model.py

# This code is adapted from https://github.com/THUDM/CogVideo/blob/ff423aa169978fb2f636f761e348631fa3178b03/cogvideo_pipeline.py

from __future__ import annotations

import argparse
import logging
import os
import pathlib
import shutil
import subprocess
import sys
import tempfile
import time
import zipfile
from typing import Any

if os.getenv('SYSTEM') == 'spaces':
    subprocess.run('pip install icetk==0.0.4'.split())
    subprocess.run('pip install SwissArmyTransformer==0.2.9'.split())
    subprocess.run(
        'pip install git+https://github.com/Sleepychord/Image-Local-Attention@43fee31'
        .split())
    #subprocess.run('git clone https://github.com/NVIDIA/apex'.split())
    #subprocess.run('git checkout 1403c21'.split(), cwd='apex')
    #with open('patch.apex') as f:
    #    subprocess.run('patch -p1'.split(), cwd='apex', stdin=f)
    #subprocess.run(
    #    'pip install -v --disable-pip-version-check --no-cache-dir --global-option --cpp_ext --global-option --cuda_ext ./'
    #    .split(),
    #    cwd='apex')
    #subprocess.run('rm -rf apex'.split())
    with open('patch') as f:
        subprocess.run('patch -p1'.split(), cwd='CogVideo', stdin=f)

    from huggingface_hub import hf_hub_download

    def download_and_extract_icetk_models() -> None:
        icetk_model_dir = pathlib.Path('/home/user/.icetk_models')
        icetk_model_dir.mkdir()
        path = hf_hub_download('THUDM/icetk',
                               'models.zip',
                               use_auth_token=os.getenv('HF_TOKEN'))
        with zipfile.ZipFile(path) as f:
            f.extractall(path=icetk_model_dir.as_posix())

    def download_and_extract_cogvideo_models(name: str) -> None:
        path = hf_hub_download('THUDM/CogVideo',
                               name,
                               use_auth_token=os.getenv('HF_TOKEN'))
        with zipfile.ZipFile(path) as f:
            f.extractall('pretrained')
        os.remove(path)

    def download_and_extract_cogview2_models(name: str) -> None:
        path = hf_hub_download('THUDM/CogView2', name)
        with zipfile.ZipFile(path) as f:
            f.extractall()
        shutil.move('/home/user/app/sharefs/cogview-new/cogview2-dsr',
                    'pretrained')
        shutil.rmtree('/home/user/app/sharefs/')
        os.remove(path)

    download_and_extract_icetk_models()
    download_and_extract_cogvideo_models('cogvideo-stage1.zip')
    #download_and_extract_cogvideo_models('cogvideo-stage2.zip')
    #download_and_extract_cogview2_models('cogview2-dsr.zip')

    os.environ['SAT_HOME'] = '/home/user/app/pretrained'

import gradio as gr
import imageio.v2 as iio
import numpy as np
import torch
from icetk import IceTokenizer
from SwissArmyTransformer import get_args
from SwissArmyTransformer.arguments import set_random_seed
from SwissArmyTransformer.generation.sampling_strategies import BaseStrategy
from SwissArmyTransformer.resources import auto_create

app_dir = pathlib.Path(__file__).parent
submodule_dir = app_dir / 'CogVideo'
sys.path.insert(0, submodule_dir.as_posix())

from coglm_strategy import CoglmStrategy
from models.cogvideo_cache_model import CogVideoCacheModel
from sr_pipeline import DirectSuperResolution

formatter = logging.Formatter(
    '[%(asctime)s] %(name)s %(levelname)s: %(message)s',
    datefmt='%Y-%m-%d %H:%M:%S')
stream_handler = logging.StreamHandler(stream=sys.stdout)
stream_handler.setLevel(logging.INFO)
stream_handler.setFormatter(formatter)
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
logger.propagate = False
logger.addHandler(stream_handler)

ICETK_MODEL_DIR = app_dir / 'icetk_models'


def get_masks_and_position_ids_stage1(data, textlen, framelen):
    # Extract batch size and sequence length.
    tokens = data
    seq_length = len(data[0])
    # Attention mask (lower triangular).
    attention_mask = torch.ones((1, textlen + framelen, textlen + framelen),
                                device=data.device)
    attention_mask[:, :textlen, textlen:] = 0
    attention_mask[:, textlen:, textlen:].tril_()
    attention_mask.unsqueeze_(1)
    # Unaligned version
    position_ids = torch.zeros(seq_length,
                               dtype=torch.long,
                               device=data.device)
    torch.arange(textlen,
                 out=position_ids[:textlen],
                 dtype=torch.long,
                 device=data.device)
    torch.arange(512,
                 512 + seq_length - textlen,
                 out=position_ids[textlen:],
                 dtype=torch.long,
                 device=data.device)
    position_ids = position_ids.unsqueeze(0)

    return tokens, attention_mask, position_ids


def get_masks_and_position_ids_stage2(data, textlen, framelen):
    # Extract batch size and sequence length.
    tokens = data
    seq_length = len(data[0])

    # Attention mask (lower triangular).
    attention_mask = torch.ones((1, textlen + framelen, textlen + framelen),
                                device=data.device)
    attention_mask[:, :textlen, textlen:] = 0
    attention_mask[:, textlen:, textlen:].tril_()
    attention_mask.unsqueeze_(1)

