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 .util import (
    checkpoint,
    avg_pool_nd,
    zero_module,
    normalization,
    timestep_embedding,
)
from ..attention import SpatialTransformer
from comfy.ldm.util import exists
import comfy.ops

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

    @abstractmethod
    def forward(self, x, emb):
        """
        Apply the module to `x` given `emb` timestep embeddings.
        """

#This is needed because accelerate makes a copy of transformer_options which breaks "current_index"
def forward_timestep_embed(ts, x, emb, context=None, transformer_options={}, output_shape=None):
    for layer in ts:
        if isinstance(layer, TimestepBlock):
            x = layer(x, emb)
        elif isinstance(layer, SpatialTransformer):
            x = layer(x, context, transformer_options)
            if "current_index" in transformer_options:
                transformer_options["current_index"] += 1
        elif isinstance(layer, Upsample):
            x = layer(x, output_shape=output_shape)
        else:
            x = layer(x)
    return x

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, *args, **kwargs):
        return forward_timestep_embed(self, *args, **kwargs)

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, out_channels=None, padding=1, dtype=None, device=None, operations=comfy.ops):
        super().__init__()
        self.channels = channels
        self.out_channels = out_channels or channels
        self.use_conv = use_conv
        self.dims = dims
        if use_conv:
            self.conv = operations.conv_nd(dims, self.channels, self.out_channels, 3, padding=padding, dtype=dtype, device=device)

    def forward(self, x, output_shape=None):
        assert x.shape[1] == self.channels
        if self.dims == 3:
            shape = [x.shape[2], x.shape[3] * 2, x.shape[4] * 2]
            if output_shape is not None:
                shape[1] = output_shape[3]
                shape[2] = output_shape[4]
        else:
            shape = [x.shape[2] * 2, x.shape[3] * 2]
            if output_shape is not None:
                shape[0] = output_shape[2]
                shape[1] = output_shape[3]

        x = F.interpolate(x, size=shape, 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, out_channels=None, padding=1, dtype=None, device=None, operations=comfy.ops):
        super().__init__()
        self.channels = channels
        self.out_channels = out_channels or channels
        self.use_conv = use_conv
        self.dims = dims
        stride = 2 if dims != 3 else (1, 2, 2)
        if use_conv:
            self.op = operations.conv_nd(
                dims, self.channels, self.out_channels, 3, stride=stride, padding=padding, dtype=dtype, device=device
            )
        else:
            assert self.channels == self.out_channels
            self.op = avg_pool_nd(dims, kernel_size=stride, stride=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.
    :param up: if True, use this block for upsampling.
    :param down: if True, use this block for downsampling.
    """

    def __init__(
        self,
        channels,
        emb_channels,
        dropout,
        out_channels=None,
        use_conv=False,
        use_scale_shift_norm=False,
        dims=2,
        use_checkpoint=False,
        up=False,
        down=False,
        dtype=None,
        device=None,
        operations=comfy.ops
    ):
        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(
            nn.GroupNorm(32, channels, dtype=dtype, device=device),
            nn.SiLU(),
            operations.conv_nd(dims, channels, self.out_channels, 3, padding=1, dtype=dtype, device=device),
        )

        self.updown = up or down

        if up:
            self.h_upd = Upsample(channels, False, dims, dtype=dtype, device=device)
            self.x_upd = Upsample(channels, False, dims, dtype=dtype, device=device)
        elif down:
            self.h_upd = Downsample(channels, False, dims, dtype=dtype, device=device)
            self.x_upd = Downsample(channels, False, dims, dtype=dtype, device=device)
        else:
            self.h_upd = self.x_upd = nn.Identity()

        self.emb_layers = nn.Sequential(
            nn.SiLU(),
            operations.Linear(
                emb_channels,
                2 * self.out_channels if use_scale_shift_norm else self.out_channels, dtype=dtype, device=device
            ),
        )
        self.out_layers = nn.Sequential(
            nn.GroupNorm(32, self.out_channels, dtype=dtype, device=device),
            nn.SiLU(),
            nn.Dropout(p=dropout),
            zero_module(
                operations.conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1, dtype=dtype, device=device)
            ),
        )

