from inspect import isfunction
import math
import torch
import torch.nn.functional as F
from torch import nn, einsum
from einops import rearrange, repeat

from ldm.modules.diffusionmodules.util import checkpoint


def exists(val):
    return val is not None


def uniq(arr):
    return{el: True for el in arr}.keys()


def default(val, d):
    if exists(val):
        return val
    return d() if isfunction(d) else d


def max_neg_value(t):
    return -torch.finfo(t.dtype).max


def init_(tensor):
    dim = tensor.shape[-1]
    std = 1 / math.sqrt(dim)
    tensor.uniform_(-std, std)
    return tensor


# feedforward
class GEGLU(nn.Module):
    def __init__(self, dim_in, dim_out):
        super().__init__()
        self.proj = nn.Linear(dim_in, dim_out * 2)

    def forward(self, x):
        x, gate = self.proj(x).chunk(2, dim=-1)
        return x * F.gelu(gate)


# feedforward
class Rank_1_GEGLU(nn.Module):
    def __init__(self, dim_in, dim_out):
        super().__init__()
        self.proj = nn.Linear(dim_in, dim_out * 2)
        
        # -------- #
        self.proj_rank_1 = Rank_1_Linear(dim_in, dim_out * 2)

    def forward(self, x):
        # x, gate = self.proj(x).chunk(2, dim=-1)
        # return x * F.gelu(gate)
        
        # modulate linear layer
        proj_weight = self.proj.weight
        proj_bias   = self.proj.bias
        
        x, gate     = self.proj_rank_1(x, proj_weight, proj_bias).chunk(2, dim=-1)
        
        return x * F.gelu(gate)
    
    
class FeedForward(nn.Module):
    def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
        super().__init__()
        inner_dim = int(dim * mult)
        dim_out = default(dim_out, dim)
        project_in = nn.Sequential(
            nn.Linear(dim, inner_dim),
            nn.GELU()
        ) if not glu else GEGLU(dim, inner_dim)

        self.net = nn.Sequential(
            project_in,
            nn.Dropout(dropout),
            nn.Linear(inner_dim, dim_out)
        )

    def forward(self, x):
        return self.net(x)



# bug not fixed yet
class Rank_1_FeedForward(nn.Module):
    def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
        super().__init__()
        inner_dim = int(dim * mult)
        dim_out = default(dim_out, dim)
        
        # ---------------------------- #
        
        project_in = nn.Sequential(
            nn.Linear(dim, inner_dim),
            nn.GELU()
        ) if not glu else GEGLU(dim, inner_dim)

        self.project_in_rank_1 = nn.Sequential(
            Rank_1_Linear(dim, inner_dim),
            nn.GELU()
        ) if not glu else Rank_1_GEGLU(dim, inner_dim)
        
        self.net = nn.Sequential(
            project_in,
            nn.Dropout(dropout),
            nn.Linear(inner_dim, dim_out)
        )
        self.net_rank_1 = nn.Sequential(
            self.project_in_rank_1,
            nn.Dropout(dropout),
            Rank_1_Linear(inner_dim, dim_out)
        )
        
        # ---------------------------- #
        
    def forward(self, x):
        
        # ---------------------------- #
        # modulate linear layers
        
        # return self.net(x)
        for i, net_module in enumerate(self.net_rank_1):
            if i == 0:  ## nn.Sequential()[0] -> self.project_in_rank_1
                try:
                    for j, _ in enumerate(net_module):
                        if j == 0:
                            proj_in_weight = self.net[0][0].weight
                            proj_in_bias   = self.net[0][0].bias
                            out            = self.project_in_rank_1[0](x, proj_in_weight, proj_in_bias)  # rank_1_linear forward
                        else:
                            out            = self.project_in_rank_1[j](out)
                except:
                    out                    = net_module(x)
            elif i == 1:
                out = net_module(out)
            else:  # rank_1_linear forward
                net_weight             = self.net[i].weight
                net_bias               = self.net[i].bias
                
                out = net_module(out, net_weight, net_bias)
        return out
                
    
def zero_module(module):
    """
    Zero out the parameters of a module and return it.
    """
    for p in module.parameters():
        p.detach().zero_()
    return module


def Normalize(in_channels):
    return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)


class LinearAttention(nn.Module):
    def __init__(self, dim, heads=4, dim_head=32):
        super().__init__()
        self.heads = heads
        hidden_dim = dim_head * heads
        self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias = False)
        self.to_out = nn.Conv2d(hidden_dim, dim, 1)

