"""ResNet, ResNetV2, and ResNeXt models for Keras.

# Reference papers

- [Deep Residual Learning for Image Recognition]
  (https://arxiv.org/abs/1512.03385) (CVPR 2016 Best Paper Award)
- [Identity Mappings in Deep Residual Networks]
  (https://arxiv.org/abs/1603.05027) (ECCV 2016)
- [Aggregated Residual Transformations for Deep Neural Networks]
  (https://arxiv.org/abs/1611.05431) (CVPR 2017)

# Reference implementations

- [TensorNets]
  (https://github.com/taehoonlee/tensornets/blob/master/tensornets/resnets.py)
- [Caffe ResNet]
  (https://github.com/KaimingHe/deep-residual-networks/tree/master/prototxt)
- [Torch ResNetV2]
  (https://github.com/facebook/fb.resnet.torch/blob/master/models/preresnet.lua)
- [Torch ResNeXt]
  (https://github.com/facebookresearch/ResNeXt/blob/master/models/resnext.lua)

"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os
import numpy as np

from . import get_submodules_from_kwargs
from .imagenet_utils import _obtain_input_shape


backend = None
layers = None
models = None
keras_utils = None


BASE_WEIGHTS_PATH = (
    'https://github.com/keras-team/keras-applications/'
    'releases/download/resnet/')
WEIGHTS_HASHES = {
    'resnet50': ('2cb95161c43110f7111970584f804107',
                 '4d473c1dd8becc155b73f8504c6f6626'),
    'resnet101': ('f1aeb4b969a6efcfb50fad2f0c20cfc5',
                  '88cf7a10940856eca736dc7b7e228a21'),
    'resnet152': ('100835be76be38e30d865e96f2aaae62',
                  'ee4c566cf9a93f14d82f913c2dc6dd0c'),
    'resnet50v2': ('3ef43a0b657b3be2300d5770ece849e0',
                   'fac2f116257151a9d068a22e544a4917'),
    'resnet101v2': ('6343647c601c52e1368623803854d971',
                    'c0ed64b8031c3730f411d2eb4eea35b5'),
    'resnet152v2': ('a49b44d1979771252814e80f8ec446f9',
                    'ed17cf2e0169df9d443503ef94b23b33'),
    'resnext50': ('67a5b30d522ed92f75a1f16eef299d1a',
                  '62527c363bdd9ec598bed41947b379fc'),
    'resnext101': ('34fb605428fcc7aa4d62f44404c11509',
                   '0f678c91647380debd923963594981b3')
}


def block1(x, filters, kernel_size=3, stride=1,
           conv_shortcut=True, name=None):
    """A residual block.

    # Arguments
        x: input tensor.
        filters: integer, filters of the bottleneck layer.
        kernel_size: default 3, kernel size of the bottleneck layer.
        stride: default 1, stride of the first layer.
        conv_shortcut: default True, use convolution shortcut if True,
            otherwise identity shortcut.
        name: string, block label.

    # Returns
        Output tensor for the residual block.
    """
    bn_axis = 3 if backend.image_data_format() == 'channels_last' else 1

    if conv_shortcut is True:
        shortcut = layers.Conv2D(4 * filters, 1, strides=stride,
                                 name=name + '_0_conv')(x)
        shortcut = layers.BatchNormalization(axis=bn_axis, epsilon=1.001e-5,
                                             name=name + '_0_bn')(shortcut)
    else:
        shortcut = x

    x = layers.Conv2D(filters, 1, strides=stride, name=name + '_1_conv')(x)
    x = layers.BatchNormalization(axis=bn_axis, epsilon=1.001e-5,
                                  name=name + '_1_bn')(x)
    x = layers.Activation('relu', name=name + '_1_relu')(x)

    x = layers.Conv2D(filters, kernel_size, padding='SAME',
                      name=name + '_2_conv')(x)
    x = layers.BatchNormalization(axis=bn_axis, epsilon=1.001e-5,
                                  name=name + '_2_bn')(x)
    x = layers.Activation('relu', name=name + '_2_relu')(x)

    x = layers.Conv2D(4 * filters, 1, name=name + '_3_conv')(x)
    x = layers.BatchNormalization(axis=bn_axis, epsilon=1.001e-5,
                                  name=name + '_3_bn')(x)

    x = layers.Add(name=name + '_add')([shortcut, x])
    x = layers.Activation('relu', name=name + '_out')(x)
    return x


def stack1(x, filters, blocks, stride1=2, name=None):
    """A set of stacked residual blocks.

