import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
from torch import einsum


class VectorQuantizer2(nn.Module):
    """
    Improved version over VectorQuantizer, can be used as a drop-in replacement. Mostly
    avoids costly matrix multiplications and allows for post-hoc remapping of indices.
    """

    # NOTE: due to a bug the beta term was applied to the wrong term. for
    # backwards compatibility we use the buggy version by default, but you can
    # specify legacy=False to fix it.
    def __init__(self,
                 n_e,
                 e_dim,
                 beta,
                 remap=None,
                 unknown_index='random',
                 sane_index_shape=False,
                 legacy=True):
        super().__init__()
        self.n_e = n_e
        self.e_dim = e_dim
        self.beta = beta
        self.legacy = legacy

        self.embedding = nn.Embedding(self.n_e, self.e_dim)
        self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e)

        self.remap = remap
        if self.remap is not None:
            self.register_buffer('used', torch.tensor(np.load(self.remap)))
            self.re_embed = self.used.shape[0]
            self.unknown_index = unknown_index  # "random" or "extra" or integer
            if self.unknown_index == 'extra':
                self.unknown_index = self.re_embed
                self.re_embed = self.re_embed + 1
            print(f'Remapping {self.n_e} indices to {self.re_embed} indices. '
                  f'Using {self.unknown_index} for unknown indices.')
        else:
            self.re_embed = n_e

        self.sane_index_shape = sane_index_shape

    def remap_to_used(self, inds):
        ishape = inds.shape
        assert len(ishape) > 1
        inds = inds.reshape(ishape[0], -1)
        used = self.used.to(inds)
        match = (inds[:, :, None] == used[None, None, ...]).long()
        new = match.argmax(-1)
        unknown = match.sum(2) < 1
        if self.unknown_index == 'random':
            new[unknown] = torch.randint(
                0, self.re_embed,
                size=new[unknown].shape).to(device=new.device)
        else:
            new[unknown] = self.unknown_index
        return new.reshape(ishape)

    def unmap_to_all(self, inds):
        ishape = inds.shape
        assert len(ishape) > 1
        inds = inds.reshape(ishape[0], -1)
        used = self.used.to(inds)
        if self.re_embed > self.used.shape[0]:  # extra token
            inds[inds >= self.used.shape[0]] = 0  # simply set to zero
        back = torch.gather(used[None, :][inds.shape[0] * [0], :], 1, inds)
        return back.reshape(ishape)

    def forward(self, z, temp=None, rescale_logits=False, return_logits=False):
        assert temp is None or temp == 1.0, 'Only for interface compatible with Gumbel'
        assert rescale_logits == False, 'Only for interface compatible with Gumbel'
        assert return_logits == False, 'Only for interface compatible with Gumbel'
        # reshape z -> (batch, height, width, channel) and flatten
        z = rearrange(z, 'b c h w -> b h w c').contiguous()
        z_flattened = z.view(-1, self.e_dim)
        # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z

        d = (torch.sum(z_flattened**2, dim=1, keepdim=True) +
             torch.sum(self.embedding.weight**2, dim=1) -
             2 * torch.einsum('bd,dn->bn', z_flattened,
                              rearrange(self.embedding.weight, 'n d -> d n')))

        min_encoding_indices = torch.argmin(d, dim=1)
        z_q = self.embedding(min_encoding_indices).view(z.shape)
        perplexity = None
        min_encodings = None

        # compute loss for embedding
        if not self.legacy:
            loss = self.beta * torch.mean((z_q.detach() - z)**2) + torch.mean(
                (z_q - z.detach())**2)
        else:
            loss = torch.mean((z_q.detach() - z)**2) + self.beta * torch.mean(
                (z_q - z.detach())**2)

        # preserve gradients
        z_q = z + (z_q - z).detach()

        # reshape back to match original input shape
        z_q = rearrange(z_q, 'b h w c -> b c h w').contiguous()

        if self.remap is not None:
            min_encoding_indices = min_encoding_indices.reshape(
                z.shape[0], -1)  # add batch axis
            min_encoding_indices = self.remap_to_used(min_encoding_indices)
            min_encoding_indices = min_encoding_indices.reshape(-1,
                                                                1)  # flatten

        if self.sane_index_shape:
            min_encoding_indices = min_encoding_indices.reshape(
                z_q.shape[0], z_q.shape[2], z_q.shape[3])

        return z_q, loss, (perplexity, min_encodings, min_encoding_indices)

    def get_codebook_entry(self, indices, shape):
        # shape specifying (batch, height, width, channel)
        if self.remap is not None:
            indices = indices.reshape(shape[0], -1)  # add batch axis
            indices = self.unmap_to_all(indices)
            indices = indices.reshape(-1)  # flatten again