    def forward(self, x):
        b, c, h, w = x.shape
        qkv = self.to_qkv(x)
        q, k, v = rearrange(qkv, 'b (qkv heads c) h w -> qkv b heads c (h w)', heads = self.heads, qkv=3)
        k = k.softmax(dim=-1)  
        context = torch.einsum('bhdn,bhen->bhde', k, v)
        out = torch.einsum('bhde,bhdn->bhen', context, q)
        out = rearrange(out, 'b heads c (h w) -> b (heads c) h w', heads=self.heads, h=h, w=w)
        return self.to_out(out)


class SpatialSelfAttention(nn.Module):
    def __init__(self, in_channels):
        super().__init__()
        self.in_channels = in_channels

        self.norm = Normalize(in_channels)
        self.q = torch.nn.Conv2d(in_channels,
                                 in_channels,
                                 kernel_size=1,
                                 stride=1,
                                 padding=0)
        self.k = torch.nn.Conv2d(in_channels,
                                 in_channels,
                                 kernel_size=1,
                                 stride=1,
                                 padding=0)
        self.v = torch.nn.Conv2d(in_channels,
                                 in_channels,
                                 kernel_size=1,
                                 stride=1,
                                 padding=0)
        self.proj_out = torch.nn.Conv2d(in_channels,
                                        in_channels,
                                        kernel_size=1,
                                        stride=1,
                                        padding=0)

    def forward(self, x):
        h_ = x
        h_ = self.norm(h_)
        q = self.q(h_)
        k = self.k(h_)
        v = self.v(h_)

        # compute attention
        b,c,h,w = q.shape
        q = rearrange(q, 'b c h w -> b (h w) c')
        k = rearrange(k, 'b c h w -> b c (h w)')
        w_ = torch.einsum('bij,bjk->bik', q, k)

        w_ = w_ * (int(c)**(-0.5))
        w_ = torch.nn.functional.softmax(w_, dim=2)

        # attend to values
        v = rearrange(v, 'b c h w -> b c (h w)')
        w_ = rearrange(w_, 'b i j -> b j i')
        h_ = torch.einsum('bij,bjk->bik', v, w_)
        h_ = rearrange(h_, 'b c (h w) -> b c h w', h=h)
        h_ = self.proj_out(h_)

        return x+h_


class CrossAttention(nn.Module):
    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.):
        super().__init__()
        inner_dim = dim_head * heads
        context_dim = default(context_dim, query_dim)

        self.scale = dim_head ** -0.5
        self.heads = heads

        self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
        self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
        self.to_v = nn.Linear(context_dim, inner_dim, bias=False)

        self.to_out = nn.Sequential(
            nn.Linear(inner_dim, query_dim),
            nn.Dropout(dropout)
        )

    def forward(self, x, context=None, mask=None):
        h = self.heads

        q = self.to_q(x)
        context = default(context, x)
        k = self.to_k(context)
        v = self.to_v(context)

        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))

        sim = einsum('b i d, b j d -> b i j', q, k) * self.scale

        if exists(mask):
            mask = rearrange(mask, 'b ... -> b (...)')
            max_neg_value = -torch.finfo(sim.dtype).max
            mask = repeat(mask, 'b j -> (b h) () j', h=h)
            sim.masked_fill_(~mask, max_neg_value)

        # attention, what we cannot get enough of
        attn = sim.softmax(dim=-1)

        out = einsum('b i j, b j d -> b i d', attn, v)
        out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
        return self.to_out(out)


class Rank_1_CrossAttention(nn.Module):
    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.):
        super().__init__()
        inner_dim = dim_head * heads
        context_dim = default(context_dim, query_dim)

        self.scale = dim_head ** -0.5
        self.heads = heads

        # ------------------------------------------------------ #
        
        self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
        self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
        self.to_v = nn.Linear(context_dim, inner_dim, bias=False)

        self.to_q_rank_1 = Rank_1_Linear(query_dim, inner_dim, bias=False)
        self.to_k_rank_1 = Rank_1_Linear(context_dim, inner_dim, bias=False)
        self.to_v_rank_1 = Rank_1_Linear(context_dim, inner_dim, bias=False)
        
        
        self.to_out = nn.Sequential(
            nn.Linear(inner_dim, query_dim),
            nn.Dropout(dropout)
        )
        self.to_out_rank_1 = nn.Sequential(
            Rank_1_Linear(inner_dim, query_dim),
            nn.Dropout(dropout)
        )
    
