# coding: utf8
# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

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

import paddle
import paddle.fluid as fluid
from paddle.fluid.initializer import MSRA
from paddle.fluid.param_attr import ParamAttr

from utils.config import cfg


def conv_bn_layer(input,
                  filter_size,
                  num_filters,
                  stride=1,
                  padding=1,
                  num_groups=1,
                  if_act=True,
                  name=None):
    conv = fluid.layers.conv2d(
        input=input,
        num_filters=num_filters,
        filter_size=filter_size,
        stride=stride,
        padding=(filter_size - 1) // 2,
        groups=num_groups,
        act=None,
        #        param_attr=ParamAttr(initializer=MSRA(), learning_rate=1.0, name=name + '_weights'),
        param_attr=ParamAttr(
            initializer=fluid.initializer.Normal(scale=0.001),
            learning_rate=1.0,
            name=name + '_weights'),
        bias_attr=False)
    bn_name = name + '_bn'
    bn = fluid.layers.batch_norm(
        input=conv,
        param_attr=ParamAttr(
            name=bn_name + "_scale",
            initializer=fluid.initializer.Constant(1.0)),
        bias_attr=ParamAttr(
            name=bn_name + "_offset",
            initializer=fluid.initializer.Constant(0.0)),
        moving_mean_name=bn_name + '_mean',
        moving_variance_name=bn_name + '_variance')
    if if_act:
        bn = fluid.layers.relu(bn)
    return bn


def basic_block(input, num_filters, stride=1, downsample=False, name=None):
    residual = input
    conv = conv_bn_layer(
        input=input,
        filter_size=3,
        num_filters=num_filters,
        stride=stride,
        name=name + '_conv1')
    conv = conv_bn_layer(
        input=conv,
        filter_size=3,
        num_filters=num_filters,
        if_act=False,
        name=name + '_conv2')
    if downsample:
        residual = conv_bn_layer(
            input=input,
            filter_size=1,
            num_filters=num_filters,
            if_act=False,
            name=name + '_downsample')
    return fluid.layers.elementwise_add(x=residual, y=conv, act='relu')


def bottleneck_block(input, num_filters, stride=1, downsample=False, name=None):
    residual = input
    conv = conv_bn_layer(
        input=input,
        filter_size=1,
        num_filters=num_filters,
        name=name + '_conv1')
    conv = conv_bn_layer(
        input=conv,
        filter_size=3,
        num_filters=num_filters,
        stride=stride,
        name=name + '_conv2')
    conv = conv_bn_layer(
        input=conv,
        filter_size=1,
        num_filters=num_filters * 4,
        if_act=False,
        name=name + '_conv3')
    if downsample:
        residual = conv_bn_layer(
            input=input,
            filter_size=1,
            num_filters=num_filters * 4,
            if_act=False,
            name=name + '_downsample')
    return fluid.layers.elementwise_add(x=residual, y=conv, act='relu')


def fuse_layers(x, channels, multi_scale_output=True, name=None):
    out = []
    for i in range(len(channels) if multi_scale_output else 1):
        residual = x[i]
        shape = residual.shape
        width = shape[-1]
        height = shape[-2]
        for j in range(len(channels)):
            if j > i:
                y = conv_bn_layer(
                    x[j],
                    filter_size=1,
                    num_filters=channels[i],
                    if_act=False,
                    name=name + '_layer_' + str(i + 1) + '_' + str(j + 1))
                y = fluid.layers.resize_bilinear(
                    input=y, out_shape=[height, width])
                residual = fluid.layers.elementwise_add(
                    x=residual, y=y, act=None)
            elif j < i:
                y = x[j]
                for k in range(i - j):
                    if k == i - j - 1:
                        y = conv_bn_layer(
                            y,
                            filter_size=3,
                            num_filters=channels[i],
                            stride=2,
                            if_act=False,
                            name=name + '_layer_' + str(i + 1) + '_' +
                            str(j + 1) + '_' + str(k + 1))
                    else:
                        y = conv_bn_layer(
                            y,
                            filter_size=3,
                            num_filters=channels[j],
                            stride=2,
                            name=name + '_layer_' + str(i + 1) + '_' +
                            str(j + 1) + '_' + str(k + 1))
                residual = fluid.layers.elementwise_add(
                    x=residual, y=y, act=None)

        residual = fluid.layers.relu(residual)
        out.append(residual)
    return out


def branches(x, block_num, channels, name=None):
    out = []
    for i in range(len(channels)):
        residual = x[i]
        for j in range(block_num):
            residual = basic_block(
                residual,
                channels[i],
                name=name + '_branch_layer_' + str(i + 1) + '_' + str(j + 1))
        out.append(residual)
    return out


def high_resolution_module(x, channels, multi_scale_output=True, name=None):
    residual = branches(x, 4, channels, name=name)
    out = fuse_layers(
        residual, channels, multi_scale_output=multi_scale_output, name=name)
    return out


