import gc
import logging
import os
from pathlib import Path
from typing import Callable, Iterator, List, Optional, Tuple

import torch
import torch.nn as nn
from torch.optim import Optimizer
from torch.optim.lr_scheduler import _LRScheduler as LRScheduler
from torch.utils.data import DataLoader

from colossalai.checkpoint_io import CheckpointIndexFile, CheckpointIO, GeneralCheckpointIO
from colossalai.checkpoint_io.utils import (
    get_model_base_filenames,
    get_optimizer_base_filenames,
    load_shard_state_dict,
    save_config_file,
    save_state_dict,
    save_state_dict_shards,
)
from colossalai.cluster import DistCoordinator
from colossalai.interface import ModelWrapper, OptimizerWrapper
from colossalai.utils import get_current_device
from colossalai.zero import GeminiDDP, GeminiOptimizer
from colossalai.zero.gemini.memory_tracer import MemStats

from .dp_plugin_base import DPPluginBase

__all__ = ['GeminiPlugin']

SUPPORTED_PRECISION = ['fp16', 'bf16']
PRECISION_STR_TO_DTYPE = {'fp16': torch.half, 'bf16': torch.bfloat16}


class GeminiCheckpointIO(GeneralCheckpointIO):

    def __init__(self) -> None:
        super().__init__()
        self.coordinator = DistCoordinator()

    def save_unsharded_model(self, model: GeminiDDP, checkpoint: str, gather_dtensor: bool, use_safetensors: bool):
        """
        Save sharded model to checkpoint but only on master process.
        The model should be unwrapped in self.load_model via ModelWrapper.unwrap.
        As there is communication when getting state dict, model.state_dict() must be called on all processes.
        """
        state_dict = model.state_dict(only_rank_0=True)
        if self.coordinator.is_master():
            save_state_dict(state_dict, checkpoint, use_safetensors)

    def load_unsharded_model(self, model: GeminiDDP, checkpoint: str, strict: bool = True):
        """
        Load model from checkpoint with automatic unwrapping.
        The model should be unwrapped in self.load_model via ModelWrapper.unwrap.
        """
        super().load_unsharded_model(model, checkpoint, strict=strict)

    def save_unsharded_optimizer(self, optimizer: Optimizer, checkpoint: str, gather_dtensor: bool):
        """
        Save unsharded optimizer state dict to checkpoint.
        After calling optimizer.state_dict(), the complete optimizer states will be collected on master rank.
        As there is communication when getting state dict, optimizer.state_dict() must be called on all processes.
        The saving process will only be executed by master rank.
        """
        state_dict = optimizer.state_dict()
        if self.coordinator.is_master():
            save_state_dict(state_dict, checkpoint, use_safetensors=False)

    def load_unsharded_optimizer(self, optimizer: Optimizer, checkpoint: str):
        """
        Loading unsharded optimizer from checkpoint file.
        For each process, only loading optimizer states of parameters it controls.
        """
        super().load_unsharded_optimizer(optimizer, checkpoint)

    def save_sharded_model(self,
                           model: GeminiDDP,
                           checkpoint_path: str,
                           gather_dtensor: bool = False,
                           prefix: Optional[str] = None,
                           max_shard_size: int = 1024,
                           use_safetensors: bool = False):
        """
        Save sharded model.
        As there is communication when getting state dict, model.state_dict() must be called on all processes.
        """
        if os.path.isfile(checkpoint_path):
            logging.error(f"Provided path ({checkpoint_path}) should be a directory, not a file")
            return

        Path(checkpoint_path).mkdir(parents=True, exist_ok=True)

        state_dict_shard = model.state_dict_shard(max_shard_size=max_shard_size, only_rank_0=True, dtype=torch.float32)
        weights_name, save_index_file = get_model_base_filenames(prefix, use_safetensors)
        index_file = CheckpointIndexFile(checkpoint_path)

        # Save shards of optimizer states.
        is_master = self.coordinator.is_master()
        total_size = save_state_dict_shards(sharded_state_dict=state_dict_shard,
                                            checkpoint=checkpoint_path,
                                            index_file=index_file,
                                            base_filename=weights_name,
                                            is_master=is_master,
                                            use_safetensors=use_safetensors)

        # only save the index file on the master rank
        if self.coordinator.is_master():
            index_file.append_meta_data("total_size", total_size)
            index_file.write_index_file(save_index_file)
            save_config_file(model.module, checkpoint_path)
            logging.info(f"The model is split into checkpoint shards. "
                         f"You can find where each parameters has been saved in the "
                         f"index located at {save_index_file}.")

