






'''simple docstring'''







import os
import time

import numpy as np
import onnxruntime as ort


_A     :      Optional[Any]   					='''1'''
_A     :      Any   					='''0'''
_A     :      Union[str, Any]   					='''1'''

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

_A     :      List[Any]   					=128
_A     :      Dict   					=1
_A     :      List[str]   					=np.ones((batch, sequence), dtype=np.intaa)
_A     :      Union[str, Any]   					=np.ones((batch, sequence), dtype=np.intaa)
_A     :      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...''')
_A     :      int   					=time.time()
_A     :      List[str]   					=2_000
_A     :      List[str]   					={}
for iter in range(max_iters):
			_A     :      List[Any]   					=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_000 / max_iters))









'''simple docstring'''







def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	list:

							# bit count represents no. of bits in the gray code
							if bit_count < 0:
														raise ValueError("""The given input must be positive"""				)

							# get the generated string sequence
							lowerCamelCase__ :     List[str]	  =	gray_code_sequence_string(UpperCamelCase				)
							#
							# convert them to integers
							for i in range(len(UpperCamelCase				)				):
														lowerCamelCase__ :     int	  =	int(sequence[i]    ,      2				)

							return sequence





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	list:

							# The approach is a recursive one
							# Base case achieved when either n = 0 or n=1
							if bit_count == 0:
														return ["0"]

							if bit_count == 1:
														return ["0", "1"]

							lowerCamelCase__ :     Optional[Any]	  =	1 << bit_count  # defines the length of the sequence
							# 1<< n is equivalent to 2^n

							# recursive answer will generate answer for n-1 bits
							lowerCamelCase__ :     Any	  =	gray_code_sequence_string(bit_count - 1				)

							lowerCamelCase__ :     Union[str, Any]	  =	[]

							# append 0 to first half of the smaller sequence generated
							for i in range(seq_len // 2				):
														lowerCamelCase__ :     Optional[int]	  =	"""0""" + smaller_sequence[i]
														sequence.append(UpperCamelCase				)

							# append 1 to second half ... start from the end of the list
							for i in reversed(range(seq_len // 2				)				):
														lowerCamelCase__ :     int	  =	"""1""" + smaller_sequence[i]
														sequence.append(UpperCamelCase				)

							return sequence


if __name__ == "__main__":
			import doctest

			doctest.testmod()

