




"""simple docstring"""




import unittest

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

from ...test_tokenization_common import TokenizerTesterMixin


a_            =   get_tests_dir('fixtures/spiece.model')

@require_sentencepiece
@require_tokenizers
class        UpperCAmelCase_  (	snake_case ,			unittest.TestCase						):
		UpperCamelCase									=ErnieMTokenizer
		UpperCamelCase									=False
		UpperCamelCase									=True
		UpperCamelCase									=False
		UpperCamelCase									=False
		def 					_lowerCamelCase    (		self   )		->   Optional[int]:
					super().setUp()

					# We have a SentencePiece fixture for testing
					__lowercase	:       List[Any]				=   ErnieMTokenizer(UpperCamelCase_							,		unk_token='''<unk>'''							,		pad_token='''<pad>'''   )
					tokenizer.save_pretrained(self.tmpdirname   )
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_   )		->   Optional[Any]:
					__lowercase	:       Tuple				=   '''this is a test'''
					__lowercase	:       List[Any]				=   '''this is a test'''
					return input_text, output_text
		def 					_lowerCamelCase    (		self   )		->   Optional[int]:
					__lowercase	:       Tuple				=   '''<pad>'''
					__lowercase	:       Dict				=   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   )		->   List[str]:
					__lowercase	:       Tuple				=   list(self.get_tokenizer().get_vocab().keys()   )

					self.assertEqual(vocab_keys[0]							,		'''<pad>'''   )
					self.assertEqual(vocab_keys[1]							,		'''<unk>'''   )
					self.assertEqual(vocab_keys[-1]							,		'''▁eloquent'''   )
					self.assertEqual(len(UpperCamelCase_   )							,		3_00_00   )
		def 					_lowerCamelCase    (		self   )		->   Any:
					self.assertEqual(self.get_tokenizer().vocab_size							,		3_00_00   )
		def 					_lowerCamelCase    (		self   )		->   Union[str, Any]:
					if not self.test_rust_tokenizer:
								return

					__lowercase	:       str				=   self.get_tokenizer()
					__lowercase	:       List[Any]				=   self.get_rust_tokenizer()

					__lowercase	:       Tuple				=   '''I was born in 92000, and this is falsé.'''

					__lowercase	:       List[Any]				=   tokenizer.tokenize(UpperCamelCase_   )
					__lowercase	:       Union[str, Any]				=   rust_tokenizer.tokenize(UpperCamelCase_   )
					self.assertListEqual(UpperCamelCase_							,		UpperCamelCase_   )

					__lowercase	:       int				=   tokenizer.encode(UpperCamelCase_							,		add_special_tokens=UpperCamelCase_   )
					__lowercase	:       List[Any]				=   rust_tokenizer.encode(UpperCamelCase_							,		add_special_tokens=UpperCamelCase_   )
					self.assertListEqual(UpperCamelCase_							,		UpperCamelCase_   )

					__lowercase	:       Union[str, Any]				=   self.get_rust_tokenizer()
					__lowercase	:       str				=   tokenizer.encode(UpperCamelCase_   )
					__lowercase	:       Optional[Any]				=   rust_tokenizer.encode(UpperCamelCase_   )
					self.assertListEqual(UpperCamelCase_							,		UpperCamelCase_   )
		def 					_lowerCamelCase    (		self   )		->   Tuple:
					__lowercase	:       Tuple				=   ErnieMTokenizer(UpperCamelCase_							,		do_lower_case=UpperCamelCase_							,		unk_token='''<unk>'''							,		pad_token='''<pad>'''   )

					__lowercase	:       int				=   tokenizer.tokenize('''This is a test'''   )
					self.assertListEqual(UpperCamelCase_							,		['''▁this''', '''▁is''', '''▁a''', '''▁test''']   )

					self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase_   )							,		[48, 25, 21, 12_89]   )

					__lowercase	:       str				=   tokenizer.tokenize('''I was born in 92000, and this is falsé.'''   )
					# ErnieMTokenizer(paddlenlp implementation) outputs '9' instead of '_9' so to mimic that '_9' is changed to '9'
					self.assertListEqual(
					    UpperCamelCase_							,		['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''é''', '''.''']   )
					__lowercase	:       List[Any]				=   tokenizer.convert_tokens_to_ids(UpperCamelCase_   )
					self.assertListEqual(UpperCamelCase_							,		[31, 23, 3_86, 19, 5_18, 30_50, 15, 17, 48, 25, 82_56, 18, 1, 9]   )

