# 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.

import numpy as np
import torch
from torch import nn
from typing import Any, Optional, Tuple, Type


class LayerNorm2d(nn.Module):
    def __init__(self, num_channels: int, eps: float = 1e-6) -> None:
        super().__init__()
        self.weight = nn.Parameter(torch.ones(num_channels))
        self.bias = nn.Parameter(torch.zeros(num_channels))
        self.eps = eps

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        u = x.mean(-3, keepdim=True)
        s = (x - u).pow(2).mean(-3, keepdim=True)
        x = (x - u) / torch.sqrt(s + self.eps)
        x = self.weight[:, None, None] * x + self.bias[:, None, None]
        return x


class PromptEncoder(nn.Module):
    def __init__(
        self,
        embed_dim: int,
        image_embedding_size: Tuple[int, int],
        input_image_size: Tuple[int, int],
        mask_in_chans: int,
        activation: Type[nn.Module] = nn.GELU,
    ) -> None:
        """
        Encodes prompts for input to SAM's mask decoder.

        Arguments:
          embed_dim (int): The prompts' embedding dimension
          image_embedding_size (tuple(int, int)): The spatial size of the
            image embedding, as (H, W).
          input_image_size (int): The padded size of the image as input
            to the image encoder, as (H, W).
          mask_in_chans (int): The number of hidden channels used for
            encoding input masks.
          activation (nn.Module): The activation to use when encoding
            input masks.
        """
        super().__init__()
        self.embed_dim = embed_dim
        self.input_image_size = input_image_size
        self.image_embedding_size = image_embedding_size
        self.pe_layer = PositionEmbeddingRandom(embed_dim // 2)

        self.num_point_embeddings: int = 4  # pos/neg point + 2 box corners
        point_embeddings = [nn.Embedding(1, embed_dim) for i in range(self.num_point_embeddings)]
        self.point_embeddings = nn.ModuleList(point_embeddings)
        self.not_a_point_embed = nn.Embedding(1, embed_dim)

        self.mask_input_size = (4 * image_embedding_size[0], 4 * image_embedding_size[1])
        self.mask_downscaling = nn.Sequential(
            nn.Conv2d(1, mask_in_chans // 4, kernel_size=2, stride=2),
            LayerNorm2d(mask_in_chans // 4),
            activation(),
            nn.Conv2d(mask_in_chans // 4, mask_in_chans, kernel_size=2, stride=2),
            LayerNorm2d(mask_in_chans),
            activation(),
            nn.Conv2d(mask_in_chans, embed_dim, kernel_size=1),
        )
        self.no_mask_embed = nn.Embedding(1, embed_dim)

    def get_dense_pe(self) -> torch.Tensor:
        """
        Returns the positional encoding used to encode point prompts,
        applied to a dense set of points the shape of the image encoding.

        Returns:
          torch.Tensor: Positional encoding with shape
            1x(embed_dim)x(embedding_h)x(embedding_w)
        """
        return self.pe_layer(self.image_embedding_size).unsqueeze(0)

    def _embed_points(
        self,
        points: torch.Tensor,
        labels: torch.Tensor,
        pad: bool,
    ) -> torch.Tensor:
        """Embeds point prompts."""
        points = points + 0.5  # Shift to center of pixel
        if pad:
            if points.is_nested:
                points = torch.cat([points, torch.zeros_like(points)], dim=2)
                labels = torch.cat([labels, -torch.ones_like(labels)], dim=2)
            else:
                padding_point = torch.zeros((points.shape[0], 1, 2), device=points.device, dtype=points.dtype)
                padding_label = -torch.ones((labels.shape[0], 1), device=labels.device, dtype=points.dtype)
                points = torch.cat([points, padding_point], dim=1)
                labels = torch.cat([labels, padding_label], dim=1)
        point_embedding = self.pe_layer.forward_with_coords(points, self.input_image_size)
        point_embedding = torch.where((labels == -1).unsqueeze(-1).expand_as(point_embedding),
                                      torch.zeros_like(point_embedding) + self.not_a_point_embed.weight,
                                      point_embedding)
        point_embedding = torch.where((labels == 0).unsqueeze(-1).expand_as(point_embedding),
                                      point_embedding + self.point_embeddings[0].weight,
                                      point_embedding)
        point_embedding = torch.where((labels == 1).unsqueeze(-1).expand_as(point_embedding),
                                      point_embedding + self.point_embeddings[1].weight,
                                      point_embedding)
        return point_embedding

    def _embed_boxes(self, boxes: torch.Tensor) -> torch.Tensor:
        """Embeds box prompts."""
        boxes = boxes + 0.5  # Shift to center of pixel
        coords = boxes.reshape(-1, 2, 2)
        corner_embedding = self.pe_layer.forward_with_coords(coords, self.input_image_size)
        corner_embedding[:, 0, :] += self.point_embeddings[2].weight
        corner_embedding[:, 1, :] += self.point_embeddings[3].weight
        return corner_embedding

