import math
from typing import Optional

import torch
import torch.nn.functional as F
from torch import nn

try:
    import xformers.ops
    MEM_EFFICIENT_ATTN = True
except ImportError:
    MEM_EFFICIENT_ATTN = False

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.
    Uses three q, k, v linear layers to compute attention.

    Parameters:
        channels (:obj:`int`): The number of channels in the input and output.
        num_head_channels (:obj:`int`, *optional*):
            The number of channels in each head. If None, then `num_heads` = 1.
        num_groups (:obj:`int`, *optional*, defaults to 32): The number of groups to use for group norm.
        rescale_output_factor (:obj:`float`, *optional*, defaults to 1.0): The factor to rescale the output by.
        eps (:obj:`float`, *optional*, defaults to 1e-5): The epsilon value to use for group norm.
    """

    def __init__(
        self,
        channels: int,
        num_head_channels: Optional[int] = None,
        num_groups: int = 32,
        rescale_output_factor: float = 1.0,
        eps: float = 1e-5,
    ):
        super().__init__()
        self.channels = channels

        self.num_heads = channels // num_head_channels if num_head_channels is not None else 1
        self.num_head_size = num_head_channels
        self.group_norm = nn.GroupNorm(num_channels=channels, num_groups=num_groups, eps=eps, affine=True)

        # define q,k,v as linear layers
        self.query = nn.Linear(channels, channels)
        self.key = nn.Linear(channels, channels)
        self.value = nn.Linear(channels, channels)

        self.rescale_output_factor = rescale_output_factor
        self.proj_attn = nn.Linear(channels, channels, 1)

    def transpose_for_scores(self, projection: torch.Tensor) -> torch.Tensor:
        new_projection_shape = projection.size()[:-1] + (self.num_heads, -1)
        # move heads to 2nd position (B, T, H * D) -> (B, T, H, D) -> (B, H, T, D)
        new_projection = projection.view(new_projection_shape).permute(0, 2, 1, 3)
        return new_projection

    def forward(self, hidden_states):
        residual = hidden_states
        batch, channel, height, width = hidden_states.shape

        # norm
        hidden_states = self.group_norm(hidden_states)

        hidden_states = hidden_states.view(batch, channel, height * width).transpose(1, 2)

        # proj to q, k, v
        query_proj = self.query(hidden_states)
        key_proj = self.key(hidden_states)
        value_proj = self.value(hidden_states)

        # transpose
        query_states = self.transpose_for_scores(query_proj)
        key_states = self.transpose_for_scores(key_proj)
        value_states = self.transpose_for_scores(value_proj)

        # get scores
        scale = 1 / math.sqrt(math.sqrt(self.channels / self.num_heads))
        attention_scores = torch.matmul(query_states * scale, key_states.transpose(-1, -2) * scale)  # TODO: use baddmm
        attention_probs = torch.softmax(attention_scores.float(), dim=-1).type(attention_scores.dtype)

        # compute attention output
        hidden_states = torch.matmul(attention_probs, value_states)

        hidden_states = hidden_states.permute(0, 2, 1, 3).contiguous()
        new_hidden_states_shape = hidden_states.size()[:-2] + (self.channels,)
        hidden_states = hidden_states.view(new_hidden_states_shape)

        # compute next hidden_states
        hidden_states = self.proj_attn(hidden_states)
        hidden_states = hidden_states.transpose(-1, -2).reshape(batch, channel, height, width)

        # res connect and rescale
        hidden_states = (hidden_states + residual) / self.rescale_output_factor
        return hidden_states


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.

