from abc import abstractmethod

import math

import numpy as np
import torch as th
import torch.nn as nn
import torch.nn.functional as F

from .fp16_util import convert_module_to_f16, convert_module_to_f32
from .nn import (
    SiLU,
    conv_nd,
    linear,
    avg_pool_nd,
    zero_module,
    normalization,
    timestep_embedding,
    checkpoint,
)
from .nn import reparameterize, GaussianConvEncoder, CausalModeling, MultivariateCausalFlow


class TimestepBlock(nn.Module):
    """
    Any module where forward() takes timestep embeddings as a second argument.
    """

    @abstractmethod
    def forward(self, x, emb): # IMAGE REP AND TEMPORAL EMBEDDINGS
        """
        Apply the module to `x` given `emb` timestep embeddings.
        """

# HOW WE EMBED THE TIMESTEP (WRAPPER) - IF TIME EMBED INCLUDED -> FUSE, ELSE -> JUST USE IMAGE REP (CONV2D WILL GET TIMSTEP + IMAGE REP?)
class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
    """
    A sequential module that passes timestep embeddings to the children that
    support it as an extra input.
    """

    def forward(self, x, emb):
        for layer in self:
            if isinstance(layer, TimestepBlock):
                x = layer(x, emb)
            else:
                x = layer(x)
        return x


class Upsample(nn.Module):
    """
    An upsampling layer with an optional convolution.

    :param channels: channels in the inputs and outputs.
    :param use_conv: a bool determining if a convolution is applied.
    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
                 upsampling occurs in the inner-two dimensions.
    """

    def __init__(self, channels, use_conv, dims=2):
        super().__init__()
        self.channels = channels
        self.use_conv = use_conv
        self.dims = dims
        if use_conv:
            self.conv = conv_nd(dims, channels, channels, 3, padding=1)

    def forward(self, x):
        assert x.shape[1] == self.channels
        if self.dims == 3:
            x = F.interpolate(
                x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest"
            )
        else:
            x = F.interpolate(x, scale_factor=2, mode="nearest")
        if self.use_conv:
            x = self.conv(x)
        return x


class Downsample(nn.Module):
    """
    A downsampling layer with an optional convolution.

    :param channels: channels in the inputs and outputs.
    :param use_conv: a bool determining if a convolution is applied.
    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
                 downsampling occurs in the inner-two dimensions.
    """

    def __init__(self, channels, use_conv, dims=2):
        super().__init__()
        self.channels = channels
        self.use_conv = use_conv
        self.dims = dims
        stride = 2 if dims != 3 else (1, 2, 2)
        if use_conv:
            self.op = conv_nd(dims, channels, channels, 3, stride=stride, padding=1)
        else:
            self.op = avg_pool_nd(stride)

    def forward(self, x):
        assert x.shape[1] == self.channels
        return self.op(x)


class ResBlock(TimestepBlock):
    """
    A residual block that can optionally change the number of channels.

    :param channels: the number of input channels.
    :param emb_channels: the number of timestep embedding channels.
    :param dropout: the rate of dropout.
    :param out_channels: if specified, the number of out channels.
    :param use_conv: if True and out_channels is specified, use a spatial
        convolution instead of a smaller 1x1 convolution to change the
        channels in the skip connection.
    :param dims: determines if the signal is 1D, 2D, or 3D.
    :param use_checkpoint: if True, use gradient checkpointing on this module.
    """

    def __init__(
        self,
        channels,
        emb_channels,
        dropout,
        out_channels=None,
        use_conv=False,
        use_scale_shift_norm=False,
        dims=2,
        use_checkpoint=False,
    ):
        super().__init__()
        self.channels = channels
        self.emb_channels = emb_channels
        self.dropout = dropout
        self.out_channels = out_channels or channels
        self.use_conv = use_conv
        self.use_checkpoint = use_checkpoint
        self.use_scale_shift_norm = use_scale_shift_norm

        self.in_layers = nn.Sequential(
            normalization(channels),
            SiLU(),
            conv_nd(dims, channels, self.out_channels, 3, padding=1),
        )
        self.emb_layers = nn.Sequential(
            SiLU(),
            linear(
                emb_channels,
                2 * self.out_channels if use_scale_shift_norm else self.out_channels,
            ),
        )
        self.out_layers = nn.Sequential(
            normalization(self.out_channels),
            SiLU(),
            nn.Dropout(p=dropout),
            zero_module(
                conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1)
            ),
        )

        if self.out_channels == channels:
            self.skip_connection = nn.Identity()
        elif use_conv:
            self.skip_connection = conv_nd(
                dims, channels, self.out_channels, 3, padding=1
            )
        else:
            self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)

    def forward(self, x, emb):
        """
        Apply the block to a Tensor, conditioned on a timestep embedding.

