import math
import warnings

import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import (build_conv_layer, build_norm_layer, constant_init,
                      kaiming_init, normal_init, trunc_normal_init)
from mmcv.cnn.bricks.transformer import FFN, MultiheadAttention
from mmcv.runner import BaseModule, ModuleList, _load_checkpoint
from torch.nn.modules.batchnorm import _BatchNorm
from torch.nn.modules.utils import _pair as to_2tuple

from mmseg.utils import get_root_logger
from ..builder import BACKBONES
from ..utils import vit_convert


class TransformerEncoderLayer(BaseModule):
    """Implements one encoder layer in Vision Transformer.

    Args:
        embed_dims (int): The feature dimension.
        num_heads (int): Parallel attention heads.
        feedforward_channels (int): The hidden dimension for FFNs.
        drop_rate (float): Probability of an element to be zeroed
            after the feed forward layer. Default: 0.0.
        attn_drop_rate (float): The drop out rate for attention layer.
            Default: 0.0.
        drop_path_rate (float): stochastic depth rate. Default 0.0.
        num_fcs (int): The number of fully-connected layers for FFNs.
            Default: 2.
        qkv_bias (bool): enable bias for qkv if True. Default: True
        act_cfg (dict): The activation config for FFNs.
            Defalut: dict(type='GELU').
        norm_cfg (dict): Config dict for normalization layer.
            Default: dict(type='LN').
        batch_first (bool): Key, Query and Value are shape of
            (batch, n, embed_dim)
            or (n, batch, embed_dim). Default: True.
    """

    def __init__(self,
                 embed_dims,
                 num_heads,
                 feedforward_channels,
                 drop_rate=0.,
                 attn_drop_rate=0.,
                 drop_path_rate=0.,
                 num_fcs=2,
                 qkv_bias=True,
                 act_cfg=dict(type='GELU'),
                 norm_cfg=dict(type='LN'),
                 batch_first=True):
        super(TransformerEncoderLayer, self).__init__()

        self.norm1_name, norm1 = build_norm_layer(
            norm_cfg, embed_dims, postfix=1)
        self.add_module(self.norm1_name, norm1)

        self.attn = MultiheadAttention(
            embed_dims=embed_dims,
            num_heads=num_heads,
            attn_drop=attn_drop_rate,
            proj_drop=drop_rate,
            dropout_layer=dict(type='DropPath', drop_prob=drop_path_rate),
            batch_first=batch_first,
            bias=qkv_bias)

        self.norm2_name, norm2 = build_norm_layer(
            norm_cfg, embed_dims, postfix=2)
        self.add_module(self.norm2_name, norm2)

        self.ffn = FFN(
            embed_dims=embed_dims,
            feedforward_channels=feedforward_channels,
            num_fcs=num_fcs,
            ffn_drop=drop_rate,
            dropout_layer=dict(type='DropPath', drop_prob=drop_path_rate),
            act_cfg=act_cfg)

    @property
    def norm1(self):
        return getattr(self, self.norm1_name)

    @property
    def norm2(self):
        return getattr(self, self.norm2_name)

    def forward(self, x):
        x = self.attn(self.norm1(x), identity=x)
        x = self.ffn(self.norm2(x), identity=x)
        return x


# Modified from pytorch-image-models
class PatchEmbed(BaseModule):
    """Image to Patch Embedding.

    Args:
        patch_size (int): The size of one patch
        in_channels (int): The num of input channels.
        embed_dims (int): The dimensions of embedding.
        norm_cfg (dict, optional): Config dict for normalization layer.
        conv_cfg (dict, optional): The config dict for conv layers.
            Default: None.
    """

    def __init__(self,
                 patch_size=16,
                 in_channels=3,
                 embed_dims=768,
                 norm_cfg=None,
                 conv_cfg=None):
        super(PatchEmbed, self).__init__()

        # Use conv layer to embed
        self.projection = build_conv_layer(
            conv_cfg,
            in_channels,
            embed_dims,
            kernel_size=patch_size,
            stride=patch_size)

        if norm_cfg is not None:
            self.norm = build_norm_layer(norm_cfg, embed_dims)[1]
        else:
            self.norm = None

    def forward(self, x):
        x = self.projection(x).flatten(2).transpose(1, 2)

        if self.norm is not None:
            x = self.norm(x)

        return x


@BACKBONES.register_module()
class VisionTransformer(BaseModule):
    """Vision Transformer.

