import os
from collections import OrderedDict
from typing import Any, Callable, Dict, List, Optional

import torch
import torch.distributed as dist
import torch.nn as nn
from coati.models.bloom import BLOOMRM, BLOOMActor, BLOOMCritic
from coati.models.gpt import GPTRM, GPTActor, GPTCritic
from coati.models.llama import LlamaActor, LlamaCritic, LlamaRM
from coati.models.opt import OPTRM, OPTActor, OPTCritic
from coati.trainer.strategies import DDPStrategy, GeminiStrategy, LowLevelZeroStrategy
from transformers import AutoTokenizer, BloomTokenizerFast, GPT2Tokenizer, LlamaTokenizer


def is_rank_0() -> bool:
    return not dist.is_initialized() or dist.get_rank() == 0


def get_rank() -> int:
    return dist.get_rank() if dist.is_initialized() else 0


def get_world_size() -> int:
    return dist.get_world_size() if dist.is_initialized() else 1


def get_actor_from_args(model: str, pretrained: str = None, config=None, lora_rank=0):
    if model == 'gpt2':
        actor = GPTActor(pretrained=pretrained, config=config, lora_rank=lora_rank)
    elif model == 'bloom':
        actor = BLOOMActor(pretrained=pretrained, config=config, lora_rank=lora_rank)
    elif model == 'opt':
        actor = OPTActor(pretrained=pretrained, config=config, lora_rank=lora_rank)
    elif model == 'llama':
        actor = LlamaActor(pretrained=pretrained, config=config, lora_rank=lora_rank)
    else:
        raise ValueError(f'Unsupported actor model "{model}"')
    return actor


def get_critic_from_args(model: str, pretrained: str = None, config=None, lora_rank=0):
    if model == 'gpt2':
        critic = GPTCritic(pretrained=pretrained, lora_rank=lora_rank, config=config, use_action_mask=True)
    elif model == 'bloom':
        critic = BLOOMCritic(pretrained=pretrained, lora_rank=lora_rank, config=config, use_action_mask=True)
    elif model == 'opt':
        critic = OPTCritic(pretrained=pretrained, lora_rank=lora_rank, config=config, use_action_mask=True)
    elif model == 'llama':
        critic = LlamaCritic(pretrained=pretrained, lora_rank=lora_rank, config=config, use_action_mask=True)
    else:
        raise ValueError(f'Unsupported reward model "{model}"')
    return critic


def get_reward_model_from_args(model: str, pretrained: str = None, config=None):
    if model == 'gpt2':
        reward_model = GPTRM(pretrained=pretrained, config=config)
    elif model == 'bloom':
        reward_model = BLOOMRM(pretrained=pretrained, config=config)
    elif model == 'opt':
        reward_model = OPTRM(pretrained=pretrained, config=config)
    elif model == 'llama':
        reward_model = LlamaRM(pretrained=pretrained, config=config)
    else:
        raise ValueError(f'Unsupported reward model "{model}"')
    return reward_model


def get_strategy_from_args(strategy: str):
    if strategy == 'ddp':
        strategy_ = DDPStrategy()
    elif strategy == 'colossalai_gemini':
        strategy_ = GeminiStrategy(placement_policy='cuda', initial_scale=2**5)
    elif strategy == 'colossalai_zero2':
        strategy_ = LowLevelZeroStrategy(stage=2, placement_policy='cuda')
    elif strategy == 'colossalai_gemini_cpu':
        strategy_ = GeminiStrategy(placement_policy='cpu', initial_scale=2**5)
    elif strategy == 'colossalai_zero2_cpu':
        strategy_ = LowLevelZeroStrategy(stage=2, placement_policy='cpu')
    else:
        raise ValueError(f'Unsupported strategy "{strategy}"')
    return strategy_


def get_tokenizer_from_args(model: str, **kwargs):
    if model == 'gpt2':
        tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
    elif model == 'bloom':
        tokenizer = BloomTokenizerFast.from_pretrained('bigscience/bloom-560m')
    elif model == 'opt':
        tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m")
    elif model == 'llama':
        pretrain_path = kwargs["pretrain"]
        tokenizer = AutoTokenizer.from_pretrained(pretrain_path)
    else:
        raise ValueError(f'Unsupported model "{model}"')

    tokenizer.pad_token = tokenizer.eos_token
    return tokenizer


def set_dist_env(env_info: Dict[str, str]):
    os.environ["RANK"] = env_info['rank']
    os.environ["LOCAL_RANK"] = env_info['local_rank']
    os.environ["WORLD_SIZE"] = env_info['world_size']
    os.environ['MASTER_PORT'] = env_info['master_port']
    os.environ['MASTER_ADDR'] = env_info['master_addr']


def get_model_numel(model: nn.Module) -> int:
    numel = sum(p.numel() for p in model.parameters())
    return numel


def get_receivers_per_sender(sender_idx: int, num_senders: int, num_receivers: int, allow_idle_sender: bool) -> list:
    target_receivers = []
    if num_senders <= num_receivers or allow_idle_sender:
        # a sender will send data to one or more receivers
        # a receiver only has one sender
        for i in range(num_receivers):
            if i % num_senders == sender_idx:
                target_receivers.append(i)
    else:
        # a sender will send data to one receiver
        # a receiver may have more than one sender
        target_receivers.append(sender_idx % num_receivers)
    return target_receivers


def state_dict_to(state_dict: Dict[str, Any],
                  dtype: torch.dtype = torch.float16,
                  device: torch.device = torch.device('cpu')):
    '''
        keep state_dict intact
    '''
    new_state_dict = OrderedDict()
    for k, v in state_dict.items():
        new_state_dict[k] = v.to(dtype=dtype, device=device)
    return new_state_dict
