'''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


lowercase_  		=			get_tests_dir('''fixtures/spiece.model''')
@require_sentencepiece
@require_tokenizers
class 	SCREAMING_SNAKE_CASE      (    __lowercase     ,      unittest.TestCase):







		"""simple docstring"""
		lowercase :   Dict							=				ErnieMTokenizer
		lowercase :   List[Any]							=				False
		lowercase :   Optional[int]							=				True
		lowercase :   Any							=				False
		lowercase :   int							=				False






		def 		UpperCamelCase__							( self					)			->  List[Any]:
								super().setUp()

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






		def 		UpperCamelCase__							( self		,    __A					)			->  Union[str, Any]:
								_lowerCAmelCase					    ='this is a test'
								_lowerCAmelCase					    ='this is a test'
								return input_text, output_text






		def 		UpperCamelCase__							( self					)			->  int:
								_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 		UpperCamelCase__							( self					)			->  Optional[Any]:
								_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_0000					)






		def 		UpperCamelCase__							( self					)			->  int:
								self.assertEqual(self.get_tokenizer().vocab_size		,    3_0000					)






		def 		UpperCamelCase__							( self					)			->  List[Any]:
								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 		UpperCamelCase__							( self					)			->  Union[str, Any]:
								_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, 1289]					)

								_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, 386, 19, 518, 3050, 15, 17, 48, 25, 8256, 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 		UpperCamelCase__							( self					)			->  Optional[int]:
								_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 		UpperCamelCase__							( self					)			->  Optional[int]:
								# fmt: off
								_lowerCAmelCase					    ={'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.',
								    ]		,    )






'''simple docstring'''

from .prime_check import Test

Test()





