import numpy as np
import tensorflow as tf
from tensorflow.keras import layers as tfkl
from tensorflow_probability import distributions as tfd
from tensorflow.keras.mixed_precision import experimental as prec

import tools


class RSSM(tools.Module):

    def __init__(self, stoch=30, deter=200, hidden=200, act=tf.nn.elu):
        super().__init__()
        self._activation = act
        self._stoch_size = stoch
        self._deter_size = deter
        self._hidden_size = hidden
        self._cell = tfkl.GRUCell(self._deter_size)

    def initial(self, batch_size):
        dtype = prec.global_policy().compute_dtype
        return dict(
            mean=tf.zeros([batch_size, self._stoch_size], dtype),
            std=tf.zeros([batch_size, self._stoch_size], dtype),
            stoch=tf.zeros([batch_size, self._stoch_size], dtype),
            deter=self._cell.get_initial_state(None, batch_size, dtype))


    @tf.function
    def observe(self, embed, action, state=None):
        if state is None:
            state = self.initial(tf.shape(action)[0])
        embed = tf.transpose(embed, [1, 0, 2])
        action = tf.transpose(action, [1, 0, 2])
        post, prior = tools.static_scan(
            lambda prev, inputs: self.obs_step(
                prev[0], *inputs), 
            (action, embed), (state, state))
        post = {k: tf.transpose(v, [1, 0, 2]) for k, v in post.items()}
        prior = {k: tf.transpose(v, [1, 0, 2]) for k, v in prior.items()}
        return post, prior

    @tf.function
    def imagine(self, action, state=None):
        if state is None:
            state = self.initial(tf.shape(action)[0])
        assert isinstance(state, dict), state
        action = tf.transpose(action, [1, 0, 2])
        prior = tools.static_scan(self.img_step, action, state)
        prior = {k: tf.transpose(v, [1, 0, 2]) for k, v in prior.items()}
        return prior

    def get_feat(self, state):
        return tf.concat([state['stoch'], state['deter']], -1)

    def get_dist(self, state):
        return tfd.MultivariateNormalDiag(state['mean'], state['std'])

    @tf.function
    def obs_step(self, prev_state, prev_action, embed):
        prior = self.img_step(prev_state, prev_action)
        x = tf.concat([prior['deter'], embed], -1)
        x = self.get('obs1', tfkl.Dense, self._hidden_size,
                     self._activation)(x)
        x = self.get('obs2', tfkl.Dense, 2 * self._stoch_size, None)(x)
        mean, std = tf.split(x, 2, -1)
        std = tf.nn.softplus(std) + 0.1
        stoch = self.get_dist({'mean': mean, 'std': std}).sample()
        post = {'mean': mean, 'std': std,
                'stoch': stoch, 'deter': prior['deter']}
        return post, prior

    @tf.function
    def img_step(self, prev_state, prev_action):
        x = tf.concat([prev_state['stoch'], prev_action], -1)
        x = self.get('img1', tfkl.Dense, self._hidden_size,
                     self._activation)(x)
        x, deter = self._cell(x, [prev_state['deter']])
        deter = deter[0]  # Keras wraps the state in a list.
        x = self.get('img2', tfkl.Dense, self._hidden_size,
                     self._activation)(x)
        x = self.get('img3', tfkl.Dense, 2 * self._stoch_size, None)(x)
        mean, std = tf.split(x, 2, -1)
        std = tf.nn.softplus(std) + 0.1
        stoch = self.get_dist({'mean': mean, 'std': std}).sample()
        prior = {'mean': mean, 'std': std, 'stoch': stoch, 'deter': deter}
        return prior


class ConvEncoder(tools.Module):

    def __init__(self, depth=32, act=tf.nn.relu, image_size=64):
        self._act = act
        self._depth = depth
        self._image_size = image_size

        if image_size == 64:
            self._outdim = 32 * self._depth
            self._kernel_sizes = [4, 4, 4, 4]
        elif image_size == 32:
            self._outdim = 8 * self._depth
            self._kernel_sizes = [3, 3, 3, 3]
        elif image_size == 84:
            self._outdim = 72 * self._depth
            self._kernel_sizes = [4, 4, 4, 4]
        else:
            raise NotImplementedError