        :param x: an [N x C x ...] Tensor of features.
        :param emb: an [N x emb_channels] Tensor of timestep embeddings.
        :return: an [N x C x ...] Tensor of outputs.
        """
        return checkpoint(
            self._forward, (x, emb), self.parameters(), self.use_checkpoint
        )

    def _forward(self, x, emb):
        h = self.in_layers(x)
        emb_out = self.emb_layers(emb).type(h.dtype)
        while len(emb_out.shape) < len(h.shape):
            emb_out = emb_out[..., None]
        if self.use_scale_shift_norm:
            out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
            scale, shift = th.chunk(emb_out, 2, dim=1)
            h = out_norm(h) * (1 + scale) + shift
            h = out_rest(h)
        else:
            h = h + emb_out
            h = self.out_layers(h)
        return self.skip_connection(x) + h


class AttentionBlock(nn.Module):
    """
    An attention block that allows spatial positions to attend to each other.

    Originally ported from here, but adapted to the N-d case.
    https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.
    """

    def __init__(self, channels, num_heads=1, use_checkpoint=False):
        super().__init__()
        self.channels = channels
        self.num_heads = num_heads
        self.use_checkpoint = use_checkpoint

        self.norm = normalization(channels)
        self.qkv = conv_nd(1, channels, channels * 3, 1)
        self.attention = QKVAttention()
        self.proj_out = zero_module(conv_nd(1, channels, channels, 1))

    def forward(self, x):
        return checkpoint(self._forward, (x,), self.parameters(), self.use_checkpoint)

    def _forward(self, x):
        b, c, *spatial = x.shape
        x = x.reshape(b, c, -1)
        qkv = self.qkv(self.norm(x))
        qkv = qkv.reshape(b * self.num_heads, -1, qkv.shape[2])
        h = self.attention(qkv)
        h = h.reshape(b, -1, h.shape[-1])
        h = self.proj_out(h)
        return (x + h).reshape(b, c, *spatial)


class QKVAttention(nn.Module):
    """
    A module which performs QKV attention.
    """

    def forward(self, qkv):
        """
        Apply QKV attention.

        :param qkv: an [N x (C * 3) x T] tensor of Qs, Ks, and Vs.
        :return: an [N x C x T] tensor after attention.
        """
        ch = qkv.shape[1] // 3
        q, k, v = th.split(qkv, ch, dim=1)
        scale = 1 / math.sqrt(math.sqrt(ch))
        weight = th.einsum(
            "bct,bcs->bts", q * scale, k * scale
        )  # More stable with f16 than dividing afterwards
        weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
        return th.einsum("bts,bcs->bct", weight, v)

    @staticmethod
    def count_flops(model, _x, y):
        """
        A counter for the `thop` package to count the operations in an
        attention operation.

        Meant to be used like:

            macs, params = thop.profile(
                model,
                inputs=(inputs, timestamps),
                custom_ops={QKVAttention: QKVAttention.count_flops},
            )

        """
        b, c, *spatial = y[0].shape
        num_spatial = int(np.prod(spatial))
        # We perform two matmuls with the same number of ops.
        # The first computes the weight matrix, the second computes
        # the combination of the value vectors.
        matmul_ops = 2 * b * (num_spatial ** 2) * c
        model.total_ops += th.DoubleTensor([matmul_ops])


class UNetModel(nn.Module):
    """
    The full UNet model with attention and timestep embedding.

    :param in_channels: channels in the input Tensor.
    :param model_channels: base channel count for the model.
    :param out_channels: channels in the output Tensor.
    :param num_res_blocks: number of residual blocks per downsample.
    :param attention_resolutions: a collection of downsample rates at which
        attention will take place. May be a set, list, or tuple.
        For example, if this contains 4, then at 4x downsampling, attention
        will be used.
    :param dropout: the dropout probability.
    :param channel_mult: channel multiplier for each level of the UNet.
    :param conv_resample: if True, use learned convolutions for upsampling and
        downsampling.
    :param dims: determines if the signal is 1D, 2D, or 3D.
    :param num_classes: if specified (as an int), then this model will be
        class-conditional with `num_classes` classes.
    :param use_checkpoint: use gradient checkpointing to reduce memory usage.
    :param num_heads: the number of attention heads in each attention layer.
    """

