# Copyright (c) Facebook, Inc. and its affiliates.

import torch
import torch.nn as nn


def tie_weights(src, trg):
    assert type(src) == type(trg)
    trg.weight = src.weight
    trg.bias = src.bias


OUT_DIM = {2: 39, 4: 35, 6: 31}


class PixelEncoder(nn.Module):
    """Convolutional encoder of pixels observations."""

    def __init__(self, obs_shape, feature_dim, num_layers=2, num_filters=32):
        super().__init__()

        assert len(obs_shape) == 3

        self.feature_dim = feature_dim
        self.num_layers = num_layers

        self.convs = nn.ModuleList([nn.Conv2d(obs_shape[0], num_filters, 3, stride=2)])
        for i in range(num_layers - 1):
            self.convs.append(nn.Conv2d(num_filters, num_filters, 3, stride=1))

        out_dim = OUT_DIM[num_layers]
        self.fc = nn.Linear(num_filters * out_dim * out_dim, self.feature_dim)
        self.ln = nn.LayerNorm(self.feature_dim)

        self.outputs = dict()

    def reparameterize(self, mu, logstd):
        std = torch.exp(logstd)
        eps = torch.randn_like(std)
        return mu + eps * std

    def forward_conv(self, obs):
        obs = obs / 255.0
        self.outputs["obs"] = obs

        conv = torch.relu(self.convs[0](obs))
        self.outputs["conv1"] = conv

        for i in range(1, self.num_layers):
            conv = torch.relu(self.convs[i](conv))
            self.outputs["conv%s" % (i + 1)] = conv

        h = conv.view(conv.size(0), -1)
        return h

    def forward(self, obs, detach=False):
        h = self.forward_conv(obs)

        if detach:
            h = h.detach()

        h_fc = self.fc(h)
        self.outputs["fc"] = h_fc

        h_norm = self.ln(h_fc)
        self.outputs["ln"] = h_norm

        out = torch.tanh(h_norm)
        self.outputs["tanh"] = out

        return out

    def copy_conv_weights_from(self, source):
        """Tie convolutional layers"""
        # only tie conv layers
        for i in range(self.num_layers):
            tie_weights(src=source.convs[i], trg=self.convs[i])

    def log(self, L, step, log_freq):
        if step % log_freq != 0:
            return

        for k, v in self.outputs.items():
            L.log_histogram("train_encoder/%s_hist" % k, v, step)
            if len(v.shape) > 2:
                L.log_image("train_encoder/%s_img" % k, v[0], step)

        for i in range(self.num_layers):
            L.log_param("train_encoder/conv%s" % (i + 1), self.convs[i], step)
        L.log_param("train_encoder/fc", self.fc, step)
        L.log_param("train_encoder/ln", self.ln, step)


class IdentityEncoder(nn.Module):
    def __init__(self, obs_shape, feature_dim, num_layers, num_filters):
        super().__init__()

        assert len(obs_shape) == 1
        self.feature_dim = obs_shape[0]

    def forward(self, obs, detach=False):
        return obs

    def copy_conv_weights_from(self, source):
        pass

    def log(self, L, step, log_freq):
        pass


class LinearEncoder(nn.Module):
    def __init__(self, obs_shape, feature_dim, num_layers, num_filters):
        super().__init__()
        self.feature_dim = feature_dim
        self.linear = nn.Linear(obs_shape, feature_dim)

    def forward(self, obs, detach=False):
        if detach:
            return self.linear(obs).detach()
        else:
            return self.linear(obs)

    def copy_conv_weights_from(self, source):
        pass

    def log(self, L, step, log_freq):
        pass


class MLPEncoder(nn.Module):
    def __init__(self, obs_shape, feature_dim, num_layers, num_filters):
        super().__init__()
        self.feature_dim = feature_dim
        self.model = nn.ModuleList()
        self.model.append(nn.Linear(obs_shape, 512))

        for i in range(num_layers - 1):
            self.model.append(nn.Linear(512, 512))
        self.model.append(nn.Linear(512, feature_dim))

    def forward(self, obs, detach=False):
        for i in range(len(self.model)):
            obs = torch.relu(self.model[i](obs))
        if detach:
            return obs.detach()
        else:
            return obs

    def copy_conv_weights_from(self, source):
        pass

    def log(self, L, step, log_freq):
        pass


class VariationalEncoder(nn.Module):
    def __init__(self, obs_shape, feature_dim, num_layers, num_filters):
        super().__init__()
        self.feature_dim = feature_dim
        self.model = nn.ModuleList()
        self.model.append(nn.Linear(obs_shape, 200))

        for i in range(num_layers - 1):
            self.model.append(nn.Linear(200, 200))
        self.model.append(nn.Linear(200, feature_dim * 2))

    def forward(self, obs, deterministic=False, detach=False):
        mu, logvar = self.encode(obs)
        if deterministic:
            obs = mu
        else:
            std = torch.exp(0.5 * logvar)
            eps = torch.randn_like(std)
            obs = mu + eps * std
        if detach:
            return obs.detach()
        else:
            return obs

    def encode(self, obs):
        for i in range(len(self.model)):
            obs = torch.relu(self.model[i](obs))

        return obs[:, : self.feature_dim], obs[:, self.feature_dim :]

    def copy_conv_weights_from(self, source):
        pass

    def log(self, L, step, log_freq):
        pass


_AVAILABLE_ENCODERS = {
    "pixel": PixelEncoder,
    "identity": IdentityEncoder,
    "linear": LinearEncoder,
    "mlp": MLPEncoder,
    "variational": VariationalEncoder,
}


def make_encoder(encoder_type, obs_shape, feature_dim, num_layers, num_filters):
    assert encoder_type in _AVAILABLE_ENCODERS
    return _AVAILABLE_ENCODERS[encoder_type](
        obs_shape, feature_dim, num_layers, num_filters
    )