    # Unaligned version
    position_ids = torch.zeros(seq_length,
                               dtype=torch.long,
                               device=data.device)
    torch.arange(textlen,
                 out=position_ids[:textlen],
                 dtype=torch.long,
                 device=data.device)
    frame_num = (seq_length - textlen) // framelen
    assert frame_num == 5
    torch.arange(512,
                 512 + framelen,
                 out=position_ids[textlen:textlen + framelen],
                 dtype=torch.long,
                 device=data.device)
    torch.arange(512 + framelen * 2,
                 512 + framelen * 3,
                 out=position_ids[textlen + framelen:textlen + framelen * 2],
                 dtype=torch.long,
                 device=data.device)
    torch.arange(512 + framelen * (frame_num - 1),
                 512 + framelen * frame_num,
                 out=position_ids[textlen + framelen * 2:textlen +
                                  framelen * 3],
                 dtype=torch.long,
                 device=data.device)
    torch.arange(512 + framelen * 1,
                 512 + framelen * 2,
                 out=position_ids[textlen + framelen * 3:textlen +
                                  framelen * 4],
                 dtype=torch.long,
                 device=data.device)
    torch.arange(512 + framelen * 3,
                 512 + framelen * 4,
                 out=position_ids[textlen + framelen * 4:textlen +
                                  framelen * 5],
                 dtype=torch.long,
                 device=data.device)

    position_ids = position_ids.unsqueeze(0)

    return tokens, attention_mask, position_ids


def my_update_mems(hiddens, mems_buffers, mems_indexs,
                   limited_spatial_channel_mem, text_len, frame_len):
    if hiddens is None:
        return None, mems_indexs
    mem_num = len(hiddens)
    ret_mem = []
    with torch.no_grad():
        for id in range(mem_num):
            if hiddens[id][0] is None:
                ret_mem.append(None)
            else:
                if id == 0 and limited_spatial_channel_mem and mems_indexs[
                        id] + hiddens[0][0].shape[1] >= text_len + frame_len:
                    if mems_indexs[id] == 0:
                        for layer, hidden in enumerate(hiddens[id]):
                            mems_buffers[id][
                                layer, :, :text_len] = hidden.expand(
                                    mems_buffers[id].shape[1], -1,
                                    -1)[:, :text_len]
                    new_mem_len_part2 = (mems_indexs[id] +
                                         hiddens[0][0].shape[1] -
                                         text_len) % frame_len
                    if new_mem_len_part2 > 0:
                        for layer, hidden in enumerate(hiddens[id]):
                            mems_buffers[id][
                                layer, :, text_len:text_len +
                                new_mem_len_part2] = hidden.expand(
                                    mems_buffers[id].shape[1], -1,
                                    -1)[:, -new_mem_len_part2:]
                    mems_indexs[id] = text_len + new_mem_len_part2
                else:
                    for layer, hidden in enumerate(hiddens[id]):
                        mems_buffers[id][layer, :,
                                         mems_indexs[id]:mems_indexs[id] +
                                         hidden.shape[1]] = hidden.expand(
                                             mems_buffers[id].shape[1], -1, -1)
                    mems_indexs[id] += hidden.shape[1]
                ret_mem.append(mems_buffers[id][:, :, :mems_indexs[id]])
    return ret_mem, mems_indexs


def calc_next_tokens_frame_begin_id(text_len, frame_len, total_len):
    # The fisrt token's position id of the frame that the next token belongs to;
    if total_len < text_len:
        return None
    return (total_len - text_len) // frame_len * frame_len + text_len


def my_filling_sequence(
        model,
        tokenizer,
        args,
        seq,
        batch_size,
        get_masks_and_position_ids,
        text_len,
        frame_len,
        strategy=BaseStrategy(),
        strategy2=BaseStrategy(),
        mems=None,
        log_text_attention_weights=0,  # default to 0: no artificial change
        mode_stage1=True,
        enforce_no_swin=False,
        guider_seq=None,
        guider_text_len=0,
        guidance_alpha=1,
        limited_spatial_channel_mem=False,  # 空间通道的存储限制在本帧内
        **kw_args):
    '''
        seq: [2, 3, 5, ..., -1(to be generated), -1, ...]
        mems: [num_layers, batch_size, len_mems(index), mem_hidden_size]
            cache, should be first mems.shape[1] parts of context_tokens.
            mems are the first-level citizens here, but we don't assume what is memorized.
            input mems are used when multi-phase generation.
    '''
    if guider_seq is not None:
        logger.debug('Using Guidance In Inference')
    if limited_spatial_channel_mem:
        logger.debug("Limit spatial-channel's mem to current frame")
    assert len(seq.shape) == 2

    # building the initial tokens, attention_mask, and position_ids
    actual_context_length = 0

    while seq[-1][
            actual_context_length] >= 0:  # the last seq has least given tokens
        actual_context_length += 1  # [0, context_length-1] are given
    assert actual_context_length > 0
    current_frame_num = (actual_context_length - text_len) // frame_len
    assert current_frame_num >= 0
    context_length = text_len + current_frame_num * frame_len

    tokens, attention_mask, position_ids = get_masks_and_position_ids(
        seq, text_len, frame_len)
    tokens = tokens[..., :context_length]
    input_tokens = tokens.clone()

    if guider_seq is not None:
        guider_index_delta = text_len - guider_text_len
        guider_tokens, guider_attention_mask, guider_position_ids = get_masks_and_position_ids(
            guider_seq, guider_text_len, frame_len)
        guider_tokens = guider_tokens[..., :context_length -
                                      guider_index_delta]
        guider_input_tokens = guider_tokens.clone()

    for fid in range(current_frame_num):
        input_tokens[:, text_len + 400 * fid] = tokenizer['<start_of_image>']
        if guider_seq is not None:
            guider_input_tokens[:, guider_text_len +
                                400 * fid] = tokenizer['<start_of_image>']

    attention_mask = attention_mask.type_as(next(
        model.parameters()))  # if fp16
    # initialize generation
    counter = context_length - 1  # Last fixed index is ``counter''
    index = 0  # Next forward starting index, also the length of cache.
    mems_buffers_on_GPU = False
    mems_indexs = [0, 0]
    mems_len = [(400 + 74) if limited_spatial_channel_mem else 5 * 400 + 74,
                5 * 400 + 74]
    mems_buffers = [
        torch.zeros(args.num_layers,
                    batch_size,
                    mem_len,
                    args.hidden_size * 2,
                    dtype=next(model.parameters()).dtype)
        for mem_len in mems_len
    ]

