import math
from enum import Enum
from typing import Any, Dict, Set, Tuple

import torch
import torch.distributed as dist
from torch.nn import Parameter
from torch.optim import Optimizer

from colossalai.amp.naive_amp.grad_scaler import DynamicGradScaler
from colossalai.gemini.chunk import Chunk, ChunkManager
from colossalai.logging import get_dist_logger
from colossalai.nn.optimizer import ColossalaiOptimizer
from colossalai.nn.parallel.data_parallel import ZeroDDP
from colossalai.utils import disposable, get_current_device


class OptimState(Enum):
    SCALED = 0
    UNSCALED = 1


class ZeroOptimizer(ColossalaiOptimizer):
    """A wrapper for optimizer. ``ZeroDDP`` and ``ZeroOptimizer`` implement Zero Redundancy Optimizer (ZeRO state-3).

    Note:
        You must use ``ZeroDDP`` with ``ZeroOptimizer``.

    Note:
        Make sure you set ``placement_policy`` of ``GeminiManager`` to `"auto"`,
        if you set ``gpu_margin_mem_ratio > 0``.

    Args:
        optim (Optimizer): An Optimizer instance.
        module (ZeroDDP): A ``ZeroDDP`` instance.
        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**32.
        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.
        """

    def __init__(self,
                 optim: Optimizer,
                 module: ZeroDDP,
                 gpu_margin_mem_ratio: float = 0.0,
                 initial_scale: float = 2**32,
                 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,
                 clipping_norm: float = 0.0,
                 norm_type: float = 2.0,
                 **defaults: Any):
        super().__init__(optim)
        assert isinstance(module, ZeroDDP)
        self.module = module
        self.gemini_manager = module.gemini_manager
        self.chunk_manager: ChunkManager = self.gemini_manager.chunk_manager
        self.optim_state = OptimState.UNSCALED
        self.param_to_range: Dict[Parameter, Tuple[int, int]] = dict()
        self.param_to_chunk32: Dict[Parameter, Chunk] = dict()
        self.chunk16_set: Set[Chunk] = set()
        self.clipping_flag = clipping_norm > 0.0
        self.max_norm = clipping_norm

        if self.clipping_flag:
            assert norm_type == 2.0, "ZeroOptimizer only supports L2 norm now"

        params_list = [p for p in module.parameters() if not getattr(p, '_ddp_to_ignore', False)]
        for p, fp32_p in zip(params_list, module.fp32_params):
            chunk_16 = self.chunk_manager.get_chunk(p)
            if chunk_16 not in self.chunk16_set:
                chunk_16.l2_norm_flag = self.clipping_flag
                self.chunk16_set.add(chunk_16)

        self.__init__optimizer()

        # Grad scaler
        self.grad_scaler = DynamicGradScaler(initial_scale=initial_scale,
                                             min_scale=min_scale,
                                             growth_factor=growth_factor,
                                             backoff_factor=backoff_factor,
                                             growth_interval=growth_interval,
                                             hysteresis=hysteresis,
                                             max_scale=max_scale)
        self._found_overflow: torch.Tensor = torch.zeros(1, dtype=torch.int64, device=get_current_device())
        self._logger = get_dist_logger()

        self.gpu_margin_mem_ratio: float = float(gpu_margin_mem_ratio)
        assert 0.0 <= self.gpu_margin_mem_ratio <= 1.0, f'gpu_margin_mem_ratio must >=0.0 and <=1.0'
        # Only move fp32 shards from CPU to GPU when user allows and inner optimizer is valid
        # Inner optimizer must support optimizing hybrid (CPU and CUDA) tensors,
        # and it must set `num_fp32_shards_per_param` correctly
        self._should_move_fp32_params_h2d: bool = self.gemini_manager.is_cuda_margin_mem_avail and self.gpu_margin_mem_ratio > 0.0 and getattr(
            optim, 'num_fp32_shards_per_param', 0) >= 2
        if self.gpu_margin_mem_ratio > 0.0 and not self.gemini_manager.is_cuda_margin_mem_avail:
            self._logger.warning(f'gpu_margin_mem_ratio is meaningless when placement_policy is not "auto"', ranks=[0])

        self._register_states = disposable(self._register_states_)

    def _set_grad_ptr(self):
        for group in self.param_groups:
            for fake_param in group['params']:
                chunk32 = self.param_to_chunk32[fake_param]
                begin, end = self.param_to_range[fake_param]
                chunk16 = chunk32.paired_chunk

                fake_param.data = chunk16.payload[begin:end]
                fake_param.grad = fake_param.data
                fake_param.data = chunk32.payload[begin:end]

    def _update_fp16_params(self):
        none_tensor = torch.empty([0])
        for group in self.param_groups:
            for fake_param in group['params']:
                assert fake_param.grad is None
                fake_param.data = none_tensor

        for chunk16 in self.chunk16_set:
            chunk16.optim_update()

    def _check_overflow(self):
        # clear previous overflow record
        self._found_overflow.fill_(self.module.overflow_counter)

