from __future__ import annotations

import hashlib
import json
import os
from pathlib import Path
from typing import Any, Dict, Iterable, List, Sequence

import torch


CAMERA_ORDER = [
    "CAM_FRONT",
    "CAM_FRONT_LEFT",
    "CAM_FRONT_RIGHT",
    "CAM_BACK",
    "CAM_BACK_LEFT",
    "CAM_BACK_RIGHT",
]

SYSTEM_PROMPT = (
    "You are an expert autonomous-driving assistant. Analyze the camera "
    "views carefully and answer the driving-scene question accurately, "
    "safely, and concisely. Do not invent objects that are not visible."
)


def camera_names(num_views: int) -> List[str]:
    if not 1 <= num_views <= len(CAMERA_ORDER):
        raise ValueError(f"num_views must be in [1, 6], got {num_views}")
    return CAMERA_ORDER[:num_views]


def load_rows(data_dir: str, split: str) -> List[Dict[str, Any]]:
    root = Path(data_dir)
    candidates = [
        root / f"{split}.json",
        root / f"drivelm_{split}.json",
        root / f"{split}.jsonl",
    ]
    path = next((item for item in candidates if item.is_file()), None)
    if path is None:
        raise FileNotFoundError(
            f"No {split} JSON/JSONL file under {root}. Expected one of: "
            + ", ".join(str(item) for item in candidates)
        )
    if path.suffix == ".jsonl":
        rows = []
        with path.open("r", encoding="utf-8") as handle:
            for line_no, line in enumerate(handle, 1):
                if line.strip():
                    row = json.loads(line)
                    if not isinstance(row, dict):
                        raise TypeError(f"{path}:{line_no} is not an object")
                    rows.append(row)
        return rows
    with path.open("r", encoding="utf-8") as handle:
        payload = json.load(handle)
    if not isinstance(payload, list):
        raise TypeError(
            f"{path} must be a list of flattened QA rows. Convert raw DriveLM first."
        )
    return payload


def normalized_row(row: Dict[str, Any]) -> Dict[str, Any]:
    question = str(row.get("question", row.get("query", ""))).strip()
    answer = str(row.get("answer", row.get("response", ""))).strip()
    image_paths = row.get("image_paths") or {}
    if not isinstance(image_paths, dict):
        raise TypeError("image_paths must be an object keyed by camera name")
    return {
        "scene_id": str(row.get("scene_id", "")),
        "frame_token": str(row.get("frame_token", "")),
        "task_type": str(row.get("task_type", row.get("category", "unknown"))),
        "question": question,
        "answer": answer,
        "image_paths": {str(key): str(value) for key, value in image_paths.items()},
    }


def validate_image_paths(
    image_paths: Dict[str, str],
    num_views: int,
    allow_missing: bool = False,
) -> List[str]:
    selected = []
    missing = []
    for camera in camera_names(num_views):
        value = str(image_paths.get(camera, ""))
        if not value or not os.path.isfile(value):
            missing.append(f"{camera}={value!r}")
        selected.append(value)
    if missing and not allow_missing:
        raise FileNotFoundError("Missing required camera images: " + "; ".join(missing))
    return selected


def build_messages(
    question: str,
    image_paths: Dict[str, str],
    num_views: int,
    answer: str | None = None,
) -> List[Dict[str, Any]]:
    paths = validate_image_paths(image_paths, num_views, allow_missing=False)
    content: List[Dict[str, str]] = [
        {"type": "image", "path": path} for path in paths
    ]
    content.append({"type": "text", "text": question})
    messages: List[Dict[str, Any]] = [
        {
            "role": "system",
            "content": [{"type": "text", "text": SYSTEM_PROMPT}],
        },
        {"role": "user", "content": content},
    ]
    if answer is not None:
        messages.append(
            {
                "role": "assistant",
                "content": [{"type": "text", "text": answer}],
            }
        )
    return messages


def apply_chat_template(
    processor,
    messages: Sequence[Dict[str, Any]],
    *,
    add_generation_prompt: bool,
    max_length: int,
):
    return processor.apply_chat_template(
        list(messages),
        add_generation_prompt=add_generation_prompt,
        tokenize=True,
        return_dict=True,
        return_tensors="pt",
        truncation=True,
        max_length=max_length,
    )


def move_to_device(batch: Dict[str, Any], device: torch.device) -> Dict[str, Any]:
    return {
        key: value.to(device) if torch.is_tensor(value) else value
        for key, value in batch.items()
    }


def append_response_ids(
    prompt_batch: Dict[str, Any],
    response_ids: torch.Tensor,
) -> tuple[Dict[str, Any], int]:
    input_ids = prompt_batch["input_ids"]
    if input_ids.shape[0] != 1 or response_ids.shape[0] != 1:
        raise ValueError("The first online OPD implementation requires batch size 1")
    prompt_len = int(input_ids.shape[1])
    result: Dict[str, Any] = {}
    for key, value in prompt_batch.items():
        if key in {"input_ids", "attention_mask", "position_ids", "cache_position"}:
            continue
        result[key] = value
    result["input_ids"] = torch.cat([input_ids, response_ids], dim=1)
    prompt_mask = prompt_batch.get("attention_mask", torch.ones_like(input_ids))
    response_mask = torch.ones_like(response_ids, dtype=prompt_mask.dtype)
    result["attention_mask"] = torch.cat([prompt_mask, response_mask], dim=1)
    return result, prompt_len


def tokenizer_fingerprint(tokenizer) -> str:
    """Hash token-id mapping and special tokens; OPD requires an exact match."""
    digest = hashlib.sha256()
    digest.update(str(len(tokenizer)).encode("utf-8"))
    for index in range(len(tokenizer)):
        token = tokenizer.convert_ids_to_tokens(index)
        digest.update(index.to_bytes(4, "little", signed=False))
        digest.update(str(token).encode("utf-8", errors="surrogatepass"))
        digest.update(b"\0")
    digest.update(
        json.dumps(
            tokenizer.special_tokens_map,
            ensure_ascii=False,
            sort_keys=True,
        ).encode("utf-8")
    )
    return digest.hexdigest()


def infer_input_device(model) -> torch.device:
    for parameter in model.parameters():
        if parameter.device.type != "meta":
            return parameter.device
    raise RuntimeError("Could not infer a real model input device")