# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
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"""
Run this command to interactively debug:
PYTHONPATH=. python cosmos_predict1/tokenizer/training/datasets/video_dataset.py

Adapted from:
https://github.com/bytedance/IRASim/blob/main/dataset/dataset_3D.py
"""

import traceback
import warnings
from concurrent.futures import ThreadPoolExecutor, as_completed
from glob import glob

import numpy as np
import torch
from decord import VideoReader, cpu
from torch.utils.data import Dataset
from torchvision import transforms as T
from tqdm import tqdm

from cosmos_predict1.diffusion.training.datasets.dataset_utils import ToTensorVideo


class Dataset(Dataset):
    def __init__(
        self,
        video_pattern,
        sequence_interval=1,
        start_frame_interval=1,
        num_video_frames=25,
    ):
        """Dataset class for loading image-text-to-video generation data.

        Args:
            video_pattern (str): path/to/videos/*.mp4
            sequence_interval (int): Interval between sampled frames in a sequence
            num_frames (int): Number of frames to load per sequence
            video_size (list): Target size [H,W] for video frames

        Returns dict with:
            - video: RGB frames tensor [T,C,H,W]
            - video_name: Dict with episode/frame metadata
        """

        super().__init__()
        self.video_directory_or_pattern = video_pattern
        self.start_frame_interval = start_frame_interval
        self.sequence_interval = sequence_interval
        self.sequence_length = num_video_frames

        self.video_paths = sorted(glob(str(video_pattern)))
        print(f"{len(self.video_paths)} videos in total")

        self.samples = self._init_samples(self.video_paths)
        self.samples = sorted(self.samples, key=lambda x: (x["video_path"], x["frame_ids"][0]))
        print(f"{len(self.samples)} samples in total")
        self.wrong_number = 0
        self.preprocess = T.Compose(
            [
                ToTensorVideo(),
            ]
        )

    def __str__(self):
        return f"{len(self.video_paths)} samples from {self.video_directory_or_pattern}"

    def _init_samples(self, video_paths):
        samples = []
        with ThreadPoolExecutor(32) as executor:
            future_to_video_path = {
                executor.submit(self._load_and_process_video_path, video_path): video_path for video_path in video_paths
            }
            for future in tqdm(as_completed(future_to_video_path), total=len(video_paths)):
                samples.extend(future.result())
        return samples

    def _load_and_process_video_path(self, video_path):
        vr = VideoReader(video_path, ctx=cpu(0), num_threads=2)
        n_frames = len(vr)

        samples = []
        for frame_i in range(0, n_frames, self.start_frame_interval):
            sample = dict()
            sample["video_path"] = video_path
            sample["frame_ids"] = []
            curr_frame_i = frame_i
            while True:
                if curr_frame_i > (n_frames - 1):
                    break
                sample["frame_ids"].append(curr_frame_i)
                if len(sample["frame_ids"]) == self.sequence_length:
                    break
                curr_frame_i += self.sequence_interval
            # make sure there are sequence_length number of frames
            if len(sample["frame_ids"]) == self.sequence_length:
                samples.append(sample)
        return samples

    def __len__(self):
        return len(self.samples)

    def _load_video(self, video_path, frame_ids):
        vr = VideoReader(video_path, ctx=cpu(0), num_threads=2)
        assert (np.array(frame_ids) < len(vr)).all()
        assert (np.array(frame_ids) >= 0).all()
        vr.seek(0)
        frame_data = vr.get_batch(frame_ids).asnumpy()
        return frame_data

    def _get_frames(self, video_path, frame_ids):
        frames = self._load_video(video_path, frame_ids)
        frames = frames.astype(np.uint8)
        frames = torch.from_numpy(frames).permute(0, 3, 1, 2)  # (l, c, h, data)
        frames = self.preprocess(frames)
        frames = torch.clamp(frames * 255.0, 0, 255).to(torch.uint8)
        return frames

    def __getitem__(self, index):
        try:
            sample = self.samples[index]
            video_path = sample["video_path"]
            frame_ids = sample["frame_ids"]

            data = dict()

            video = self._get_frames(video_path, frame_ids)
            video = video.permute(1, 0, 2, 3)  # Rearrange from [T, C, H, W] to [C, T, H, W]
            data["video"] = video
            data["video_name"] = {
                "video_path": video_path,
                "start_frame_id": str(frame_ids[0]),
            }
            data["fps"] = 24
            data["image_size"] = torch.tensor([704, 1280, 704, 1280])  # .cuda()  # TODO: Does this matter?
            data["num_frames"] = self.sequence_length
            data["padding_mask"] = torch.zeros(1, 704, 1280)  # .cuda()

            return data
        except Exception:
            warnings.warn(
                f"Invalid data encountered: {self.samples[index]['video_path']}. Skipped "
                f"(by randomly sampling another sample in the same dataset)."
            )
            warnings.warn("FULL TRACEBACK:")
            warnings.warn(traceback.format_exc())
            self.wrong_number += 1
            print(self.wrong_number)
            return self[np.random.randint(len(self.samples))]


if __name__ == "__main__":
    dataset = Dataset(
        video_directory_or_pattern="assets/example_training_data/videos/*.mp4",
        sequence_interval=1,
        num_frames=57,
        video_size=[240, 360],
    )

    indices = [0, 13, 200, -1]
    for idx in indices:
        data = dataset[idx]
        print((f"{idx=} " f"{data['video'].sum()=}\n" f"{data['video'].shape=}\n" f"{data['video_name']=}\n" "---"))