import argparse
from copy import deepcopy

import torch
import torch.distributed as dist
import torch.nn as nn
from coati.models.base import RewardModel
from coati.models.opt import OPTActor, OPTCritic
from coati.trainer import PPOTrainer
from coati.trainer.callbacks import PerformanceEvaluator
from coati.trainer.strategies import DDPStrategy, GeminiStrategy, LowLevelZeroStrategy, Strategy
from torch.optim import Adam
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
from transformers.models.opt.configuration_opt import OPTConfig

from colossalai.nn.optimizer import HybridAdam


def get_model_numel(model: nn.Module, strategy: Strategy) -> int:
    numel = sum(p.numel() for p in model.parameters())
    if isinstance(strategy, GeminiStrategy) and strategy.shard_init:
        numel *= dist.get_world_size()
    return numel


def preprocess_batch(samples) -> dict:
    input_ids = torch.stack(samples)
    attention_mask = torch.ones_like(input_ids, dtype=torch.long)
    return {"input_ids": input_ids, "attention_mask": attention_mask}


def print_rank_0(*args, **kwargs) -> None:
    if dist.get_rank() == 0:
        print(*args, **kwargs)


def print_model_numel(model_dict: dict) -> None:
    B = 1024**3
    M = 1024**2
    K = 1024
    outputs = ""
    for name, numel in model_dict.items():
        outputs += f"{name}: "
        if numel >= B:
            outputs += f"{numel / B:.2f} B\n"
        elif numel >= M:
            outputs += f"{numel / M:.2f} M\n"
        elif numel >= K:
            outputs += f"{numel / K:.2f} K\n"
        else:
            outputs += f"{numel}\n"
    print_rank_0(outputs)


def get_gpt_config(model_name: str) -> OPTConfig:
    model_map = {
        "125m": OPTConfig.from_pretrained("facebook/opt-125m"),
        "350m": OPTConfig(hidden_size=1024, ffn_dim=4096, num_hidden_layers=24, num_attention_heads=16),
        "700m": OPTConfig(hidden_size=1280, ffn_dim=5120, num_hidden_layers=36, num_attention_heads=20),
        "1.3b": OPTConfig.from_pretrained("facebook/opt-1.3b"),
        "2.7b": OPTConfig.from_pretrained("facebook/opt-2.7b"),
        "3.5b": OPTConfig(hidden_size=3072, ffn_dim=12288, num_hidden_layers=32, num_attention_heads=32),
        "5.5b": OPTConfig(hidden_size=3840, ffn_dim=15360, num_hidden_layers=32, num_attention_heads=32),
        "6.7b": OPTConfig.from_pretrained("facebook/opt-6.7b"),
        "10b": OPTConfig(hidden_size=5120, ffn_dim=20480, num_hidden_layers=32, num_attention_heads=32),
        "13b": OPTConfig.from_pretrained("facebook/opt-13b"),
    }
    try:
        return model_map[model_name]
    except KeyError:
        raise ValueError(f'Unknown model "{model_name}"')


def main(args):
    if args.strategy == "ddp":
        strategy = DDPStrategy()
    elif args.strategy == "colossalai_gemini":
        strategy = GeminiStrategy(placement_policy="static",initial_scale=2**5)
    elif args.strategy == "colossalai_gemini_cpu":
        strategy = GeminiStrategy(placement_policy="static", offload_optim_frac=1.0, offload_param_frac=1.0, initial_scale=2**5)
    elif args.strategy == "colossalai_zero2":
        strategy = LowLevelZeroStrategy(stage=2, placement_policy="cuda")
    elif args.strategy == "colossalai_zero2_cpu":
        strategy = LowLevelZeroStrategy(stage=2, placement_policy="cpu")
    elif args.strategy == "colossalai_zero1":
        strategy = LowLevelZeroStrategy(stage=1, placement_policy="cuda")
    elif args.strategy == "colossalai_zero1_cpu":
        strategy = LowLevelZeroStrategy(stage=1, placement_policy="cpu")
    else:
        raise ValueError(f'Unsupported strategy "{args.strategy}"')

    torch.cuda.set_per_process_memory_fraction(args.cuda_mem_frac)

