#    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.


import argparse
from copy import deepcopy
from typing import Tuple, Union, List

import numpy as np
from batchgenerators.augmentations.utils import resize_segmentation
from e2enet.inference.segmentation_export import save_segmentation_nifti_from_softmax, save_segmentation_nifti
from batchgenerators.utilities.file_and_folder_operations import *
from multiprocessing import Process, Queue, Manager
import torch
import SimpleITK as sitk
import shutil
from multiprocessing import Pool
from e2enet.postprocessing.connected_components import load_remove_save, load_postprocessing
from e2enet.training.model_restore import load_model_and_checkpoint_files
from e2enet.training.network_training.nnUNetTrainer import nnUNetTrainer
from e2enet.utilities.one_hot_encoding import to_one_hot


def preprocess_save_to_queue(preprocess_fn, q, list_of_lists, output_files, segs_from_prev_stage, classes,
                             transpose_forward):
    # suppress output
    # sys.stdout = open(os.devnull, 'w')

    errors_in = []
    for i, l in enumerate(list_of_lists):
        try:
            output_file = output_files[i]
            print("preprocessing", output_file)
            d, _, dct = preprocess_fn(l)
            # print(output_file, dct)
            if segs_from_prev_stage[i] is not None:
                assert isfile(segs_from_prev_stage[i]) and segs_from_prev_stage[i].endswith(
                    ".nii.gz"), "segs_from_prev_stage" \
                                " must point to a " \
                                "segmentation file"
                seg_prev = sitk.GetArrayFromImage(sitk.ReadImage(segs_from_prev_stage[i]))
                # check to see if shapes match
                img = sitk.GetArrayFromImage(sitk.ReadImage(l[0]))
                assert all([i == j for i, j in zip(seg_prev.shape, img.shape)]), "image and segmentation from previous " \
                                                                                 "stage don't have the same pixel array " \
                                                                                 "shape! image: %s, seg_prev: %s" % \
                                                                                 (l[0], segs_from_prev_stage[i])
                seg_prev = seg_prev.transpose(transpose_forward)
                seg_reshaped = resize_segmentation(seg_prev, d.shape[1:], order=1)
                seg_reshaped = to_one_hot(seg_reshaped, classes)
                d = np.vstack((d, seg_reshaped)).astype(np.float32)
            """There is a problem with python process communication that prevents us from communicating objects 
            larger than 2 GB between processes (basically when the length of the pickle string that will be sent is 
            communicated by the multiprocessing.Pipe object then the placeholder (I think) does not allow for long 
            enough strings (lol). This could be fixed by changing i to l (for long) but that would require manually 
            patching system python code. We circumvent that problem here by saving softmax_pred to a npy file that will 
            then be read (and finally deleted) by the Process. save_segmentation_nifti_from_softmax can take either 
            filename or np.ndarray and will handle this automatically"""
            print(d.shape)
            if np.prod(d.shape) > (2e9 / 4 * 0.85):  # *0.85 just to be save, 4 because float32 is 4 bytes
                print(
                    "This output is too large for python process-process communication. "
                    "Saving output temporarily to disk")
                np.save(output_file[:-7] + ".npy", d)
                d = output_file[:-7] + ".npy"
            q.put((output_file, (d, dct)))
        except KeyboardInterrupt:
            raise KeyboardInterrupt
        except Exception as e:
            print("error in", l)
            print(e)
    q.put("end")
    if len(errors_in) > 0:
        print("There were some errors in the following cases:", errors_in)
        print("These cases were ignored.")
    else:
        print("This worker has ended successfully, no errors to report")
    # restore output
    # sys.stdout = sys.__stdout__


def preprocess_multithreaded(trainer, list_of_lists, output_files, num_processes=2, segs_from_prev_stage=None):
    if segs_from_prev_stage is None:
        segs_from_prev_stage = [None] * len(list_of_lists)

    num_processes = min(len(list_of_lists), num_processes)

    classes = list(range(1, trainer.num_classes))
    # assert isinstance(trainer, nnUNetTrainer)
    q = Queue(1)
    processes = []
    for i in range(num_processes):
        pr = Process(target=preprocess_save_to_queue, args=(trainer.preprocess_patient, q,
                                                            list_of_lists[i::num_processes],
                                                            output_files[i::num_processes],
                                                            segs_from_prev_stage[i::num_processes],
                                                            classes, trainer.plans['transpose_forward']))
        pr.start()
        processes.append(pr)

    try:
        end_ctr = 0
        while end_ctr != num_processes:
            item = q.get()
            if item == "end":
                end_ctr += 1
                continue
            else:
                yield item

    finally:
        for p in processes:
            if p.is_alive():
                p.terminate()  # this should not happen but better safe than sorry right
            p.join()

        q.close()

def preprocess_pure(trainer, list_of_lists, output_files, num_processes=2, segs_from_prev_stage=None):
    if segs_from_prev_stage is None:
        segs_from_prev_stage = [None] * len(list_of_lists)

    num_processes = min(len(list_of_lists), num_processes)

