import torch
import torch.nn as nn
import torch.nn.functional as F

import math
import logging
from functools import partial
from itertools import repeat
import collections.abc
from .helpers import _create_vision_transformer, trunc_normal_, _init_vit_weights, named_apply, _load_weights

_logger = logging.getLogger(__name__)

vit_model = {}
def register_model(name):
    def re(cls):
        vit_model[name] = cls
        return cls
    return re

def drop_path(x, drop_prob: float = 0., training: bool = False):
    """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
    This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
    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 ... I've opted for
    changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use
    'survival rate' as the argument.
    """
    if drop_prob == 0. or not training:
        return x
    keep_prob = 1 - drop_prob
    shape = (x.shape[0],) + (1,) * (x.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
    random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)
    random_tensor.floor_()  # binarize
    output = x.div(keep_prob) * random_tensor
    return output

class DropPath(nn.Module):
    """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)

def _ntuple(n):
    def parse(x):
        if isinstance(x, collections.abc.Iterable):
            return x
        return tuple(repeat(x, n))
    return parse
to_2tuple = _ntuple(2)

class PatchEmbed(nn.Module):
    """ 2D Image to Patch Embedding
    """
    def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768, norm_layer=None, flatten=True):
        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.grid_size = (img_size[0] // patch_size[0], img_size[1] // patch_size[1])
        self.num_patches = self.grid_size[0] * self.grid_size[1]
        self.flatten = flatten

        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
        self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()

    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)
        if self.flatten:
            x = x.flatten(2).transpose(1, 2)  # BCHW -> BNC
        x = self.norm(x)
        return x

class Mlp(nn.Module):
    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.drop(x)
        x = self.fc2(x)
        x = self.drop(x)
        return x

class Attention(nn.Module):
    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
        # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
        self.scale = qk_scale or head_dim ** -0.5

        self.qkv = nn.Linear(dim, dim * 3, bias=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):
        B, N, C = x.shape
        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
        q, k, v = qkv[0], qkv[1], qkv[2]   # make torchscript happy (cannot use tensor as tuple)

        attn = (q @ k.transpose(-2, -1)) * self.scale
        attn = attn.softmax(dim=-1)

        attn = self.attn_drop(attn)

        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
        x = self.proj(x)
        return x

class Block(nn.Module):

    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, AttentionModule=Attention):
        super().__init__()
        self.norm1 = norm_layer(dim)
        self.attn = AttentionModule(
            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 nn.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)

    def forward(self, x):
        y = self.attn(self.norm1(x))

        x = x + self.drop_path(y)
        x = x + self.drop_path(self.mlp(self.norm2(x)))
        return x


class VisionTransformer(nn.Module):
    """ Vision Transformer
    A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale`
        - https://arxiv.org/abs/2010.11929
    Includes distillation token & head support for `DeiT: Data-efficient Image Transformers`
        - https://arxiv.org/abs/2012.12877
    """

    def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12,
                 num_heads=12, mlp_ratio=4., qkv_bias=True, distilled=False,
                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0., embed_layer=PatchEmbed, norm_layer=None,
                 act_layer=None, weight_init=''):
        """
        Args:
            img_size (int, tuple): input image size
            patch_size (int, tuple): patch size
            in_chans (int): number of input channels
            num_classes (int): number of classes for classification head
            embed_dim (int): embedding dimension
            depth (int): depth of transformer
            num_heads (int): number of attention heads
            mlp_ratio (int): ratio of mlp hidden dim to embedding dim
            qkv_bias (bool): enable bias for qkv if True
            distilled (bool): model includes a distillation token and head as in DeiT models
            drop_rate (float): dropout rate
            attn_drop_rate (float): attention dropout rate
            drop_path_rate (float): stochastic depth rate
            embed_layer (nn.Module): patch embedding layer
            norm_layer: (nn.Module): normalization layer
            weight_init: (str): weight init scheme
        """
        super().__init__()
        self.num_classes = num_classes
        self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
        self.num_tokens = 2 if distilled else 1
        norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
        act_layer = act_layer or nn.GELU

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

        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
        self.dist_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) if distilled else None
        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + self.num_tokens, embed_dim))
        self.pos_drop = nn.Dropout(p=drop_rate)

