# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# This implementation refers to: https://github.com/facebookresearch/segment-anything

import sys
import os
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), "."))

from functools import partial
from typing import Optional, Tuple, Type

import paddle
import paddle.nn as nn
import paddle.nn.functional as F

from .common import LayerNorm2d, MLPBlock


# This class and its supporting functions below lightly adapted from the ViTDet backbone available at: https://github.com/facebookresearch/detectron2/blob/main/detectron2/modeling/backbone/vit.py # noqa
class ImageEncoderViT(nn.Layer):
    def __init__(
            self,
            img_size: int=1024,
            patch_size: int=16,
            in_chans: int=3,
            embed_dim: int=768,
            depth: int=12,
            num_heads: int=12,
            mlp_ratio: float=4.0,
            out_chans: int=256,
            qkv_bias: bool=True,
            norm_layer: Type[nn.Layer]=nn.LayerNorm,
            act_layer: Type[nn.Layer]=nn.GELU,
            use_abs_pos: bool=True,
            use_rel_pos: bool=False,
            rel_pos_zero_init: bool=True,
            window_size: int=0,
            global_attn_indexes: Tuple[int, ...]=(), ) -> None:
        """
        Args:
            img_size (int): Input image size.
            patch_size (int): Patch size.
            in_chans (int): Number of input image channels.
            embed_dim (int): Patch embedding dimension.
            depth (int): Depth of ViT.
            num_heads (int): Number of attention heads in each ViT block.
            mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
            qkv_bias (bool): If True, add a learnable bias to query, key, value.
            norm_layer (nn.Layer): Normalization layer.
            act_layer (nn.Layer): Activation layer.
            use_abs_pos (bool): If True, use absolute positional embeddings.
            use_rel_pos (bool): If True, add relative positional embeddings to the attention map.
            rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
            window_size (int): Window size for window attention blocks.
            global_attn_indexes (list): Indexes for blocks using global attention.
        """
        super().__init__()
        self.img_size = img_size

        self.patch_embed = PatchEmbed(
            kernel_size=(patch_size, patch_size),
            stride=(patch_size, patch_size),
            in_chans=in_chans,
            embed_dim=embed_dim, )

        self.pos_embed = None
        if use_abs_pos:
            # Initialize absolute positional embedding with pretrain image size.
            self.pos_embed = paddle.create_parameter(
                shape=[
                    1, img_size // patch_size, img_size // patch_size, embed_dim
                ],
                dtype='float32',
                default_initializer=nn.initializer.Constant(value=0.0))
            # self.pos_embed = nn.Parameter(paddle.zeros(1, img_size // patch_size, img_size // patch_size, embed_dim))

        self.blocks = nn.LayerList()
        for i in range(depth):
            block = Block(
                dim=embed_dim,
                num_heads=num_heads,
                mlp_ratio=mlp_ratio,
                qkv_bias=qkv_bias,
                norm_layer=norm_layer,
                act_layer=act_layer,
                use_rel_pos=use_rel_pos,
                rel_pos_zero_init=rel_pos_zero_init,
                window_size=window_size if i not in global_attn_indexes else 0,
                input_size=(img_size // patch_size, img_size // patch_size), )
            self.blocks.append(block)

        self.neck = nn.Sequential(
            nn.Conv2D(
                embed_dim,
                out_chans,
                kernel_size=1,
                bias_attr=False, ),
            LayerNorm2d(out_chans),
            nn.Conv2D(
                out_chans,
                out_chans,
                kernel_size=3,
                padding=1,
                bias_attr=False, ),
            LayerNorm2d(out_chans), )

    def forward(self, x: paddle.Tensor) -> paddle.Tensor:
        x = self.patch_embed(x)
        if self.pos_embed is not None:
            x = x + self.pos_embed

        for blk in self.blocks:
            x = blk(x)

        x = self.neck(x.transpose([0, 3, 1, 2]))

        return x


class Block(nn.Layer):
    """Transformer blocks with support of window attention and residual propagation blocks"""