    if guider_seq is not None:
        guider_attention_mask = guider_attention_mask.type_as(
            next(model.parameters()))  # if fp16
        guider_mems_buffers = [
            torch.zeros(args.num_layers,
                        batch_size,
                        mem_len,
                        args.hidden_size * 2,
                        dtype=next(model.parameters()).dtype)
            for mem_len in mems_len
        ]
        guider_mems_indexs = [0, 0]
        guider_mems = None

    torch.cuda.empty_cache()
    # step-by-step generation
    while counter < len(seq[0]) - 1:
        # we have generated counter+1 tokens
        # Now, we want to generate seq[counter + 1],
        # token[:, index: counter+1] needs forwarding.
        if index == 0:
            group_size = 2 if (input_tokens.shape[0] == batch_size
                               and not mode_stage1) else batch_size

            logits_all = None
            for batch_idx in range(0, input_tokens.shape[0], group_size):
                logits, *output_per_layers = model(
                    input_tokens[batch_idx:batch_idx + group_size, index:],
                    position_ids[..., index:counter + 1],
                    attention_mask,  # TODO memlen
                    mems=mems,
                    text_len=text_len,
                    frame_len=frame_len,
                    counter=counter,
                    log_text_attention_weights=log_text_attention_weights,
                    enforce_no_swin=enforce_no_swin,
                    **kw_args)
                logits_all = torch.cat(
                    (logits_all,
                     logits), dim=0) if logits_all is not None else logits
                mem_kv01 = [[o['mem_kv'][0] for o in output_per_layers],
                            [o['mem_kv'][1] for o in output_per_layers]]
                next_tokens_frame_begin_id = calc_next_tokens_frame_begin_id(
                    text_len, frame_len, mem_kv01[0][0].shape[1])
                for id, mem_kv in enumerate(mem_kv01):
                    for layer, mem_kv_perlayer in enumerate(mem_kv):
                        if limited_spatial_channel_mem and id == 0:
                            mems_buffers[id][
                                layer, batch_idx:batch_idx + group_size, :
                                text_len] = mem_kv_perlayer.expand(
                                    min(group_size,
                                        input_tokens.shape[0] - batch_idx), -1,
                                    -1)[:, :text_len]
                            mems_buffers[id][layer, batch_idx:batch_idx+group_size, text_len:text_len+mem_kv_perlayer.shape[1]-next_tokens_frame_begin_id] =\
                                mem_kv_perlayer.expand(min(group_size, input_tokens.shape[0]-batch_idx), -1, -1)[:, next_tokens_frame_begin_id:]
                        else:
                            mems_buffers[id][
                                layer, batch_idx:batch_idx +
                                group_size, :mem_kv_perlayer.
                                shape[1]] = mem_kv_perlayer.expand(
                                    min(group_size,
                                        input_tokens.shape[0] - batch_idx), -1,
                                    -1)
                mems_indexs[0], mems_indexs[1] = mem_kv01[0][0].shape[
                    1], mem_kv01[1][0].shape[1]
                if limited_spatial_channel_mem:
                    mems_indexs[0] -= (next_tokens_frame_begin_id - text_len)

            mems = [
                mems_buffers[id][:, :, :mems_indexs[id]] for id in range(2)
            ]
            logits = logits_all

            # Guider
            if guider_seq is not None:
                guider_logits_all = None
                for batch_idx in range(0, guider_input_tokens.shape[0],
                                       group_size):
                    guider_logits, *guider_output_per_layers = model(
                        guider_input_tokens[batch_idx:batch_idx + group_size,
                                            max(index -
                                                guider_index_delta, 0):],
                        guider_position_ids[
                            ...,
                            max(index - guider_index_delta, 0):counter + 1 -
                            guider_index_delta],
                        guider_attention_mask,
                        mems=guider_mems,
                        text_len=guider_text_len,
                        frame_len=frame_len,
                        counter=counter - guider_index_delta,
                        log_text_attention_weights=log_text_attention_weights,
                        enforce_no_swin=enforce_no_swin,
                        **kw_args)
                    guider_logits_all = torch.cat(
                        (guider_logits_all, guider_logits), dim=0
                    ) if guider_logits_all is not None else guider_logits
                    guider_mem_kv01 = [[
                        o['mem_kv'][0] for o in guider_output_per_layers
                    ], [o['mem_kv'][1] for o in guider_output_per_layers]]
                    for id, guider_mem_kv in enumerate(guider_mem_kv01):
                        for layer, guider_mem_kv_perlayer in enumerate(
                                guider_mem_kv):
                            if limited_spatial_channel_mem and id == 0:
                                guider_mems_buffers[id][
                                    layer, batch_idx:batch_idx + group_size, :
                                    guider_text_len] = guider_mem_kv_perlayer.expand(
                                        min(group_size,
                                            input_tokens.shape[0] - batch_idx),
                                        -1, -1)[:, :guider_text_len]
                                guider_next_tokens_frame_begin_id = calc_next_tokens_frame_begin_id(
                                    guider_text_len, frame_len,
                                    guider_mem_kv_perlayer.shape[1])
                                guider_mems_buffers[id][layer, batch_idx:batch_idx+group_size, guider_text_len:guider_text_len+guider_mem_kv_perlayer.shape[1]-guider_next_tokens_frame_begin_id] =\
                                    guider_mem_kv_perlayer.expand(min(group_size, input_tokens.shape[0]-batch_idx), -1, -1)[:, guider_next_tokens_frame_begin_id:]
                            else:
                                guider_mems_buffers[id][
                                    layer, batch_idx:batch_idx +
                                    group_size, :guider_mem_kv_perlayer.
                                    shape[1]] = guider_mem_kv_perlayer.expand(
                                        min(group_size,
                                            input_tokens.shape[0] - batch_idx),
                                        -1, -1)
                    guider_mems_indexs[0], guider_mems_indexs[
                        1] = guider_mem_kv01[0][0].shape[1], guider_mem_kv01[
                            1][0].shape[1]
                    if limited_spatial_channel_mem:
                        guider_mems_indexs[0] -= (
                            guider_next_tokens_frame_begin_id -
                            guider_text_len)
                guider_mems = [
                    guider_mems_buffers[id][:, :, :guider_mems_indexs[id]]
                    for id in range(2)
                ]
                guider_logits = guider_logits_all
        else:
            if not mems_buffers_on_GPU:
                if not mode_stage1:
                    torch.cuda.empty_cache()
                    for idx, mem in enumerate(mems):
                        mems[idx] = mem.to(next(model.parameters()).device)
                    if guider_seq is not None:
                        for idx, mem in enumerate(guider_mems):
                            guider_mems[idx] = mem.to(
                                next(model.parameters()).device)
                else:
                    torch.cuda.empty_cache()
                    for idx, mem_buffer in enumerate(mems_buffers):
                        mems_buffers[idx] = mem_buffer.to(
                            next(model.parameters()).device)
                    mems = [
                        mems_buffers[id][:, :, :mems_indexs[id]]
                        for id in range(2)
                    ]
                    if guider_seq is not None:
                        for idx, guider_mem_buffer in enumerate(
                                guider_mems_buffers):
                            guider_mems_buffers[idx] = guider_mem_buffer.to(
                                next(model.parameters()).device)
                        guider_mems = [
                            guider_mems_buffers[id]
                            [:, :, :guider_mems_indexs[id]] for id in range(2)
                        ]
                    mems_buffers_on_GPU = True