    # Arguments
        x: input tensor.
        filters: integer, filters of the bottleneck layer in a block.
        blocks: integer, blocks in the stacked blocks.
        stride1: default 2, stride of the first layer in the first block.
        name: string, stack label.

    # Returns
        Output tensor for the stacked blocks.
    """
    x = block1(x, filters, stride=stride1, name=name + '_block1')
    for i in range(2, blocks + 1):
        x = block1(x, filters, conv_shortcut=False, name=name + '_block' + str(i))
    return x


def block2(x, filters, kernel_size=3, stride=1,
           conv_shortcut=False, name=None):
    """A residual block.

    # Arguments
        x: input tensor.
        filters: integer, filters of the bottleneck layer.
        kernel_size: default 3, kernel size of the bottleneck layer.
        stride: default 1, stride of the first layer.
        conv_shortcut: default False, use convolution shortcut if True,
            otherwise identity shortcut.
        name: string, block label.

    # Returns
        Output tensor for the residual block.
    """
    bn_axis = 3 if backend.image_data_format() == 'channels_last' else 1

    preact = layers.BatchNormalization(axis=bn_axis, epsilon=1.001e-5,
                                       name=name + '_preact_bn')(x)
    preact = layers.Activation('relu', name=name + '_preact_relu')(preact)

    if conv_shortcut is True:
        shortcut = layers.Conv2D(4 * filters, 1, strides=stride,
                                 name=name + '_0_conv')(preact)
    else:
        shortcut = layers.MaxPooling2D(1, strides=stride)(x) if stride > 1 else x

    x = layers.Conv2D(filters, 1, strides=1, use_bias=False,
                      name=name + '_1_conv')(preact)
    x = layers.BatchNormalization(axis=bn_axis, epsilon=1.001e-5,
                                  name=name + '_1_bn')(x)
    x = layers.Activation('relu', name=name + '_1_relu')(x)

    x = layers.ZeroPadding2D(padding=((1, 1), (1, 1)), name=name + '_2_pad')(x)
    x = layers.Conv2D(filters, kernel_size, strides=stride,
                      use_bias=False, name=name + '_2_conv')(x)
    x = layers.BatchNormalization(axis=bn_axis, epsilon=1.001e-5,
                                  name=name + '_2_bn')(x)
    x = layers.Activation('relu', name=name + '_2_relu')(x)

    x = layers.Conv2D(4 * filters, 1, name=name + '_3_conv')(x)
    x = layers.Add(name=name + '_out')([shortcut, x])
    return x


def stack2(x, filters, blocks, stride1=2, name=None):
    """A set of stacked residual blocks.

    # Arguments
        x: input tensor.
        filters: integer, filters of the bottleneck layer in a block.
        blocks: integer, blocks in the stacked blocks.
        stride1: default 2, stride of the first layer in the first block.
        name: string, stack label.

    # Returns
        Output tensor for the stacked blocks.
    """
    x = block2(x, filters, conv_shortcut=True, name=name + '_block1')
    for i in range(2, blocks):
        x = block2(x, filters, name=name + '_block' + str(i))
    x = block2(x, filters, stride=stride1, name=name + '_block' + str(blocks))
    return x


def block3(x, filters, kernel_size=3, stride=1, groups=32,
           conv_shortcut=True, name=None):
    """A residual block.

    # Arguments
        x: input tensor.
        filters: integer, filters of the bottleneck layer.
        kernel_size: default 3, kernel size of the bottleneck layer.
        stride: default 1, stride of the first layer.
        groups: default 32, group size for grouped convolution.
        conv_shortcut: default True, use convolution shortcut if True,
            otherwise identity shortcut.
        name: string, block label.

    # Returns
        Output tensor for the residual block.
    """
    bn_axis = 3 if backend.image_data_format() == 'channels_last' else 1

    if conv_shortcut is True:
        shortcut = layers.Conv2D((64 // groups) * filters, 1, strides=stride,
                                 use_bias=False, name=name + '_0_conv')(x)
        shortcut = layers.BatchNormalization(axis=bn_axis, epsilon=1.001e-5,
                                             name=name + '_0_bn')(shortcut)
    else:
        shortcut = x

    x = layers.Conv2D(filters, 1, use_bias=False, name=name + '_1_conv')(x)
    x = layers.BatchNormalization(axis=bn_axis, epsilon=1.001e-5,
                                  name=name + '_1_bn')(x)
    x = layers.Activation('relu', name=name + '_1_relu')(x)

