646 lines
26 KiB
Python
646 lines
26 KiB
Python
import datetime
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import json
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import logging
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import os
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import time
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from wrapt_timeout_decorator import *
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from lib_results_logger import log_task_completion
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logger = logging.getLogger("desktopenv.experiment")
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def run_single_example(agent, env, example, max_steps, instruction, args, example_result_dir, scores):
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runtime_logger = setup_logger(example, example_result_dir)
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# Reset environment first to get fresh VM IP
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env.reset(task_config=example)
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# Reset agent with fresh VM IP (for snapshot reverts)
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try:
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agent.reset(runtime_logger, vm_ip=env.vm_ip)
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except Exception as e:
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agent.reset(vm_ip=env.vm_ip)
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time.sleep(60) # Wait for the environment to be ready
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obs = env._get_obs() # Get the initial observation
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done = False
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step_idx = 0
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env.controller.start_recording()
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while not done and step_idx < max_steps:
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response, actions = agent.predict(
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instruction,
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obs
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)
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for action in actions:
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# Capture the timestamp before executing the action
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action_timestamp = datetime.datetime.now().strftime("%Y%m%d@%H%M%S%f")
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logger.info("Step %d: %s", step_idx + 1, action)
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obs, reward, done, info = env.step(action, args.sleep_after_execution)
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logger.info("Reward: %.2f", reward)
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logger.info("Done: %s", done)
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# Save screenshot and trajectory information
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with open(os.path.join(example_result_dir, f"step_{step_idx + 1}_{action_timestamp}.png"),
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"wb") as _f:
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_f.write(obs['screenshot'])
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with open(os.path.join(example_result_dir, "traj.jsonl"), "a") as f:
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f.write(json.dumps({
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"step_num": step_idx + 1,
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"action_timestamp": action_timestamp,
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"action": action,
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"response": response,
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"reward": reward,
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"done": done,
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"info": info,
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"screenshot_file": f"step_{step_idx + 1}_{action_timestamp}.png"
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}))
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f.write("\n")
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if done:
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logger.info("The episode is done.")
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break
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step_idx += 1
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time.sleep(20) # Wait for the environment to settle
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result = env.evaluate()
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logger.info("Result: %.2f", result)
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scores.append(result)
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with open(os.path.join(example_result_dir, "result.txt"), "w", encoding="utf-8") as f:
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f.write(f"{result}\n")
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# Log task completion to results.json
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log_task_completion(example, result, example_result_dir, args)
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env.controller.end_recording(os.path.join(example_result_dir, "recording.mp4"))
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def setup_logger(example, example_result_dir):
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runtime_logger = logging.getLogger(f"desktopenv.example.{example['id']}")
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runtime_logger.setLevel(logging.DEBUG)
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runtime_logger.addHandler(logging.FileHandler(os.path.join(example_result_dir, "runtime.log")))
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return runtime_logger
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def run_single_example_human(env, example, max_steps, instruction, args, example_result_dir, scores):
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runtime_logger = setup_logger(example, example_result_dir)
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env.reset(task_config=example)
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time.sleep(60) # Wait for the environment to be ready
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obs = env._get_obs() # Get the initial observation
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# Save initial screenshot
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with open(os.path.join(example_result_dir, "initial_state.png"), "wb") as _f:
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_f.write(obs['screenshot'])
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# Save trajectory information
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with open(os.path.join(example_result_dir, "traj.jsonl"), "a") as f:
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f.write(json.dumps({
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"instruction": instruction,
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"initial_state": "initial_state.png"
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}))
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f.write("\n")
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# Evaluate the result
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result = env.evaluate()
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logger.info("Result: %.2f", result)
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scores.append(result)
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with open(os.path.join(example_result_dir, "result.txt"), "w", encoding="utf-8") as f:
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f.write(f"{result}\n")
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def run_single_example_agi(agent, env, example, max_steps, instruction, args, example_result_dir, scores):
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runtime_logger = setup_logger(example, example_result_dir)
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agent.reset(runtime_logger)
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env.reset(task_config=example)
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time.sleep(60) # Wait for the environment to be ready
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obs = env._get_obs() # Get the initial observation
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done = False
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step_idx = 0
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env.controller.start_recording()
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while not done and step_idx < max_steps:
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response, actions = agent.predict(
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instruction,
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obs
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)
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done = not response.get('state_correct', False)
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for action in actions:
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# Capture the timestamp before executing the action
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action_timestamp = datetime.datetime.now().strftime("%Y%m%d@%H%M%S")
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logger.info("Step %d: %s", step_idx + 1, action)
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obs, reward, done, info, step_info = agent.step(action)
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if not done:
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if not response.get('state_correct', False):
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done = True
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logger.info("Reward: %.2f", reward)
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logger.info("Done: %s", done)
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# Save screenshot and trajectory information
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with open(os.path.join(example_result_dir, f"step_{step_idx + 1}_{action_timestamp}.png"),
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"wb") as _f:
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_f.write(obs['screenshot'])
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# Remove pending checks if they exist which will cause issues with json serialization
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if action.get('pending_checks', None):
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del action['pending_checks']
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with open(os.path.join(example_result_dir, "traj.jsonl"), "a") as f:
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f.write(json.dumps({
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"step_num": step_idx + 1,
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"action_timestamp": action_timestamp,
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"action": action,
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"reward": reward,
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"done": done,
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"info": info,
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"screenshot_file": f"step_{step_idx + 1}_{action_timestamp}.png"
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}))
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f.write("\n")
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if done:
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logger.info("The episode is done.")
