import os, sys, time

import torch

import torch.nn as nn

import torch.nn.functional as F

from timm.models.layers import trunc_normal_, DropPath

from timm.models.registry import register_model

sys.path.append(os.getcwd())

import utilities.runUtils as rutl

##------------------------------------------------------------------------------

# ConvNext Taken From: https://github.com/facebookresearch/ConvNeXt/blob/main/models/convnext.py

class Block(nn.Module):

    r""" ConvNeXt Block. There are two equivalent implementations:

    (1) DwConv -> LayerNorm (channels_first) -> 1x1 Conv -> GELU -> 1x1 Conv; all in (N, C, H, W)

    (2) DwConv -> Permute to (N, H, W, C); LayerNorm (channels_last) -> Linear -> GELU -> Linear; Permute back

    We use (2) as we find it slightly faster in PyTorch

    Args:

        dim (int): Number of input channels.

        drop_path (float): Stochastic depth rate. Default: 0.0

        layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6.

    """

    def __init__(self, dim, drop_path=0., layer_scale_init_value=1e-6):

        super().__init__()

        self.dwconv = nn.Conv2d(dim, dim, kernel_size=7, padding=3, groups=dim) # depthwise conv

        self.norm = LayerNorm(dim, eps=1e-6)

        self.pwconv1 = nn.Linear(dim, 4 * dim) # pointwise/1x1 convs, implemented with linear layers

        self.act = nn.GELU()

        self.pwconv2 = nn.Linear(4 * dim, dim)

        self.gamma = nn.Parameter(layer_scale_init_value * torch.ones((dim)),

                                    requires_grad=True) if layer_scale_init_value > 0 else None

        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()

    def forward(self, x):

        input = x

        x = self.dwconv(x)

        x = x.permute(0, 2, 3, 1) # (N, C, H, W) -> (N, H, W, C)

        x = self.norm(x)

        x = self.pwconv1(x)

        x = self.act(x)

        x = self.pwconv2(x)

        if self.gamma is not None:

            x = self.gamma * x

        x = x.permute(0, 3, 1, 2) # (N, H, W, C) -> (N, C, H, W)

        x = input + self.drop_path(x)

        return x

class ConvNeXt(nn.Module):

    r""" ConvNeXt

        A PyTorch impl of : `A ConvNet for the 2020s`  -

          https://arxiv.org/pdf/2201.03545.pdf

    Args:

        in_chans (int): Number of input image channels. Default: 3

        num_classes (int): Number of classes for classification head. Default: 1000

        depths (tuple(int)): Number of blocks at each stage. Default: [3, 3, 9, 3]

        dims (int): Feature dimension at each stage. Default: [96, 192, 384, 768]

        drop_path_rate (float): Stochastic depth rate. Default: 0.

        layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6.

        head_init_scale (float): Init scaling value for classifier weights and biases. Default: 1.

    """

    def __init__(self, in_chans=3, num_classes=1000,

                 depths=[3, 3, 9, 3], dims=[96, 192, 384, 768], drop_path_rate=0.,

                 layer_scale_init_value=1e-6, head_init_scale=1.,

                 ):

        super().__init__()

        self.downsample_layers = nn.ModuleList() # stem and 3 intermediate downsampling conv layers

        stem = nn.Sequential(

            nn.Conv2d(in_chans, dims[0], kernel_size=4, stride=4),

            LayerNorm(dims[0], eps=1e-6, data_format="channels_first")

        )

        self.downsample_layers.append(stem)

        for i in range(3):

            downsample_layer = nn.Sequential(

                    LayerNorm(dims[i], eps=1e-6, data_format="channels_first"),

                    nn.Conv2d(dims[i], dims[i+1], kernel_size=2, stride=2),

            )

            self.downsample_layers.append(downsample_layer)

        self.stages = nn.ModuleList() # 4 feature resolution stages, each consisting of multiple residual blocks

        dp_rates=[x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]

        cur = 0

        for i in range(4):

            stage = nn.Sequential(

                *[Block(dim=dims[i], drop_path=dp_rates[cur + j],

                layer_scale_init_value=layer_scale_init_value) for j in range(depths[i])]

