#    Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
#
#    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 copy import deepcopy

import numpy as np
from nnunet.experiment_planning.common_utils import get_pool_and_conv_props
from nnunet.experiment_planning.experiment_planner_baseline_3DUNet import ExperimentPlanner
from nnunet.network_architecture.generic_UNet import Generic_UNet
from nnunet.paths import *


class ExperimentPlannerPoolBasedOnSpacing(ExperimentPlanner):
    def __init__(self, folder_with_cropped_data, preprocessed_output_folder):
        super(ExperimentPlannerPoolBasedOnSpacing, self).__init__(folder_with_cropped_data, preprocessed_output_folder)
        self.data_identifier = "nnUNetData_poolBasedOnSpacing"
        self.plans_fname = join(self.preprocessed_output_folder,
                                "nnUNetPlans" +  "poolBasedOnSpacing_plans_3D.pkl")

    def get_properties_for_stage(self, current_spacing, original_spacing, original_shape, num_cases,
                                 num_modalities, num_classes):
        """
        ExperimentPlanner configures pooling so that we pool late. Meaning that if the number of pooling per axis is
        (2, 3, 3), then the first pooling operation will always pool axes 1 and 2 and not 0, irrespective of spacing.
        This can cause a larger memory footprint, so it can be beneficial to revise this.

        Here we are pooling based on the spacing of the data.

        """
        new_median_shape = np.round(original_spacing / current_spacing * original_shape).astype(int)
        dataset_num_voxels = np.prod(new_median_shape) * num_cases

        # the next line is what we had before as a default. The patch size had the same aspect ratio as the median shape of a patient. We swapped t
        # input_patch_size = new_median_shape

        # compute how many voxels are one mm
        input_patch_size = 1 / np.array(current_spacing)

        # normalize voxels per mm
        input_patch_size /= input_patch_size.mean()

        # create an isotropic patch of size 512x512x512mm
        input_patch_size *= 1 / min(input_patch_size) * 512  # to get a starting value
        input_patch_size = np.round(input_patch_size).astype(int)

        # clip it to the median shape of the dataset because patches larger then that make not much sense
        input_patch_size = [min(i, j) for i, j in zip(input_patch_size, new_median_shape)]

        network_num_pool_per_axis, pool_op_kernel_sizes, conv_kernel_sizes, new_shp, \
        shape_must_be_divisible_by = get_pool_and_conv_props(current_spacing, input_patch_size,
                                                             self.unet_featuremap_min_edge_length,
                                                             self.unet_max_numpool)

        ref = Generic_UNet.use_this_for_batch_size_computation_3D
        here = Generic_UNet.compute_approx_vram_consumption(new_shp, network_num_pool_per_axis,
                                                            self.unet_base_num_features,
                                                            self.unet_max_num_filters, num_modalities,
                                                            num_classes,
                                                            pool_op_kernel_sizes, conv_per_stage=self.conv_per_stage)
        while here > ref:
            axis_to_be_reduced = np.argsort(new_shp / new_median_shape)[-1]

            tmp = deepcopy(new_shp)
            tmp[axis_to_be_reduced] -= shape_must_be_divisible_by[axis_to_be_reduced]
            _, _, _, _, shape_must_be_divisible_by_new = \
                get_pool_and_conv_props(current_spacing, tmp,
                                        self.unet_featuremap_min_edge_length,
                                        self.unet_max_numpool,
                                        )
            new_shp[axis_to_be_reduced] -= shape_must_be_divisible_by_new[axis_to_be_reduced]

            # we have to recompute numpool now:
            network_num_pool_per_axis, pool_op_kernel_sizes, conv_kernel_sizes, new_shp, \
            shape_must_be_divisible_by = get_pool_and_conv_props(current_spacing, new_shp,
                                                                 self.unet_featuremap_min_edge_length,
                                                                 self.unet_max_numpool,
                                                                 )

            here = Generic_UNet.compute_approx_vram_consumption(new_shp, network_num_pool_per_axis,
                                                                self.unet_base_num_features,
                                                                self.unet_max_num_filters, num_modalities,
                                                                num_classes, pool_op_kernel_sizes,
                                                                conv_per_stage=self.conv_per_stage)
            print(new_shp)

        input_patch_size = new_shp

        batch_size = Generic_UNet.DEFAULT_BATCH_SIZE_3D  # This is what wirks with 128**3
        batch_size = int(np.floor(max(ref / here, 1) * batch_size))

        # check if batch size is too large
        max_batch_size = np.round(self.batch_size_covers_max_percent_of_dataset * dataset_num_voxels /
                                  np.prod(input_patch_size, dtype=np.int64)).astype(int)
        max_batch_size = max(max_batch_size, self.unet_min_batch_size)
        batch_size = max(1, min(batch_size, max_batch_size))

        do_dummy_2D_data_aug = (max(input_patch_size) / input_patch_size[
            0]) > self.anisotropy_threshold

        plan = {
            'batch_size': batch_size,
            'num_pool_per_axis': network_num_pool_per_axis,
            'patch_size': input_patch_size,
            'median_patient_size_in_voxels': new_median_shape,
            'current_spacing': current_spacing,
            'original_spacing': original_spacing,
            'do_dummy_2D_data_aug': do_dummy_2D_data_aug,
            'pool_op_kernel_sizes': pool_op_kernel_sizes,
            'conv_kernel_sizes': conv_kernel_sizes,
        }
        return plan