def 	lowerCamelCase					(	UpperCamelCase     :				int = 1_00_00_00							)   ->    int:
		_lowerCamelCase				   =  [i - 1 for i in range(limit + 1							)]

		for i in range(2			,       limit + 1							):
				if phi[i] == i - 1:
						for j in range(2 * i			,       limit + 1			,       UpperCamelCase							):
								phi[j] -= phi[j] // i

		return sum(phi[2 : limit + 1]							)


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

"""simple docstring"""

import os
import time

import numpy as np
import onnxruntime as ort


SCREAMING_SNAKE_CASE_            =    '''1'''
SCREAMING_SNAKE_CASE_            =    '''0'''
SCREAMING_SNAKE_CASE_            =    '''1'''

SCREAMING_SNAKE_CASE_            =    ort.SessionOptions()
SCREAMING_SNAKE_CASE_            =    ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print('''Create inference session...''')
SCREAMING_SNAKE_CASE_            =    ['''TensorrtExecutionProvider''', '''CUDAExecutionProvider''']
SCREAMING_SNAKE_CASE_            =    ort.InferenceSession('''model.onnx''', sess_options=sess_opt, providers=execution_provider)
SCREAMING_SNAKE_CASE_            =    ort.RunOptions()

SCREAMING_SNAKE_CASE_            =    128
SCREAMING_SNAKE_CASE_            =    1
SCREAMING_SNAKE_CASE_            =    np.ones((batch, sequence), dtype=np.intaa)
SCREAMING_SNAKE_CASE_            =    np.ones((batch, sequence), dtype=np.intaa)
SCREAMING_SNAKE_CASE_            =    np.ones((batch, sequence), dtype=np.intaa)

print('''Warm up phase...''')
sess.run(
    None,
    {
        sess.get_inputs()[0].name: input_ids,
        sess.get_inputs()[1].name: attention_mask,
        sess.get_inputs()[2].name: token_type_ids,
    },
    run_options=run_opt,
)

print('''Start inference...''')
SCREAMING_SNAKE_CASE_            =    time.time()
SCREAMING_SNAKE_CASE_            =    2000
SCREAMING_SNAKE_CASE_            =    {}
for iter in range(max_iters):
   SCREAMING_SNAKE_CASE_            =    sess.run(
       None,
       {
           sess.get_inputs()[0].name: input_ids,
           sess.get_inputs()[1].name: attention_mask,
           sess.get_inputs()[2].name: token_type_ids,
       },
       run_options=run_opt,
   )
print('''Average Inference Time = {:.3f} ms'''.format((time.time() - start_time) * 1000 / max_iters))