    Parameters:
        in_channels (:obj:`int`): The number of channels in the input and output.
        n_heads (:obj:`int`): The number of heads to use for multi-head attention.
        d_head (:obj:`int`): The number of channels in each head.
        depth (:obj:`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use.
        dropout (:obj:`float`, *optional*, defaults to 0.1): The dropout probability to use.
        context_dim (:obj:`int`, *optional*): The number of context dimensions to use.
    """

    def __init__(
        self,
        in_channels: int,
        n_heads: int,
        d_head: int,
        depth: int = 1,
        dropout: float = 0.0,
        num_groups: int = 32,
        context_dim: Optional[int] = None,
    ):
        super().__init__()
        self.n_heads = n_heads
        self.d_head = d_head
        self.in_channels = in_channels
        inner_dim = n_heads * d_head
        self.norm = torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True)

        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 = nn.Conv2d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0)

    def _set_attention_slice(self, slice_size):
        for block in self.transformer_blocks:
            block._set_attention_slice(slice_size)

    def forward(self, hidden_states, context=None):
        # note: if no context is given, cross-attention defaults to self-attention
        batch, channel, height, weight = hidden_states.shape
        residual = hidden_states
        hidden_states = self.norm(hidden_states)
        hidden_states = self.proj_in(hidden_states)
        inner_dim = hidden_states.shape[1]
        hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * weight, inner_dim)
        for block in self.transformer_blocks:
            hidden_states = block(hidden_states, context=context)
        hidden_states = hidden_states.reshape(batch, height, weight, inner_dim).permute(0, 3, 1, 2)
        hidden_states = self.proj_out(hidden_states)
        return hidden_states + residual


class BasicTransformerBlock(nn.Module):
    r"""
    A basic Transformer block.

    Parameters:
        dim (:obj:`int`): The number of channels in the input and output.
        n_heads (:obj:`int`): The number of heads to use for multi-head attention.
        d_head (:obj:`int`): The number of channels in each head.
        dropout (:obj:`float`, *optional*, defaults to 0.0): The dropout probability to use.
        context_dim (:obj:`int`, *optional*): The size of the context vector for cross attention.
        gated_ff (:obj:`bool`, *optional*, defaults to :obj:`False`): Whether to use a gated feed-forward network.
        checkpoint (:obj:`bool`, *optional*, defaults to :obj:`False`): Whether to use checkpointing.
    """

    def __init__(
        self,
        dim: int,
        n_heads: int,
        d_head: int,
        dropout=0.0,
        context_dim: Optional[int] = None,
        gated_ff: bool = True,
        checkpoint: bool = 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 _set_attention_slice(self, slice_size):
        self.attn1._slice_size = slice_size
        self.attn2._slice_size = slice_size

    def forward(self, hidden_states, context=None):
        hidden_states = hidden_states.contiguous() if hidden_states.device.type == "mps" else hidden_states
        hidden_states = self.attn1(self.norm1(hidden_states)) + hidden_states
        hidden_states = self.attn2(self.norm2(hidden_states), context=context) + hidden_states
        hidden_states = self.ff(self.norm3(hidden_states)) + hidden_states
        return hidden_states




# $$$ FlashAttentionを使うCrossAttention
# based on https://github.com/lucidrains/memory-efficient-attention-pytorch/blob/main/memory_efficient_attention_pytorch/flash_attention.py

# constants

EPSILON = 1e-6

# helper functions

def exists(val):
    return val is not None

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

# flash attention forwards and backwards

# https://arxiv.org/abs/2205.14135

from torch.autograd.function import Function
class FlashAttentionFunction(Function):
    @staticmethod
    @torch.no_grad()
    def forward(ctx, q, k, v, mask, causal, q_bucket_size, k_bucket_size):
        """ Algorithm 2 in the paper """

        device = q.device
        dtype = q.dtype
        max_neg_value = -torch.finfo(q.dtype).max
        qk_len_diff = max(k.shape[-2] - q.shape[-2], 0)

        o = torch.zeros_like(q)
        all_row_sums = torch.zeros((*q.shape[:-1], 1), dtype=dtype, device = device)
        all_row_maxes = torch.full((*q.shape[:-1], 1), max_neg_value, dtype=dtype, device = device)

        scale = (q.shape[-1] ** -0.5)

        if not exists(mask):
            mask = (None,) * math.ceil(q.shape[-2] / q_bucket_size)
        else:
            mask = rearrange(mask, 'b n -> b 1 1 n')
            mask = mask.split(q_bucket_size, dim = -1)

        row_splits = zip(
            q.split(q_bucket_size, dim = -2),
            o.split(q_bucket_size, dim = -2),
            mask,
            all_row_sums.split(q_bucket_size, dim = -2),
            all_row_maxes.split(q_bucket_size, dim = -2),
        )

