import math
from inspect import isfunction
from typing import Any, Optional

import torch
import torch.nn.functional as F
from einops import rearrange, repeat
from packaging import version
from torch import nn

if version.parse(torch.__version__) >= version.parse('2.0.0'):
    SDP_IS_AVAILABLE = True
    from torch.backends.cuda import SDPBackend, sdp_kernel

    BACKEND_MAP = {
        SDPBackend.MATH: {
            'enable_math': True,
            'enable_flash': False,
            'enable_mem_efficient': False,
        },
        SDPBackend.FLASH_ATTENTION: {
            'enable_math': False,
            'enable_flash': True,
            'enable_mem_efficient': False,
        },
        SDPBackend.EFFICIENT_ATTENTION: {
            'enable_math': False,
            'enable_flash': False,
            'enable_mem_efficient': True,
        },
        None: {
            'enable_math': True,
            'enable_flash': True,
            'enable_mem_efficient': True
        },
    }
else:
    from contextlib import nullcontext

    SDP_IS_AVAILABLE = False
    sdp_kernel = nullcontext
    BACKEND_MAP = {}
    print(
        f'No SDP backend available, likely because you are running in pytorch versions < 2.0. In fact, '
        f'you are using PyTorch {torch.__version__}. You might want to consider upgrading.'
    )

try:
    import xformers
    import xformers.ops

    XFORMERS_IS_AVAILABLE = True
except:
    XFORMERS_IS_AVAILABLE = False
    print("no module 'xformers'. Processing without...")

from .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)


class FeedForward(nn.Module):

    def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.0):
        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)


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=0.0,
        backend=None,
    ):
        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))
        self.backend = backend

    def forward(
        self,
        x,
        context=None,
        mask=None,
        additional_tokens=None,
        n_times_crossframe_attn_in_self=0,
    ):
        h = self.heads

        if additional_tokens is not None:
            # get the number of masked tokens at the beginning of the output sequence
            n_tokens_to_mask = additional_tokens.shape[1]
            # add additional token
            x = torch.cat([additional_tokens, x], dim=1)

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

        if n_times_crossframe_attn_in_self:
            # reprogramming cross-frame attention as in https://arxiv.org/abs/2303.13439
            assert x.shape[0] % n_times_crossframe_attn_in_self == 0
            n_cp = x.shape[0] // n_times_crossframe_attn_in_self
            k = repeat(k[::n_times_crossframe_attn_in_self],
                       'b ... -> (b n) ...',
                       n=n_cp)
            v = repeat(v[::n_times_crossframe_attn_in_self],
                       'b ... -> (b n) ...',
                       n=n_cp)

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

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

        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
        sim = sim.softmax(dim=-1)

        out = einsum('b i j, b j d -> b i d', sim, v)
        """
        ## new
        with sdp_kernel(**BACKEND_MAP[self.backend]):
            # print("dispatching into backend", self.backend, "q/k/v shape: ", q.shape, k.shape, v.shape)
            out = F.scaled_dot_product_attention(
                q, k, v,
                attn_mask=mask)  # scale is dim_head ** -0.5 per default

        del q, k, v
        out = rearrange(out, 'b h n d -> b n (h d)', h=h)

        if additional_tokens is not None:
            # remove additional token
            out = out[:, n_tokens_to_mask:]
        return self.to_out(out)


class MemoryEfficientCrossAttention(nn.Module):
    # https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223
    def __init__(self,
                 query_dim,
                 context_dim=None,
                 heads=8,
                 dim_head=64,
                 dropout=0.0,
                 **kwargs):
        super().__init__()
        print(
            f'Setting up {self.__class__.__name__}. Query dim is {query_dim}, context_dim is {context_dim} and using '
            f'{heads} heads with a dimension of {dim_head}.')
        inner_dim = dim_head * heads
        context_dim = default(context_dim, query_dim)

        self.heads = heads
        self.dim_head = dim_head

        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))
        self.attention_op: Optional[Any] = None

    def forward(
        self,
        x,
        context=None,
        mask=None,
        additional_tokens=None,
        n_times_crossframe_attn_in_self=0,
    ):
        if additional_tokens is not None:
            # get the number of masked tokens at the beginning of the output sequence
            n_tokens_to_mask = additional_tokens.shape[1]
            # add additional token
            x = torch.cat([additional_tokens, x], dim=1)
        q = self.to_q(x)
        context = default(context, x)
        k = self.to_k(context)
        v = self.to_v(context)