    classes = list(range(1, trainer.num_classes))
    # assert isinstance(trainer, nnUNetTrainer)

    preprocess_fn=trainer.preprocess_patient
    transpose_forward = trainer.plans['transpose_forward']

    errors_in = []
    q = []
    for i, l in enumerate(list_of_lists):
        try:
            output_file = output_files[i]
            print("preprocessing", output_file)
            d, _, dct = preprocess_fn(l)
            # print(output_file, dct)
            if segs_from_prev_stage[i] is not None:
                assert isfile(segs_from_prev_stage[i]) and segs_from_prev_stage[i].endswith(
                    ".nii.gz"), "segs_from_prev_stage" \
                                " must point to a " \
                                "segmentation file"
                seg_prev = sitk.GetArrayFromImage(sitk.ReadImage(segs_from_prev_stage[i]))
                # check to see if shapes match
                img = sitk.GetArrayFromImage(sitk.ReadImage(l[0]))
                assert all([i == j for i, j in zip(seg_prev.shape, img.shape)]), "image and segmentation from previous " \
                                                                                 "stage don't have the same pixel array " \
                                                                                 "shape! image: %s, seg_prev: %s" % \
                                                                                 (l[0], segs_from_prev_stage[i])
                seg_prev = seg_prev.transpose(transpose_forward)
                seg_reshaped = resize_segmentation(seg_prev, d.shape[1:], order=1)
                seg_reshaped = to_one_hot(seg_reshaped, classes)
                d = np.vstack((d, seg_reshaped)).astype(np.float32)
            """There is a problem with python process communication that prevents us from communicating objects 
            larger than 2 GB between processes (basically when the length of the pickle string that will be sent is 
            communicated by the multiprocessing.Pipe object then the placeholder (I think) does not allow for long 
            enough strings (lol). This could be fixed by changing i to l (for long) but that would require manually 
            patching system python code. We circumvent that problem here by saving softmax_pred to a npy file that will 
            then be read (and finally deleted) by the Process. save_segmentation_nifti_from_softmax can take either 
            filename or np.ndarray and will handle this automatically"""
            print(d.shape)
            if np.prod(d.shape) > (2e9 / 4 * 0.85):  # *0.85 just to be save, 4 because float32 is 4 bytes
                print(
                    "This output is too large for python process-process communication. "
                    "Saving output temporarily to disk")
                np.save(output_file[:-7] + ".npy", d)
                d = output_file[:-7] + ".npy"
            q.append((output_file, (d, dct)))
        except KeyboardInterrupt:
            raise KeyboardInterrupt
        except Exception as e:
            print("error in", l)
            print(e)
    if len(errors_in) > 0:
        print("There were some errors in the following cases:", errors_in)
        print("These cases were ignored.")
    else:
        print("This worker has ended successfully, no errors to report")

    return q

def predict_cases(model, list_of_lists, output_filenames, folds, save_npz, num_threads_preprocessing,
                  num_threads_nifti_save, segs_from_prev_stage=None, do_tta=True, mixed_precision=True,
                  overwrite_existing=False,
                  all_in_gpu=False, step_size=0.5, checkpoint_name="model_final_checkpoint",
                  segmentation_export_kwargs: dict = None, disable_postprocessing: bool = False):
    """
    :param segmentation_export_kwargs:
    :param model: folder where the model is saved, must contain fold_x subfolders
    :param list_of_lists: [[case0_0000.nii.gz, case0_0001.nii.gz], [case1_0000.nii.gz, case1_0001.nii.gz], ...]
    :param output_filenames: [output_file_case0.nii.gz, output_file_case1.nii.gz, ...]
    :param folds: default: (0, 1, 2, 3, 4) (but can also be 'all' or a subset of the five folds, for example use (0, )
    for using only fold_0
    :param save_npz: default: False
    :param num_threads_preprocessing:
    :param num_threads_nifti_save:
    :param segs_from_prev_stage:
    :param do_tta: default: True, can be set to False for a 8x speedup at the cost of a reduced segmentation quality
    :param overwrite_existing: default: True
    :param mixed_precision: if None then we take no action. If True/False we overwrite what the model has in its init
    :return:
    """
    assert len(list_of_lists) == len(output_filenames)
    if segs_from_prev_stage is not None: assert len(segs_from_prev_stage) == len(output_filenames)

    pool = Pool(num_threads_nifti_save)
    results = []

    cleaned_output_files = []
    for o in output_filenames:
        dr, f = os.path.split(o)
        if len(dr) > 0:
            maybe_mkdir_p(dr)
        if not f.endswith(".nii.gz"):
            f, _ = os.path.splitext(f)
            f = f + ".nii.gz"
        cleaned_output_files.append(join(dr, f))

    if not overwrite_existing:
        print("number of cases:", len(list_of_lists))
        # if save_npz=True then we should also check for missing npz files
        not_done_idx = [i for i, j in enumerate(cleaned_output_files) if (not isfile(j)) or (save_npz and not isfile(j[:-7] + '.npz'))]

