import torch
import torch.nn as nn

""" Positional encoding embedding. Code was taken from https://github.com/bmild/nerf. """


class Embedder:
    def __init__(self, **kwargs):
        self.kwargs = kwargs
        self.create_embedding_fn()

    def create_embedding_fn(self):
        embed_fns = []
        d = self.kwargs['input_dims']
        out_dim = 0
        if self.kwargs['include_input']:
            embed_fns.append(lambda x: x)
            out_dim += d

        max_freq = self.kwargs['max_freq_log2']
        N_freqs = self.kwargs['num_freqs']

        if self.kwargs['log_sampling']:
            freq_bands = 2. ** torch.linspace(0., max_freq, N_freqs)
        else:
            freq_bands = torch.linspace(2. ** 0., 2. ** max_freq, N_freqs)

        for freq in freq_bands:
            for p_fn in self.kwargs['periodic_fns']:
                if self.kwargs['normalize']:
                    embed_fns.append(lambda x, p_fn=p_fn,
                                            freq=freq: p_fn(x * freq) / freq)
                else:
                    embed_fns.append(lambda x, p_fn=p_fn,
                                            freq=freq: p_fn(x * freq))
                out_dim += d

        self.embed_fns = embed_fns
        self.out_dim = out_dim

    def embed(self, inputs):
        return torch.cat([fn(inputs) for fn in self.embed_fns], -1)


def get_embedder(multires, normalize=False, input_dims=3):
    embed_kwargs = {
        'include_input': True,
        'input_dims': input_dims,
        'max_freq_log2': multires - 1,
        'num_freqs': multires,
        'normalize': normalize,
        'log_sampling': True,
        'periodic_fns': [torch.sin, torch.cos],
    }

    embedder_obj = Embedder(**embed_kwargs)

    def embed(x, eo=embedder_obj): return eo.embed(x)

    return embed, embedder_obj.out_dim


class Embedding(nn.Module):
    def __init__(self, in_channels, N_freqs, logscale=True, normalize=False):
        """
        Defines a function that embeds x to (x, sin(2^k x), cos(2^k x), ...)
        in_channels: number of input channels (3 for both xyz and direction)
        """
        super(Embedding, self).__init__()
        self.N_freqs = N_freqs
        self.in_channels = in_channels
        self.funcs = [torch.sin, torch.cos]
        self.out_channels = in_channels * (len(self.funcs) * N_freqs + 1)
        self.normalize = normalize

        if logscale:
            self.freq_bands = 2 ** torch.linspace(0, N_freqs - 1, N_freqs)
        else:
            self.freq_bands = torch.linspace(1, 2 ** (N_freqs - 1), N_freqs)

    def forward(self, x):
        """
        Embeds x to (x, sin(2^k x), cos(2^k x), ...)
        Different from the paper, "x" is also in the output
        See https://github.com/bmild/nerf/issues/12

        Inputs:
            x: (B, self.in_channels)

        Outputs:
            out: (B, self.out_channels)
        """
        out = [x]
        for freq in self.freq_bands:
            for func in self.funcs:
                if self.normalize:
                    out += [func(freq * x) / freq]
                else:
                    out += [func(freq * x)]

        return torch.cat(out, -1)