def transition_layer(x, in_channels, out_channels, name=None):
    num_in = len(in_channels)
    num_out = len(out_channels)
    out = []
    for i in range(num_out):
        if i < num_in:
            if in_channels[i] != out_channels[i]:
                residual = conv_bn_layer(
                    x[i],
                    filter_size=3,
                    num_filters=out_channels[i],
                    name=name + '_layer_' + str(i + 1))
                out.append(residual)
            else:
                out.append(x[i])
        else:
            residual = conv_bn_layer(
                x[-1],
                filter_size=3,
                num_filters=out_channels[i],
                stride=2,
                name=name + '_layer_' + str(i + 1))
            out.append(residual)
    return out


def stage(x, num_modules, channels, multi_scale_output=True, name=None):
    out = x
    for i in range(num_modules):
        if i == num_modules - 1 and multi_scale_output == False:
            out = high_resolution_module(
                out,
                channels,
                multi_scale_output=False,
                name=name + '_' + str(i + 1))
        else:
            out = high_resolution_module(
                out, channels, name=name + '_' + str(i + 1))

    return out


def layer1(input, name=None):
    conv = input
    for i in range(4):
        conv = bottleneck_block(
            conv,
            num_filters=64,
            downsample=True if i == 0 else False,
            name=name + '_' + str(i + 1))
    return conv


def aux_head(input, last_inp_channels, num_classes):
    x = conv_bn_layer(
        input=input,
        filter_size=1,
        num_filters=last_inp_channels,
        stride=1,
        padding=0,
        name='aux_head_conv1')
    x = fluid.layers.conv2d(
        input=x,
        num_filters=num_classes,
        filter_size=1,
        stride=1,
        padding=0,
        act=None,
        #        param_attr=ParamAttr(initializer=MSRA(), learning_rate=1.0, name='aux_head_conv2_weights'),
        param_attr=ParamAttr(
            initializer=fluid.initializer.Normal(scale=0.001),
            learning_rate=1.0,
            name='aux_head_conv2_weights'),
        bias_attr=ParamAttr(
            initializer=fluid.initializer.Constant(0.0),
            name="aux_head_conv2_bias"))
    return x


def conv3x3_ocr(input, ocr_mid_channels):
    x = conv_bn_layer(
        input=input,
        filter_size=3,
        num_filters=ocr_mid_channels,
        stride=1,
        padding=1,
        name='conv3x3_ocr')
    return x


def f_pixel(input, key_channels):
    x = conv_bn_layer(
        input=input,
        filter_size=1,
        num_filters=key_channels,
        stride=1,
        padding=0,
        name='f_pixel_conv1')
    x = conv_bn_layer(
        input=x,
        filter_size=1,
        num_filters=key_channels,
        stride=1,
        padding=0,
        name='f_pixel_conv2')
    return x


def f_object(input, key_channels):
    x = conv_bn_layer(
        input=input,
        filter_size=1,
        num_filters=key_channels,
        stride=1,
        padding=0,
        name='f_object_conv1')
    x = conv_bn_layer(
        input=x,
        filter_size=1,
        num_filters=key_channels,
        stride=1,
        padding=0,
        name='f_object_conv2')
    return x


def f_down(input, key_channels):
    x = conv_bn_layer(
        input=input,
        filter_size=1,
        num_filters=key_channels,
        stride=1,
        padding=0,
        name='f_down_conv')
    return x


def f_up(input, in_channels):
    x = conv_bn_layer(
        input=input,
        filter_size=1,
        num_filters=in_channels,
        stride=1,
        padding=0,
        name='f_up_conv')
    return x


def object_context_block(x, proxy, in_channels, key_channels, scale):
    batch_size, _, h, w = x.shape
    if scale > 1:
        x = fluid.layers.pool2d(x, pool_size=[scale, scale], pool_type='max')

    query = f_pixel(x, key_channels)
    query = fluid.layers.reshape(
        query,
        shape=[batch_size, key_channels, query.shape[2] * query.shape[3]])
    query = fluid.layers.transpose(query, perm=[0, 2, 1])

    key = f_object(proxy, key_channels)
    key = fluid.layers.reshape(
        key, shape=[batch_size, key_channels, key.shape[2] * key.shape[3]])

    value = f_down(proxy, key_channels)
    value = fluid.layers.reshape(
        value,
        shape=[batch_size, key_channels, value.shape[2] * value.shape[3]])
    value = fluid.layers.transpose(value, perm=[0, 2, 1])

    sim_map = fluid.layers.matmul(query, key)
    sim_map = (key_channels**-.5) * sim_map
    sim_map = fluid.layers.softmax(sim_map, axis=-1)

    context = fluid.layers.matmul(sim_map, value)
    context = fluid.layers.transpose(context, perm=[0, 2, 1])
    context = fluid.layers.reshape(
        context, shape=[batch_size, key_channels, x.shape[2], x.shape[3]])
    context = f_up(context, in_channels)

    if scale > 1:
        context = fluid.layers.resize_bilinear(context, out_shape=[h, w])

