##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
from os import path as osp
from typing import List, Text
import torch

__all__ = ['change_key', 'get_cell_based_tiny_net', 'get_search_spaces', 'get_cifar_models', 'get_imagenet_models', \
           'obtain_model', 'obtain_search_model', 'load_net_from_checkpoint', \
           'CellStructure', 'CellArchitectures'
           ]

# useful modules
from config_utils import dict2config
from .SharedUtils import change_key
from .cell_searchs import CellStructure, CellArchitectures


# Cell-based NAS Models
def get_cell_based_tiny_net(config):
  if isinstance(config, dict): config = dict2config(config, None) # to support the argument being a dict
  super_type = getattr(config, 'super_type', 'basic')
  group_names = ['DARTS-V1', 'DARTS-V2', 'GDAS', 'SETN', 'ENAS', 'RANDOM']
  if super_type == 'basic' and config.name in group_names:
    from .cell_searchs import nas201_super_nets as nas_super_nets
    try:
      return nas_super_nets[config.name](config.C, config.N, config.max_nodes, config.num_classes, config.space, config.affine, config.track_running_stats)
    except:
      return nas_super_nets[config.name](config.C, config.N, config.max_nodes, config.num_classes, config.space)
  elif super_type == 'nasnet-super':
    from .cell_searchs import nasnet_super_nets as nas_super_nets
    return nas_super_nets[config.name](config.C, config.N, config.steps, config.multiplier, \
                    config.stem_multiplier, config.num_classes, config.space, config.affine, config.track_running_stats)
  elif config.name == 'infer.tiny':
    from .cell_infers import TinyNetwork
    if hasattr(config, 'genotype'):
      genotype = config.genotype
    elif hasattr(config, 'arch_str'):
      genotype = CellStructure.str2structure(config.arch_str)
    else: raise ValueError('Can not find genotype from this config : {:}'.format(config))
    return TinyNetwork(config.C, config.N, genotype, config.num_classes)
  elif config.name == 'infer.shape.tiny':
    from .shape_infers import DynamicShapeTinyNet
    if isinstance(config.channels, str):
      channels = tuple([int(x) for x in config.channels.split(':')])
    else: channels = config.channels
    genotype = CellStructure.str2structure(config.genotype)
    return DynamicShapeTinyNet(channels, genotype, config.num_classes)
  elif config.name == 'infer.nasnet-cifar':
    from .cell_infers import NASNetonCIFAR
    raise NotImplementedError
  else:
    raise ValueError('invalid network name : {:}'.format(config.name))


# obtain the search space, i.e., a dict mapping the operation name into a python-function for this op
def get_search_spaces(xtype, name) -> List[Text]:
  if xtype == 'cell':
    from .cell_operations import SearchSpaceNames
    assert name in SearchSpaceNames, 'invalid name [{:}] in {:}'.format(name, SearchSpaceNames.keys())
    return SearchSpaceNames[name]
  else:
    raise ValueError('invalid search-space type is {:}'.format(xtype))


def get_cifar_models(config, extra_path=None):
  super_type = getattr(config, 'super_type', 'basic')
  if super_type == 'basic':
    from .CifarResNet      import CifarResNet
    from .CifarDenseNet    import DenseNet
    from .CifarWideResNet  import CifarWideResNet
    if config.arch == 'resnet':
      return CifarResNet(config.module, config.depth, config.class_num, config.zero_init_residual)
    elif config.arch == 'densenet':
      return DenseNet(config.growthRate, config.depth, config.reduction, config.class_num, config.bottleneck)
    elif config.arch == 'wideresnet':
      return CifarWideResNet(config.depth, config.wide_factor, config.class_num, config.dropout)
    else:
      raise ValueError('invalid module type : {:}'.format(config.arch))
  elif super_type.startswith('infer'):
    from .shape_infers import InferWidthCifarResNet
    from .shape_infers import InferDepthCifarResNet
    from .shape_infers import InferCifarResNet
    from .cell_infers import NASNetonCIFAR
    assert len(super_type.split('-')) == 2, 'invalid super_type : {:}'.format(super_type)
    infer_mode = super_type.split('-')[1]
    if infer_mode == 'width':
      return InferWidthCifarResNet(config.module, config.depth, config.xchannels, config.class_num, config.zero_init_residual)
    elif infer_mode == 'depth':
      return InferDepthCifarResNet(config.module, config.depth, config.xblocks, config.class_num, config.zero_init_residual)
    elif infer_mode == 'shape':
      return InferCifarResNet(config.module, config.depth, config.xblocks, config.xchannels, config.class_num, config.zero_init_residual)
    elif infer_mode == 'nasnet.cifar':
      genotype = config.genotype
      if extra_path is not None:  # reload genotype by extra_path
        if not osp.isfile(extra_path): raise ValueError('invalid extra_path : {:}'.format(extra_path))
        xdata = torch.load(extra_path)
        current_epoch = xdata['epoch']
        genotype = xdata['genotypes'][current_epoch-1]
      C = config.C if hasattr(config, 'C') else config.ichannel
      N = config.N if hasattr(config, 'N') else config.layers
      return NASNetonCIFAR(C, N, config.stem_multi, config.class_num, genotype, config.auxiliary)
    else:
      raise ValueError('invalid infer-mode : {:}'.format(infer_mode))
  else:
    raise ValueError('invalid super-type : {:}'.format(super_type))


