from typing import Callable, List, Optional, Tuple, Union

import torch
import torch.nn.functional as F
from diffusers.models.attention_processor import Attention
from diffusers.models.embeddings import apply_rotary_emb
from diffusers.utils import logging
from einops import rearrange


logger = logging.get_logger(__name__)  # pylint: disable=invalid-name


class FlashTripoSGAttnProcessor2_0:
    r"""
    Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). This is
    used in the Tripo2DiT model. It applies a s normalization layer and rotary embedding on query and key vector.
    """

    def __init__(self, topk=True):
        if not hasattr(F, "scaled_dot_product_attention"):
            raise ImportError(
                "AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0."
            )
        self.topk = topk

    def qkv(self, attn, q, k, v, attn_mask, dropout_p, is_causal):
        if k.shape[-2] == 3072:
            topk = 1024
        elif k.shape[-2] == 512:
            topk = 256
        else:
            topk = k.shape[-2] // 3

        if self.topk is True:
            q1 = q[:, :, ::100, :]
            sim = q1 @ k.transpose(-1, -2)
            sim = torch.mean(sim, -2)
            topk_ind = torch.topk(sim, dim=-1, k=topk).indices.squeeze(-2).unsqueeze(-1)
            topk_ind = topk_ind.expand(-1, -1, -1, v.shape[-1])
            v0 = torch.gather(v, dim=-2, index=topk_ind)
            k0 = torch.gather(k, dim=-2, index=topk_ind)
            out = F.scaled_dot_product_attention(q, k0, v0)
        elif self.topk is False:
            out = F.scaled_dot_product_attention(q, k, v)
        else:
            idx, counts = self.topk
            start = 0
            outs = []
            for grid_coord, count in zip(idx, counts):
                end = start + count
                q_chunk = q[:, :, start:end, :]
                q1 = q_chunk[:, :, ::50, :]
                sim = q1 @ k.transpose(-1, -2)
                sim = torch.mean(sim, -2)
                topk_ind = torch.topk(sim, dim=-1, k=topk).indices.squeeze(-2).unsqueeze(-1)
                topk_ind = topk_ind.expand(-1, -1, -1, v.shape[-1])
                v0 = torch.gather(v, dim=-2, index=topk_ind)
                k0 = torch.gather(k, dim=-2, index=topk_ind)
                out = F.scaled_dot_product_attention(q_chunk, k0, v0)
                outs.append(out)
                start += count
            out = torch.cat(outs, dim=-2)
        self.topk = False
        return out

    def __call__(
        self,
        attn: Attention,
        hidden_states: torch.Tensor,
        encoder_hidden_states: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        temb: Optional[torch.Tensor] = None,
        image_rotary_emb: Optional[torch.Tensor] = None,
        **kwargs,
    ) -> torch.Tensor:
        residual = hidden_states
        if attn.spatial_norm is not None:
            hidden_states = attn.spatial_norm(hidden_states, temb)

        input_ndim = hidden_states.ndim

        if input_ndim == 4:
            batch_size, channel, height, width = hidden_states.shape
            hidden_states = hidden_states.view(
                batch_size, channel, height * width
            ).transpose(1, 2)

        batch_size, sequence_length, _ = (
            hidden_states.shape
            if encoder_hidden_states is None
            else encoder_hidden_states.shape
        )

        if attention_mask is not None:
            attention_mask = attn.prepare_attention_mask(
                attention_mask, sequence_length, batch_size
            )
            # scaled_dot_product_attention expects attention_mask shape to be
            # (batch, heads, source_length, target_length)
            attention_mask = attention_mask.view(
                batch_size, attn.heads, -1, attention_mask.shape[-1]
            )

        if attn.group_norm is not None:
            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(
                1, 2
            )

        query = attn.to_q(hidden_states)

        if encoder_hidden_states is None:
            encoder_hidden_states = hidden_states
        elif attn.norm_cross:
            encoder_hidden_states = attn.norm_encoder_hidden_states(
                encoder_hidden_states
            )

        key = attn.to_k(encoder_hidden_states)
        value = attn.to_v(encoder_hidden_states)