        # ------------------------------------------------------ #

    def forward(self, x, context=None, mask=None):
        h = self.heads

        # ----------------------------------------------------- #
        # modulate linear layers
        
        # q = self.to_q(x)
        to_q_weight = self.to_q.weight
        q           = self.to_q_rank_1(x, to_q_weight)
        
        context = default(context, x)
        
        # k = self.to_k(context)
        to_k_weight = self.to_k.weight
        k           = self.to_k_rank_1(context, to_k_weight)
        
        # v = self.to_v(context)
        to_v_weight = self.to_v.weight
        v           = self.to_v_rank_1(context, to_v_weight)
        
        # ----------------------------------------------------- #

        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))

        sim = einsum('b i d, b j d -> b i j', q, k) * self.scale

        if exists(mask):
            mask = rearrange(mask, 'b ... -> b (...)')
            max_neg_value = -torch.finfo(sim.dtype).max
            mask = repeat(mask, 'b j -> (b h) () j', h=h)
            sim.masked_fill_(~mask, max_neg_value)

        # attention, what we cannot get enough of
        attn = sim.softmax(dim=-1)

        out = einsum('b i j, b j d -> b i d', attn, v)
        out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
        
        
        # ------------------------------------------------------ #
        # modulate linear layers
        
        # return self.to_out(out)
        for i, module in enumerate(self.to_out_rank_1):
            if i == 0:
                to_out_weight = self.to_out[i].weight
                to_out_bias   = self.to_out[i].bias
                out           = self.to_out_rank_1[i](out, to_out_weight, to_out_bias)
            else:
                out           = module(out)
        # ------------------------------------------------------ #
        
        return out
        
    
    
class BasicTransformerBlock(nn.Module):
    def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True):
        super().__init__()
        self.attn1 = CrossAttention(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout)  # is a self-attention
        self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)
        self.attn2 = CrossAttention(query_dim=dim, context_dim=context_dim,
                                    heads=n_heads, dim_head=d_head, dropout=dropout)  # is self-attn if context is none
        self.norm1 = nn.LayerNorm(dim)
        self.norm2 = nn.LayerNorm(dim)
        self.norm3 = nn.LayerNorm(dim)
        self.checkpoint = checkpoint

    def forward(self, x, context=None):
        return checkpoint(self._forward, (x, context), self.parameters(), self.checkpoint)

    def _forward(self, x, context=None):
        x = self.attn1(self.norm1(x)) + x
        x = self.attn2(self.norm2(x), context=context) + x
        x = self.ff(self.norm3(x)) + x
        return x


class Rank_1_BasicTransformerBlock(nn.Module):
    def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True):
        super().__init__()
        self.attn1 = Rank_1_CrossAttention(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout)  # is a self-attention
        self.ff    = FeedForward(dim, dropout=dropout, glu=gated_ff)
        self.attn2 = Rank_1_CrossAttention(query_dim=dim, context_dim=context_dim,
                                    heads=n_heads, dim_head=d_head, dropout=dropout)  # is self-attn if context is none
        self.norm1 = nn.LayerNorm(dim)
        self.norm2 = nn.LayerNorm(dim)
        self.norm3 = nn.LayerNorm(dim)
        self.checkpoint = checkpoint

    def forward(self, x, context=None):
        return checkpoint(self._forward, (x, context), self.parameters(), self.checkpoint)

    def _forward(self, x, context=None):
        x = self.attn1(self.norm1(x)) + x
        x = self.attn2(self.norm2(x), context=context) + x
        x = self.ff(self.norm3(x)) + x
        return x
    

class SpatialTransformer(nn.Module):
    """
    Transformer block for image-like data.
    First, project the input (aka embedding)
    and reshape to b, t, d.
    Then apply standard transformer action.
    Finally, reshape to image
    """
    def __init__(self, in_channels, n_heads, d_head,
                 depth=1, dropout=0., context_dim=None):
        super().__init__()
        self.in_channels = in_channels
        inner_dim = n_heads * d_head
        self.norm = Normalize(in_channels)

        self.proj_in = nn.Conv2d(in_channels,
                                 inner_dim,
                                 kernel_size=1,
                                 stride=1,
                                 padding=0)
    
        self.transformer_blocks = nn.ModuleList(
            [BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim)
                for d in range(depth)]
        )
        
        self.proj_out = zero_module(nn.Conv2d(inner_dim,
                                              in_channels,
                                              kernel_size=1,
                                              stride=1,
                                              padding=0))

    def forward(self, x, context=None):
        # note: if no context is given, cross-attention defaults to self-attention
        b, c, h, w = x.shape
        x_in = x
        x = self.norm(x)
        x = self.proj_in(x)
        x = rearrange(x, 'b c h w -> b (h w) c')
        for block in self.transformer_blocks:
            x = block(x, context=context)
        x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w)
        x = self.proj_out(x)
        return x + x_in
    

class Rank_1_SpatialTransformer(nn.Module):
    """
    Transformer block for image-like data.
    First, project the input (aka embedding)
    and reshape to b, t, d.
    Then apply standard transformer action.
    Finally, reshape to image
    """
    def __init__(self, in_channels, n_heads, d_head,
                 depth=1, dropout=0., context_dim=None):
        super().__init__()
        self.in_channels = in_channels
        inner_dim = n_heads * d_head
        self.norm = Normalize(in_channels)


