import random
from pathlib import Path
from unittest.mock import patch

import datasets
import numpy as np
import PIL.Image
import pytest
import torch

from lerobot.common.datasets.lerobot_dataset import CODEBASE_VERSION, LeRobotDataset, LeRobotDatasetMetadata
from lerobot.common.datasets.utils import (
    DEFAULT_CHUNK_SIZE,
    DEFAULT_FEATURES,
    DEFAULT_PARQUET_PATH,
    DEFAULT_VIDEO_PATH,
    get_hf_features_from_features,
    hf_transform_to_torch,
)
from tests.fixtures.constants import (
    DEFAULT_FPS,
    DUMMY_CAMERA_FEATURES,
    DUMMY_MOTOR_FEATURES,
    DUMMY_REPO_ID,
    DUMMY_ROBOT_TYPE,
    DUMMY_VIDEO_INFO,
)


def get_task_index(task_dicts: dict, task: str) -> int:
    tasks = {d["task_index"]: d["task"] for d in task_dicts}
    task_to_task_index = {task: task_idx for task_idx, task in tasks.items()}
    return task_to_task_index[task]


@pytest.fixture(scope="session")
def img_tensor_factory():
    def _create_img_tensor(height=100, width=100, channels=3, dtype=torch.float32) -> torch.Tensor:
        return torch.rand((channels, height, width), dtype=dtype)

    return _create_img_tensor


@pytest.fixture(scope="session")
def img_array_factory():
    def _create_img_array(height=100, width=100, channels=3, dtype=np.uint8) -> np.ndarray:
        if np.issubdtype(dtype, np.unsignedinteger):
            # Int array in [0, 255] range
            img_array = np.random.randint(0, 256, size=(height, width, channels), dtype=dtype)
        elif np.issubdtype(dtype, np.floating):
            # Float array in [0, 1] range
            img_array = np.random.rand(height, width, channels).astype(dtype)
        else:
            raise ValueError(dtype)
        return img_array

    return _create_img_array


@pytest.fixture(scope="session")
def img_factory(img_array_factory):
    def _create_img(height=100, width=100) -> PIL.Image.Image:
        img_array = img_array_factory(height=height, width=width)
        return PIL.Image.fromarray(img_array)

    return _create_img


@pytest.fixture(scope="session")
def features_factory():
    def _create_features(
        motor_features: dict = DUMMY_MOTOR_FEATURES,
        camera_features: dict = DUMMY_CAMERA_FEATURES,
        use_videos: bool = True,
    ) -> dict:
        if use_videos:
            camera_ft = {
                key: {"dtype": "video", **ft, **DUMMY_VIDEO_INFO} for key, ft in camera_features.items()
            }
        else:
            camera_ft = {key: {"dtype": "image", **ft} for key, ft in camera_features.items()}
        return {
            **motor_features,
            **camera_ft,
            **DEFAULT_FEATURES,
        }

    return _create_features


@pytest.fixture(scope="session")
def info_factory(features_factory):
    def _create_info(
        codebase_version: str = CODEBASE_VERSION,
        fps: int = DEFAULT_FPS,
        robot_type: str = DUMMY_ROBOT_TYPE,
        total_episodes: int = 0,
        total_frames: int = 0,
        total_tasks: int = 0,
        total_videos: int = 0,
        total_chunks: int = 0,
        chunks_size: int = DEFAULT_CHUNK_SIZE,
        data_path: str = DEFAULT_PARQUET_PATH,
        video_path: str = DEFAULT_VIDEO_PATH,
        motor_features: dict = DUMMY_MOTOR_FEATURES,
        camera_features: dict = DUMMY_CAMERA_FEATURES,
        use_videos: bool = True,
    ) -> dict:
        features = features_factory(motor_features, camera_features, use_videos)
        return {
            "codebase_version": codebase_version,
            "robot_type": robot_type,
            "total_episodes": total_episodes,
            "total_frames": total_frames,
            "total_tasks": total_tasks,
            "total_videos": total_videos,
            "total_chunks": total_chunks,
            "chunks_size": chunks_size,
            "fps": fps,
            "splits": {},
            "data_path": data_path,
            "video_path": video_path if use_videos else None,
            "features": features,
        }

