





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


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




@require_sentencepiece
@require_tokenizers
class 					UpperCamelCase_    (       UpperCamelCase__			,					unittest.TestCase      ):
							lowerCamelCase_          =   ErnieMTokenizer
							lowerCamelCase_          =   False
							lowerCamelCase_          =   True
							lowerCamelCase_          =   False
							lowerCamelCase_          =   False


							def        _snake_case					(							self      :str						)				->				List[str]:



									"""simple docstring"""



									super().setUp()

									# We have a SentencePiece fixture for testing
									SCREAMING_SNAKE_CASE__								= ErnieMTokenizer(__A							,			unk_token="""<unk>"""							,			pad_token="""<pad>"""						)
									tokenizer.save_pretrained(self.tmpdirname						)


							def        _snake_case					(							self      :List[Any]							,			__A      :List[Any]						)				->				int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= """this is a test"""
									SCREAMING_SNAKE_CASE__								= """this is a test"""
									return input_text, output_text


							def        _snake_case					(							self      :Union[str, Any]						)				->				int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= """<pad>"""
									SCREAMING_SNAKE_CASE__								= 0

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


							def        _snake_case					(							self      :Union[str, Any]						)				->				Optional[int]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= 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_0000						)


							def        _snake_case					(							self      :Tuple						)				->				List[Any]:



									"""simple docstring"""



									self.assertEqual(self.get_tokenizer().vocab_size							,			3_0000						)


							def        _snake_case					(							self      :Any						)				->				Tuple:



									"""simple docstring"""



									if not self.test_rust_tokenizer:
											return

									SCREAMING_SNAKE_CASE__								= self.get_tokenizer()
									SCREAMING_SNAKE_CASE__								= self.get_rust_tokenizer()

									SCREAMING_SNAKE_CASE__								= """I was born in 92000, and this is falsé."""

									SCREAMING_SNAKE_CASE__								= tokenizer.tokenize(__A						)
									SCREAMING_SNAKE_CASE__								= rust_tokenizer.tokenize(__A						)
									self.assertListEqual(__A							,			__A						)

									SCREAMING_SNAKE_CASE__								= tokenizer.encode(__A							,			add_special_tokens=__A						)
									SCREAMING_SNAKE_CASE__								= rust_tokenizer.encode(__A							,			add_special_tokens=__A						)
									self.assertListEqual(__A							,			__A						)

									SCREAMING_SNAKE_CASE__								= self.get_rust_tokenizer()
									SCREAMING_SNAKE_CASE__								= tokenizer.encode(__A						)
									SCREAMING_SNAKE_CASE__								= rust_tokenizer.encode(__A						)
									self.assertListEqual(__A							,			__A						)


							def        _snake_case					(							self      :Union[str, Any]						)				->				Any:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= ErnieMTokenizer(__A							,			do_lower_case=__A							,			unk_token="""<unk>"""							,			pad_token="""<pad>"""						)

									SCREAMING_SNAKE_CASE__								= tokenizer.tokenize("""This is a test"""						)
									self.assertListEqual(__A							,			["""▁this""", """▁is""", """▁a""", """▁test"""]						)

									self.assertListEqual(tokenizer.convert_tokens_to_ids(__A						)							,			[48, 25, 21, 1289]						)

									SCREAMING_SNAKE_CASE__								= 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""", """."""]						)
									SCREAMING_SNAKE_CASE__								= tokenizer.convert_tokens_to_ids(__A						)
									self.assertListEqual(__A							,			[31, 23, 386, 19, 518, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]						)

									SCREAMING_SNAKE_CASE__								= tokenizer.convert_ids_to_tokens(__A						)
									self.assertListEqual(
									    __A							,			["""▁i""", """▁was""", """▁born""", """▁in""", """9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """<unk>""", """."""]							,			)


							def        _snake_case					(							self      :Union[str, Any]						)				->				Dict:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= ErnieMTokenizer(__A							,			unk_token="""<unk>"""							,			pad_token="""<pad>"""						)

									SCREAMING_SNAKE_CASE__								= tokenizer.encode("""sequence builders"""						)
									SCREAMING_SNAKE_CASE__								= tokenizer.encode("""multi-sequence build"""						)

									SCREAMING_SNAKE_CASE__								= tokenizer.build_inputs_with_special_tokens(__A						)
									SCREAMING_SNAKE_CASE__								= 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        _snake_case					(							self      :Optional[int]						)				->				str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= {"""input_ids""": [[0, 1_1062, 8_2772, 7, 15, 8_2772, 538, 5_1529, 237, 1_7198, 1290, 206, 9, 21_5175, 1314, 136, 1_7198, 1290, 206, 9, 5_6359, 42, 12_2009, 9, 1_6466, 16, 8_7344, 4537, 9, 4717, 7_8381, 6, 15_9958, 7, 15, 2_4480, 618, 4, 527, 2_2693, 9, 304, 4, 2777, 2_4480, 9874, 4, 4_3523, 594, 4, 803, 1_8392, 3_3189, 18, 4, 4_3523, 2_4447, 5, 5, 5, 16, 100, 2_4955, 8_3658, 9626, 14_4057, 15, 839, 2_2335, 16, 136, 2_4955, 8_3658, 8_3479, 15, 3_9102, 724, 16, 678, 645, 6460, 1328, 4589, 42, 12_2009, 11_5774, 23, 3559, 1328, 4_6876, 7, 136, 5_3894, 1940, 4_2227, 4_1159, 1_7721, 823, 425, 4, 2_7512, 9_8722, 206, 136, 5531, 4970, 919, 1_7336, 5, 2], [0, 2_0080, 618, 83, 8_2775, 47, 479, 9, 1517, 73, 5_3894, 333, 8_0581, 11_0117, 1_8811, 5256, 1295, 51, 15_2526, 297, 7986, 390, 12_4416, 538, 3_5431, 214, 98, 1_5044, 2_5737, 136, 7108, 4_3701, 23, 756, 13_5355, 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, 581, 6_3773, 11_9455, 6, 14_7797, 8_8203, 7, 645, 70, 21, 3285, 1_0269, 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.""",
									    ]							,			)






from typing import Callable






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							Callable      ):

		def wrapper(UpperCamelCase__:							List[Any]      ):
				SCREAMING_SNAKE_CASE__								= function_with_docstring.__doc__
				return target_function

		return wrapper