        # ------------------------------------- #
        self.proj_in = nn.Conv2d(in_channels,
                                 inner_dim,
                                 kernel_size=1,
                                 stride=1,
                                 padding=0)
    
        self.proj_in_rank_1 = Rank_1_Conv2d(
                                 in_channels,
                                 inner_dim,
                                 kernel_size=1,
                                 stride=1,
                                 padding=0)
        
        self.transformer_blocks = nn.ModuleList(
            [Rank_1_BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim)
                for d in range(depth)]
        )
        # ------------------------------------- #
        
        
        self.proj_out = zero_module(nn.Conv2d(inner_dim,
                                              in_channels,
                                              kernel_size=1,
                                              stride=1,
                                              padding=0))

    def forward(self, x, context=None):
        # note: if no context is given, cross-attention defaults to self-attention
        b, c, h, w = x.shape
        x_in = x
        x = self.norm(x)
        
        
        # ----------------------- #
        # modulate conv layers
        
        # x = self.proj_in(x)
        
        proj_in_weight = self.proj_in.weight
        proj_in_bias   = self.proj_in.bias
        x              = self.proj_in_rank_1(x, proj_in_weight, proj_in_bias)
        
        # ----------------------- #
        
        
        x = rearrange(x, 'b c h w -> b (h w) c')
        for block in self.transformer_blocks:
            x = block(x, context=context)
        x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w)
        x = self.proj_out(x)
        return x + x_in
    
    
class Rank_1_Linear(nn.Module):
    def __init__(self, in_dim, out_dim, bias=True):
        super().__init__()
        
        self.bias = bias
        
        # KML Parameters with the same original dimension
        self.u_vector  = nn.Parameter(torch.randn(in_dim)  * 0)
        self.v_vector  = nn.Parameter(torch.randn(out_dim) * 0)
        
        if self.bias:
            self.b_vector  = nn.Parameter(torch.zeros(out_dim) * 0)

    def forward(self, input, W, b=None):
        # W: base params of weights, dim=(out_dim, in_dim)
        # Outer production to generate modulation parameters
        W_hat  = torch.ger(self.v_vector, self.u_vector).view(W.shape)

        # Hadamard production
        weight = W * (torch.ones_like(W) + W_hat)
        
        if self.bias:
            bias   = b + self.b_vector
        else:
            bias   = None

        out = F.linear(input, weight, bias=bias)

        return out
    
    
class Rank_1_Conv2d(nn.Module):
    def __init__(
        self, in_channel, out_channel, kernel_size, stride=1, padding=0, bias=True
    ):
        super().__init__()

        self.stride = stride
        self.padding = padding
        self.in_channel  = in_channel
        self.out_channel = out_channel
        self.kernel_size = kernel_size

        # --------------- KML fc/Conv parameters ------------ #
        # Conv
        self.u_vector = nn.Parameter(torch.randn(in_channel * kernel_size * kernel_size) * 0)
        self.v_vector = nn.Parameter(torch.randn(out_channel) * 0)
        self.b_vector = nn.Parameter(torch.zeros(out_channel) * 0)
        # ------------------------------------------------- #

    def forward(self, input, conv_2d_weight, conv_2d_bias):
        # --------------- KML Conv operations ------------ #
        weight_hat = torch.ger(self.u_vector, self.v_vector).view(self.out_channel, self.in_channel, self.kernel_size, self.kernel_size)
        
        # Hadamard production
        weight     = conv_2d_weight * (torch.ones_like(conv_2d_weight) + weight_hat)
        bias       = conv_2d_bias + self.b_vector
        # ------------------------------------------------- #

        out = F.conv2d(
            input,
            weight,
            bias,
            stride=self.stride,
            padding=self.padding,
        )

        return out

    def __repr__(self):
        return (
            f'{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]},'
            f' {self.weight.shape[2]}, stride={self.stride}, padding={self.padding})'
        )
        