"""Atomic safetensors writer and JSONL manifest for Predictor data."""

from __future__ import annotations

import json
import os
from pathlib import Path
from typing import Any, Mapping

import torch
from safetensors.torch import save_file

from .schema import SCHEMA_VERSION, validate_case_tensors, validate_chunk_tensors


def _cpu_contiguous(tensor: torch.Tensor, *, dtype: torch.dtype | None = None) -> torch.Tensor:
    tensor = tensor.detach()
    if dtype is not None and tensor.is_floating_point():
        tensor = tensor.to(dtype=dtype)
    return tensor.to(device="cpu").contiguous()


class PredictorDatasetWriter:
    def __init__(self, root: str | os.PathLike[str], *, save_dtype: torch.dtype = torch.bfloat16):
        self.root = Path(root).resolve()
        self.case_dir = self.root / "cases"
        self.chunk_dir = self.root / "chunks"
        self.manifest_path = self.root / "manifest.jsonl"
        self.train_eval_manifest_path = self.root / "train_eval_manifest.jsonl"
        self.save_dtype = save_dtype
        self.case_dir.mkdir(parents=True, exist_ok=True)
        self.chunk_dir.mkdir(parents=True, exist_ok=True)
        self._manifest_keys = self._load_existing_keys()

    def _load_existing_keys(self) -> set[tuple[int, int, int]]:
        keys: set[tuple[int, int, int]] = set()
        if not self.manifest_path.exists():
            return keys
        with self.manifest_path.open("r", encoding="utf-8") as handle:
            for line in handle:
                if not line.strip():
                    continue
                item = json.loads(line)
                keys.add((int(item["case_id"]), int(item["seed"]), int(item["chunk_id"])))
        return keys

    def case_path(self, case_id: int) -> Path:
        return self.case_dir / f"case_{case_id:02d}.safetensors"

    def chunk_path(self, case_id: int, seed: int, chunk_id: int) -> Path:
        return self.chunk_dir / f"case_{case_id:02d}_seed_{seed}" / f"chunk_{chunk_id:02d}.safetensors"

    def is_chunk_complete(self, case_id: int, seed: int, chunk_id: int) -> bool:
        key = (case_id, seed, chunk_id)
        return key in self._manifest_keys and self.chunk_path(case_id, seed, chunk_id).is_file()

    def _atomic_save(self, path: Path, tensors: Mapping[str, torch.Tensor]) -> None:
        path.parent.mkdir(parents=True, exist_ok=True)
        tmp_path = path.with_suffix(path.suffix + f".tmp.{os.getpid()}")
        save_file(dict(tensors), str(tmp_path))
        os.replace(tmp_path, path)

    def save_case(self, case_id: int, tensors: Mapping[str, torch.Tensor]) -> Path:
        converted = {
            name: _cpu_contiguous(tensor, dtype=self.save_dtype)
            for name, tensor in tensors.items()
        }
        validate_case_tensors(converted)
        path = self.case_path(case_id)
        self._atomic_save(path, converted)
        return path

    def save_chunk(
        self,
        *,
        case_id: int,
        seed: int,
        chunk_id: int,
        tensors: Mapping[str, torch.Tensor],
        metadata: Mapping[str, Any],
    ) -> Path:
        key = (case_id, seed, chunk_id)
        if self.is_chunk_complete(*key):
            return self.chunk_path(*key)

        converted = {}
        for name, tensor in tensors.items():
            dtype = self.save_dtype if tensor.is_floating_point() and "timestep" not in name else None
            if "timestep" in name:
                dtype = torch.float32
            converted[name] = _cpu_contiguous(tensor, dtype=dtype)
        validate_chunk_tensors(converted)

        path = self.chunk_path(*key)
        self._atomic_save(path, converted)
        record = {
            "schema_version": SCHEMA_VERSION,
            "case_id": case_id,
            "seed": seed,
            "chunk_id": chunk_id,
            "tensor_file": str(path.relative_to(self.root)),
            "case_tensor_file": str(self.case_path(case_id).relative_to(self.root)),
            **dict(metadata),
        }
        line = json.dumps(record, ensure_ascii=False, sort_keys=True) + "\n"
        for manifest in (self.manifest_path, self.train_eval_manifest_path):
            with manifest.open("a", encoding="utf-8") as handle:
                handle.write(line)
                handle.flush()
                os.fsync(handle.fileno())
        self._manifest_keys.add(key)
        return path