import torch
import logging
import os.path as osp
from datetime import datetime
from easydict import EasyDict
import os

cfg = EasyDict(__name__='Config: VideoComposer')

pmi_world_size = int(os.getenv('WORLD_SIZE', 1))
gpus_per_machine = torch.cuda.device_count()
world_size = pmi_world_size * gpus_per_machine

cfg.video_compositions = ['text', 'mask', 'depthmap', 'sketch', 'motion', 'image', 'local_image', 'single_sketch']

# dataset
cfg.root_dir = 'webvid10m/'

cfg.alpha = 0.7

cfg.misc_size = 384
cfg.depth_std = 20.0
cfg.depth_clamp = 10.0
cfg.hist_sigma = 10.0

# 
cfg.use_image_dataset = False
cfg.alpha_img = 0.7

cfg.resolution = 256
cfg.mean = [0.5, 0.5, 0.5]
cfg.std = [0.5, 0.5, 0.5]

# sketch
cfg.sketch_mean = [0.485, 0.456, 0.406]
cfg.sketch_std = [0.229, 0.224, 0.225]

# dataloader
cfg.max_words = 1000

cfg.frame_lens = [
    16,
    16,
    16,
    16,
]
cfg.feature_framerates = [
    4,
]
cfg.feature_framerate = 4
cfg.batch_sizes = {
    str(1):1,
    str(4):1,
    str(8):1,
    str(16):1,
}

cfg.chunk_size=64
cfg.num_workers = 8
cfg.prefetch_factor = 2
cfg.seed = 8888

# diffusion
cfg.num_timesteps = 1000
cfg.mean_type = 'eps'
cfg.var_type = 'fixed_small'  # NOTE: to stabilize training and avoid NaN
cfg.loss_type = 'mse'
cfg.ddim_timesteps = 50  # official: 250
cfg.ddim_eta = 0.0
cfg.clamp = 1.0
cfg.share_noise = False
cfg.use_div_loss = False

# classifier-free guidance
cfg.p_zero = 0.9
cfg.guide_scale = 6.0

# stabel diffusion
cfg.sd_checkpoint = 'v2-1_512-ema-pruned.ckpt'


# clip vision encoder
cfg.vit_image_size = 336
cfg.vit_patch_size = 14
cfg.vit_dim = 1024
cfg.vit_out_dim = 768
cfg.vit_heads = 16
cfg.vit_layers = 24
cfg.vit_mean = [0.48145466, 0.4578275, 0.40821073]
cfg.vit_std = [0.26862954, 0.26130258, 0.27577711]
cfg.clip_checkpoint = 'open_clip_pytorch_model.bin'
cfg.mvs_visual= False
# unet
cfg.unet_in_dim = 4
cfg.unet_concat_dim = 8
cfg.unet_y_dim = cfg.vit_out_dim
cfg.unet_context_dim = 1024
cfg.unet_out_dim = 8 if cfg.var_type.startswith('learned') else 4
cfg.unet_dim = 320
#cfg.unet_dim_mult = [1, 2, 3, 5]
cfg.unet_dim_mult = [1, 2, 4, 4]
cfg.unet_res_blocks = 2
cfg.unet_num_heads = 8
cfg.unet_head_dim = 64
cfg.unet_attn_scales = [1 / 1, 1 / 2, 1 / 4]
cfg.unet_dropout = 0.1
cfg.misc_dropout = 0.5
cfg.p_all_zero = 0.1
cfg.p_all_keep = 0.1
cfg.temporal_conv = False
cfg.temporal_attn_times = 1
cfg.temporal_attention = True

cfg.use_fps_condition = False
cfg.use_sim_mask = False

## Default: load 2d pretrain
cfg.pretrained = False
cfg.fix_weight = False

## Default resume
# 
cfg.resume = True
cfg.resume_step = 148000
cfg.resume_check_dir = '.'
cfg.resume_checkpoint = os.path.join(cfg.resume_check_dir,f'step_{cfg.resume_step}/non_ema_{cfg.resume_step}.pth')
#
cfg.resume_optimizer = False
if cfg.resume_optimizer:
    cfg.resume_optimizer = os.path.join(cfg.resume_check_dir,f'optimizer_step_{cfg.resume_step}.pt')


# acceleration
cfg.use_ema = True  
# for debug, no ema
if world_size<2:
    cfg.use_ema = False
cfg.load_from = None

cfg.use_checkpoint = True
cfg.use_sharded_ddp = False
cfg.use_fsdp = False 
cfg.use_fp16 = True

# training
cfg.ema_decay = 0.9999
cfg.viz_interval = 1000
cfg.save_ckp_interval = 1000

# logging
cfg.log_interval = 100
composition_strings = '_'.join(cfg.video_compositions)
### Default log_dir
cfg.log_dir = f'outputs/'
