"""Model and dataset loading, inference, and label extraction functions."""

from __future__ import annotations

import json
import os
from functools import lru_cache
from typing import Any, Dict, List, Optional, Tuple

import numpy as np
import pandas as pd
import torch
from datasets import Dataset, DatasetDict, load_dataset
from PIL import Image
from torchvision import transforms
from torchvision.transforms import functional as TF
from transformers import (
    AutoImageProcessor,
    AutoModelForImageClassification,
)

HF_REPO_ID = "raidium/curia"
HF_DATASET_ID = "raidium/CuriaBench"


class _NumpyToTensor:
    """Convert numpy arrays to tensors while preserving tensors/images."""

    def __call__(self, value: Any) -> torch.Tensor:
        if isinstance(value, (torch.Tensor, Image.Image)):
            return value  # type: ignore[return-value]
        return torch.tensor(value).unsqueeze(0)


class AdaptativeResizeMask(torch.nn.Module):
    """Resize binary masks with a fallback threshold to avoid empty masks."""

    def __init__(self, target_size: int = 512, initial_threshold: float = 0.5) -> None:
        super().__init__()
        self.target_size = target_size
        self.initial_threshold = initial_threshold

    def forward(self, mask: torch.Tensor) -> torch.Tensor:  # type: ignore[override]
        mask = mask.to(dtype=torch.float32)
        resized = TF.resize(
            mask,
            (self.target_size, self.target_size),
            interpolation=TF.InterpolationMode.BILINEAR,
            antialias=True,
        )
        binary = resized > self.initial_threshold
        if binary.sum() == 0:
            new_threshold = torch.max(resized) * 0.5
            binary = resized > new_threshold
        return binary.to(dtype=torch.float32)


@lru_cache(maxsize=1)
def make_mask_transform(crop_size: int = 512) -> transforms.Compose:
    """Return the resize transform used during training/inference."""

    return transforms.Compose(
        [
            _NumpyToTensor(),
            AdaptativeResizeMask(target_size=crop_size),
        ]
    )


def prepare_mask_for_model(mask: Any) -> Optional[torch.Tensor]:
    """Apply Curia's mask preprocessing so heads get the ROI they expect."""

    if mask is None:
        return None

    mask_transform = make_mask_transform()

    try:
        mask_arr = np.array(mask)
    except Exception:
        return None

    if mask_arr.size == 0:
        return None

    if mask_arr.ndim == 3:
        tensor = mask_transform(mask_arr.transpose(2, 0, 1))
        # Match the shape produced in simple_test.py so the model receives
        # (batch, height, width, channels) style tensors.
        tensor = tensor.transpose(1, 3).transpose(1, 2)
    else:
        tensor = mask_transform(torch.tensor([mask_arr]))
        tensor = tensor.unsqueeze(0)

    if isinstance(tensor, np.ndarray):
        tensor = torch.from_numpy(tensor)

    return tensor


@lru_cache(maxsize=1)
def load_id_to_labels() -> Dict[str, Dict[str, str]]:
    """Load the id_to_labels.json mapping file."""
    json_path = os.path.join(os.path.dirname(__file__), "id_to_labels.json")
    with open(json_path, "r") as f:
        data = json.load(f)
        # convert string keys to integers
        for head in data:
            data[head] = {int(k): v for k, v in data[head].items()}
        return data


@lru_cache(maxsize=1)
def load_processor() -> AutoImageProcessor:
    token = os.environ.get("HF_TOKEN")
    return AutoImageProcessor.from_pretrained(
        HF_REPO_ID, trust_remote_code=True, token=token
    )


@lru_cache(maxsize=None)
def load_model(head: str) -> AutoModelForImageClassification:
    token = os.environ.get("HF_TOKEN")
    model = AutoModelForImageClassification.from_pretrained(
        HF_REPO_ID,
        trust_remote_code=True,
        subfolder=head,
        token=token,
    )
    model.eval()
    return model


@lru_cache(maxsize=None)
def load_curia_dataset(subset: str) -> Any:
    token = os.environ.get("HF_TOKEN")
    ds = load_dataset(
        HF_DATASET_ID,
        subset,
        split="test",
        token=token,
    )
    if isinstance(ds, DatasetDict):
        return ds["test"]
    return ds


def to_numpy_image(image: Any) -> np.ndarray:
    """Convert dataset or user-provided imagery to a float32 numpy array."""

    if isinstance(image, np.ndarray):
        arr = image
    elif isinstance(image, Image.Image):
        arr = np.array(image)
    else:
        # Some datasets provide nested dicts or lists – attempt to coerce.
        arr = np.array(image)

    if arr.ndim == 3 and arr.shape[-1] == 3:
        # Convert RGB to grayscale by averaging channels
        arr = arr.mean(axis=-1)

    return arr.astype(np.float32)


def infer_image(
    image: np.ndarray,
    head: str,
    mask: Any | None = None,
) -> torch.Tensor:
    processor = load_processor()
    model = load_model(head)
    with torch.no_grad():
        processed = processor(images=image, return_tensors="pt")
        mask_tensor = prepare_mask_for_model(mask)
        if mask_tensor is not None:
            processed["mask"] = mask_tensor
        outputs = model(**processed)
        logits = outputs["logits"]
        probs = torch.nn.functional.softmax(logits[0], dim=-1)
    return probs