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

    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):
        if self.updown:
            in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
            h = in_rest(x)
            h = self.h_upd(h)
            x = self.x_upd(x)
            h = in_conv(h)
        else:
            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 Timestep(nn.Module):
    def __init__(self, dim):
        super().__init__()
        self.dim = dim

    def forward(self, t):
        return timestep_embedding(t, self.dim)

def apply_control(h, control, name):
    if control is not None and name in control and len(control[name]) > 0:
        ctrl = control[name].pop()
        if ctrl is not None:
            try:
                h += ctrl
            except:
                print("warning control could not be applied", h.shape, ctrl.shape)
    return h

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 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.
    :param num_heads_channels: if specified, ignore num_heads and instead use
                               a fixed channel width per attention head.
    :param num_heads_upsample: works with num_heads to set a different number
                               of heads for upsampling. Deprecated.
    :param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
    :param resblock_updown: use residual blocks for up/downsampling.
    :param use_new_attention_order: use a different attention pattern for potentially
                                    increased efficiency.
    """

    def __init__(
        self,
        image_size,
        in_channels,
        model_channels,
        out_channels,
        num_res_blocks,
        dropout=0,
        channel_mult=(1, 2, 4, 8),
        conv_resample=True,
        dims=2,
        num_classes=None,
        use_checkpoint=False,
        dtype=th.float32,
        num_heads=-1,
        num_head_channels=-1,
        num_heads_upsample=-1,
        use_scale_shift_norm=False,
        resblock_updown=False,
        use_new_attention_order=False,
        use_spatial_transformer=False,    # custom transformer support
        transformer_depth=1,              # custom transformer support
        context_dim=None,                 # custom transformer support
        n_embed=None,                     # custom support for prediction of discrete ids into codebook of first stage vq model
        legacy=True,
        disable_self_attentions=None,
        num_attention_blocks=None,
        disable_middle_self_attn=False,
        use_linear_in_transformer=False,
        adm_in_channels=None,
        transformer_depth_middle=None,
        transformer_depth_output=None,
        device=None,
        operations=comfy.ops,
    ):
        super().__init__()
        assert use_spatial_transformer == True, "use_spatial_transformer has to be true"
        if use_spatial_transformer:
            assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...'

        if context_dim is not None:
            assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'
            # from omegaconf.listconfig import ListConfig
            # if type(context_dim) == ListConfig:
            #     context_dim = list(context_dim)

        if num_heads_upsample == -1:
            num_heads_upsample = num_heads

        if num_heads == -1:
            assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set'

        if num_head_channels == -1:
            assert num_heads != -1, 'Either num_heads or num_head_channels has to be set'

        self.image_size = image_size
        self.in_channels = in_channels
        self.model_channels = model_channels
        self.out_channels = out_channels

        if isinstance(num_res_blocks, int):
            self.num_res_blocks = len(channel_mult) * [num_res_blocks]
        else:
            if len(num_res_blocks) != len(channel_mult):
                raise ValueError("provide num_res_blocks either as an int (globally constant) or "
                                 "as a list/tuple (per-level) with the same length as channel_mult")
            self.num_res_blocks = num_res_blocks

        if disable_self_attentions is not None:
            # should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not
            assert len(disable_self_attentions) == len(channel_mult)
        if num_attention_blocks is not None:
            assert len(num_attention_blocks) == len(self.num_res_blocks)

        transformer_depth = transformer_depth[:]
        transformer_depth_output = transformer_depth_output[:]

        self.dropout = dropout
        self.channel_mult = channel_mult
        self.conv_resample = conv_resample
        self.num_classes = num_classes
        self.use_checkpoint = use_checkpoint
        self.dtype = dtype
        self.num_heads = num_heads
        self.num_head_channels = num_head_channels
        self.num_heads_upsample = num_heads_upsample
        self.predict_codebook_ids = n_embed is not None

        time_embed_dim = model_channels * 4
        self.time_embed = nn.Sequential(
            operations.Linear(model_channels, time_embed_dim, dtype=self.dtype, device=device),
            nn.SiLU(),
            operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device),
        )

        if self.num_classes is not None:
            if isinstance(self.num_classes, int):
                self.label_emb = nn.Embedding(num_classes, time_embed_dim)
            elif self.num_classes == "continuous":
                print("setting up linear c_adm embedding layer")
                self.label_emb = nn.Linear(1, time_embed_dim)
            elif self.num_classes == "sequential":
                assert adm_in_channels is not None
                self.label_emb = nn.Sequential(
                    nn.Sequential(
                        operations.Linear(adm_in_channels, time_embed_dim, dtype=self.dtype, device=device),
                        nn.SiLU(),
                        operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device),
                    )
                )
            else:
                raise ValueError()

