# copyright (c) 2021 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 heavily based on https://github.com/whai362/PVT

from functools import partial
import math
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from paddle.nn.initializer import TruncatedNormal, Constant

from .vision_transformer import trunc_normal_, zeros_, ones_, to_2tuple, DropPath, Identity, drop_path

from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url

MODEL_URLS = {
    "PVT_V2_B0":
    "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/PVT_V2_B0_pretrained.pdparams",
    "PVT_V2_B1":
    "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/PVT_V2_B1_pretrained.pdparams",
    "PVT_V2_B2":
    "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/PVT_V2_B2_pretrained.pdparams",
    "PVT_V2_B2_Linear":
    "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/PVT_V2_B2_Linear_pretrained.pdparams",
    "PVT_V2_B3":
    "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/PVT_V2_B3_pretrained.pdparams",
    "PVT_V2_B4":
    "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/PVT_V2_B4_pretrained.pdparams",
    "PVT_V2_B5":
    "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/PVT_V2_B5_pretrained.pdparams",
}

__all__ = list(MODEL_URLS.keys())


@paddle.jit.not_to_static
def swapdim(x, dim1, dim2):
    a = list(range(len(x.shape)))
    a[dim1], a[dim2] = a[dim2], a[dim1]
    return x.transpose(a)


class Mlp(nn.Layer):
    def __init__(self,
                 in_features,
                 hidden_features=None,
                 out_features=None,
                 act_layer=nn.GELU,
                 drop=0.,
                 linear=False):
        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.dwconv = DWConv(hidden_features)
        self.act = act_layer()
        self.fc2 = nn.Linear(hidden_features, out_features)
        self.drop = nn.Dropout(drop)
        self.linear = linear
        if self.linear:
            self.relu = nn.ReLU()

    def forward(self, x, H, W):
        x = self.fc1(x)
        if self.linear:
            x = self.relu(x)
        x = self.dwconv(x, H, W)
        x = self.act(x)
        x = self.drop(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.,
                 sr_ratio=1,
                 linear=False):
        super().__init__()
        assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}."

        self.dim = dim
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = qk_scale or head_dim**-0.5

        self.q = nn.Linear(dim, dim, bias_attr=qkv_bias)
        self.kv = nn.Linear(dim, dim * 2, bias_attr=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)

        self.linear = linear
        self.sr_ratio = sr_ratio
        if not linear:
            if sr_ratio > 1:
                self.sr = nn.Conv2D(
                    dim, dim, kernel_size=sr_ratio, stride=sr_ratio)
                self.norm = nn.LayerNorm(dim)
        else:
            self.pool = nn.AdaptiveAvgPool2D(7)
            self.sr = nn.Conv2D(dim, dim, kernel_size=1, stride=1)
            self.norm = nn.LayerNorm(dim)
            self.act = nn.GELU()

    def forward(self, x, H, W):
        B, N, C = x.shape
        q = self.q(x).reshape(
            [B, N, self.num_heads, C // self.num_heads]).transpose(
                [0, 2, 1, 3])

        if not self.linear:
            if self.sr_ratio > 1:
                x_ = x.transpose([0, 2, 1]).reshape([B, C, H, W])
                x_ = self.sr(x_)
                h_, w_ = x_.shape[-2:]
                x_ = x_.reshape([B, C, h_ * w_]).transpose([0, 2, 1])
                x_ = self.norm(x_)
                kv = self.kv(x_)
                kv = kv.reshape([
                    B, kv.shape[2] * kv.shape[1] // 2 // C, 2, self.num_heads,
                    C // self.num_heads
                ]).transpose([2, 0, 3, 1, 4])
            else:
                kv = self.kv(x)
                kv = kv.reshape([
                    B, kv.shape[2] * kv.shape[1] // 2 // C, 2, self.num_heads,
                    C // self.num_heads
                ]).transpose([2, 0, 3, 1, 4])
        else:
            x_ = x.transpose([0, 2, 1]).reshape([B, C, H, W])
            x_ = self.sr(self.pool(x_))
            x_ = x_.reshape([B, C, x_.shape[2] * x_.shape[3]]).transpose(
                [0, 2, 1])
            x_ = self.norm(x_)
            x_ = self.act(x_)
            kv = self.kv(x_)
            kv = kv.reshape([
                B, kv.shape[2] * kv.shape[1] // 2 // C, 2, self.num_heads,
                C // self.num_heads
            ]).transpose([2, 0, 3, 1, 4])
        k, v = kv[0], kv[1]

        attn = (q @swapdim(k, -2, -1)) * self.scale
        attn = F.softmax(attn, axis=-1)
        attn = self.attn_drop(attn)

        x = swapdim((attn @v), 1, 2).reshape([B, 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,
                 sr_ratio=1,
                 linear=False):
        super().__init__()
        self.norm1 = norm_layer(dim)
        self.attn = Attention(
            dim,
            num_heads=num_heads,
            qkv_bias=qkv_bias,
            qk_scale=qk_scale,
            attn_drop=attn_drop,
            proj_drop=drop,
            sr_ratio=sr_ratio,
            linear=linear)
        # 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()
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = Mlp(in_features=dim,
                       hidden_features=mlp_hidden_dim,
                       act_layer=act_layer,
                       drop=drop,
                       linear=linear)

