"""
Inception V3, suitable for images with around 299 x 299

Reference:
Szegedy, Christian, et al. "Rethinking the Inception Architecture for Computer Vision."
arXiv preprint arXiv:1512.00567 (2015).

Adopted from https://github.com/apache/incubator-mxnet/blob/
             master/example/image-classification/symbols/inception-v3.py
"""
# pylint: disable=invalid-name,missing-docstring,unused-argument
from tvm import relay
from .init import create_workload
from . import layers

def Conv(data, num_filter, kernel=(1, 1), stride=(1, 1), pad=(0, 0), name=None, suffix=''):
    conv = layers.conv2d(
        data=data,
        channels=int(num_filter),
        kernel_size=kernel,
        strides=stride,
        padding=pad,
        name='%s%s_conv1' % (name, suffix))

    bn = layers.batch_norm_infer(data=conv, epsilon=2e-5, name='%s%s_bn' % (name, suffix))
    act = relay.nn.relu(data=bn)
    return act

def Pooling(data, kernel, stride, pad, pool_type, name):
    if pool_type == 'max':
        return relay.nn.max_pool2d(data=data, pool_size=kernel, strides=stride, padding=pad)
    elif pool_type == 'avg':
        return relay.nn.avg_pool2d(data=data, pool_size=kernel, strides=stride, padding=pad,
                                   count_include_pad=True)
    else:
        raise ValueError("Invalid pooling type: " + pool_type)

def Inception7A(data,
                num_1x1,
                num_3x3_red, num_3x3_1, num_3x3_2,
                num_5x5_red, num_5x5,
                pool, proj,
                name):
    tower_1x1 = Conv(data, num_1x1, name=('%s_conv' % name))
    tower_5x5 = Conv(data, num_5x5_red, name=('%s_tower' % name), suffix='_conv')
    tower_5x5 = Conv(tower_5x5, num_5x5, kernel=(5, 5), pad=(2, 2), name=('%s_tower' % name),
                     suffix='_conv_1')
    tower_3x3 = Conv(data, num_3x3_red, name=('%s_tower_1' % name), suffix='_conv')
    tower_3x3 = Conv(tower_3x3, num_3x3_1, kernel=(3, 3), pad=(1, 1), name=('%s_tower_1' % name),
                     suffix='_conv_1')
    tower_3x3 = Conv(tower_3x3, num_3x3_2, kernel=(3, 3), pad=(1, 1), name=('%s_tower_1' % name),
                     suffix='_conv_2')
    pooling = Pooling(data=data, kernel=(3, 3), stride=(1, 1), pad=(1, 1), pool_type=pool,
                      name=('%s_pool_%s_pool' % (pool, name)))

    cproj = Conv(pooling, proj, name=('%s_tower_2' % name), suffix='_conv')
    concat = relay.concatenate((tower_1x1, tower_5x5, tower_3x3, cproj), axis=1)
    return concat

# First Downsample
def Inception7B(data,
                num_3x3,
                num_d3x3_red, num_d3x3_1, num_d3x3_2,
                pool,
                name):
    tower_3x3 = Conv(data, num_3x3, kernel=(3, 3), pad=(0, 0), stride=(2, 2),
                     name=('%s_conv' % name))
    tower_d3x3 = Conv(data, num_d3x3_red, name=('%s_tower' % name), suffix='_conv')
    tower_d3x3 = Conv(tower_d3x3, num_d3x3_1, kernel=(3, 3), pad=(1, 1), stride=(1, 1),
                      name=('%s_tower' % name), suffix='_conv_1')
    tower_d3x3 = Conv(tower_d3x3, num_d3x3_2, kernel=(3, 3), pad=(0, 0), stride=(2, 2),
                      name=('%s_tower' % name), suffix='_conv_2')
    pooling = Pooling(data=data, kernel=(3, 3), stride=(2, 2), pad=(0, 0), pool_type="max",
                      name=('max_pool_%s_pool' % name))
    concat = relay.concatenate((tower_3x3, tower_d3x3, pooling), axis=1)
    return concat

