| """Action-space bindings and boundary conventions shared by both codecs.""" |
| from __future__ import annotations |
| from collections.abc import Mapping |
| import numpy as np |
| from pathlib import Path |
| from ..backends.actioncodec2.presets import PresetConfig |
| from .conversion import _numpy |
|
|
| class ActionSpaceMixin: |
| def __str__(self) -> str: |
| |
| return self._describe_action_spaces() |
|
|
| @property |
| def action_space(self) -> PresetConfig: |
| """The immutable tensor semantics selected for this tokenizer.""" |
| return self._action_space |
|
|
| @property |
| def action_spaces(self) -> Mapping[str, PresetConfig]: |
| """Every built-in or user-registered option in the loaded artifact.""" |
| return self.action_tokenizer.action_spaces |
|
|
| def register_action_space( |
| self, |
| preset: PresetConfig | Mapping[str, object] | str | Path, |
| ) -> PresetConfig: |
| """Register a custom action space without changing this instance's binding.""" |
| return self.action_tokenizer.register_action_space(preset) |
|
|
| def first_order_template(self, batch_size: int) -> dict[str, np.ndarray]: |
| 'Explicit zero boundary templates, shaped {component: (B,D_active)}.' |
| if type(batch_size) is not int or batch_size < 1: |
| raise ValueError("batch_size must be a positive integer") |
| return { |
| name: np.zeros((batch_size, width), dtype=np.float32) |
| for name, width in self.action_tokenizer.second_order_boundaries( |
| self.action_space |
| ).items() |
| } |
|
|
| @staticmethod |
| def _numpy_boundary(previous_first_order): |
| if previous_first_order is None: |
| return None |
| if not isinstance(previous_first_order, Mapping): |
| raise ValueError("previous_first_order must map component names to [B,D_active] arrays") |
| return {name: _numpy(value) for name, value in previous_first_order.items()} |
|
|