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break
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step_idx += 1
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result = env.evaluate()
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logger.info("Result: %.2f", result)
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scores.append(result)
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with open(os.path.join(example_result_dir, "result.txt"), "w", encoding="utf-8") as f:
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f.write(f"{result}\n")
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env.controller.end_recording(os.path.join(example_result_dir, "recording.mp4"))
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def run_single_example_openaicua(agent, env, example, max_steps, instruction, args, example_result_dir, scores):
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runtime_logger = setup_logger(example, example_result_dir)
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agent.reset(runtime_logger)
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env.reset(task_config=example)
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time.sleep(60) # Wait for the environment to be ready
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obs = env._get_obs() # Get the initial observation
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done = False
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step_idx = 0
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env.controller.start_recording()
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while not done and step_idx < max_steps:
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response, actions = agent.predict(
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instruction,
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obs
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)
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done = not response.get('state_correct', False)
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for action in actions:
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# Capture the timestamp before executing the action
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action_timestamp = datetime.datetime.now().strftime("%Y%m%d@%H%M%S")
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logger.info("Step %d: %s", step_idx + 1, action)
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obs, reward, done, info, step_info = agent.step(action)
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if not done:
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if not response.get('state_correct', False):
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done = True
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logger.info("Reward: %.2f", reward)
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logger.info("Done: %s", done)
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# Save screenshot and trajectory information
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with open(os.path.join(example_result_dir, f"step_{step_idx + 1}_{action_timestamp}.png"),
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"wb") as _f:
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_f.write(obs['screenshot'])
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# Remove pending checks if they exist which will cause issues with json serialization
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if action.get('pending_checks', None):
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del action['pending_checks']
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with open(os.path.join(example_result_dir, "traj.jsonl"), "a") as f:
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f.write(json.dumps({
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"step_num": step_idx + 1,
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"action_timestamp": action_timestamp,
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"action": action,
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"reward": reward,
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"done": done,
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"info": info,
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"screenshot_file": f"step_{step_idx + 1}_{action_timestamp}.png"
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}))
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f.write("\n")
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if done:
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logger.info("The episode is done.")
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break
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step_idx += 1
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result = env.evaluate()
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logger.info("Result: %.2f", result)
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scores.append(result)
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with open(os.path.join(example_result_dir, "result.txt"), "w", encoding="utf-8") as f:
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f.write(f"{result}\n")
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env.controller.end_recording(os.path.join(example_result_dir, "recording.mp4"))
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def run_single_example_opencua(agent, env, example, max_steps, instruction, args, example_result_dir, scores):
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runtime_logger = setup_logger(example, example_result_dir)
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agent.reset(runtime_logger)
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env.reset(task_config=example)
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time.sleep(60) # Wait for the environment to be ready
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obs = env._get_obs() # Get the initial observation
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done = False
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step_idx = 0
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env.controller.start_recording()
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while not done and step_idx < max_steps:
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response, actions, info_dict = agent.predict(instruction, obs)
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logger.info(f"Got Action: {actions}")
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# Breack if no actions
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if not actions or len(actions)==0 or actions[0]=="" or actions[0].lower().startswith("error"):
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break
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for action in actions:
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# Capture the timestamp before executing the action
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action_timestamp = datetime.datetime.now().strftime("%Y%m%d@%H%M%S")
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logger.info("Step %d: %s", step_idx + 1, action)
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obs, reward, done, info = env.step(action, args.sleep_after_execution)
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logger.info(f"Action {action} executed, reward: {reward}, done: {done}")
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# Save screenshot and trajectory information
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with open(os.path.join(example_result_dir, f"step_{step_idx + 1}_{action_timestamp}.png"),
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"wb") as _f:
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_f.write(obs['screenshot'])
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with open(os.path.join(example_result_dir, "traj.jsonl"), "a", encoding="utf-8") as f:
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f.write(json.dumps({
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"step_num": step_idx + 1,
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"action": action,
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"natural_language_action": info_dict.get("action"),
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"action_timestamp": action_timestamp,
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"response": response,
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"reward": reward,
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"done": done,
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"info": info,
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"screenshot_file": f"step_{step_idx + 1}_{action_timestamp}.png"
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}, ensure_ascii=False))
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f.write("\n")
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if done:
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logger.info("The episode is done.")
