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1bfc3f0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 | """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()}
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