code: 
  
  
  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      :						List[Any]    =		get_tests_dir("fixtures/spiece.model")
  
  
  
  
  
  
  
  @require_sentencepiece
  @require_tokenizers
  class 					__lowercase					(       a_ ,       unittest.TestCase     ):
  
  
  
  		"""simple docstring"""
  
  		UpperCamelCase    :				Optional[Any] 					=		ErnieMTokenizer
  		UpperCamelCase    :				Dict 					=		False
  		UpperCamelCase    :				List[Any] 					=		True
  		UpperCamelCase    :				str 					=		False
  		UpperCamelCase    :				Optional[Any] 					=		False
  
  
  
  
  
  
  
  		def 				__A      (   self  )     ->   str:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					super().setUp()
  
  					# We have a SentencePiece fixture for testing
  					lowerCamelCase								=     ErnieMTokenizer(A		,			unk_token="""<unk>"""		,			pad_token="""<pad>"""  )
  					tokenizer.save_pretrained(self.tmpdirname  )
  
  
  
  
  
  
  
  		def 				__A      (   self		,			A  )     ->   Any:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					lowerCamelCase								=     """this is a test"""
  					lowerCamelCase								=     """this is a test"""
  					return input_text, output_text
  
  
  
  
  
  
  
  		def 				__A      (   self  )     ->   Union[str, Any]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					lowerCamelCase								=     """<pad>"""
  					lowerCamelCase								=     0
  
  					self.assertEqual(self.get_tokenizer()._convert_token_to_id(A  )		,			A  )
  					self.assertEqual(self.get_tokenizer()._convert_id_to_token(A  )		,			A  )
  
  
  
  
  
  
  
  		def 				__A      (   self  )     ->   Any:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					lowerCamelCase								=     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(A  )		,			3_00_00  )
  
  
  
  
  
  
  
  		def 				__A      (   self  )     ->   Optional[int]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					self.assertEqual(self.get_tokenizer().vocab_size		,			3_00_00  )
  
  
  
  
  
  
  
  		def 				__A      (   self  )     ->   Dict:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					if not self.test_rust_tokenizer:
  								return
  
  					lowerCamelCase								=     self.get_tokenizer()
  					lowerCamelCase								=     self.get_rust_tokenizer()
  
  					lowerCamelCase								=     """I was born in 92000, and this is falsé."""
  
  					lowerCamelCase								=     tokenizer.tokenize(A  )
  					lowerCamelCase								=     rust_tokenizer.tokenize(A  )
  					self.assertListEqual(A		,			A  )
  
  					lowerCamelCase								=     tokenizer.encode(A		,			add_special_tokens=A  )
  					lowerCamelCase								=     rust_tokenizer.encode(A		,			add_special_tokens=A  )
  					self.assertListEqual(A		,			A  )
  
  					lowerCamelCase								=     self.get_rust_tokenizer()
  					lowerCamelCase								=     tokenizer.encode(A  )
  					lowerCamelCase								=     rust_tokenizer.encode(A  )
  					self.assertListEqual(A		,			A  )
  
  
  
  
  
  
  
  		def 				__A      (   self  )     ->   Any:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					lowerCamelCase								=     ErnieMTokenizer(A		,			do_lower_case=A		,			unk_token="""<unk>"""		,			pad_token="""<pad>"""  )
  
  					lowerCamelCase								=     tokenizer.tokenize("""This is a test"""  )
  					self.assertListEqual(A		,			["""▁this""", """▁is""", """▁a""", """▁test"""]  )
  
  					self.assertListEqual(tokenizer.convert_tokens_to_ids(A  )		,			[48, 25, 21, 12_89]  )
  
  					lowerCamelCase								=     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(
  					    A		,			["""▁i""", """▁was""", """▁born""", """▁in""", """9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """é""", """."""]  )
  					lowerCamelCase								=     tokenizer.convert_tokens_to_ids(A  )
  					self.assertListEqual(A		,			[31, 23, 3_86, 19, 5_18, 30_50, 15, 17, 48, 25, 82_56, 18, 1, 9]  )
  
  					lowerCamelCase								=     tokenizer.convert_ids_to_tokens(A  )
  					self.assertListEqual(
  					    A		,			["""▁i""", """▁was""", """▁born""", """▁in""", """9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """<unk>""", """."""]		,			)
  
  
  
  
  
  
  
  		def 				__A      (   self  )     ->   Any:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					lowerCamelCase								=     ErnieMTokenizer(A		,			unk_token="""<unk>"""		,			pad_token="""<pad>"""  )
  
  					lowerCamelCase								=     tokenizer.encode("""sequence builders"""  )
  					lowerCamelCase								=     tokenizer.encode("""multi-sequence build"""  )
  
  					lowerCamelCase								=     tokenizer.build_inputs_with_special_tokens(A  )
  					lowerCamelCase								=     tokenizer.build_inputs_with_special_tokens(A		,			A  )
  
  					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 				__A      (   self  )     ->   Union[str, Any]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					lowerCamelCase								=     {"""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=A		,			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.""",
  					    ]		,			)
  
  
style_context: 
  
  
  import warnings
  
  from .generation import FlaxGenerationMixin
  
  
  
  
  
  
  
  class 					__lowercase					(       a_     ):
  
  
  
  		"""simple docstring"""
  
  		warnings.warn(
  		    "Importing `FlaxGenerationMixin` from `src/transformers/generation_flax_utils.py` is deprecated and will "
  		    "be removed in Transformers v5. Import as `from transformers import FlaxGenerationMixin` instead." ,       a_ ,       )
  
  