




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


@require_sentencepiece
@require_tokenizers
class     __SCREAMING_SNAKE_CASE							(		lowerCamelCase       ,					unittest.TestCase  ):
					_lowerCamelCase		  =			ErnieMTokenizer
					_lowerCamelCase		  =			False
					_lowerCamelCase		  =			True
					_lowerCamelCase		  =			False
					_lowerCamelCase		  =			False

					def      lowercase_							(    self ):







						'''simple docstring'''




						super().setUp()

						# We have a SentencePiece fixture for testing
						lowerCamelCase_							:		List[str]							       =							ErnieMTokenizer(__lowercase			,	unk_token='<unk>'			,	pad_token='<pad>' )
						tokenizer.save_pretrained(self.tmpdirname )

					def      lowercase_							(    self			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[Any]							       =							'this is a test'
						lowerCamelCase_							:		Tuple							       =							'this is a test'
						return input_text, output_text

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Union[str, Any]							       =							'<pad>'
						lowerCamelCase_							:		Dict							       =							0

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

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		List[Any]							       =							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(__lowercase )			,	30_000 )

					def      lowercase_							(    self ):







						'''simple docstring'''




						self.assertEqual(self.get_tokenizer().vocab_size			,	30_000 )

					def      lowercase_							(    self ):







						'''simple docstring'''




						if not self.test_rust_tokenizer:
							return

						lowerCamelCase_							:		Dict							       =							self.get_tokenizer()
						lowerCamelCase_							:		List[Any]							       =							self.get_rust_tokenizer()

						lowerCamelCase_							:		Dict							       =							'I was born in 92000, and this is falsé.'

						lowerCamelCase_							:		Tuple							       =							tokenizer.tokenize(__lowercase )
						lowerCamelCase_							:		List[Any]							       =							rust_tokenizer.tokenize(__lowercase )
						self.assertListEqual(__lowercase			,	__lowercase )

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

						lowerCamelCase_							:		str							       =							self.get_rust_tokenizer()
						lowerCamelCase_							:		List[str]							       =							tokenizer.encode(__lowercase )
						lowerCamelCase_							:		str							       =							rust_tokenizer.encode(__lowercase )
						self.assertListEqual(__lowercase			,	__lowercase )

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[int]							       =							ErnieMTokenizer(__lowercase			,	do_lower_case=__lowercase			,	unk_token='<unk>'			,	pad_token='<pad>' )

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

						self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowercase )			,	[48, 25, 21, 1_289] )

						lowerCamelCase_							:		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(
						    __lowercase			,	['▁i', '▁was', '▁born', '▁in', '9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', 'é', '.'] )
						lowerCamelCase_							:		Optional[Any]							       =							tokenizer.convert_tokens_to_ids(__lowercase )
						self.assertListEqual(__lowercase			,	[31, 23, 386, 19, 518, 3_050, 15, 17, 48, 25, 8_256, 18, 1, 9] )

						lowerCamelCase_							:		List[Any]							       =							tokenizer.convert_ids_to_tokens(__lowercase )
						self.assertListEqual(
						    __lowercase			,	['▁i', '▁was', '▁born', '▁in', '9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '.']			,	)

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Any							       =							ErnieMTokenizer(__lowercase			,	unk_token='<unk>'			,	pad_token='<pad>' )

						lowerCamelCase_							:		Union[str, Any]							       =							tokenizer.encode('sequence builders' )
						lowerCamelCase_							:		Tuple							       =							tokenizer.encode('multi-sequence build' )

						lowerCamelCase_							:		List[str]							       =							tokenizer.build_inputs_with_special_tokens(__lowercase )
						lowerCamelCase_							:		Optional[int]							       =							tokenizer.build_inputs_with_special_tokens(__lowercase			,	__lowercase )

						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      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[Any]							       =							{'input_ids': [[0, 11_062, 82_772, 7, 15, 82_772, 538, 51_529, 237, 17_198, 1_290, 206, 9, 215_175, 1_314, 136, 17_198, 1_290, 206, 9, 56_359, 42, 122_009, 9, 16_466, 16, 87_344, 4_537, 9, 4_717, 78_381, 6, 159_958, 7, 15, 24_480, 618, 4, 527, 22_693, 9, 304, 4, 2_777, 24_480, 9_874, 4, 43_523, 594, 4, 803, 18_392, 33_189, 18, 4, 43_523, 24_447, 5, 5, 5, 16, 100, 24_955, 83_658, 9_626, 144_057, 15, 839, 22_335, 16, 136, 24_955, 83_658, 83_479, 15, 39_102, 724, 16, 678, 645, 6_460, 1_328, 4_589, 42, 122_009, 115_774, 23, 3_559, 1_328, 46_876, 7, 136, 53_894, 1_940, 42_227, 41_159, 17_721, 823, 425, 4, 27_512, 98_722, 206, 136, 5_531, 4_970, 919, 17_336, 5, 2], [0, 20_080, 618, 83, 82_775, 47, 479, 9, 1_517, 73, 53_894, 333, 80_581, 110_117, 18_811, 5_256, 1_295, 51, 152_526, 297, 7_986, 390, 124_416, 538, 35_431, 214, 98, 15_044, 25_737, 136, 7_108, 43_701, 23, 756, 135_355, 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, 63_773, 119_455, 6, 147_797, 88_203, 7, 645, 70, 21, 3_285, 10_269, 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=__lowercase			,	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  snake_case (							A      )							->							List[str]:




	'''simple docstring'''


	def wrapper(A      ):
		lowerCamelCase_							:		Dict							       =							function_with_docstring.__doc__
		return target_function

	return wrapper