        cleaned_output_files = [cleaned_output_files[i] for i in not_done_idx]
        list_of_lists = [list_of_lists[i] for i in not_done_idx]
        if segs_from_prev_stage is not None:
            segs_from_prev_stage = [segs_from_prev_stage[i] for i in not_done_idx]

        print("number of cases that still need to be predicted:", len(cleaned_output_files))

    print("emptying cuda cache")
    torch.cuda.empty_cache()

    print("loading parameters for folds,", folds)
    trainer, params = load_model_and_checkpoint_files(model, folds, mixed_precision=mixed_precision,
                                                      checkpoint_name=checkpoint_name)

    if segmentation_export_kwargs is None:
        if 'segmentation_export_params' in trainer.plans.keys():
            force_separate_z = trainer.plans['segmentation_export_params']['force_separate_z']
            interpolation_order = trainer.plans['segmentation_export_params']['interpolation_order']
            interpolation_order_z = trainer.plans['segmentation_export_params']['interpolation_order_z']
        else:
            force_separate_z = None
            interpolation_order = 1
            interpolation_order_z = 0
    else:
        force_separate_z = segmentation_export_kwargs['force_separate_z']
        interpolation_order = segmentation_export_kwargs['interpolation_order']
        interpolation_order_z = segmentation_export_kwargs['interpolation_order_z']

    ## 进行预处理，得到处理后的数据返回list，但是还没保持预处理后的数据
    print("starting preprocessing generator")
    preprocessing = preprocess_multithreaded(trainer, list_of_lists, cleaned_output_files, num_threads_preprocessing,
                                             segs_from_prev_stage)

    # preprocessing = preprocess_pure(trainer, list_of_lists, cleaned_output_files, num_threads_preprocessing,
    #                                 segs_from_prev_stage)
    print("starting prediction...")
    all_output_files = []
    for preprocessed in preprocessing:
        output_filename, (d, dct) = preprocessed
        all_output_files.append(all_output_files)
        if isinstance(d, str):
            data = np.load(d)
            os.remove(d)
            d = data

        print("predicting", output_filename)
        trainer.load_checkpoint_ram(params[0], False)
        softmax = trainer.predict_preprocessed_data_return_seg_and_softmax(
            d, do_mirroring=do_tta, mirror_axes=trainer.data_aug_params['mirror_axes'], use_sliding_window=True,
            step_size=step_size, use_gaussian=True, all_in_gpu=all_in_gpu,
            mixed_precision=mixed_precision)[1]

        for p in params[1:]:
            trainer.load_checkpoint_ram(p, False)
            softmax += trainer.predict_preprocessed_data_return_seg_and_softmax(
                d, do_mirroring=do_tta, mirror_axes=trainer.data_aug_params['mirror_axes'], use_sliding_window=True,
                step_size=step_size, use_gaussian=True, all_in_gpu=all_in_gpu,
                mixed_precision=mixed_precision)[1]

        if len(params) > 1:
            softmax /= len(params)

        transpose_forward = trainer.plans.get('transpose_forward')
        if transpose_forward is not None:
            transpose_backward = trainer.plans.get('transpose_backward')
            softmax = softmax.transpose([0] + [i + 1 for i in transpose_backward])

        if save_npz:
            npz_file = output_filename[:-7] + ".npz"
        else:
            npz_file = None

        if hasattr(trainer, 'regions_class_order'):
            region_class_order = trainer.regions_class_order
        else:
            region_class_order = None

        """There is a problem with python process communication that prevents us from communicating objects 
        larger than 2 GB between processes (basically when the length of the pickle string that will be sent is 
        communicated by the multiprocessing.Pipe object then the placeholder (I think) does not allow for long 
        enough strings (lol). This could be fixed by changing i to l (for long) but that would require manually 
        patching system python code. We circumvent that problem here by saving softmax_pred to a npy file that will 
        then be read (and finally deleted) by the Process. save_segmentation_nifti_from_softmax can take either 
        filename or np.ndarray and will handle this automatically"""
        bytes_per_voxel = 4
        if all_in_gpu:
            bytes_per_voxel = 2  # if all_in_gpu then the return value is half (float16)
        if np.prod(softmax.shape) > (2e9 / bytes_per_voxel * 0.85):  # * 0.85 just to be save
            print(
                "This output is too large for python process-process communication. Saving output temporarily to disk")
            np.save(output_filename[:-7] + ".npy", softmax)
            softmax = output_filename[:-7] + ".npy"

        results.append(pool.starmap_async(save_segmentation_nifti_from_softmax,
                                          ((softmax, output_filename, dct, interpolation_order, region_class_order,
                                            None, None,
                                            npz_file, None, force_separate_z, interpolation_order_z),)
                                          ))