            logits, *output_per_layers = model(
                input_tokens[:, index:],
                position_ids[..., index:counter + 1],
                attention_mask,  # TODO memlen
                mems=mems,
                text_len=text_len,
                frame_len=frame_len,
                counter=counter,
                log_text_attention_weights=log_text_attention_weights,
                enforce_no_swin=enforce_no_swin,
                limited_spatial_channel_mem=limited_spatial_channel_mem,
                **kw_args)
            mem_kv0, mem_kv1 = [o['mem_kv'][0] for o in output_per_layers
                                ], [o['mem_kv'][1] for o in output_per_layers]

            if guider_seq is not None:
                guider_logits, *guider_output_per_layers = model(
                    guider_input_tokens[:,
                                        max(index - guider_index_delta, 0):],
                    guider_position_ids[...,
                                        max(index -
                                            guider_index_delta, 0):counter +
                                        1 - guider_index_delta],
                    guider_attention_mask,
                    mems=guider_mems,
                    text_len=guider_text_len,
                    frame_len=frame_len,
                    counter=counter - guider_index_delta,
                    log_text_attention_weights=0,
                    enforce_no_swin=enforce_no_swin,
                    limited_spatial_channel_mem=limited_spatial_channel_mem,
                    **kw_args)
                guider_mem_kv0, guider_mem_kv1 = [
                    o['mem_kv'][0] for o in guider_output_per_layers
                ], [o['mem_kv'][1] for o in guider_output_per_layers]

            if not mems_buffers_on_GPU:
                torch.cuda.empty_cache()
                for idx, mem_buffer in enumerate(mems_buffers):
                    mems_buffers[idx] = mem_buffer.to(
                        next(model.parameters()).device)
                if guider_seq is not None:
                    for idx, guider_mem_buffer in enumerate(
                            guider_mems_buffers):
                        guider_mems_buffers[idx] = guider_mem_buffer.to(
                            next(model.parameters()).device)
                mems_buffers_on_GPU = True

            mems, mems_indexs = my_update_mems([mem_kv0, mem_kv1],
                                               mems_buffers, mems_indexs,
                                               limited_spatial_channel_mem,
                                               text_len, frame_len)
            if guider_seq is not None:
                guider_mems, guider_mems_indexs = my_update_mems(
                    [guider_mem_kv0, guider_mem_kv1], guider_mems_buffers,
                    guider_mems_indexs, limited_spatial_channel_mem,
                    guider_text_len, frame_len)

        counter += 1
        index = counter

        logits = logits[:, -1].expand(batch_size,
                                      -1)  # [batch size, vocab size]
        tokens = tokens.expand(batch_size, -1)
        if guider_seq is not None:
            guider_logits = guider_logits[:, -1].expand(batch_size, -1)
            guider_tokens = guider_tokens.expand(batch_size, -1)

        if seq[-1][counter].item() < 0:
            # sampling
            guided_logits = guider_logits + (
                logits - guider_logits
            ) * guidance_alpha if guider_seq is not None else logits
            if mode_stage1 and counter < text_len + 400:
                tokens, mems = strategy.forward(guided_logits, tokens, mems)
            else:
                tokens, mems = strategy2.forward(guided_logits, tokens, mems)
            if guider_seq is not None:
                guider_tokens = torch.cat((guider_tokens, tokens[:, -1:]),
                                          dim=1)

            if seq[0][counter].item() >= 0:
                for si in range(seq.shape[0]):
                    if seq[si][counter].item() >= 0:
                        tokens[si, -1] = seq[si, counter]
                        if guider_seq is not None:
                            guider_tokens[si,
                                          -1] = guider_seq[si, counter -
                                                           guider_index_delta]

        else:
            tokens = torch.cat(
                (tokens, seq[:, counter:counter + 1].clone().expand(
                    tokens.shape[0], 1).to(device=tokens.device,
                                           dtype=tokens.dtype)),
                dim=1)
            if guider_seq is not None:
                guider_tokens = torch.cat(
                    (guider_tokens,
                     guider_seq[:, counter - guider_index_delta:counter + 1 -
                                guider_index_delta].clone().expand(
                                    guider_tokens.shape[0], 1).to(
                                        device=guider_tokens.device,
                                        dtype=guider_tokens.dtype)),
                    dim=1)

        input_tokens = tokens.clone()
        if guider_seq is not None:
            guider_input_tokens = guider_tokens.clone()
        if (index - text_len - 1) // 400 < (input_tokens.shape[-1] - text_len -
                                            1) // 400:
            boi_idx = ((index - text_len - 1) // 400 + 1) * 400 + text_len
            while boi_idx < input_tokens.shape[-1]:
                input_tokens[:, boi_idx] = tokenizer['<start_of_image>']
                if guider_seq is not None:
                    guider_input_tokens[:, boi_idx -
                                        guider_index_delta] = tokenizer[
                                            '<start_of_image>']
                boi_idx += 400

        if strategy.is_done:
            break
    return strategy.finalize(tokens, mems)


class InferenceModel_Sequential(CogVideoCacheModel):
    def __init__(self, args, transformer=None, parallel_output=True):
        super().__init__(args,
                         transformer=transformer,
                         parallel_output=parallel_output,
                         window_size=-1,
                         cogvideo_stage=1)

