src/videogen_hub/pipelines/cogvideo/cogvideo_src/cogvideo_pipeline.py

# -*- encoding: utf-8 -*-
"""
@File    :   cogvideo_pipeline.py
@Time    :   2022/07/15 11:24:56
@Author  :   Wenyi Hong 
@Version :   1.0
@Contact :   [email protected]
"""

# here put the import lib

import os
import sys
import torch
import argparse
import time
from torchvision.utils import save_image
import stat
from videogen_hub.depend.icetk import icetk as tokenizer
import logging, sys

import torch.distributed as dist

tokenizer.add_special_tokens(
    ["<start_of_image>", "<start_of_english>", "<start_of_chinese>"]
)


from SwissArmyTransformer import get_args
from SwissArmyTransformer.data_utils import BinaryDataset, make_loaders
from SwissArmyTransformer.generation.sampling_strategies import BaseStrategy
from SwissArmyTransformer.generation.utils import (
    timed_name,
    save_multiple_images,
    generate_continually,
)
from SwissArmyTransformer.resources import auto_create

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


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 my_save_multiple_images(imgs, path, subdir, debug=True):
    # imgs: list of tensor images
    if debug:
        imgs = torch.cat(imgs, dim=0)
        print("\nSave to: ", path, flush=True)
        save_image(imgs, path, normalize=True)
    else:
        print("\nSave to: ", path, flush=True)
        single_frame_path = os.path.join(path, subdir)
        os.makedirs(single_frame_path, exist_ok=True)
        for i in range(len(imgs)):
            save_image(
                imgs[i],
                os.path.join(single_frame_path, f'{str(i).rjust(4,"0")}.jpg'),
                normalize=True,
            )
            os.chmod(
                os.path.join(single_frame_path, f'{str(i).rjust(4,"0")}.jpg'),
                stat.S_IRWXO + stat.S_IRWXG + stat.S_IRWXU,
            )
        save_image(
            torch.cat(imgs, dim=0),
            os.path.join(single_frame_path, f"frame_concat.jpg"),
            normalize=True,
        )
        os.chmod(
            os.path.join(single_frame_path, f"frame_concat.jpg"),
            stat.S_IRWXO + stat.S_IRWXG + stat.S_IRWXU,
        )


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,
    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:
        logging.debug("Using Guidance In Inference")
    if limited_spatial_channel_mem:
        logging.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 main(args):
    assert int(args.stage_1) + int(args.stage_2) + int(args.both_stages) == 1
    rank_id = args.device % args.parallel_size
    generate_frame_num = args.generate_frame_num

    if args.stage_1 or args.both_stages:
        model_stage1, args = InferenceModel_Sequential.from_pretrained(
            args, "cogvideo-stage1"
        )
        model_stage1.eval()
        if args.both_stages:
            model_stage1 = model_stage1.cpu()

    if args.stage_2 or 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()

    invalid_slices = [slice(tokenizer.num_image_tokens, None)]
    strategy_cogview2 = CoglmStrategy(invalid_slices, temperature=1.0, top_k=16)
    strategy_cogvideo = CoglmStrategy(
        invalid_slices,
        temperature=args.temperature,
        top_k=args.top_k,
        temperature2=args.coglm_temperature2,
    )
    if not args.stage_1:
        from sr_pipeline import DirectSuperResolution

        dsr_path = auto_create(
            "cogview2-dsr", path=None
        )  # path=os.getenv('SAT_HOME', '~/.sat_models')
        dsr = DirectSuperResolution(args, dsr_path, max_bz=12, onCUDA=False)

    def process_stage2(
        model,
        seq_text,
        duration,
        video_raw_text=None,
        video_guidance_text="视频",
        parent_given_tokens=None,
        conddir=None,
        outputdir=None,
        gpu_rank=0,
        gpu_parallel_size=1,
    ):
        stage2_starttime = time.time()
        use_guidance = args.use_guidance_stage2
        if args.both_stages:
            move_start_time = time.time()
            logging.debug("moving stage-2 model to cuda")
            model = model.cuda()
            logging.debug(
                "moving in stage-2 model takes time: {:.2f}".format(
                    time.time() - move_start_time
                )
            )

        try:
            if parent_given_tokens is None:
                assert conddir is not None
                parent_given_tokens = torch.load(
                    os.path.join(conddir, "frame_tokens.pt"), map_location="cpu"
                )
            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:
            logging.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

            logging.info(
                "[Stage2: Generating Frames, Frame Rate {:d}]\nraw text: {:s}".format(
                    int(4 / duration), tokenizer.decode(enc_text)
                )
            )

            # generation
            seq = (
                torch.cuda.LongTensor(seq, device=args.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.cuda.LongTensor(guider_seq, device=args.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 = args.max_inference_batch_size

            assert generate_batchsize_total < mbz or generate_batchsize_total % mbz == 0
            output_list = []
            start_time = time.time()
            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,
                        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=strategy_cogview2,
                        strategy2=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=args.guidance_alpha,
                        limited_spatial_channel_mem=True,
                    )[0]
                )
            logging.info(
                "Duration {:.2f}, Taken time {:.2f}\n".format(
                    duration, time.time() - start_time
                )
            )

