# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.

# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

# Copyright (C) 2024-present Naver Corporation. All rights reserved.
# Licensed under CC BY-NC-SA 4.0 (non-commercial use only).
#
# --------------------------------------------------------
# PatchEmbed implementation for DUST3R,
# in particular ManyAR_PatchEmbed that Handle images with non-square aspect ratio
# --------------------------------------------------------
import torch
from stream3r.croco.models.blocks import PatchEmbed


def get_patch_embed(patch_embed_cls, img_size, patch_size, enc_embed_dim):
    assert patch_embed_cls in ["PatchEmbedDust3R", "ManyAR_PatchEmbed"]
    patch_embed = eval(patch_embed_cls)(img_size, patch_size, 3, enc_embed_dim)
    return patch_embed


class PatchEmbedDust3R(PatchEmbed):
    def forward(self, x, **kw):
        B, C, H, W = x.shape
        assert (
            H % self.patch_size[0] == 0
        ), f"Input image height ({H}) is not a multiple of patch size ({self.patch_size[0]})."
        assert (
            W % self.patch_size[1] == 0
        ), f"Input image width ({W}) is not a multiple of patch size ({self.patch_size[1]})."
        x = self.proj(x)
        pos = self.position_getter(B, x.size(2), x.size(3), x.device)
        if self.flatten:
            x = x.flatten(2).transpose(1, 2)  # BCHW -> BNC
        x = self.norm(x)
        return x, pos


class ManyAR_PatchEmbed(PatchEmbed):
    """Handle images with non-square aspect ratio.
    All images in the same batch have the same aspect ratio.
    true_shape = [(height, width) ...] indicates the actual shape of each image.
    """

    def __init__(
        self,
        img_size=224,
        patch_size=16,
        in_chans=3,
        embed_dim=768,
        norm_layer=None,
        flatten=True,
    ):
        self.embed_dim = embed_dim
        super().__init__(img_size, patch_size, in_chans, embed_dim, norm_layer, flatten)

    def forward(self, img, true_shape):
        if not self.training:
            x = img
            B, C, H, W = x.shape
            assert (
                H % self.patch_size[0] == 0
            ), f"Input image height ({H}) is not a multiple of patch size ({self.patch_size[0]})."
            assert (
                W % self.patch_size[1] == 0
            ), f"Input image width ({W}) is not a multiple of patch size ({self.patch_size[1]})."
            x = self.proj(x)
            pos = self.position_getter(B, x.size(2), x.size(3), x.device)
            if self.flatten:
                x = x.flatten(2).transpose(1, 2)  # BCHW -> BNC
            x = self.norm(x)
            return x, pos 

        B, C, H, W = img.shape
        assert W >= H, f"img should be in landscape mode, but got {W=} {H=}"
        assert (
            H % self.patch_size[0] == 0
        ), f"Input image height ({H}) is not a multiple of patch size ({self.patch_size[0]})."
        assert (
            W % self.patch_size[1] == 0
        ), f"Input image width ({W}) is not a multiple of patch size ({self.patch_size[1]})."
        assert true_shape.shape == (
            B,
            2,
        ), f"true_shape has the wrong shape={true_shape.shape}"

        # size expressed in tokens
        W //= self.patch_size[0]
        H //= self.patch_size[1]
        n_tokens = H * W

        height, width = true_shape.T
        is_landscape = width >= height
        is_portrait = ~is_landscape

        # linear projection, transposed if necessary
        if is_landscape.any():
            new_landscape_content = self.proj(img[is_landscape])
            new_landscape_content = new_landscape_content.permute(0, 2, 3, 1).flatten(1, 2)
        if is_portrait.any():
            new_protrait_content = self.proj(img[is_portrait].swapaxes(-1, -2))
            new_protrait_content = new_protrait_content.permute(0, 2, 3, 1).flatten(1, 2)

        # allocate space for result and set the content
        x = img.new_empty((B, n_tokens, self.embed_dim), dtype=next(self.named_parameters())[1].dtype)  # dynamically set dtype based on the current precision
        if is_landscape.any():
            x[is_landscape] = new_landscape_content.to(x.dtype)
        if is_portrait.any():
            x[is_portrait] = new_protrait_content.to(x.dtype)

        # allocate space for result and set the content
        pos = img.new_empty((B, n_tokens, 2), dtype=torch.int64)
        if is_landscape.any():
            pos[is_landscape] = self.position_getter(1, H, W, pos.device).expand(is_landscape.sum(), -1, -1)
        if is_portrait.any():
            pos[is_portrait] = self.position_getter(1, W, H, pos.device).expand(is_portrait.sum(), -1, -1)

        x = self.norm(x)
        return x, pos