# copyright (c) 2022 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 math
import paddle
from paddle import ParamAttr
import paddle.nn as nn
import paddle.nn.functional as F

from ppocr.modeling.necks.rnn import Im2Seq, EncoderWithRNN, EncoderWithFC, SequenceEncoder, EncoderWithSVTR, trunc_normal_, zeros_
from .rec_ctc_head import CTCHead
from .rec_sar_head import SARHead
from .rec_nrtr_head import Transformer


class FCTranspose(nn.Layer):
    def __init__(self, in_channels, out_channels, only_transpose=False):
        super().__init__()
        self.only_transpose = only_transpose
        if not self.only_transpose:
            self.fc = nn.Linear(in_channels, out_channels, bias_attr=False)

    def forward(self, x):
        if self.only_transpose:
            return x.transpose([0, 2, 1])
        else:
            return self.fc(x.transpose([0, 2, 1]))

class AddPos(nn.Layer):
    def  __init__(self, dim, w):
        super().__init__()
        self.dec_pos_embed = self.create_parameter(
                            shape=[1, w, dim], default_initializer=zeros_)
        self.add_parameter("dec_pos_embed", self.dec_pos_embed)
        trunc_normal_(self.dec_pos_embed)
    
    def forward(self,x):
        x = x + self.dec_pos_embed[:, :paddle.shape(x)[1], :]
        return x


class MultiHead(nn.Layer):
    def __init__(self, in_channels, out_channels_list, **kwargs):
        super().__init__()
        self.head_list = kwargs.pop('head_list')
        self.use_pool = kwargs.get('use_pool', False)
        self.use_pos = kwargs.get('use_pos', False)
        self.in_channels = in_channels
        if self.use_pool:
            self.pool = nn.AvgPool2D(kernel_size=[3, 2], stride=[3, 2], padding=0)
        self.gtc_head = 'sar'
        assert len(self.head_list) >= 2
        for idx, head_name in enumerate(self.head_list):
            name = list(head_name)[0]
            if name == 'SARHead':
                # sar head
                sar_args = self.head_list[idx][name]
                self.sar_head = eval(name)(in_channels=in_channels, \
                    out_channels=out_channels_list['SARLabelDecode'], **sar_args)
            elif name == 'NRTRHead':
                gtc_args = self.head_list[idx][name]
                max_text_length = gtc_args.get('max_text_length', 25)
                nrtr_dim = gtc_args.get('nrtr_dim', 256)
                num_decoder_layers = gtc_args.get('num_decoder_layers', 4)
                if self.use_pos:
                    self.before_gtc = nn.Sequential(
                    nn.Flatten(2), FCTranspose(in_channels, nrtr_dim), AddPos(nrtr_dim, 80))
                else:
                    self.before_gtc = nn.Sequential(
                    nn.Flatten(2), FCTranspose(in_channels, nrtr_dim))
                
                self.gtc_head = Transformer(
                    d_model=nrtr_dim,
                    nhead=nrtr_dim // 32,
                    num_encoder_layers=-1,
                    beam_size=-1,
                    num_decoder_layers=num_decoder_layers,
                    max_len=max_text_length,
                    dim_feedforward=nrtr_dim * 4,
                    out_channels=out_channels_list['NRTRLabelDecode'])
            elif name == 'CTCHead':
                # ctc neck
                self.encoder_reshape = Im2Seq(in_channels)
                neck_args = self.head_list[idx][name]['Neck']
                encoder_type = neck_args.pop('name')
                self.ctc_encoder = SequenceEncoder(in_channels=in_channels, \
                    encoder_type=encoder_type, **neck_args)
                # ctc head
                head_args = self.head_list[idx][name]['Head']
                self.ctc_head = eval(name)(in_channels=self.ctc_encoder.out_channels, \
                    out_channels=out_channels_list['CTCLabelDecode'], **head_args)
            else:
                raise NotImplementedError(
                    '{} is not supported in MultiHead yet'.format(name))

    def forward(self, x, targets=None):
        if self.use_pool:
            x = self.pool(x.reshape([0, 3, -1, self.in_channels]).transpose([0, 3, 1, 2]))
        ctc_encoder = self.ctc_encoder(x)
        ctc_out = self.ctc_head(ctc_encoder, targets)
        head_out = dict()
        head_out['ctc'] = ctc_out
        head_out['ctc_neck'] = ctc_encoder
        # eval mode
        if not self.training:
            return ctc_out
        if self.gtc_head == 'sar':
            sar_out = self.sar_head(x, targets[1:])
            head_out['sar'] = sar_out
        else:
            gtc_out = self.gtc_head(self.before_gtc(x), targets[1:])
            head_out['nrtr'] = gtc_out
        return head_out