



def 						UpperCAmelCase__   (   UpperCAmelCase__			:int = 10**6       ):






 '''simple docstring'''

 if n <= 0:
  raise ValueError("Please enter an integer greater than 0"       )

 a							=		list(range(n + 1       )       )
 for number in range(2			,       n + 1       ):
  if phi[number] == number:
   phi[number] -= 1
   for multiple in range(number * 2			,       n + 1			,       UpperCAmelCase__       ):
    a							=		(phi[multiple] // number) * (number - 1)

 a							=		1
 for number in range(1			,       n + 1       ):
  if (answer / phi[answer]) < (number / phi[number]):
   a							=		number

 return answer


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









'''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


A:   Dict						=					get_tests_dir("fixtures/spiece.model")






@require_sentencepiece
@require_tokenizers
class 				__magic_name__  (						UpperCAmelCase_,    unittest.TestCase					):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_       :   int   =	ErnieMTokenizer
	SCREAMING_SNAKE_CASE_       :   Optional[Any]   =	False
	SCREAMING_SNAKE_CASE_       :   Dict   =	True
	SCREAMING_SNAKE_CASE_       :   Union[str, Any]   =	False
	SCREAMING_SNAKE_CASE_       :   Dict   =	False
	def 							lowerCamelCase__		(				self							)    ->	Any:
							super().setUp()

							# We have a SentencePiece fixture for testing
							lowercase_   : List[Any]   	=      ErnieMTokenizer(_lowercase	,   unk_token='<unk>'	,   pad_token='<pad>'							)
							tokenizer.save_pretrained(self.tmpdirname							)
	def 							lowerCamelCase__		(				self	,   _lowercase							)    ->	Optional[int]:
							lowercase_   : Optional[int]   	=      'this is a test'
							lowercase_   : Dict   	=      'this is a test'
							return input_text, output_text
	def 							lowerCamelCase__		(				self							)    ->	Any:
							lowercase_   : str   	=      '<pad>'
							lowercase_   : Optional[Any]   	=      0

							self.assertEqual(self.get_tokenizer()._convert_token_to_id(_lowercase							)	,   _lowercase							)
							self.assertEqual(self.get_tokenizer()._convert_id_to_token(_lowercase							)	,   _lowercase							)
	def 							lowerCamelCase__		(				self							)    ->	Dict:
							lowercase_   : List[str]   	=      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							)	,   3_0000							)
	def 							lowerCamelCase__		(				self							)    ->	Dict:
							self.assertEqual(self.get_tokenizer().vocab_size	,   3_0000							)
	def 							lowerCamelCase__		(				self							)    ->	List[str]:
							if not self.test_rust_tokenizer:
													return

							lowercase_   : Optional[int]   	=      self.get_tokenizer()
							lowercase_   : Optional[Any]   	=      self.get_rust_tokenizer()

							lowercase_   : Tuple   	=      'I was born in 92000, and this is falsé.'

							lowercase_   : Union[str, Any]   	=      tokenizer.tokenize(_lowercase							)
							lowercase_   : List[Any]   	=      rust_tokenizer.tokenize(_lowercase							)
							self.assertListEqual(_lowercase	,   _lowercase							)

							lowercase_   : Any   	=      tokenizer.encode(_lowercase	,   add_special_tokens=_lowercase							)
							lowercase_   : List[Any]   	=      rust_tokenizer.encode(_lowercase	,   add_special_tokens=_lowercase							)
							self.assertListEqual(_lowercase	,   _lowercase							)

							lowercase_   : Union[str, Any]   	=      self.get_rust_tokenizer()
							lowercase_   : Optional[Any]   	=      tokenizer.encode(_lowercase							)
							lowercase_   : str   	=      rust_tokenizer.encode(_lowercase							)
							self.assertListEqual(_lowercase	,   _lowercase							)
	def 							lowerCamelCase__		(				self							)    ->	Tuple:
							lowercase_   : Optional[int]   	=      ErnieMTokenizer(_lowercase	,   do_lower_case=_lowercase	,   unk_token='<unk>'	,   pad_token='<pad>'							)

							lowercase_   : Tuple   	=      tokenizer.tokenize('This is a test'							)
							self.assertListEqual(_lowercase	,   ['▁this', '▁is', '▁a', '▁test']							)

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

							lowercase_   : Union[str, Any]   	=      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', '.']							)
							lowercase_   : int   	=      tokenizer.convert_tokens_to_ids(_lowercase							)
							self.assertListEqual(_lowercase	,   [31, 23, 386, 19, 518, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]							)

							lowercase_   : Optional[int]   	=      tokenizer.convert_ids_to_tokens(_lowercase							)
							self.assertListEqual(
							    _lowercase	,   ['▁i', '▁was', '▁born', '▁in', '9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '.']	,   )
	def 							lowerCamelCase__		(				self							)    ->	Any:
							lowercase_   : Tuple   	=      ErnieMTokenizer(_lowercase	,   unk_token='<unk>'	,   pad_token='<pad>'							)

							lowercase_   : List[Any]   	=      tokenizer.encode('sequence builders'							)
							lowercase_   : Optional[int]   	=      tokenizer.encode('multi-sequence build'							)

							lowercase_   : Union[str, Any]   	=      tokenizer.build_inputs_with_special_tokens(_lowercase							)
							lowercase_   : 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 							lowerCamelCase__		(				self							)    ->	Tuple:
							# fmt: off
							lowercase_   : Dict   	=      {'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=_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.',
							    ]	,   )
