from __future__ import annotations

from collections.abc import Callable
from pathlib import Path
from typing import ClassVar, Protocol, cast, override

import torch
import torch.nn as nn
from beartype import beartype
from einops import rearrange
from jaxtyping import Float
import jaxtyping
from torch import Tensor

from opengait.modeling.backbones.resnet import ResNet9
from opengait.modeling.modules import (
    HorizontalPoolingPyramid,
    PackSequenceWrapper as TemporalPool,
    SeparateBNNecks,
    SeparateFCs,
)
from opengait.utils import common as common_utils


JaxtypedDecorator = Callable[[Callable[..., object]], Callable[..., object]]
JaxtypedFactory = Callable[..., JaxtypedDecorator]
jaxtyped = cast(JaxtypedFactory, jaxtyping.jaxtyped)
ConfigLoader = Callable[[str], dict[str, object]]
config_loader = cast(ConfigLoader, common_utils.config_loader)


class TemporalPoolLike(Protocol):
    def __call__(
        self,
        seqs: Tensor,
        seqL: object,
        dim: int = 2,
        options: dict[str, int] | None = None,
    ) -> object: ...


class HppLike(Protocol):
    def __call__(self, x: Tensor) -> Tensor: ...


class FCsLike(Protocol):
    def __call__(self, x: Tensor) -> Tensor: ...


class BNNecksLike(Protocol):
    def __call__(self, x: Tensor) -> tuple[Tensor, Tensor]: ...


class ScoNetDemo(nn.Module):
    LABEL_MAP: ClassVar[dict[int, str]] = {0: "negative", 1: "neutral", 2: "positive"}
    cfg_path: str
    cfg: dict[str, object]
    backbone: ResNet9
    temporal_pool: TemporalPoolLike
    hpp: HppLike
    fcs: FCsLike
    bn_necks: BNNecksLike
    device: torch.device

    @jaxtyped(typechecker=beartype)
    def __init__(
        self,
        cfg_path: str | Path = "configs/sconet/sconet_scoliosis1k.yaml",
        checkpoint_path: str | Path | None = None,
        device: str | torch.device | None = None,
    ) -> None:
        super().__init__()
        resolved_cfg = self._resolve_path(cfg_path)
        self.cfg_path = str(resolved_cfg)

        self.cfg = config_loader(self.cfg_path)

        model_cfg = self._extract_model_cfg(self.cfg)
        backbone_cfg = self._extract_dict(model_cfg, "backbone_cfg")

        if backbone_cfg.get("type") != "ResNet9":
            raise ValueError(
                "ScoNetDemo currently supports backbone type ResNet9 only."
            )

        self.backbone = ResNet9(
            block=self._extract_str(backbone_cfg, "block"),
            channels=self._extract_int_list(backbone_cfg, "channels"),
            in_channel=self._extract_int(backbone_cfg, "in_channel", default=1),
            layers=self._extract_int_list(backbone_cfg, "layers"),
            strides=self._extract_int_list(backbone_cfg, "strides"),
            maxpool=self._extract_bool(backbone_cfg, "maxpool", default=True),
        )

        fcs_cfg = self._extract_dict(model_cfg, "SeparateFCs")
        bn_cfg = self._extract_dict(model_cfg, "SeparateBNNecks")
        bin_num = self._extract_int_list(model_cfg, "bin_num")

        self.temporal_pool = cast(TemporalPoolLike, TemporalPool(torch.max))
        self.hpp = cast(HppLike, HorizontalPoolingPyramid(bin_num=bin_num))
        self.fcs = cast(
            FCsLike,
            SeparateFCs(
                parts_num=self._extract_int(fcs_cfg, "parts_num"),
                in_channels=self._extract_int(fcs_cfg, "in_channels"),
                out_channels=self._extract_int(fcs_cfg, "out_channels"),
                norm=self._extract_bool(fcs_cfg, "norm", default=False),
            ),
        )
        self.bn_necks = cast(
            BNNecksLike,
            SeparateBNNecks(
                parts_num=self._extract_int(bn_cfg, "parts_num"),
                in_channels=self._extract_int(bn_cfg, "in_channels"),
                class_num=self._extract_int(bn_cfg, "class_num"),
                norm=self._extract_bool(bn_cfg, "norm", default=True),
                parallel_BN1d=self._extract_bool(bn_cfg, "parallel_BN1d", default=True),
            ),
        )

        self.device = (
            torch.device(device) if device is not None else torch.device("cpu")
        )
        _ = self.to(self.device)

        if checkpoint_path is not None:
            _ = self.load_checkpoint(checkpoint_path)

        _ = self.eval()

    @staticmethod
    def _resolve_path(path: str | Path) -> Path:
        candidate = Path(path)
        if candidate.is_file():
            return candidate
        if candidate.is_absolute():
            return candidate
        repo_root = Path(__file__).resolve().parents[2]
        return repo_root / candidate

    @staticmethod
    def _extract_model_cfg(cfg: dict[str, object]) -> dict[str, object]:
        model_cfg_obj = cfg.get("model_cfg")
        if not isinstance(model_cfg_obj, dict):
            raise TypeError("model_cfg must be a dictionary.")
        return cast(dict[str, object], model_cfg_obj)

    @staticmethod
    def _extract_dict(container: dict[str, object], key: str) -> dict[str, object]:
        value = container.get(key)
        if not isinstance(value, dict):
            raise TypeError(f"{key} must be a dictionary.")
        return cast(dict[str, object], value)

    @staticmethod
    def _extract_str(container: dict[str, object], key: str) -> str:
        value = container.get(key)
        if not isinstance(value, str):
            raise TypeError(f"{key} must be a string.")
        return value