        # all-reduce across global group
        dist.all_reduce(self._found_overflow)

        return self._found_overflow.item() > 0

    def _calc_global_norm(self) -> float:
        norm_sqr: float = 0.0
        group_to_norm = dict()
        for c16 in self.chunk16_set:
            assert c16.l2_norm is not None

            if c16.is_gathered:
                norm_sqr += c16.l2_norm
            else:
                # this chunk is sharded, use communication to collect total norm
                if c16.torch_pg not in group_to_norm:
                    group_to_norm[c16.torch_pg] = 0.0
                group_to_norm[c16.torch_pg] += c16.l2_norm

            c16.l2_norm = None    # clear l2 norm

        comm_buffer = torch.zeros(1, dtype=torch.float, device=get_current_device())
        for group, part_norm in group_to_norm.items():
            comm_buffer.fill_(part_norm)
            dist.all_reduce(comm_buffer, group=group)
            norm_sqr += comm_buffer.item()

        global_norm = math.sqrt(norm_sqr)
        return global_norm

    def _unscale_and_clip_grads(self):
        assert self.optim_state == OptimState.SCALED

        combined_scale = self.loss_scale
        if self.clipping_flag:
            total_norm = self._calc_global_norm()
            clip = ((total_norm / self.loss_scale) + 1e-6) / self.max_norm
            if clip > 1:
                combined_scale = clip * self.loss_scale

        for group in self.optim.param_groups:
            for p in group['params']:
                if p.grad is not None:
                    p.grad.data.div_(combined_scale)
        self.optim_state = OptimState.UNSCALED

    @property
    def loss_scale(self):
        return self.grad_scaler.scale.item()

    def zero_grad(self, *args, **kwargs):
        self.module.overflow_counter = 0
        return self.optim.zero_grad(set_to_none=True)

    def step(self, *args, **kwargs):
        self._maybe_move_fp32_params()
        self._set_grad_ptr()

        found_inf = self._check_overflow()
        if found_inf:
            self.optim_state = OptimState.UNSCALED    # no need to unscale grad
            self.grad_scaler.update(found_inf)    # update gradient scaler
            self._logger.info(f'Found overflow. Skip step')
            self.zero_grad()    # reset all gradients
            self._update_fp16_params()
            return

        # unscale grads if scaled
        if self.optim_state == OptimState.SCALED:
            self._unscale_and_clip_grads()
        self.grad_scaler.update(found_inf)

        ret = self.optim.step(*args, **kwargs)
        self._register_states()
        self.zero_grad()
        self._update_fp16_params()
        return ret

    def clip_grad_norm(self, model: torch.nn.Module, max_norm: float, norm_type: float = 2.0):
        raise NotImplementedError

    def backward(self, loss: torch.Tensor):
        loss = self.loss_scale * loss
        self.optim_state = OptimState.SCALED
        self.module.backward(loss)

    def backward_by_grad(self, tensor: torch.Tensor, grad: torch.Tensor):
        # This function is called except the last stage of pipeline parallel
        # It receives the scaled grad from the previous rank
        # No need to scale the grad again
        # Need to unscale when optimizing
        self.optim_state = OptimState.SCALED
        self.module.backward_by_grad(tensor, grad)

    def _maybe_move_fp32_params(self):
        if self._should_move_fp32_params_h2d:
            self._should_move_fp32_params_h2d = False
            available_cuda_margin_mem = self.gemini_manager.cuda_margin_mem * self.gpu_margin_mem_ratio
            fp32_params_available_cuda_margin_mem = available_cuda_margin_mem / self.optim.num_fp32_shards_per_param
            fp32_params_used_cuda_margin_mem = 0

            for group in self.param_groups:
                for fake_param in group['params']:
                    chunk32 = self.param_to_chunk32[fake_param]
                    chunk16 = chunk32.paired_chunk

                    if chunk32.device_type == 'cuda':
                        continue

                    if fp32_params_used_cuda_margin_mem + chunk32.payload_mem < fp32_params_available_cuda_margin_mem:
                        self.chunk_manager.move_chunk(chunk32, get_current_device())
                        # stores grad now
                        self.chunk_manager.move_chunk(chunk16, get_current_device())
                        self.module.set_chunk_grad_device(chunk16, get_current_device())
                        fp32_params_used_cuda_margin_mem += chunk32.payload_mem

            for group in self.param_groups:
                for fake_param in group['params']:
                    chunk32 = self.param_to_chunk32[fake_param]
                    if chunk32.device_type == 'cuda':
                        state = self.optim.state[fake_param]
                        for k, v in state.items():
                            if isinstance(v, torch.Tensor):
                                state[k] = v.to(get_current_device())

    def _register_states_(self):
        for group in self.optim.param_groups:
            for p in group['params']:
                state = self.optim.state[p]
                for val in state.values():
                    if isinstance(val, torch.Tensor):
                        self.chunk_manager.add_extern_static_tensor(val)

    def __init__optimizer(self):

        def get_range_pair(local_chunk: Chunk, local_param: Parameter):
            param_info = local_chunk.tensors_info[local_param]
            if local_chunk.keep_gathered:
                return param_info.offset, param_info.end
            begin = max(0, param_info.offset - local_chunk.shard_begin)
            end = min(local_chunk.shard_size, param_info.end - local_chunk.shard_begin)
            return begin, end

        for group in self.optim.param_groups:
            fake_params_list = list()

            for param in group['params']:
                chunk16 = self.chunk_manager.get_chunk(param)
                range_pair = get_range_pair(chunk16, param)
                if range_pair[0] >= range_pair[1]:
                    continue

                fake_param = torch.nn.Parameter(torch.empty([0]))
                self.param_to_chunk32[fake_param] = chunk16.paired_chunk
                self.param_to_range[fake_param] = range_pair

                fake_params_list.append(fake_param)

            group['params'] = fake_params_list
