# copyright (c) 2023 PaddlePaddle Authors. All Rights Reserve.
#
# 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.

# Code was based on https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
# reference: https://arxiv.org/abs/2010.11929

from collections.abc import Callable

import numpy as np
import paddle
import paddle.nn as nn
from paddle.nn.initializer import TruncatedNormal, Constant, Normal
from ppdet.core.workspace import register, serializable

trunc_normal_ = TruncatedNormal(std=.02)


def to_2tuple(x):
    if isinstance(x, (list, tuple)):
        return x
    return tuple([x] * 2)


def drop_path(x, drop_prob=0., training=False):
    """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
    the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
    See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ...
    """
    if drop_prob == 0. or not training:
        return x
    keep_prob = paddle.to_tensor(1.0 - drop_prob).astype(x.dtype)
    shape = (paddle.shape(x)[0], ) + (1, ) * (x.ndim - 1)
    random_tensor = keep_prob + paddle.rand(shape).astype(x.dtype)
    random_tensor = paddle.floor(random_tensor)  # binarize
    output = x.divide(keep_prob) * random_tensor
    return output


class DropPath(nn.Layer):
    """Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).
    """

    def __init__(self, drop_prob=None):
        super(DropPath, self).__init__()
        self.drop_prob = drop_prob

    def forward(self, x):
        return drop_path(x, self.drop_prob, self.training)


class Identity(nn.Layer):
    def __init__(self):
        super(Identity, self).__init__()

    def forward(self, input):
        return input


class Mlp(nn.Layer):
    def __init__(self,
                 in_features,
                 hidden_features=None,
                 out_features=None,
                 act_layer=nn.GELU,
                 drop=0.):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1 = nn.Linear(in_features, hidden_features)
        self.act = act_layer()
        self.fc2 = nn.Linear(hidden_features, out_features)
        self.drop = nn.Dropout(drop)

    def forward(self, x):
        x = self.fc1(x)
        x = self.act(x)

        x = self.fc2(x)
        x = self.drop(x)
        return x


class Attention(nn.Layer):
    def __init__(self,
                 dim,
                 num_heads=8,
                 qkv_bias=False,
                 qk_scale=None,
                 attn_drop=0.,
                 proj_drop=0.):
        super().__init__()
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = qk_scale or head_dim**-0.5

        self.qkv = nn.Linear(dim, dim * 3, bias_attr=qkv_bias)

        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)

    def forward(self, x):

        N, C = x.shape[1:]
        qkv = self.qkv(x).reshape((-1, N, 3, self.num_heads, C //
                                   self.num_heads)).transpose((2, 0, 3, 1, 4))

        q, k, v = qkv[0], qkv[1], qkv[2]

        attn = (q.matmul(k.transpose((0, 1, 3, 2)))) * self.scale
        attn = nn.functional.softmax(attn, axis=-1)
        attn = self.attn_drop(attn)

        x = (attn.matmul(v)).transpose((0, 2, 1, 3)).reshape((-1, N, C))
        x = self.proj(x)

        x = self.proj_drop(x)
        return x


class Block(nn.Layer):
    def __init__(self,
                 dim,
                 num_heads,
                 mlp_ratio=4.,
                 qkv_bias=False,
                 qk_scale=None,
                 drop=0.,
                 attn_drop=0.,
                 drop_path=0.,
                 act_layer=nn.GELU,
                 norm_layer='nn.LayerNorm',
                 epsilon=1e-5):
        super().__init__()
        if isinstance(norm_layer, str):
            self.norm1 = eval(norm_layer)(dim, epsilon=epsilon)
        elif isinstance(norm_layer, Callable):
            self.norm1 = norm_layer(dim)
        else:
            raise TypeError(
                "The norm_layer must be str or paddle.nn.layer.Layer class")
        self.attn = Attention(
            dim,
            num_heads=num_heads,
            qkv_bias=qkv_bias,
            qk_scale=qk_scale,
            attn_drop=attn_drop,
            proj_drop=drop)
        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
        self.drop_path = DropPath(drop_path) if drop_path > 0. else Identity()
        if isinstance(norm_layer, str):
            self.norm2 = eval(norm_layer)(dim, epsilon=epsilon)
        elif isinstance(norm_layer, Callable):
            self.norm2 = norm_layer(dim)
        else:
            raise TypeError(
                "The norm_layer must be str or paddle.nn.layer.Layer class")
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = Mlp(in_features=dim,
                       hidden_features=mlp_hidden_dim,
                       act_layer=act_layer,
                       drop=drop)