    def _embed_masks(self, masks: torch.Tensor) -> torch.Tensor:
        """Embeds mask inputs."""
        mask_embedding = self.mask_downscaling(masks)
        return mask_embedding

    def _get_batch_size(
        self,
        points: Optional[Tuple[torch.Tensor, torch.Tensor]],
        boxes: Optional[torch.Tensor],
        masks: Optional[torch.Tensor],
    ) -> int:
        """
        Gets the batch size of the output given the batch size of the input prompts.
        """
        if points is not None:
            return points[0].shape[0]
        elif boxes is not None:
            return boxes.shape[0]
        elif masks is not None:
            return masks.shape[0]
        else:
            return 1

    def _get_device(self) -> torch.device:
        return self.point_embeddings[0].weight.device

    def forward(
        self,
        points: Optional[Tuple[torch.Tensor, torch.Tensor]],
        boxes: Optional[torch.Tensor],
        masks: Optional[torch.Tensor],
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        """
        Embeds different types of prompts, returning both sparse and dense
        embeddings.

        Arguments:
          points (tuple(torch.Tensor, torch.Tensor) or none): point coordinates
            and labels to embed.
          boxes (torch.Tensor or none): boxes to embed
          masks (torch.Tensor or none): masks to embed

        Returns:
          torch.Tensor: sparse embeddings for the points and boxes, with shape
            BxNx(embed_dim), where N is determined by the number of input points
            and boxes.
          torch.Tensor: dense embeddings for the masks, in the shape
            Bx(embed_dim)x(embed_H)x(embed_W)
        """
        bs = self._get_batch_size(points, boxes, masks)
        if points is not None:
            coords, labels = points
            sparse_embeddings = self._embed_points(coords, labels, pad=(boxes is None))
        if boxes is not None:
            sparse_embeddings = self._embed_boxes(boxes)

        if masks is not None:
            dense_embeddings = self._embed_masks(masks)
        else:
            if sparse_embeddings.is_nested:
                embed_weight = (
                    self.no_mask_embed.weight.squeeze(0).unsqueeze(-1).unsqueeze(-1).expand(
                        -1, self.image_embedding_size[0], self.image_embedding_size[1])
                )
                dense_embeddings = (
                    torch.zeros_like(sparse_embeddings.unsqueeze(2).prod(dim=-1, keepdim=True).prod(
                        dim=-2, keepdim=True)) + embed_weight
                )
            else:
                dense_embeddings = self.no_mask_embed.weight.reshape(1, -1, 1, 1).expand(
                    bs, -1, self.image_embedding_size[0], self.image_embedding_size[1])

        return sparse_embeddings.to(dense_embeddings.dtype), dense_embeddings


class PositionEmbeddingRandom(nn.Module):
    """
    Positional encoding using random spatial frequencies.
    """

    def __init__(self, num_pos_feats: int = 64, scale: Optional[float] = None) -> None:
        super().__init__()
        if scale is None or scale <= 0.0:
            scale = 1.0
        self.register_buffer(
            "positional_encoding_gaussian_matrix",
            scale * torch.randn((2, num_pos_feats)),
        )

    def _pe_encoding(self, coords: torch.Tensor) -> torch.Tensor:
        """Positionally encode points that are normalized to [0,1]."""
        # assuming coords are in [0, 1]^2 square and have d_1 x ... x d_n x 2 shape
        coords = 2 * coords - 1
        coords = coords.to(self.positional_encoding_gaussian_matrix.dtype) @ self.positional_encoding_gaussian_matrix
        coords = 2 * np.pi * coords
        # outputs d_1 x ... x d_n x C shape
        return torch.cat([torch.sin(coords), torch.cos(coords)], dim=-1)

    def forward(self, size: Tuple[int, int]) -> torch.Tensor:
        """Generate positional encoding for a grid of the specified size."""
        h, w = size
        device: Any = self.positional_encoding_gaussian_matrix.device
        grid = torch.ones((h, w), device=device, dtype=self.positional_encoding_gaussian_matrix.dtype)
        y_embed = grid.cumsum(dim=0) - 0.5
        x_embed = grid.cumsum(dim=1) - 0.5
        y_embed = y_embed / h
        x_embed = x_embed / w

        pe = self._pe_encoding(torch.stack([x_embed, y_embed], dim=-1))
        return pe.permute(2, 0, 1)  # C x H x W

    def forward_with_coords(
        self, coords_input: torch.Tensor, image_size: Tuple[int, int]
    ) -> torch.Tensor:
        # Take advantage of square image size to simplify normalization
        assert image_size[1] == image_size[0]
        return self._pe_encoding(coords_input / image_size[1])  # B x N x C