# coding=utf-8
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#
# 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
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import torch
import torch.nn as nn

from .attention import MultiheadAttention


class OriginalConditionalAttentionEncoder(nn.Module):
    """Original implementation of Conditional Self-Attention

    Remove norm and dropout layer for test simplicity
    """

    def __init__(self, d_model, nhead):
        super().__init__()
        self.sa_qcontent_proj = nn.Linear(d_model, d_model)
        self.sa_qpos_proj = nn.Linear(d_model, d_model)
        self.sa_kcontent_proj = nn.Linear(d_model, d_model)
        self.sa_kpos_proj = nn.Linear(d_model, d_model)
        self.sa_v_proj = nn.Linear(d_model, d_model)
        self.self_attn = MultiheadAttention(d_model, nhead, dropout=0.0, vdim=d_model)

    def forward(self, tgt, query_pos):
        q_content = self.sa_qcontent_proj(tgt)
        q_pos = self.sa_qpos_proj(query_pos)
        k_content = self.sa_kcontent_proj(tgt)
        k_pos = self.sa_kpos_proj(query_pos)
        v = self.sa_v_proj(tgt)
        q = q_content + q_pos
        k = k_content + k_pos

        tgt2 = self.self_attn(q, k, v)
        return tgt2


class OriginalConditionalAttentionDecoder(nn.Module):
    """Original implementation of Conditional Attention Decoder

    Remove norm and dropout layer for test simplicity
    """

    def __init__(self, d_model, nhead):
        super().__init__()
        # self-attn
        self.sa_qcontent_proj = nn.Linear(d_model, d_model)
        self.sa_qpos_proj = nn.Linear(d_model, d_model)
        self.sa_kcontent_proj = nn.Linear(d_model, d_model)
        self.sa_kpos_proj = nn.Linear(d_model, d_model)
        self.sa_v_proj = nn.Linear(d_model, d_model)
        self.self_attn = MultiheadAttention(d_model, nhead, dropout=0.0, vdim=d_model)

        # cross-attn
        self.ca_qcontent_proj = nn.Linear(d_model, d_model)
        self.ca_qpos_proj = nn.Linear(d_model, d_model)
        self.ca_kcontent_proj = nn.Linear(d_model, d_model)
        self.ca_kpos_proj = nn.Linear(d_model, d_model)
        self.ca_v_proj = nn.Linear(d_model, d_model)
        self.ca_qpos_sine_proj = nn.Linear(d_model, d_model)
        self.cross_attn = MultiheadAttention(d_model * 2, nhead, dropout=0.0, vdim=d_model)

        self.nhead = nhead

    def forward(self, tgt, memory, query_pos, pos, query_sine_embed, is_first=True):
        # self attention
        q_content = self.sa_qcontent_proj(
            tgt
        )  # target is the input of the first decoder layer. zero by default.
        q_pos = self.sa_qpos_proj(query_pos)
        k_content = self.sa_kcontent_proj(tgt)
        k_pos = self.sa_kpos_proj(query_pos)
        v = self.sa_v_proj(tgt)
        q = q_content + q_pos
        k = k_content + k_pos

        tgt2 = self.self_attn(q, k, v)[0]

        tgt = tgt + tgt2
        # ========================================

        # cross attention
        q_content = self.ca_qcontent_proj(tgt)
        k_content = self.ca_kcontent_proj(memory)
        v = self.ca_v_proj(memory)

        num_queries, bs, n_model = q_content.shape
        hw, _, _ = k_content.shape

        k_pos = self.ca_kpos_proj(pos)

        # For the first decoder layer, we concatenate the positional embedding predicted from
        # the object query (the positional embedding) into the original query (key) in DETR.
        if is_first:
            q_pos = self.ca_qpos_proj(query_pos)
            q = q_content + q_pos
            k = k_content + k_pos
        else:
            q = q_content
            k = k_content

        q = q.view(num_queries, bs, self.nhead, n_model // self.nhead)
        query_sine_embed = self.ca_qpos_sine_proj(query_sine_embed)
        query_sine_embed = query_sine_embed.view(num_queries, bs, self.nhead, n_model // self.nhead)
        q = torch.cat([q, query_sine_embed], dim=3).view(num_queries, bs, n_model * 2)
        k = k.view(hw, bs, self.nhead, n_model // self.nhead)
        k_pos = k_pos.view(hw, bs, self.nhead, n_model // self.nhead)
        k = torch.cat([k, k_pos], dim=3).view(hw, bs, n_model * 2)

        tgt2 = self.cross_attn(query=q, key=k, value=v)[0]

        return tgt2 + tgt