


"""simple docstring"""






import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available


if is_sentencepiece_available():
	from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


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

_lowerCamelCase     =  {'target_lang': 'fi', 'source_lang': 'en'}
_lowerCamelCase     =  '>>zh<<'
_lowerCamelCase     =  'Helsinki-NLP/'

if is_torch_available():
	_lowerCamelCase     =  'pt'
elif is_tf_available():
	_lowerCamelCase     =  'tf'
else:
	_lowerCamelCase     =  'jax'


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





				"""simple docstring"""







				_lowerCAmelCase      :      Any      							=					MarianTokenizer
				_lowerCAmelCase      :      List[str]      							=					False
				_lowerCAmelCase      :      Dict      							=					True



				def   lowerCAmelCase__		(						self     ):
								super().setUp()
								SCREAMING_SNAKE_CASE__     							=				["</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est", "\u0120", "<pad>"]
								SCREAMING_SNAKE_CASE__     							=				dict(zip(UpperCAmelCase__       ,		range(len(UpperCAmelCase__     )     )     )     )
								SCREAMING_SNAKE_CASE__     							=				Path(self.tmpdirname     )
								save_json(UpperCAmelCase__       ,		save_dir / VOCAB_FILES_NAMES["vocab"]     )
								save_json(UpperCAmelCase__       ,		save_dir / VOCAB_FILES_NAMES["tokenizer_config_file"]     )
								if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
												copyfile(UpperCAmelCase__       ,		save_dir / VOCAB_FILES_NAMES["source_spm"]     )
												copyfile(UpperCAmelCase__       ,		save_dir / VOCAB_FILES_NAMES["target_spm"]     )

								SCREAMING_SNAKE_CASE__     							=				MarianTokenizer.from_pretrained(self.tmpdirname     )
								tokenizer.save_pretrained(self.tmpdirname     )



				def   lowerCAmelCase__		(						self       ,		**UpperCAmelCase__     ):
								return MarianTokenizer.from_pretrained(self.tmpdirname       ,		**UpperCAmelCase__     )



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__     ):
								return (
								    "This is a test",
								    "This is a test",
								)



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				"</s>"
								SCREAMING_SNAKE_CASE__     							=				0

								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]       ,		"<unk>"     )
								self.assertEqual(vocab_keys[-1]       ,		"<pad>"     )
								self.assertEqual(len(UpperCAmelCase__     )       ,		9     )



				def   lowerCAmelCase__		(						self     ):
								self.assertEqual(self.get_tokenizer().vocab_size       ,		9     )



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				MarianTokenizer.from_pretrained(f'''{ORG_NAME}opus-mt-en-de'''     )
								SCREAMING_SNAKE_CASE__     							=				en_de_tokenizer(["I am a small frog"]       ,		return_tensors=UpperCAmelCase__     )
								self.assertIsInstance(UpperCAmelCase__       ,		UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				[38, 121, 14, 697, 3_8848, 0]
								self.assertListEqual(UpperCAmelCase__       ,		batch.input_ids[0]     )

								SCREAMING_SNAKE_CASE__     							=				tempfile.mkdtemp()
								en_de_tokenizer.save_pretrained(UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				[x.name for x in Path(UpperCAmelCase__     ).glob("*"     )]
								self.assertIn("source.spm"       ,		UpperCAmelCase__     )
								MarianTokenizer.from_pretrained(UpperCAmelCase__     )



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				self.get_tokenizer()

								SCREAMING_SNAKE_CASE__     							=				tok(
								    ["I am a small frog" * 1000, "I am a small frog"]       ,		padding=UpperCAmelCase__       ,		truncation=UpperCAmelCase__       ,		return_tensors=UpperCAmelCase__     )
								self.assertIsInstance(UpperCAmelCase__       ,		UpperCAmelCase__     )
								self.assertEqual(batch.input_ids.shape       ,		(2, 512)     )



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				self.get_tokenizer()
								SCREAMING_SNAKE_CASE__     							=				tok(["I am a tiny frog", "I am a small frog"]       ,		padding=UpperCAmelCase__       ,		return_tensors=UpperCAmelCase__     )
								self.assertIsInstance(UpperCAmelCase__       ,		UpperCAmelCase__     )
								self.assertEqual(batch_smaller.input_ids.shape       ,		(2, 10)     )



				@slow
				def   lowerCAmelCase__		(						self     ):
								# fmt: off
								SCREAMING_SNAKE_CASE__     							=				{"input_ids": [[4_3495, 462, 20, 4_2164, 1369, 52, 464, 132, 1703, 492, 13, 7491, 3_8999, 6, 8, 464, 132, 1703, 492, 13, 4669, 3_7867, 13, 7525, 27, 1593, 988, 13, 3_3972, 7029, 6, 20, 8251, 383, 2, 270, 5866, 3788, 2, 2353, 8251, 1_2338, 2, 1_3958, 387, 2, 3629, 6953, 188, 2900, 2, 1_3958, 8011, 1_1501, 23, 8460, 4073, 3_4009, 20, 435, 1_1439, 27, 8, 8460, 4073, 6004, 20, 9988, 375, 27, 33, 266, 1945, 1076, 1350, 3_7867, 3288, 5, 577, 1076, 4374, 8, 5082, 5, 2_6453, 257, 556, 403, 2, 242, 132, 383, 316, 492, 8, 1_0767, 6, 316, 304, 4239, 3, 0], [148, 1_5722, 19, 1839, 12, 1350, 13, 2_2327, 5082, 5418, 4_7567, 3_5938, 59, 318, 1_9552, 108, 2183, 54, 1_4976, 4835, 32, 547, 1114, 8, 315, 2417, 5, 92, 1_9088, 3, 0, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100], [36, 6395, 1_2570, 3_9147, 1_1597, 6, 266, 4, 4_5405, 7296, 3, 0, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100]], "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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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="Helsinki-NLP/opus-mt-en-de"       ,		revision="1a8c2263da11e68e50938f97e10cd57820bd504c"       ,		decode_kwargs={"use_source_tokenizer": True}       ,		)






				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				MarianTokenizer.from_pretrained("hf-internal-testing/test-marian-two-vocabs"     )

								SCREAMING_SNAKE_CASE__     							=				"Tämä on testi"
								SCREAMING_SNAKE_CASE__     							=				"This is a test"

								SCREAMING_SNAKE_CASE__     							=				[76, 7, 2047, 2]
								SCREAMING_SNAKE_CASE__     							=				[69, 12, 11, 940, 2]

								SCREAMING_SNAKE_CASE__     							=				tokenizer(UpperCAmelCase__     ).input_ids
								self.assertListEqual(UpperCAmelCase__       ,		UpperCAmelCase__     )

								SCREAMING_SNAKE_CASE__     							=				tokenizer(text_target=UpperCAmelCase__     ).input_ids
								self.assertListEqual(UpperCAmelCase__       ,		UpperCAmelCase__     )

								SCREAMING_SNAKE_CASE__     							=				tokenizer.decode(UpperCAmelCase__       ,		skip_special_tokens=UpperCAmelCase__     )
								self.assertEqual(UpperCAmelCase__       ,		UpperCAmelCase__     )




"""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)}""")