    return _create_info


@pytest.fixture(scope="session")
def stats_factory():
    def _create_stats(
        features: dict[str] | None = None,
    ) -> dict:
        stats = {}
        for key, ft in features.items():
            shape = ft["shape"]
            dtype = ft["dtype"]
            if dtype in ["image", "video"]:
                stats[key] = {
                    "max": np.full((3, 1, 1), 1, dtype=np.float32).tolist(),
                    "mean": np.full((3, 1, 1), 0.5, dtype=np.float32).tolist(),
                    "min": np.full((3, 1, 1), 0, dtype=np.float32).tolist(),
                    "std": np.full((3, 1, 1), 0.25, dtype=np.float32).tolist(),
                }
            else:
                stats[key] = {
                    "max": np.full(shape, 1, dtype=dtype).tolist(),
                    "mean": np.full(shape, 0.5, dtype=dtype).tolist(),
                    "min": np.full(shape, 0, dtype=dtype).tolist(),
                    "std": np.full(shape, 0.25, dtype=dtype).tolist(),
                }
        return stats

    return _create_stats


@pytest.fixture(scope="session")
def tasks_factory():
    def _create_tasks(total_tasks: int = 3) -> int:
        tasks_list = []
        for i in range(total_tasks):
            task_dict = {"task_index": i, "task": f"Perform action {i}."}
            tasks_list.append(task_dict)
        return tasks_list

    return _create_tasks


@pytest.fixture(scope="session")
def episodes_factory(tasks_factory):
    def _create_episodes(
        total_episodes: int = 3,
        total_frames: int = 400,
        tasks: dict | None = None,
        multi_task: bool = False,
    ):
        if total_episodes <= 0 or total_frames <= 0:
            raise ValueError("num_episodes and total_length must be positive integers.")
        if total_frames < total_episodes:
            raise ValueError("total_length must be greater than or equal to num_episodes.")

        if not tasks:
            min_tasks = 2 if multi_task else 1
            total_tasks = random.randint(min_tasks, total_episodes)
            tasks = tasks_factory(total_tasks)

        if total_episodes < len(tasks) and not multi_task:
            raise ValueError("The number of tasks should be less than the number of episodes.")

        # Generate random lengths that sum up to total_length
        lengths = np.random.multinomial(total_frames, [1 / total_episodes] * total_episodes).tolist()

        tasks_list = [task_dict["task"] for task_dict in tasks]
        num_tasks_available = len(tasks_list)

        episodes_list = []
        remaining_tasks = tasks_list.copy()
        for ep_idx in range(total_episodes):
            num_tasks_in_episode = random.randint(1, min(3, num_tasks_available)) if multi_task else 1
            tasks_to_sample = remaining_tasks if remaining_tasks else tasks_list
            episode_tasks = random.sample(tasks_to_sample, min(num_tasks_in_episode, len(tasks_to_sample)))
            if remaining_tasks:
                for task in episode_tasks:
                    remaining_tasks.remove(task)

            episodes_list.append(
                {
                    "episode_index": ep_idx,
                    "tasks": episode_tasks,
                    "length": lengths[ep_idx],
                }
            )

        return episodes_list

    return _create_episodes


@pytest.fixture(scope="session")
def hf_dataset_factory(features_factory, tasks_factory, episodes_factory, img_array_factory):
    def _create_hf_dataset(
        features: dict | None = None,
        tasks: list[dict] | None = None,
        episodes: list[dict] | None = None,
        fps: int = DEFAULT_FPS,
    ) -> datasets.Dataset:
        if not tasks:
            tasks = tasks_factory()
        if not episodes:
            episodes = episodes_factory()
        if not features:
            features = features_factory()

        timestamp_col = np.array([], dtype=np.float32)
        frame_index_col = np.array([], dtype=np.int64)
        episode_index_col = np.array([], dtype=np.int64)
        task_index = np.array([], dtype=np.int64)
        for ep_dict in episodes:
            timestamp_col = np.concatenate((timestamp_col, np.arange(ep_dict["length"]) / fps))
            frame_index_col = np.concatenate((frame_index_col, np.arange(ep_dict["length"], dtype=int)))
            episode_index_col = np.concatenate(
                (episode_index_col, np.full(ep_dict["length"], ep_dict["episode_index"], dtype=int))
            )
            ep_task_index = get_task_index(tasks, ep_dict["tasks"][0])
            task_index = np.concatenate((task_index, np.full(ep_dict["length"], ep_task_index, dtype=int)))

        index_col = np.arange(len(episode_index_col))

        robot_cols = {}
        for key, ft in features.items():
            if ft["dtype"] == "image":
                robot_cols[key] = [
                    img_array_factory(height=ft["shapes"][1], width=ft["shapes"][0])
                    for _ in range(len(index_col))
                ]
            elif ft["shape"][0] > 1 and ft["dtype"] != "video":
                robot_cols[key] = np.random.random((len(index_col), ft["shape"][0])).astype(ft["dtype"])

