import torch
from torch import nn


class LitEma(nn.Module):
    def __init__(self, model, decay=0.9999, use_num_upates=True):
        super().__init__()
        if decay < 0.0 or decay > 1.0:
            raise ValueError('Decay must be between 0 and 1')

        self.m_name2s_name = {}
        self.register_buffer('decay', torch.tensor(decay, dtype=torch.float32))
        self.register_buffer('num_updates', torch.tensor(0, dtype=torch.int) if use_num_upates
        else torch.tensor(-1, dtype=torch.int))

        for name, p in model.named_parameters():
            if p.requires_grad:
                # remove as '.'-character is not allowed in buffers
                s_name = name.replace('.', '')
                self.m_name2s_name.update({name: s_name})
                self.register_buffer(s_name, p.clone().detach().data)

        self.collected_params = []

    def reset_num_updates(self):
        del self.num_updates
        self.register_buffer('num_updates', torch.tensor(0, dtype=torch.int))

    def forward(self, model):
        decay = self.decay

        if self.num_updates >= 0:
            self.num_updates += 1
            decay = min(self.decay, (1 + self.num_updates) / (10 + self.num_updates))

        one_minus_decay = 1.0 - decay

        with torch.no_grad():
            m_param = dict(model.named_parameters())
            shadow_params = dict(self.named_buffers())

            for key in m_param:
                if m_param[key].requires_grad:
                    sname = self.m_name2s_name[key]
                    shadow_params[sname] = shadow_params[sname].type_as(m_param[key])
                    shadow_params[sname].sub_(one_minus_decay * (shadow_params[sname] - m_param[key]))
                else:
                    assert not key in self.m_name2s_name

    def copy_to(self, model):
        """
        Copying the ema state (i.e., buffers) to the targeted model
        Input:
            model: targeted model
        """
        m_param = dict(model.named_parameters())
        shadow_params = dict(self.named_buffers())
        for key in m_param:
            if m_param[key].requires_grad:
                m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data)
            else:
                assert not key in self.m_name2s_name

    def store(self, parameters):
        """
        Save the parameters of the targeted model into the temporary pool for restoring later.
        Args:
          parameters: parameters of the targeted model.
                      Iterable of `torch.nn.Parameter`; the parameters to be temporarily stored.
        """
        self.collected_params = [param.clone() for param in parameters]

    def restore(self, parameters):
        """
        Restore the parameters from the temporaty pool (stored with the `store` method).
        Useful to validate the model with EMA parameters without affecting the
        original optimization process. Store the parameters before the
        `copy_to` method. After validation (or model saving), use this to
        restore the former parameters.
        Args:
          parameters: Iterable of `torch.nn.Parameter`; the parameters to be
            updated with the stored parameters.
        """
        for c_param, param in zip(self.collected_params, parameters):
            param.data.copy_(c_param.data)

    def resume(self, ckpt, num_updates):
        """
        Resume from the targeted checkpoint, i.e., copying the checkpoints to ema buffers
        Input:
            model: targerted model
        """
        self.register_buffer('num_updates', torch.tensor(num_updates, dtype=torch.int))

        shadow_params = dict(self.named_buffers())
        for key, value in ckpt.items():
            try:
                shadow_params[self.m_name2s_name[key]].data.copy_(value.data)
            except:
                if key.startswith('module') and key not in shadow_params:
                    key = key[7:]
                shadow_params[self.m_name2s_name[key]].data.copy_(value.data)