    def __init__(
            self,
            dim: int,
            num_heads: int,
            mlp_ratio: float=4.0,
            qkv_bias: bool=True,
            norm_layer=nn.LayerNorm,
            act_layer=nn.GELU,
            use_rel_pos: bool=False,
            rel_pos_zero_init: bool=True,
            window_size: int=0,
            input_size: Optional[Tuple[int, int]]=None, ) -> None:
        """
        Args:
            dim (int): Number of input channels.
            num_heads (int): Number of attention heads in each ViT block.
            mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
            qkv_bias (bool): If True, add a learnable bias to query, key, value.
            norm_layer (nn.Layer): Normalization layer.
            act_layer (nn.Layer): Activation layer.
            use_rel_pos (bool): If True, add relative positional embeddings to the attention map.
            rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
            window_size (int): Window size for window attention blocks. If it equals 0, then
                use global attention.
            input_size (int or None): Input resolution for calculating the relative positional
                parameter size.
        """
        super().__init__()
        self.norm1 = norm_layer(dim)
        self.attn = Attention(
            dim,
            num_heads=num_heads,
            qkv_bias=qkv_bias,
            use_rel_pos=use_rel_pos,
            rel_pos_zero_init=rel_pos_zero_init,
            input_size=input_size
            if window_size == 0 else (window_size, window_size), )

        self.norm2 = norm_layer(dim)
        self.mlp = MLPBlock(
            embedding_dim=dim, mlp_dim=int(dim * mlp_ratio), act=act_layer)

        self.window_size = window_size

    def forward(self, x: paddle.Tensor) -> paddle.Tensor:
        shortcut = x
        x = self.norm1(x)
        # Window partition
        if self.window_size > 0:
            H, W = x.shape[1], x.shape[2]
            x, pad_hw = window_partition(x, self.window_size)

        x = self.attn(x)
        # Reverse window partition
        if self.window_size > 0:
            x = window_unpartition(x, self.window_size, pad_hw, (H, W))

        x = shortcut + x
        x = x + self.mlp(self.norm2(x))

        return x


class Attention(nn.Layer):
    """Multi-head Attention block with relative position embeddings."""

    def __init__(
            self,
            dim: int,
            num_heads: int=8,
            qkv_bias: bool=True,
            use_rel_pos: bool=False,
            rel_pos_zero_init: bool=True,
            input_size: Optional[Tuple[int, int]]=None, ) -> None:
        """
        Args:
            dim (int): Number of input channels.
            num_heads (int): Number of attention heads.
            qkv_bias (bool:  If True, add a learnable bias to query, key, value.
            rel_pos (bool): If True, add relative positional embeddings to the attention map.
            rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
            input_size (int or None): Input resolution for calculating the relative positional
                parameter size.
        """
        super().__init__()
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = head_dim**-0.5

        self.qkv = nn.Linear(
            dim, dim * 3, bias_attr=qkv_bias if not qkv_bias else None)
        self.proj = nn.Linear(dim, dim)

        self.use_rel_pos = use_rel_pos
        if self.use_rel_pos:
            assert (
                input_size is not None
            ), "Input size must be provided if using relative positional encoding."
            # initialize relative positional embeddings
            self.rel_pos_h = paddle.create_parameter(
                shape=[2 * input_size[0] - 1, head_dim],
                dtype='float32',
                default_initializer=nn.initializer.Constant(value=0.0))
            self.rel_pos_w = paddle.create_parameter(
                shape=[2 * input_size[1] - 1, head_dim],
                dtype='float32',
                default_initializer=nn.initializer.Constant(value=0.0))
            # self.rel_pos_h = nn.Parameter(paddle.zeros(2 * input_size[0] - 1, head_dim))
            # self.rel_pos_w = nn.Parameter(paddle.zeros(2 * input_size[1] - 1, head_dim))

    def forward(self, x: paddle.Tensor) -> paddle.Tensor:
        B, H, W, _ = x.shape
        # qkv with shape (3, B, nHead, H * W, C)
        qkv = self.qkv(x).reshape(
            shape=[B, H * W, 3, self.num_heads, -1]).transpose([2, 0, 3, 1, 4])
        # q, k, v with shape (B * nHead, H * W, C)

        q, k, v = qkv.reshape(shape=[3, B * self.num_heads, H * W, -1]).unbind(
            axis=0)