    @staticmethod
    def _extract_int(
        container: dict[str, object], key: str, default: int | None = None
    ) -> int:
        value = container.get(key, default)
        if not isinstance(value, int):
            raise TypeError(f"{key} must be an int.")
        return value

    @staticmethod
    def _extract_bool(
        container: dict[str, object], key: str, default: bool | None = None
    ) -> bool:
        value = container.get(key, default)
        if not isinstance(value, bool):
            raise TypeError(f"{key} must be a bool.")
        return value

    @staticmethod
    def _extract_int_list(container: dict[str, object], key: str) -> list[int]:
        value = container.get(key)
        if not isinstance(value, list):
            raise TypeError(f"{key} must be a list[int].")
        values = cast(list[object], value)
        if not all(isinstance(v, int) for v in values):
            raise TypeError(f"{key} must be a list[int].")
        return cast(list[int], values)

    @staticmethod
    def _normalize_state_dict(
        state_dict_obj: dict[object, object],
    ) -> dict[str, Tensor]:
        prefix_remap: tuple[tuple[str, str], ...] = (
            ("Backbone.forward_block.", "backbone."),
            ("FCs.", "fcs."),
            ("BNNecks.", "bn_necks."),
        )
        cleaned_state_dict: dict[str, Tensor] = {}
        for key_obj, value_obj in state_dict_obj.items():
            if not isinstance(key_obj, str):
                raise TypeError("Checkpoint state_dict keys must be strings.")
            if not isinstance(value_obj, Tensor):
                raise TypeError("Checkpoint state_dict values must be torch.Tensor.")
            key = key_obj[7:] if key_obj.startswith("module.") else key_obj
            for source_prefix, target_prefix in prefix_remap:
                if key.startswith(source_prefix):
                    key = f"{target_prefix}{key[len(source_prefix) :]}"
                    break
            if key in cleaned_state_dict:
                raise RuntimeError(
                    f"Checkpoint key normalization collision detected for key '{key}'."
                )
            cleaned_state_dict[key] = value_obj
        return cleaned_state_dict

    @jaxtyped(typechecker=beartype)
    def load_checkpoint(
        self,
        checkpoint_path: str | Path,
        map_location: str | torch.device | None = None,
        strict: bool = True,
    ) -> None:
        resolved_ckpt = self._resolve_path(checkpoint_path)
        checkpoint_obj = cast(
            object,
            torch.load(
                str(resolved_ckpt),
                map_location=map_location if map_location is not None else self.device,
            ),
        )

        state_dict_obj: object = checkpoint_obj
        if isinstance(checkpoint_obj, dict) and "model" in checkpoint_obj:
            state_dict_obj = cast(dict[str, object], checkpoint_obj)["model"]

        if not isinstance(state_dict_obj, dict):
            raise TypeError("Unsupported checkpoint format.")

        cleaned_state_dict = self._normalize_state_dict(
            cast(dict[object, object], state_dict_obj)
        )
        try:
            _ = self.load_state_dict(cleaned_state_dict, strict=strict)
        except RuntimeError as exc:
            raise RuntimeError(
                f"Failed to load ScoNetDemo checkpoint after key normalization from '{resolved_ckpt}'."
            ) from exc
        _ = self.eval()

    def _prepare_sils(self, sils: Tensor) -> Tensor:
        if sils.ndim == 4:
            sils = sils.unsqueeze(1)
        elif sils.ndim == 5 and sils.shape[1] != 1 and sils.shape[2] == 1:
            sils = rearrange(sils, "b s c h w -> b c s h w")

        if sils.ndim != 5 or sils.shape[1] != 1:
            raise ValueError("Expected sils shape [B, 1, S, H, W] or [B, S, H, W].")

        return sils.float().to(self.device)

    def _forward_backbone(self, sils: Tensor) -> Tensor:
        batch, channels, seq, height, width = sils.shape
        framewise = sils.transpose(1, 2).reshape(batch * seq, channels, height, width)
        frame_feats = cast(Tensor, self.backbone(framewise))
        _, out_channels, out_h, out_w = frame_feats.shape
        return (
            frame_feats.reshape(batch, seq, out_channels, out_h, out_w)
            .transpose(1, 2)
            .contiguous()
        )

    @override
    @jaxtyped(typechecker=beartype)
    def forward(self, sils: Float[Tensor, "batch 1 seq 64 44"]) -> dict[str, Tensor]:
        with torch.inference_mode():
            prepared_sils = self._prepare_sils(sils)
            outs = self._forward_backbone(prepared_sils)

            pooled_obj = self.temporal_pool(outs, None, options={"dim": 2})
            if (
                not isinstance(pooled_obj, tuple)
                or not pooled_obj
                or not isinstance(pooled_obj[0], Tensor)
            ):
                raise TypeError("TemporalPool output is invalid.")
            pooled = pooled_obj[0]

            feat = self.hpp(pooled)
            embed_1 = self.fcs(feat)
            _, logits = self.bn_necks(embed_1)

            mean_logits = logits.mean(dim=-1)
            pred_ids = torch.argmax(mean_logits, dim=-1)
            probs = torch.softmax(mean_logits, dim=-1)
            confidence = torch.gather(
                probs, dim=-1, index=pred_ids.unsqueeze(-1)
            ).squeeze(-1)

            return {"logits": logits, "label": pred_ids, "confidence": confidence}

    @jaxtyped(typechecker=beartype)
    def predict(self, sils: Float[Tensor, "batch 1 seq 64 44"]) -> tuple[str, float]:
        outputs = cast(dict[str, Tensor], self.forward(sils))
        labels = outputs["label"]
        confidence = outputs["confidence"]

        if labels.numel() != 1:
            raise ValueError("predict expects batch size 1.")

        label_id = int(labels.item())
        return self.LABEL_MAP[label_id], float(confidence.item())