def Inception7C(data,
                num_1x1,
                num_d7_red, num_d7_1, num_d7_2,
                num_q7_red, num_q7_1, num_q7_2, num_q7_3, num_q7_4,
                pool, proj,
                name):
    tower_1x1 = Conv(data=data, num_filter=num_1x1, kernel=(1, 1), name=('%s_conv' % name))
    tower_d7 = Conv(data=data, num_filter=num_d7_red, name=('%s_tower' % name), suffix='_conv')
    tower_d7 = Conv(data=tower_d7, num_filter=num_d7_1, kernel=(1, 7), pad=(0, 3),
                    name=('%s_tower' % name), suffix='_conv_1')
    tower_d7 = Conv(data=tower_d7, num_filter=num_d7_2, kernel=(7, 1), pad=(3, 0),
                    name=('%s_tower' % name), suffix='_conv_2')
    tower_q7 = Conv(data=data, num_filter=num_q7_red, name=('%s_tower_1' % name), suffix='_conv')
    tower_q7 = Conv(data=tower_q7, num_filter=num_q7_1, kernel=(7, 1), pad=(3, 0),
                    name=('%s_tower_1' % name), suffix='_conv_1')
    tower_q7 = Conv(data=tower_q7, num_filter=num_q7_2, kernel=(1, 7), pad=(0, 3),
                    name=('%s_tower_1' % name), suffix='_conv_2')
    tower_q7 = Conv(data=tower_q7, num_filter=num_q7_3, kernel=(7, 1), pad=(3, 0),
                    name=('%s_tower_1' % name), suffix='_conv_3')
    tower_q7 = Conv(data=tower_q7, num_filter=num_q7_4, kernel=(1, 7), pad=(0, 3),
                    name=('%s_tower_1' % name), suffix='_conv_4')
    pooling = Pooling(data=data, kernel=(3, 3), stride=(1, 1), pad=(1, 1), pool_type=pool,
                      name=('%s_pool_%s_pool' % (pool, name)))
    cproj = Conv(data=pooling, num_filter=proj, kernel=(1, 1),
                 name=('%s_tower_2' % name), suffix='_conv')
    # concat
    concat = relay.concatenate((tower_1x1, tower_d7, tower_q7, cproj), axis=1)
    return concat

def Inception7D(data,
                num_3x3_red, num_3x3,
                num_d7_3x3_red, num_d7_1, num_d7_2, num_d7_3x3,
                pool,
                name):
    tower_3x3 = Conv(data=data, num_filter=num_3x3_red, name=('%s_tower' % name),
                     suffix='_conv')
    tower_3x3 = Conv(data=tower_3x3, num_filter=num_3x3, kernel=(3, 3), pad=(0, 0), stride=(2, 2),
                     name=('%s_tower' % name), suffix='_conv_1')
    tower_d7_3x3 = Conv(data=data, num_filter=num_d7_3x3_red, name=('%s_tower_1' % name),
                        suffix='_conv')
    tower_d7_3x3 = Conv(data=tower_d7_3x3, num_filter=num_d7_1, kernel=(1, 7), pad=(0, 3),
                        name=('%s_tower_1' % name), suffix='_conv_1')
    tower_d7_3x3 = Conv(data=tower_d7_3x3, num_filter=num_d7_2, kernel=(7, 1), pad=(3, 0),
                        name=('%s_tower_1' % name), suffix='_conv_2')
    tower_d7_3x3 = Conv(data=tower_d7_3x3, num_filter=num_d7_3x3, kernel=(3, 3), stride=(2, 2),
                        name=('%s_tower_1' % name), suffix='_conv_3')
    pooling = Pooling(data=data, kernel=(3, 3), stride=(2, 2), pool_type=pool, pad=(0, 0),
                      name=('%s_pool_%s_pool' % (pool, name)))
    # concat
    concat = relay.concatenate((tower_3x3, tower_d7_3x3, pooling), axis=1)
    return concat

def Inception7E(data,
                num_1x1,
                num_d3_red, num_d3_1, num_d3_2,
                num_3x3_d3_red, num_3x3, num_3x3_d3_1, num_3x3_d3_2,
                pool, proj,
                name):
    tower_1x1 = Conv(data=data, num_filter=num_1x1, kernel=(1, 1), name=('%s_conv' % name))
    tower_d3 = Conv(data=data, num_filter=num_d3_red, name=('%s_tower' % name), suffix='_conv')
    tower_d3_a = Conv(data=tower_d3, num_filter=num_d3_1, kernel=(1, 3), pad=(0, 1),
                      name=('%s_tower' % name), suffix='_mixed_conv')
    tower_d3_b = Conv(data=tower_d3, num_filter=num_d3_2, kernel=(3, 1), pad=(1, 0),
                      name=('%s_tower' % name), suffix='_mixed_conv_1')
    tower_3x3_d3 = Conv(data=data, num_filter=num_3x3_d3_red, name=('%s_tower_1' % name),
                        suffix='_conv')
    tower_3x3_d3 = Conv(data=tower_3x3_d3, num_filter=num_3x3, kernel=(3, 3), pad=(1, 1),
                        name=('%s_tower_1' % name), suffix='_conv_1')
    tower_3x3_d3_a = Conv(data=tower_3x3_d3, num_filter=num_3x3_d3_1, kernel=(1, 3), pad=(0, 1),
                          name=('%s_tower_1' % name), suffix='_mixed_conv')
    tower_3x3_d3_b = Conv(data=tower_3x3_d3, num_filter=num_3x3_d3_2, kernel=(3, 1), pad=(1, 0),
                          name=('%s_tower_1' % name), suffix='_mixed_conv_1')
    pooling = Pooling(data=data, kernel=(3, 3), stride=(1, 1), pad=(1, 1), pool_type=pool,
                      name=('%s_pool_%s_pool' % (pool, name)))
    cproj = Conv(data=pooling, num_filter=proj, kernel=(1, 1), name=('%s_tower_2' % name),
                 suffix='_conv')
    # concat
    concat = relay.concatenate(
        (tower_1x1, tower_d3_a, tower_d3_b, tower_3x3_d3_a, tower_3x3_d3_b, cproj), axis=1)
    return concat