        if n_times_crossframe_attn_in_self:
            # reprogramming cross-frame attention as in https://arxiv.org/abs/2303.13439
            assert x.shape[0] % n_times_crossframe_attn_in_self == 0
            # n_cp = x.shape[0]//n_times_crossframe_attn_in_self
            k = repeat(
                k[::n_times_crossframe_attn_in_self],
                'b ... -> (b n) ...',
                n=n_times_crossframe_attn_in_self,
            )
            v = repeat(
                v[::n_times_crossframe_attn_in_self],
                'b ... -> (b n) ...',
                n=n_times_crossframe_attn_in_self,
            )

        b, _, _ = q.shape
        q, k, v = map(
            lambda t: t.unsqueeze(3).reshape(b, t.shape[
                1], self.heads, self.dim_head).permute(0, 2, 1, 3).reshape(
                    b * self.heads, t.shape[1], self.dim_head).contiguous(),
            (q, k, v),
        )

        # actually compute the attention, what we cannot get enough of
        out = xformers.ops.memory_efficient_attention(q,
                                                      k,
                                                      v,
                                                      attn_bias=None,
                                                      op=self.attention_op)

        # TODO: Use this directly in the attention operation, as a bias
        if exists(mask):
            raise NotImplementedError
        out = (out.unsqueeze(0).reshape(
            b, self.heads, out.shape[1],
            self.dim_head).permute(0, 2, 1,
                                   3).reshape(b, out.shape[1],
                                              self.heads * self.dim_head))
        if additional_tokens is not None:
            # remove additional token
            out = out[:, n_tokens_to_mask:]
        return self.to_out(out)


class BasicTransformerBlock(nn.Module):
    ATTENTION_MODES = {
        'softmax': CrossAttention,  # vanilla attention
        'softmax-xformers': MemoryEfficientCrossAttention,  # ampere
    }

    def __init__(
        self,
        dim,
        n_heads,
        d_head,
        dropout=0.0,
        context_dim=None,
        gated_ff=True,
        checkpoint=True,
        disable_self_attn=False,
        attn_mode='softmax',
        sdp_backend=None,
    ):
        super().__init__()
        assert attn_mode in self.ATTENTION_MODES
        if attn_mode != 'softmax' and not XFORMERS_IS_AVAILABLE:
            print(
                f"Attention mode '{attn_mode}' is not available. Falling back to native attention. "
                f'This is not a problem in Pytorch >= 2.0. FYI, you are running with PyTorch version {torch.__version__}'
            )
            attn_mode = 'softmax'
        elif attn_mode == 'softmax' and not SDP_IS_AVAILABLE:
            print(
                'We do not support vanilla attention anymore, as it is too expensive. Sorry.'
            )
            if not XFORMERS_IS_AVAILABLE:
                assert False, "Please install xformers via e.g. 'pip install xformers==0.0.16'"
            else:
                print('Falling back to xformers efficient attention.')
                attn_mode = 'softmax-xformers'
        attn_cls = self.ATTENTION_MODES[attn_mode]
        if version.parse(torch.__version__) >= version.parse('2.0.0'):
            assert sdp_backend is None or isinstance(sdp_backend, SDPBackend)
        else:
            assert sdp_backend is None
        self.disable_self_attn = disable_self_attn
        self.attn1 = attn_cls(
            query_dim=dim,
            heads=n_heads,
            dim_head=d_head,
            dropout=dropout,
            context_dim=context_dim if self.disable_self_attn else None,
            backend=sdp_backend,
        )  # is a self-attention if not self.disable_self_attn
        self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)
        self.attn2 = attn_cls(
            query_dim=dim,
            context_dim=context_dim,
            heads=n_heads,
            dim_head=d_head,
            dropout=dropout,
            backend=sdp_backend,
        )  # 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
        if self.checkpoint:
            print(f'{self.__class__.__name__} is using checkpointing')

    def forward(self,
                x,
                context=None,
                additional_tokens=None,
                n_times_crossframe_attn_in_self=0):
        kwargs = {'x': x}

        if context is not None:
            kwargs.update({'context': context})

        if additional_tokens is not None:
            kwargs.update({'additional_tokens': additional_tokens})

        if n_times_crossframe_attn_in_self:
            kwargs.update({
                'n_times_crossframe_attn_in_self':
                n_times_crossframe_attn_in_self
            })