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break
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step_idx += 1
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time.sleep(20) # Wait for the environment to settle
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result = env.evaluate()
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logger.info("Result: %.2f", result)
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scores.append(result)
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with open(os.path.join(example_result_dir, "result.txt"), "w", encoding="utf-8") as f:
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f.write(f"{result}\n")
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env.controller.end_recording(os.path.join(example_result_dir, "recording.mp4"))
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def run_single_example_autoglm(agent, env, example, max_steps, instruction, args, example_result_dir, scores):
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runtime_logger = setup_logger(example, example_result_dir)
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try:
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agent.reset(runtime_logger)
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except Exception as e:
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agent.reset()
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env.reset(task_config=example)
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time.sleep(60) # Wait for the environment to be ready
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obs = env._get_obs() # Get the initial observation
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done = False
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step_idx = 0
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env.controller.start_recording()
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while not done and step_idx < max_steps:
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response, actions = agent.predict(
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instruction,
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obs
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)
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for action in actions:
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# Capture the timestamp before executing the action
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action_timestamp = datetime.datetime.now().strftime("%Y%m%d@%H%M%S")
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logger.info("Step %d: %s", step_idx + 1, action)
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obs, reward, done, info = env.step(action, args.sleep_after_execution)
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logger.info("Reward: %.2f", reward)
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logger.info("Done: %s", done)
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# Save screenshot and trajectory information
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with open(os.path.join(example_result_dir, f"step_{step_idx + 1}_{action_timestamp}.png"),
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"wb") as _f:
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_f.write(obs['screenshot'])
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with open(os.path.join(example_result_dir, "traj.jsonl"), "a") as f:
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f.write(json.dumps({
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"step_num": step_idx + 1,
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"action_timestamp": action_timestamp,
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"action": action,
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"response": response,
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"reward": reward,
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"done": done,
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"info": info,
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"screenshot_file": f"step_{step_idx + 1}_{action_timestamp}.png"
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}))
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f.write("\n")
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if done:
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logger.info("The episode is done.")
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break
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# Invalid Action
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if not actions:
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obs = env._get_obs() # update observation
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step_idx += 1
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if not done: # not completed the task yet
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env.action_history.append('FAIL')
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result = env.evaluate()
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logger.info("Result: %.2f", result)
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scores.append(result)
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with open(os.path.join(example_result_dir, "result.txt"), "w", encoding="utf-8") as f:
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f.write(f"{result}\n")
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env.controller.end_recording(os.path.join(example_result_dir, "recording.mp4"))
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def run_single_example_mano(agent, env, example, max_steps, instruction, args, example_result_dir, scores):
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runtime_logger = setup_logger(example, example_result_dir)
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agent.reset(runtime_logger)
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env.reset(task_config=example)
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time.sleep(60) # Wait for the environment to be ready
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obs = env._get_obs() # Get the initial observation
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done = False
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step_idx = 0
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env.controller.start_recording()
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with open(os.path.join(example_result_dir, f"step_0.png"),
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"wb") as _f:
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_f.write(obs['screenshot'])
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while not done and step_idx < max_steps:
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response, actions = agent.predict(
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instruction,
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obs
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)
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if len(actions) > 1:
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if (("pyautogui.hotkey('shift')" in actions[0] or "pyautogui.hotkey('ctrl')" in actions[0])
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and "pyautogui.click" in actions[1]):
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hotkey_type = 'shift' if "shift" in actions[0] else 'ctrl'
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action = f"pyautogui.keyDown('{hotkey_type}')\n{actions[1]}\npyautogui.keyUp('{hotkey_type}')"
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actions = [action]
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for action in actions:
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# Capture the timestamp before executing the action
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action_timestamp = datetime.datetime.now().strftime("%Y%m%d@%H%M%S")
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logger.info("Step %d: %s", step_idx + 1, action)
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obs, reward, done, info = env.step(action, args.sleep_after_execution)
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logger.info("Reward: %.2f", reward)
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logger.info("Done: %s", done)
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# Save screenshot and trajectory information
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with open(os.path.join(example_result_dir, f"step_{step_idx + 1}_{action_timestamp}.png"),
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"wb") as _f:
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_f.write(obs['screenshot'])
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with open(os.path.join(example_result_dir, "traj.jsonl"), "a") as f:
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f.write(json.dumps({
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"step_num": step_idx + 1,
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"action_timestamp": action_timestamp,
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"action": action,
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"reward": reward,
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"done": done,
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"info": info,
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"screenshot_file": f"step_{step_idx + 1}_{action_timestamp}.png",
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"response":response
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}))
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f.write("\n")
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if done:
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logger.info("The episode is done.")