        self.input_blocks = nn.ModuleList(
            [
                TimestepEmbedSequential(
                    operations.conv_nd(dims, in_channels, model_channels, 3, padding=1, dtype=self.dtype, device=device)
                )
            ]
        )
        self._feature_size = model_channels
        input_block_chans = [model_channels]
        ch = model_channels
        ds = 1
        for level, mult in enumerate(channel_mult):
            for nr in range(self.num_res_blocks[level]):
                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,
                        dtype=self.dtype,
                        device=device,
                        operations=operations,
                    )
                ]
                ch = mult * model_channels
                num_transformers = transformer_depth.pop(0)
                if num_transformers > 0:
                    if num_head_channels == -1:
                        dim_head = ch // num_heads
                    else:
                        num_heads = ch // num_head_channels
                        dim_head = num_head_channels
                    if legacy:
                        #num_heads = 1
                        dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
                    if exists(disable_self_attentions):
                        disabled_sa = disable_self_attentions[level]
                    else:
                        disabled_sa = False

                    if not exists(num_attention_blocks) or nr < num_attention_blocks[level]:
                        layers.append(SpatialTransformer(
                                ch, num_heads, dim_head, depth=num_transformers, context_dim=context_dim,
                                disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer,
                                use_checkpoint=use_checkpoint, dtype=self.dtype, device=device, operations=operations
                            )
                        )
                self.input_blocks.append(TimestepEmbedSequential(*layers))
                self._feature_size += ch
                input_block_chans.append(ch)
            if level != len(channel_mult) - 1:
                out_ch = ch
                self.input_blocks.append(
                    TimestepEmbedSequential(
                        ResBlock(
                            ch,
                            time_embed_dim,
                            dropout,
                            out_channels=out_ch,
                            dims=dims,
                            use_checkpoint=use_checkpoint,
                            use_scale_shift_norm=use_scale_shift_norm,
                            down=True,
                            dtype=self.dtype,
                            device=device,
                            operations=operations
                        )
                        if resblock_updown
                        else Downsample(
                            ch, conv_resample, dims=dims, out_channels=out_ch, dtype=self.dtype, device=device, operations=operations
                        )
                    )
                )
                ch = out_ch
                input_block_chans.append(ch)
                ds *= 2
                self._feature_size += ch

        if num_head_channels == -1:
            dim_head = ch // num_heads
        else:
            num_heads = ch // num_head_channels
            dim_head = num_head_channels
        if legacy:
            #num_heads = 1
            dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
        mid_block = [
            ResBlock(
                ch,
                time_embed_dim,
                dropout,
                dims=dims,
                use_checkpoint=use_checkpoint,
                use_scale_shift_norm=use_scale_shift_norm,
                dtype=self.dtype,
                device=device,
                operations=operations
            )]
        if transformer_depth_middle >= 0:
            mid_block += [SpatialTransformer(  # always uses a self-attn
                            ch, num_heads, dim_head, depth=transformer_depth_middle, context_dim=context_dim,
                            disable_self_attn=disable_middle_self_attn, use_linear=use_linear_in_transformer,
                            use_checkpoint=use_checkpoint, dtype=self.dtype, device=device, operations=operations
                        ),
            ResBlock(
                ch,
                time_embed_dim,
                dropout,
                dims=dims,
                use_checkpoint=use_checkpoint,
                use_scale_shift_norm=use_scale_shift_norm,
                dtype=self.dtype,
                device=device,
                operations=operations
            )]
        self.middle_block = TimestepEmbedSequential(*mid_block)
        self._feature_size += ch

        self.output_blocks = nn.ModuleList([])
        for level, mult in list(enumerate(channel_mult))[::-1]:
            for i in range(self.num_res_blocks[level] + 1):
                ich = input_block_chans.pop()
                layers = [
                    ResBlock(
                        ch + ich,
                        time_embed_dim,
                        dropout,
                        out_channels=model_channels * mult,
                        dims=dims,
                        use_checkpoint=use_checkpoint,
                        use_scale_shift_norm=use_scale_shift_norm,
                        dtype=self.dtype,
                        device=device,
                        operations=operations
                    )
                ]
                ch = model_channels * mult
                num_transformers = transformer_depth_output.pop()
                if num_transformers > 0:
                    if num_head_channels == -1:
                        dim_head = ch // num_heads
                    else:
                        num_heads = ch // num_head_channels
                        dim_head = num_head_channels
                    if legacy:
                        #num_heads = 1
                        dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
                    if exists(disable_self_attentions):
                        disabled_sa = disable_self_attentions[level]
                    else:
                        disabled_sa = False