    def load_sharded_model(self,
                           model: GeminiDDP,
                           checkpoint_index_file: Path,
                           strict: bool = False,
                           use_safetensors: bool = False):
        """
        Load shard model, load model from multiple files.
        """
        return super().load_sharded_model(model, checkpoint_index_file, strict, use_safetensors, load_sub_module=False)

    def save_sharded_optimizer(self, optimizer: Optimizer, checkpoint: Path, gather_dtensor: bool, prefix: str,
                               size_per_shard: int):
        """
        Save sharded optimizer state dict to checkpoint folder.
        As there is communication when getting state dict, this must be called on all processes.
        """

        assert isinstance(optimizer, GeminiOptimizer)

        if os.path.isfile(checkpoint):
            logging.error(f"Provided path ({checkpoint}) should be a directory, not a file")
            return

        Path(checkpoint).mkdir(parents=True, exist_ok=True)

        # Preparing file paths and index file.
        states_name, save_index_file, param_group_file = get_optimizer_base_filenames(prefix)
        index_file = CheckpointIndexFile(checkpoint)

        # Store the information of param groups to param_group_file.
        index_file.append_meta_data("param_groups", param_group_file)
        group_file_path = os.path.join(checkpoint, param_group_file)
        param_groups = optimizer.get_param_groups_for_saving()
        torch.save(param_groups, group_file_path)

        # States are broken into shards within max_shard_size.
        state_dict_shard = optimizer.state_shard(prefix=prefix, max_shard_size=size_per_shard, only_rank_0=True)

        # Save shards of optimizer states.
        is_master = self.coordinator.is_master()
        total_size = save_state_dict_shards(sharded_state_dict=state_dict_shard,
                                            checkpoint=checkpoint,
                                            index_file=index_file,
                                            base_filename=states_name,
                                            is_master=is_master,
                                            use_safetensors=False)

        # Wrap up index file. Only save it on master rank.
        if self.coordinator.is_master():
            index_file.append_meta_data("total_size", total_size)
            index_file.write_index_file(save_index_file)
            logging.info(f"The optimizer is going to be split to checkpoint shards. "
                         f"You can find where each parameters has been saved in the "
                         f"index located at {save_index_file}.")

    def load_sharded_optimizer(self, optimizer: Optimizer, checkpoint_index_file: Path, prefix: str):
        """
        Loading sharded optimizer from checkpoint folder, with index file given.
        For each process, only loading optimizer states of parameters it controls.
        """

        if not os.path.isfile(checkpoint_index_file):
            logging.error(f"Provided path ({checkpoint_index_file}) should be a file")

        assert isinstance(optimizer, GeminiOptimizer)

        # Read checkpoint index file.
        ckpt_index_file = CheckpointIndexFile.from_file(checkpoint_index_file)

        # Load param_groups.
        param_group_path = ckpt_index_file.get_param_group_filename()
        if param_group_path is None:
            raise RuntimeError(f'Invalid index file path {checkpoint_index_file} for an optimizer. \
                               Lacking param group file under current directory.')
        saved_param_groups = torch.load(param_group_path)
        optimizer.load_param_groups(saved_param_groups)

        checkpoint_files, _ = ckpt_index_file.get_checkpoint_filenames()

        # Load optimizer states from shard files under checkpoint path.
        # For each file, only load the states managed by current process.
        for shard_file in checkpoint_files:
            state_dict_shard = load_shard_state_dict(Path(shard_file), use_safetensors=False)
            optimizer.load_param_states(state_dict_shard)
            del state_dict_shard
            gc.collect()

        optimizer.optimizer_loading_epilogue()

    def save_lr_scheduler(self, lr_scheduler: LRScheduler, checkpoint: str):
        """
        Save model to checkpoint but only on master process.
        """
        if self.coordinator.is_master():
            super().save_lr_scheduler(lr_scheduler, checkpoint)


class GeminiPlugin(DPPluginBase):
    """
    Plugin for Gemini.

