minor

from gymnasium.utils import seeding

from vagen.env.utils.context_utils import parse_llm_raw_response, convert_numpy_to_PIL

from .env_config import PrimitiveSkillEnvConfig

from .maniskill.utils import build_env, handel_info, get_workspace_limits

from .maniskill.utils import build_env, handle_info, get_workspace_limits

from .prompts import system_prompt, init_observation_template, action_template

import vagen.env.primitive_skill.maniskill.env

        obs=self._render(info,init_obs=False,valid_actions=valid_actions)

        output_info["metrics"] = metrics

        self.total_reward += reward

        if isinstance(done, np.ndarray):

            done = done.item()

        return obs,reward,done,output_info

    def system_prompt(self):

    def _render(self,info,init_obs=False,valid_actions=None):

        new_info=handel_info(info.copy())

        new_info=handle_info(info.copy())

        object_positions=new_info['obj_positions']

        other_information=new_info['other_info']

        instruction=self.env.instruction()

    return env

def handel_info(info):

def handle_info(info):

    obj_positions = {}

    other_info = {}

    # Remove specific keys

    info.pop('is_success', None)

    info.pop('num_timesteps', None)

    info.pop('elapsed_steps', None)

    info.pop('skill_success', None)

    info.pop('reward_components', None)

    for k, v in info.items():

        if k.endswith('_pos'):

            # convert to cm round to 2 decimal places

            obj_positions[k] = tuple(np.round(v*1000, 0).astype(int))

            # Convert position arrays to integer tuples in cm

            obj_positions[k] = tuple(np.round(v * 1000, 0).astype(int).tolist())

        elif k.endswith('_value'):

            # Convert value arrays to integers in cm

            other_info[k] = np.round(v * 1000, 0).astype(int).item()

        elif k.endswith('_size'):

            other_info[k] = tuple(np.round(v*1000, 0).astype(int))

            # Convert size arrays to integer tuples in cm

            other_info[k] = tuple(np.round(v * 1000, 0).astype(int).tolist())

        else:

            if isinstance(v, np.ndarray) and v.ndim == 0:

            # Handle all other cases

            if isinstance(v, np.ndarray):

                if v.ndim == 0:  # Scalar array

                    other_info[k] = v.item()

                else:  # Multi-dimensional array

                    other_info[k] = tuple(v.flatten().tolist())

            else:

                other_info[k] = v

    return {

        'obj_positions': obj_positions,

        'other_info': other_info