


from __future__ import annotations

import math

snake_case__			 =						'''2020.9.26'''
snake_case__			 =						'''xcodz-dot, cclaus, dhruvmanila'''


def 				lowerCamelCase__	(      a	:   float	,  a	:   float	,  a	:   float	,  a	:   float	,  a	:   float				)    ->	tuple[float, float]:





			"""simple docstring"""
			if not all(isinstance(a	,  (float, int)				) for val in locals().values()				):
						a__ :str						  =    F'''Input values must either be float or int: {list(locals().values()				)}'''
						raise TypeError(a				)
			a__ :int						  =    ((x * distance) / (z + distance)) * scale
			a__ :List[str]						  =    ((y * distance) / (z + distance)) * scale
			return projected_x, projected_y


def 				lowerCamelCase__	(      a	:   float	,  a	:   float	,  a	:   float	,  a	:   str	,  a	:   float				)    ->	tuple[float, float, float]:





			"""simple docstring"""
			if not isinstance(a	,  a				):
						raise TypeError("Axis must be a str"				)
			a__ :Optional[Any]						  =    locals()
			del input_variables["axis"]
			if not all(isinstance(a	,  (float, int)				) for val in input_variables.values()				):
						a__ :Optional[Any]						  =    (
						    "Input values except axis must either be float or int: "
						    F'''{list(input_variables.values()				)}'''
						)
						raise TypeError(a				)
			a__ :Union[str, Any]						  =    (angle % 360) / 450 * 180 / math.pi
			if axis == "z":
						a__ :Dict						  =    x * math.cos(a				) - y * math.sin(a				)
						a__ :Optional[Any]						  =    y * math.cos(a				) + x * math.sin(a				)
						a__ :Tuple						  =    z
			elif axis == "x":
						a__ :List[Any]						  =    y * math.cos(a				) - z * math.sin(a				)
						a__ :List[Any]						  =    z * math.cos(a				) + y * math.sin(a				)
						a__ :Any						  =    x
			elif axis == "y":
						a__ :Dict						  =    x * math.cos(a				) - z * math.sin(a				)
						a__ :List[str]						  =    z * math.cos(a				) + x * math.sin(a				)
						a__ :Any						  =    y
			else:
						raise ValueError("not a valid axis, choose one of 'x', 'y', 'z'"				)

			return new_x, new_y, new_z


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		print(f'''{convert_to_ad(1.0, 2.0, 3.0, 10.0, 10.0) = }''')
		print(f'''{rotate(1.0, 2.0, 3.0, "y", 90.0) = }''')






import os
import time

import numpy as np
import onnxruntime as ort


snake_case__			 =						'''1'''
snake_case__			 =						'''0'''
snake_case__			 =						'''1'''

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

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