    # TODO: check it

    def final_forward(self, logits, **kwargs):
        logits_parallel = logits
        logits_parallel = torch.nn.functional.linear(
            logits_parallel.float(),
            self.transformer.word_embeddings.weight[:20000].float())
        return logits_parallel


class InferenceModel_Interpolate(CogVideoCacheModel):
    def __init__(self, args, transformer=None, parallel_output=True):
        super().__init__(args,
                         transformer=transformer,
                         parallel_output=parallel_output,
                         window_size=10,
                         cogvideo_stage=2)

    # TODO: check it

    def final_forward(self, logits, **kwargs):
        logits_parallel = logits
        logits_parallel = torch.nn.functional.linear(
            logits_parallel.float(),
            self.transformer.word_embeddings.weight[:20000].float())
        return logits_parallel


def get_default_args() -> argparse.Namespace:
    known = argparse.Namespace(generate_frame_num=5,
                               coglm_temperature2=0.89,
                               use_guidance_stage1=True,
                               use_guidance_stage2=False,
                               guidance_alpha=3.0,
                               stage_1=True,
                               stage_2=False,
                               both_stages=False,
                               parallel_size=1,
                               stage1_max_inference_batch_size=-1,
                               multi_gpu=False,
                               layout='64, 464, 2064',
                               window_size=10,
                               additional_seqlen=2000,
                               cogvideo_stage=1)

    args_list = [
        '--tokenizer-type',
        'fake',
        '--mode',
        'inference',
        '--distributed-backend',
        'nccl',
        '--fp16',
        '--model-parallel-size',
        '1',
        '--temperature',
        '1.05',
        '--top_k',
        '12',
        '--sandwich-ln',
        '--seed',
        '1234',
        '--num-workers',
        '0',
        '--batch-size',
        '1',
        '--max-inference-batch-size',
        '8',
    ]
    args = get_args(args_list)
    args = argparse.Namespace(**vars(args), **vars(known))
    args.layout = [int(x) for x in args.layout.split(',')]
    args.do_train = False
    return args


class Model:
    def __init__(self, only_first_stage: bool = False):
        self.args = get_default_args()
        if only_first_stage:
            self.args.stage_1 = True
            self.args.both_stages = False
        else:
            self.args.stage_1 = False
            self.args.both_stages = True

        self.tokenizer = self.load_tokenizer()

        self.model_stage1, self.args = self.load_model_stage1()
        self.model_stage2, self.args = self.load_model_stage2()

        self.strategy_cogview2, self.strategy_cogvideo = self.load_strategies()
        self.dsr = self.load_dsr()

        self.device = torch.device(self.args.device)

    def load_tokenizer(self) -> IceTokenizer:
        logger.info('--- load_tokenizer ---')
        start = time.perf_counter()

        tokenizer = IceTokenizer(ICETK_MODEL_DIR.as_posix())
        tokenizer.add_special_tokens(
            ['<start_of_image>', '<start_of_english>', '<start_of_chinese>'])

        elapsed = time.perf_counter() - start
        logger.info(f'--- done ({elapsed=:.3f}) ---')
        return tokenizer

    def load_model_stage1(
            self) -> tuple[CogVideoCacheModel, argparse.Namespace]:
        logger.info('--- load_model_stage1 ---')
        start = time.perf_counter()

        args = self.args
        model_stage1, args = InferenceModel_Sequential.from_pretrained(
            args, 'cogvideo-stage1')
        model_stage1.eval()
        if args.both_stages:
            model_stage1 = model_stage1.cpu()

        elapsed = time.perf_counter() - start
        logger.info(f'--- done ({elapsed=:.3f}) ---')
        return model_stage1, args

    def load_model_stage2(
            self) -> tuple[CogVideoCacheModel | None, argparse.Namespace]:
        logger.info('--- load_model_stage2 ---')
        start = time.perf_counter()

        args = self.args
        if args.both_stages:
            model_stage2, args = InferenceModel_Interpolate.from_pretrained(
                args, 'cogvideo-stage2')
            model_stage2.eval()
            if args.both_stages:
                model_stage2 = model_stage2.cpu()
        else:
            model_stage2 = None

        elapsed = time.perf_counter() - start
        logger.info(f'--- done ({elapsed=:.3f}) ---')
        return model_stage2, args

    def load_strategies(self) -> tuple[CoglmStrategy, CoglmStrategy]:
        logger.info('--- load_strategies ---')
        start = time.perf_counter()

        invalid_slices = [slice(self.tokenizer.num_image_tokens, None)]
        strategy_cogview2 = CoglmStrategy(invalid_slices,
                                          temperature=1.0,
                                          top_k=16)
        strategy_cogvideo = CoglmStrategy(
            invalid_slices,
            temperature=self.args.temperature,
            top_k=self.args.top_k,
            temperature2=self.args.coglm_temperature2)

        elapsed = time.perf_counter() - start
        logger.info(f'--- done ({elapsed=:.3f}) ---')
        return strategy_cogview2, strategy_cogvideo

    def load_dsr(self) -> DirectSuperResolution | None:
        logger.info('--- load_dsr ---')
        start = time.perf_counter()

        if self.args.both_stages:
            path = auto_create('cogview2-dsr', path=None)
            dsr = DirectSuperResolution(self.args,
                                        path,
                                        max_bz=12,
                                        onCUDA=False)
        else:
            dsr = None

        elapsed = time.perf_counter() - start
        logger.info(f'--- done ({elapsed=:.3f}) ---')
        return dsr

