import math
from typing import TYPE_CHECKING, Callable, Dict, List, Optional, Union
from transformers.trainer import Trainer
import torch
from torch import nn
from transformers.optimization import get_scheduler
from transformers.utils.versions import require_version

from ..extras.logging import get_logger
from ..extras.packages import is_galore_available
from ..hparams import FinetuningArguments, ModelArguments
from ..model import load_model_and_tokenizer, load_valuehead_params


if is_galore_available():
    from galore_torch import GaLoreAdafactor, GaLoreAdamW, GaLoreAdamW8bit


if TYPE_CHECKING:
    from datasets import Dataset, IterableDataset
    from transformers import Seq2SeqTrainingArguments
    from transformers.modeling_utils import PreTrainedModel
    from trl import AutoModelForCausalLMWithValueHead

    from ..hparams import DataArguments


logger = get_logger(__name__)


class DummyOptimizer(torch.optim.Optimizer):
    def __init__(self, *args, **kwargs):
        dummy_tensor = torch.randn(1, 1)
        super().__init__([dummy_tensor], {"lr": 1e-3})

    def zero_grad(self, set_to_none: bool = True) -> None:
        pass

    def step(self, closure: Optional[Callable[[], float]] = None) -> Optional[float]:
        pass


def create_modelcard_and_push(
    trainer: "Trainer",
    model_args: "ModelArguments",
    data_args: "DataArguments",
    training_args: "Seq2SeqTrainingArguments",
    finetuning_args: "FinetuningArguments",
) -> None:
    kwargs = {
        "tasks": "text-generation",
        "finetuned_from": model_args.model_name_or_path,
        "dataset": [dataset.strip() for dataset in data_args.dataset.split(",")],
        "tags": ["llama-factory", finetuning_args.finetuning_type],
    }
    if not training_args.do_train:
        pass
    elif training_args.push_to_hub:
        trainer.push_to_hub(**kwargs)
    else:
        trainer.create_model_card(license="other", **kwargs)  # prevent from connecting to hub


def create_ref_model(
    model_args: "ModelArguments", finetuning_args: "FinetuningArguments", add_valuehead: bool = False
) -> Union["PreTrainedModel", "AutoModelForCausalLMWithValueHead"]:
    r"""
    Creates reference model for PPO/DPO training. Evaluation mode is not supported.

    The valuehead parameter is randomly initialized since it is useless for PPO training.
    """
    if finetuning_args.ref_model is not None:
        ref_model_args_dict = model_args.to_dict()
        ref_model_args_dict.update(
            dict(
                model_name_or_path=finetuning_args.ref_model,
                adapter_name_or_path=finetuning_args.ref_model_adapters,
                quantization_bit=finetuning_args.ref_model_quantization_bit,
            )
        )
        ref_model_args = ModelArguments(**ref_model_args_dict)
        ref_finetuning_args = FinetuningArguments(finetuning_type="lora")
        ref_model, _ = load_model_and_tokenizer(
            ref_model_args, ref_finetuning_args, is_trainable=False, add_valuehead=add_valuehead
        )
        logger.info("Created reference model from {}".format(finetuning_args.ref_model))
    else:
        if finetuning_args.finetuning_type == "lora":
            ref_model = None
        else:
            ref_model, _ = load_model_and_tokenizer(
                model_args, finetuning_args, is_trainable=False, add_valuehead=add_valuehead
            )
            logger.info("Created reference model from the model itself.")

