"""
Evaluation scripts for measuring improvement after fine-tuning.
"""

import logging
from pathlib import Path
from typing import Dict, List, Optional
import numpy as np
import torch

from ..utils.wandb_utils import finish_wandb, init_wandb, log_metrics
from .ba_validator import BAValidator

logger = logging.getLogger(__name__)


def evaluate_ba_agreement(
    model: torch.nn.Module,
    sequences: List[Path],
    ba_validator: BAValidator,
    threshold: float = 2.0,  # degrees
    use_wandb: bool = True,
    wandb_project: str = "ylff",
    wandb_name: Optional[str] = None,
) -> Dict:
    """
    Evaluate model agreement with BA.

    Args:
        model: Model to evaluate
        sequences: List of sequence paths
        ba_validator: BA validator
        threshold: Agreement threshold (degrees)

    Returns:
        Dictionary with evaluation metrics
    """
    model.eval()

    agreement_count = 0
    total_count = 0
    rotation_errors = []
    translation_errors = []

    for seq_path in sequences:
        # Load images
        images = load_images(seq_path)
        if len(images) == 0:
            continue

        # Run model
        with torch.no_grad():
            output = model.inference(images)

        poses_model = output.extrinsics

        # Validate with BA
        result = ba_validator.validate(
            images=images,
            poses_model=poses_model,
        )

        if result["status"] == "ba_failed":
            continue

        total_count += 1
        error = result["error"]

        if error < threshold:
            agreement_count += 1

        rotation_errors.append(error)
        if "error_metrics" in result:
            translation_errors.extend(result["error_metrics"].get("translation_errors", []))

    agreement_rate = agreement_count / total_count if total_count > 0 else 0.0
    mean_rot_error = np.mean(rotation_errors) if rotation_errors else 0.0
    mean_trans_error = np.mean(translation_errors) if translation_errors else 0.0

    metrics = {
        "agreement_rate": agreement_rate,
        "agreement_count": agreement_count,
        "total_count": total_count,
        "mean_rotation_error_deg": mean_rot_error,
        "mean_translation_error": mean_trans_error,
        "rotation_errors": rotation_errors,
        "translation_errors": translation_errors,
    }

    # Log to wandb
    if use_wandb:
        wandb_run = init_wandb(
            project=wandb_project,
            name=wandb_name or f"eval-ba-agreement-{len(sequences)}-seqs",
            config={
                "task": "evaluation",
                "threshold": threshold,
                "num_sequences": len(sequences),
            },
            tags=["evaluation", "ba-agreement"],
        )

        if wandb_run:
            log_metrics(
                {
                    "eval/agreement_rate": agreement_rate,
                    "eval/agreement_count": agreement_count,
                    "eval/total_count": total_count,
                    "eval/mean_rotation_error_deg": mean_rot_error,
                    "eval/mean_translation_error": mean_trans_error,
                }
            )
            finish_wandb()

    return metrics


def load_images(sequence_path: Path) -> List[np.ndarray]:
    """Load images from sequence directory."""
    import cv2

    image_extensions = {".jpg", ".jpeg", ".png", ".JPG", ".JPEG", ".PNG"}
    image_paths = sorted([p for p in sequence_path.iterdir() if p.suffix in image_extensions])

    images = []
    for img_path in image_paths:
        img = cv2.imread(str(img_path))
        if img is None:
            continue
        img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        images.append(img_rgb)

    return images