        for ind, (qc, oc, row_mask, row_sums, row_maxes) in enumerate(row_splits):
            q_start_index = ind * q_bucket_size - qk_len_diff

            col_splits = zip(
                k.split(k_bucket_size, dim = -2),
                v.split(k_bucket_size, dim = -2),
            )

            for k_ind, (kc, vc) in enumerate(col_splits):
                k_start_index = k_ind * k_bucket_size

                attn_weights = einsum('... i d, ... j d -> ... i j', qc, kc) * scale

                if exists(row_mask):
                    attn_weights.masked_fill_(~row_mask, max_neg_value)

                if causal and q_start_index < (k_start_index + k_bucket_size - 1):
                    causal_mask = torch.ones((qc.shape[-2], kc.shape[-2]), dtype = torch.bool, device = device).triu(q_start_index - k_start_index + 1)
                    attn_weights.masked_fill_(causal_mask, max_neg_value)

                block_row_maxes = attn_weights.amax(dim = -1, keepdims = True)
                attn_weights -= block_row_maxes
                exp_weights = torch.exp(attn_weights)

                if exists(row_mask):
                    exp_weights.masked_fill_(~row_mask, 0.)

                block_row_sums = exp_weights.sum(dim = -1, keepdims = True).clamp(min = EPSILON)

                new_row_maxes = torch.maximum(block_row_maxes, row_maxes)

                exp_values = einsum('... i j, ... j d -> ... i d', exp_weights, vc)

                exp_row_max_diff = torch.exp(row_maxes - new_row_maxes)
                exp_block_row_max_diff = torch.exp(block_row_maxes - new_row_maxes)

                new_row_sums = exp_row_max_diff * row_sums + exp_block_row_max_diff * block_row_sums

                oc.mul_((row_sums / new_row_sums) * exp_row_max_diff).add_((exp_block_row_max_diff / new_row_sums) * exp_values)

                row_maxes.copy_(new_row_maxes)
                row_sums.copy_(new_row_sums)

        ctx.args = (causal, scale, mask, q_bucket_size, k_bucket_size)
        ctx.save_for_backward(q, k, v, o, all_row_sums, all_row_maxes)

        return o

    @staticmethod
    @torch.no_grad()
    def backward(ctx, do):
        """ Algorithm 4 in the paper """

        causal, scale, mask, q_bucket_size, k_bucket_size = ctx.args
        q, k, v, o, l, m = ctx.saved_tensors

        device = q.device

        max_neg_value = -torch.finfo(q.dtype).max
        qk_len_diff = max(k.shape[-2] - q.shape[-2], 0)

        dq = torch.zeros_like(q)
        dk = torch.zeros_like(k)
        dv = torch.zeros_like(v)

        row_splits = zip(
            q.split(q_bucket_size, dim = -2),
            o.split(q_bucket_size, dim = -2),
            do.split(q_bucket_size, dim = -2),
            mask,
            l.split(q_bucket_size, dim = -2),
            m.split(q_bucket_size, dim = -2),
            dq.split(q_bucket_size, dim = -2)
        )

        for ind, (qc, oc, doc, row_mask, lc, mc, dqc) in enumerate(row_splits):
            q_start_index = ind * q_bucket_size - qk_len_diff

            col_splits = zip(
                k.split(k_bucket_size, dim = -2),
                v.split(k_bucket_size, dim = -2),
                dk.split(k_bucket_size, dim = -2),
                dv.split(k_bucket_size, dim = -2),
            )

            for k_ind, (kc, vc, dkc, dvc) in enumerate(col_splits):
                k_start_index = k_ind * k_bucket_size

                attn_weights = einsum('... i d, ... j d -> ... i j', qc, kc) * scale

                if causal and q_start_index < (k_start_index + k_bucket_size - 1):
                    causal_mask = torch.ones((qc.shape[-2], kc.shape[-2]), dtype = torch.bool, device = device).triu(q_start_index - k_start_index + 1)
                    attn_weights.masked_fill_(causal_mask, max_neg_value)

                exp_attn_weights = torch.exp(attn_weights - mc)

                if exists(row_mask):
                    exp_attn_weights.masked_fill_(~row_mask, 0.)