            )

            self.stages.append(stage)

            cur += depths[i]

        self.norm = nn.LayerNorm(dims[-1], eps=1e-6) # final norm layer

        self.head = nn.Linear(dims[-1], num_classes)

        self.apply(self._init_weights)

        self.head.weight.data.mul_(head_init_scale)

        self.head.bias.data.mul_(head_init_scale)

    def _init_weights(self, m):

        if isinstance(m, (nn.Conv2d, nn.Linear)):

            trunc_normal_(m.weight, std=.02)

            nn.init.constant_(m.bias, 0)

    def forward_features(self, x):

        for i in range(4):

            x = self.downsample_layers[i](x)

            x = self.stages[i](x)

        return self.norm(x.mean([-2, -1])) # global average pooling, (N, C, H, W) -> (N, C)

    def forward(self, x):

        x = self.forward_features(x)

        x = self.head(x)

        return x

class LayerNorm(nn.Module):

    r""" LayerNorm that supports two data formats: channels_last (default) or channels_first.

    The ordering of the dimensions in the inputs. channels_last corresponds to inputs with

    shape (batch_size, height, width, channels) while channels_first corresponds to inputs

    with shape (batch_size, channels, height, width).

    """

    def __init__(self, normalized_shape, eps=1e-6, data_format="channels_last"):

        super().__init__()

        self.weight = nn.Parameter(torch.ones(normalized_shape))

        self.bias = nn.Parameter(torch.zeros(normalized_shape))

        self.eps = eps

        self.data_format = data_format

        if self.data_format not in ["channels_last", "channels_first"]:

            raise NotImplementedError

        self.normalized_shape = (normalized_shape, )

    def forward(self, x):

        if self.data_format == "channels_last":

            return F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)

        elif self.data_format == "channels_first":

            u = x.mean(1, keepdim=True)

            s = (x - u).pow(2).mean(1, keepdim=True)

            x = (x - u) / torch.sqrt(s + self.eps)

            x = self.weight[:, None, None] * x + self.bias[:, None, None]

            return x

model_urls = {

    "convnext_tiny_1k": "https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth",

    "convnext_small_1k": "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth",

    "convnext_base_1k": "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth",

    "convnext_large_1k": "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth",

    "convnext_tiny_22k": "https://dl.fbaipublicfiles.com/convnext/convnext_tiny_22k_224.pth",

    "convnext_small_22k": "https://dl.fbaipublicfiles.com/convnext/convnext_small_22k_224.pth",

    "convnext_base_22k": "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth",

    "convnext_large_22k": "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth",

    "convnext_xlarge_22k": "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth",

}

@register_model

def convnext_tiny(pretrained=False,in_22k=False, **kwargs):

    model = ConvNeXt(depths=[3, 3, 9, 3], dims=[96, 192, 384, 768], **kwargs)

    if pretrained:

        url = model_urls['convnext_tiny_22k'] if in_22k else model_urls['convnext_tiny_1k']

        checkpoint = torch.hub.load_state_dict_from_url(url=url, map_location="cpu", check_hash=True)

        model.load_state_dict(checkpoint["model"])

    return model

@register_model

def convnext_small(pretrained=False,in_22k=False, **kwargs):

    model = ConvNeXt(depths=[3, 3, 27, 3], dims=[96, 192, 384, 768], **kwargs)

    if pretrained:

        url = model_urls['convnext_small_22k'] if in_22k else model_urls['convnext_small_1k']

        checkpoint = torch.hub.load_state_dict_from_url(url=url, map_location="cpu")

        model.load_state_dict(checkpoint["model"])

    return model

@register_model

def convnext_base(pretrained=False, in_22k=False, **kwargs):

    model = ConvNeXt(depths=[3, 3, 27, 3], dims=[128, 256, 512, 1024], **kwargs)

    if pretrained:

        url = model_urls['convnext_base_22k'] if in_22k else model_urls['convnext_base_1k']

        checkpoint = torch.hub.load_state_dict_from_url(url=url, map_location="cpu")

        model.load_state_dict(checkpoint["model"])