            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 args.both_stages:
            move_start_time = time.time()
            logging.debug("moving stage 2 model to cpu")
            model = model.cpu()
            torch.cuda.empty_cache()
            logging.debug(
                "moving out model2 takes time: {:.2f}".format(
                    time.time() - move_start_time
                )
            )

        logging.info(
            "CogVideo Stage2 completed. Taken time {:.2f}\n".format(
                time.time() - stage2_starttime
            )
        )

        # decoding
        # imgs = [torch.nn.functional.interpolate(tokenizer.decode(image_ids=seq.tolist()), size=(480, 480)) for seq in output_tokens_merge]
        # os.makedirs(output_dir_full_path, exist_ok=True)
        # my_save_multiple_images(imgs, output_dir_full_path,subdir="frames", debug=False)
        # torch.save(output_tokens_merge.cpu(), os.path.join(output_dir_full_path, 'frame_token.pt'))
        # os.system(f"gifmaker -i '{output_dir_full_path}'/frames/0*.jpg -o '{output_dir_full_path}/{str(float(duration))}_concat.gif' -d 0.2")

        # direct super-resolution by CogView2
        logging.info("[Direct super-resolution]")
        dsr_starttime = time.time()
        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.cuda.LongTensor(enc_text, device=args.device)
            .unsqueeze(0)
            .repeat(parent_given_tokens_2d.shape[0], 1)
        )
        sred_tokens = 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(
                    torch.nn.functional.interpolate(decoded_sr_img, size=(480, 480))
                )
            decoded_sr_videos.append(decoded_sr_imgs)

        for sample_i in range(sample_num):
            my_save_multiple_images(
                decoded_sr_videos[sample_i],
                outputdir,
                subdir=f"frames/{sample_i+sample_num*gpu_rank}",
                debug=False,
            )
            os.system(
                f"gifmaker -i '{outputdir}'/frames/'{sample_i+sample_num*gpu_rank}'/0*.jpg -o '{outputdir}/{sample_i+sample_num*gpu_rank}.gif' -d 0.125"
            )

        logging.info(
            "Direct super-resolution completed. Taken time {:.2f}\n".format(
                time.time() - dsr_starttime
            )
        )

        return True

    def process_stage1(
        model,
        seq_text,
        duration,
        video_raw_text=None,
        video_guidance_text="视频",
        image_text_suffix="",
        outputdir=None,
        batch_size=1,
    ):
        process_start_time = time.time()
        use_guide = args.use_guidance_stage1
        if args.both_stages:
            move_start_time = time.time()
            logging.debug("moving stage 1 model to cuda")
            model = model.cuda()
            logging.debug(
                "moving in model1 takes time: {:.2f}".format(
                    time.time() - move_start_time
                )
            )

        if video_raw_text is None:
            video_raw_text = seq_text
        mbz = (
            args.stage1_max_inference_batch_size
            if args.stage1_max_inference_batch_size > 0
            else 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
        )  # IV!!  # test local!!! # test randboi!!!
        logging.info(
            "[Generating First Frame with CogView2]Raw text: {:s}".format(
                tokenizer.decode(enc_text)
            )
        )
        text_len_1st = len(seq_1st) - frame_len * 1 - 1

        seq_1st = torch.cuda.LongTensor(seq_1st, device=args.device).unsqueeze(0)
        output_list_1st = []
        for tim in range(max(batch_size // mbz, 1)):
            start_time = time.time()
            output_list_1st.append(
                my_filling_sequence(
                    model,
                    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=strategy_cogview2,
                    strategy2=strategy_cogvideo,
                    log_text_attention_weights=1.4,
                    enforce_no_swin=True,
                    mode_stage1=True,
                )[0]
            )
            logging.info(
                "[First Frame]Taken time {:.2f}\n".format(time.time() - start_time)
            )
        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(str(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
        )
        logging.info(
            "[Stage1: Generating Subsequent Frames, Frame Rate {:.1f}]\nraw text: {:s}".format(
                4 / duration, tokenizer.decode(enc_text_video)
            )
        )

        text_len = len(seq) - frame_len * generate_frame_num - 1
        guider_text_len = len(guider_seq) - frame_len * generate_frame_num - 1
        seq = (
            torch.cuda.LongTensor(seq, device=args.device)
            .unsqueeze(0)
            .repeat(batch_size, 1)
        )
        guider_seq = (
            torch.cuda.LongTensor(guider_seq, device=args.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)):
            start_time = time.time()
            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,
                    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=strategy_cogview2,
                    strategy2=strategy_cogvideo,
                    log_text_attention_weights=video_log_text_attention_weights,
                    guider_seq=guider_seq2,
                    guider_text_len=guider_text_len,
                    guidance_alpha=args.guidance_alpha,
                    limited_spatial_channel_mem=True,
                    mode_stage1=True,
                )[0]
            )

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

        if args.both_stages:
            move_start_time = time.time()
            logging.debug("moving stage 1 model to cpu")
            model = model.cpu()
            torch.cuda.empty_cache()
            logging.debug(
                "moving in model1 takes time: {:.2f}".format(
                    time.time() - move_start_time
                )
            )