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

        return x


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

    def __init__(self,
                 img_size=224,
                 patch_size=7,
                 stride=4,
                 in_chans=3,
                 embed_dim=768):
        super().__init__()
        img_size = to_2tuple(img_size)
        patch_size = to_2tuple(patch_size)

        self.img_size = img_size
        self.patch_size = patch_size
        self.H, self.W = img_size[0] // patch_size[0], img_size[
            1] // patch_size[1]
        self.num_patches = self.H * self.W
        self.proj = nn.Conv2D(
            in_chans,
            embed_dim,
            kernel_size=patch_size,
            stride=stride,
            padding=(patch_size[0] // 2, patch_size[1] // 2))
        self.norm = nn.LayerNorm(embed_dim)

    def forward(self, x):
        x = self.proj(x)
        _, _, H, W = x.shape
        x = x.flatten(2)
        x = swapdim(x, 1, 2)
        x = self.norm(x)

        return x, H, W


class PyramidVisionTransformerV2(nn.Layer):
    def __init__(self,
                 img_size=224,
                 patch_size=16,
                 in_chans=3,
                 class_num=1000,
                 embed_dims=[64, 128, 256, 512],
                 num_heads=[1, 2, 4, 8],
                 mlp_ratios=[4, 4, 4, 4],
                 qkv_bias=False,
                 qk_scale=None,
                 drop_rate=0.,
                 attn_drop_rate=0.,
                 drop_path_rate=0.,
                 norm_layer=nn.LayerNorm,
                 depths=[3, 4, 6, 3],
                 sr_ratios=[8, 4, 2, 1],
                 num_stages=4,
                 linear=False):
        super().__init__()
        self.class_num = class_num
        self.depths = depths
        self.num_stages = num_stages

        dpr = [x for x in paddle.linspace(0, drop_path_rate, sum(depths))
               ]  # stochastic depth decay rule
        cur = 0

        for i in range(num_stages):
            patch_embed = OverlapPatchEmbed(
                img_size=img_size if i == 0 else img_size // (2**(i + 1)),
                patch_size=7 if i == 0 else 3,
                stride=4 if i == 0 else 2,
                in_chans=in_chans if i == 0 else embed_dims[i - 1],
                embed_dim=embed_dims[i])

            block = nn.LayerList([
                Block(
                    dim=embed_dims[i],
                    num_heads=num_heads[i],
                    mlp_ratio=mlp_ratios[i],
                    qkv_bias=qkv_bias,
                    qk_scale=qk_scale,
                    drop=drop_rate,
                    attn_drop=attn_drop_rate,
                    drop_path=dpr[cur + j],
                    norm_layer=norm_layer,
                    sr_ratio=sr_ratios[i],
                    linear=linear) for j in range(depths[i])
            ])
            norm = norm_layer(embed_dims[i])
            cur += depths[i]

            setattr(self, f"patch_embed{i + 1}", patch_embed)
            setattr(self, f"block{i + 1}", block)
            setattr(self, f"norm{i + 1}", norm)

        # classification head
        self.head = nn.Linear(embed_dims[3],
                              class_num) if class_num > 0 else Identity()

        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight)
            if isinstance(m, nn.Linear) and m.bias is not None:
                zeros_(m.bias)
        elif isinstance(m, nn.LayerNorm):
            zeros_(m.bias)
            ones_(m.weight)

    def forward_features(self, x):
        B = x.shape[0]

        for i in range(self.num_stages):
            patch_embed = getattr(self, f"patch_embed{i + 1}")
            block = getattr(self, f"block{i + 1}")
            norm = getattr(self, f"norm{i + 1}")
            x, H, W = patch_embed(x)
            for blk in block:
                x = blk(x, H, W)
            x = norm(x)
            if i != self.num_stages - 1:
                x = x.reshape([B, H, W, x.shape[2]]).transpose([0, 3, 1, 2])

        return x.mean(axis=1)

    def forward(self, x):
        x = self.forward_features(x)
        x = self.head(x)

        return x


class DWConv(nn.Layer):
    def __init__(self, dim=768):
        super().__init__()
        self.dwconv = nn.Conv2D(dim, dim, 3, 1, 1, bias_attr=True, groups=dim)

    def forward(self, x, H, W):
        B, N, C = x.shape
        x = swapdim(x, 1, 2)
        x = x.reshape([B, C, H, W])
        x = self.dwconv(x)
        x = x.flatten(2)
        x = swapdim(x, 1, 2)