        # return mixed_checkpoint(self._forward, kwargs, self.parameters(), self.checkpoint)
        return checkpoint(self._forward, (x, context), self.parameters(),
                          self.checkpoint)

    def _forward(self,
                 x,
                 context=None,
                 additional_tokens=None,
                 n_times_crossframe_attn_in_self=0):
        x = (self.attn1(
            self.norm1(x),
            context=context if self.disable_self_attn else None,
            additional_tokens=additional_tokens,
            n_times_crossframe_attn_in_self=n_times_crossframe_attn_in_self
            if not self.disable_self_attn else 0,
        ) + x)
        x = self.attn2(self.norm2(x),
                       context=context,
                       additional_tokens=additional_tokens) + x
        x = self.ff(self.norm3(x)) + x
        return x


class BasicTransformerSingleLayerBlock(nn.Module):
    ATTENTION_MODES = {
        'softmax': CrossAttention,  # vanilla attention
        'softmax-xformers':
        MemoryEfficientCrossAttention,  # on the A100s not quite as fast as the above version
        # (todo might depend on head_dim, check, falls back to semi-optimized kernels for dim!=[16,32,64,128])
    }

    def __init__(
        self,
        dim,
        n_heads,
        d_head,
        dropout=0.0,
        context_dim=None,
        gated_ff=True,
        checkpoint=True,
        attn_mode='softmax',
    ):
        super().__init__()
        assert attn_mode in self.ATTENTION_MODES
        attn_cls = self.ATTENTION_MODES[attn_mode]
        self.attn1 = attn_cls(
            query_dim=dim,
            heads=n_heads,
            dim_head=d_head,
            dropout=dropout,
            context_dim=context_dim,
        )
        self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)
        self.norm1 = nn.LayerNorm(dim)
        self.norm2 = 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), context=context) + x
        x = self.ff(self.norm2(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
    NEW: use_linear for more efficiency instead of the 1x1 convs
    """

    def __init__(
        self,
        in_channels,
        n_heads,
        d_head,
        depth=1,
        dropout=0.0,
        context_dim=None,
        disable_self_attn=False,
        use_linear=False,
        attn_type='softmax',
        use_checkpoint=True,
        # sdp_backend=SDPBackend.FLASH_ATTENTION
        sdp_backend=None,
    ):
        super().__init__()
        print(
            f'constructing {self.__class__.__name__} of depth {depth} w/ {in_channels} channels and {n_heads} heads'
        )
        from omegaconf import ListConfig

        if exists(context_dim) and not isinstance(context_dim,
                                                  (list, ListConfig)):
            context_dim = [context_dim]
        if exists(context_dim) and isinstance(context_dim, list):
            if depth != len(context_dim):
                print(
                    f'WARNING: {self.__class__.__name__}: Found context dims {context_dim} of depth {len(context_dim)}, '
                    f"which does not match the specified 'depth' of {depth}. Setting context_dim to {depth * [context_dim[0]]} now."
                )
                # depth does not match context dims.
                assert all(
                    map(lambda x: x == context_dim[0], context_dim)
                ), 'need homogenous context_dim to match depth automatically'
                context_dim = depth * [context_dim[0]]
        elif context_dim is None:
            context_dim = [None] * depth
        self.in_channels = in_channels
        inner_dim = n_heads * d_head
        self.norm = Normalize(in_channels)
        if not use_linear:
            self.proj_in = nn.Conv2d(in_channels,
                                     inner_dim,
                                     kernel_size=1,
                                     stride=1,
                                     padding=0)
        else:
            self.proj_in = nn.Linear(in_channels, inner_dim)

        self.transformer_blocks = nn.ModuleList([
            BasicTransformerBlock(
                inner_dim,
                n_heads,
                d_head,
                dropout=dropout,
                context_dim=context_dim[d],
                disable_self_attn=disable_self_attn,
                attn_mode=attn_type,
                checkpoint=use_checkpoint,
                sdp_backend=sdp_backend,
            ) for d in range(depth)
        ])
        if not use_linear:
            self.proj_out = zero_module(
                nn.Conv2d(inner_dim,
                          in_channels,
                          kernel_size=1,
                          stride=1,
                          padding=0))
        else:
            # self.proj_out = zero_module(nn.Linear(in_channels, inner_dim))
            self.proj_out = zero_module(nn.Linear(inner_dim, in_channels))
        self.use_linear = use_linear

    def forward(self, x, context=None):
        # note: if no context is given, cross-attention defaults to self-attention
        if not isinstance(context, list):
            context = [context]
        b, c, h, w = x.shape
        x_in = x
        x = self.norm(x)
        if not self.use_linear:
            x = self.proj_in(x)
        x = rearrange(x, 'b c h w -> b (h w) c').contiguous()
        if self.use_linear:
            x = self.proj_in(x)
        for i, block in enumerate(self.transformer_blocks):
            if i > 0 and len(context) == 1:
                i = 0  # use same context for each block
            x = block(x, context=context[i])
        if self.use_linear:
            x = self.proj_out(x)
        x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w).contiguous()
        if not self.use_linear:
            x = self.proj_out(x)
        return x + x_in
