import torch
import torch.nn as nn
import torch.nn.functional as F
from diffusers.models.normalization import RMSNorm
from typing import Optional


class FluxAttnProcessor(nn.Module):
    def __init__(self, hidden_size, cross_attention_dim=None, scale=1.0, num_tokens=4):
        super().__init__()

        self.hidden_size = hidden_size
        self.cross_attention_dim = cross_attention_dim
        self.scale = scale
        self.num_tokens = num_tokens

        self.to_k_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
        self.to_v_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)

        self.norm_added_k = RMSNorm(128, eps=1e-5, elementwise_affine=False)

    def __call__(
            self,
            attn,
            hidden_states: torch.FloatTensor,
            image_emb: torch.FloatTensor,
            encoder_hidden_states: torch.FloatTensor = None,
            attention_mask: Optional[torch.FloatTensor] = None,
            image_rotary_emb: Optional[torch.Tensor] = None,
    ) -> torch.FloatTensor:
        batch_size, _, _ = hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape

        query = attn.to_q(hidden_states)
        key = attn.to_k(hidden_states)
        value = attn.to_v(hidden_states)

        inner_dim = key.shape[-1]
        head_dim = inner_dim // attn.heads

        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)

        if image_emb is not None:
            ip_hidden_states = image_emb
            ip_hidden_states_key_proj = self.to_k_ip(ip_hidden_states)
            ip_hidden_states_value_proj = self.to_v_ip(ip_hidden_states)

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

            ip_hidden_states_key_proj = self.norm_added_k(ip_hidden_states_key_proj)

            ip_hidden_states = F.scaled_dot_product_attention(query,
                                                              ip_hidden_states_key_proj,
                                                              ip_hidden_states_value_proj,
                                                              dropout_p=0.0, is_causal=False)

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

        if encoder_hidden_states is not None:
            encoder_hidden_states_query_proj = attn.add_q_proj(encoder_hidden_states)
            encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states)
            encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states)

            encoder_hidden_states_query_proj = encoder_hidden_states_query_proj.view(
                batch_size, -1, attn.heads, head_dim
            ).transpose(1, 2)
            encoder_hidden_states_key_proj = encoder_hidden_states_key_proj.view(
                batch_size, -1, attn.heads, head_dim
            ).transpose(1, 2)
            encoder_hidden_states_value_proj = encoder_hidden_states_value_proj.view(
                batch_size, -1, attn.heads, head_dim
            ).transpose(1, 2)

            if attn.norm_added_q is not None:
                encoder_hidden_states_query_proj = attn.norm_added_q(encoder_hidden_states_query_proj)
            if attn.norm_added_k is not None:
                encoder_hidden_states_key_proj = attn.norm_added_k(encoder_hidden_states_key_proj)

            query = torch.cat([encoder_hidden_states_query_proj, query], dim=2)
            key = torch.cat([encoder_hidden_states_key_proj, key], dim=2)
            value = torch.cat([encoder_hidden_states_value_proj, value], dim=2)

        if image_rotary_emb is not None:
            from diffusers.models.embeddings import apply_rotary_emb

            query = apply_rotary_emb(query, image_rotary_emb)
            key = apply_rotary_emb(key, image_rotary_emb)

        hidden_states = F.scaled_dot_product_attention(query, key, value, 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)

        if encoder_hidden_states is not None:

            encoder_hidden_states, hidden_states = (
                hidden_states[:, : encoder_hidden_states.shape[1]],
                hidden_states[:, encoder_hidden_states.shape[1]:],
            )
            if image_emb is not None:
                hidden_states = hidden_states + self.scale * ip_hidden_states

            hidden_states = attn.to_out[0](hidden_states)
            hidden_states = attn.to_out[1](hidden_states)
            encoder_hidden_states = attn.to_add_out(encoder_hidden_states)

            return hidden_states, encoder_hidden_states
        else:
            if image_emb is not None:
                hidden_states = hidden_states + self.scale * ip_hidden_states

            return hidden_states