# -*- coding: utf-8 -*-
#
# This file is part of UniRig.
# 
# This file is derived from https://github.com/NeuralCarver/Michelangelo
#
# Copyright (c) https://github.com/NeuralCarver/Michelangelo original authors
# Copyright (c) 2025 VAST-AI-Research and contributors.
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program.  If not, see <https://www.gnu.org/licenses/>.

import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional
import os

from .checkpoint import checkpoint

def init_linear(l, stddev):
    nn.init.normal_(l.weight, std=stddev)
    if l.bias is not None:
        nn.init.constant_(l.bias, 0.0)

def flash_attention(q, k, v):
    with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=True, enable_mem_efficient=False):
        q = q.transpose(1, 2)
        k = k.transpose(1, 2)
        v = v.transpose(1, 2)
        out = F.scaled_dot_product_attention(q, k, v)
        out = out.transpose(1, 2)
        # print("use flash atten 2")   

    return out

class MultiheadAttention(nn.Module):
    def __init__(
        self,
        *,
        device: torch.device,
        dtype: torch.dtype,
        n_ctx: int,
        width: int,
        heads: int,
        init_scale: float,
        qkv_bias: bool,
        flash: bool = False
    ):
        super().__init__()
        self.n_ctx = n_ctx
        self.width = width
        self.heads = heads
        self.c_qkv = nn.Linear(width, width * 3, bias=qkv_bias, device=device, dtype=dtype)
        self.c_proj = nn.Linear(width, width, device=device, dtype=dtype)
        self.attention = QKVMultiheadAttention(device=device, dtype=dtype, heads=heads, n_ctx=n_ctx, flash=flash)
        init_linear(self.c_qkv, init_scale)
        init_linear(self.c_proj, init_scale)

    def forward(self, x):
        x = self.c_qkv(x)
        x = checkpoint(self.attention, (x,), (), False)
        x = self.c_proj(x)
        return x


class QKVMultiheadAttention(nn.Module):
    def __init__(self, *, device: torch.device, dtype: torch.dtype, heads: int, n_ctx: int, flash: bool = False):
        super().__init__()
        self.device = device
        self.dtype = dtype
        self.heads = heads
        self.n_ctx = n_ctx
        self.flash = flash

    def forward(self, qkv):
        bs, n_ctx, width = qkv.shape
        attn_ch = width // self.heads // 3
        scale = 1 / math.sqrt(math.sqrt(attn_ch))
        qkv = qkv.view(bs, n_ctx, self.heads, -1)
        q, k, v = torch.split(qkv, attn_ch, dim=-1)

        if self.flash:
            out = flash_attention(q, k, v)
            out = out.reshape(out.shape[0], out.shape[1], -1)
        else:
            weight = torch.einsum(
                "bthc,bshc->bhts", q * scale, k * scale
            )  # More stable with f16 than dividing afterwards
            wdtype = weight.dtype
            weight = torch.softmax(weight.float(), dim=-1).type(wdtype)
            out = torch.einsum("bhts,bshc->bthc", weight, v).reshape(bs, n_ctx, -1)

        return out


class ResidualAttentionBlock(nn.Module):
    def __init__(
        self,
        *,
        device: torch.device,
        dtype: torch.dtype,
        n_ctx: int,
        width: int,
        heads: int,
        init_scale: float = 1.0,
        qkv_bias: bool = True,
        flash: bool = False,
        use_checkpoint: bool = False
    ):
        super().__init__()

        self.use_checkpoint = use_checkpoint

        self.attn = MultiheadAttention(
            device=device,
            dtype=dtype,
            n_ctx=n_ctx,
            width=width,
            heads=heads,
            init_scale=init_scale,
            qkv_bias=qkv_bias,
            flash=flash
        )
        self.ln_1 = nn.LayerNorm(width, device=device, dtype=dtype)
        self.mlp = MLP(device=device, dtype=dtype, width=width, init_scale=init_scale)
        self.ln_2 = nn.LayerNorm(width, device=device, dtype=dtype)

    def _forward(self, x: torch.Tensor):
        x = x + self.attn(self.ln_1(x))
        x = x + self.mlp(self.ln_2(x))
        return x