    print("inference done. Now waiting for the segmentation export to finish...")
    _ = [i.get() for i in results]
    # now apply postprocessing
    # first load the postprocessing properties if they are present. Else raise a well visible warning
    if not disable_postprocessing:
        results = []
        pp_file = join(model, "postprocessing.json")
        if isfile(pp_file):
            print("postprocessing...")
            shutil.copy(pp_file, os.path.abspath(os.path.dirname(output_filenames[0])))
            # for_which_classes stores for which of the classes everything but the largest connected component needs to be
            # removed
            for_which_classes, min_valid_obj_size = load_postprocessing(pp_file)
            results.append(pool.starmap_async(load_remove_save,
                                              zip(output_filenames, output_filenames,
                                                  [for_which_classes] * len(output_filenames),
                                                  [min_valid_obj_size] * len(output_filenames))))
            _ = [i.get() for i in results]
        else:
            print("WARNING! Cannot run postprocessing because the postprocessing file is missing. Make sure to run "
                  "consolidate_folds in the output folder of the model first!\nThe folder you need to run this in is "
                  "%s" % model)

    pool.close()
    pool.join()


def predict_cases_fast(model, list_of_lists, output_filenames, folds, num_threads_preprocessing,
                       num_threads_nifti_save, segs_from_prev_stage=None, do_tta=True, mixed_precision=True,
                       overwrite_existing=False,
                       all_in_gpu=False, step_size=0.5, checkpoint_name="model_final_checkpoint",
                       segmentation_export_kwargs: dict = None, disable_postprocessing: bool = False):
    assert len(list_of_lists) == len(output_filenames)
    if segs_from_prev_stage is not None: assert len(segs_from_prev_stage) == len(output_filenames)

    pool = Pool(num_threads_nifti_save)
    results = []

    cleaned_output_files = []
    for o in output_filenames:
        dr, f = os.path.split(o)
        if len(dr) > 0:
            maybe_mkdir_p(dr)
        if not f.endswith(".nii.gz"):
            f, _ = os.path.splitext(f)
            f = f + ".nii.gz"
        cleaned_output_files.append(join(dr, f))

    if not overwrite_existing:
        print("number of cases:", len(list_of_lists))
        not_done_idx = [i for i, j in enumerate(cleaned_output_files) if not isfile(j)]

        cleaned_output_files = [cleaned_output_files[i] for i in not_done_idx]
        list_of_lists = [list_of_lists[i] for i in not_done_idx]
        if segs_from_prev_stage is not None:
            segs_from_prev_stage = [segs_from_prev_stage[i] for i in not_done_idx]

        print("number of cases that still need to be predicted:", len(cleaned_output_files))

    print("emptying cuda cache")
    torch.cuda.empty_cache()

    print("loading parameters for folds,", folds)
    trainer, params = load_model_and_checkpoint_files(model, folds, mixed_precision=mixed_precision,
                                                      checkpoint_name=checkpoint_name)

    if segmentation_export_kwargs is None:
        if 'segmentation_export_params' in trainer.plans.keys():
            force_separate_z = trainer.plans['segmentation_export_params']['force_separate_z']
            interpolation_order = trainer.plans['segmentation_export_params']['interpolation_order']
            interpolation_order_z = trainer.plans['segmentation_export_params']['interpolation_order_z']
        else:
            force_separate_z = None
            interpolation_order = 1
            interpolation_order_z = 0
    else:
        force_separate_z = segmentation_export_kwargs['force_separate_z']
        interpolation_order = segmentation_export_kwargs['interpolation_order']
        interpolation_order_z = segmentation_export_kwargs['interpolation_order_z']

    print("starting preprocessing generator")
    # preprocessing = preprocess_multithreaded(trainer, list_of_lists, cleaned_output_files, num_threads_preprocessing,
    #                                          segs_from_prev_stage)

    preprocessing = preprocess_pure(trainer, list_of_lists, cleaned_output_files, num_threads_preprocessing,
                                             segs_from_prev_stage)

    print("starting prediction...")
    aa = preprocessing
    for preprocessed in preprocessing:
        print("getting data from preprocessor")
        output_filename, (d, dct) = preprocessed
        print("got something")
        if isinstance(d, str):
            print("what I got is a string, so I need to load a file")
            data = np.load(d)
            os.remove(d)
            d = data

        # preallocate the output arrays
        # same dtype as the return value in predict_preprocessed_data_return_seg_and_softmax (saves time)
        softmax_aggr = None  # np.zeros((trainer.num_classes, *d.shape[1:]), dtype=np.float16)
        all_seg_outputs = np.zeros((len(params), *d.shape[1:]), dtype=int)
        print("predicting", output_filename)

        for i, p in enumerate(params):
            trainer.load_checkpoint_ram(p, False)

            res = trainer.predict_preprocessed_data_return_seg_and_softmax(d, do_mirroring=do_tta,
                                                                           mirror_axes=trainer.data_aug_params['mirror_axes'],
                                                                           use_sliding_window=True,
                                                                           step_size=step_size, use_gaussian=True,
                                                                           all_in_gpu=all_in_gpu,
                                                                           mixed_precision=mixed_precision)

            if len(params) > 1:
                # otherwise we dont need this and we can save ourselves the time it takes to copy that
                print("aggregating softmax")
                if softmax_aggr is None:
                    softmax_aggr = res[1]
                else:
                    softmax_aggr += res[1]
            all_seg_outputs[i] = res[0]