def get_imagenet_models(config):
  super_type = getattr(config, 'super_type', 'basic')
  if super_type == 'basic':
    from .ImageNet_ResNet import ResNet
    from .ImageNet_MobileNetV2 import MobileNetV2
    if config.arch == 'resnet':
      return ResNet(config.block_name, config.layers, config.deep_stem, config.class_num, config.zero_init_residual, config.groups, config.width_per_group)
    elif config.arch == 'mobilenet_v2':
      return MobileNetV2(config.class_num, config.width_multi, config.input_channel, config.last_channel, 'InvertedResidual', config.dropout)
    else:
      raise ValueError('invalid arch : {:}'.format( config.arch ))
  elif super_type.startswith('infer'): # NAS searched architecture
    assert len(super_type.split('-')) == 2, 'invalid super_type : {:}'.format(super_type)
    infer_mode = super_type.split('-')[1]
    if infer_mode == 'shape':
      from .shape_infers import InferImagenetResNet
      from .shape_infers import InferMobileNetV2
      if config.arch == 'resnet':
        return InferImagenetResNet(config.block_name, config.layers, config.xblocks, config.xchannels, config.deep_stem, config.class_num, config.zero_init_residual)
      elif config.arch == "MobileNetV2":
        return InferMobileNetV2(config.class_num, config.xchannels, config.xblocks, config.dropout)
      else:
        raise ValueError('invalid arch-mode : {:}'.format(config.arch))
    else:
      raise ValueError('invalid infer-mode : {:}'.format(infer_mode))
  else:
    raise ValueError('invalid super-type : {:}'.format(super_type))


# Try to obtain the network by config.
def obtain_model(config, extra_path=None):
  if config.dataset == 'cifar':
    return get_cifar_models(config, extra_path)
  elif config.dataset == 'imagenet':
    return get_imagenet_models(config)
  else:
    raise ValueError('invalid dataset in the model config : {:}'.format(config))


def obtain_search_model(config):
  if config.dataset == 'cifar':
    if config.arch == 'resnet':
      from .shape_searchs import SearchWidthCifarResNet
      from .shape_searchs import SearchDepthCifarResNet
      from .shape_searchs import SearchShapeCifarResNet
      if config.search_mode == 'width':
        return SearchWidthCifarResNet(config.module, config.depth, config.class_num)
      elif config.search_mode == 'depth':
        return SearchDepthCifarResNet(config.module, config.depth, config.class_num)
      elif config.search_mode == 'shape':
        return SearchShapeCifarResNet(config.module, config.depth, config.class_num)
      else: raise ValueError('invalid search mode : {:}'.format(config.search_mode))
    elif config.arch == 'simres':
      from .shape_searchs import SearchWidthSimResNet
      if config.search_mode == 'width':
        return SearchWidthSimResNet(config.depth, config.class_num)
      else: raise ValueError('invalid search mode : {:}'.format(config.search_mode))
    else:
      raise ValueError('invalid arch : {:} for dataset [{:}]'.format(config.arch, config.dataset))
  elif config.dataset == 'imagenet':
    from .shape_searchs import SearchShapeImagenetResNet
    assert config.search_mode == 'shape', 'invalid search-mode : {:}'.format( config.search_mode )
    if config.arch == 'resnet':
      return SearchShapeImagenetResNet(config.block_name, config.layers, config.deep_stem, config.class_num)
    else:
      raise ValueError('invalid model config : {:}'.format(config))
  else:
    raise ValueError('invalid dataset in the model config : {:}'.format(config))


def load_net_from_checkpoint(checkpoint):
  assert osp.isfile(checkpoint), 'checkpoint {:} does not exist'.format(checkpoint)
  checkpoint   = torch.load(checkpoint)
  model_config = dict2config(checkpoint['model-config'], None)
  model        = obtain_model(model_config)
  model.load_state_dict(checkpoint['base-model'])
  return model