




'''simple docstring'''





from __future__ import annotations




def        lowerCamelCase  (				_snake_case  :						str       ):





    '''simple docstring'''


    lowercase__		       =						[True] * limit
    lowercase__		       =						False
    lowercase__		       =						False
    lowercase__		       =						True

    for i in range(3     ,int(limit**0.5 + 1       )     ,2       ):
        lowercase__		       =						i * 2
        while index < limit:
            lowercase__		       =						False
            lowercase__		       =						index + i

    lowercase__		       =						[2]

    for i in range(3     ,_SCREAMING_SNAKE_CASE     ,2       ):
        if is_prime[i]:
            primes.append(_SCREAMING_SNAKE_CASE       )

    return primes




def        lowerCamelCase  (				_snake_case  :						List[str] = 1_000_000       ):





    '''simple docstring'''


    lowercase__		       =						prime_sieve(_SCREAMING_SNAKE_CASE       )
    lowercase__		       =						0
    lowercase__		       =						0

    for i in range(len(_SCREAMING_SNAKE_CASE       )       ):
        for j in range(i + length     ,len(_SCREAMING_SNAKE_CASE       )       ):
            lowercase__		       =						sum(primes[i:j]       )
            if sol >= ceiling:
                break

            if sol in primes:
                lowercase__		       =						j - i
                lowercase__		       =						sol

    return largest


if __name__ == "__main__":
 print(f'''{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))




