





'''simple docstring'''


from collections import defaultdict
from math import ceil, sqrt







def    _lowerCamelCase  (__lowerCamelCase  :     Tuple = 100_0000					,  __lowerCamelCase  :     Any = 10     )   ->    int:
   a__     = defaultdict(__lowerCamelCase     )

   for outer_width in range(3					,  (t_limit // 4) + 2     ):
      if outer_width * outer_width > t_limit:
         a__     = max(
             ceil(sqrt(outer_width * outer_width - t_limit     )     )					,  1     )
      else:
         a__     = 1

      hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2

      for hole_width in range(__lowerCamelCase					,  outer_width - 1					,  2     ):
         count[outer_width * outer_width - hole_width * hole_width] += 1

   return sum(1 for n in count.values() if 1 <= n <= 10     )


if __name__ == "__main__":
  print(f"""{solution() = }""")










'''simple docstring'''


import os
import time

import numpy as np
import onnxruntime as ort


lowerCAmelCase_   :   Optional[int]       =					"1"
lowerCAmelCase_   :   List[str]       =					"0"
lowerCAmelCase_   :   Tuple       =					"1"

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

lowerCAmelCase_   :   Optional[int]       =					128
lowerCAmelCase_   :   Union[str, Any]       =					1
lowerCAmelCase_   :   List[str]       =					np.ones((batch, sequence), dtype=np.intaa)
lowerCAmelCase_   :   Optional[Any]       =					np.ones((batch, sequence), dtype=np.intaa)
lowerCAmelCase_   :   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_   :   Tuple       =					time.time()
lowerCAmelCase_   :   int       =					2000
lowerCAmelCase_   :   Optional[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) * 1000 / max_iters))