    model_config = get_gpt_config(args.model)
    critic_config = get_gpt_config(args.critic_model)
    with strategy.model_init_context():
        actor = OPTActor(config=model_config, lora_rank=args.lora_rank).cuda()
        critic = OPTCritic(config=critic_config, lora_rank=args.lora_rank).cuda()

        initial_model = deepcopy(actor).cuda().half()
        reward_model = RewardModel(deepcopy(critic.model), deepcopy(critic.value_head)).cuda().half()

    if args.use_kernels:
        from coati.kernels import convert_to_xformer_model

        actor, critic, initial_model, reward_model = map(
            convert_to_xformer_model, (actor, critic, initial_model, reward_model)
        )

    actor_numel = get_model_numel(actor, strategy)
    critic_numel = get_model_numel(critic, strategy)
    initial_model_numel = get_model_numel(initial_model, strategy)
    reward_model_numel = get_model_numel(reward_model, strategy)
    print_model_numel(
        {
            "Actor": actor_numel,
            "Critic": critic_numel,
            "Initial model": initial_model_numel,
            "Reward model": reward_model_numel,
        }
    )
    performance_evaluator = PerformanceEvaluator(
        actor_numel,
        critic_numel,
        initial_model_numel,
        reward_model_numel,
        enable_grad_checkpoint=False,
        ignore_episodes=1,
    )

    if args.strategy.startswith("colossalai"):
        actor_optim = HybridAdam(actor.parameters(), lr=5e-6)
        critic_optim = HybridAdam(critic.parameters(), lr=5e-6)
    else:
        actor_optim = Adam(actor.parameters(), lr=5e-6)
        critic_optim = Adam(critic.parameters(), lr=5e-6)

    tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m")
    tokenizer.pad_token = tokenizer.eos_token
    tokenizer.padding_side = "left"

    (actor, actor_optim), (critic, critic_optim) = strategy.prepare((actor, actor_optim), (critic, critic_optim))

    random_prompts = torch.randint(tokenizer.vocab_size, (1000, 256), device=torch.cuda.current_device())
    dataloader = DataLoader(
        random_prompts, batch_size=args.experience_batch_size, shuffle=True, collate_fn=preprocess_batch
    )

    trainer = PPOTrainer(
        strategy,
        actor,
        critic,
        reward_model,
        initial_model,
        actor_optim,
        critic_optim,
        tokenizer=tokenizer,
        ptx_coef=0,
        train_batch_size=args.train_batch_size,
        offload_inference_models=args.offload_inference_models,
        max_length=512,
        do_sample=True,
        temperature=1.0,
        top_k=50,
        use_cache=True,
        callbacks=[performance_evaluator],
    )

    trainer.fit(
        prompt_dataloader=dataloader,
        pretrain_dataloader=None,
        num_episodes=args.num_episodes,
        num_update_steps=args.num_update_steps,
        num_collect_steps=args.num_collect_steps,
    )

    print_rank_0(f"Peak CUDA mem: {torch.cuda.max_memory_allocated()/1024**3:.2f} GB")


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--model", default="125m")
    parser.add_argument("--critic_model", default="125m")
    parser.add_argument(
        "--strategy",
        choices=[
            "ddp",
            "colossalai_gemini",
            "colossalai_gemini_cpu",
            "colossalai_zero2",
            "colossalai_zero2_cpu",
            "colossalai_zero1",
            "colossalai_zero1_cpu",
        ],
        default="ddp",
    )
    parser.add_argument("--num_episodes", type=int, default=3)
    parser.add_argument("--num_collect_steps", type=int, default=8)
    parser.add_argument("--num_update_steps", type=int, default=1)
    parser.add_argument("--train_batch_size", type=int, default=8)
    parser.add_argument("--experience_batch_size", type=int, default=8)
    parser.add_argument("--lora_rank", type=int, default=0)
    parser.add_argument("--cuda_mem_frac", type=float, default=1.0)
    parser.add_argument("--offload_inference_models", action="store_true", default=False)
    parser.add_argument("--use_kernels", action="store_true", default=False)
    args = parser.parse_args()
    main(args)