    A PyTorch implement of : `An Image is Worth 16x16 Words:
    Transformers for Image Recognition at Scale` -
        https://arxiv.org/abs/2010.11929

    Args:
        img_size (int | tuple): Input image size. Default: 224.
        patch_size (int): The patch size. Default: 16.
        in_channels (int): Number of input channels. Default: 3.
        embed_dims (int): embedding dimension. Default: 768.
        num_layers (int): depth of transformer. Default: 12.
        num_heads (int): number of attention heads. Default: 12.
        mlp_ratio (int): ratio of mlp hidden dim to embedding dim.
            Default: 4.
        out_indices (list | tuple | int): Output from which stages.
            Default: -1.
        qkv_bias (bool): enable bias for qkv if True. Default: True.
        drop_rate (float): Probability of an element to be zeroed.
            Default 0.0
        attn_drop_rate (float): The drop out rate for attention layer.
            Default 0.0
        drop_path_rate (float): stochastic depth rate. Default 0.0
        with_cls_token (bool): If concatenating class token into image tokens
            as transformer input. Default: True.
        norm_cfg (dict): Config dict for normalization layer.
            Default: dict(type='LN')
        act_cfg (dict): The activation config for FFNs.
            Defalut: dict(type='GELU').
        patch_norm (bool): Whether to add a norm in PatchEmbed Block.
            Default: False.
        final_norm (bool): Whether to add a additional layer to normalize
            final feature map. Default: False.
        out_shape (str): Select the output format of feature information.
            Default: NCHW.
        interpolate_mode (str): Select the interpolate mode for position
            embeding vector resize. Default: bicubic.
        num_fcs (int): The number of fully-connected layers for FFNs.
            Default: 2.
        norm_eval (bool): Whether to set norm layers to eval mode, namely,
            freeze running stats (mean and var). Note: Effect on Batch Norm
            and its variants only. Default: False.
        with_cp (bool): Use checkpoint or not. Using checkpoint will save
            some memory while slowing down the training speed. Default: False.
        pretrain_style (str): Choose to use timm or mmcls pretrain weights.
            Default: timm.
        pretrained (str, optional): model pretrained path. Default: None.
        init_cfg (dict or list[dict], optional): Initialization config dict.
            Default: None.
    """

    def __init__(self,
                 img_size=224,
                 patch_size=16,
                 in_channels=3,
                 embed_dims=768,
                 num_layers=12,
                 num_heads=12,
                 mlp_ratio=4,
                 out_indices=-1,
                 qkv_bias=True,
                 drop_rate=0.,
                 attn_drop_rate=0.,
                 drop_path_rate=0.,
                 with_cls_token=True,
                 norm_cfg=dict(type='LN'),
                 act_cfg=dict(type='GELU'),
                 patch_norm=False,
                 final_norm=False,
                 out_shape='NCHW',
                 interpolate_mode='bicubic',
                 num_fcs=2,
                 norm_eval=False,
                 with_cp=False,
                 pretrain_style='timm',
                 pretrained=None,
                 init_cfg=None):
        super(VisionTransformer, self).__init__()

        if isinstance(img_size, int):
            img_size = to_2tuple(img_size)
        elif isinstance(img_size, tuple):
            if len(img_size) == 1:
                img_size = to_2tuple(img_size[0])
            assert len(img_size) == 2, \
                f'The size of image should have length 1 or 2, ' \
                f'but got {len(img_size)}'

        assert pretrain_style in ['timm', 'mmcls']

        assert out_shape in ['NLC',
                             'NCHW'], 'output shape must be "NLC" or "NCHW".'