                    if not exists(num_attention_blocks) or i < num_attention_blocks[level]:
                        layers.append(
                            SpatialTransformer(
                                ch, num_heads, dim_head, depth=num_transformers, context_dim=context_dim,
                                disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer,
                                use_checkpoint=use_checkpoint, dtype=self.dtype, device=device, operations=operations
                            )
                        )
                if level and i == self.num_res_blocks[level]:
                    out_ch = ch
                    layers.append(
                        ResBlock(
                            ch,
                            time_embed_dim,
                            dropout,
                            out_channels=out_ch,
                            dims=dims,
                            use_checkpoint=use_checkpoint,
                            use_scale_shift_norm=use_scale_shift_norm,
                            up=True,
                            dtype=self.dtype,
                            device=device,
                            operations=operations
                        )
                        if resblock_updown
                        else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch, dtype=self.dtype, device=device, operations=operations)
                    )
                    ds //= 2
                self.output_blocks.append(TimestepEmbedSequential(*layers))
                self._feature_size += ch

        self.out = nn.Sequential(
            nn.GroupNorm(32, ch, dtype=self.dtype, device=device),
            nn.SiLU(),
            zero_module(operations.conv_nd(dims, model_channels, out_channels, 3, padding=1, dtype=self.dtype, device=device)),
        )
        if self.predict_codebook_ids:
            self.id_predictor = nn.Sequential(
            nn.GroupNorm(32, ch, dtype=self.dtype, device=device),
            operations.conv_nd(dims, model_channels, n_embed, 1, dtype=self.dtype, device=device),
            #nn.LogSoftmax(dim=1)  # change to cross_entropy and produce non-normalized logits
        )

    def forward(self, x, timesteps=None, context=None, y=None, control=None, transformer_options={}, **kwargs):
        """
        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 context: conditioning plugged in via crossattn
        :param y: an [N] Tensor of labels, if class-conditional.
        :return: an [N x C x ...] Tensor of outputs.
        """
        transformer_options["original_shape"] = list(x.shape)
        transformer_options["current_index"] = 0
        transformer_patches = transformer_options.get("patches", {})

        assert (y is not None) == (
            self.num_classes is not None
        ), "must specify y if and only if the model is class-conditional"
        hs = []
        t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(self.dtype)
        emb = self.time_embed(t_emb)

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

        h = x.type(self.dtype)
        for id, module in enumerate(self.input_blocks):
            transformer_options["block"] = ("input", id)
            h = forward_timestep_embed(module, h, emb, context, transformer_options)
            h = apply_control(h, control, 'input')
            if "input_block_patch" in transformer_patches:
                patch = transformer_patches["input_block_patch"]
                for p in patch:
                    h = p(h, transformer_options)

            hs.append(h)
            if "input_block_patch_after_skip" in transformer_patches:
                patch = transformer_patches["input_block_patch_after_skip"]
                for p in patch:
                    h = p(h, transformer_options)

        transformer_options["block"] = ("middle", 0)
        h = forward_timestep_embed(self.middle_block, h, emb, context, transformer_options)
        h = apply_control(h, control, 'middle')

        for id, module in enumerate(self.output_blocks):
            transformer_options["block"] = ("output", id)
            hsp = hs.pop()
            hsp = apply_control(hsp, control, 'output')

            if "output_block_patch" in transformer_patches:
                patch = transformer_patches["output_block_patch"]
                for p in patch:
                    h, hsp = p(h, hsp, transformer_options)

            h = th.cat([h, hsp], dim=1)
            del hsp
            if len(hs) > 0:
                output_shape = hs[-1].shape
            else:
                output_shape = None
            h = forward_timestep_embed(module, h, emb, context, transformer_options, output_shape)
        h = h.type(x.dtype)
        if self.predict_codebook_ids:
            return self.id_predictor(h)
        else:
            return self.out(h)