    @torch.inference_mode()
    def process_stage1(self,
                       model,
                       seq_text,
                       duration,
                       video_raw_text=None,
                       video_guidance_text='视频',
                       image_text_suffix='',
                       batch_size=1):
        process_start_time = time.perf_counter()

        generate_frame_num = self.args.generate_frame_num
        tokenizer = self.tokenizer
        use_guide = self.args.use_guidance_stage1

        if next(model.parameters()).device != self.device:
            move_start_time = time.perf_counter()
            logger.debug('moving stage 1 model to cuda')

            model = model.to(self.device)

            elapsed = time.perf_counter() - move_start_time
            logger.debug(f'moving in model1 takes time: {elapsed:.2f}')

        if video_raw_text is None:
            video_raw_text = seq_text
        mbz = self.args.stage1_max_inference_batch_size if self.args.stage1_max_inference_batch_size > 0 else self.args.max_inference_batch_size
        assert batch_size < mbz or batch_size % mbz == 0
        frame_len = 400

        # generate the first frame:
        enc_text = tokenizer.encode(seq_text + image_text_suffix)
        seq_1st = enc_text + [tokenizer['<start_of_image>']] + [-1] * 400
        logger.info(
            f'[Generating First Frame with CogView2] Raw text: {tokenizer.decode(enc_text):s}'
        )
        text_len_1st = len(seq_1st) - frame_len * 1 - 1

        seq_1st = torch.tensor(seq_1st, dtype=torch.long,
                               device=self.device).unsqueeze(0)
        output_list_1st = []
        for tim in range(max(batch_size // mbz, 1)):
            start_time = time.perf_counter()
            output_list_1st.append(
                my_filling_sequence(
                    model,
                    tokenizer,
                    self.args,
                    seq_1st.clone(),
                    batch_size=min(batch_size, mbz),
                    get_masks_and_position_ids=
                    get_masks_and_position_ids_stage1,
                    text_len=text_len_1st,
                    frame_len=frame_len,
                    strategy=self.strategy_cogview2,
                    strategy2=self.strategy_cogvideo,
                    log_text_attention_weights=1.4,
                    enforce_no_swin=True,
                    mode_stage1=True,
                )[0])
            elapsed = time.perf_counter() - start_time
            logger.info(f'[First Frame] Elapsed: {elapsed:.2f}')
        output_tokens_1st = torch.cat(output_list_1st, dim=0)
        given_tokens = output_tokens_1st[:, text_len_1st + 1:text_len_1st +
                                         401].unsqueeze(
                                             1
                                         )  # given_tokens.shape: [bs, frame_num, 400]

        # generate subsequent frames:
        total_frames = generate_frame_num
        enc_duration = tokenizer.encode(f'{float(duration)}秒')
        if use_guide:
            video_raw_text = video_raw_text + ' 视频'
        enc_text_video = tokenizer.encode(video_raw_text)
        seq = enc_duration + [tokenizer['<n>']] + enc_text_video + [
            tokenizer['<start_of_image>']
        ] + [-1] * 400 * generate_frame_num
        guider_seq = enc_duration + [tokenizer['<n>']] + tokenizer.encode(
            video_guidance_text) + [tokenizer['<start_of_image>']
                                    ] + [-1] * 400 * generate_frame_num
        logger.info(
            f'[Stage1: Generating Subsequent Frames, Frame Rate {4/duration:.1f}] raw text: {tokenizer.decode(enc_text_video):s}'
        )

        text_len = len(seq) - frame_len * generate_frame_num - 1
        guider_text_len = len(guider_seq) - frame_len * generate_frame_num - 1
        seq = torch.tensor(seq, dtype=torch.long,
                           device=self.device).unsqueeze(0).repeat(
                               batch_size, 1)
        guider_seq = torch.tensor(guider_seq,
                                  dtype=torch.long,
                                  device=self.device).unsqueeze(0).repeat(
                                      batch_size, 1)

        for given_frame_id in range(given_tokens.shape[1]):
            seq[:, text_len + 1 + given_frame_id * 400:text_len + 1 +
                (given_frame_id + 1) * 400] = given_tokens[:, given_frame_id]
            guider_seq[:, guider_text_len + 1 +
                       given_frame_id * 400:guider_text_len + 1 +
                       (given_frame_id + 1) *
                       400] = given_tokens[:, given_frame_id]
        output_list = []

        if use_guide:
            video_log_text_attention_weights = 0
        else:
            guider_seq = None
            video_log_text_attention_weights = 1.4

        for tim in range(max(batch_size // mbz, 1)):
            input_seq = seq[:min(batch_size, mbz)].clone(
            ) if tim == 0 else seq[mbz * tim:mbz * (tim + 1)].clone()
            guider_seq2 = (guider_seq[:min(batch_size, mbz)].clone()
                           if tim == 0 else guider_seq[mbz * tim:mbz *
                                                       (tim + 1)].clone()
                           ) if guider_seq is not None else None
            output_list.append(
                my_filling_sequence(
                    model,
                    tokenizer,
                    self.args,
                    input_seq,
                    batch_size=min(batch_size, mbz),
                    get_masks_and_position_ids=
                    get_masks_and_position_ids_stage1,
                    text_len=text_len,
                    frame_len=frame_len,
                    strategy=self.strategy_cogview2,
                    strategy2=self.strategy_cogvideo,
                    log_text_attention_weights=video_log_text_attention_weights,
                    guider_seq=guider_seq2,
                    guider_text_len=guider_text_len,
                    guidance_alpha=self.args.guidance_alpha,
                    limited_spatial_channel_mem=True,
                    mode_stage1=True,
                )[0])

        output_tokens = torch.cat(output_list, dim=0)[:, 1 + text_len:]

        if self.args.both_stages:
            move_start_time = time.perf_counter()
            logger.debug('moving stage 1 model to cpu')
            model = model.cpu()
            torch.cuda.empty_cache()
            elapsed = time.perf_counter() - move_start_time
            logger.debug(f'moving in model1 takes time: {elapsed:.2f}')