        # NOTE that tripo2 split heads first then split qkv or kv, like .view(..., attn.heads, 3, dim)
        # instead of .view(..., 3, attn.heads, dim). So we need to re-split here.
        if not attn.is_cross_attention:
            qkv = torch.cat((query, key, value), dim=-1)
            split_size = qkv.shape[-1] // attn.heads // 3
            qkv = qkv.view(batch_size, -1, attn.heads, split_size * 3)
            query, key, value = torch.split(qkv, split_size, dim=-1)
        else:
            kv = torch.cat((key, value), dim=-1)
            split_size = kv.shape[-1] // attn.heads // 2
            kv = kv.view(batch_size, -1, attn.heads, split_size * 2)
            key, value = torch.split(kv, split_size, dim=-1)

        head_dim = key.shape[-1]

        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)

        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)

        if attn.norm_q is not None:
            query = attn.norm_q(query)
        if attn.norm_k is not None:
            key = attn.norm_k(key)

        # Apply RoPE if needed
        if image_rotary_emb is not None:
            query = apply_rotary_emb(query, image_rotary_emb)
            if not attn.is_cross_attention:
                key = apply_rotary_emb(key, image_rotary_emb)

        # flashvdm topk
        hidden_states = self.qkv(attn, query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False)   

        hidden_states = hidden_states.transpose(1, 2).reshape(
            batch_size, -1, attn.heads * head_dim
        )
        hidden_states = hidden_states.to(query.dtype)

        # linear proj
        hidden_states = attn.to_out[0](hidden_states)
        # dropout
        hidden_states = attn.to_out[1](hidden_states)

        if input_ndim == 4:
            hidden_states = hidden_states.transpose(-1, -2).reshape(
                batch_size, channel, height, width
            )

        if attn.residual_connection:
            hidden_states = hidden_states + residual

        hidden_states = hidden_states / attn.rescale_output_factor

        return hidden_states

class TripoSGAttnProcessor2_0:
    r"""
    Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). This is
    used in the TripoSG model. It applies a s normalization layer and rotary embedding on query and key vector.
    """

    def __init__(self):
        if not hasattr(F, "scaled_dot_product_attention"):
            raise ImportError(
                "AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0."
            )

    def __call__(
        self,
        attn: Attention,
        hidden_states: torch.Tensor,
        encoder_hidden_states: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        temb: Optional[torch.Tensor] = None,
        image_rotary_emb: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        residual = hidden_states
        if attn.spatial_norm is not None:
            hidden_states = attn.spatial_norm(hidden_states, temb)

        input_ndim = hidden_states.ndim

        if input_ndim == 4:
            batch_size, channel, height, width = hidden_states.shape
            hidden_states = hidden_states.view(
                batch_size, channel, height * width
            ).transpose(1, 2)

        batch_size, sequence_length, _ = (
            hidden_states.shape
            if encoder_hidden_states is None
            else encoder_hidden_states.shape
        )

        if attention_mask is not None:
            attention_mask = attn.prepare_attention_mask(
                attention_mask, sequence_length, batch_size
            )
            # scaled_dot_product_attention expects attention_mask shape to be
            # (batch, heads, source_length, target_length)
            attention_mask = attention_mask.view(
                batch_size, attn.heads, -1, attention_mask.shape[-1]
            )

        if attn.group_norm is not None:
            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(
                1, 2
            )

        query = attn.to_q(hidden_states)

        if encoder_hidden_states is None:
            encoder_hidden_states = hidden_states
        elif attn.norm_cross:
            encoder_hidden_states = attn.norm_encoder_hidden_states(
                encoder_hidden_states
            )

        key = attn.to_k(encoder_hidden_states)
        value = attn.to_v(encoder_hidden_states)

        # NOTE that pre-trained models split heads first then split qkv or kv, like .view(..., attn.heads, 3, dim)
        # instead of .view(..., 3, attn.heads, dim). So we need to re-split here.
        if not attn.is_cross_attention:
            qkv = torch.cat((query, key, value), dim=-1)
            split_size = qkv.shape[-1] // attn.heads // 3
            qkv = qkv.view(batch_size, -1, attn.heads, split_size * 3)
            query, key, value = torch.split(qkv, split_size, dim=-1)
        else:
            kv = torch.cat((key, value), dim=-1)
            split_size = kv.shape[-1] // attn.heads // 2
            kv = kv.view(batch_size, -1, attn.heads, split_size * 2)
            key, value = torch.split(kv, split_size, dim=-1)

        head_dim = key.shape[-1]

        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)

        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)

        if attn.norm_q is not None:
            query = attn.norm_q(query)
        if attn.norm_k is not None:
            key = attn.norm_k(key)

        # Apply RoPE if needed
        if image_rotary_emb is not None:
            query = apply_rotary_emb(query, image_rotary_emb)
            if not attn.is_cross_attention:
                key = apply_rotary_emb(key, image_rotary_emb)

