from __future__ import annotations
from datetime import datetime, date
from typing import Any, Dict, Optional, Callable
from sqlalchemy import inspect

ISO_DT_FORMATS = ("%Y-%m-%dT%H:%M:%S.%f",
                  "%Y-%m-%dT%H:%M:%S",
                  "%Y-%m-%d %H:%M",
                  "%Y-%m-%d")

def filter_to_columns(data: dict, model_cls):
    cols = {c.key for c in inspect(model_cls).mapper.columns}
    return {k: v for k, v in data.items() if k in cols}

def _parse_dt_maybe(x: Any) -> Any:
    if isinstance(x, (datetime, date)):
        return x
    if isinstance(x, str):
        s = x.strip().replace("Z", "+00:00")  # tolerate Zulu
        for fmt in ISO_DT_FORMATS:
            try:
                return datetime.strptime(s, fmt)
            except ValueError:
                pass
        try:
            return datetime.fromisoformat(s)
        except Exception:
            return x
    return x

def _normalize_for_compare(x: Any) -> Any:
    if isinstance(x, (str, datetime, date)):
        return _parse_dt_maybe(x)
    return x

def deep_diff(
    old: Any,
    new: Any,
    *,
    path: str = "",
    ignore_keys: Optional[set] = None,
    list_mode: str = "index",  # "index" or "set"
    custom_equal: Optional[Callable[[str, Any, Any], bool]] = None,
) -> Dict[str, Dict[str, Any]]:
    if ignore_keys is None:
        ignore_keys = set()

    out: Dict[str, Dict[str, Any]] = {"added": {}, "removed": {}, "changed": {}}

    def mark_changed(p, a, b):
        out["changed"][p] = {"from": a, "to": b}

    def rec(o, n, pfx):
        if custom_equal and custom_equal(pfx.rstrip("."), o, n):
            return

        if isinstance(o, dict) and isinstance(n, dict):
            o_keys = set(o.keys())
            n_keys = set(n.keys())

            for k in sorted(o_keys - n_keys):
                if k not in ignore_keys:
                    out["removed"][f"{pfx}{k}"] = o[k]

            for k in sorted(n_keys - o_keys):
                if k not in ignore_keys:
                    out["added"][f"{pfx}{k}"] = n[k]

            for k in sorted(o_keys & n_keys):
                if k not in ignore_keys:
                    rec(o[k], n[k], f"{pfx}{k}.")
            return

        if isinstance(o, list) and isinstance(n, list):
            if list_mode == "set":
                if set(o) != set(n):
                    mark_changed(pfx.rstrip("."), o, n)
            else:
                max_len = max(len(o), len(n))
                for i in range(max_len):
                    key = f"{pfx}[{i}]"
                    if i >= len(o):
                        out["added"][key] = n[i]
                    elif i >= len(n):
                        out["removed"][key] = o[i]
                    else:
                        rec(o[i], n[i], f"{key}.")
            return

        a = _normalize_for_compare(o)
        b = _normalize_for_compare(n)
        if a != b:
            mark_changed(pfx.rstrip("."), o, n)

    rec(old, new, path)
    return out

def diff_to_patch(diff: Dict[str, Dict[str, Any]]) -> Dict[str, Any]:
    """
    Produce a shallow patch of changed/added top-level fields.
    Only includes leaf paths without dots/brackets; useful for simple UPDATEs.
    """
    patch: Dict[str, Any] = {}
    for k, v in diff["added"].items():
        if "." not in k and "[" not in k:
            patch[k] = v
    for k, v in diff["changed"].items():
        if "." not in k and "[" not in k:
            patch[k] = v["to"]
    return patch

def normalize_payload(payload: dict, model):
    """
    Coerce incoming JSON into SQLAlchemy column types for the given model.
    - "" or None -> None
    - Integer/Boolean/Date/DateTime handled by column type
    """
    from sqlalchemy import Integer, Boolean, DateTime, Date
    out: Dict[str, Any] = {}

    mapper = inspect(model).mapper
    cols = {c.key: c.type for c in mapper.columns}

    for field, value in payload.items():
        if value == "" or value is None:
            out[field] = None
            continue

        coltype = cols.get(field)
        if coltype is None:
            out[field] = value
            continue

        tname = coltype.__class__.__name__.lower()

        if "integer" in tname:
            out[field] = int(value)
        elif "boolean" in tname:
            out[field] = value if isinstance(value, bool) else str(value).lower() in ("1", "true", "yes", "on")
        elif "datetime" in tname:
            out[field] = value if isinstance(value, datetime) else _parse_dt_maybe(value)
        elif "date" in tname:
            v = _parse_dt_maybe(value)
            out[field] = v.date() if isinstance(v, datetime) else v
        else:
            out[field] = value

    return out