    def __call__(self, obs):
        kwargs = dict(strides=2, activation=self._act)
        x = tf.reshape(obs['image'], (-1,) + tuple(obs['image'].shape[-3:]))
        x = self.get('h1', tfkl.Conv2D, 1 * self._depth,
                     self._kernel_sizes[0], **kwargs)(x)
        x = self.get('h2', tfkl.Conv2D, 2 * self._depth,
                     self._kernel_sizes[1], **kwargs)(x)
        x = self.get('h3', tfkl.Conv2D, 4 * self._depth,
                     self._kernel_sizes[2], **kwargs)(x)
        x = self.get('h4', tfkl.Conv2D, 8 * self._depth,
                     self._kernel_sizes[3], **kwargs)(x)
        shape = tf.concat([tf.shape(obs['image'])[:-3], [self._outdim]], 0)
        return tf.reshape(x, shape)


class ConvDecoder(tools.Module):

    def __init__(self, depth=32, act=tf.nn.relu, shape=(64, 64, 3)):
        self._act = act
        self._depth = depth
        self._shape = shape

        if shape[0] == 64:
            self._outdim = 32 * self._depth
            self._kernel_sizes = [5, 5, 6, 6]
        elif shape[0] == 32:
            self._outdim = 8 * self._depth
            self._kernel_sizes = [3, 3, 3, 4]
        elif shape[0] == 84:
            self._outdim = 72 * self._depth
            self._kernel_sizes = [7, 6, 6, 6]
        else:
            raise NotImplementedError

    def __call__(self, features):
        kwargs = dict(strides=2, activation=self._act)
        x = self.get('h1', tfkl.Dense, self._outdim, None)(features)
        x = tf.reshape(x, [-1, 1, 1, self._outdim])
        x = self.get('h2', tfkl.Conv2DTranspose,
                     4 * self._depth, self._kernel_sizes[0], **kwargs)(x)
        x = self.get('h3', tfkl.Conv2DTranspose,
                     2 * self._depth, self._kernel_sizes[1], **kwargs)(x)
        x = self.get('h4', tfkl.Conv2DTranspose,
                     1 * self._depth, self._kernel_sizes[2], **kwargs)(x)
        x = self.get('h5', tfkl.Conv2DTranspose,
                     self._shape[-1], self._kernel_sizes[3], strides=2)(x)
        mean = tf.reshape(x, tf.concat(
            [tf.shape(features)[:-1], self._shape], 0))
        return tfd.Independent(tfd.Normal(mean, 1), len(self._shape))


class ConvDecoderMask(tools.Module):

    def __init__(self, depth=32, act=tf.nn.relu, shape=(64, 64, 3)):
        self._act = act
        self._depth = depth
        self._shape = shape

        if shape[0] == 64:
            self._outdim = 32 * self._depth
            self._kernel_sizes = [5, 5, 6, 6]
        elif shape[0] == 32:
            self._outdim = 8 * self._depth
            self._kernel_sizes = [3, 3, 3, 4]
        elif shape[0] == 84:
            self._outdim = 72 * self._depth
            self._kernel_sizes = [7, 6, 6, 6]
        else:
            raise NotImplementedError

    def __call__(self, features):
        kwargs = dict(strides=2, activation=self._act)
        x = self.get('h1', tfkl.Dense, self._outdim, None)(features)
        x = tf.reshape(x, [-1, 1, 1, self._outdim])
        x = self.get('h2', tfkl.Conv2DTranspose,
                     4 * self._depth, self._kernel_sizes[0], **kwargs)(x)
        x = self.get('h3', tfkl.Conv2DTranspose,
                     2 * self._depth, self._kernel_sizes[1], **kwargs)(x)
        x = self.get('h4', tfkl.Conv2DTranspose,
                     1 * self._depth, self._kernel_sizes[2], **kwargs)(x)
        x = self.get('h5', tfkl.Conv2DTranspose,
                     3 + self._shape[-1], self._kernel_sizes[3], strides=2)(x)
        mean, mask = tf.split(x, [3, 3], -1)
        mean = tf.reshape(mean, tf.concat(
            [tf.shape(features)[:-1], self._shape], 0))
        mask = tf.reshape(mask, tf.concat(
            [tf.shape(features)[:-1], self._shape], 0))
        return tfd.Independent(tfd.Normal(mean, 1), len(self._shape)), mask


