import torch
import torch.nn as nn
import torch.nn.functional as F


class FreqEncoder(nn.Module):
    def __init__(
        self,
        input_dim,
        max_freq_log2,
        N_freqs,
        log_sampling=True,
        include_input=True,
        periodic_fns=(torch.sin, torch.cos),
    ):

        super().__init__()

        self.input_dim = input_dim
        self.include_input = include_input
        self.periodic_fns = periodic_fns

        self.output_dim = 0
        if self.include_input:
            self.output_dim += self.input_dim

        self.output_dim += self.input_dim * N_freqs * len(self.periodic_fns)

        if log_sampling:
            self.freq_bands = 2.0 ** torch.linspace(0.0, max_freq_log2, N_freqs)
        else:
            self.freq_bands = torch.linspace(2.0 ** 0.0, 2.0 ** max_freq_log2, N_freqs)

        self.freq_bands = self.freq_bands.numpy().tolist()

    def forward(self, input, **kwargs):

        out = []
        if self.include_input:
            out.append(input)

        for i in range(len(self.freq_bands)):
            freq = self.freq_bands[i]
            for p_fn in self.periodic_fns:
                out.append(p_fn(input * freq))

        out = torch.cat(out, dim=-1)

        return out


def get_encoder(
    encoding,
    input_dim=3,
    multires=6,
    degree=4,
    num_levels=14,
    level_dim=2,
    base_resolution=16,
    log2_hashmap_size=19,
    desired_resolution=4096,
    align_corners=False,
    **kwargs
):

    if encoding == "None":
        return lambda x, **kwargs: x, input_dim

    elif encoding == "frequency":
        encoder = FreqEncoder(
            input_dim=input_dim,
            max_freq_log2=multires - 1,
            N_freqs=multires,
            log_sampling=True,
        )

    elif encoding == "sphere_harmonics":
        from shencoder import SHEncoder

        encoder = SHEncoder(input_dim=input_dim, degree=degree)

    elif encoding == "hashgrid":
        from gridencoder import GridEncoder

        encoder = GridEncoder(
            input_dim=input_dim,
            num_levels=num_levels,
            level_dim=level_dim,
            base_resolution=base_resolution,
            log2_hashmap_size=log2_hashmap_size,
            desired_resolution=desired_resolution,
            gridtype="hash",
            align_corners=align_corners,
        )

    elif encoding == "tiledgrid":
        from gridencoder import GridEncoder

        encoder = GridEncoder(
            input_dim=input_dim,
            num_levels=num_levels,
            level_dim=level_dim,
            base_resolution=base_resolution,
            log2_hashmap_size=log2_hashmap_size,
            desired_resolution=desired_resolution,
            gridtype="tiled",
            align_corners=align_corners,
        )

    elif encoding == "ash":
        from ashencoder import AshEncoder

        encoder = AshEncoder(
            input_dim=input_dim,
            output_dim=16,
            log2_hashmap_size=log2_hashmap_size,
            resolution=desired_resolution,
        )

    else:
        raise NotImplementedError()

    return encoder, encoder.output_dim