    return model

@register_model

def convnext_large(pretrained=False, in_22k=False, **kwargs):

    model = ConvNeXt(depths=[3, 3, 27, 3], dims=[192, 384, 768, 1536], **kwargs)

    if pretrained:

        url = model_urls['convnext_large_22k'] if in_22k else model_urls['convnext_large_1k']

        checkpoint = torch.hub.load_state_dict_from_url(url=url, map_location="cpu")

        model.load_state_dict(checkpoint["model"])

    return model

@register_model

def convnext_xlarge(pretrained=False, in_22k=False, **kwargs):

    model = ConvNeXt(depths=[3, 3, 27, 3], dims=[256, 512, 1024, 2048], **kwargs)

    if pretrained:

        assert in_22k, "only ImageNet-22K pre-trained ConvNeXt-XL is available; please set in_22k=True"

        url = model_urls['convnext_xlarge_22k']

        checkpoint = torch.hub.load_state_dict_from_url(url=url, map_location="cpu")

        model.load_state_dict(checkpoint["model"])

    return model

##==================== Task Specific Class======================================

class ClassifierNet(nn.Module):

    def __init__(self, args):

        super().__init__()

        rutl.START_SEED()

        self.args = args

        # Feature Extractor

        self.backbone, self.feat_outsize = self._load_convnext_backbone()

        self.feat_dropout = nn.Dropout(p=self.args.featx_dropout)

        # Classifier

        sizes = [self.feat_outsize] + list(args.classifier)

        layers = []

        for i in range(len(sizes) - 2):

            layers.append(nn.Linear(sizes[i], sizes[i + 1], bias=False))

            layers.append(nn.LayerNorm(sizes[i + 1]))  #watchout this is LayerNorm

            layers.append(nn.ReLU(inplace=True))

            layers.append(nn.Dropout(p=self.args.clsfy_dropout))

        layers.append(nn.Linear(sizes[-2], sizes[-1], bias=False))

        self.classifier = nn.Sequential(*layers)

    def forward(self, x):

        x = self.backbone(x)

        x = self.feat_dropout(x)

        out = self.classifier(x)

        return out

    def _load_convnext_backbone(self):

        ## pretrain setting

        pretrain = False; imgnet22k = False

        head_default = None

        if self.args.featx_pretrain in ["DEFAULT", "IMAGENET-1K"]:

            pretrain = True

            head_default = 1000

        elif self.args.featx_pretrain == "IMAGENET-22K":

            pretrain = True; imgnet22k = True

            head_default = 21841

        elif self.args.featx_pretrain not in [None, "NONE", "none"]:

            raise ValueError(f"Unknown pretrain weight type requested {self.args.featx_pretrain}" )

        ## Model loading

        if self.args.feature_extract == 'convnext-tiny':

            backbone = convnext_tiny(pretrained=pretrain, in_22k=imgnet22k,

                                        num_classes = head_default)

            outfeat_size = 768

        elif self.args.feature_extract == 'convnext-small':

            backbone = convnext_small(pretrained=pretrain, in_22k=imgnet22k,

                                        num_classes = head_default)

            outfeat_size = 768

        elif self.args.feature_extract == 'convnext-base':

            backbone = convnext_base(pretrained=pretrain, in_22k=imgnet22k,

                                        num_classes = head_default)

            outfeat_size = 1024

        elif self.args.feature_extract == 'convnext-large':

            backbone = convnext_large(pretrained=pretrain, in_22k=imgnet22k,

                                        num_classes = head_default)

            outfeat_size = 1536

        else:

            raise ValueError(f"Unknown Model Implementation called in {os.path.basename(__file__)}")

        backbone.head = nn.Identity() #remove fc of default arc

        # pretrain from external file

        if os.path.exists(self.args.featx_pretrain):

            backbone = self._load_weights_from_file(backbone,

                                self.args.featx_pretrain )

            print("Loaded:", self.args.featx_pretrain )

        return backbone, outfeat_size

if __name__ == "__main__":

    from torchsummary import summary

    model = convnext_large()

    summary(model, (3, 224, 224))