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break
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step_idx += 1
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result = env.evaluate()
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logger.info("Result: %.2f", result)
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scores.append(result)
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with open(os.path.join(example_result_dir, "result.txt"), "w", encoding="utf-8") as f:
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f.write(f"{result}\n")
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env.controller.end_recording(os.path.join(example_result_dir, "recording.mp4"))
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def run_single_example_uipath(agent, env, example, max_steps, instruction, args, example_result_dir, scores):
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runtime_logger = setup_logger(example, example_result_dir)
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try:
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agent.reset(runtime_logger)
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except Exception as e:
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agent.reset()
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env.reset(task_config=example)
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time.sleep(60) # Wait for the environment to be ready
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obs = env._get_obs() # Get the initial observation
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done = False
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step_idx = 0
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env.controller.start_recording()
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while not done and step_idx < max_steps:
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response, actions = agent.predict(
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instruction,
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obs,
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args,
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step_idx
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)
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for action in actions:
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# Capture the timestamp before executing the action
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action_timestamp = datetime.datetime.now().strftime("%Y%m%d@%H%M%S")
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logger.info("Step %d: %s", step_idx + 1, action)
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obs, reward, done, info = env.step(action, args.sleep_after_execution)
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logger.info("Reward: %.2f", reward)
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logger.info("Done: %s", done)
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# Save screenshot and trajectory information
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with open(os.path.join(example_result_dir, f"step_{step_idx + 1}_{action_timestamp}.png"),
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"wb") as _f:
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_f.write(obs['screenshot'])
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with open(os.path.join(example_result_dir, "traj.jsonl"), "a") as f:
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f.write(json.dumps({
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"step_num": step_idx + 1,
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"action_timestamp": action_timestamp,
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"action": action,
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"response": response,
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"reward": reward,
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"done": done,
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"info": info,
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"screenshot_file": f"step_{step_idx + 1}_{action_timestamp}.png"
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}))
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f.write("\n")
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if done:
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logger.info("The episode is done.")
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break
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step_idx += 1
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result = env.evaluate()
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logger.info("Result: %.2f", result)
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scores.append(result)
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with open(os.path.join(example_result_dir, "result.txt"), "w", encoding="utf-8") as f:
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f.write(f"{result}\n")
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env.controller.end_recording(os.path.join(example_result_dir, "recording.mp4"))
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from mm_agents.os_symphony.utils.common_utils import draw_coordinates
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from mm_agents.os_symphony.utils.process_context import set_current_result_dir
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logger = logging.getLogger("desktopenv.experiment")
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def run_single_example_os_symphony(agent, env, example, max_steps, instruction, args, example_result_dir, scores):
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set_current_result_dir(example_result_dir)
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agent.reset(result_dir=example_result_dir)
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env.reset(task_config=example)
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time.sleep(30) # Wait for the environment to be ready
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obs = env._get_obs() # Get the initial observation
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done = False
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step_idx = 0
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# env.controller.start_recording()
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start_time = time.time()
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while not done and step_idx < max_steps:
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response, actions = agent.predict(
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instruction,
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obs,
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step_idx == max_steps - 1
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)
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for action in actions:
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# Save screenshot and trajectory information
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if "reflection" in response and response["reflection"].get("is_milestone"):
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img_name = f"step_{step_idx + 1}_milestone.png"
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else:
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img_name = f"step_{step_idx + 1}.png"
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with open(os.path.join(example_result_dir, img_name),
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"wb") as _f:
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_f.write(obs['screenshot'])
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if "coordinates" in response and response["coordinates"]:
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draw_coordinates(
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image_bytes=obs['screenshot'],
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coordinates=response["coordinates"],
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save_path=os.path.join(example_result_dir, img_name[:-4] + "_draw.png")
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)
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logger.info("Step %d: %s", step_idx + 1, action)
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obs, reward, done, info = env.step(action, args.sleep_after_execution)
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logger.info("Done: %s", done)
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with open(os.path.join(example_result_dir, "traj.jsonl"), "a", encoding="utf-8") as f:
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f.write(json.dumps({
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"instruction": instruction,
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"step_num": step_idx + 1,
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"action": action,
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"response": response,
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"done": done,
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"info": info,
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"screenshot_file": img_name
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}))
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f.write("\n")
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with open(os.path.join(example_result_dir, f"traj_{step_idx+1}.json"), "w", encoding="utf-8") as f:
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json.dump({
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"step_num": step_idx + 1,
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"action": action,
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"response": response,
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"done": done,
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"info": info,
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"screenshot_file": img_name
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}, f, indent=4, ensure_ascii=False)
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if done:
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logger.info("The episode is done.")
