



'''simple docstring'''



import os
import time

import numpy as np
import onnxruntime as ort


lowerCAmelCase_     :       Tuple								=						'1'
lowerCAmelCase_     :       List[Any]								=						'0'
lowerCAmelCase_     :       Optional[int]								=						'1'

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

lowerCAmelCase_     :       List[Any]								=						128
lowerCAmelCase_     :       Any								=						1
lowerCAmelCase_     :       List[str]								=						np.ones((batch, sequence), dtype=np.intaa)
lowerCAmelCase_     :       Tuple								=						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_     :       Any								=						time.time()
lowerCAmelCase_     :       Optional[Any]								=						2000
lowerCAmelCase_     :       str								=						{}
for iter in range(max_iters):
							lowerCAmelCase_     :       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) * 1000 / max_iters))









'''simple docstring'''



import darl  # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline


lowerCAmelCase_     :       int								=						{
    'n_samples': 64,
    'horizon': 32,
    'num_inference_steps': 20,
    'n_guide_steps': 2,  # can set to 0 for faster sampling, does not use value network
    'scale_grad_by_std': True,
    'scale': 0.1,
    'eta': 0.0,
    't_grad_cutoff': 2,
    'device': 'cpu',
}


if __name__ == "__main__":
							lowerCAmelCase_     :       int								=						'hopper-medium-v2'
							lowerCAmelCase_     :       str								=						gym.make(env_name)

							lowerCAmelCase_     :       Union[str, Any]								=						ValueGuidedRLPipeline.from_pretrained(
							    'bglick13/hopper-medium-v2-value-function-hor32',
							    env=env,
							)

							env.seed(0)
							lowerCAmelCase_     :       Any								=						env.reset()
							lowerCAmelCase_     :       Any								=						0
							lowerCAmelCase_     :       Tuple								=						0
							lowerCAmelCase_     :       str								=						1000
							lowerCAmelCase_     :       int								=						[obs.copy()]
							try:
														for t in tqdm.tqdm(range(T)):
																					# call the policy
																					lowerCAmelCase_     :       int								=						pipeline(obs, planning_horizon=32)

																					# execute action in environment
																					lowerCAmelCase_			,							lowerCAmelCase_			,							lowerCAmelCase_			,							lowerCAmelCase_     :       Union[str, Any]								=						env.step(denorm_actions)
																					lowerCAmelCase_     :       Optional[int]								=						env.get_normalized_score(total_reward)

																					# update return
																					total_reward += reward
																					total_score += score
																					print(
																					    f'Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:'
																					    f' {total_score}'
																					)

																					# save observations for rendering
																					rollout.append(next_observation.copy())

																					lowerCAmelCase_     :       Union[str, Any]								=						next_observation
							except KeyboardInterrupt:
														pass

							print(f'Total reward: {total_reward}')




