


"""simple docstring"""






def 							A_					(   snake_case__		)				->     float:
			return -num if num < 0 else num





def 							A_					(   snake_case__		)				->     int:
			if len(snake_case__		) == 0:
						raise ValueError('''abs_min() arg is an empty sequence'''		)
			_UpperCamelCase				:str				    =  x[0]
			for i in x:
						if abs_val(snake_case__		) < abs_val(snake_case__		):
									_UpperCamelCase				:Optional[int]				    =  i
			return j





def 							A_					(   snake_case__		)				->     int:
			if len(snake_case__		) == 0:
						raise ValueError('''abs_max() arg is an empty sequence'''		)
			_UpperCamelCase				:List[Any]				    =  x[0]
			for i in x:
						if abs(snake_case__		) > abs(snake_case__		):
									_UpperCamelCase				:Optional[int]				    =  i
			return j





def 							A_					(   snake_case__		)				->     int:
			if len(snake_case__		) == 0:
						raise ValueError('''abs_max_sort() arg is an empty sequence'''		)
			return sorted(snake_case__					,				key=snake_case__		)[-1]





def 							A_					(   )				->     Dict:
			assert abs_val(0		) == 0
			assert abs_val(34		) == 34
			assert abs_val(-10_00_00_00_00_00		) == 10_00_00_00_00_00

			_UpperCamelCase				:Optional[int]				    =  [-3, -1, 2, -11]
			assert abs_max(snake_case__		) == -11
			assert abs_max_sort(snake_case__		) == -11
			assert abs_min(snake_case__		) == -1


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					test_abs_val()
					print(abs_val(-34))  # --> 34





"""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


UpperCamelCase__							:Optional[int]       =  get_tests_dir("""fixtures/spiece.model""")






@require_sentencepiece
@require_tokenizers
class     A(     lowerCamelCase__							,       unittest.TestCase		):

		"""simple docstring"""





		A         =       ErnieMTokenizer
		A         =       False
		A         =       True
		A         =       False
		A         =       False
		def      _UpperCamelCase(   self      )			->  Union[str, Any]:

					"""simple docstring"""



					super().setUp()

					# We have a SentencePiece fixture for testing
					_UpperCamelCase				:int				    =  ErnieMTokenizer(SCREAMING_SNAKE_CASE__				,							unk_token='''<unk>'''				,							pad_token='''<pad>'''      )
					tokenizer.save_pretrained(self.tmpdirname      )
		def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__      )			->  List[Any]:

					"""simple docstring"""



					_UpperCamelCase				:List[Any]				    =  '''this is a test'''
					_UpperCamelCase				:Optional[Any]				    =  '''this is a test'''
					return input_text, output_text
		def      _UpperCamelCase(   self      )			->  Union[str, Any]:

					"""simple docstring"""



					_UpperCamelCase				:Dict				    =  '''<pad>'''
					_UpperCamelCase				:Optional[Any]				    =  0

					self.assertEqual(self.get_tokenizer()._convert_token_to_id(SCREAMING_SNAKE_CASE__      )				,							SCREAMING_SNAKE_CASE__      )
					self.assertEqual(self.get_tokenizer()._convert_id_to_token(SCREAMING_SNAKE_CASE__      )				,							SCREAMING_SNAKE_CASE__      )
		def      _UpperCamelCase(   self      )			->  int:

					"""simple docstring"""



					_UpperCamelCase				:Dict				    =  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(SCREAMING_SNAKE_CASE__      )				,							3_00_00      )
		def      _UpperCamelCase(   self      )			->  str:

					"""simple docstring"""



					self.assertEqual(self.get_tokenizer().vocab_size				,							3_00_00      )
		def      _UpperCamelCase(   self      )			->  Dict:

					"""simple docstring"""



					if not self.test_rust_tokenizer:
								return

					_UpperCamelCase				:Tuple				    =  self.get_tokenizer()
					_UpperCamelCase				:str				    =  self.get_rust_tokenizer()

					_UpperCamelCase				:List[Any]				    =  '''I was born in 92000, and this is falsé.'''

					_UpperCamelCase				:Any				    =  tokenizer.tokenize(SCREAMING_SNAKE_CASE__      )
					_UpperCamelCase				:List[Any]				    =  rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE__      )
					self.assertListEqual(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )

					_UpperCamelCase				:List[str]				    =  tokenizer.encode(SCREAMING_SNAKE_CASE__				,							add_special_tokens=SCREAMING_SNAKE_CASE__      )
					_UpperCamelCase				:List[str]				    =  rust_tokenizer.encode(SCREAMING_SNAKE_CASE__				,							add_special_tokens=SCREAMING_SNAKE_CASE__      )
					self.assertListEqual(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )

					_UpperCamelCase				:Any				    =  self.get_rust_tokenizer()
					_UpperCamelCase				:List[Any]				    =  tokenizer.encode(SCREAMING_SNAKE_CASE__      )
					_UpperCamelCase				:int				    =  rust_tokenizer.encode(SCREAMING_SNAKE_CASE__      )
					self.assertListEqual(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )
		def      _UpperCamelCase(   self      )			->  Dict:

					"""simple docstring"""



					_UpperCamelCase				:Any				    =  ErnieMTokenizer(SCREAMING_SNAKE_CASE__				,							do_lower_case=SCREAMING_SNAKE_CASE__				,							unk_token='''<unk>'''				,							pad_token='''<pad>'''      )

					_UpperCamelCase				:List[str]				    =  tokenizer.tokenize('''This is a test'''      )
					self.assertListEqual(SCREAMING_SNAKE_CASE__				,							['''▁this''', '''▁is''', '''▁a''', '''▁test''']      )

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

					_UpperCamelCase				: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(
					    SCREAMING_SNAKE_CASE__				,							['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''é''', '''.''']      )
					_UpperCamelCase				:Optional[int]				    =  tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__      )
					self.assertListEqual(SCREAMING_SNAKE_CASE__				,							[31, 23, 3_86, 19, 5_18, 30_50, 15, 17, 48, 25, 82_56, 18, 1, 9]      )

					_UpperCamelCase				:Dict				    =  tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__      )
					self.assertListEqual(
					    SCREAMING_SNAKE_CASE__				,							['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''.''']				,							)
		def      _UpperCamelCase(   self      )			->  List[str]:

					"""simple docstring"""



					_UpperCamelCase				:Dict				    =  ErnieMTokenizer(SCREAMING_SNAKE_CASE__				,							unk_token='''<unk>'''				,							pad_token='''<pad>'''      )

					_UpperCamelCase				:str				    =  tokenizer.encode('''sequence builders'''      )
					_UpperCamelCase				:Optional[Any]				    =  tokenizer.encode('''multi-sequence build'''      )

					_UpperCamelCase				:int				    =  tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE__      )
					_UpperCamelCase				:Union[str, Any]				    =  tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )

					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      _UpperCamelCase(   self      )			->  int:

					"""simple docstring"""



					_UpperCamelCase				:List[str]				    =  {'''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=SCREAMING_SNAKE_CASE__				,							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.''',
					    ]				,							)

