

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


__A	:   int	=					get_tests_dir('fixtures/spiece.model')


@require_sentencepiece
@require_tokenizers
class 			_SCREAMING_SNAKE_CASE      (  lowerCAmelCase__ ,    unittest.TestCase):
						_UpperCamelCase:List[Any]	=		ErnieMTokenizer
						_UpperCamelCase:List[Any]	=		False
						_UpperCamelCase:Dict	=		True
						_UpperCamelCase:str	=		False
						_UpperCamelCase:int	=		False







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

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







						def        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE							)->							str:
													lowerCamelCase_								="""this is a test"""
													lowerCamelCase_								="""this is a test"""
													return input_text, output_text







						def        _snake_case				(			self							)->							Any:
													lowerCamelCase_								="""<pad>"""
													lowerCamelCase_								=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        _snake_case				(			self							)->							str:
													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(_SCREAMING_SNAKE_CASE							)     ,						3_0000							)







						def        _snake_case				(			self							)->							Optional[int]:
													self.assertEqual(self.get_tokenizer().vocab_size     ,						3_0000							)







						def        _snake_case				(			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(_SCREAMING_SNAKE_CASE							)
													lowerCamelCase_								=rust_tokenizer.tokenize(_SCREAMING_SNAKE_CASE							)
													self.assertListEqual(_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE							)

													lowerCamelCase_								=tokenizer.encode(_SCREAMING_SNAKE_CASE     ,						add_special_tokens=_SCREAMING_SNAKE_CASE							)
													lowerCamelCase_								=rust_tokenizer.encode(_SCREAMING_SNAKE_CASE     ,						add_special_tokens=_SCREAMING_SNAKE_CASE							)
													self.assertListEqual(_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE							)

													lowerCamelCase_								=self.get_rust_tokenizer()
													lowerCamelCase_								=tokenizer.encode(_SCREAMING_SNAKE_CASE							)
													lowerCamelCase_								=rust_tokenizer.encode(_SCREAMING_SNAKE_CASE							)
													self.assertListEqual(_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE							)







						def        _snake_case				(			self							)->							List[str]:
													lowerCamelCase_								=ErnieMTokenizer(_SCREAMING_SNAKE_CASE     ,						do_lower_case=_SCREAMING_SNAKE_CASE     ,						unk_token="""<unk>"""     ,						pad_token="""<pad>"""							)

													lowerCamelCase_								=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, 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(
													    _SCREAMING_SNAKE_CASE     ,						["""▁i""", """▁was""", """▁born""", """▁in""", """9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """é""", """."""]							)
													lowerCamelCase_								=tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE							)
													self.assertListEqual(_SCREAMING_SNAKE_CASE     ,						[31, 23, 386, 19, 518, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]							)

													lowerCamelCase_								=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        _snake_case				(			self							)->							Optional[Any]:
													lowerCamelCase_								=ErnieMTokenizer(_SCREAMING_SNAKE_CASE     ,						unk_token="""<unk>"""     ,						pad_token="""<pad>"""							)

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

													lowerCamelCase_								=tokenizer.build_inputs_with_special_tokens(_SCREAMING_SNAKE_CASE							)
													lowerCamelCase_								=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        _snake_case				(			self							)->							List[str]:
													# 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=_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.""",
													    ]     ,						)



from math import factorial




def        __UpperCamelCase     (      _A					:							Union[str, Any]      ,  _A					:							Tuple		)				->Any:



							"""simple docstring"""
							return factorial(_A		) / (factorial(_A		) * factorial(n - r		))




def        __UpperCamelCase     (      )				->Any:



							"""simple docstring"""
							lowerCamelCase_								=0

							for i in range(1      ,  101		):
														for j in range(1      ,  i + 1		):
																					if combinations(_A      ,  _A		) > 1E6:
																												total += 1
							return total


if __name__ == "__main__":
	print(solution())