        return x


def _load_pretrained(pretrained, model, model_url, use_ssld=False):
    if pretrained is False:
        pass
    elif pretrained is True:
        load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
    elif isinstance(pretrained, str):
        load_dygraph_pretrain(model, pretrained)
    else:
        raise RuntimeError(
            "pretrained type is not available. Please use `string` or `boolean` type."
        )


def PVT_V2_B0(pretrained=False, use_ssld=False, **kwargs):
    model = PyramidVisionTransformerV2(
        patch_size=4,
        embed_dims=[32, 64, 160, 256],
        num_heads=[1, 2, 5, 8],
        mlp_ratios=[8, 8, 4, 4],
        qkv_bias=True,
        norm_layer=partial(
            nn.LayerNorm, epsilon=1e-6),
        depths=[2, 2, 2, 2],
        sr_ratios=[8, 4, 2, 1],
        **kwargs)
    _load_pretrained(
        pretrained, model, MODEL_URLS["PVT_V2_B0"], use_ssld=use_ssld)
    return model


def PVT_V2_B1(pretrained=False, use_ssld=False, **kwargs):
    model = PyramidVisionTransformerV2(
        patch_size=4,
        embed_dims=[64, 128, 320, 512],
        num_heads=[1, 2, 5, 8],
        mlp_ratios=[8, 8, 4, 4],
        qkv_bias=True,
        norm_layer=partial(
            nn.LayerNorm, epsilon=1e-6),
        depths=[2, 2, 2, 2],
        sr_ratios=[8, 4, 2, 1],
        **kwargs)
    _load_pretrained(
        pretrained, model, MODEL_URLS["PVT_V2_B1"], use_ssld=use_ssld)
    return model


def PVT_V2_B2(pretrained=False, use_ssld=False, **kwargs):
    model = PyramidVisionTransformerV2(
        patch_size=4,
        embed_dims=[64, 128, 320, 512],
        num_heads=[1, 2, 5, 8],
        mlp_ratios=[8, 8, 4, 4],
        qkv_bias=True,
        norm_layer=partial(
            nn.LayerNorm, epsilon=1e-6),
        depths=[3, 4, 6, 3],
        sr_ratios=[8, 4, 2, 1],
        **kwargs)
    _load_pretrained(
        pretrained, model, MODEL_URLS["PVT_V2_B2"], use_ssld=use_ssld)
    return model


def PVT_V2_B3(pretrained=False, use_ssld=False, **kwargs):
    model = PyramidVisionTransformerV2(
        patch_size=4,
        embed_dims=[64, 128, 320, 512],
        num_heads=[1, 2, 5, 8],
        mlp_ratios=[8, 8, 4, 4],
        qkv_bias=True,
        norm_layer=partial(
            nn.LayerNorm, epsilon=1e-6),
        depths=[3, 4, 18, 3],
        sr_ratios=[8, 4, 2, 1],
        **kwargs)
    _load_pretrained(
        pretrained, model, MODEL_URLS["PVT_V2_B3"], use_ssld=use_ssld)
    return model


def PVT_V2_B4(pretrained=False, use_ssld=False, **kwargs):
    model = PyramidVisionTransformerV2(
        patch_size=4,
        embed_dims=[64, 128, 320, 512],
        num_heads=[1, 2, 5, 8],
        mlp_ratios=[8, 8, 4, 4],
        qkv_bias=True,
        norm_layer=partial(
            nn.LayerNorm, epsilon=1e-6),
        depths=[3, 8, 27, 3],
        sr_ratios=[8, 4, 2, 1],
        **kwargs)
    _load_pretrained(
        pretrained, model, MODEL_URLS["PVT_V2_B4"], use_ssld=use_ssld)
    return model


def PVT_V2_B5(pretrained=False, use_ssld=False, **kwargs):
    model = PyramidVisionTransformerV2(
        patch_size=4,
        embed_dims=[64, 128, 320, 512],
        num_heads=[1, 2, 5, 8],
        mlp_ratios=[4, 4, 4, 4],
        qkv_bias=True,
        norm_layer=partial(
            nn.LayerNorm, epsilon=1e-6),
        depths=[3, 6, 40, 3],
        sr_ratios=[8, 4, 2, 1],
        **kwargs)
    _load_pretrained(
        pretrained, model, MODEL_URLS["PVT_V2_B5"], use_ssld=use_ssld)
    return model


def PVT_V2_B2_Linear(pretrained=False, use_ssld=False, **kwargs):
    model = PyramidVisionTransformerV2(
        patch_size=4,
        embed_dims=[64, 128, 320, 512],
        num_heads=[1, 2, 5, 8],
        mlp_ratios=[8, 8, 4, 4],
        qkv_bias=True,
        norm_layer=partial(
            nn.LayerNorm, epsilon=1e-6),
        depths=[3, 4, 6, 3],
        sr_ratios=[8, 4, 2, 1],
        linear=True,
        **kwargs)
    _load_pretrained(
        pretrained, model, MODEL_URLS["PVT_V2_B2_Linear"], use_ssld=use_ssld)
    return model