




import heapq







def 		_A(       UpperCamelCase__							:			Union[str, Any]				)    -> set[int]:
							'''simple docstring'''


							__lowercase      =    []

							# 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(UpperCamelCase__				,       [-1 * len(UpperCamelCase__				), (key, value)]				)

							# chosen_vertices = set of chosen vertices
							__lowercase      =    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
														__lowercase      =    heapq.heappop(UpperCamelCase__				)[1][0]
														chosen_vertices.add(UpperCamelCase__				)

														# 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]:
																												__lowercase      =    elem[1][1].index(UpperCamelCase__				)
																												del elem[1][1][index]
																												elem[0] += 1
        # re-order the queue
														heapq.heapify(UpperCamelCase__				)
							return chosen_vertices


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				UpperCAmelCase__				     = {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)}""")












import unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase__				     = get_tests_dir("fixtures/test_sentencepiece_with_bytefallback.model")







@require_sentencepiece
@require_tokenizers
class a (     __SCREAMING_SNAKE_CASE       ,						unittest.TestCase  ):
						"""simple docstring"""

						UpperCamelCase_	:       List[str]       	=		GPTSwaTokenizer
						UpperCamelCase_	:       Union[str, Any]       	=		False
						UpperCamelCase_	:       Union[str, Any]       	=		True
						UpperCamelCase_	:       Optional[Any]       	=		False






						def     UpperCAmelCase_      ( self :     int				)    ->						Optional[int]:
													"""simple docstring"""


													super().setUp()

													# We have a SentencePiece fixture for testing
													__lowercase      =    GPTSwaTokenizer(lowerCamelCase__				,							eos_token='''<unk>'''				,							bos_token='''<unk>'''				,							pad_token='''<unk>'''				)

													tokenizer.save_pretrained(self.tmpdirname				)






						def     UpperCAmelCase_      ( self :     int				,							lowerCamelCase__ :     Dict				)    ->						Dict:
													"""simple docstring"""


													__lowercase      =    '''This is a test'''
													__lowercase      =    '''This is a test'''
													return input_text, output_text






						def     UpperCAmelCase_      ( self :     Optional[Any]				)    ->						Union[str, Any]:
													"""simple docstring"""


													__lowercase      =    '''<s>'''
													__lowercase      =    1

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






						def     UpperCAmelCase_      ( self :     Dict				)    ->						Optional[int]:
													"""simple docstring"""


													__lowercase      =    list(self.get_tokenizer().get_vocab().keys()				)

													self.assertEqual(vocab_keys[0]				,							'''<unk>'''				)
													self.assertEqual(vocab_keys[1]				,							'''<s>'''				)
													self.assertEqual(vocab_keys[-1]				,							'''j'''				)
													self.assertEqual(len(lowerCamelCase__				)				,							2_000				)






						def     UpperCAmelCase_      ( self :     Dict				)    ->						str:
													"""simple docstring"""


													self.assertEqual(self.get_tokenizer().vocab_size				,							2_000				)






						def     UpperCAmelCase_      ( self :     List[str]				)    ->						Dict:
													"""simple docstring"""


													__lowercase      =    GPTSwaTokenizer(lowerCamelCase__				)

													__lowercase      =    tokenizer.tokenize('''This is a test'''				)
													self.assertListEqual(lowerCamelCase__				,							['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']				)

													self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase__				)				,							[465, 287, 265, 631, 842]				)

													__lowercase      =    tokenizer.tokenize('''I was born in 92000, and this is falsé.'''				)
													# fmt: off
													self.assertListEqual(
													    lowerCamelCase__				,							['''▁I''', '''▁was''', '''▁bor''', '''n''', '''▁in''', '''▁''', '''<0x39>''', '''2''', '''0''', '''0''', '''0''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁f''', '''al''', '''s''', '''<0xC3>''', '''<0xA9>''', '''.''']				,							)
													# fmt: on

													__lowercase      =    tokenizer.convert_tokens_to_ids(lowerCamelCase__				)
													self.assertListEqual(
													    lowerCamelCase__				,							[262, 272, 1_525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260]				,							)

													__lowercase      =    tokenizer.convert_ids_to_tokens(lowerCamelCase__				)
													# fmt: off
													self.assertListEqual(
													    lowerCamelCase__				,							['''▁I''', '''▁was''', '''▁bor''', '''n''', '''▁in''', '''▁''', '''<0x39>''', '''2''', '''0''', '''0''', '''0''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁f''', '''al''', '''s''', '''<0xC3>''', '''<0xA9>''', '''.''']				)
													# fmt: on






						def     UpperCAmelCase_      ( self :     Tuple				)    ->						Tuple:
													"""simple docstring"""


													__lowercase      =    GPTSwaTokenizer(lowerCamelCase__				)
													__lowercase      =    ['''This is a test''', '''I was born in 92000, and this is falsé.''']
													__lowercase      =    [
													    [465, 287, 265, 631, 842],
													    [262, 272, 1_525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260],
													]

													# Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
													for text, expected_ids in zip(lowerCamelCase__				,							lowerCamelCase__				):
																				self.assertListEqual(tokenizer.encode_fast(lowerCamelCase__				)				,							lowerCamelCase__				)

													# Test that decode_fast returns the input text
													for text, token_ids in zip(lowerCamelCase__				,							lowerCamelCase__				):
																				self.assertEqual(tokenizer.decode_fast(lowerCamelCase__				)				,							lowerCamelCase__				)






						@slow
						def     UpperCAmelCase_      ( self :     List[str]				)    ->						int:
													"""simple docstring"""


													__lowercase      =    [
													    '''<|python|>def fibonacci(n)\n    if n < 0:\n        print(\'Incorrect input\')''',
													    '''Hey there, how are you doing this fine day?''',
													    '''This is a text with a trailing spaces followed by a dot     .''',
													    '''Häj sväjs lillebrör! =)''',
													    '''Det är inget fel på Mr. Cool''',
													]

													# fmt: off
													__lowercase      =    {'''input_ids''': [[63_423, 5, 6_811, 14_954, 282, 816, 3_821, 63_466, 63_425, 63_462, 18, 63_978, 678, 301, 1_320, 63_423, 63_455, 63_458, 18, 63_982, 4_246, 3_940, 1_901, 47_789, 5_547, 18_994], [19_630, 1_100, 63_446, 1_342, 633, 544, 4_488, 593, 5_102, 2_416, 63_495, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_652, 428, 268, 1_936, 515, 268, 58_593, 22_413, 9_106, 546, 268, 33_213, 63_979, 698, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [55_130, 63_450, 924, 63_449, 2_249, 4_062, 1_558, 318, 63_504, 21_498, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [509, 377, 2_827, 2_559, 332, 6_575, 63_443, 26_801, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''token_type_ids''': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [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], [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]]}
													# fmt: on
													self.tokenizer_integration_test_util(
													    expected_encoding=lowerCamelCase__				,							model_name='''AI-Sweden/gpt-sw3-126m'''				,							sequences=lowerCamelCase__				,							)






