Send back the complete code with all the fixes. Fix each of ...

Prompt

Send back the complete code with all the fixes. Fix each of the listed errors one by one, making sure to actually correct them so that there are 0 errors remaining. Keep the original imports, since the files exist. Write out every single character; do not abbreviate anything. Fix every error. There must be exactly one file. Do not write anything else; just output the complete code, and it must not contain any comments. Never, under any circumstances, use simplified, substitute, dummy, simulated, or fake code. Write the entire file as complete, unabridged, production-ready code in a single code block. It must be 100% error-free, a complete, error-free file, and must be submitted as a downloadable file. These requirements are mandatory and must be strictly adhered to. If no list of errors is provided, you must find all the errors and fix them. If there were comments in the original code, delete them. And most importantly: YOU MUST NEVER SIMPLIFY! import torch from torchvision import transforms import numpy as np from skimage.color import rgb2lab, lab2rgb import skimage.transform from PIL import Image import os from tqdm import tqdm from moviepy.editor import VideoFileClip, AudioFileClip from moviepy.tools import cvsecs import cv2 from pdb import set_trace def lab_to_rgb(L, ab): """ Takes a batch of images """ L = (L + 1.) * 50. ab = ab * 110. Lab = torch.cat([L, ab], dim=1).permute(0, 2, 3, 1).cpu().numpy() rgb_imgs = [] for img in Lab: img_rgb = lab2rgb(img) rgb_imgs.append(img_rgb) return np.stack(rgb_imgs, axis=0) SIZE = 256 def get_L(img): img = transforms.Resize( (SIZE, SIZE), transforms.InterpolationMode.BICUBIC)(img) img = np.array(img) img_lab = rgb2lab(img).astype("float32") img_lab = transforms.ToTensor()(img_lab) L = img_lab[[0], ...] / 50. - 1. # Between -1 and 1 return L def get_predictions(model, L): # model.L = L.to(model.device) model.eval() with torch.no_grad(): model.L = L.to(torch.device('cpu')) model.forward() fake_color = model.fake_color.detach() fake_imgs = lab_to_rgb(L, fake_color) return fake_imgs def colorize_img(model, img): L = get_L(img) L = L[None] # put in list fake_imgs = get_predictions(model, L) fake_img = fake_imgs[0] # get out of list resized_fake_img = skimage.transform.resize( fake_img, img.size[::-1]) # reshape to original size return resized_fake_img def valid_start_end(duration, start_input, end_input): start = start_input end = end_input if start == '': start = 0 if end == '': end = duration try: start = cvsecs(start) end = cvsecs(end) except BaseException: # start, end aren't actual time values. raise Exception("Invalid start, end values") # make it minimal maximum length start = max(start, 0) end = min(duration, end) # start must be less than end if start >= end: raise Exception("Start must be before end.") return start, end def colorize_vid(path_input, model, fps, start_input, end_input): original_video = VideoFileClip(path_input) # validate start, end start, end = valid_start_end( original_video.duration, start_input, end_input) input_video = original_video.subclip(start, end) if isinstance(fps, int): used_fps = fps nframes = np.round(fps * input_video.duration) else: used_fps = input_video.fps nframes = input_video.reader.nframes print( f"Colorizing output with FPS: {fps}, nframes: {nframes}, resolution: {input_video.size}.") frames = input_video.iter_frames(fps=used_fps) # create tmp path that is same as input path but with '_tmp.[suffix]' base_path, suffix = os.path.splitext(path_input) path_video_tmp = base_path + "_tmp" + suffix # create video writer for output size = input_video.size out = cv2.VideoWriter( path_video_tmp, cv2.VideoWriter_fourcc( *'mp4v'), used_fps, size) # out = cv2.VideoWriter(path_video_tmp, cv2.VideoWriter_fourcc(*'DIVX'), used_fps, size) for frame in tqdm(frames, total=nframes): # get colorized frame color_frame = colorize_img(model, Image.fromarray(frame)) if color_frame.max() <= 1: color_frame = (color_frame * 255).astype(np.uint8) color_frame = cv2.cvtColor(color_frame, cv2.COLOR_BGR2RGB) out.write(color_frame) out.release() # create output path that is same as input path but with '_out.[suffix]' path_output = base_path + "_out" + suffix # for some reason, subclip doesn't save audio. so make tmp audio file path_audio_tmp = base_path + "audio_tmp.mp3" input_video.audio.write_audiofile(path_audio_tmp, logger=None) input_audio = AudioFileClip(path_audio_tmp) output_video = VideoFileClip(path_video_tmp) output_video = output_video.set_audio(input_audio) output_video.write_videofile(path_output, logger=None) os.remove(path_video_tmp) os.remove(path_audio_tmp) print("Done.") return path_output