    return context


def ocr_gather_head(feats, probs, scale=1):
    feats = fluid.layers.reshape(
        feats,
        shape=[feats.shape[0], feats.shape[1], feats.shape[2] * feats.shape[3]])
    feats = fluid.layers.transpose(feats, perm=[0, 2, 1])
    probs = fluid.layers.reshape(
        probs,
        shape=[probs.shape[0], probs.shape[1], probs.shape[2] * probs.shape[3]])
    probs = fluid.layers.softmax(scale * probs, axis=2)
    ocr_context = fluid.layers.matmul(probs, feats)
    ocr_context = fluid.layers.transpose(ocr_context, perm=[0, 2, 1])
    ocr_context = fluid.layers.unsqueeze(ocr_context, axes=[3])
    return ocr_context


def ocr_distri_head(feats,
                    proxy_feats,
                    ocr_mid_channels,
                    ocr_key_channels,
                    scale=1,
                    dropout=0.05):
    context = object_context_block(feats, proxy_feats, ocr_mid_channels,
                                   ocr_key_channels, scale)
    x = fluid.layers.concat([context, feats], axis=1)
    x = conv_bn_layer(
        input=x,
        filter_size=1,
        num_filters=ocr_mid_channels,
        stride=1,
        padding=0,
        name='spatial_ocr_conv')
    x = fluid.layers.dropout(x, dropout_prob=dropout)
    return x


def cls_head(input, num_classes):
    x = fluid.layers.conv2d(
        input=input,
        num_filters=num_classes,
        filter_size=1,
        stride=1,
        padding=0,
        act=None,
        #        param_attr=ParamAttr(initializer=MSRA(), learning_rate=1.0, name='cls_head_conv_weights'),
        param_attr=ParamAttr(
            initializer=fluid.initializer.Normal(scale=0.001),
            learning_rate=1.0,
            name='cls_head_conv_weights'),
        bias_attr=ParamAttr(
            initializer=fluid.initializer.Constant(0.0),
            name="cls_head_conv_bias"))
    return x


def ocr_module(input, last_inp_channels, num_classes, ocr_mid_channels,
               ocr_key_channels):
    out_aux = aux_head(input, last_inp_channels, num_classes)
    feats = conv3x3_ocr(input, ocr_mid_channels)
    context = ocr_gather_head(feats, out_aux)
    feats = ocr_distri_head(feats, context, ocr_mid_channels, ocr_key_channels)
    out = cls_head(feats, num_classes)
    return out, out_aux


def high_resolution_ocr_net(input, num_classes):

    channels_2 = cfg.MODEL.HRNET.STAGE2.NUM_CHANNELS
    channels_3 = cfg.MODEL.HRNET.STAGE3.NUM_CHANNELS
    channels_4 = cfg.MODEL.HRNET.STAGE4.NUM_CHANNELS

    num_modules_2 = cfg.MODEL.HRNET.STAGE2.NUM_MODULES
    num_modules_3 = cfg.MODEL.HRNET.STAGE3.NUM_MODULES
    num_modules_4 = cfg.MODEL.HRNET.STAGE4.NUM_MODULES

    ocr_mid_channels = cfg.MODEL.OCR.OCR_MID_CHANNELS
    ocr_key_channels = cfg.MODEL.OCR.OCR_KEY_CHANNELS

    last_inp_channels = sum(channels_4)

    x = conv_bn_layer(
        input=input,
        filter_size=3,
        num_filters=64,
        stride=2,
        if_act=True,
        name='layer1_1')
    x = conv_bn_layer(
        input=x,
        filter_size=3,
        num_filters=64,
        stride=2,
        if_act=True,
        name='layer1_2')

    la1 = layer1(x, name='layer2')
    tr1 = transition_layer([la1], [256], channels_2, name='tr1')
    st2 = stage(tr1, num_modules_2, channels_2, name='st2')
    tr2 = transition_layer(st2, channels_2, channels_3, name='tr2')
    st3 = stage(tr2, num_modules_3, channels_3, name='st3')
    tr3 = transition_layer(st3, channels_3, channels_4, name='tr3')
    st4 = stage(tr3, num_modules_4, channels_4, name='st4')

    # upsample
    shape = st4[0].shape
    height, width = shape[-2], shape[-1]
    st4[1] = fluid.layers.resize_bilinear(st4[1], out_shape=[height, width])
    st4[2] = fluid.layers.resize_bilinear(st4[2], out_shape=[height, width])
    st4[3] = fluid.layers.resize_bilinear(st4[3], out_shape=[height, width])

    feats = fluid.layers.concat(st4, axis=1)

    out, out_aux = ocr_module(feats, last_inp_channels, num_classes,
                              ocr_mid_channels, ocr_key_channels)

    out = fluid.layers.resize_bilinear(out, input.shape[2:])
    out_aux = fluid.layers.resize_bilinear(out_aux, input.shape[2:])

    return out, out_aux


def ocrnet(input, num_classes):
    logit = high_resolution_ocr_net(input, num_classes)
    return logit