"""Load SWD factor checkpoints into an already constructed PyTorch model."""

from __future__ import annotations

import json
from dataclasses import dataclass
from pathlib import Path
from typing import Any, Literal

import torch
from safetensors.torch import load_file
from torch import nn


@dataclass(frozen=True)
class AppliedReplacement:
    module_path: str
    input_dim: int
    rank: int
    output_dim: int
    mode: str


class SWDLinear(nn.Module):
    """Two-factor linear map with explicit scalar bottleneck activations."""

    def __init__(
        self,
        read: torch.Tensor,
        write: torch.Tensor,
        bias: torch.Tensor | None = None,
    ) -> None:
        super().__init__()
        if read.ndim != 2 or write.ndim != 2 or read.shape[1] != write.shape[0]:
            raise ValueError(
                f"Invalid SWD shapes: read={tuple(read.shape)}, write={tuple(write.shape)}"
            )
        self.read = nn.Parameter(read.detach().contiguous(), requires_grad=False)
        self.write = nn.Parameter(write.detach().contiguous(), requires_grad=False)
        self.bias = (
            None
            if bias is None
            else nn.Parameter(bias.detach().contiguous(), requires_grad=False)
        )

    @property
    def in_features(self) -> int:
        return int(self.read.shape[0])

    @property
    def rank(self) -> int:
        return int(self.read.shape[1])

    @property
    def out_features(self) -> int:
        return int(self.write.shape[1])

    def component_activations(self, inputs: torch.Tensor) -> torch.Tensor:
        return inputs.matmul(self.read)

    def forward(self, inputs: torch.Tensor) -> torch.Tensor:
        outputs = self.component_activations(inputs).matmul(self.write)
        if self.bias is not None:
            outputs = outputs + self.bias
        return outputs


def _get_child(module: Any, name: str) -> Any:
    if name.isdigit() and isinstance(module, (nn.ModuleList, nn.Sequential)):
        return module[int(name)]
    return getattr(module, name)


def _set_child(module: Any, name: str, value: nn.Module) -> None:
    if name.isdigit() and isinstance(module, (nn.ModuleList, nn.Sequential)):
        module[int(name)] = value
        return
    setattr(module, name, value)


def _resolve_parent(model: nn.Module, module_path: str) -> tuple[Any, str, nn.Module]:
    parts = module_path.split(".")
    if not parts or any(not part for part in parts):
        raise ValueError(f"Invalid module path: {module_path!r}")
    parent: Any = model
    for part in parts[:-1]:
        parent = _get_child(parent, part)
    leaf = parts[-1]
    target = _get_child(parent, leaf)
    if not isinstance(target, nn.Module):
        raise TypeError(f"Target at {module_path!r} is not an nn.Module")
    return parent, leaf, target


def _module_device_dtype(module: nn.Module) -> tuple[torch.device, torch.dtype]:
    weight = getattr(module, "weight", None)
    if not torch.is_tensor(weight):
        raise TypeError("Target module must expose a materialized weight tensor")
    if weight.device.type == "meta":
        raise ValueError("Load/materialize the base model before applying SWD factors")
    if not weight.dtype.is_floating_point:
        raise TypeError(f"Unsupported target weight dtype: {weight.dtype}")
    return weight.device, weight.dtype


def _validate_base_weight(
    module: nn.Module,
    *,
    module_path: str,
    input_dim: int,
    output_dim: int,
    layout: str,
) -> None:
    weight = getattr(module, "weight", None)
    if not torch.is_tensor(weight):
        raise TypeError(f"{module_path} does not expose a weight tensor")
    expected = (
        (input_dim, output_dim) if layout == "in_out" else (output_dim, input_dim)
    )
    if tuple(weight.shape) != expected:
        raise ValueError(
            f"Base weight mismatch at {module_path}: got {tuple(weight.shape)}, expected {expected}"
        )


def _select_bias(
    module: nn.Module,
    tensors: dict[str, torch.Tensor],
    spec: dict[str, Any],
    *,
    device: torch.device,
    dtype: torch.dtype,
) -> torch.Tensor | None:
    policy = spec["bias_policy"]
    if policy == "checkpoint":
        bias = tensors[spec["bias_key"]]
    elif policy == "preserve_base":
        bias = getattr(module, "bias", None)
    elif policy == "none":
        bias = None
    else:
        raise ValueError(f"Unknown bias policy: {policy}")
    return None if bias is None else bias.detach().to(device=device, dtype=dtype)


def _fold_into_module(
    module: nn.Module,
    read: torch.Tensor,
    write: torch.Tensor,
    bias: torch.Tensor | None,
    *,
    layout: str,
) -> None:
    dense_in_out = read.matmul(write)
    dense = dense_in_out if layout == "in_out" else dense_in_out.transpose(0, 1)
    weight = getattr(module, "weight")
    with torch.no_grad():
        weight.copy_(dense.to(device=weight.device, dtype=weight.dtype))
        existing_bias = getattr(module, "bias", None)
        if bias is not None:
            if existing_bias is None:
                raise ValueError("Checkpoint provides bias but base module has no bias parameter")
            existing_bias.copy_(bias.to(existing_bias.device, existing_bias.dtype))


def load_swd_config(checkpoint_dir: str | Path) -> dict[str, Any]:
    path = Path(checkpoint_dir) / "config.json"
    with path.open(encoding="utf-8") as handle:
        config = json.load(handle)
    if config.get("schema_version") != "swd_factor_checkpoint_v1":
        raise ValueError(f"Unsupported SWD schema: {config.get('schema_version')!r}")
    return config


def apply_swd_checkpoint(
    model: nn.Module,
    checkpoint_dir: str | Path,
    *,
    mode: Literal["factorized", "folded"] = "factorized",
) -> list[AppliedReplacement]:
    """Apply one release checkpoint to a loaded base model.

    ``factorized`` installs :class:`SWDLinear` modules and preserves explicit
    bottleneck activations. ``folded`` writes ``read @ write`` into the existing
    dense modules for conventional inference.
    """

    if mode not in {"factorized", "folded"}:
        raise ValueError(f"Unknown mode: {mode}")
    root = Path(checkpoint_dir)
    config = load_swd_config(root)
    tensors = load_file(root / config["weights_file"], device="cpu")
    applied: list[AppliedReplacement] = []

    for spec in config["module_replacements"]:
        path = spec["module_path"]
        parent, leaf, module = _resolve_parent(model, path)
        input_dim = int(spec["input_dim"])
        rank = int(spec["rank"])
        output_dim = int(spec["output_dim"])
        layout = spec["base_weight_layout"]
        _validate_base_weight(
            module,
            module_path=path,
            input_dim=input_dim,
            output_dim=output_dim,
            layout=layout,
        )
        device, dtype = _module_device_dtype(module)
        read = tensors[spec["read_key"]]
        write = tensors[spec["write_key"]]
        if tuple(read.shape) != (input_dim, rank):
            raise ValueError(f"Read tensor mismatch for {path}: {tuple(read.shape)}")
        if tuple(write.shape) != (rank, output_dim):
            raise ValueError(f"Write tensor mismatch for {path}: {tuple(write.shape)}")
        read = read.to(device=device, dtype=dtype)
        write = write.to(device=device, dtype=dtype)
        bias = _select_bias(module, tensors, spec, device=device, dtype=dtype)

        if mode == "factorized":
            _set_child(parent, leaf, SWDLinear(read, write, bias))
        else:
            _fold_into_module(module, read, write, bias, layout=layout)

        applied.append(
            AppliedReplacement(path, input_dim, rank, output_dim, mode)
        )
    return applied