"""
Gr00t data exporter for LeRobot-format datasets.
"""

import copy
from dataclasses import asdict, dataclass
from functools import partial
import json
import os
from pathlib import Path
import shutil
from typing import Any, Optional

import datasets
from datasets import load_dataset
from datasets.utils import disable_progress_bars
from huggingface_hub.errors import RepositoryNotFoundError
from lerobot.common.datasets.lerobot_dataset import (
    LeRobotDataset,
    LeRobotDatasetMetadata,
    compute_episode_stats,
)
from lerobot.common.datasets.utils import (
    check_timestamps_sync,
    get_episode_data_index,
    validate_episode_buffer,
    validate_frame,
)
import numpy as np
from PIL import Image as PILImage
import torch
from torchvision import transforms

from gear_sonic.data.video_writer import VideoWriter

disable_progress_bars()


# ---------------------------------------------------------------------------
# ArgsConfig (inlined from decoupled_wbc.control.main.config_template)
# ---------------------------------------------------------------------------


@dataclass
class ArgsConfig:
    """Minimal config dataclass for script_config serialization."""

    def update(
        self,
        config_dict: dict,
        strict: bool = False,
        skip_keys: list[str] = [],
        allowed_keys: list[str] | None = None,
    ):
        for k, v in config_dict.items():
            if k in skip_keys:
                continue
            if allowed_keys is not None and k not in allowed_keys:
                continue
            if strict and not hasattr(self, k):
                raise ValueError(f"Config {k} not found in {self.__class__.__name__}")
            if not strict and not hasattr(self, k):
                continue
            setattr(self, k, v)

    @classmethod
    def from_dict(
        cls,
        config_dict: dict,
        strict: bool = False,
        skip_keys: list[str] = [],
        allowed_keys: list[str] | None = None,
    ):
        instance = cls()
        instance.update(
            config_dict=config_dict, strict=strict, skip_keys=skip_keys, allowed_keys=allowed_keys
        )
        return instance

    def to_dict(self):
        return asdict(self)

    def get(self, key: str, default: Any = None):
        return getattr(self, key) if hasattr(self, key) else default


# ---------------------------------------------------------------------------
# Gr00tDatasetMetadata
# ---------------------------------------------------------------------------


class Gr00tDatasetMetadata(LeRobotDatasetMetadata):
    """Additional metadata on top of LeRobotDatasetMetadata:
    - modality_config: Written to ``meta/modality.json``
    - discarded_episode_indices: Written to ``meta/info.json``
    """

    MODALITY_CONFIG_REL_PATH = Path("meta/modality.json")

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        with open(self.root / self.MODALITY_CONFIG_REL_PATH, "rb") as f:
            self.modality_config = json.load(f)

    @classmethod
    def create(
        cls,
        modality_config: dict,
        script_config: dict,
        *args,
        **kwargs,
    ):
        cls.validate_modality_config(modality_config)

        obj = super().create(*args, **kwargs)

        obj.info["script_config"] = script_config
        obj.info["discarded_episode_indices"] = []
        with open(obj.root / "meta" / "info.json", "w") as f:
            json.dump(obj.info, f, indent=4)

        obj.__class__ = cls
        with open(obj.root / cls.MODALITY_CONFIG_REL_PATH, "w") as f:
            json.dump(modality_config, f, indent=4)
        obj.modality_config = modality_config
        return obj

    @staticmethod
    def validate_modality_config(modality_config: dict) -> None:
        valid_keys = ["state", "action", "video", "annotation"]
        if not all(key in modality_config for key in valid_keys):
            raise ValueError(
                f"Modality config must contain all of the following keys: {valid_keys}"
            )
        for key in valid_keys:
            if key not in modality_config:
                raise ValueError(f"Modality config must contain a '{key}' key")


# ---------------------------------------------------------------------------
# Gr00tDataExporter
# ---------------------------------------------------------------------------


class Gr00tDataExporter(LeRobotDataset):
    """Exports data collected for a single session to LeRobot Dataset.

    Lifecycle:
    1. Create a Gr00tDataExporter object
    2. Add frames using add_frame()
    3. Save the episode using save_episode()
       - Flushes the episode buffer to disk
       - Closes the video writers
       - Creates new video writer and ep buffer for the next episode
    """

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.video_writers = self.create_video_writer()

    @property
    def repo_id(self):
        return self.meta.repo_id

    @property
    def root(self):
        return self.meta.root

    @property
    def local_files_only(self):
        return self.meta.local_files_only