        if isinstance(pretrained, str) or pretrained is None:
            warnings.warn('DeprecationWarning: pretrained is a deprecated, '
                          'please use "init_cfg" instead')
        else:
            raise TypeError('pretrained must be a str or None')

        self.img_size = img_size
        self.patch_size = patch_size
        self.out_shape = out_shape
        self.interpolate_mode = interpolate_mode
        self.norm_eval = norm_eval
        self.with_cp = with_cp
        self.pretrain_style = pretrain_style
        self.pretrained = pretrained
        self.init_cfg = init_cfg

        self.patch_embed = PatchEmbed(
            patch_size=patch_size,
            in_channels=in_channels,
            embed_dims=embed_dims,
            norm_cfg=norm_cfg if patch_norm else None)

        num_patches = (img_size[0] // patch_size) * \
            (img_size[1] // patch_size)

        self.with_cls_token = with_cls_token
        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dims))
        self.pos_embed = nn.Parameter(
            torch.zeros(1, num_patches + 1, embed_dims))
        self.drop_after_pos = nn.Dropout(p=drop_rate)

        if isinstance(out_indices, int):
            if out_indices == -1:
                out_indices = num_layers - 1
            self.out_indices = [out_indices]
        elif isinstance(out_indices, list) or isinstance(out_indices, tuple):
            self.out_indices = out_indices
        else:
            raise TypeError('out_indices must be type of int, list or tuple')

        dpr = [
            x.item() for x in torch.linspace(0, drop_path_rate, num_layers)
        ]  # stochastic depth decay rule

        self.layers = ModuleList()
        for i in range(num_layers):
            self.layers.append(
                TransformerEncoderLayer(
                    embed_dims=embed_dims,
                    num_heads=num_heads,
                    feedforward_channels=mlp_ratio * embed_dims,
                    attn_drop_rate=attn_drop_rate,
                    drop_rate=drop_rate,
                    drop_path_rate=dpr[i],
                    num_fcs=num_fcs,
                    qkv_bias=qkv_bias,
                    act_cfg=act_cfg,
                    norm_cfg=norm_cfg,
                    batch_first=True))

        self.final_norm = final_norm
        self.out_shape = out_shape
        if final_norm:
            self.norm1_name, norm1 = build_norm_layer(
                norm_cfg, embed_dims, postfix=1)
            self.add_module(self.norm1_name, norm1)

    @property
    def norm1(self):
        return getattr(self, self.norm1_name)

    def init_weights(self):
        if isinstance(self.pretrained, str):
            logger = get_root_logger()
            checkpoint = _load_checkpoint(
                self.pretrained, logger=logger, map_location='cpu')
            if 'state_dict' in checkpoint:
                state_dict = checkpoint['state_dict']
            elif 'model' in checkpoint:
                state_dict = checkpoint['model']
            else:
                state_dict = checkpoint

            if self.pretrain_style == 'timm':
                # Because the refactor of vit is blocked by mmcls,
                # so we firstly use timm pretrain weights to train
                # downstream model.
                state_dict = vit_convert(state_dict)

            if 'pos_embed' in state_dict.keys():
                if self.pos_embed.shape != state_dict['pos_embed'].shape:
                    logger.info(msg=f'Resize the pos_embed shape from '
                                f'{state_dict["pos_embed"].shape} to '
                                f'{self.pos_embed.shape}')
                    h, w = self.img_size
                    pos_size = int(
                        math.sqrt(state_dict['pos_embed'].shape[1] - 1))
                    state_dict['pos_embed'] = self.resize_pos_embed(
                        state_dict['pos_embed'], (h, w), (pos_size, pos_size),
                        self.patch_size, self.interpolate_mode)

            self.load_state_dict(state_dict, False)

        elif self.pretrained is None:
            super(VisionTransformer, self).init_weights()
            # We only implement the 'jax_impl' initialization implemented at
            # https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py#L353  # noqa: E501
            trunc_normal_init(self.pos_embed, std=.02)
            trunc_normal_init(self.cls_token, std=.02)
            for n, m in self.named_modules():
                if isinstance(m, nn.Linear):
                    trunc_normal_init(m.weight, std=.02)
                    if m.bias is not None:
                        if 'ffn' in n:
                            normal_init(m.bias, std=1e-6)
                        else:
                            constant_init(m.bias, 0)
                elif isinstance(m, nn.Conv2d):
                    kaiming_init(m.weight, mode='fan_in')
                    if m.bias is not None:
                        constant_init(m.bias, 0)
                elif isinstance(m, (_BatchNorm, nn.GroupNorm, nn.LayerNorm)):
                    constant_init(m.bias, 0)
                    constant_init(m.weight, 1.0)

    def _pos_embeding(self, img, patched_img, pos_embed):
        """Positiong embeding method.