        attn = (q * self.scale) @k.transpose([0, 2, 1])

        if self.use_rel_pos:
            attn = add_decomposed_rel_pos(attn, q, self.rel_pos_h,
                                          self.rel_pos_w, (H, W), (H, W))

        attn = F.softmax(attn, axis=-1)
        x = (attn @v).reshape([B, self.num_heads, H, W, -1]).transpose(
            [0, 2, 3, 1, 4]).reshape([B, H, W, -1])
        x = self.proj(x)

        return x


def window_partition(x: paddle.Tensor,
                     window_size: int) -> Tuple[paddle.Tensor, Tuple[int, int]]:
    """
    Partition into non-overlapping windows with padding if needed.
    Args:
        x (tensor): input tokens with [B, H, W, C].
        window_size (int): window size.

    Returns:
        windows: windows after partition with [B * num_windows, window_size, window_size, C].
        (Hp, Wp): padded height and width before partition
    """
    B, H, W, C = x.shape

    pad_h = (window_size - H % window_size) % window_size
    pad_w = (window_size - W % window_size) % window_size
    if pad_h > 0 or pad_w > 0:
        x = paddle.nn.functional.pad(x=x, pad=(0, 0, 0, pad_w, 0, pad_h, 0,
                                               0))  # 每个维度分两位数进行pad
    Hp, Wp = H + pad_h, W + pad_w

    x = x.reshape(
        [B, Hp // window_size, window_size, Wp // window_size, window_size, C])
    windows = x.transpose([0, 1, 3, 2, 4, 5]).reshape(
        [-1, window_size, window_size, C])
    return windows, (Hp, Wp)


def window_unpartition(windows: paddle.Tensor,
                       window_size: int,
                       pad_hw: Tuple[int, int],
                       hw: Tuple[int, int]) -> paddle.Tensor:
    """
    Window unpartition into original sequences and removing padding.
    Args:
        x (tensor): input tokens with [B * num_windows, window_size, window_size, C].
        window_size (int): window size.
        pad_hw (Tuple): padded height and width (Hp, Wp).
        hw (Tuple): original height and width (H, W) before padding.

    Returns:
        x: unpartitioned sequences with [B, H, W, C].
    """
    Hp, Wp = pad_hw
    H, W = hw
    B = windows.shape[0] // (Hp * Wp // window_size // window_size)
    x = windows.reshape([
        B, Hp // window_size, Wp // window_size, window_size, window_size, -1
    ])
    x = x.transpose([0, 1, 3, 2, 4, 5]).reshape([B, Hp, Wp, -1])

    if Hp > H or Wp > W:
        x = x[:, :H, :W, :]
    return x


def get_rel_pos(q_size: int, k_size: int,
                rel_pos: paddle.Tensor) -> paddle.Tensor:
    """
    Get relative positional embeddings according to the relative positions of
        query and key sizes.
    Args:
        q_size (int): size of query q.
        k_size (int): size of key k.
        rel_pos (Tensor): relative position embeddings (L, C).

    Returns:
        Extracted positional embeddings according to relative positions.
    """
    max_rel_dist = int(2 * max(q_size, k_size) - 1)
    # Interpolate rel pos if needed.
    if rel_pos.shape[0] != max_rel_dist:
        # Interpolate rel pos.
        rel_pos_resized = F.interpolate(
            rel_pos.reshape(1, rel_pos.shape[0], -1).transpose([0, 2, 1]),
            size=max_rel_dist,
            mode="linear", )
        rel_pos_resized = rel_pos_resized.reshape([-1, max_rel_dist]).transpose(
            [1, 0])
    else:
        rel_pos_resized = rel_pos