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


class MultiheadCrossAttention(nn.Module):
    def __init__(
        self,
        *,
        device: torch.device,
        dtype: torch.dtype,
        width: int,
        heads: int,
        init_scale: float,
        qkv_bias: bool = True,
        flash: bool = False,
        n_data: Optional[int] = None,
        data_width: Optional[int] = None,
    ):
        super().__init__()
        self.n_data = n_data
        self.width = width
        self.heads = heads
        self.data_width = width if data_width is None else data_width
        self.c_q = nn.Linear(width, width, bias=qkv_bias, device=device, dtype=dtype)
        self.c_kv = nn.Linear(self.data_width, width * 2, bias=qkv_bias, device=device, dtype=dtype)
        self.c_proj = nn.Linear(width, width, device=device, dtype=dtype)
        self.attention = QKVMultiheadCrossAttention(
            device=device, dtype=dtype, heads=heads, n_data=n_data, flash=flash
        )
        init_linear(self.c_q, init_scale)
        init_linear(self.c_kv, init_scale)
        init_linear(self.c_proj, init_scale)

    def forward(self, x, data):
        x = self.c_q(x)
        data = self.c_kv(data)
        x = checkpoint(self.attention, (x, data), (), False)
        x = self.c_proj(x)
        return x


class QKVMultiheadCrossAttention(nn.Module):
    def __init__(self, *, device: torch.device, dtype: torch.dtype, heads: int,
                 flash: bool = False, n_data: Optional[int] = None):

        super().__init__()
        self.device = device
        self.dtype = dtype
        self.heads = heads
        self.n_data = n_data
        self.flash = flash

    def forward(self, q, kv):
        _, n_ctx, _ = q.shape
        bs, n_data, width = kv.shape
        attn_ch = width // self.heads // 2
        scale = 1 / math.sqrt(math.sqrt(attn_ch))
        q = q.view(bs, n_ctx, self.heads, -1)
        kv = kv.view(bs, n_data, self.heads, -1)
        k, v = torch.split(kv, attn_ch, dim=-1)

        if self.flash:
            out = flash_attention(q, k, v)
            out = out.reshape(out.shape[0], out.shape[1], -1)
        else:
            weight = torch.einsum(
                "bthc,bshc->bhts", q * scale, k * scale
            )  # More stable with f16 than dividing afterwards
            wdtype = weight.dtype
            weight = torch.softmax(weight.float(), dim=-1).type(wdtype)
            out = torch.einsum("bhts,bshc->bthc", weight, v).reshape(bs, n_ctx, -1)

        return out


class ResidualCrossAttentionBlock(nn.Module):
    def __init__(
        self,
        *,
        device: Optional[torch.device],
        dtype: Optional[torch.dtype],
        n_data: Optional[int] = None,
        width: int,
        heads: int,
        data_width: Optional[int] = None,
        mlp_width_scale: int = 4,
        init_scale: float = 0.25,
        qkv_bias: bool = True,
        flash: bool = False
    ):
        super().__init__()

        if data_width is None:
            data_width = width

        self.attn = MultiheadCrossAttention(
            device=device,
            dtype=dtype,
            n_data=n_data,
            width=width,
            heads=heads,
            data_width=data_width,
            init_scale=init_scale,
            qkv_bias=qkv_bias,
            flash=flash,
        )
        self.ln_1 = nn.LayerNorm(width, device=device, dtype=dtype)
        self.ln_2 = nn.LayerNorm(data_width, device=device, dtype=dtype)
        self.mlp = MLP(device=device, dtype=dtype, width=width, hidden_width_scale=mlp_width_scale, init_scale=init_scale)
        self.ln_3 = nn.LayerNorm(width, device=device, dtype=dtype)

    def forward(self, x: torch.Tensor, data: torch.Tensor):
        x = x + self.attn(self.ln_1(x), self.ln_2(data))
        x = x + self.mlp(self.ln_3(x))
        return x


class MLP(nn.Module):
    def __init__(self, *,
                 device: Optional[torch.device],
                 dtype: Optional[torch.dtype],
                 width: int,
                 hidden_width_scale: int = 4,
                 init_scale: float):
        super().__init__()
        self.width = width
        self.c_fc = nn.Linear(width, width * hidden_width_scale, device=device, dtype=dtype)
        self.c_proj = nn.Linear(width * hidden_width_scale, width, device=device, dtype=dtype)
        self.gelu = nn.GELU()
        init_linear(self.c_fc, init_scale)
        init_linear(self.c_proj, init_scale)

    def forward(self, x):
        return self.c_proj(self.gelu(self.c_fc(x)))


class Transformer(nn.Module):
    def __init__(
        self,
        *,
        device: Optional[torch.device],
        dtype: Optional[torch.dtype],
        n_ctx: int,
        width: int,
        layers: int,
        heads: int,
        init_scale: float = 0.25,
        qkv_bias: bool = True,
        flash: bool = False,
        use_checkpoint: bool = False
    ):
        super().__init__()
        self.n_ctx = n_ctx
        self.width = width
        self.layers = layers
        self.resblocks = nn.ModuleList(
            [
                ResidualAttentionBlock(
                    device=device,
                    dtype=dtype,
                    n_ctx=n_ctx,
                    width=width,
                    heads=heads,
                    init_scale=init_scale,
                    qkv_bias=qkv_bias,
                    flash=flash,
                    use_checkpoint=use_checkpoint
                )
                for _ in range(layers)
            ]
        )

    def forward(self, x: torch.Tensor):
        for block in self.resblocks:
            x = block(x)
        return x