



'''simple docstring'''





def        lowerCamelCase  (				_snake_case  :						List[str]     ,_snake_case  :						int       ):





    '''simple docstring'''


    return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2




def        lowerCamelCase  (				_snake_case  :						Union[str, Any]     ,_snake_case  :						Dict=0       ):





    '''simple docstring'''


    return sorted(_snake_case     ,key=lambda _snake_case       : x[column]       )




def        lowerCamelCase  (				_snake_case  :						Optional[int]     ,_snake_case  :						Dict     ,_snake_case  :						int=float("inf"       )       ):





    '''simple docstring'''


    for i in range(points_counts - 1       ):
        for j in range(i + 1     ,_snake_case       ):
            lowercase__		       =						euclidean_distance_sqr(points[i]     ,points[j]       )
            if current_dis < min_dis:
                lowercase__		       =						current_dis
    return min_dis




def        lowerCamelCase  (				_snake_case  :						List[str]     ,_snake_case  :						Union[str, Any]     ,_snake_case  :						Any=float("inf"       )       ):





    '''simple docstring'''


    for i in range(min(6     ,points_counts - 1       )     ,_snake_case       ):
        for j in range(max(0     ,i - 6       )     ,_snake_case       ):
            lowercase__		       =						euclidean_distance_sqr(points[i]     ,points[j]       )
            if current_dis < min_dis:
                lowercase__		       =						current_dis
    return min_dis




def        lowerCamelCase  (				_snake_case  :						List[Any]     ,_snake_case  :						str     ,_snake_case  :						Optional[Any]       ):





    '''simple docstring'''


    if points_counts <= 3:
        return dis_between_closest_pair(_snake_case     ,_snake_case       )

    # recursion
    lowercase__		       =						points_counts // 2
    lowercase__		       =						closest_pair_of_points_sqr(
        _snake_case     ,points_sorted_on_y[:mid]     ,_snake_case       )
    lowercase__		       =						closest_pair_of_points_sqr(
        _snake_case     ,points_sorted_on_y[mid:]     ,points_counts - mid       )
    lowercase__		       =						min(_snake_case     ,_snake_case       )
    lowercase__		       =						[]
    for point in points_sorted_on_x:
        if abs(point[0] - points_sorted_on_x[mid][0]       ) < closest_pair_dis:
            cross_strip.append(_snake_case       )

    lowercase__		       =						dis_between_closest_in_strip(
        _snake_case     ,len(_snake_case       )     ,_snake_case       )
    return min(_snake_case     ,_snake_case       )




def        lowerCamelCase  (				_snake_case  :						Union[str, Any]     ,_snake_case  :						Union[str, Any]       ):





    '''simple docstring'''


    lowercase__		       =						column_based_sort(_snake_case     ,column=0       )
    lowercase__		       =						column_based_sort(_snake_case     ,column=1       )
    return (
        closest_pair_of_points_sqr(
            _snake_case     ,_snake_case     ,_snake_case       )
    ) ** 0.5


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE__      					=     [(2, 3), (12, 30), (40, 50), (5, 1), (12, 10), (3, 4)]
 print("Distance:", closest_pair_of_points(points, len(points)))









'''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))