    @property
    def video_keys(self):
        return self.meta.video_keys

    @classmethod
    def create(
        cls,
        save_root: str | Path,
        fps: int,
        features: dict,
        modality_config: dict,
        task: str,
        script_config: ArgsConfig | dict = None,
        robot_type: str | None = None,
        tolerance_s: float = 1e-4,
        vcodec: str = "h264",
        overwrite_existing: bool = False,
    ) -> "Gr00tDataExporter":
        if script_config is None:
            script_config = {}

        obj = cls.__new__(cls)
        repo_id = "tmp/tmp_dataset"

        if overwrite_existing and (Path(save_root)).exists():
            print(
                f"Found existing dataset at {save_root}",
                "Cleaning up this directory since overwrite_existing is True.",
            )
            shutil.rmtree(save_root)

        if (Path(save_root)).exists():
            try:
                obj.meta = Gr00tDatasetMetadata(
                    repo_id=repo_id,
                    root=save_root,
                )
            except RepositoryNotFoundError as e:
                raise ValueError(
                    f"Failed to resume from corrupted dataset. "
                    f"Please manually check the dataset at {save_root}"
                ) from e
        else:
            if not isinstance(script_config, dict):
                script_config = script_config.to_dict()
            obj.meta = Gr00tDatasetMetadata.create(
                repo_id=repo_id,
                fps=fps,
                root=save_root,
                robot=None,
                robot_type=robot_type,
                features=features,
                modality_config=modality_config,
                script_config=script_config,
                use_videos=True,
            )

        obj.tolerance_s = tolerance_s
        obj.video_backend = "pyav"
        obj.vcodec = vcodec
        obj.task = task
        obj.image_writer = None

        obj.episode_buffer = obj.create_episode_buffer()

        obj.episodes = None
        obj.hf_dataset = obj.create_hf_dataset()
        obj.image_transforms = None
        obj.delta_timestamps = None
        obj.delta_indices = None
        obj.episode_data_index = None
        obj.video_writers = obj.create_video_writer()
        return obj

    def create_video_writer(self) -> dict[str, VideoWriter]:
        video_writers = {}
        for key in self.meta.video_keys:
            video_writers[key] = VideoWriter(
                self.root
                / self.meta.get_video_file_path(self.episode_buffer["episode_index"], key),
                self.meta.shapes[key][1],
                self.meta.shapes[key][0],
                self.fps,
                self.vcodec,
            )
        return video_writers

    def add_frame(self, frame: dict) -> None:
        """Add a frame to the episode buffer. Videos are handled by the video_writer."""
        frame = copy.deepcopy(frame)
        frame["task"] = frame.get("task", self.task)

        for name in frame:
            if isinstance(frame[name], torch.Tensor):
                frame[name] = frame[name].numpy()

        validate_frame(frame, self.features)

        if self.episode_buffer is None:
            self.episode_buffer = self.create_episode_buffer()

        frame_index = self.episode_buffer["size"]
        timestamp = frame.pop("timestamp") if "timestamp" in frame else frame_index / self.fps
        self.episode_buffer["frame_index"].append(frame_index)
        self.episode_buffer["timestamp"].append(timestamp)

        for key in frame:
            if key == "task":
                self.episode_buffer["task"].append(frame["task"])
                continue

            if key not in self.features:
                raise ValueError(
                    f"An element of the frame is not in the features. "
                    f"'{key}' not in '{self.features.keys()}'."
                )

            if self.features[key]["dtype"] in ["image", "video"]:
                img_path = self._get_image_file_path(
                    episode_index=self.episode_buffer["episode_index"],
                    image_key=key,
                    frame_index=frame_index,
                )
                if frame_index == 0:
                    img_path.parent.mkdir(parents=True, exist_ok=True)

                self.video_writers[key].add_frame(frame[key])
                self.episode_buffer[key].append(str(img_path))
            else:
                self.episode_buffer[key].append(frame[key])

        self.episode_buffer["size"] += 1

    def stop_video_writers(self):
        if not hasattr(self, "video_writers"):
            raise RuntimeError(
                "Can't stop video writers because they haven't been initialized. Call create() first."
            )
        for key in self.video_writers:
            self.video_writers[key].stop()

    def skip_and_start_new_episode(self) -> None:
        """Skip the current episode and start a new one."""
        self.stop_video_writers()
        self.episode_buffer = self.create_episode_buffer()
        self.video_writers = self.create_video_writer()

    def save_episode(self, episode_data: dict | None = None) -> None:
        if not episode_data:
            episode_buffer = self.episode_buffer

        validate_episode_buffer(episode_buffer, self.meta.total_episodes, self.features)

        episode_length = episode_buffer.pop("size")
        tasks = episode_buffer.pop("task")
        episode_tasks = list(set(tasks))
        episode_index = episode_buffer["episode_index"]

        episode_buffer["index"] = np.arange(
            self.meta.total_frames, self.meta.total_frames + episode_length
        )
        episode_buffer["episode_index"] = np.full((episode_length,), episode_index)

        for task in episode_tasks:
            task_index = self.meta.get_task_index(task)
            if task_index is None:
                self.meta.add_task(task)

        episode_buffer["task_index"] = np.array([self.meta.get_task_index(task) for task in tasks])

