from dataclasses import dataclass

import torch
from torch import Tensor, nn
import numpy as np

from flux.modules.layers import (DoubleStreamBlock, EmbedND, LastLayer,
                                 MLPEmbedder, SingleStreamBlock,
                                 timestep_embedding)


@dataclass
class FluxParams:
    in_channels: int
    out_channels: int
    vec_in_dim: int
    context_in_dim: int
    hidden_size: int
    mlp_ratio: float
    num_heads: int
    depth: int
    depth_single_blocks: int
    axes_dim: list[int]
    theta: int
    qkv_bias: bool
    guidance_embed: bool


class Flux(nn.Module):
    """
    Transformer model for flow matching on sequences.
    """

    def __init__(self, params: FluxParams):
        super().__init__()

        self.params = params
        self.in_channels = params.in_channels
        self.out_channels = params.out_channels
        if params.hidden_size % params.num_heads != 0:
            raise ValueError(
                f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}"
            )
        pe_dim = params.hidden_size // params.num_heads
        if sum(params.axes_dim) != pe_dim:
            raise ValueError(f"Got {params.axes_dim} but expected positional dim {pe_dim}")
        self.hidden_size = params.hidden_size
        self.num_heads = params.num_heads
        self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim)
        self.img_in = nn.Linear(self.in_channels, self.hidden_size, bias=True)
        self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size)
        self.vector_in = MLPEmbedder(params.vec_in_dim, self.hidden_size)
        self.guidance_in = (
            MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size) if params.guidance_embed else nn.Identity()
        )
        self.txt_in = nn.Linear(params.context_in_dim, self.hidden_size)

        self.double_blocks = nn.ModuleList(
            [
                DoubleStreamBlock(
                    self.hidden_size,
                    self.num_heads,
                    mlp_ratio=params.mlp_ratio,
                    qkv_bias=params.qkv_bias,
                )
                for _ in range(params.depth)
            ]
        )

        self.single_blocks = nn.ModuleList(
            [
                SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio)
                for _ in range(params.depth_single_blocks)
            ]
        )

        self.final_layer = LastLayer(self.hidden_size, 1, self.out_channels)
        self._sequential_offload = False

    def enable_sequential_cpu_offload(self):
        self._sequential_offload = True

    def forward(
        self,
        img: Tensor,
        img_ids: Tensor,
        txt: Tensor,
        txt_ids: Tensor,
        timesteps: Tensor,
        y: Tensor,
        guidance: Tensor | None = None,
        info = None,
        ref_img: Tensor | None = None,        # ← NEW
        ref_img_ids: Tensor | None = None,    # ← NEW
    ) -> Tensor:
        if img.ndim != 3 or txt.ndim != 3:
            raise ValueError("Input img and txt tensors must have 3 dimensions.")

        # Ensure inputs match the model's dtype (NF4 can silently upcast to float32)
        target_dtype = self.img_in.weight.dtype
        img = img.to(target_dtype)
        txt = txt.to(target_dtype)
        if y.dtype != target_dtype:
            y = y.to(target_dtype)

        # running on sequences img
        img = self.img_in(img)
        original_img_seq_len = img.shape[1]   # ← REMEMBER: how many original img tokens

        vec = self.time_in(timestep_embedding(timesteps, 256))
        if self.params.guidance_embed:
            if guidance is None:
                raise ValueError("Didn't get guidance strength for guidance distilled model.")
            vec = vec + self.guidance_in(timestep_embedding(guidance, 256))
        vec = vec + self.vector_in(y)
        txt = self.txt_in(txt)

        # ═══════════════════════════════════════════════════════
        # NEW: Concatenate reference tokens into image stream
        # ═══════════════════════════════════════════════════════
        if ref_img is not None and ref_img_ids is not None:
            ref = self.img_in(ref_img)        # project reference patches same way
            img = torch.cat([img, ref], dim=1)
            img_ids = torch.cat([img_ids, ref_img_ids], dim=1)

        if ref_img is not None:
            print(f"[Flux.forward] Attending to {ref_img.shape[1]} ref tokens + {original_img_seq_len} img tokens")
        # ═══════════════════════════════════════════════════════

        ids = torch.cat((txt_ids, img_ids), dim=1)
        pe = self.pe_embedder(ids)
        inject_pe = pe.clone()
        if not info['inverse']:
            # Defensive clamp: GSAM indices may exceed seq_len for non-16-divisible images
            seq_len = pe.shape[2]

            # Initialize accumulated lists for tracking all processed IDs
            accumulated_target_ids = []
            accumulated_ref_ids = []
            for artifact_data in info['artifact_data']:
                if artifact_data['artifact_type'] == 'addition' and info['addition']:
                    ref_ids = artifact_data['reference_patch_indices'].copy()
                    target_ids = artifact_data['target_patch_indices'].copy()
                    ref_ids = [max(0, min(int(i), seq_len - 1)) for i in ref_ids]
                    target_ids = [max(0, min(int(i), seq_len - 1)) for i in target_ids]
                    if len(target_ids) > 0 and len(ref_ids) > 0:
                        inject_pe[:,:,target_ids,:,:,:] = inject_pe[:,:,ref_ids,:,:,:]

