# Copyright (c) MONAI Consortium
# 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 typing import Optional, Sequence, Union

import torch
import torch.nn as nn

from monai.networks.blocks import Convolution, UpSample, ResidualUnit
from monai.networks.layers.factories import Conv, Pool
from monai.utils import deprecated_arg, ensure_tuple_rep

__all__ = ["BasicUnet", "Basicunet", "basicunet", "BasicUNet"]


class TwoConv(nn.Sequential):
    """two convolutions."""

    def __init__(
        self,
        spatial_dims: int,
        in_chns: int,
        out_chns: int,
        act: Union[str, tuple],
        norm: Union[str, tuple],
        bias: bool,
        dropout: Union[float, tuple] = 0.0,
    ):
        """
        Args:
            spatial_dims: number of spatial dimensions.
            in_chns: number of input channels.
            out_chns: number of output channels.
            act: activation type and arguments.
            norm: feature normalization type and arguments.
            bias: whether to have a bias term in convolution blocks.
            dropout: dropout ratio. Defaults to no dropout.

        """
        super().__init__()

        conv_0 = Convolution(spatial_dims, in_chns, out_chns, act=act, norm=norm, dropout=dropout, bias=bias, padding=1)
        conv_1 = Convolution(
            spatial_dims, out_chns, out_chns, act=act, norm=norm, dropout=dropout, bias=bias, padding=1
        )
        self.add_module("conv_0", conv_0)
        self.add_module("conv_1", conv_1)


class Down(nn.Sequential):
    """maxpooling downsampling and two convolutions."""

    def __init__(
        self,
        spatial_dims: int,
        in_chns: int,
        out_chns: int,
        act: Union[str, tuple],
        norm: Union[str, tuple],
        bias: bool,
        dropout: Union[float, tuple] = 0.0,
    ):
        """
        Args:
            spatial_dims: number of spatial dimensions.
            in_chns: number of input channels.
            out_chns: number of output channels.
            act: activation type and arguments.
            norm: feature normalization type and arguments.
            bias: whether to have a bias term in convolution blocks.
            dropout: dropout ratio. Defaults to no dropout.

        """
        super().__init__()
        max_pooling = Pool["MAX", spatial_dims](kernel_size=2)
        convs = TwoConv(spatial_dims, in_chns, out_chns, act, norm, bias, dropout)
        self.add_module("max_pooling", max_pooling)
        self.add_module("convs", convs)


class UpCat(nn.Module):
    """upsampling, concatenation with the encoder feature map, two convolutions"""

    def __init__(
        self,
        spatial_dims: int,
        in_chns: int,
        cat_chns: int,
        out_chns: int,
        act: Union[str, tuple],
        norm: Union[str, tuple],
        bias: bool,
        dropout: Union[float, tuple] = 0.0,
        upsample: str = "deconv",
        pre_conv: Optional[Union[nn.Module, str]] = "default",
        interp_mode: str = "linear",
        align_corners: Optional[bool] = True,
        halves: bool = True,
        is_pad: bool = True,
    ):
        """
        Args:
            spatial_dims: number of spatial dimensions.
            in_chns: number of input channels to be upsampled.
            cat_chns: number of channels from the encoder.
            out_chns: number of output channels.
            act: activation type and arguments.
            norm: feature normalization type and arguments.
            bias: whether to have a bias term in convolution blocks.
            dropout: dropout ratio. Defaults to no dropout.
            upsample: upsampling mode, available options are
                ``"deconv"``, ``"pixelshuffle"``, ``"nontrainable"``.
            pre_conv: a conv block applied before upsampling.
                Only used in the "nontrainable" or "pixelshuffle" mode.
            interp_mode: {``"nearest"``, ``"linear"``, ``"bilinear"``, ``"bicubic"``, ``"trilinear"``}
                Only used in the "nontrainable" mode.
            align_corners: set the align_corners parameter for upsample. Defaults to True.
                Only used in the "nontrainable" mode.
            halves: whether to halve the number of channels during upsampling.
                This parameter does not work on ``nontrainable`` mode if ``pre_conv`` is `None`.
            is_pad: whether to pad upsampling features to fit features from encoder. Defaults to True.