        print("obtaining segmentation map")
        if len(params) > 1:
            # we dont need to normalize the softmax by 1 / len(params) because this would not change the outcome of the argmax
            seg = softmax_aggr.argmax(0)
        else:
            seg = all_seg_outputs[0]

        print("applying transpose_backward")
        transpose_forward = trainer.plans.get('transpose_forward')
        if transpose_forward is not None:
            transpose_backward = trainer.plans.get('transpose_backward')
            seg = seg.transpose([i for i in transpose_backward])

        if hasattr(trainer, 'regions_class_order'):
            region_class_order = trainer.regions_class_order
        else:
            region_class_order = None
        assert region_class_order is None, "predict_cases_fast can only work with regular softmax predictions " \
                                           "and is therefore unable to handle trainer classes with region_class_order"

        print("initializing segmentation export")
        results.append(pool.starmap_async(save_segmentation_nifti,
                                          ((seg, output_filename, dct, interpolation_order, force_separate_z,
                                            interpolation_order_z),)
                                          ))
        print("done")

    print("inference done. Now waiting for the segmentation export to finish...")
    _ = [i.get() for i in results]
    # now apply postprocessing
    # first load the postprocessing properties if they are present. Else raise a well visible warning

    if not disable_postprocessing:
        results = []
        pp_file = join(model, "postprocessing.json")
        if isfile(pp_file):
            print("postprocessing...")
            shutil.copy(pp_file, os.path.dirname(output_filenames[0]))
            # for_which_classes stores for which of the classes everything but the largest connected component needs to be
            # removed
            for_which_classes, min_valid_obj_size = load_postprocessing(pp_file)
            results.append(pool.starmap_async(load_remove_save,
                                              zip(output_filenames, output_filenames,
                                                  [for_which_classes] * len(output_filenames),
                                                  [min_valid_obj_size] * len(output_filenames))))
            _ = [i.get() for i in results]
        else:
            print("WARNING! Cannot run postprocessing because the postprocessing file is missing. Make sure to run "
                  "consolidate_folds in the output folder of the model first!\nThe folder you need to run this in is "
                  "%s" % model)

    pool.close()
    pool.join()


def predict_cases_fastest(model, list_of_lists, output_filenames, folds, num_threads_preprocessing,
                          num_threads_nifti_save, segs_from_prev_stage=None, do_tta=True, mixed_precision=True,
                          overwrite_existing=False, all_in_gpu=False, step_size=0.5,
                          checkpoint_name="model_final_checkpoint", disable_postprocessing: bool = False):
    assert len(list_of_lists) == len(output_filenames)
    if segs_from_prev_stage is not None: assert len(segs_from_prev_stage) == len(output_filenames)

    pool = Pool(num_threads_nifti_save)
    results = []

    cleaned_output_files = []
    for o in output_filenames:
        dr, f = os.path.split(o)
        if len(dr) > 0:
            maybe_mkdir_p(dr)
        if not f.endswith(".nii.gz"):
            f, _ = os.path.splitext(f)
            f = f + ".nii.gz"
        cleaned_output_files.append(join(dr, f))

    if not overwrite_existing:
        print("number of cases:", len(list_of_lists))
        not_done_idx = [i for i, j in enumerate(cleaned_output_files) if not isfile(j)]

        cleaned_output_files = [cleaned_output_files[i] for i in not_done_idx]
        list_of_lists = [list_of_lists[i] for i in not_done_idx]
        if segs_from_prev_stage is not None:
            segs_from_prev_stage = [segs_from_prev_stage[i] for i in not_done_idx]

        print("number of cases that still need to be predicted:", len(cleaned_output_files))

    print("emptying cuda cache")
    torch.cuda.empty_cache()

    print("loading parameters for folds,", folds)
    trainer, params = load_model_and_checkpoint_files(model, folds, mixed_precision=mixed_precision,
                                                      checkpoint_name=checkpoint_name)

    print("starting preprocessing generator")
    preprocessing = preprocess_multithreaded(trainer, list_of_lists, cleaned_output_files, num_threads_preprocessing,
                                             segs_from_prev_stage)

    print("starting prediction...")
    for preprocessed in preprocessing:
        print("getting data from preprocessor")
        output_filename, (d, dct) = preprocessed
        print("got something")
        if isinstance(d, str):
            print("what I got is a string, so I need to load a file")
            data = np.load(d)
            os.remove(d)
            d = data