					__lowercase	:       List[Any]				=   tokenizer.convert_ids_to_tokens(UpperCamelCase_   )
					self.assertListEqual(
					    UpperCamelCase_							,		['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''.''']							,		)
		def 					_lowerCamelCase    (		self   )		->   List[Any]:
					__lowercase	:       Optional[Any]				=   ErnieMTokenizer(UpperCamelCase_							,		unk_token='''<unk>'''							,		pad_token='''<pad>'''   )

					__lowercase	:       Dict				=   tokenizer.encode('''sequence builders'''   )
					__lowercase	:       List[Any]				=   tokenizer.encode('''multi-sequence build'''   )

					__lowercase	:       List[str]				=   tokenizer.build_inputs_with_special_tokens(UpperCamelCase_   )
					__lowercase	:       Union[str, Any]				=   tokenizer.build_inputs_with_special_tokens(UpperCamelCase_							,		UpperCamelCase_   )

					assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
					assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + [
					    tokenizer.sep_token_id
					] + text_a + [tokenizer.sep_token_id]
		@slow
		def 					_lowerCamelCase    (		self   )		->   Optional[int]:
					# fmt: off
					__lowercase	:       Union[str, Any]				=   {'''input_ids''': [[0, 1_10_62, 8_27_72, 7, 15, 8_27_72, 5_38, 5_15_29, 2_37, 1_71_98, 12_90, 2_06, 9, 21_51_75, 13_14, 1_36, 1_71_98, 12_90, 2_06, 9, 5_63_59, 42, 12_20_09, 9, 1_64_66, 16, 8_73_44, 45_37, 9, 47_17, 7_83_81, 6, 15_99_58, 7, 15, 2_44_80, 6_18, 4, 5_27, 2_26_93, 9, 3_04, 4, 27_77, 2_44_80, 98_74, 4, 4_35_23, 5_94, 4, 8_03, 1_83_92, 3_31_89, 18, 4, 4_35_23, 2_44_47, 5, 5, 5, 16, 1_00, 2_49_55, 8_36_58, 96_26, 14_40_57, 15, 8_39, 2_23_35, 16, 1_36, 2_49_55, 8_36_58, 8_34_79, 15, 3_91_02, 7_24, 16, 6_78, 6_45, 64_60, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 35_59, 13_28, 4_68_76, 7, 1_36, 5_38_94, 19_40, 4_22_27, 4_11_59, 1_77_21, 8_23, 4_25, 4, 2_75_12, 9_87_22, 2_06, 1_36, 55_31, 49_70, 9_19, 1_73_36, 5, 2], [0, 2_00_80, 6_18, 83, 8_27_75, 47, 4_79, 9, 15_17, 73, 5_38_94, 3_33, 8_05_81, 11_01_17, 1_88_11, 52_56, 12_95, 51, 15_25_26, 2_97, 79_86, 3_90, 12_44_16, 5_38, 3_54_31, 2_14, 98, 1_50_44, 2_57_37, 1_36, 71_08, 4_37_01, 23, 7_56, 13_53_55, 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, 1, 1, 1, 1], [0, 5_81, 6_37_73, 11_94_55, 6, 14_77_97, 8_82_03, 7, 6_45, 70, 21, 32_85, 1_02_69, 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, 1, 1, 1, 1]]}
					# fmt: on

					self.tokenizer_integration_test_util(
					    expected_encoding=UpperCamelCase_							,		model_name='''susnato/ernie-m-base_pytorch'''							,		sequences=[
					        '''Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides '''
					        '''general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural '''
					        '''Language Understanding (NLU) and Natural Language Generation (NLG) with over32+ pretrained '''
					        '''models in100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.''',
					        '''BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly '''
					        '''conditioning on both left and right context in all layers.''',
					        '''The quick brown fox jumps over the lazy dog.''',
					    ]							,		)









"""simple docstring"""




# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.
from queue import Queue







def       __UpperCAmelCase      (				__UpperCamelCase					):
			__lowercase	:       int				=   Queue()
			__lowercase	:       str				=   [False] * len(__UpperCamelCase					)
			__lowercase	:       str				=   [-1] * len(__UpperCamelCase					)

			def bfs():
						while not queue.empty():
									__lowercase	:       int				=   queue.get()
									__lowercase	:       List[str]				=   True

									for neighbour in graph[u]:
												if neighbour == u:
															return False

												if color[neighbour] == -1:
															__lowercase	:       str				=   1 - color[u]
															queue.put(__UpperCamelCase					)

												elif color[neighbour] == color[u]:
															return False

						return True

			for i in range(len(__UpperCamelCase					)					):
						if not visited[i]:
									queue.put(__UpperCamelCase					)
									__lowercase	:       int				=   0
									if bfs() is False:
												return False

			return True


if __name__ == "__main__":
			# Adjacency List of graph
			print(check_bipartite({0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2]}))