        # decoding
        res = []
        for seq in output_tokens:
            decoded_imgs = [
                self.postprocess(
                    torch.nn.functional.interpolate(tokenizer.decode(
                        image_ids=seq.tolist()[i * 400:(i + 1) * 400]),
                                                    size=(480, 480))[0])
                for i in range(total_frames)
            ]
            res.append(decoded_imgs)  # only the last image (target)

        assert len(res) == batch_size
        tokens = output_tokens[:, :+total_frames * 400].reshape(
            -1, total_frames, 400).cpu()

        elapsed = time.perf_counter() - process_start_time
        logger.info(f'--- done ({elapsed=:.3f}) ---')
        return tokens, res[0]

    @torch.inference_mode()
    def process_stage2(self,
                       model,
                       seq_text,
                       duration,
                       parent_given_tokens,
                       video_raw_text=None,
                       video_guidance_text='视频',
                       gpu_rank=0,
                       gpu_parallel_size=1):
        process_start_time = time.perf_counter()

        generate_frame_num = self.args.generate_frame_num
        tokenizer = self.tokenizer
        use_guidance = self.args.use_guidance_stage2

        stage2_start_time = time.perf_counter()

        if next(model.parameters()).device != self.device:
            move_start_time = time.perf_counter()
            logger.debug('moving stage-2 model to cuda')

            model = model.to(self.device)

            elapsed = time.perf_counter() - move_start_time
            logger.debug(f'moving in stage-2 model takes time: {elapsed:.2f}')

        try:
            sample_num_allgpu = parent_given_tokens.shape[0]
            sample_num = sample_num_allgpu // gpu_parallel_size
            assert sample_num * gpu_parallel_size == sample_num_allgpu
            parent_given_tokens = parent_given_tokens[gpu_rank *
                                                      sample_num:(gpu_rank +
                                                                  1) *
                                                      sample_num]
        except:
            logger.critical('No frame_tokens found in interpolation, skip')
            return False, []

        # CogVideo Stage2 Generation
        while duration >= 0.5:  # TODO: You can change the boundary to change the frame rate
            parent_given_tokens_num = parent_given_tokens.shape[1]
            generate_batchsize_persample = (parent_given_tokens_num - 1) // 2
            generate_batchsize_total = generate_batchsize_persample * sample_num
            total_frames = generate_frame_num
            frame_len = 400
            enc_text = tokenizer.encode(seq_text)
            enc_duration = tokenizer.encode(str(float(duration)) + '秒')
            seq = enc_duration + [tokenizer['<n>']] + enc_text + [
                tokenizer['<start_of_image>']
            ] + [-1] * 400 * generate_frame_num
            text_len = len(seq) - frame_len * generate_frame_num - 1

            logger.info(
                f'[Stage2: Generating Frames, Frame Rate {int(4/duration):d}] raw text: {tokenizer.decode(enc_text):s}'
            )

            # generation
            seq = torch.tensor(seq, dtype=torch.long,
                               device=self.device).unsqueeze(0).repeat(
                                   generate_batchsize_total, 1)
            for sample_i in range(sample_num):
                for i in range(generate_batchsize_persample):
                    seq[sample_i * generate_batchsize_persample +
                        i][text_len + 1:text_len + 1 +
                           400] = parent_given_tokens[sample_i][2 * i]
                    seq[sample_i * generate_batchsize_persample +
                        i][text_len + 1 + 400:text_len + 1 +
                           800] = parent_given_tokens[sample_i][2 * i + 1]
                    seq[sample_i * generate_batchsize_persample +
                        i][text_len + 1 + 800:text_len + 1 +
                           1200] = parent_given_tokens[sample_i][2 * i + 2]

            if use_guidance:
                guider_seq = enc_duration + [
                    tokenizer['<n>']
                ] + tokenizer.encode(video_guidance_text) + [
                    tokenizer['<start_of_image>']
                ] + [-1] * 400 * generate_frame_num
                guider_text_len = len(
                    guider_seq) - frame_len * generate_frame_num - 1
                guider_seq = torch.tensor(
                    guider_seq, dtype=torch.long,
                    device=self.device).unsqueeze(0).repeat(
                        generate_batchsize_total, 1)
                for sample_i in range(sample_num):
                    for i in range(generate_batchsize_persample):
                        guider_seq[sample_i * generate_batchsize_persample +
                                   i][text_len + 1:text_len + 1 +
                                      400] = parent_given_tokens[sample_i][2 *
                                                                           i]
                        guider_seq[sample_i * generate_batchsize_persample +
                                   i][text_len + 1 + 400:text_len + 1 +
                                      800] = parent_given_tokens[sample_i][2 *
                                                                           i +
                                                                           1]
                        guider_seq[sample_i * generate_batchsize_persample +
                                   i][text_len + 1 + 800:text_len + 1 +
                                      1200] = parent_given_tokens[sample_i][2 *
                                                                            i +
                                                                            2]
                video_log_text_attention_weights = 0
            else:
                guider_seq = None
                guider_text_len = 0
                video_log_text_attention_weights = 1.4