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

        assert len(imgs) == batch_size
        save_tokens = (
            output_tokens[:, : +total_frames * 400].reshape(-1, total_frames, 400).cpu()
        )
        if outputdir is not None:
            for clip_i in range(len(imgs)):
                # os.makedirs(output_dir_full_paths[clip_i], exist_ok=True)
                my_save_multiple_images(
                    imgs[clip_i], outputdir, subdir=f"frames/{clip_i}", debug=False
                )
                os.system(
                    f"gifmaker -i '{outputdir}'/frames/'{clip_i}'/0*.jpg -o '{outputdir}/{clip_i}.gif' -d 0.25"
                )
            torch.save(save_tokens, os.path.join(outputdir, "frame_tokens.pt"))

        logging.info(
            "CogVideo Stage1 completed. Taken time {:.2f}\n".format(
                time.time() - process_start_time
            )
        )

        return save_tokens

    # ======================================================================================================

    if args.stage_1 or args.both_stages:
        if args.input_source != "interactive":
            with open(args.input_source, "r") as fin:
                promptlist = fin.readlines()
            promptlist = [p.strip() for p in promptlist]
        else:
            promptlist = None

        now_qi = -1
        while True:
            now_qi += 1

            if promptlist is not None:  # with input-source
                if args.multi_gpu:
                    if now_qi % dist.get_world_size() != dist.get_rank():
                        continue
                    rk = dist.get_rank()
                else:
                    rk = 0
                raw_text = promptlist[now_qi]
                raw_text = raw_text.strip()
                print(f"Working on Line No. {now_qi} on {rk}... [{raw_text}]")
            else:  # interactive
                raw_text = input("\nPlease Input Query (stop to exit) >>> ")
                raw_text = raw_text.strip()
                if not raw_text:
                    print("Query should not be empty!")
                    continue
                if raw_text == "stop":
                    return

            try:
                path = os.path.join(args.output_path, f"{now_qi}_{raw_text}")
                parent_given_tokens = process_stage1(
                    model_stage1,
                    raw_text,
                    duration=4.0,
                    video_raw_text=raw_text,
                    video_guidance_text="视频",
                    image_text_suffix=" 高清摄影",
                    outputdir=path if args.stage_1 else None,
                    batch_size=args.batch_size,
                )
                if args.both_stages:
                    process_stage2(
                        model_stage2,
                        raw_text,
                        duration=2.0,
                        video_raw_text=raw_text + " 视频",
                        video_guidance_text="视频",
                        parent_given_tokens=parent_given_tokens,
                        outputdir=path,
                        gpu_rank=0,
                        gpu_parallel_size=1,
                    )  # TODO: 修改
            except (ValueError, FileNotFoundError) as e:
                print(e)
                continue

    elif args.stage_2:
        sample_dirs = os.listdir(args.output_path)
        for sample in sample_dirs:
            raw_text = sample.split("_")[-1]
            path = os.path.join(args.output_path, sample, "Interp")
            parent_given_tokens = torch.load(
                os.path.join(args.output_path, sample, "frame_tokens.pt")
            )

            process_stage2(
                raw_text,
                duration=2.0,
                video_raw_text=raw_text + " 视频",
                video_guidance_text="视频",
                parent_given_tokens=parent_given_tokens,
                outputdir=path,
                gpu_rank=0,
                gpu_parallel_size=1,
            )  # TODO: 修改

    else:
        assert False


if __name__ == "__main__":
    logging.basicConfig(stream=sys.stderr, level=logging.DEBUG)

    py_parser = argparse.ArgumentParser(add_help=False)
    py_parser.add_argument("--generate-frame-num", type=int, default=5)
    py_parser.add_argument("--coglm-temperature2", type=float, default=0.89)
    # py_parser.add_argument("--interp-duration", type=float, default=-1) # -1是顺序生成，0是超分，0.5/1/2是插帧
    # py_parser.add_argument("--total-duration", type=float, default=4.0) # 整个的时间
    py_parser.add_argument("--use-guidance-stage1", action="store_true")
    py_parser.add_argument("--use-guidance-stage2", action="store_true")
    py_parser.add_argument("--guidance-alpha", type=float, default=3.0)
    py_parser.add_argument(
        "--stage-1", action="store_true"
    )  # stage 1: sequential generation
    py_parser.add_argument("--stage-2", action="store_true")  # stage 2: interp + dsr
    py_parser.add_argument(
        "--both-stages", action="store_true"
    )  # stage 1&2: sequential generation; interp + dsr
    py_parser.add_argument("--parallel-size", type=int, default=1)
    py_parser.add_argument(
        "--stage1-max-inference-batch-size", type=int, default=-1
    )  # -1: use max-inference-batch-size
    py_parser.add_argument("--multi-gpu", action="store_true")

    CogVideoCacheModel.add_model_specific_args(py_parser)

    known, args_list = py_parser.parse_known_args()
    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

    torch.cuda.set_device(args.device)

    with torch.no_grad():
        main(args)