        hf_features = get_hf_features_from_features(features)
        dataset = datasets.Dataset.from_dict(
            {
                **robot_cols,
                "timestamp": timestamp_col,
                "frame_index": frame_index_col,
                "episode_index": episode_index_col,
                "index": index_col,
                "task_index": task_index,
            },
            features=hf_features,
        )
        dataset.set_transform(hf_transform_to_torch)
        return dataset

    return _create_hf_dataset


@pytest.fixture(scope="session")
def lerobot_dataset_metadata_factory(
    info_factory,
    stats_factory,
    tasks_factory,
    episodes_factory,
    mock_snapshot_download_factory,
):
    def _create_lerobot_dataset_metadata(
        root: Path,
        repo_id: str = DUMMY_REPO_ID,
        info: dict | None = None,
        stats: dict | None = None,
        tasks: list[dict] | None = None,
        episodes: list[dict] | None = None,
        local_files_only: bool = False,
    ) -> LeRobotDatasetMetadata:
        if not info:
            info = info_factory()
        if not stats:
            stats = stats_factory(features=info["features"])
        if not tasks:
            tasks = tasks_factory(total_tasks=info["total_tasks"])
        if not episodes:
            episodes = episodes_factory(
                total_episodes=info["total_episodes"], total_frames=info["total_frames"], tasks=tasks
            )

        mock_snapshot_download = mock_snapshot_download_factory(
            info=info,
            stats=stats,
            tasks=tasks,
            episodes=episodes,
        )
        with (
            patch(
                "lerobot.common.datasets.lerobot_dataset.get_hub_safe_version"
            ) as mock_get_hub_safe_version_patch,
            patch(
                "lerobot.common.datasets.lerobot_dataset.snapshot_download"
            ) as mock_snapshot_download_patch,
        ):
            mock_get_hub_safe_version_patch.side_effect = lambda repo_id, version: version
            mock_snapshot_download_patch.side_effect = mock_snapshot_download

            return LeRobotDatasetMetadata(repo_id=repo_id, root=root, local_files_only=local_files_only)

    return _create_lerobot_dataset_metadata


@pytest.fixture(scope="session")
def lerobot_dataset_factory(
    info_factory,
    stats_factory,
    tasks_factory,
    episodes_factory,
    hf_dataset_factory,
    mock_snapshot_download_factory,
    lerobot_dataset_metadata_factory,
):
    def _create_lerobot_dataset(
        root: Path,
        repo_id: str = DUMMY_REPO_ID,
        total_episodes: int = 3,
        total_frames: int = 150,
        total_tasks: int = 1,
        multi_task: bool = False,
        info: dict | None = None,
        stats: dict | None = None,
        tasks: list[dict] | None = None,
        episode_dicts: list[dict] | None = None,
        hf_dataset: datasets.Dataset | None = None,
        **kwargs,
    ) -> LeRobotDataset:
        if not info:
            info = info_factory(
                total_episodes=total_episodes, total_frames=total_frames, total_tasks=total_tasks
            )
        if not stats:
            stats = stats_factory(features=info["features"])
        if not tasks:
            tasks = tasks_factory(total_tasks=info["total_tasks"])
        if not episode_dicts:
            episode_dicts = episodes_factory(
                total_episodes=info["total_episodes"],
                total_frames=info["total_frames"],
                tasks=tasks,
                multi_task=multi_task,
            )
        if not hf_dataset:
            hf_dataset = hf_dataset_factory(tasks=tasks, episodes=episode_dicts, fps=info["fps"])

        mock_snapshot_download = mock_snapshot_download_factory(
            info=info,
            stats=stats,
            tasks=tasks,
            episodes=episode_dicts,
            hf_dataset=hf_dataset,
        )
        mock_metadata = lerobot_dataset_metadata_factory(
            root=root,
            repo_id=repo_id,
            info=info,
            stats=stats,
            tasks=tasks,
            episodes=episode_dicts,
            local_files_only=kwargs.get("local_files_only", False),
        )
        with (
            patch("lerobot.common.datasets.lerobot_dataset.LeRobotDatasetMetadata") as mock_metadata_patch,
            patch(
                "lerobot.common.datasets.lerobot_dataset.snapshot_download"
            ) as mock_snapshot_download_patch,
        ):
            mock_metadata_patch.return_value = mock_metadata
            mock_snapshot_download_patch.side_effect = mock_snapshot_download

            return LeRobotDataset(repo_id=repo_id, root=root, **kwargs)

    return _create_lerobot_dataset