                    # Accumulate IDs
                    if info['inject']:
                        accumulated_target_ids.extend(target_ids)
                        accumulated_ref_ids.extend(ref_ids)

                elif artifact_data['artifact_type'] == 'removal' and info['removal']:
                    ref_ids = artifact_data['reference_patch_indices'].copy()
                    target_ids = artifact_data['target_patch_indices'].copy()

                    ref_ids = [max(0, min(int(i), seq_len - 1)) for i in ref_ids]
                    target_ids = [max(0, min(int(i), seq_len - 1)) for i in target_ids]

                    # ref_ids = get_closest_patch_inds(info['patch_h'], info['patch_w'], target_ids, ref_ids)
                    if len(target_ids) > 0 and len(ref_ids) > 0:
                        inject_pe[:,:,target_ids,:,:,:] = inject_pe[:,:,ref_ids,:,:,:]
                    # Accumulate IDs (after target_ids modification)
                    if info['inject']:
                        accumulated_target_ids.extend(target_ids)
                        accumulated_ref_ids.extend(ref_ids)

                elif artifact_data['artifact_type'] == 'distortion' and info['distortion']:
                    ref_ids = artifact_data['reference_patch_indices'].copy()
                    target_ids = artifact_data['target_patch_indices'].copy()
                    ref_ids = [max(0, min(int(i), seq_len - 1)) for i in ref_ids]
                    target_ids = [max(0, min(int(i), seq_len - 1)) for i in target_ids]

                    if len(ref_ids) == 0:
                        # For distortion with no reference patches, shuffle target patches
                        ref_ids = target_ids.copy()
                        np.random.shuffle(ref_ids)
                    # Ensure target_ids and ref_ids are different for distortion
                    if len(target_ids) > 0 and len(ref_ids) > 0:
                        inject_pe[:,:,target_ids,:,:,:] = inject_pe[:,:,ref_ids,:,:,:]
                    # Accumulate IDs (after any ref_ids modification)
                    if info['inject']:
                        accumulated_target_ids.extend(target_ids)
                        accumulated_ref_ids.extend(ref_ids)

                elif artifact_data['artifact_type'] == 'fusion' and info['fusion']:
                    ref_ids = artifact_data['reference_patch_indices'].copy()
                    target_ids = artifact_data['target_patch_indices'].copy()
                    ref_ids = [max(0, min(int(i), seq_len - 1)) for i in ref_ids]
                    target_ids = [max(0, min(int(i), seq_len - 1)) for i in target_ids]

                    # np.random.shuffle(ref_ids)
                    if len(target_ids) > 0 and len(ref_ids) > 0:
                        inject_pe[:,:,target_ids,:,:,:] = inject_pe[:,:,ref_ids,:,:,:]
                        
                    # Accumulate IDs
                    if info['inject']:
                        accumulated_target_ids.extend(target_ids)
                        accumulated_ref_ids.extend(ref_ids)

            info['patch_ids'] = accumulated_target_ids
            info['patch_ref_ids'] = accumulated_ref_ids 
        info['timesteps'] = timesteps


        if self._sequential_offload:
            for block in self.double_blocks:
                block = block.to(img.device)
                img, txt = block(img=img, txt=txt, vec=vec, pe=inject_pe, info=info)
                block = block.cpu()
                torch.cuda.empty_cache()
        else:
            for block in self.double_blocks:
                img, txt = block(img=img, txt=txt, vec=vec, pe=inject_pe, info=info)

        cnt = 0
        img = torch.cat((txt, img), 1) 
        info['type'] = 'single'
        if self._sequential_offload:
            for block in self.single_blocks:
                block = block.to(img.device)
                info['id'] = cnt
                if cnt < 19:
                    img, info = block(img, vec=vec, pe=inject_pe, info=info)
                else:
                    img, info = block(img, vec=vec, pe=pe, info=info)
                block = block.cpu()
                torch.cuda.empty_cache()
                cnt += 1
        else:
            for block in self.single_blocks:
                info['id'] = cnt
                if cnt < 19:
                    img, info = block(img, vec=vec, pe=inject_pe, info=info)
                else:
                    img, info = block(img, vec=vec, pe=pe, info=info)
                cnt += 1
            

        # ═══════════════════════════════════════════════════════
        # MODIFIED: Extract only ORIGINAL img tokens
        # Before: img = img[:, txt.shape[1] :, ...]  (gets img + ref)
        # After:  img = img[:, txt.shape[1] : txt.shape[1] + original_img_seq_len, ...]
        # ═══════════════════════════════════════════════════════
        img = img[:, txt.shape[1] : txt.shape[1] + original_img_seq_len, ...]

        img = self.final_layer(img, vec)  # (N, T, patch_size ** 2 * out_channels)
        return img, info