"""Qwen2.5VL encoder with delayed normalization"""

import torch
from einops import rearrange
from transformers.models.qwen2_5_vl.modeling_qwen2_5_vl import (
    Qwen2_5_VisionTransformerPretrainedModel,
)


class _LinearPatchEmbedProj(torch.nn.Module):
    """Replaces Conv3d in Qwen's patch embed after collapsing the temporal dim.

    The training codebase replaces the Conv3d with a plain Linear for efficiency
    when temporal_patch_size=1.  The checkpoint therefore stores the weight under
    ``patch_embed.proj.linear.weight`` rather than ``patch_embed.proj.weight``,
    so this wrapper is required for state-dict key compatibility.
    """

    def __init__(self, in_features: int, out_features: int) -> None:
        super().__init__()
        self.linear = torch.nn.Linear(in_features, out_features, bias=False)

    @property
    def weight(self) -> torch.Tensor:
        return self.linear.weight

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        n = x.shape[0]
        return self.linear(x.reshape(n, -1)).view(n, -1, 1, 1, 1)


def _replace_patch_embed_proj(visual: "Qwen2_5_VisionTransformerPretrainedModel") -> None:
    """Swap the Conv3d patch-embed proj to a Linear so parameter names match the checkpoint."""
    proj = visual.patch_embed.proj
    if not isinstance(proj, torch.nn.Conv3d):
        return
    _, in_ch, _t, p, _ = proj.weight.shape
    visual.patch_embed.temporal_patch_size = 1
    visual.patch_embed.proj = _LinearPatchEmbedProj(in_ch * p * p, proj.out_channels)  # type: ignore[assignment]


def prepare_for_qwen_encoder(
    x: torch.Tensor | list[torch.Tensor], mean: torch.Tensor, std: torch.Tensor
) -> tuple[torch.Tensor, torch.Tensor]:
    """
    Preprocessing for Qwen encoder
    Image mean and std come from processor.image_processor.image_mean and image_std
    """
    grid_thw = torch.Tensor([[1, img.shape[0], img.shape[1]] for img in x]).to(x[0].device)
    hws_flatten_shape = torch.prod(grid_thw, dim=-1)
    x = torch.cat(
        [img.reshape((int(hws_flatten_shape[idx].item()), -1)) for idx, img in enumerate(x)],
        dim=0,
    )
    assert x.min() >= 0.0 and x.max() <= 1.0
    og_shape = x.shape
    x = rearrange(x, "L (c d) -> L c d", c=3)
    x = (x - mean) / std
    x = x.view(og_shape).to(torch.bfloat16)
    return x, grid_thw


class Qwen25VLEncoder(torch.nn.Module):
    """Qwen2.5 VL encoder with pre/post processing compatible with our CA implementation"""

    def __init__(
        self,
        visual: "Qwen2_5_VisionTransformerPretrainedModel",
    ):
        super().__init__()
        self.visual = visual
        # Match training checkpoint: Conv3d is replaced by a Linear for t=1 images
        _replace_patch_embed_proj(self.visual)
        self.image_mean = torch.tensor(self.visual.config.image_mean).view(1, 3, 1)
        self.image_std = torch.tensor(self.visual.config.image_std).view(1, 3, 1)

    def forward(
        self, x: torch.Tensor | list[torch.Tensor]
    ) -> dict[str, torch.Tensor | list[torch.Tensor]]:
        x, grid_thw = prepare_for_qwen_encoder(
            x, mean=self.image_mean.to(x[0].device), std=self.image_std.to(x[0].device)
        )

        grid_thw = grid_thw.type(torch.int)
        assert len(x) == grid_thw.prod(dim=1).sum()
        out = self.visual(x, grid_thw=grid_thw)

        split_sizes = (grid_thw.prod(dim=-1) // self.visual.spatial_merge_size**2).tolist()
        embeds = list(torch.split(out, split_sizes, dim=0))  # Ni * (seq, C)
        return {"image_embeds": embeds, "grid_thw": grid_thw}