"""
Global configuration for velocity prediction.
"""

from dataclasses import dataclass, field
from pathlib import Path


# ──────────────────────────── Dataset paths ────────────────────────────

DATASET_ROOT = Path(__file__).resolve().parents[2] / "dataset"

# Velocity normalization stats (computed from forward scenes only, 28363 frames)
# Yaw-compensated horizontal velocity: [v_right, v_forward]
VELOCITY_MEAN = [-2.902497, 3.837231]   # [v_right, v_forward]
VELOCITY_STD  = [3.453774,  3.722085]   # [v_right, v_forward]

# TRAIN_SCENES = [
#     "indoor_forward_3", "indoor_forward_5", "indoor_forward_6",
#     "indoor_forward_7", "indoor_forward_9", "indoor_forward_10",
#     "indoor_45_2", "indoor_45_4", "indoor_45_9", "indoor_45_12",
# ]
# VAL_SCENES = [
#     "indoor_45_13", "indoor_45_14",
# ]
# TEST_SCENES = [
#     "outdoor_forward_1", "outdoor_forward_3", "outdoor_forward_5",
#     "outdoor_45_1",
# ]

TRAIN_SCENES = [
    "indoor_forward_3", "indoor_forward_9", "indoor_forward_10",  # Easy
    "indoor_forward_5", "indoor_forward_6",                       # Medium
    "outdoor_forward_3"                                           # Medium 室外
]
VAL_SCENES = [
    "indoor_forward_7",      # Hard 室内
    "outdoor_forward_1"      # Easy 室外
    # "indoor_forward_3", "indoor_forward_9", "indoor_forward_10",  # Easy
]
TEST_SCENES = [
    # "indoor_forward_9",
    # "indoor_forward_9","indoor_forward_3",
    # "indoor_forward_7",      # Hard 室内
    "outdoor_forward_1",     # Easy 室外
    # "outdoor_forward_5"      # Hard 室外
    # "indoor_forward_3", "indoor_forward_9", "indoor_forward_10",  # Easy
]


# ──────────────────────────── Model architecture ────────────────────────────

@dataclass
class CNNConfig:
    in_channels: int = 1
    channels: tuple = (32, 64, 128)            # per-layer output channels
    kernel_size: int = 3
    stride: int = 2                            # strided conv replaces conv+pool
    use_bn: bool = True


@dataclass
class PoseMLPConfig:
    input_dim: int = 3
    hidden_dim: int = 32
    output_dim: int = 64


@dataclass
class GRUConfig:
    input_size: int = 192       # CNN(128) + PoseMLP(64)
    hidden_size: int = 128
    num_layers: int = 1
    dropout: float = 0.0


@dataclass
class HeadConfig:
    input_dim: int = 128        # GRU hidden_size
    hidden_dim: int = 64
    output_dim: int = 2         # [v_right, v_forward]


@dataclass
class ModelConfig:
    cnn: CNNConfig = field(default_factory=CNNConfig)
    pose_mlp: PoseMLPConfig = field(default_factory=PoseMLPConfig)
    gru: GRUConfig = field(default_factory=GRUConfig)
    head: HeadConfig = field(default_factory=HeadConfig)


# ──────────────────────────── Training ────────────────────────────

@dataclass
class TrainConfig:
    seq_len: int = 128                    # frames per training sequence
    batch_size: int = 4
    epochs: int = 300
    lr: float = 1e-3
    weight_decay: float = 1e-5
    lr_scheduler_step: int = 30
    lr_scheduler_gamma: float = 0.5
    num_workers: int = 4
    seed: int = 42

    # Sliding window: stride=1 → full overlap, stride=seq_len → non-overlapping
    sliding_window_stride: int = 64

    # Event simulation
    event_threshold: float = 0.1
    event_use_log: bool = True
    event_auto_threshold: bool = False

    # Logging / checkpoint
    log_dir: str = "logs"
    checkpoint_dir: str = "checkpoints"
    log_interval: int = 10
    save_interval: int = 10


# ──────────────────────────── Singleton instances ────────────────────────────

model_cfg = ModelConfig()
train_cfg = TrainConfig()