    return ref_model


def create_reward_model(
    model: "AutoModelForCausalLMWithValueHead", model_args: "ModelArguments", finetuning_args: "FinetuningArguments"
) -> "AutoModelForCausalLMWithValueHead":
    r"""
    Creates reward model for PPO training.
    """
    if finetuning_args.reward_model_type == "api":
        assert finetuning_args.reward_model.startswith("http"), "Please provide full url."
        logger.info("Use reward server {}".format(finetuning_args.reward_model))
        return finetuning_args.reward_model
    elif finetuning_args.reward_model_type == "lora":
        model.pretrained_model.load_adapter(finetuning_args.reward_model, "reward")
        for name, param in model.named_parameters():  # https://github.com/huggingface/peft/issues/1090
            if "default" in name:
                param.data = param.data.to(torch.float32)  # trainable params should in fp32
        vhead_params = load_valuehead_params(finetuning_args.reward_model, model_args)
        assert vhead_params is not None, "Reward model is not correctly loaded."
        model.register_buffer("reward_head_weight", vhead_params["v_head.summary.weight"], persistent=False)
        model.register_buffer("reward_head_bias", vhead_params["v_head.summary.bias"], persistent=False)
        model.register_buffer(
            "default_head_weight", torch.zeros_like(vhead_params["v_head.summary.weight"]), persistent=False
        )
        model.register_buffer(
            "default_head_bias", torch.zeros_like(vhead_params["v_head.summary.bias"]), persistent=False
        )
        logger.info("Loaded adapter weights of reward model from {}".format(finetuning_args.reward_model))
        return None
    else:
        reward_model_args_dict = model_args.to_dict()
        reward_model_args_dict.update(
            dict(
                model_name_or_path=finetuning_args.reward_model,
                adapter_name_or_path=finetuning_args.reward_model_adapters,
                quantization_bit=finetuning_args.reward_model_quantization_bit,
            )
        )
        reward_model_args = ModelArguments(**reward_model_args_dict)
        reward_finetuning_args = FinetuningArguments(finetuning_type="lora")
        reward_model, _ = load_model_and_tokenizer(
            reward_model_args, reward_finetuning_args, is_trainable=False, add_valuehead=True
        )
        logger.info("Loaded full weights of reward model from {}".format(finetuning_args.reward_model))
        logger.warning("Please ensure the ppo model and reward model share SAME tokenizer and vocabulary.")
        return reward_model


def create_custom_optimzer(
    model: "PreTrainedModel",
    dataset: Union["Dataset", "IterableDataset"],
    training_args: "Seq2SeqTrainingArguments",
    finetuning_args: "FinetuningArguments",
) -> Optional["torch.optim.Optimizer"]:
    if not finetuning_args.use_galore:
        return None

    require_version("galore_torch", "To fix: pip install git+https://github.com/hiyouga/GaLore.git")
    galore_params: List[torch.nn.Parameter] = []
    galore_targets = finetuning_args.galore_target.split(",")

    for name, module in model.named_modules():
        if isinstance(module, torch.nn.Linear) and any(target in name for target in galore_targets):
            for param in module.parameters():
                if param.requires_grad and len(param.shape) > 1:
                    galore_params.append(param)

    id_galore_params = {id(param) for param in galore_params}
    trainable_params = filter(lambda param: param.requires_grad, model.parameters())
    non_galore_params = [param for param in trainable_params if id(param) not in id_galore_params]

    if training_args.optim == "adamw_torch":
        optim_class = GaLoreAdamW
        optim_kwargs = {
            "lr": training_args.learning_rate,
            "eps": training_args.adam_epsilon,
            "betas": (training_args.adam_beta1, training_args.adam_beta2),
            "weight_decay": training_args.weight_decay,
        }

    elif training_args.optim in ["adamw_bnb_8bit", "adamw_8bit", "paged_adamw_8bit"]:
        optim_class = GaLoreAdamW8bit
        optim_kwargs = {
            "lr": training_args.learning_rate,
            "eps": training_args.adam_epsilon,
            "betas": (training_args.adam_beta1, training_args.adam_beta2),
            "weight_decay": training_args.weight_decay,
            "optim_bits": 8,
            "is_paged": "paged" in training_args.optim,
        }

    elif training_args.optim == "adafactor":
        optim_class = GaLoreAdafactor
        optim_kwargs = {
            "lr": training_args.learning_rate,
            "weight_decay": training_args.weight_decay,
        }

    else:
        raise NotImplementedError("Unknow optim: {}".format(training_args.optim))

    galore_kwargs = {
        "rank": finetuning_args.galore_rank,
        "update_proj_gap": finetuning_args.galore_update_interval,
        "scale": finetuning_args.galore_scale,
        "proj_type": finetuning_args.galore_proj_type,
    }

    if finetuning_args.galore_layerwise:
        if training_args.gradient_accumulation_steps != 1:
            raise ValueError("Per-layer GaLore does not support gradient accumulation.")