        Resize the pos_embed, if the input image size doesn't match
            the training size.
        Args:
            img (torch.Tensor): The inference image tensor, the shape
                must be [B, C, H, W].
            patched_img (torch.Tensor): The patched image, it should be
                shape of [B, L1, C].
            pos_embed (torch.Tensor): The pos_embed weighs, it should be
                shape of [B, L2, c].
        Return:
            torch.Tensor: The pos encoded image feature.
        """
        assert patched_img.ndim == 3 and pos_embed.ndim == 3, \
            'the shapes of patched_img and pos_embed must be [B, L, C]'
        x_len, pos_len = patched_img.shape[1], pos_embed.shape[1]
        if x_len != pos_len:
            if pos_len == (self.img_size[0] // self.patch_size) * (
                    self.img_size[1] // self.patch_size) + 1:
                pos_h = self.img_size[0] // self.patch_size
                pos_w = self.img_size[1] // self.patch_size
            else:
                raise ValueError(
                    'Unexpected shape of pos_embed, got {}.'.format(
                        pos_embed.shape))
            pos_embed = self.resize_pos_embed(pos_embed, img.shape[2:],
                                              (pos_h, pos_w), self.patch_size,
                                              self.interpolate_mode)
        return self.drop_after_pos(patched_img + pos_embed)

    @staticmethod
    def resize_pos_embed(pos_embed, input_shpae, pos_shape, patch_size, mode):
        """Resize pos_embed weights.

        Resize pos_embed using bicubic interpolate method.
        Args:
            pos_embed (torch.Tensor): pos_embed weights.
            input_shpae (tuple): Tuple for (input_h, intput_w).
            pos_shape (tuple): Tuple for (pos_h, pos_w).
            patch_size (int): Patch size.
        Return:
            torch.Tensor: The resized pos_embed of shape [B, L_new, C]
        """
        assert pos_embed.ndim == 3, 'shape of pos_embed must be [B, L, C]'
        input_h, input_w = input_shpae
        pos_h, pos_w = pos_shape
        cls_token_weight = pos_embed[:, 0]
        pos_embed_weight = pos_embed[:, (-1 * pos_h * pos_w):]
        pos_embed_weight = pos_embed_weight.reshape(
            1, pos_h, pos_w, pos_embed.shape[2]).permute(0, 3, 1, 2)
        pos_embed_weight = F.interpolate(
            pos_embed_weight,
            size=[input_h // patch_size, input_w // patch_size],
            align_corners=False,
            mode=mode)
        cls_token_weight = cls_token_weight.unsqueeze(1)
        pos_embed_weight = torch.flatten(pos_embed_weight, 2).transpose(1, 2)
        pos_embed = torch.cat((cls_token_weight, pos_embed_weight), dim=1)
        return pos_embed

    def forward(self, inputs):
        B = inputs.shape[0]

        x = self.patch_embed(inputs)

        # stole cls_tokens impl from Phil Wang, thanks
        cls_tokens = self.cls_token.expand(B, -1, -1)
        x = torch.cat((cls_tokens, x), dim=1)
        x = self._pos_embeding(inputs, x, self.pos_embed)

        if not self.with_cls_token:
            # Remove class token for transformer encoder input
            x = x[:, 1:]

        outs = []
        for i, layer in enumerate(self.layers):
            x = layer(x)
            if i == len(self.layers) - 1:
                if self.final_norm:
                    x = self.norm1(x)
            if i in self.out_indices:
                if self.with_cls_token:
                    # Remove class token and reshape token for decoder head
                    out = x[:, 1:]
                else:
                    out = x
                if self.out_shape == 'NCHW':
                    B, _, C = out.shape
                    out = out.reshape(B, inputs.shape[2] // self.patch_size,
                                      inputs.shape[3] // self.patch_size,
                                      C).permute(0, 3, 1, 2)
                outs.append(out)

        return tuple(outs)

    def train(self, mode=True):
        super(VisionTransformer, self).train(mode)
        if mode and self.norm_eval:
            for m in self.modules():
                if isinstance(m, nn.LayerNorm):
                    m.eval()