        # preallocate the output arrays
        # same dtype as the return value in predict_preprocessed_data_return_seg_and_softmax (saves time)
        all_softmax_outputs = np.zeros((len(params), trainer.num_classes, *d.shape[1:]), dtype=np.float16)
        all_seg_outputs = np.zeros((len(params), *d.shape[1:]), dtype=int)
        print("predicting", output_filename)

        for i, p in enumerate(params):
            trainer.load_checkpoint_ram(p, False)
            res = trainer.predict_preprocessed_data_return_seg_and_softmax(d, do_mirroring=do_tta,
                                                                           mirror_axes=trainer.data_aug_params['mirror_axes'],
                                                                           use_sliding_window=True,
                                                                           step_size=step_size, use_gaussian=True,
                                                                           all_in_gpu=all_in_gpu,
                                                                           mixed_precision=mixed_precision)
            if len(params) > 1:
                # otherwise we dont need this and we can save ourselves the time it takes to copy that
                all_softmax_outputs[i] = res[1]
            all_seg_outputs[i] = res[0]

        if hasattr(trainer, 'regions_class_order'):
            region_class_order = trainer.regions_class_order
        else:
            region_class_order = None
        assert region_class_order is None, "predict_cases_fastest can only work with regular softmax predictions " \
                                           "and is therefore unable to handle trainer classes with region_class_order"

        print("aggregating predictions")
        if len(params) > 1:
            softmax_mean = np.mean(all_softmax_outputs, 0)
            seg = softmax_mean.argmax(0)
        else:
            seg = all_seg_outputs[0]

        print("applying transpose_backward")
        transpose_forward = trainer.plans.get('transpose_forward')
        if transpose_forward is not None:
            transpose_backward = trainer.plans.get('transpose_backward')
            seg = seg.transpose([i for i in transpose_backward])

        print("initializing segmentation export")
        results.append(pool.starmap_async(save_segmentation_nifti,
                                          ((seg, output_filename, dct, 0, None),)
                                          ))
        print("done")

    print("inference done. Now waiting for the segmentation export to finish...")
    _ = [i.get() for i in results]
    # now apply postprocessing
    # first load the postprocessing properties if they are present. Else raise a well visible warning
    if not disable_postprocessing:
        results = []
        pp_file = join(model, "postprocessing.json")
        if isfile(pp_file):
            print("postprocessing...")
            shutil.copy(pp_file, os.path.dirname(output_filenames[0]))
            # for_which_classes stores for which of the classes everything but the largest connected component needs to be
            # removed
            for_which_classes, min_valid_obj_size = load_postprocessing(pp_file)
            results.append(pool.starmap_async(load_remove_save,
                                              zip(output_filenames, output_filenames,
                                                  [for_which_classes] * len(output_filenames),
                                                  [min_valid_obj_size] * len(output_filenames))))
            _ = [i.get() for i in results]
        else:
            print("WARNING! Cannot run postprocessing because the postprocessing file is missing. Make sure to run "
                  "consolidate_folds in the output folder of the model first!\nThe folder you need to run this in is "
                  "%s" % model)

    pool.close()
    pool.join()


def check_input_folder_and_return_caseIDs(input_folder, expected_num_modalities):
    print("This model expects %d input modalities for each image" % expected_num_modalities)
    files = subfiles(input_folder, suffix=".nii.gz", join=False, sort=True)

    maybe_case_ids = np.unique([i[:-12] for i in files])

    remaining = deepcopy(files)
    missing = []

    assert len(files) > 0, "input folder did not contain any images (expected to find .nii.gz file endings)"

    # now check if all required files are present and that no unexpected files are remaining
    for c in maybe_case_ids:
        for n in range(expected_num_modalities):
            expected_output_file = c + "_%04.0d.nii.gz" % n
            if not isfile(join(input_folder, expected_output_file)):
                missing.append(expected_output_file)
            else:
                remaining.remove(expected_output_file)

    print("Found %d unique case ids, here are some examples:" % len(maybe_case_ids),
          np.random.choice(maybe_case_ids, min(len(maybe_case_ids), 10)))
    print("If they don't look right, make sure to double check your filenames. They must end with _0000.nii.gz etc")

    if len(remaining) > 0:
        print("found %d unexpected remaining files in the folder. Here are some examples:" % len(remaining),
              np.random.choice(remaining, min(len(remaining), 10)))

    if len(missing) > 0:
        print("Some files are missing:")
        print(missing)
        raise RuntimeError("missing files in input_folder")

    return maybe_case_ids


def predict_from_folder(model: str, input_folder: str, output_folder: str, folds: Union[Tuple[int], List[int]],
                        save_npz: bool, num_threads_preprocessing: int, num_threads_nifti_save: int,
                        lowres_segmentations: Union[str, None],
                        part_id: int, num_parts: int, tta: bool, mixed_precision: bool = True,
                        overwrite_existing: bool = True, mode: str = 'normal', overwrite_all_in_gpu: bool = None,
                        step_size: float = 0.5, checkpoint_name: str = "model_final_checkpoint",
                        segmentation_export_kwargs: dict = None, disable_postprocessing: bool = False):
    """
        here we use the standard naming scheme to generate list_of_lists and output_files needed by predict_cases

    :param model:
    :param input_folder:
    :param output_folder:
    :param folds:
    :param save_npz:
    :param num_threads_preprocessing:
    :param num_threads_nifti_save:
    :param lowres_segmentations:
    :param part_id:
    :param num_parts:
    :param tta:
    :param mixed_precision:
    :param overwrite_existing: if not None then it will be overwritten with whatever is in there. None is default (no overwrite)
    :return:
    """
    maybe_mkdir_p(output_folder)
    shutil.copy(join(model, 'plans.pkl'), output_folder)

    assert isfile(join(model, "plans.pkl")), "Folder with saved model weights must contain a plans.pkl file"
    expected_num_modalities = load_pickle(join(model, "plans.pkl"))['num_modalities']