    # Scale the coords with short length if shapes for q and k are different.
    q_coords = paddle.arange(end=q_size)[:, None] * max(k_size / q_size, 1.0)
    k_coords = paddle.arange(end=k_size)[None, :] * max(q_size / k_size, 1.0)
    relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size /
                                                                 k_size, 1.0)

    return rel_pos_resized[relative_coords.cast('int64')]


def add_decomposed_rel_pos(
        attn: paddle.Tensor,
        q: paddle.Tensor,
        rel_pos_h: paddle.Tensor,
        rel_pos_w: paddle.Tensor,
        q_size: Tuple[int, int],
        k_size: Tuple[int, int], ) -> paddle.Tensor:
    """
    Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`.
    https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py   # noqa B950
    Args:
        attn (Tensor): attention map.
        q (Tensor): query q in the attention layer with shape (B, q_h * q_w, C).
        rel_pos_h (Tensor): relative position embeddings (Lh, C) for height axis.
        rel_pos_w (Tensor): relative position embeddings (Lw, C) for width axis.
        q_size (Tuple): spatial sequence size of query q with (q_h, q_w).
        k_size (Tuple): spatial sequence size of key k with (k_h, k_w).

    Returns:
        attn (Tensor): attention map with added relative positional embeddings.
    """
    q_h, q_w = q_size
    k_h, k_w = k_size
    Rh = get_rel_pos(q_h, k_h, rel_pos_h)
    Rw = get_rel_pos(q_w, k_w, rel_pos_w)

    B, _, dim = q.shape
    r_q = q.reshape([B, q_h, q_w, dim])
    rel_h = paddle.einsum("bhwc,hkc->bhwk", r_q, Rh)
    rel_w = paddle.einsum("bhwc,wkc->bhwk", r_q, Rw)

    attn = (attn.reshape([B, q_h, q_w, k_h, k_w]) + rel_h[:, :, :, :, None] +
            rel_w[:, :, :, None, :]).reshape([B, q_h * q_w, k_h * k_w])

    return attn


class PatchEmbed(nn.Layer):
    """
    Image to Patch Embedding.
    """

    def __init__(
            self,
            kernel_size: Tuple[int, int]=(16, 16),
            stride: Tuple[int, int]=(16, 16),
            padding: Tuple[int, int]=(0, 0),
            in_chans: int=3,
            embed_dim: int=768, ) -> None:
        """
        Args:
            kernel_size (Tuple): kernel size of the projection layer.
            stride (Tuple): stride of the projection layer.
            padding (Tuple): padding size of the projection layer.
            in_chans (int): Number of input image channels.
            embed_dim (int):  embed_dim (int): Patch embedding dimension.
        """
        super().__init__()

        self.proj = nn.Conv2D(
            in_chans,
            embed_dim,
            kernel_size=kernel_size,
            stride=stride,
            padding=padding)

    def forward(self, x: paddle.Tensor) -> paddle.Tensor:
        x = self.proj(x)
        # B C H W -> B H W C
        x = paddle.transpose(x, [0, 2, 3, 1])
        return x


if __name__ == '__main__':
    import numpy as np
    import paddle
    from padiff import auto_diff
    import torch
    from segment_anything.modeling import ImageEncoderViT as ImageEncoderViT_torch

    image_encoder_t = ImageEncoderViT_torch(
        depth=12,
        embed_dim=768,
        img_size=1024,
        mlp_ratio=4,
        norm_layer=partial(
            torch.nn.LayerNorm, eps=1e-6),
        num_heads=12,
        patch_size=16,
        qkv_bias=True,
        use_rel_pos=True,
        global_attn_indexes=[2, 5, 8, 11],
        window_size=14,
        out_chans=256, )

    image_encoder = ImageEncoderViT(
        depth=12,
        embed_dim=768,
        img_size=1024,
        mlp_ratio=4,
        norm_layer=partial(
            paddle.nn.LayerNorm, epsilon=1e-6),
        num_heads=12,
        patch_size=16,
        qkv_bias=True,
        use_rel_pos=True,
        global_attn_indexes=[2, 5, 8, 11],
        window_size=14,
        out_chans=256, )

    # Generate random numbers of shape (4, 3, 128, 128)
    random_numbers = np.random.rand(1, 3, 1024, 1024).astype("float32")
    inp = ({
        'x': paddle.to_tensor(random_numbers)
    }, {
        'x': torch.as_tensor(random_numbers)
    })

    auto_diff(
        image_encoder,
        image_encoder_t,
        inp,
        auto_weights=True,
        options={
            'atol': 0.001,
            'rtol': 0,
            'compare_mode': 'mean',
            'single_step': False
        })
