import os
from copy import deepcopy

from torch import nn as nn

from timm.layers import Conv2dSame, BatchNormAct2d, Linear

__all__ = ['extract_layer', 'set_layer', 'adapt_model_from_string', 'adapt_model_from_file']


def extract_layer(model, layer):
    layer = layer.split('.')
    module = model
    if hasattr(model, 'module') and layer[0] != 'module':
        module = model.module
    if not hasattr(model, 'module') and layer[0] == 'module':
        layer = layer[1:]
    for l in layer:
        if hasattr(module, l):
            if not l.isdigit():
                module = getattr(module, l)
            else:
                module = module[int(l)]
        else:
            return module
    return module


def set_layer(model, layer, val):
    layer = layer.split('.')
    module = model
    if hasattr(model, 'module') and layer[0] != 'module':
        module = model.module
    lst_index = 0
    module2 = module
    for l in layer:
        if hasattr(module2, l):
            if not l.isdigit():
                module2 = getattr(module2, l)
            else:
                module2 = module2[int(l)]
            lst_index += 1
    lst_index -= 1
    for l in layer[:lst_index]:
        if not l.isdigit():
            module = getattr(module, l)
        else:
            module = module[int(l)]
    l = layer[lst_index]
    setattr(module, l, val)


def adapt_model_from_string(parent_module, model_string):
    separator = '***'
    state_dict = {}
    lst_shape = model_string.split(separator)
    for k in lst_shape:
        k = k.split(':')
        key = k[0]
        shape = k[1][1:-1].split(',')
        if shape[0] != '':
            state_dict[key] = [int(i) for i in shape]

    new_module = deepcopy(parent_module)
    for n, m in parent_module.named_modules():
        old_module = extract_layer(parent_module, n)
        if isinstance(old_module, nn.Conv2d) or isinstance(old_module, Conv2dSame):
            if isinstance(old_module, Conv2dSame):
                conv = Conv2dSame
            else:
                conv = nn.Conv2d
            s = state_dict[n + '.weight']
            in_channels = s[1]
            out_channels = s[0]
            g = 1
            if old_module.groups > 1:
                in_channels = out_channels
                g = in_channels
            new_conv = conv(
                in_channels=in_channels, out_channels=out_channels, kernel_size=old_module.kernel_size,
                bias=old_module.bias is not None, padding=old_module.padding, dilation=old_module.dilation,
                groups=g, stride=old_module.stride)
            set_layer(new_module, n, new_conv)
        elif isinstance(old_module, BatchNormAct2d):
            new_bn = BatchNormAct2d(
                state_dict[n + '.weight'][0], eps=old_module.eps, momentum=old_module.momentum,
                affine=old_module.affine, track_running_stats=True)
            new_bn.drop = old_module.drop
            new_bn.act = old_module.act
            set_layer(new_module, n, new_bn)
        elif isinstance(old_module, nn.BatchNorm2d):
            new_bn = nn.BatchNorm2d(
                num_features=state_dict[n + '.weight'][0], eps=old_module.eps, momentum=old_module.momentum,
                affine=old_module.affine, track_running_stats=True)
            set_layer(new_module, n, new_bn)
        elif isinstance(old_module, nn.Linear):
            # FIXME extra checks to ensure this is actually the FC classifier layer and not a diff Linear layer?
            num_features = state_dict[n + '.weight'][1]
            new_fc = Linear(
                in_features=num_features, out_features=old_module.out_features, bias=old_module.bias is not None)
            set_layer(new_module, n, new_fc)
            if hasattr(new_module, 'num_features'):
                new_module.num_features = num_features
    new_module.eval()
    parent_module.eval()

    return new_module


def adapt_model_from_file(parent_module, model_variant):
    adapt_file = os.path.join(os.path.dirname(__file__), '_pruned', model_variant + '.txt')
    with open(adapt_file, 'r') as f:
        return adapt_model_from_string(parent_module, f.read().strip())