    # check input folder integrity
    case_ids = check_input_folder_and_return_caseIDs(input_folder, expected_num_modalities)

    output_files = [join(output_folder, i + ".nii.gz") for i in case_ids]
    all_files = subfiles(input_folder, suffix=".nii.gz", join=False, sort=True)
    list_of_lists = [[join(input_folder, i) for i in all_files if i[:len(j)].startswith(j) and
                      len(i) == (len(j) + 12)] for j in case_ids]

    if lowres_segmentations is not None:
        assert isdir(lowres_segmentations), "if lowres_segmentations is not None then it must point to a directory"
        lowres_segmentations = [join(lowres_segmentations, i + ".nii.gz") for i in case_ids]
        assert all([isfile(i) for i in lowres_segmentations]), "not all lowres_segmentations files are present. " \
                                                               "(I was searching for case_id.nii.gz in that folder)"
        lowres_segmentations = lowres_segmentations[part_id::num_parts]
    else:
        lowres_segmentations = None

    if mode == "normal":
        if overwrite_all_in_gpu is None:
            all_in_gpu = False
        else:
            all_in_gpu = overwrite_all_in_gpu

        return predict_cases(model, list_of_lists[part_id::num_parts], output_files[part_id::num_parts], folds,
                             save_npz, num_threads_preprocessing, num_threads_nifti_save, lowres_segmentations, tta,
                             mixed_precision=mixed_precision, overwrite_existing=overwrite_existing,
                             all_in_gpu=all_in_gpu,
                             step_size=step_size, checkpoint_name=checkpoint_name,
                             segmentation_export_kwargs=segmentation_export_kwargs,
                             disable_postprocessing=disable_postprocessing)
    elif mode == "fast":
        if overwrite_all_in_gpu is None:
            all_in_gpu = False
        else:
            all_in_gpu = overwrite_all_in_gpu

        assert save_npz is False
        return predict_cases_fast(model, list_of_lists[part_id::num_parts], output_files[part_id::num_parts], folds,
                                  num_threads_preprocessing, num_threads_nifti_save, lowres_segmentations,
                                  tta, mixed_precision=mixed_precision, overwrite_existing=overwrite_existing,
                                  all_in_gpu=all_in_gpu,
                                  step_size=step_size, checkpoint_name=checkpoint_name,
                                  segmentation_export_kwargs=segmentation_export_kwargs,
                                  disable_postprocessing=disable_postprocessing)
    elif mode == "fastest":
        if overwrite_all_in_gpu is None:
            all_in_gpu = False
        else:
            all_in_gpu = overwrite_all_in_gpu

        assert save_npz is False
        return predict_cases_fastest(model, list_of_lists[part_id::num_parts], output_files[part_id::num_parts], folds,
                                     num_threads_preprocessing, num_threads_nifti_save, lowres_segmentations,
                                     tta, mixed_precision=mixed_precision, overwrite_existing=overwrite_existing,
                                     all_in_gpu=all_in_gpu,
                                     step_size=step_size, checkpoint_name=checkpoint_name,
                                     disable_postprocessing=disable_postprocessing)
    else:
        raise ValueError("unrecognized mode. Must be normal, fast or fastest")