                p = exp_attn_weights / lc

                dv_chunk = einsum('... i j, ... i d -> ... j d', p, doc)
                dp = einsum('... i d, ... j d -> ... i j', doc, vc)

                D = (doc * oc).sum(dim = -1, keepdims = True)
                ds = p * scale * (dp - D)

                dq_chunk = einsum('... i j, ... j d -> ... i d', ds, kc)
                dk_chunk = einsum('... i j, ... i d -> ... j d', ds, qc)

                dqc.add_(dq_chunk)
                dkc.add_(dk_chunk)
                dvc.add_(dv_chunk)

        return dq, dk, dv, None, None, None, None


# based on https://github.com/gammagec/Dreambooth-SD-optimized/blob/main/ldm/modules/attention.py
class CrossAttention(nn.Module):
  def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.):
    super().__init__()
    # print("$$$ CrossAttention with FlashAttention")
    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.flash_func = FlashAttentionFunction
    self.q_bucket_size = 512 # 256 #  # 128                 # 小さいほうが遅くなるが省メモリのはず
    self.k_bucket_size = 1024 # 512 # # 256

  def forward(self, x, context=None, mask=None):
    # with autocast('cuda'):
    h = self.heads

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

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

    out = self.flash_func.apply(q, k, v, mask, False, self.q_bucket_size, self.k_bucket_size)

    out = rearrange(out, 'b h n d -> b n (h d)')

    return self.to_out(out)


# $$$ 省メモリなCrossAttention
# copy from https://github.com/gammagec/Dreambooth-SD-optimized 一部変更
from torch import nn, einsum, autocast
from einops import rearrange, repeat

def exists(val):
    return val is not None

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

class CrossAttentionDBSDOPT(nn.Module):
    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.):
        # print("$$$ memory efficient CrossAttention")
        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):
        #with autocast('cuda'):
        h = self.heads

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

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

        r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], dtype=q.dtype, device=q.device)

        # valid values for steps = 2,4,8,16,32,64
        # higher steps is slower but less memory usage
        # at 16 can run 1920x1536 on a 3090, at 64 can run over 1920x1920
        # speed seems to be impacted more on 30x series cards
        steps = 16
        slice_size = q.shape[1] // steps if q.shape[1] % steps == 0 else q.shape[1]
        for i in range(0, q.shape[1], slice_size):
            end = i + slice_size
            s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k)
            s1 *= self.scale
            s2 = s1.softmax(dim=-1)
            del s1
            r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v)
            del s2
        r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)
        del r1

        return self.to_out(r2)


class CrossAttentionOrg(nn.Module):
    r"""
    A cross attention layer.

    Parameters:
        query_dim (:obj:`int`): The number of channels in the query.
        context_dim (:obj:`int`, *optional*):
            The number of channels in the context. If not given, defaults to `query_dim`.
        heads (:obj:`int`,  *optional*, defaults to 8): The number of heads to use for multi-head attention.
        dim_head (:obj:`int`,  *optional*, defaults to 64): The number of channels in each head.
        dropout (:obj:`float`, *optional*, defaults to 0.0): The dropout probability to use.
    """

    def __init__(
        self, query_dim: int, context_dim: Optional[int] = None, heads: int = 8, dim_head: int = 64, dropout: int = 0.0
    ):
        super().__init__()
        inner_dim = dim_head * heads
        context_dim = context_dim if context_dim is not None else query_dim

        self.scale = dim_head**-0.5
        self.heads = heads
        self.dim_head = dim_head
        # for slice_size > 0 the attention score computation
        # is split across the batch axis to save memory
        # You can set slice_size with `set_attention_slice`
        self._slice_size = None