    c = filters // groups
    x = layers.ZeroPadding2D(padding=((1, 1), (1, 1)), name=name + '_2_pad')(x)
    x = layers.DepthwiseConv2D(kernel_size, strides=stride, depth_multiplier=c,
                               use_bias=False, name=name + '_2_conv')(x)
    kernel = np.zeros((1, 1, filters * c, filters), dtype=np.float32)
    for i in range(filters):
        start = (i // c) * c * c + i % c
        end = start + c * c
        kernel[:, :, start:end:c, i] = 1.
    x = layers.Conv2D(filters, 1, use_bias=False, trainable=False,
                      kernel_initializer={'class_name': 'Constant',
                                          'config': {'value': kernel}},
                      name=name + '_2_gconv')(x)
    x = layers.BatchNormalization(axis=bn_axis, epsilon=1.001e-5,
                                  name=name + '_2_bn')(x)
    x = layers.Activation('relu', name=name + '_2_relu')(x)

    x = layers.Conv2D((64 // groups) * filters, 1,
                      use_bias=False, name=name + '_3_conv')(x)
    x = layers.BatchNormalization(axis=bn_axis, epsilon=1.001e-5,
                                  name=name + '_3_bn')(x)

    x = layers.Add(name=name + '_add')([shortcut, x])
    x = layers.Activation('relu', name=name + '_out')(x)
    return x


def stack3(x, filters, blocks, stride1=2, groups=32, name=None):
    """A set of stacked residual blocks.

    # Arguments
        x: input tensor.
        filters: integer, filters of the bottleneck layer in a block.
        blocks: integer, blocks in the stacked blocks.
        stride1: default 2, stride of the first layer in the first block.
        groups: default 32, group size for grouped convolution.
        name: string, stack label.

    # Returns
        Output tensor for the stacked blocks.
    """
    x = block3(x, filters, stride=stride1, groups=groups, name=name + '_block1')
    for i in range(2, blocks + 1):
        x = block3(x, filters, groups=groups, conv_shortcut=False,
                   name=name + '_block' + str(i))
    return x


def ResNet(stack_fn,
           preact,
           use_bias,
           model_name='resnet',
           include_top=True,
           weights='imagenet',
           input_tensor=None,
           input_shape=None,
           pooling=None,
           classes=1000,
           **kwargs):
    """Instantiates the ResNet, ResNetV2, and ResNeXt architecture.

    Optionally loads weights pre-trained on ImageNet.
    Note that the data format convention used by the model is
    the one specified in your Keras config at `~/.keras/keras.json`.

    # Arguments
        stack_fn: a function that returns output tensor for the
            stacked residual blocks.
        preact: whether to use pre-activation or not
            (True for ResNetV2, False for ResNet and ResNeXt).
        use_bias: whether to use biases for convolutional layers or not
            (True for ResNet and ResNetV2, False for ResNeXt).
        model_name: string, model name.
        include_top: whether to include the fully-connected
            layer at the top of the network.
        weights: one of `None` (random initialization),
              'imagenet' (pre-training on ImageNet),
              or the path to the weights file to be loaded.
        input_tensor: optional Keras tensor
            (i.e. output of `layers.Input()`)
            to use as image input for the model.
        input_shape: optional shape tuple, only to be specified
            if `include_top` is False (otherwise the input shape
            has to be `(224, 224, 3)` (with `channels_last` data format)
            or `(3, 224, 224)` (with `channels_first` data format).
            It should have exactly 3 inputs channels.
        pooling: optional pooling mode for feature extraction
            when `include_top` is `False`.
            - `None` means that the output of the model will be
                the 4D tensor output of the
                last convolutional layer.
            - `avg` means that global average pooling
                will be applied to the output of the
                last convolutional layer, and thus
                the output of the model will be a 2D tensor.
            - `max` means that global max pooling will
                be applied.
        classes: optional number of classes to classify images
            into, only to be specified if `include_top` is True, and
            if no `weights` argument is specified.