        # get quantized latent vectors
        z_q = self.embedding(indices)

        if shape is not None:
            z_q = z_q.view(shape)
            # reshape back to match original input shape
            z_q = z_q.permute(0, 3, 1, 2).contiguous()

        return z_q


class GumbelQuantize(nn.Module):
    """
    credit to @karpathy: https://github.com/karpathy/deep-vector-quantization/blob/main/model.py (thanks!)
    Gumbel Softmax trick quantizer
    Categorical Reparameterization with Gumbel-Softmax, Jang et al. 2016
    https://arxiv.org/abs/1611.01144
    """

    def __init__(
        self,
        num_hiddens,
        embedding_dim,
        n_embed,
        straight_through=True,
        kl_weight=5e-4,
        temp_init=1.0,
        use_vqinterface=True,
        remap=None,
        unknown_index='random',
    ):
        super().__init__()

        self.embedding_dim = embedding_dim
        self.n_embed = n_embed

        self.straight_through = straight_through
        self.temperature = temp_init
        self.kl_weight = kl_weight

        self.proj = nn.Conv2d(num_hiddens, n_embed, 1)
        self.embed = nn.Embedding(n_embed, embedding_dim)

        self.use_vqinterface = use_vqinterface

        self.remap = remap
        if self.remap is not None:
            self.register_buffer('used', torch.tensor(np.load(self.remap)))
            self.re_embed = self.used.shape[0]
            self.unknown_index = unknown_index  # "random" or "extra" or integer
            if self.unknown_index == 'extra':
                self.unknown_index = self.re_embed
                self.re_embed = self.re_embed + 1
            print(
                f'Remapping {self.n_embed} indices to {self.re_embed} indices. '
                f'Using {self.unknown_index} for unknown indices.')
        else:
            self.re_embed = n_embed

    def remap_to_used(self, inds):
        ishape = inds.shape
        assert len(ishape) > 1
        inds = inds.reshape(ishape[0], -1)
        used = self.used.to(inds)
        match = (inds[:, :, None] == used[None, None, ...]).long()
        new = match.argmax(-1)
        unknown = match.sum(2) < 1
        if self.unknown_index == 'random':
            new[unknown] = torch.randint(
                0, self.re_embed,
                size=new[unknown].shape).to(device=new.device)
        else:
            new[unknown] = self.unknown_index
        return new.reshape(ishape)

    def unmap_to_all(self, inds):
        ishape = inds.shape
        assert len(ishape) > 1
        inds = inds.reshape(ishape[0], -1)
        used = self.used.to(inds)
        if self.re_embed > self.used.shape[0]:  # extra token
            inds[inds >= self.used.shape[0]] = 0  # simply set to zero
        back = torch.gather(used[None, :][inds.shape[0] * [0], :], 1, inds)
        return back.reshape(ishape)

    def forward(self, z, temp=None, return_logits=False):
        # force hard = True when we are in eval mode, as we must quantize. actually, always true seems to work
        hard = self.straight_through if self.training else True
        temp = self.temperature if temp is None else temp

        logits = self.proj(z)
        if self.remap is not None:
            # continue only with used logits
            full_zeros = torch.zeros_like(logits)
            logits = logits[:, self.used, ...]

        soft_one_hot = F.gumbel_softmax(logits, tau=temp, dim=1, hard=hard)
        if self.remap is not None:
            # go back to all entries but unused set to zero
            full_zeros[:, self.used, ...] = soft_one_hot
            soft_one_hot = full_zeros
        z_q = einsum('b n h w, n d -> b d h w', soft_one_hot,
                     self.embed.weight)

        # + kl divergence to the prior loss
        qy = F.softmax(logits, dim=1)
        diff = self.kl_weight * torch.sum(
            qy * torch.log(qy * self.n_embed + 1e-10), dim=1).mean()

        ind = soft_one_hot.argmax(dim=1)
        if self.remap is not None:
            ind = self.remap_to_used(ind)
        if self.use_vqinterface:
            if return_logits:
                return z_q, diff, (None, None, ind), logits
            return z_q, diff, (None, None, ind)
        return z_q, diff, ind

    def get_codebook_entry(self, indices, shape):
        b, h, w, c = shape
        assert b * h * w == indices.shape[0]
        indices = rearrange(indices, '(b h w) -> b h w', b=b, h=h, w=w)
        if self.remap is not None:
            indices = self.unmap_to_all(indices)
        one_hot = F.one_hot(indices,
                            num_classes=self.n_embed).permute(0, 3, 1,
                                                              2).float()
        z_q = einsum('b n h w, n d -> b d h w', one_hot, self.embed.weight)
        return z_q