def get_net(batch_size,
            num_classes,
            image_shape,
            dtype):
    """Get network a Inception v3 network.

    batch_size : int
        The batch size used in the model

    num_classes : int, optional
        Number of claseses

    image_shape : tuple, optional
        The input image shape

    dtype : str, optional
        The data type

    Returns
    -------
    net : relay.Function
        The dataflow.
    """
    data_shape = (batch_size,) + image_shape
    data = relay.var("data",
                     shape=data_shape,
                     dtype=dtype)

    # stage 1
    conv = Conv(data, 32, kernel=(3, 3), stride=(2, 2), name="conv")
    conv_1 = Conv(conv, 32, kernel=(3, 3), name="conv_1")
    conv_2 = Conv(conv_1, 64, kernel=(3, 3), pad=(1, 1), name="conv_2")
    pool = Pooling(data=conv_2, kernel=(3, 3), stride=(2, 2), pool_type="max", pad=(0, 0),
                   name="pool")
    # stage 2
    conv_3 = Conv(pool, 80, kernel=(1, 1), name="conv_3")
    conv_4 = Conv(conv_3, 192, kernel=(3, 3), name="conv_4")
    pool1 = Pooling(data=conv_4, kernel=(3, 3), stride=(2, 2), pool_type="max", pad=(0, 0),
                    name="pool1")

    # stage 3
    in3a = Inception7A(pool1, 64,
                       64, 96, 96,
                       48, 64,
                       "avg", 32, "mixed")

    in3b = Inception7A(in3a, 64,
                       64, 96, 96,
                       48, 64,
                       "avg", 64, "mixed_1")
    in3c = Inception7A(in3b, 64,
                       64, 96, 96,
                       48, 64,
                       "avg", 64, "mixed_2")
    in3d = Inception7B(in3c, 384,
                       64, 96, 96,
                       "max", "mixed_3")
    # stage 4
    in4a = Inception7C(in3d, 192,
                       128, 128, 192,
                       128, 128, 128, 128, 192,
                       "avg", 192, "mixed_4")
    in4b = Inception7C(in4a, 192,
                       160, 160, 192,
                       160, 160, 160, 160, 192,
                       "avg", 192, "mixed_5")
    in4c = Inception7C(in4b, 192,
                       160, 160, 192,
                       160, 160, 160, 160, 192,
                       "avg", 192, "mixed_6")
    in4d = Inception7C(in4c, 192,
                       192, 192, 192,
                       192, 192, 192, 192, 192,
                       "avg", 192, "mixed_7")
    in4e = Inception7D(in4d, 192, 320,
                       192, 192, 192, 192,
                       "max", "mixed_8")
    # stage 5
    in5a = Inception7E(in4e, 320,
                       384, 384, 384,
                       448, 384, 384, 384,
                       "avg", 192, "mixed_9")
    in5b = Inception7E(in5a, 320,
                       384, 384, 384,
                       448, 384, 384, 384,
                       "max", 192, "mixed_10")

    # pool
    pool = Pooling(data=in5b, kernel=(8, 8), stride=(1, 1), pool_type="avg", pad=(0, 0),
                   name="global_pool")

    flatten = relay.nn.batch_flatten(pool)
    fc1 = relay.nn.dense(flatten, relay.var("fc1_weight"), units=num_classes)
    fc1 = relay.nn.bias_add(fc1, relay.var("fc2_bias"))
    inception_v3 = relay.nn.softmax(data=fc1)
    args = relay.ir_pass.free_vars(inception_v3)
    return relay.Function(args, inception_v3)

def get_workload(batch_size=1, num_classes=1000,
                 image_shape=(3, 299, 299), dtype="float32"):
    """Get benchmark workload for InceptionV3

    Parameters
    ----------
    batch_size : int
        The batch size used in the model

    num_classes : int, optional
        Number of classes

    image_shape : tuple, optional
        The input image shape

    dtype : str, optional
        The data type

    Returns
    -------
    net : nnvm.Symbol
        The computational graph

    params : dict of str to NDArray
        The parameters.
    """
    net = get_net(batch_size, num_classes, image_shape, dtype)
    return create_workload(net)