    def __init__(
        self,
        in_channels,
        model_channels,
        out_channels,
        num_res_blocks,
        attention_resolutions,
        dropout=0,
        channel_mult=(1, 2, 4, 8),
        conv_resample=True,
        dims=2,
        num_classes=None,
        c_dim=None,
        rep_dim=None,
        causal_modeling=False,
        flow_based=False,
        use_checkpoint=False,
        num_heads=1,
        num_heads_upsample=-1,
        use_scale_shift_norm=False,
        masking=False,
        n_vars=4
    ):
        super().__init__()

        if num_heads_upsample == -1:
            num_heads_upsample = num_heads

        self.in_channels = in_channels
        self.model_channels = model_channels
        self.out_channels = out_channels
        self.num_res_blocks = num_res_blocks
        self.attention_resolutions = attention_resolutions
        self.dropout = dropout
        self.channel_mult = channel_mult
        self.conv_resample = conv_resample
        self.num_classes = num_classes
        self.c_dim = c_dim
        self.rep_dim = rep_dim
        self.use_checkpoint = use_checkpoint
        self.num_heads = num_heads
        self.num_heads_upsample = num_heads_upsample
        self.causal_modeling = causal_modeling
        self.flow_based = flow_based
        self.masking = masking
        self.drop_prob = 0.5
        self.n_vars = n_vars

        time_embed_dim = model_channels * 4
        # time_embed_dim = model_channels

        # MLP FOR TIME EMBEDDING (TRANSFORMS SINUSOIDS)
        self.time_embed = nn.Sequential(
            linear(model_channels, time_embed_dim),
            SiLU(),
            linear(time_embed_dim, time_embed_dim),
        )   

        
        # CONTEXT EMBEDDING (NEED TO CHANGE THIS FOR CONTINUOUS LABEL VECTORS OR REPRESENTATIONS)
        if self.num_classes is not None:
            self.label_emb = nn.Embedding(num_classes, time_embed_dim)


        if self.c_dim is not None:
            # print(time_embed_dim)
            self.c_emb = nn.Sequential(
                linear(self.c_dim, 256),
                SiLU(),
                linear(256, time_embed_dim)
            )

        # TODO: REPRESENTATION CONDITIONING
        # TRAIN AN ENCODER THAT LEARNS A REPRESENTATION 
        if self.rep_dim is not None:
            self.rep_emb = GaussianConvEncoder(in_channels=in_channels, latent_dim=self.rep_dim, num_vars=4)
            
            self.up_emb = nn.Linear(self.rep_dim, time_embed_dim)
            # self.rep_emb = GaussianEncoder(in_channels=in_channels, latent_dim=rep_dim, num_var=4)

        if self.causal_modeling:
            self.causal_mask = CausalModeling(latent_dim=rep_dim, num_var=self.n_vars, learn=False)

        if self.flow_based:
            self.causal_flow = MultivariateCausalFlow(dim=2, k=256)

        # INPUT BLOCKS (DOWNSAMPLING)
        self.input_blocks = nn.ModuleList(
            [
                TimestepEmbedSequential(
                    conv_nd(dims, in_channels, model_channels, 3, padding=1)  # TIMESTEPEMBED (CONV2D)
                )
            ]
        )
        input_block_chans = [model_channels]
        ch = model_channels
        ds = 1

        # FOR ALL THE CHANNEL MULTIPLIERS, CREATE RESNET BLOCK FOR CONVOLUTIONS (3 PER CHANNEL MULT)
        for level, mult in enumerate(channel_mult):
            for _ in range(num_res_blocks):
                layers = [
                    ResBlock(
                        ch,
                        time_embed_dim,
                        dropout,
                        out_channels=mult * model_channels,
                        dims=dims,
                        use_checkpoint=use_checkpoint,
                        use_scale_shift_norm=use_scale_shift_norm,
                    )
                ]
                ch = mult * model_channels

                # DS TELLS US HOW MANY TIMES WE HAVE DOWNSAMPLED --> IF ENOUGH TIMES, START ADDING ATTENTION BLOCKS
                if ds in attention_resolutions:
                    layers.append(
                        AttentionBlock(
                            ch, use_checkpoint=use_checkpoint, num_heads=num_heads
                        )
                    )
                # APPEND THESE ATTENTION BLOCKS TO INPUT BLOCKS LIST
                self.input_blocks.append(TimestepEmbedSequential(*layers))
                input_block_chans.append(ch)

            if level != len(channel_mult) - 1: # IF WE ARE NOT AT LAST LEVEL OF DOWNSAMPLING, ADD A DOWNSAMPLING BLOCK <-----------
                self.input_blocks.append(
                    TimestepEmbedSequential(Downsample(ch, conv_resample, dims=dims))
                )
                input_block_chans.append(ch)
                ds *= 2




