




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_      )       ->	bool:

    """simple docstring"""


    return not any(
        neighbour == 1 and colored_vertices[i] == color
        for i, neighbour in enumerate(lowercase_      )      )




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_					,     lowercase_      )       ->	bool:

    """simple docstring"""


    if index == len(lowercase_      ):
        return True

    # Recursive Step
    for i in range(lowercase_      ):
        if valid_coloring(graph[index]					,     lowercase_					,     lowercase_      ):
            # Color current vertex
            __UpperCamelCase  		=						i
            # Validate coloring
            if util_color(lowercase_					,     lowercase_					,     lowercase_					,     index + 1      ):
                return True
            # Backtrack
            __UpperCamelCase  		=						-1
    return False




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_      )       ->	list[int]:

    """simple docstring"""


    __UpperCamelCase  		=						[-1] * len(lowercase_      )

    if util_color(lowercase_					,     lowercase_					,     lowercase_					,     0      ):
        return colored_vertices

    return []












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))