        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
        self.blocks = nn.Sequential(*[
            Block(
                dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, drop=drop_rate,
                attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, act_layer=act_layer)
            for i in range(depth)])
        self.norm = norm_layer(embed_dim)

        # Classifier head(s)
        self.pre_logits = nn.Identity()
        self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
        self.head_dist = None
        if distilled:
            self.head_dist = nn.Linear(self.embed_dim, self.num_classes) if num_classes > 0 else nn.Identity()

        self.init_weights(weight_init)

    def init_weights(self, mode=''):
        assert mode in ('jax', 'jax_nlhb', 'nlhb', '')
        head_bias = -math.log(self.num_classes) if 'nlhb' in mode else 0.
        trunc_normal_(self.pos_embed, std=.02)
        if self.dist_token is not None:
            trunc_normal_(self.dist_token, std=.02)
        if mode.startswith('jax'):
            # leave cls token as zeros to match jax impl
            named_apply(partial(_init_vit_weights, head_bias=head_bias, jax_impl=True), self)
        else:
            trunc_normal_(self.cls_token, std=.02)
            self.apply(_init_vit_weights)

    def _init_weights(self, m):
        # this fn left here for compat with downstream users
        _init_vit_weights(m)

    @torch.jit.ignore()
    def load_pretrained(self, checkpoint_path, prefix=''):
        _load_weights(self, checkpoint_path, prefix)

    @torch.jit.ignore
    def no_weight_decay(self):
        return {'pos_embed', 'cls_token', 'dist_token'}

    def get_classifier(self):
        if self.dist_token is None:
            return self.head
        else:
            return self.head, self.head_dist

    def reset_classifier(self, num_classes, global_pool=''):
        self.num_classes = num_classes
        self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
        if self.num_tokens == 2:
            self.head_dist = nn.Linear(self.embed_dim, self.num_classes) if num_classes > 0 else nn.Identity()

    def forward_features(self, x):
        x = self.patch_embed(x)
        cls_token = self.cls_token.expand(x.shape[0], -1, -1)  # stole cls_tokens impl from Phil Wang, thanks
        if self.dist_token is None:
            x = torch.cat((cls_token, x), dim=1)
        else:
            x = torch.cat((cls_token, self.dist_token.expand(x.shape[0], -1, -1), x), dim=1)
        x = self.pos_drop(x + self.pos_embed)
        x = self.blocks(x)
        x = self.norm(x)
        return x[:, 0]


@register_model('vit_base_patch16_224')
def vit_base_patch16_224(pretrained=False, args=None, VisionTransformerModule=VisionTransformer):
    """ ViT-Base (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929).
    ImageNet-1k weights fine-tuned from in21k @ 224x224, source https://github.com/google-research/vision_transformer.
    """
    model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, args=args, VisionTransformerModule=VisionTransformerModule)
    model = _create_vision_transformer('vit_base_patch16_224', pretrained=pretrained, **model_kwargs)
    return model

@register_model('vit_small_patch16_224')
def vit_small_patch16_224(pretrained=False, args=None, VisionTransformerModule=VisionTransformer):
    """ ViT-Small (ViT-S/16)
    NOTE I've replaced my previous 'small' model definition and weights with the small variant from the DeiT paper
    """
    model_kwargs = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6, args=args, VisionTransformerModule=VisionTransformerModule)
    model = _create_vision_transformer('vit_small_patch16_224', pretrained=pretrained, **model_kwargs)
    return model


class VT(VisionTransformer):
    def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12,
                 num_heads=12, mlp_ratio=4., qkv_bias=True,
                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0., distilled=False,
                 args=None):

        super(VisionTransformer, self).__init__()
        self.num_classes = num_classes
        self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
        norm_layer = partial(nn.LayerNorm, eps=1e-6)
        self.distilled = distilled

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

        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
        if distilled:
            self.dist_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
            self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 2, embed_dim))
        else:
            self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim))
        self.pos_drop = nn.Dropout(p=drop_rate)