        for key, ft in self.features.items():
            if key in ["index", "episode_index", "task_index"] or ft["dtype"] in ["image", "video"]:
                continue
            episode_buffer[key] = np.stack(episode_buffer[key])

        self._wait_image_writer()
        self._save_episode_table(episode_buffer, episode_index)

        non_video_features = {k: v for k, v in self.features.items() if v["dtype"] not in ["video"]}
        non_vid_ep_buffer = {
            k: v for k, v in episode_buffer.items() if k in non_video_features.keys()
        }
        ep_stats = compute_episode_stats(non_vid_ep_buffer, non_video_features)

        if len(self.meta.video_keys) > 0:
            video_paths = self.encode_episode_videos(episode_index)
            for key in self.meta.video_keys:
                episode_buffer[key] = video_paths[key]

        self.meta.save_episode(episode_index, episode_length, episode_tasks, ep_stats)

        ep_data_index = get_episode_data_index(self.meta.episodes, [episode_index])
        ep_data_index_np = {k: t.numpy() for k, t in ep_data_index.items()}
        check_timestamps_sync(
            episode_buffer["timestamp"],
            episode_buffer["episode_index"],
            ep_data_index_np,
            self.fps,
            self.tolerance_s,
        )

        video_files = list(self.root.rglob("*.mp4"))
        assert len(video_files) == self.num_episodes * len(self.meta.video_keys)

        parquet_files = list(self.root.rglob("*.parquet"))
        assert len(parquet_files) == self.num_episodes

        img_dir = self.root / "images"
        if img_dir.is_dir():
            shutil.rmtree(self.root / "images")

        if not episode_data:
            self.episode_buffer = self.create_episode_buffer()
            self.video_writers = self.create_video_writer()

        for key in self.meta.video_keys:
            video_path = os.path.join(self.root, self.meta.get_video_file_path(episode_index, key))
            if not os.path.exists(video_path):
                raise FileNotFoundError(
                    f"Video path: {video_path} does not exist for episode {episode_index}"
                )

        parquet_path = os.path.join(self.root, self.meta.get_data_file_path(episode_index))
        if not os.path.exists(parquet_path):
            raise FileNotFoundError(
                f"Parquet path: {parquet_path} does not exist for episode {episode_index}"
            )

    def encode_episode_videos(self, episode_index: int) -> dict:
        video_paths = {}
        for key in self.meta.video_keys:
            video_paths[key] = self.video_writers[key].stop()
        return video_paths

    def save_episode_as_discarded(self) -> None:
        """Flag ongoing episode as discarded and save it to disk."""
        self.meta.info["discarded_episode_indices"] = self.meta.info.get(
            "discarded_episode_indices", []
        ) + [self.episode_buffer["episode_index"]]
        self.save_episode()


# ---------------------------------------------------------------------------
# HF dataset helpers (for loading saved datasets)
# ---------------------------------------------------------------------------


def hf_transform_to_torch_by_features(
    features: datasets.Sequence, items_dict: dict[torch.Tensor | None]
):
    for key in items_dict:
        first_item = items_dict[key][0]
        if isinstance(first_item, PILImage.Image):
            to_tensor = transforms.ToTensor()
            items_dict[key] = [to_tensor(img) for img in items_dict[key]]
        elif first_item is None:
            pass
        else:
            if isinstance(features[key], datasets.Value):
                dtype_str = features[key].dtype
            elif isinstance(features[key], datasets.Sequence):
                assert isinstance(features[key].feature, datasets.Value)
                dtype_str = features[key].feature.dtype
            else:
                raise ValueError(f"Unsupported feature type for key '{key}': {features[key]}")
            dtype_mapping = {
                "float32": torch.float32,
                "float64": torch.float64,
                "int32": torch.int32,
                "int64": torch.int64,
            }
            items_dict[key] = [
                torch.tensor(x, dtype=dtype_mapping[dtype_str]) for x in items_dict[key]
            ]
    return items_dict


class TypedLeRobotDataset(LeRobotDataset):
    def __init__(self, load_video=True, *args, **kwargs):
        super().__init__(*args, **kwargs)
        if not load_video:
            video_keys = []
            for key in self.meta.features.keys():
                if self.meta.features[key]["dtype"] == "video":
                    video_keys.append(key)
            for key in video_keys:
                self.meta.features.pop(key)

    def load_hf_dataset(self) -> datasets.Dataset:
        if self.episodes is None:
            path = str(self.root / "data")
            hf_dataset = load_dataset("parquet", data_dir=path, split="train")
        else:
            files = [
                str(self.root / self.meta.get_data_file_path(ep_idx)) for ep_idx in self.episodes
            ]
            hf_dataset = load_dataset("parquet", data_files=files, split="train")

        hf_dataset.set_transform(partial(hf_transform_to_torch_by_features, hf_dataset.features))
        return hf_dataset