    # Returns
        A Keras model instance.

    # Raises
        ValueError: in case of invalid argument for `weights`,
            or invalid input shape.
    """
    global backend, layers, models, keras_utils
    backend, layers, models, keras_utils = get_submodules_from_kwargs(kwargs)

    if not (weights in {'imagenet', None} or os.path.exists(weights)):
        raise ValueError('The `weights` argument should be either '
                         '`None` (random initialization), `imagenet` '
                         '(pre-training on ImageNet), '
                         'or the path to the weights file to be loaded.')

    if weights == 'imagenet' and include_top and classes != 1000:
        raise ValueError('If using `weights` as `"imagenet"` with `include_top`'
                         ' as true, `classes` should be 1000')

    # Determine proper input shape
    input_shape = _obtain_input_shape(input_shape,
                                      default_size=224,
                                      min_size=32,
                                      data_format=backend.image_data_format(),
                                      require_flatten=include_top,
                                      weights=weights)

    if input_tensor is None:
        img_input = layers.Input(shape=input_shape)
    else:
        if not backend.is_keras_tensor(input_tensor):
            img_input = layers.Input(tensor=input_tensor, shape=input_shape)
        else:
            img_input = input_tensor

    bn_axis = 3 if backend.image_data_format() == 'channels_last' else 1

    x = layers.ZeroPadding2D(padding=((3, 3), (3, 3)), name='conv1_pad')(img_input)
    x = layers.Conv2D(64, 7, strides=2, use_bias=use_bias, name='conv1_conv')(x)

    if preact is False:
        x = layers.BatchNormalization(axis=bn_axis, epsilon=1.001e-5,
                                      name='conv1_bn')(x)
        x = layers.Activation('relu', name='conv1_relu')(x)

    x = layers.ZeroPadding2D(padding=((1, 1), (1, 1)), name='pool1_pad')(x)
    x = layers.MaxPooling2D(3, strides=2, name='pool1_pool')(x)

    x = stack_fn(x)

    if preact is True:
        x = layers.BatchNormalization(axis=bn_axis, epsilon=1.001e-5,
                                      name='post_bn')(x)
        x = layers.Activation('relu', name='post_relu')(x)

    if include_top:
        x = layers.GlobalAveragePooling2D(name='avg_pool')(x)
        x = layers.Dense(classes, activation='softmax', name='probs')(x)
    else:
        if pooling == 'avg':
            x = layers.GlobalAveragePooling2D(name='avg_pool')(x)
        elif pooling == 'max':
            x = layers.GlobalMaxPooling2D(name='max_pool')(x)

    # Ensure that the model takes into account
    # any potential predecessors of `input_tensor`.
    if input_tensor is not None:
        inputs = keras_utils.get_source_inputs(input_tensor)
    else:
        inputs = img_input

    # Create model.
    model = models.Model(inputs, x, name=model_name)

    # Load weights.
    if (weights == 'imagenet') and (model_name in WEIGHTS_HASHES):
        if include_top:
            file_name = model_name + '_weights_tf_dim_ordering_tf_kernels.h5'
            file_hash = WEIGHTS_HASHES[model_name][0]
        else:
            file_name = model_name + '_weights_tf_dim_ordering_tf_kernels_notop.h5'
            file_hash = WEIGHTS_HASHES[model_name][1]
        weights_path = keras_utils.get_file(file_name,
                                            BASE_WEIGHTS_PATH + file_name,
                                            cache_subdir='models',
                                            file_hash=file_hash)
        by_name = True if 'resnext' in model_name else False
        model.load_weights(weights_path, by_name=by_name)
    elif weights is not None:
        model.load_weights(weights)

    return model


def ResNet50(include_top=True,
             weights='imagenet',
             input_tensor=None,
             input_shape=None,
             pooling=None,
             classes=1000,
             **kwargs):
    def stack_fn(x):
        x = stack1(x, 64, 3, stride1=1, name='conv2')
        x = stack1(x, 128, 4, name='conv3')
        x = stack1(x, 256, 6, name='conv4')
        x = stack1(x, 512, 3, name='conv5')
        return x
    return ResNet(stack_fn, False, True, 'resnet50',
                  include_top, weights,
                  input_tensor, input_shape,
                  pooling, classes,
                  **kwargs)


def ResNet101(include_top=True,
              weights='imagenet',
              input_tensor=None,
              input_shape=None,
              pooling=None,
              classes=1000,
              **kwargs):
    def stack_fn(x):
        x = stack1(x, 64, 3, stride1=1, name='conv2')
        x = stack1(x, 128, 4, name='conv3')
        x = stack1(x, 256, 23, name='conv4')
        x = stack1(x, 512, 3, name='conv5')
        return x
    return ResNet(stack_fn, False, True, 'resnet101',
                  include_top, weights,
                  input_tensor, input_shape,
                  pooling, classes,
                  **kwargs)