        # the output of sdp = (batch, num_heads, seq_len, head_dim)
        # TODO: add support for attn.scale when we move to Torch 2.1
        hidden_states = F.scaled_dot_product_attention(
            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
        )

        hidden_states = hidden_states.transpose(1, 2).reshape(
            batch_size, -1, attn.heads * head_dim
        )
        hidden_states = hidden_states.to(query.dtype)

        # linear proj
        hidden_states = attn.to_out[0](hidden_states)
        # dropout
        hidden_states = attn.to_out[1](hidden_states)

        if input_ndim == 4:
            hidden_states = hidden_states.transpose(-1, -2).reshape(
                batch_size, channel, height, width
            )

        if attn.residual_connection:
            hidden_states = hidden_states + residual

        hidden_states = hidden_states / attn.rescale_output_factor

        return hidden_states


class FusedTripoSGAttnProcessor2_0:
    r"""
    Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0) with fused
    projection layers. This is used in the HunyuanDiT model. It applies a s normalization layer and rotary embedding on
    query and key vector.
    """

    def __init__(self):
        if not hasattr(F, "scaled_dot_product_attention"):
            raise ImportError(
                "FusedTripoSGAttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0."
            )

    def __call__(
        self,
        attn: Attention,
        hidden_states: torch.Tensor,
        encoder_hidden_states: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        temb: Optional[torch.Tensor] = None,
        image_rotary_emb: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        residual = hidden_states
        if attn.spatial_norm is not None:
            hidden_states = attn.spatial_norm(hidden_states, temb)

        input_ndim = hidden_states.ndim

        if input_ndim == 4:
            batch_size, channel, height, width = hidden_states.shape
            hidden_states = hidden_states.view(
                batch_size, channel, height * width
            ).transpose(1, 2)

        batch_size, sequence_length, _ = (
            hidden_states.shape
            if encoder_hidden_states is None
            else encoder_hidden_states.shape
        )

        if attention_mask is not None:
            attention_mask = attn.prepare_attention_mask(
                attention_mask, sequence_length, batch_size
            )
            # scaled_dot_product_attention expects attention_mask shape to be
            # (batch, heads, source_length, target_length)
            attention_mask = attention_mask.view(
                batch_size, attn.heads, -1, attention_mask.shape[-1]
            )

        if attn.group_norm is not None:
            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(
                1, 2
            )

        # NOTE that pre-trained split heads first, then split qkv
        if encoder_hidden_states is None:
            qkv = attn.to_qkv(hidden_states)
            split_size = qkv.shape[-1] // attn.heads // 3
            qkv = qkv.view(batch_size, -1, attn.heads, split_size * 3)
            query, key, value = torch.split(qkv, split_size, dim=-1)
        else:
            if attn.norm_cross:
                encoder_hidden_states = attn.norm_encoder_hidden_states(
                    encoder_hidden_states
                )
            query = attn.to_q(hidden_states)

            kv = attn.to_kv(encoder_hidden_states)
            split_size = kv.shape[-1] // attn.heads // 2
            kv = kv.view(batch_size, -1, attn.heads, split_size * 2)
            key, value = torch.split(kv, split_size, dim=-1)

        head_dim = key.shape[-1]

        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)

        if attn.norm_q is not None:
            query = attn.norm_q(query)
        if attn.norm_k is not None:
            key = attn.norm_k(key)

        # Apply RoPE if needed
        if image_rotary_emb is not None:
            query = apply_rotary_emb(query, image_rotary_emb)
            if not attn.is_cross_attention:
                key = apply_rotary_emb(key, image_rotary_emb)

        # the output of sdp = (batch, num_heads, seq_len, head_dim)
        # TODO: add support for attn.scale when we move to Torch 2.1
        hidden_states = F.scaled_dot_product_attention(
            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
        )

        hidden_states = hidden_states.transpose(1, 2).reshape(
            batch_size, -1, attn.heads * head_dim
        )
        hidden_states = hidden_states.to(query.dtype)

        # linear proj
        hidden_states = attn.to_out[0](hidden_states)
        # dropout
        hidden_states = attn.to_out[1](hidden_states)

        if input_ndim == 4:
            hidden_states = hidden_states.transpose(-1, -2).reshape(
                batch_size, channel, height, width
            )

        if attn.residual_connection:
            hidden_states = hidden_states + residual

        hidden_states = hidden_states / attn.rescale_output_factor

        return hidden_states