        """
        super().__init__()
        if upsample == "nontrainable" and pre_conv is None:
            up_chns = in_chns
        else:
            up_chns = in_chns // 2 if halves else in_chns
        self.upsample = UpSample(
            spatial_dims,
            in_chns,
            up_chns,
            2,
            mode=upsample,
            pre_conv=pre_conv,
            interp_mode=interp_mode,
            align_corners=align_corners,
        )
        self.convs = TwoConv(spatial_dims, cat_chns + up_chns, out_chns, act, norm, bias, dropout)
        self.is_pad = is_pad

    def forward(self, x: torch.Tensor, x_e: Optional[torch.Tensor]):
        """

        Args:
            x: features to be upsampled.
            x_e: features from the encoder.
        """
        x_0 = self.upsample(x)

        if x_e is not None:
            if self.is_pad:
                # handling spatial shapes due to the 2x maxpooling with odd edge lengths.
                dimensions = len(x.shape) - 2
                sp = [0] * (dimensions * 2)
                for i in range(dimensions):
                    if x_e.shape[-i - 1] != x_0.shape[-i - 1]:
                        sp[i * 2 + 1] = 1
                x_0 = torch.nn.functional.pad(x_0, sp, "replicate")
            x = self.convs(torch.cat([x_e, x_0], dim=1))  # input channels: (cat_chns + up_chns)
        else:
            x = self.convs(x_0)

        return x


class Unet_decoder(nn.Module):
    def __init__(
        self,
        spatial_dims: int = 3,
        out_channels: int = 2,
        features: Sequence[int] = (32, 32, 64, 128, 256, 32),
        act: Union[str, tuple] = ("LeakyReLU", {"negative_slope": 0.1, "inplace": True}),
        norm: Union[str, tuple] = ("instance", {"affine": True}),
        bias: bool = True,
        dropout: Union[float, tuple] = 0.0,
        upsample: str = "deconv",
        dimensions: Optional[int] = None,
    ):
        super().__init__()
        if dimensions is not None:
            spatial_dims = dimensions
        fea = ensure_tuple_rep(features, 6)
        print(f"Unet_decoder features: {fea}.")

        self.upcat_4 = UpCat(spatial_dims, fea[4], fea[3], fea[3], act, norm, bias, dropout, upsample)
        self.upcat_3 = UpCat(spatial_dims, fea[3], fea[2], fea[2], act, norm, bias, dropout, upsample)
        self.upcat_2 = UpCat(spatial_dims, fea[2], fea[1], fea[1], act, norm, bias, dropout, upsample)
        self.upcat_1 = UpCat(spatial_dims, fea[1], fea[0], fea[5], act, norm, bias, dropout, upsample, halves=False)

        self.final_conv = Conv["conv", spatial_dims](fea[5], out_channels, kernel_size=1)

    def forward(self, image_embeddings, feature_list):
        x4, x3, x2, x1, x0 = image_embeddings, feature_list[3], feature_list[2], feature_list[1], feature_list[0]
        u4 = self.upcat_4(x4, x3)
        u3 = self.upcat_3(u4, x2)
        u2 = self.upcat_2(u3, x1)
        u1 = self.upcat_1(u2, x0)

        # logits = self.final_conv(u1)
        return u1

class Unet_encoder(nn.Module):
    def __init__(
        self,
        spatial_dims: int = 3,
        in_channels: int = 1,
        features: Sequence[int] = (32, 32, 64, 128, 256, 32),
        act: Union[str, tuple] = ("LeakyReLU", {"negative_slope": 0.1, "inplace": True}),
        norm: Union[str, tuple] = ("instance", {"affine": True}),
        bias: bool = True,
        dropout: Union[float, tuple] = 0.0,
        dimensions: Optional[int] = None,
    ):
        super().__init__()
        if dimensions is not None:
            spatial_dims = dimensions
        fea = ensure_tuple_rep(features, 6)
        print(f"Unet_encoder features: {fea}.")

        self.conv_0 = TwoConv(spatial_dims, in_channels, features[0], act, norm, bias, dropout)
        self.down_1 = Down(spatial_dims, fea[0], fea[1], act, norm, bias, dropout)
        self.down_2 = Down(spatial_dims, fea[1], fea[2], act, norm, bias, dropout)
        self.down_3 = Down(spatial_dims, fea[2], fea[3], act, norm, bias, dropout)
        self.down_4 = Down(spatial_dims, fea[3], fea[4], act, norm, bias, dropout)

    def forward(self, x: torch.Tensor, deepest_only=False):
        x0 = self.conv_0(x)
        x1 = self.down_1(x0)
        x2 = self.down_2(x1)
        x3 = self.down_3(x2)
        x4 = self.down_4(x3)

        if deepest_only:
            return x4
        else:
            return x4, x3, x2, x1, x0

# BasicUnet = Basicunet = basicunet = BasicUNet