    Example:
        >>> from colossalai.booster import Booster
        >>> from colossalai.booster.plugin import GeminiPlugin
        >>>
        >>> model, train_dataset, optimizer, criterion = ...
        >>> plugin = GeminiPlugin()

        >>> train_dataloader = plugin.prepare_dataloader(train_dataset, batch_size=8)
        >>> booster = Booster(plugin=plugin)
        >>> model, optimizer, train_dataloader, criterion = booster.boost(model, optimizer, train_dataloader, criterion)

    Args:
        chunk_config_dict (dict, optional): chunk configuration dictionary.
        chunk_init_device (torch.device, optional): device to initialize the chunk.
        placement_policy (str, optional): "static" and "auto". Defaults to "static".
        shard_param_frac (float, optional): fraction of parameters to be sharded. Only for "static" placement.
            If `shard_param_frac` is 1.0, it's equal to zero-3. If `shard_param_frac` is 0.0, it's equal to zero-2. Defaults to 1.0.
        offload_optim_frac (float, optional): fraction of optimizer states to be offloaded. Only for "static" placement.
            If `shard_param_frac` is 1.0 and `offload_optim_frac` is 0.0, it's equal to old "cuda" placement. Defaults to 0.0.
        offload_param_frac (float, optional): fraction of parameters to be offloaded. Only for "static" placement.
            For efficiency, this argument is useful only when `shard_param_frac` is 1.0 and `offload_optim_frac` is 1.0.
            If `shard_param_frac` is 1.0, `offload_optim_frac` is 1.0 and `offload_param_frac` is 1.0, it's equal to old "cpu" placement.
            When using static placement, we recommend users to tune `shard_param_frac` first and then `offload_optim_frac`.
            Defaults to 0.0.
        warmup_non_model_data_ratio (float, optional): ratio of expected non-model data memory during warmup. Only for "auto" placement. Defaults to 0.8.
        steady_cuda_cap_ratio (float, optional): ratio of allowed cuda capacity for model data during steady state. Only for "auto" placement. Defaults to 0.9.
        precision (str, optional): precision. Support 'fp16' and 'bf16'. Defaults to 'fp16'.
        pin_memory (bool, optional): use pin memory on CPU. Defaults to False.
        force_outputs_fp32 (bool, optional): force outputs are fp32. Defaults to False.
        strict_ddp_mode (bool, optional): use strict ddp mode (only use dp without other parallelism). Defaults to False.
        search_range_m (int, optional): chunk size searching range divided by 2^20. Defaults to 32.
        hidden_dim (int, optional): the hidden dimension of DNN.
            Users can provide this argument to speed up searching.
            If users do not know this argument before training, it is ok. We will use a default value 1024.
        min_chunk_size_m (float, optional): the minimum chunk size divided by 2^20.
            If the aggregate size of parameters is still smaller than the minimum chunk size,
            all parameters will be compacted into one small chunk.
        memstats (MemStats, optional) the memory statistics collector by a runtime memory tracer.
        gpu_margin_mem_ratio (float, optional): The ratio of GPU remaining memory (after the first forward-backward)
            which will be used when using hybrid CPU optimizer.
            This argument is meaningless when `placement_policy` of `GeminiManager` is not "auto".
            Defaults to 0.0.
        initial_scale (float, optional): Initial scale used by DynamicGradScaler. Defaults to 2**16.
        min_scale (float, optional): Min scale used by DynamicGradScaler. Defaults to 1.
        growth_factor (float, optional): growth_factor used by DynamicGradScaler. Defaults to 2.
        backoff_factor (float, optional): backoff_factor used by DynamicGradScaler. Defaults to 0.5.
        growth_interval (float, optional): growth_interval used by DynamicGradScaler. Defaults to 1000.
        hysteresis (float, optional): hysteresis used by DynamicGradScaler. Defaults to 2.
        max_scale (int, optional): max_scale used by DynamicGradScaler. Defaults to 2**32.
        max_norm (float, optional): max_norm used for `clip_grad_norm`. You should notice that you shall not do
            clip_grad_norm by yourself when using ZeRO DDP. The ZeRO optimizer will take care of clip_grad_norm.
        norm_type (float, optional): norm_type used for `clip_grad_norm`.
        verbose (bool, optional): verbose mode. Debug info including chunk search result will be printed. Defaults to False.
    """