        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 reshape_heads_to_batch_dim(self, tensor):
        batch_size, seq_len, dim = tensor.shape
        head_size = self.heads
        tensor = tensor.reshape(batch_size, seq_len, head_size, dim // head_size)
        tensor = tensor.permute(0, 2, 1, 3).reshape(batch_size * head_size, seq_len, dim // head_size)
        return tensor

    def reshape_batch_dim_to_heads(self, tensor):
        batch_size, seq_len, dim = tensor.shape
        head_size = self.heads
        tensor = tensor.reshape(batch_size // head_size, head_size, seq_len, dim)
        tensor = tensor.permute(0, 2, 1, 3).reshape(batch_size // head_size, seq_len, dim * head_size)
        return tensor

    def forward(self, hidden_states, context=None, mask=None):
        batch_size, sequence_length, _ = hidden_states.shape
        query = self.to_q(hidden_states)
        context = context if context is not None else hidden_states
        key = self.to_k(context)
        value = self.to_v(context)

        dim = query.shape[-1]

        query = self.reshape_heads_to_batch_dim(query)
        key = self.reshape_heads_to_batch_dim(key)
        value = self.reshape_heads_to_batch_dim(value)

        # TODO(PVP) - mask is currently never used. Remember to re-implement when used
        # attention, what we cannot get enough of
        if MEM_EFFICIENT_ATTN:
            query = query.contiguous()
            key = key.contiguous()
            value = value.contiguous()
            hidden_states = xformers.ops.memory_efficient_attention(query, key, value)
        elif self._slice_size is None or query.shape[0] // self._slice_size == 1:
            hidden_states = self._attention(query, key, value)
        else:
            hidden_states = self._sliced_attention(query, key, value, sequence_length, dim)
        hidden_states = self.reshape_batch_dim_to_heads(hidden_states)
        return self.to_out(hidden_states)

    def _attention(self, query, key, value):
        # TODO: use baddbmm for better performance
        attention_scores = torch.matmul(query, key.transpose(-1, -2)) * self.scale
        attention_probs = attention_scores.softmax(dim=-1)
        # compute attention output
        hidden_states = torch.matmul(attention_probs, value)
        return hidden_states

    def _sliced_attention(self, query, key, value, sequence_length, dim):
        batch_size_attention = query.shape[0]
        hidden_states = torch.zeros(
            (batch_size_attention, sequence_length, dim // self.heads), device=query.device, dtype=query.dtype
        )
        slice_size = self._slice_size if self._slice_size is not None else hidden_states.shape[0]
        for i in range(hidden_states.shape[0] // slice_size):
            start_idx = i * slice_size
            end_idx = (i + 1) * slice_size
            attn_slice = (
                torch.matmul(query[start_idx:end_idx], key[start_idx:end_idx].transpose(1, 2)) * self.scale
            )  # TODO: use baddbmm for better performance
            attn_slice = attn_slice.softmax(dim=-1)
            attn_slice = torch.matmul(attn_slice, value[start_idx:end_idx])

            hidden_states[start_idx:end_idx] = attn_slice

        return hidden_states


class FeedForward(nn.Module):
    r"""
    A feed-forward layer.

    Parameters:
        dim (:obj:`int`): The number of channels in the input.
        dim_out (:obj:`int`, *optional*): The number of channels in the output. If not given, defaults to `dim`.
        mult (:obj:`int`, *optional*, defaults to 4): The multiplier to use for the hidden dimension.
        glu (:obj:`bool`, *optional*, defaults to :obj:`False`): Whether to use GLU activation.
        dropout (:obj:`float`, *optional*, defaults to 0.0): The dropout probability to use.
    """

    def __init__(
        self, dim: int, dim_out: Optional[int] = None, mult: int = 4, glu: bool = False, dropout: float = 0.0
    ):
        super().__init__()
        inner_dim = int(dim * mult)
        dim_out = dim_out if dim_out is not None else dim
        project_in = GEGLU(dim, inner_dim)

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

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


# feedforward
class GEGLU(nn.Module):
    r"""
    A variant of the gated linear unit activation function from https://arxiv.org/abs/2002.05202.

    Parameters:
        dim_in (:obj:`int`): The number of channels in the input.
        dim_out (:obj:`int`): The number of channels in the output.
    """

    def __init__(self, dim_in: int, dim_out: int):
        super().__init__()
        self.proj = nn.Linear(dim_in, dim_out * 2)

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