import os
import io
import av
import json
from pickle import dumps, loads
import numpy as np
import torch
from torchvision.transforms.functional import resize
import tensorflow as tf
import tensorflow_datasets as tfds
from einops import rearrange

def decode_inst(insts):
    # Utility to decode encoded language instructions
    decoded_insts = []
    for inst in insts:
        decoded_insts.append(bytes(inst[np.where(inst != 0)].tolist()).decode("utf-8"))
    return decoded_insts

def save_video(file, video):
    container = av.open(file, 'w', 'mp4')
    stream = container.add_stream('libx264', rate=30)
    stream.height = video[0].shape[0]
    stream.width = video[0].shape[1]
    stream.bit_rate = 2000000 # 2Mbps
    stream.pix_fmt = 'yuv420p'
    for i in range(len(video)):
        frame = av.VideoFrame.from_ndarray(video[i], format='rgb24')
        frame = frame.reformat(format=stream.pix_fmt)
        for packet in stream.encode(frame):
            container.mux(packet)
    # Flush stream
    for packet in stream.encode():
        container.mux(packet)
    container.close()

if __name__ == '__main__':
    tf_builder = tfds.builder_from_directory('./droid/1.0.0/')
    tf_dataset = tf_builder.as_dataset(split="train")
    skip_episode = 78663
    js_path = 'index.json'
    if os.path.exists(js_path):
        js_data = json.load(open(js_path, 'r'))
    else:
        js_data = []
    for episode_id, episode in enumerate(tf_dataset):
        file_path = episode['episode_metadata']['file_path'].numpy().decode('utf-8')
        recording_folderpath = episode['episode_metadata']['recording_folderpath'].numpy().decode('utf-8')
        if episode_id <= skip_episode or 'success' not in file_path:
            print(f'skipping {episode_id}/{len(tf_dataset)}')
            continue
        left_camera = []
        arm_camera = []
        right_camera = []
        inst = []
        skip_episode = False
        for step_id, single_step in enumerate(episode['steps']):
            if single_step['language_instruction'].numpy().decode('utf-8') not in inst:
                inst.append(single_step['language_instruction'].numpy().decode('utf-8'))
            if single_step['language_instruction_2'].numpy().decode('utf-8') not in inst:
                inst.append(single_step['language_instruction_2'].numpy().decode('utf-8'))
            if single_step['language_instruction_3'].numpy().decode('utf-8') not in inst:
                inst.append(single_step['language_instruction_3'].numpy().decode('utf-8'))
            if len(inst) == 1 and inst[0] == '':
                skip_episode = True
                break
            left_camera.append(single_step['observation']['exterior_image_1_left'].numpy())
            right_camera.append(single_step['observation']['exterior_image_2_left'].numpy())
            arm_camera.append(single_step['observation']['wrist_image_left'].numpy())
        if skip_episode:
            print(f'skipping {episode_id}/{len(tf_dataset)}')
            continue
        print(f'saving {episode_id}/{len(tf_dataset)}')
        save_video(f'droid_videos/episode_{episode_id}_left_camera.mp4', left_camera)
        save_video(f'droid_videos/episode_{episode_id}_right_camera.mp4', right_camera)
        save_video(f'droid_videos/episode_{episode_id}_arm_camera.mp4', arm_camera)
        for i in range(len(inst)):
            if inst[i] == '':
                continue
            js_data.append({"path": f'droid_videos/episode_{episode_id}_left_camera.mp4', "recording_folder": recording_folderpath, "cap": [inst[i]]})
            js_data.append({"path": f'droid_videos/episode_{episode_id}_right_camera.mp4', "recording_folder": recording_folderpath, "cap": [inst[i]]})
            js_data.append({"path": f'droid_videos/episode_{episode_id}_arm_camera.mp4', "recording_folder": recording_folderpath, "cap": [inst[i]]})
        if episode_id % 1000 < 10:
            json.dump(js_data, open(js_path, 'w'), indent=4)
    json.dump(js_data, open(js_path, 'w'), indent=4)