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("-i", '--input_folder', help="Must contain all modalities for each patient in the correct"
                                                     " order (same as training). Files must be named "
                                                     "CASENAME_XXXX.nii.gz where XXXX is the modality "
                                                     "identifier (0000, 0001, etc)", required=True)
    parser.add_argument('-o', "--output_folder", required=True, help="folder for saving predictions")
    parser.add_argument('-m', '--model_output_folder',
                        help='model output folder. Will automatically discover the folds '
                             'that were '
                             'run and use those as an ensemble', required=True)
    parser.add_argument('-f', '--folds', nargs='+', default='None', help="folds to use for prediction. Default is None "
                                                                         "which means that folds will be detected "
                                                                         "automatically in the model output folder")
    parser.add_argument('-z', '--save_npz', required=False, action='store_true', help="use this if you want to ensemble"
                                                                                      " these predictions with those of"
                                                                                      " other models. Softmax "
                                                                                      "probabilities will be saved as "
                                                                                      "compresed numpy arrays in "
                                                                                      "output_folder and can be merged "
                                                                                      "between output_folders with "
                                                                                      "merge_predictions.py")
    parser.add_argument('-l', '--lowres_segmentations', required=False, default='None', help="if model is the highres "
                                                                                             "stage of the cascade then you need to use -l to specify where the segmentations of the "
                                                                                             "corresponding lowres unet are. Here they are required to do a prediction")
    parser.add_argument("--part_id", type=int, required=False, default=0, help="Used to parallelize the prediction of "
                                                                               "the folder over several GPUs. If you "
                                                                               "want to use n GPUs to predict this "
                                                                               "folder you need to run this command "
                                                                               "n times with --part_id=0, ... n-1 and "
                                                                               "--num_parts=n (each with a different "
                                                                               "GPU (for example via "
                                                                               "CUDA_VISIBLE_DEVICES=X)")
    parser.add_argument("--num_parts", type=int, required=False, default=1,
                        help="Used to parallelize the prediction of "
                             "the folder over several GPUs. If you "
                             "want to use n GPUs to predict this "
                             "folder you need to run this command "
                             "n times with --part_id=0, ... n-1 and "
                             "--num_parts=n (each with a different "
                             "GPU (via "
                             "CUDA_VISIBLE_DEVICES=X)")
    parser.add_argument("--num_threads_preprocessing", required=False, default=6, type=int, help=
    "Determines many background processes will be used for data preprocessing. Reduce this if you "
    "run into out of memory (RAM) problems. Default: 6")
    parser.add_argument("--num_threads_nifti_save", required=False, default=2, type=int, help=
    "Determines many background processes will be used for segmentation export. Reduce this if you "
    "run into out of memory (RAM) problems. Default: 2")
    parser.add_argument("--tta", required=False, type=int, default=1, help="Set to 0 to disable test time data "
                                                                           "augmentation (speedup of factor "
                                                                           "4(2D)/8(3D)), "
                                                                           "lower quality segmentations")
    parser.add_argument("--overwrite_existing", required=False, type=int, default=1, help="Set this to 0 if you need "
                                                                                          "to resume a previous "
                                                                                          "prediction. Default: 1 "
                                                                                          "(=existing segmentations "
                                                                                          "in output_folder will be "
                                                                                          "overwritten)")
    parser.add_argument("--mode", type=str, default="normal", required=False)
    parser.add_argument("--all_in_gpu", type=str, default="None", required=False, help="can be None, False or True")
    parser.add_argument("--step_size", type=float, default=0.5, required=False, help="don't touch")
    # parser.add_argument("--interp_order", required=False, default=3, type=int,
    #                     help="order of interpolation for segmentations, has no effect if mode=fastest")
    # parser.add_argument("--interp_order_z", required=False, default=0, type=int,
    #                     help="order of interpolation along z is z is done differently")
    # parser.add_argument("--force_separate_z", required=False, default="None", type=str,
    #                     help="force_separate_z resampling. Can be None, True or False, has no effect if mode=fastest")
    parser.add_argument('--disable_mixed_precision', default=False, action='store_true', required=False,
                        help='Predictions are done with mixed precision by default. This improves speed and reduces '
                             'the required vram. If you want to disable mixed precision you can set this flag. Note '
                             'that this is not recommended (mixed precision is ~2x faster!)')

    args = parser.parse_args()
    input_folder = args.input_folder
    output_folder = args.output_folder
    part_id = args.part_id
    num_parts = args.num_parts
    model = args.model_output_folder
    folds = args.folds
    save_npz = args.save_npz
    lowres_segmentations = args.lowres_segmentations
    num_threads_preprocessing = args.num_threads_preprocessing
    num_threads_nifti_save = args.num_threads_nifti_save
    tta = args.tta
    step_size = args.step_size

    # interp_order = args.interp_order
    # interp_order_z = args.interp_order_z
    # force_separate_z = args.force_separate_z

    # if force_separate_z == "None":
    #     force_separate_z = None
    # elif force_separate_z == "False":
    #     force_separate_z = False
    # elif force_separate_z == "True":
    #     force_separate_z = True
    # else:
    #     raise ValueError("force_separate_z must be None, True or False. Given: %s" % force_separate_z)

    overwrite = args.overwrite_existing
    mode = args.mode
    all_in_gpu = args.all_in_gpu

    if lowres_segmentations == "None":
        lowres_segmentations = None

    if isinstance(folds, list):
        if folds[0] == 'all' and len(folds) == 1:
            pass
        else:
            folds = [int(i) for i in folds]
    elif folds == "None":
        folds = None
    else:
        raise ValueError("Unexpected value for argument folds")

    if tta == 0:
        tta = False
    elif tta == 1:
        tta = True
    else:
        raise ValueError("Unexpected value for tta, Use 1 or 0")

    if overwrite == 0:
        overwrite = False
    elif overwrite == 1:
        overwrite = True
    else:
        raise ValueError("Unexpected value for overwrite, Use 1 or 0")

    assert all_in_gpu in ['None', 'False', 'True']
    if all_in_gpu == "None":
        all_in_gpu = None
    elif all_in_gpu == "True":
        all_in_gpu = True
    elif all_in_gpu == "False":
        all_in_gpu = False

    predict_from_folder(model, input_folder, output_folder, folds, save_npz, num_threads_preprocessing,
                        num_threads_nifti_save, lowres_segmentations, part_id, num_parts, tta,
                        mixed_precision=not args.disable_mixed_precision,
                        overwrite_existing=overwrite, mode=mode, overwrite_all_in_gpu=all_in_gpu, step_size=step_size)