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time.sleep(60)
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break
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step_idx += 1
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end_time = time.time()
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result = float(env.evaluate())
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logger.info("Result: %.2f", result)
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scores.append(result)
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with open(os.path.join(example_result_dir, "result.txt"), "w", encoding="utf-8") as f:
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f.write(f"{result}\n")
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with open(os.path.join(example_result_dir, "time.txt"), "w", encoding="utf-8") as f:
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f.write(f"{end_time-start_time:.2f}\n")
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def run_single_example_evocua(agent, env, example, max_steps, instruction, args, example_result_dir, scores):
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"""
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Unified run function for EvoCUAAgent (supporting both S1 and S2 modes).
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"""
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runtime_logger = setup_logger(example, example_result_dir)
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# Reset Environment
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env.reset(task_config=example)
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# Reset Agent
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# Handle agent reset signature differences if any
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try:
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agent.reset(runtime_logger, vm_ip=env.vm_ip)
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except Exception:
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try:
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agent.reset(runtime_logger)
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except Exception:
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agent.reset()
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time.sleep(60) # Wait for the environment to be ready
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obs = env._get_obs() # Get the initial observation
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done = False
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step_idx = 0
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env.controller.start_recording()
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while not done and step_idx < max_steps:
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# EvoCUAAgent.predict unified signature: returns (response, actions)
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# It handles both modes internally.
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predict_res = agent.predict(instruction, obs)
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# Check return signature logic
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if len(predict_res) == 3:
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# Compatibility with S1 original signature if agent was updated to match
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response, actions, info_dict = predict_res
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else:
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response, actions = predict_res
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info_dict = {}
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logger.info(f"Step {step_idx + 1} Actions: {actions}")
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# Break if no actions (fail-safe)
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if not actions or (len(actions) == 1 and (actions[0] == "" or "error" in actions[0].lower())):
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# Allow "FAIL" or "DONE" to process through execution loop if agent outputs them as actions
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if not (actions and actions[0] in ["FAIL", "DONE"]):
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logger.warning("No valid actions returned. Breaking loop.")
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break
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for action in actions:
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action_timestamp = datetime.datetime.now().strftime("%Y%m%d@%H%M%S%f")
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logger.info("Executing action: %s", action)
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# Execute
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obs, reward, done, info = env.step(action, args.sleep_after_execution)
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logger.info("Reward: %.2f", reward)
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logger.info("Done: %s", done)
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# Save screenshot
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screenshot_file = f"step_{step_idx + 1}_{action_timestamp}.png"
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with open(os.path.join(example_result_dir, screenshot_file), "wb") as _f:
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_f.write(obs['screenshot'])
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# Log Trajectory
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log_entry = {
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"step_num": step_idx + 1,
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"action_timestamp": action_timestamp,
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"action": action,
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"response": response,
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"reward": reward,
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"done": done,
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"info": info,
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"screenshot_file": screenshot_file
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}
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# Add natural language info if available (S1 style)
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if info_dict:
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log_entry["natural_language_action"] = info_dict.get("action")
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with open(os.path.join(example_result_dir, "traj.jsonl"), "a", encoding="utf-8") as f:
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f.write(json.dumps(log_entry, ensure_ascii=False))
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f.write("\n")
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if done:
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logger.info("The episode is done.")
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break
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step_idx += 1
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time.sleep(20) # Wait for environment to settle
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result = env.evaluate()
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logger.info("Result: %.2f", result)
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scores.append(result)
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with open(os.path.join(example_result_dir, "result.txt"), "w", encoding="utf-8") as f:
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f.write(f"{result}\n")
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log_task_completion(example, result, example_result_dir, args)
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env.controller.end_recording(os.path.join(example_result_dir, "recording.mp4"))
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