

'''simple docstring'''
import os
import time

import numpy as np
import onnxruntime as ort


lowerCamelCase :      Tuple					 =     "1"
lowerCamelCase :      Optional[Any]					 =     "0"
lowerCamelCase :      Dict					 =     "1"

lowerCamelCase :      List[str]					 =     ort.SessionOptions()
lowerCamelCase :      str					 =     ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print("Create inference session...")
lowerCamelCase :      Optional[Any]					 =     ["TensorrtExecutionProvider", "CUDAExecutionProvider"]
lowerCamelCase :      Optional[int]					 =     ort.InferenceSession("model.onnx", sess_options=sess_opt, providers=execution_provider)
lowerCamelCase :      Dict					 =     ort.RunOptions()

lowerCamelCase :      Dict					 =     1_2_8
lowerCamelCase :      Optional[int]					 =     1
lowerCamelCase :      List[str]					 =     np.ones((batch, sequence), dtype=np.intaa)
lowerCamelCase :      List[str]					 =     np.ones((batch, sequence), dtype=np.intaa)
lowerCamelCase :      List[str]					 =     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...")
lowerCamelCase :      List[str]					 =     time.time()
lowerCamelCase :      Tuple					 =     2_0_0_0
lowerCamelCase :      List[Any]					 =     {}
for iter in range(max_iters):
     lowerCamelCase :      List[str]					 =     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) * 1_0_0_0 / max_iters))



'''simple docstring'''
import numpy as np
from PIL import Image


def        _lowerCAmelCase					(						_UpperCamelCase						:     np.ndarray							, _UpperCamelCase						:     int							, _UpperCamelCase						:     int				)      ->			np.ndarray:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =np.array(_UpperCamelCase				)
     if arr.shape[0] != arr.shape[1]:
          raise ValueError('The input array is not a square matrix'				)
     _SCREAMING_SNAKE_CASE							    =0
     _SCREAMING_SNAKE_CASE							    =0
     _SCREAMING_SNAKE_CASE							    =0
     _SCREAMING_SNAKE_CASE							    =0

     # compute the shape of the output matrix
     _SCREAMING_SNAKE_CASE							    =(arr.shape[0] - size) // stride + 1
     # initialize the output matrix with zeros of shape maxpool_shape
     _SCREAMING_SNAKE_CASE							    =np.zeros((maxpool_shape, maxpool_shape)				)

     while i < arr.shape[0]:
          if i + size > arr.shape[0]:
               # if the end of the matrix is reached, break
               break
          while j < arr.shape[1]:
               # if the end of the matrix is reached, break
               if j + size > arr.shape[1]:
                    break
               # compute the maximum of the pooling matrix
               _SCREAMING_SNAKE_CASE							    =np.max(arr[i : i + size, j : j + size]				)
               # shift the pooling matrix by stride of column pixels
               j += stride
               mat_j += 1

          # shift the pooling matrix by stride of row pixels
          i += stride
          mat_i += 1

          # reset the column index to 0
          _SCREAMING_SNAKE_CASE							    =0
          _SCREAMING_SNAKE_CASE							    =0

     return updated_arr


def        _lowerCAmelCase					(						_UpperCamelCase						:     np.ndarray							, _UpperCamelCase						:     int							, _UpperCamelCase						:     int				)      ->			np.ndarray:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =np.array(_UpperCamelCase				)
     if arr.shape[0] != arr.shape[1]:
          raise ValueError('The input array is not a square matrix'				)
     _SCREAMING_SNAKE_CASE							    =0
     _SCREAMING_SNAKE_CASE							    =0
     _SCREAMING_SNAKE_CASE							    =0
     _SCREAMING_SNAKE_CASE							    =0

     # compute the shape of the output matrix
     _SCREAMING_SNAKE_CASE							    =(arr.shape[0] - size) // stride + 1
     # initialize the output matrix with zeros of shape avgpool_shape
     _SCREAMING_SNAKE_CASE							    =np.zeros((avgpool_shape, avgpool_shape)				)

     while i < arr.shape[0]:
          # if the end of the matrix is reached, break
          if i + size > arr.shape[0]:
               break
          while j < arr.shape[1]:
               # if the end of the matrix is reached, break
               if j + size > arr.shape[1]:
                    break
               # compute the average of the pooling matrix
               _SCREAMING_SNAKE_CASE							    =int(np.average(arr[i : i + size, j : j + size]				)				)
               # shift the pooling matrix by stride of column pixels
               j += stride
               mat_j += 1

          # shift the pooling matrix by stride of row pixels
          i += stride
          mat_i += 1
          # reset the column index to 0
          _SCREAMING_SNAKE_CASE							    =0
          _SCREAMING_SNAKE_CASE							    =0

     return updated_arr


# Main Function
if __name__ == "__main__":
     from doctest import testmod

     testmod(name="avgpooling", verbose=True)

     # Loading the image
     lowerCamelCase :      Optional[Any]					 =     Image.open("path_to_image")

     # Converting the image to numpy array and maxpooling, displaying the result
     # Ensure that the image is a square matrix

     Image.fromarray(maxpooling(np.array(image), size=3, stride=2)).show()

     # Converting the image to numpy array and averagepooling, displaying the result
     # Ensure that the image is a square matrix

     Image.fromarray(avgpooling(np.array(image), size=3, stride=2)).show()