def ResNet152(include_top=True,
              weights='imagenet',
              input_tensor=None,
              input_shape=None,
              pooling=None,
              classes=1000,
              **kwargs):
    def stack_fn(x):
        x = stack1(x, 64, 3, stride1=1, name='conv2')
        x = stack1(x, 128, 8, name='conv3')
        x = stack1(x, 256, 36, name='conv4')
        x = stack1(x, 512, 3, name='conv5')
        return x
    return ResNet(stack_fn, False, True, 'resnet152',
                  include_top, weights,
                  input_tensor, input_shape,
                  pooling, classes,
                  **kwargs)


def ResNet50V2(include_top=True,
               weights='imagenet',
               input_tensor=None,
               input_shape=None,
               pooling=None,
               classes=1000,
               **kwargs):
    def stack_fn(x):
        x = stack2(x, 64, 3, name='conv2')
        x = stack2(x, 128, 4, name='conv3')
        x = stack2(x, 256, 6, name='conv4')
        x = stack2(x, 512, 3, stride1=1, name='conv5')
        return x
    return ResNet(stack_fn, True, True, 'resnet50v2',
                  include_top, weights,
                  input_tensor, input_shape,
                  pooling, classes,
                  **kwargs)


def ResNet101V2(include_top=True,
                weights='imagenet',
                input_tensor=None,
                input_shape=None,
                pooling=None,
                classes=1000,
                **kwargs):
    def stack_fn(x):
        x = stack2(x, 64, 3, name='conv2')
        x = stack2(x, 128, 4, name='conv3')
        x = stack2(x, 256, 23, name='conv4')
        x = stack2(x, 512, 3, stride1=1, name='conv5')
        return x
    return ResNet(stack_fn, True, True, 'resnet101v2',
                  include_top, weights,
                  input_tensor, input_shape,
                  pooling, classes,
                  **kwargs)


def ResNet152V2(include_top=True,
                weights='imagenet',
                input_tensor=None,
                input_shape=None,
                pooling=None,
                classes=1000,
                **kwargs):
    def stack_fn(x):
        x = stack2(x, 64, 3, name='conv2')
        x = stack2(x, 128, 8, name='conv3')
        x = stack2(x, 256, 36, name='conv4')
        x = stack2(x, 512, 3, stride1=1, name='conv5')
        return x
    return ResNet(stack_fn, True, True, 'resnet152v2',
                  include_top, weights,
                  input_tensor, input_shape,
                  pooling, classes,
                  **kwargs)


def ResNeXt50(include_top=True,
              weights='imagenet',
              input_tensor=None,
              input_shape=None,
              pooling=None,
              classes=1000,
              **kwargs):
    def stack_fn(x):
        x = stack3(x, 128, 3, stride1=1, name='conv2')
        x = stack3(x, 256, 4, name='conv3')
        x = stack3(x, 512, 6, name='conv4')
        x = stack3(x, 1024, 3, name='conv5')
        return x
    return ResNet(stack_fn, False, False, 'resnext50',
                  include_top, weights,
                  input_tensor, input_shape,
                  pooling, classes,
                  **kwargs)


def ResNeXt101(include_top=True,
               weights='imagenet',
               input_tensor=None,
               input_shape=None,
               pooling=None,
               classes=1000,
               **kwargs):
    def stack_fn(x):
        x = stack3(x, 128, 3, stride1=1, name='conv2')
        x = stack3(x, 256, 4, name='conv3')
        x = stack3(x, 512, 23, name='conv4')
        x = stack3(x, 1024, 3, name='conv5')
        return x
    return ResNet(stack_fn, False, False, 'resnext101',
                  include_top, weights,
                  input_tensor, input_shape,
                  pooling, classes,
                  **kwargs)


setattr(ResNet50, '__doc__', ResNet.__doc__)
setattr(ResNet101, '__doc__', ResNet.__doc__)
setattr(ResNet152, '__doc__', ResNet.__doc__)
setattr(ResNet50V2, '__doc__', ResNet.__doc__)
setattr(ResNet101V2, '__doc__', ResNet.__doc__)
setattr(ResNet152V2, '__doc__', ResNet.__doc__)
setattr(ResNeXt50, '__doc__', ResNet.__doc__)
setattr(ResNeXt101, '__doc__', ResNet.__doc__)