    def __init__(
        self,
        chunk_config_dict: Optional[dict] = None,
        chunk_init_device: Optional[torch.device] = None,
        placement_policy: str = "static",
        shard_param_frac: float = 1.0,    # only for static placement
        offload_optim_frac: float = 0.0,    # only for static placement
        offload_param_frac: float = 0.0,    # only for static placement
        warmup_non_model_data_ratio: float = 0.8,    # only for auto placement
        steady_cuda_cap_ratio: float = 0.9,    # only for auto placement
        precision: str = "fp16",
        pin_memory: bool = False,
        force_outputs_fp32: bool = False,
        strict_ddp_mode: bool = False,
        search_range_m: int = 32,
        hidden_dim: Optional[int] = None,
        min_chunk_size_m: float = 32,
        memstats: Optional[MemStats] = None,
        gpu_margin_mem_ratio: float = 0.0,
        initial_scale: float = 2**16,
        min_scale: float = 1,
        growth_factor: float = 2,
        backoff_factor: float = 0.5,
        growth_interval: int = 1000,
        hysteresis: int = 2,
        max_scale: float = 2**32,
        max_norm: float = 0.0,
        norm_type: float = 2.0,
        verbose: bool = False,
    ) -> None:
        super().__init__()
        assert precision in SUPPORTED_PRECISION, f'precision {precision} is not supported'
        self.gemini_config = dict(
            chunk_config_dict=chunk_config_dict,
            chunk_init_device=(chunk_init_device or get_current_device()),
            placement_policy=placement_policy,
            shard_param_frac=shard_param_frac,
            offload_optim_frac=offload_optim_frac,
            offload_param_frac=offload_param_frac,
            warmup_non_model_data_ratio=warmup_non_model_data_ratio,
            steady_cuda_cap_ratio=steady_cuda_cap_ratio,
            pin_memory=pin_memory,
            force_outputs_fp32=force_outputs_fp32,
            strict_ddp_mode=strict_ddp_mode,
            search_range_m=search_range_m,
            hidden_dim=hidden_dim,
            min_chunk_size_m=min_chunk_size_m,
            memstats=memstats,
            mixed_precision=PRECISION_STR_TO_DTYPE[precision],
        )
        self.zero_optim_config = dict(gpu_margin_mem_ratio=gpu_margin_mem_ratio,)
        self.optim_kwargs = dict(initial_scale=initial_scale,
                                 growth_factor=growth_factor,
                                 backoff_factor=backoff_factor,
                                 growth_interval=growth_interval,
                                 hysteresis=hysteresis,
                                 min_scale=min_scale,
                                 max_scale=max_scale,
                                 max_norm=max_norm,
                                 norm_type=norm_type)
        self.verbose = verbose

    def support_no_sync(self) -> bool:
        return False

    def control_precision(self) -> bool:
        return True

    def supported_precisions(self) -> List[str]:
        return SUPPORTED_PRECISION

    def control_device(self) -> bool:
        return True

    def supported_devices(self) -> List[str]:
        return ['cuda']

    def configure(
        self,
        model: nn.Module,
        optimizer: Optional[Optimizer] = None,
        criterion: Optional[Callable] = None,
        dataloader: Optional[DataLoader] = None,
        lr_scheduler: Optional[LRScheduler] = None,
    ) -> Tuple[nn.Module, OptimizerWrapper, Callable, DataLoader, LRScheduler]:

        if not isinstance(model, ModelWrapper):
            # convert model to sync bn
            # FIXME(ver217): gemini does not support sync bn
            # In torch/nn/modules/_functions.py, line 22, ``mean, invstd = torch.batch_norm_stats(input, eps)`` will get fp32 mean and invstd even though the input is fp16.
            # This inconsistency of dtype will cause the error.
            # We have two possible solutions:
            # 1. keep batch norm always in fp32. This is hard for gemini, as it use chunks.
            # 2. patch sync bn or write a new on. This is relatively easy, but we need to test it.
            # model = nn.SyncBatchNorm.convert_sync_batchnorm(model, None)

            # wrap the model with Gemini
            model = GeminiDDP(model, **self.gemini_config, verbose=self.verbose)

        if optimizer is not None and \
                not isinstance(optimizer, OptimizerWrapper):
            optimizer = GeminiOptimizer(optimizer,
                                        model.unwrap(),
                                        **self.zero_optim_config,
                                        **self.optim_kwargs,
                                        verbose=self.verbose)

        return model, optimizer, criterion, dataloader, lr_scheduler

    def control_checkpoint_io(self) -> bool:
        return True

    def get_checkpoint_io(self) -> CheckpointIO:
        return GeminiCheckpointIO()

    def no_sync(self, model: nn.Module, optimizer: OptimizerWrapper) -> Iterator[None]:
        raise NotImplementedError
