from __future__ import absolute_import
import torch
import os

from .osnet import *
from .irse import *

# from .pcb import *
# from .mlfn import *
# from .hacnn import *
# from .senet import *
# from .mudeep import *
# from .nasnet import *
# from .resnet import *
# from .densenet import *
# from .xception import *
# from .osnet_ain import *
# from .resnetmid import *
# from .shufflenet import *
# from .squeezenet import *
# from .inceptionv4 import *
# from .mobilenetv2 import *
# from .resnet_ibn_a import *
# from .resnet_ibn_b import *
# from .shufflenetv2 import *
# from .inceptionresnetv2 import *

__model_factory = {
    # image classification models
    'osnet_x1_0_msmt17': osnet_x1_0,
    'osnet_x0_75_msmt17': osnet_x0_75,
    'osnet_x0_5_msmt17': osnet_x0_5,
    'osnet_x0_25_msmt17': osnet_x0_25,
    'ir_se': ir_se,
    'ir': ir,
    # 'resnet18': resnet18,
    # 'resnet34': resnet34,
    # 'resnet50': resnet50,
    # 'resnet101': resnet101,
    # 'resnet152': resnet152,
    # 'resnext50_32x4d': resnext50_32x4d,
    # 'resnext101_32x8d': resnext101_32x8d,
    # 'resnet50_fc512': resnet50_fc512,
    # 'se_resnet50': se_resnet50,
    # 'se_resnet50_fc512': se_resnet50_fc512,
    # 'se_resnet101': se_resnet101,
    # 'se_resnext50_32x4d': se_resnext50_32x4d,
    # 'se_resnext101_32x4d': se_resnext101_32x4d,
    # 'densenet121': densenet121,
    # 'densenet169': densenet169,
    # 'densenet201': densenet201,
    # 'densenet161': densenet161,
    # 'densenet121_fc512': densenet121_fc512,
    # 'inceptionresnetv2': inceptionresnetv2,
    # 'inceptionv4': inceptionv4,
    # 'xception': xception,
    # 'resnet50_ibn_a': resnet50_ibn_a,
    # 'resnet50_ibn_b': resnet50_ibn_b,
    # # lightweight models
    # 'nasnsetmobile': nasnetamobile,
    # 'mobilenetv2_x1_0': mobilenetv2_x1_0,
    # 'mobilenetv2_x1_4': mobilenetv2_x1_4,
    # 'shufflenet': shufflenet,
    # 'squeezenet1_0': squeezenet1_0,
    # 'squeezenet1_0_fc512': squeezenet1_0_fc512,
    # 'squeezenet1_1': squeezenet1_1,
    # 'shufflenet_v2_x0_5': shufflenet_v2_x0_5,
    # 'shufflenet_v2_x1_0': shufflenet_v2_x1_0,
    # 'shufflenet_v2_x1_5': shufflenet_v2_x1_5,
    # 'shufflenet_v2_x2_0': shufflenet_v2_x2_0,
    # # reid-specific models
    # 'mudeep': MuDeep,
    # 'resnet50mid': resnet50mid,
    # 'hacnn': HACNN,
    # 'pcb_p6': pcb_p6,
    # 'pcb_p4': pcb_p4,
    # 'mlfn': mlfn,
    # 'osnet_ibn_x1_0': osnet_ibn_x1_0,
    # 'osnet_ain_x1_0': osnet_ain_x1_0,
    # 'osnet_ain_x0_75': osnet_ain_x0_75,
    # 'osnet_ain_x0_5': osnet_ain_x0_5,
    # 'osnet_ain_x0_25': osnet_ain_x0_25
}


def show_avai_models():
    """Displays available models.

    Examples::
        >>> from torchreid import models
        >>> models.show_avai_models()
    """
    print(list(__model_factory.keys()))

def _extract_model_key(name: str):
    # 避免有模型檔案名稱像是"model_ir_se50.pth" → "ir_se"
    name = os.path.basename(name)
    name = name.split('.')[0]
    for key in __model_factory.keys():
        if key in name:
            return key
    raise KeyError(f"Cannot infer model type from '{name}'")


def build_model(
    name, num_layers=50, drop_ratio=0, num_classes=1, loss='softmax', pretrained=True, use_gpu=True
):
    """A function wrapper for building a model.

    Args:
        name (str): model name.
        num_classes (int): number of training identities.
        loss (str, optional): loss function to optimize the model. Currently
            supports "softmax" and "triplet". Default is "softmax".
        pretrained (bool, optional): whether to load ImageNet-pretrained weights.
            Default is True.
        use_gpu (bool, optional): whether to use gpu. Default is True.

    Returns:
        nn.Module

    Examples::
        >>> from torchreid import models
        >>> model = models.build_model('resnet50', 751, loss='softmax')
    """
    name = _extract_model_key(name)
    avai_models = list(__model_factory.keys())

    if name not in avai_models:
        raise KeyError(
            'Unknown model: {}. Must be one of {}'.format(name, avai_models)
        )

    if name in ['ir', 'ir_se']:
        return __model_factory[name](
        num_layers=num_layers,
        drop_ratio=drop_ratio
    )

    else:
        return __model_factory[name](
            num_classes=num_classes,
            loss=loss,
            pretrained=pretrained,
            use_gpu=use_gpu
        )
