerCAmelCase__		(						self     ):
								# fmt: off
								SCREAMING_SNAKE_CASE__     							=				{"input_ids": [[0, 1_1062, 8_2772, 7, 15, 8_2772, 538, 5_1529, 237, 1_7198, 1290, 206, 9, 21_5175, 1314, 136, 1_7198, 1290, 206, 9, 5_6359, 42, 12_2009, 9, 1_6466, 16, 8_7344, 4537, 9, 4717, 7_8381, 6, 15_9958, 7, 15, 2_4480, 618, 4, 527, 2_2693, 5428, 4, 2777, 2_4480, 9874, 4, 4_3523, 594, 4, 803, 1_8392, 3_3189, 18, 4, 4_3523, 2_4447, 1_2399, 100, 2_4955, 8_3658, 9626, 14_4057, 15, 839, 2_2335, 16, 136, 2_4955, 8_3658, 8_3479, 15, 3_9102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 12_2009, 11_5774, 23, 805, 1328, 4_6876, 7, 136, 5_3894, 1940, 4_2227, 4_1159, 1_7721, 823, 425, 4, 2_7512, 9_8722, 206, 136, 5531, 4970, 919, 1_7336, 5, 2], [0, 2_0080, 618, 83, 8_2775, 47, 479, 9, 1517, 73, 5_3894, 333, 8_0581, 11_0117, 1_8811, 5256, 1295, 51, 15_2526, 297, 7986, 390, 12_4416, 538, 3_5431, 214, 98, 1_5044, 2_5737, 136, 7108, 4_3701, 23, 756, 13_5355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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, 581, 6_3773, 11_9455, 6, 14_7797, 8_8203, 7, 645, 70, 21, 3285, 1_0269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], "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, 1, 1, 1, 1, 1, 1, 1, 1, 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, 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, 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, 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="xlm-roberta-base"       ,		revision="d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3"       ,		)




"""simple docstring"""






import heapq


def __lowercase     (      lowerCamelCase_  :      dict  ):
				SCREAMING_SNAKE_CASE__     							=				[]

				# for each node and his adjacency list add them and the rank of the node to queue
				# using heapq module the queue will be filled like a Priority Queue
				# heapq works with a min priority queue, so I used -1*len(v) to build it
				for key, value in graph.items():
								# O(log(n))
								heapq.heappush(lowerCamelCase_						,					[-1 * len(lowerCamelCase_  ), (key, value)]  )

				# chosen_vertices = set of chosen vertices
				SCREAMING_SNAKE_CASE__     							=				set()

				# while queue isn't empty and there are still edges
				#   (queue[0][0] is the rank of the node with max rank)
				while queue and queue[0][0] != 0:
								# extract vertex with max rank from queue and add it to chosen_vertices
								SCREAMING_SNAKE_CASE__     							=				heapq.heappop(lowerCamelCase_  )[1][0]
								chosen_vertices.add(lowerCamelCase_  )

								# Remove all arcs adjacent to argmax
								for elem in queue:
												# if v haven't adjacent node, skip
												if elem[0] == 0:
																continue
												# if argmax is reachable from elem
												# remove argmax from elem's adjacent list and update his rank
												if argmax in elem[1][1]:
																SCREAMING_SNAKE_CASE__     							=				elem[1][1].index(lowerCamelCase_  )
																del elem[1][1][index]
																elem[0] += 1
        # re-order the queue
								heapq.heapify(lowerCamelCase_  )
				return chosen_vertices


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	_lowerCamelCase     =  {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
	print(f"""Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}""")