        if training_args.max_steps > 0:
            num_training_steps = training_args.max_steps
        else:
            total_train_batch_size = training_args.per_device_train_batch_size * training_args.world_size
            num_training_steps = training_args.num_train_epochs * math.ceil(len(dataset) / total_train_batch_size)

        optimizer_dict: Dict["torch.Tensor", "torch.optim.Optimizer"] = {}
        for param in non_galore_params:
            param_groups = [dict(params=[param])]
            optimizer_dict[param] = optim_class(param_groups, **optim_kwargs)
        for param in galore_params:
            param_groups = [dict(params=[param], **galore_kwargs)]
            optimizer_dict[param] = optim_class(param_groups, **optim_kwargs)

        scheduler_dict: Dict["torch.Tensor", "torch.optim.lr_scheduler.LRScheduler"] = {}
        for param in non_galore_params + galore_params:
            scheduler_dict[param] = get_scheduler(
                training_args.lr_scheduler_type,
                optimizer=optimizer_dict[param],
                num_warmup_steps=training_args.get_warmup_steps(num_training_steps) * 2,
                num_training_steps=num_training_steps * 2,
            )

        def optimizer_hook(param: "torch.Tensor"):
            if param.grad is not None:
                optimizer_dict[param].step()
                optimizer_dict[param].zero_grad()
                scheduler_dict[param].step()

        for param in non_galore_params + galore_params:
            param.register_post_accumulate_grad_hook(optimizer_hook)

        optimizer = DummyOptimizer()
    else:
        param_groups = [dict(params=non_galore_params), dict(params=galore_params, **galore_kwargs)]
        optimizer = optim_class(param_groups, **optim_kwargs)

    logger.info("Using GaLore optimizer, may cause hanging at the start of training, wait patiently.")
    return optimizer


def optimizer_group_callback(model, lora_lr_ratio, **defaults):
    "lora plus"
    params = []
    names = set()
    for name, param in model.named_parameters():
        if "default" in name and ('lora_B' in name or
                                  'lora_embedding_B' in name):
            params.append(param)
            names.add(name)
    if params:
        assert 'lr' in defaults
        return names, {
            'params': params,
            'lr': defaults['lr'] * lora_lr_ratio,
        }
    return None, None


def create_lora_plus_optimizer(
        model: "PreTrainedModel",
        training_args: "Seq2SeqTrainingArguments",
        finetuning_args: "FinetuningArguments",
) -> Optional["torch.optim.Optimizer"]:
    if finetuning_args.lora_lr_ratio is None:
        return None
    all_param_names = set()
    param_groups = []
    param_names, param_group = optimizer_group_callback(
        model, lora_lr_ratio=finetuning_args.lora_lr_ratio,
        lr=training_args.learning_rate,
        weight_decay=training_args.weight_decay)
    if param_names and all_param_names & param_names:
        raise ValueError(
            'Cannot set one parameter to different param groups')
    if param_names and param_group:
        all_param_names.update(param_names)
        param_groups.append(param_group)

    opt_model = model
    decay_parameters = Trainer.get_decay_parameter_names(None, opt_model)
    param_groups.extend([
        {
            'params': [
                p for n, p in opt_model.named_parameters()
                if (n in decay_parameters and n not in all_param_names and p.requires_grad)
            ],
            'weight_decay':
                training_args.weight_decay,
        },
        {
            'params': [
                p for n, p in opt_model.named_parameters()
                if (n not in decay_parameters and n not in all_param_names and p.requires_grad)
            ],
            'weight_decay':
                0.0,
        },
    ])

    optimizer_cls, optimizer_kwargs = Trainer.get_optimizer_cls_and_kwargs(training_args)

    optimizer = optimizer_cls(param_groups, **optimizer_kwargs)

    if optimizer_cls.__name__ == 'Adam8bit':
        import bitsandbytes

        manager = bitsandbytes.optim.GlobalOptimManager.get_instance()

        skipped = 0
        for module in opt_model.modules():
            if isinstance(module, nn.Embedding):
                skipped += sum({
                                   p.data_ptr(): p.numel()
                                   for p in module.parameters()
                               }.values())
                logger.info(
                    f'skipped {module}: {skipped / 2 ** 20}M params')
                manager.register_module_override(
                    module, 'weight', {'optim_bits': 32})
                logger.debug(
                    f'bitsandbytes: will optimize {module} in fp32')
        logger.info(f'skipped: {skipped / 2 ** 20}M params')

    return optimizer