            mbz = self.args.max_inference_batch_size

            assert generate_batchsize_total < mbz or generate_batchsize_total % mbz == 0
            output_list = []
            start_time = time.perf_counter()
            for tim in range(max(generate_batchsize_total // mbz, 1)):
                input_seq = seq[:min(generate_batchsize_total, mbz)].clone(
                ) if tim == 0 else seq[mbz * tim:mbz * (tim + 1)].clone()
                guider_seq2 = (
                    guider_seq[:min(generate_batchsize_total, mbz)].clone()
                    if tim == 0 else guider_seq[mbz * tim:mbz *
                                                (tim + 1)].clone()
                ) if guider_seq is not None else None
                output_list.append(
                    my_filling_sequence(
                        model,
                        tokenizer,
                        self.args,
                        input_seq,
                        batch_size=min(generate_batchsize_total, mbz),
                        get_masks_and_position_ids=
                        get_masks_and_position_ids_stage2,
                        text_len=text_len,
                        frame_len=frame_len,
                        strategy=self.strategy_cogview2,
                        strategy2=self.strategy_cogvideo,
                        log_text_attention_weights=
                        video_log_text_attention_weights,
                        mode_stage1=False,
                        guider_seq=guider_seq2,
                        guider_text_len=guider_text_len,
                        guidance_alpha=self.args.guidance_alpha,
                        limited_spatial_channel_mem=True,
                    )[0])
            elapsed = time.perf_counter() - start_time
            logger.info(f'Duration {duration:.2f}, Elapsed: {elapsed:.2f}\n')

            output_tokens = torch.cat(output_list, dim=0)
            output_tokens = output_tokens[:, text_len + 1:text_len + 1 +
                                          (total_frames) * 400].reshape(
                                              sample_num, -1,
                                              400 * total_frames)
            output_tokens_merge = torch.cat(
                (output_tokens[:, :, :1 * 400], output_tokens[:, :,
                                                              400 * 3:4 * 400],
                 output_tokens[:, :, 400 * 1:2 * 400],
                 output_tokens[:, :, 400 * 4:(total_frames) * 400]),
                dim=2).reshape(sample_num, -1, 400)

            output_tokens_merge = torch.cat(
                (output_tokens_merge, output_tokens[:, -1:, 400 * 2:3 * 400]),
                dim=1)
            duration /= 2
            parent_given_tokens = output_tokens_merge

        if self.args.both_stages:
            move_start_time = time.perf_counter()
            logger.debug('moving stage 2 model to cpu')
            model = model.cpu()
            torch.cuda.empty_cache()
            elapsed = time.perf_counter() - move_start_time
            logger.debug(f'moving out model2 takes time: {elapsed:.2f}')

        elapsed = time.perf_counter() - stage2_start_time
        logger.info(f'CogVideo Stage2 completed. Elapsed: {elapsed:.2f}\n')

        # direct super-resolution by CogView2
        logger.info('[Direct super-resolution]')
        dsr_start_time = time.perf_counter()

        enc_text = tokenizer.encode(seq_text)
        frame_num_per_sample = parent_given_tokens.shape[1]
        parent_given_tokens_2d = parent_given_tokens.reshape(-1, 400)
        text_seq = torch.tensor(enc_text, dtype=torch.long,
                                device=self.device).unsqueeze(0).repeat(
                                    parent_given_tokens_2d.shape[0], 1)
        sred_tokens = self.dsr(text_seq, parent_given_tokens_2d)

        decoded_sr_videos = []
        for sample_i in range(sample_num):
            decoded_sr_imgs = []
            for frame_i in range(frame_num_per_sample):
                decoded_sr_img = tokenizer.decode(
                    image_ids=sred_tokens[frame_i + sample_i *
                                          frame_num_per_sample][-3600:])
                decoded_sr_imgs.append(
                    self.postprocess(
                        torch.nn.functional.interpolate(decoded_sr_img,
                                                        size=(480, 480))[0]))
            decoded_sr_videos.append(decoded_sr_imgs)

        elapsed = time.perf_counter() - dsr_start_time
        logger.info(
            f'Direct super-resolution completed. Elapsed: {elapsed:.2f}')

        elapsed = time.perf_counter() - process_start_time
        logger.info(f'--- done ({elapsed=:.3f}) ---')
        return True, decoded_sr_videos[0]

    @staticmethod
    def postprocess(tensor: torch.Tensor) -> np.ndarray:
        return tensor.cpu().mul(255).add_(0.5).clamp_(0, 255).permute(
            1, 2, 0).to(torch.uint8).numpy()

    def run(self, text: str, seed: int,
            only_first_stage: bool) -> list[np.ndarray]:
        logger.info('==================== run ====================')
        start = time.perf_counter()

        set_random_seed(seed)
        self.args.seed = seed

        if only_first_stage:
            self.args.stage_1 = True
            self.args.both_stages = False
        else:
            self.args.stage_1 = False
            self.args.both_stages = True

        parent_given_tokens, res = self.process_stage1(
            self.model_stage1,
            text,
            duration=4.0,
            video_raw_text=text,
            video_guidance_text='视频',
            image_text_suffix=' 高清摄影',
            batch_size=self.args.batch_size)
        if not only_first_stage:
            _, res = self.process_stage2(
                self.model_stage2,
                text,
                duration=2.0,
                parent_given_tokens=parent_given_tokens,
                video_raw_text=text + ' 视频',
                video_guidance_text='视频',
                gpu_rank=0,
                gpu_parallel_size=1)  # TODO: 修改

        elapsed = time.perf_counter() - start
        logger.info(f'Elapsed: {elapsed:.3f}')
        logger.info('==================== done ====================')
        return res


class AppModel(Model):
    def __init__(self, only_first_stage: bool):
        super().__init__(only_first_stage)
        self.translator = gr.Interface.load(
            'spaces/chinhon/translation_eng2ch')

    def to_video(self, frames: list[np.ndarray]) -> str:
        out_file = tempfile.NamedTemporaryFile(suffix='.mp4', delete=False)
        if self.args.stage_1:
            fps = 4
        else:
            fps = 8
        writer = iio.get_writer(out_file.name, fps=fps)
        for frame in frames:
            writer.append_data(frame)
        writer.close()
        return out_file.name

    def run_with_translation(
            self, text: str, translate: bool, seed: int,
            only_first_stage: bool) -> tuple[str | None, str | None]:
        logger.info(f'{text=}, {translate=}, {seed=}, {only_first_stage=}')
        if translate:
            text = translated_text = self.translator(text)
        else:
            translated_text = None
        frames = self.run(text, seed, only_first_stage)
        video_path = self.to_video(frames)
        return translated_text, video_path