class ConvDecoderMaskEnsemble(tools.Module):
    """
    ensemble two convdecoder with <Normal, mask> outputs
    """

    def __init__(self, decoder1, decoder2, precision):
        self._decoder1 = decoder1
        self._decoder2 = decoder2
        self._precision = 'float' + str(precision)
        self._shape = decoder1._shape

    def __call__(self, feat1, feat2):
        kwargs = dict(strides=1, activation=tf.nn.sigmoid)
        pred1, mask1 = self._decoder1(feat1)
        pred2, mask2 = self._decoder2(feat2)
        mean1 = pred1.submodules[0].loc
        mean2 = pred2.submodules[0].loc
        mask_feat = tf.concat([mask1, mask2], -1)
        mask = self.get('mask1', tfkl.Conv2D, 1, 1, **kwargs)(mask_feat)
        mask_use1 = mask
        mask_use2 = 1-mask
        mean = mean1 * tf.cast(mask_use1, self._precision) + \
            mean2 * tf.cast(mask_use2, self._precision)
        return tfd.Independent(tfd.Normal(mean, 1), len(self._shape)), pred1, pred2, tf.cast(mask_use1, self._precision)


class InverseDecoder(tools.Module):

    def __init__(self, shape, layers, units, act=tf.nn.elu):
        self._shape = shape
        self._layers = layers
        self._units = units
        self._act = act

    def __call__(self, features):
        x = tf.concat([features[:, :-1], features[:, 1:]], -1)
        for index in range(self._layers):
            x = self.get(f'h{index}', tfkl.Dense, self._units, self._act)(x)
        x = self.get(f'hout', tfkl.Dense, np.prod(self._shape))(x)
        return tfd.Independent(tfd.Normal(x, 1), 1)


class DenseDecoder(tools.Module):

    def __init__(self, shape, layers, units, dist='normal', act=tf.nn.elu):
        self._shape = shape
        self._layers = layers
        self._units = units
        self._dist = dist
        self._act = act

    def __call__(self, features):
        x = features
        for index in range(self._layers):
            x = self.get(f'h{index}', tfkl.Dense, self._units, self._act)(x)
        x = self.get(f'hout', tfkl.Dense, np.prod(self._shape))(x)
        x = tf.reshape(x, tf.concat([tf.shape(features)[:-1], self._shape], 0))
        if self._dist == 'normal':
            return tfd.Independent(tfd.Normal(x, 1), len(self._shape))
        if self._dist == 'binary':
            return tfd.Independent(tfd.Bernoulli(x), len(self._shape))
        raise NotImplementedError(self._dist)


class ActionDecoder(tools.Module):

    def __init__(
            self, size, layers, units, dist='tanh_normal', act=tf.nn.elu,
            min_std=1e-4, init_std=5, mean_scale=5):
        self._size = size
        self._layers = layers
        self._units = units
        self._dist = dist
        self._act = act
        self._min_std = min_std
        self._init_std = init_std
        self._mean_scale = mean_scale

    def __call__(self, features):
        raw_init_std = np.log(np.exp(self._init_std) - 1)
        x = features
        for index in range(self._layers):
            x = self.get(f'h{index}', tfkl.Dense, self._units, self._act)(x)
        if self._dist == 'tanh_normal':
            # https://www.desmos.com/calculator/rcmcf5jwe7
            x = self.get(f'hout', tfkl.Dense, 2 * self._size)(x)
            mean, std = tf.split(x, 2, -1)
            mean = self._mean_scale * tf.tanh(mean / self._mean_scale)
            std = tf.nn.softplus(std + raw_init_std) + self._min_std
            dist = tfd.Normal(mean, std)
            dist = tfd.TransformedDistribution(dist, tools.TanhBijector())
            dist = tfd.Independent(dist, 1)
            dist = tools.SampleDist(dist)
        elif self._dist == 'onehot':
            x = self.get(f'hout', tfkl.Dense, self._size)(x)
            dist = tools.OneHotDist(x)
        else:
            raise NotImplementedError(dist)
        return dist