        # MIDDLE BLOCKS (TRANSFORMATIONS & ATTENTION)
        self.middle_block = TimestepEmbedSequential( # ADD SOME MORE RESBLOCKS
            ResBlock(
                ch,
                time_embed_dim,
                dropout,
                dims=dims,
                use_checkpoint=use_checkpoint,
                use_scale_shift_norm=use_scale_shift_norm,
            ),
            AttentionBlock(ch, use_checkpoint=use_checkpoint, num_heads=num_heads), # ADD AN ATTENTION BLOCK
            ResBlock( # ADD SOME MORE RESBLOCKS
                ch,
                time_embed_dim,
                dropout,
                dims=dims,
                use_checkpoint=use_checkpoint,
                use_scale_shift_norm=use_scale_shift_norm,
            ),
        )




        # OUTPUT BLOCKS (UPSAMPLING) -> BASICALLY DO REVESE OF INPUT BLOCKS
        self.output_blocks = nn.ModuleList([])
        for level, mult in list(enumerate(channel_mult))[::-1]: # REVERSE CHANNEL MULTIPLIERS
            for i in range(num_res_blocks + 1):
                layers = [
                    ResBlock( # RESBLOCKS
                        ch + input_block_chans.pop(),
                        time_embed_dim,
                        dropout,
                        out_channels=model_channels * mult,
                        dims=dims,
                        use_checkpoint=use_checkpoint,
                        use_scale_shift_norm=use_scale_shift_norm,
                    )
                ]
                ch = model_channels * mult
                # ATTENTION
                if ds in attention_resolutions:
                    layers.append(
                        AttentionBlock(
                            ch,
                            use_checkpoint=use_checkpoint,
                            num_heads=num_heads_upsample,
                        )
                    )

                # UPSAMPLING BLOCKS
                if level and i == num_res_blocks:
                    layers.append(Upsample(ch, conv_resample, dims=dims))
                    ds //= 2
                self.output_blocks.append(TimestepEmbedSequential(*layers))


        # OUTPUT BLOCK (OUTPUTS PREDICTION OF SAME SIZE AS ORIGINAL IMAGE)
        self.out = nn.Sequential(
            normalization(ch), # GROUP NORMALIZATION
            SiLU(),
            zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)), # ZERO WEIGHT IN LAST LAYER
        )

    def convert_to_fp16(self):
        """
        Convert the torso of the model to float16.
        """
        self.input_blocks.apply(convert_module_to_f16)
        self.middle_block.apply(convert_module_to_f16)
        self.output_blocks.apply(convert_module_to_f16)

    def convert_to_fp32(self):
        """
        Convert the torso of the model to float32.
        """
        self.input_blocks.apply(convert_module_to_f32)
        self.middle_block.apply(convert_module_to_f32)
        self.output_blocks.apply(convert_module_to_f32)

    @property
    def inner_dtype(self):
        """
        Get the dtype used by the torso of the model.
        """
        return next(self.input_blocks.parameters()).dtype


    def forward(self, x, timesteps, y=None, c=None, x_start=None, z=None, A=None, mask=None):
        """
        Apply the model to an input batch.

        :param x: an [N x C x ...] Tensor of inputs.
        :param timesteps: a 1-D batch of timesteps.
        :param y: an [N] Tensor of labels, if class-conditional.
        :param c: an [N x L] Tensor of other labels, if conditional model.
        :param x_start: an [N x C x ...] Tensor of start data (t=0).
        :param z: an [N x K] Tensor for representation.
        :param z: an [N x L x L] Tensor for causal graph encoding relations.
        :return: an [N x C x ...] Tensor of outputs.
        """

        assert (y is not None) == (
            self.num_classes is not None
        ), "must specify y if and only if the model is class-conditional"


        hs = []
        emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))

        # CLASS EMBEDDING
        if self.num_classes is not None:
            assert y.shape == (x.shape[0],)
            emb = emb + self.label_emb(y)

        # OTHER LABEL EMBEDDING
        if self.c_dim is not None:
            emb = emb + self.c_emb(c)

        # REPRESENTATION EMBEDDING
        if self.rep_dim is not None:
            if z is not None:
                up_z = self.up_emb(z)
                emb = emb + up_z
                # emb = emb + z
                mu = None
                var = None
                z_post = None
                mask = None
            else:
                mu, var = self.rep_emb.encode(x_start)
                