        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
        self.blocks = nn.Sequential(*[
            Block(
                dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias,
                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer)
            for i in range(depth)])
        self.norm = norm_layer(embed_dim)

        self.pre_logits = nn.Identity()
        self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
        if distilled:
            self.head_dist = nn.Linear(self.embed_dim, self.num_classes) if num_classes > 0 else nn.Identity()


    def forward_features(self, x):
        x = self.patch_embed(x)
        cls_tokens = self.cls_token.expand(x.shape[0], -1, -1)  # stole cls_tokens impl from Phil Wang, thanks

        if self.distilled:
            dist_tokens = self.dist_token.expand(x.shape[0], -1, -1)
            x = torch.cat((cls_tokens, dist_tokens, x), dim=1)
        else:
            x = torch.cat((cls_tokens, x), dim=1)
        x = x + self.pos_embed
        x = self.pos_drop(x)

        for layer, blk in enumerate(self.blocks):
            x = blk(x)

        x = self.norm(x)
        x = x[:, 0]

        return x


class ViTNet(nn.Module):
    def __init__(self, base_net='vit_base_patch16_224', use_bottleneck=True, bottleneck_dim=1024, width=1024, class_num=31, args=None):
        super(ViTNet, self).__init__()

        self.base_network = vit_model[base_net](pretrained=True, args=args, VisionTransformerModule=VT)
        self.use_bottleneck = use_bottleneck

        if self.use_bottleneck:
            self.bottleneck_layer = [nn.Linear(self.base_network.embed_dim, bottleneck_dim), nn.BatchNorm1d(bottleneck_dim), nn.ReLU(), nn.Dropout(0.5)]
            self.bottleneck = nn.Sequential(*self.bottleneck_layer)

        classifier_dim = bottleneck_dim if use_bottleneck else self.base_network.embed_dim
        self.classifier_layer = [nn.Linear(classifier_dim, width), nn.ReLU(), nn.Dropout(0.5), nn.Linear(width, class_num)]
        self.classifier = nn.Sequential(*self.classifier_layer)

        if self.use_bottleneck:
            self.bottleneck[0].weight.data.normal_(0, 0.005)
            self.bottleneck[0].bias.data.fill_(0.1)

        for dep in range(2):
            self.classifier[dep * 3].weight.data.normal_(0, 0.01)
            self.classifier[dep * 3].bias.data.fill_(0.0)

        self.parameter_list = [
                             {"params":self.base_network.parameters(), "lr":0.1},
                             {"params":self.classifier.parameters(), "lr":1}]

        if self.use_bottleneck:
            self.parameter_list.extend([{"params":self.bottleneck.parameters(), "lr":1}])


    def forward(self, inputs):
        features = self.base_network.forward_features(inputs)
        if self.use_bottleneck:
            features = self.bottleneck(features)

        outputs = self.classifier(features)

        return outputs

class ViT(object):
    def __init__(self, base_net='vit_base_patch16_224', bottleneck_dim=1024, class_num=31, use_gpu=True, args=None):
        self.c_net = ViTNet(base_net, args.use_bottleneck, bottleneck_dim, class_num, args)
        self.use_gpu = use_gpu
        self.is_train = False
        self.class_num = class_num
        if self.use_gpu:
            self.c_net = self.c_net.cuda()

    def to_dicts(self):
        return self.c_net.state_dict()

    def from_dicts(self, dicts):
        self.c_net.load_state_dict(dicts)

    def get_loss(self, inputs_source, inputs_target, labels_source, labels_target, args=None):

        outputs = self.c_net(inputs_source)

        classifier_loss = nn.CrossEntropyLoss()(outputs, labels_source)
        total_loss = classifier_loss

        if args.use_tensorboard:
            all_losses = {}
            all_losses.update({'classifier_loss': classifier_loss})

            for key, value in all_losses.items():
                if torch.is_tensor(value):
                    args.writer.add_scalar(key, value.item(), self.iter_num)
                else:
                    args.writer.add_scalar(key, value, self.iter_num)
            args.writer.flush()

        return total_loss

    def __call__(self, inputs):
        return self.forward(inputs)

    def forward(self, inputs):
        outputs = self.c_net(inputs)
        return outputs

    def predict(self, inputs, domain='target', output='prob'):
        outputs = self.c_net(inputs)
        if output == 'prob':
            softmax_outputs = F.softmax(outputs)
            return softmax_outputs
        elif output == 'score':
            return outputs
        else:
            raise NotImplementedError('Invalid output')

    def get_parameter_list(self):
        return self.c_net.parameter_list

    def set_train(self, mode):
        self.c_net.train(mode)
        self.is_train = mode