    def forward(self, x):
        x = x + self.drop_path(self.attn(self.norm1(x)))
        x = x + self.drop_path(self.mlp(self.norm2(x)))

        return x


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

    def __init__(self,
                 img_size=224,
                 patch_size=16,
                 in_chans=3,
                 embed_dim=768,
                 ratio=1):
        super().__init__()
        img_size = to_2tuple(img_size)
        patch_size = to_2tuple(patch_size)

        num_patches = (img_size[1] // patch_size[1]) * (
            img_size[0] // patch_size[0]) * (ratio**2)
        self.img_size = img_size
        self.patch_size = patch_size
        self.num_patches = num_patches

        self.proj = nn.Conv2D(
            in_chans,
            embed_dim,
            kernel_size=patch_size,
            stride=(patch_size[0] // ratio),
            padding=(4 + 2 * (ratio // 2 - 1), 4 + 2 * (ratio // 2 - 1)))

    def forward(self, x):
        B, C, H, W = x.shape
        assert H == self.img_size[0] and W == self.img_size[1], \
            f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."

        x = self.proj(x)
        return x


@register
@serializable
class ViT(nn.Layer):
    """ Vision Transformer with support for patch input

        This module is different from ppdet's VisionTransformer (from ppdet/modeling/backbones/visio_transformer.py),
        the main differences are:
        1.the module PatchEmbed.proj has padding set,padding=(4 + 2 * (ratio // 2 - 1), 4 + 2 * (ratio // 2 - 1),
          VisionTransformer dose not
        2.Attention module qkv is standard.but VisionTransformer provide more options
        3.MLP module only one Dropout,and VisionTransformer twice;
        4.VisionTransformer provide fpn layer,but the module does not.
        
    """

    def __init__(self,
                 img_size=224,
                 patch_size=16,
                 in_chans=3,
                 embed_dim=768,
                 depth=12,
                 num_heads=12,
                 mlp_ratio=4,
                 qkv_bias=False,
                 qk_scale=None,
                 drop_rate=0.,
                 attn_drop_rate=0.,
                 drop_path_rate=0.,
                 norm_layer='nn.LayerNorm',
                 epsilon=1e-5,
                 ratio=1,
                 pretrained=None,
                 **kwargs):
        super().__init__()

        self.pretrained = pretrained
        self.num_features = self.embed_dim = embed_dim

        self.patch_embed = PatchEmbed(
            img_size=img_size,
            patch_size=patch_size,
            in_chans=in_chans,
            embed_dim=embed_dim,
            ratio=ratio)
        num_patches = self.patch_embed.num_patches

        self.pos_embed = self.create_parameter(
            shape=(1, num_patches + 1, embed_dim),
            default_initializer=trunc_normal_)
        self.add_parameter("pos_embed", self.pos_embed)

        dpr = np.linspace(0, drop_path_rate, depth, dtype='float32')

        self.blocks = nn.LayerList([
            Block(
                dim=embed_dim,
                num_heads=num_heads,
                mlp_ratio=mlp_ratio,
                qkv_bias=qkv_bias,
                qk_scale=qk_scale,
                drop=drop_rate,
                attn_drop=attn_drop_rate,
                drop_path=dpr[i],
                norm_layer=norm_layer,
                epsilon=epsilon) for i in range(depth)
        ])

        self.last_norm = eval(norm_layer)(embed_dim, epsilon=epsilon)
        trunc_normal_(self.pos_embed)
        self._init_weights()

    def _init_weights(self):
        pretrained = self.pretrained

        if pretrained:

            if 'http' in pretrained:  #URL
                path = paddle.utils.download.get_weights_path_from_url(
                    pretrained)
            else:  #model in local path
                path = pretrained

            load_state_dict = paddle.load(path)
            self.set_state_dict(load_state_dict)
            print("Load load_state_dict:", path)

    def forward_features(self, x):

        B = paddle.shape(x)[0]
        x = self.patch_embed(x)
        B, D, Hp, Wp = x.shape
        x = x.flatten(2).transpose([0, 2, 1])
        x = x + self.pos_embed[:, 1:] + self.pos_embed[:, :1]

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

        x = self.last_norm(x)
        xp = paddle.reshape(
            paddle.transpose(
                x, perm=[0, 2, 1]), shape=[B, -1, Hp, Wp])

        return xp