                # CAUSAL MASKING OF REPRESENTATION
                if self.causal_modeling:
                    if self.n_vars == 2:
                        A = th.tensor([[0, 1], [0, 0]], dtype=th.float32).to(mu.device)
                    else:
                        # CIRCUIT
                        A = th.tensor([[0, 1, 1, 1], [0, 0, 0, 1], [0, 0, 0, 1], [0, 0, 0, 0]], dtype=th.float32).to(mu.device)

                        # PENDULUM
                        # A = th.tensor([[0, 0, 1, 1], [0, 0, 1, 1], [0, 0, 0, 0], [0, 0, 0, 0]], dtype=th.float32).to(mu.device)

                    if self.flow_based:
                        C = th.eye(A.shape[0]).to(mu.device) - A
                        mask = None
                        z_post, log_det = self.causal_flow.flow(mu, C)

                        log_det, log_prob = self.causal_flow.reverse(z_post, C)

                        mask = -th.mean(log_det)
                    else:
                        z_pre = self.causal_mask.causal_masking(mu, A)
                        z_post = self.causal_mask.nonlinearity_add_back_noise(mu, z_pre).to(mu.device)
                    
                    z = reparameterize(z_post, var * 0.001)
                else:
                    z = reparameterize(mu, var * 0.001)
                    z_post = None
                    mask = None

                
                # CLASSIFIER-FREE MASKING MECHANISM
                if self.masking:
                    context_mask = th.bernoulli(th.zeros(z.shape[0])+ (1-self.drop_prob)).to(z.device)
                    base_context_mask = context_mask
                    context_mask = context_mask[:, None]
                    context_mask = context_mask.repeat(1, 512)
                    # context_mask = context_mask.repeat(1, 64)

                    
                    z = z * context_mask
                    z = z.float()
                    
                    z_post = (z_post * context_mask).float()
                    mask = base_context_mask
                    # var = (var * context_mask).float()

                up_z = self.up_emb(z)
                emb = emb + up_z
                # emb = emb + z
        else:
            mu, var, z_post, mask = None, None, None, None


        h = x.type(self.inner_dtype)
        for module in self.input_blocks:
            h = module(h, emb)
            hs.append(h)
        h = self.middle_block(h, emb)
        for module in self.output_blocks:
            cat_in = th.cat([h, hs.pop()], dim=1)
            h = module(cat_in, emb)
        h = h.type(x.dtype)

        return self.out(h), mu, var, z_post, mask

    def get_feature_vectors(self, x, timesteps, y=None):
        """
        Apply the model and return all of the intermediate tensors.

        :param x: an [N x C x ...] Tensor of inputs.
        :param timesteps: a 1-D batch of timesteps.
        :param y: an [N] Tensor of labels, if class-conditional.
        :return: a dict with the following keys:
                 - 'down': a list of hidden state tensors from downsampling.
                 - 'middle': the tensor of the output of the lowest-resolution
                             block in the model.
                 - 'up': a list of hidden state tensors from upsampling.
        """
        hs = []
        emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
        if self.num_classes is not None:
            assert y.shape == (x.shape[0],)
            emb = emb + self.label_emb(y)
        result = dict(down=[], up=[])
        h = x.type(self.inner_dtype)
        for module in self.input_blocks:
            h = module(h, emb)
            hs.append(h)
            result["down"].append(h.type(x.dtype))
        h = self.middle_block(h, emb)
        result["middle"] = h.type(x.dtype)
        for module in self.output_blocks:
            cat_in = th.cat([h, hs.pop()], dim=1)
            h = module(cat_in, emb)
            result["up"].append(h.type(x.dtype))
        return result


class SuperResModel(UNetModel):
    """
    A UNetModel that performs super-resolution.

    Expects an extra kwarg `low_res` to condition on a low-resolution image.
    """

    def __init__(self, in_channels, *args, **kwargs):
        super().__init__(in_channels * 2, *args, **kwargs)

    def forward(self, x, timesteps, low_res=None, **kwargs):
        _, _, new_height, new_width = x.shape
        upsampled = F.interpolate(low_res, (new_height, new_width), mode="bilinear")
        x = th.cat([x, upsampled], dim=1)
        return super().forward(x, timesteps, **kwargs)

    def get_feature_vectors(self, x, timesteps, low_res=None, **kwargs):
        _, new_height, new_width, _ = x.shape
        upsampled = F.interpolate(low_res, (new_height, new_width), mode="bilinear")
        x = th.cat([x, upsampled], dim=1)
        return super().get_feature_vectors(x, timesteps, **kwargs)

