import torch.nn as nn
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.models.registry import register_model
from .helpers import build_model_with_cfg


def _cfg(url='', **kwargs):
    return {
        'url': url,
        'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
        'crop_pct': .96, 'interpolation': 'bicubic',
        'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD, 'classifier': 'head',
        'first_conv': 'stem.0',
        **kwargs
    }


default_cfgs = {
    'convmixer_1536_20': _cfg(url='https://github.com/tmp-iclr/convmixer/releases/download/timm-v1.0/convmixer_1536_20_ks9_p7.pth.tar'),
    'convmixer_768_32': _cfg(url='https://github.com/tmp-iclr/convmixer/releases/download/timm-v1.0/convmixer_768_32_ks7_p7_relu.pth.tar'),
    'convmixer_1024_20_ks9_p14': _cfg(url='https://github.com/tmp-iclr/convmixer/releases/download/timm-v1.0/convmixer_1024_20_ks9_p14.pth.tar')
}


class Residual(nn.Module):
    def __init__(self, fn):
        super().__init__()
        self.fn = fn

    def forward(self, x):
        return self.fn(x) + x


class ConvMixer(nn.Module):
    def __init__(self, dim, depth, kernel_size=9, patch_size=7, in_chans=3, num_classes=1000, activation=nn.GELU, **kwargs):
        super().__init__()
        self.num_classes = num_classes
        self.num_features = dim
        self.head = nn.Linear(dim, num_classes) if num_classes > 0 else nn.Identity()
        self.stem = nn.Sequential(
            nn.Conv2d(in_chans, dim, kernel_size=patch_size, stride=patch_size),
            activation(),
            nn.BatchNorm2d(dim)
        )
        self.blocks = nn.Sequential(
            *[nn.Sequential(
                    Residual(nn.Sequential(
                        nn.Conv2d(dim, dim, kernel_size, groups=dim, padding="same"),
                        activation(),
                        nn.BatchNorm2d(dim)
                    )),
                    nn.Conv2d(dim, dim, kernel_size=1),
                    activation(),
                    nn.BatchNorm2d(dim)
            ) for i in range(depth)]
        )
        self.pooling = nn.Sequential(
            nn.AdaptiveAvgPool2d((1, 1)),
            nn.Flatten()
        )

    def get_classifier(self):
        return self.head

    def reset_classifier(self, num_classes, global_pool=''):
        self.num_classes = num_classes
        self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
          
    def forward_features(self, x):
        x = self.stem(x)
        x = self.blocks(x)
        return x
    
    def forward(self, x):
        x = self.forward_features(x)
        x = self.pooling(x)
        x = self.head(x)
        return x


def _create_convmixer(variant, pretrained=False, **kwargs):
    return build_model_with_cfg(ConvMixer, variant, pretrained, **kwargs)


@register_model
def convmixer_1536_20(pretrained=False, **kwargs):
    model_args = dict(dim=1536, depth=20, kernel_size=9, patch_size=7, **kwargs)
    return _create_convmixer('convmixer_1536_20', pretrained, **model_args)


@register_model
def convmixer_768_32(pretrained=False, **kwargs):
    model_args = dict(dim=768, depth=32, kernel_size=7, patch_size=7, activation=nn.ReLU, **kwargs)
    return _create_convmixer('convmixer_768_32', pretrained, **model_args)


@register_model
def convmixer_1024_20_ks9_p14(pretrained=False, **kwargs):
    model_args = dict(dim=1024, depth=20, kernel_size=9, patch_size=14, **kwargs)
    return _create_convmixer('convmixer_1024_20_ks9_p14', pretrained, **model_args)