




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	Optional[Any]:

    """simple docstring"""


    __UpperCamelCase  		=						[]
    __UpperCamelCase  		=						set({'''(''', '''[''', '''{'''}      )
    __UpperCamelCase  		=						set({''')''', ''']''', '''}'''}      )
    __UpperCamelCase  		=						{'''{''': '''}''', '''[''': ''']''', '''(''': ''')'''}

    for i in range(len(lowercase_      )      ):
        if s[i] in open_brackets:
            stack.append(s[i]      )

        elif s[i] in closed_brackets and (
            len(lowercase_      ) == 0 or (len(lowercase_      ) > 0 and open_to_closed[stack.pop()] != s[i])
        ):
            return False

    return len(lowercase_      ) == 0




def 					__SCREAMING_SNAKE_CASE   (  )       ->	int:

    """simple docstring"""


    __UpperCamelCase  		=						input('''Enter sequence of brackets: '''      )
    if is_balanced(lowercase_      ):
        print(lowercase_					,     '''is balanced'''      )
    else:
        print(lowercase_					,     '''is not balanced'''      )


if __name__ == "__main__":
 main()












import os
import time

import numpy as np
import onnxruntime as ort


a_   		=     "1"
a_   		=     "0"
a_   		=     "1"

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

a_   		=     128
a_   		=     1
a_   		=     np.ones((batch, sequence), dtype=np.intaa)
a_   		=     np.ones((batch, sequence), dtype=np.intaa)
a_   		=     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...")
a_   		=     time.time()
a_   		=     2000
a_   		=     {}
for iter in range(max_iters):
 a_   		=     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))






