# Once for All: Train One Network and Specialize it for Efficient Deployment
# <NAME>, <NAME>, <NAME>, <NAME>, <NAME>
# International Conference on Learning Representations (ICLR), 2020.

import numpy as np
import time
import random
import os
import math

import torch
import torchvision.transforms.functional as F
import torchvision.transforms as transforms
from torch.utils.data.distributed import DistributedSampler


class DataProvider:
    SUB_SEED = 937162211  # random seed for sampling subset
    VALID_SEED = 2147483647  # random seed for the validation set

    @staticmethod
    def name():
        """ Return name of the dataset """
        raise NotImplementedError

    @property
    def data_shape(self):
        """ Return shape as python list of one data entry """
        raise NotImplementedError

    @property
    def n_classes(self):
        """ Return `int` of num classes """
        raise NotImplementedError

    @property
    def save_path(self):
        """ local path to save the data """
        raise NotImplementedError

    @property
    def data_url(self):
        """ link to download the data """
        raise NotImplementedError

    @staticmethod
    def random_sample_valid_set(train_size, valid_size):
        assert train_size > valid_size
        g = torch.Generator()
        g.manual_seed(DataProvider.VALID_SEED)  # set random seed before sampling validation set
        rand_indexes = torch.randperm(train_size, generator=g).tolist()

        valid_indexes = rand_indexes[:valid_size]
        train_indexes = rand_indexes[valid_size:]
        return train_indexes, valid_indexes

    @staticmethod
    def labels_to_one_hot(n_classes, labels):
        new_labels = np.zeros((labels.shape[0], n_classes), dtype=np.float32)
        new_labels[range(labels.shape[0]), labels] = np.ones(labels.shape)
        return new_labels


class MyRandomResizedCrop(transforms.RandomResizedCrop):
    ACTIVE_SIZE = 224
    IMAGE_SIZE_LIST = [224]

    CONTINUOUS = False
    SYNC_DISTRIBUTED = False

    EPOCH = 0

    def __call__(self, img):
        i, j, h, w = self.get_params(img, self.scale, self.ratio)
        return F.resized_crop(
            img, i, j, h, w, (MyRandomResizedCrop.ACTIVE_SIZE, MyRandomResizedCrop.ACTIVE_SIZE), self.interpolation
        )

    @staticmethod
    def get_candidate_image_size():
        if MyRandomResizedCrop.CONTINUOUS:
            min_size = min(MyRandomResizedCrop.IMAGE_SIZE_LIST)
            max_size = max(MyRandomResizedCrop.IMAGE_SIZE_LIST)
            candidate_sizes = []
            for i in range(min_size, max_size + 1):
                if i % 4 == 0:
                    candidate_sizes.append(i)
        else:
            candidate_sizes = MyRandomResizedCrop.IMAGE_SIZE_LIST

        relative_probs = None
        return candidate_sizes, relative_probs

    @staticmethod
    def sample_image_size(batch_id):
        if MyRandomResizedCrop.SYNC_DISTRIBUTED:
            _seed = int('%d%.3d' % (batch_id, MyRandomResizedCrop.EPOCH))
        else:
            _seed = os.getpid() + time.time()
        random.seed(_seed)
        candidate_sizes, relative_probs = MyRandomResizedCrop.get_candidate_image_size()
        MyRandomResizedCrop.ACTIVE_SIZE = random.choices(candidate_sizes, weights=relative_probs)[0]


class MyDistributedSampler(DistributedSampler):
    """ Allow Subset Sampler in Distributed Training """

    def __init__(self, dataset, num_replicas=None, rank=None, sub_index_list=None):
        super(MyDistributedSampler, self).__init__(dataset, num_replicas, rank)
        self.sub_index_list = sub_index_list  # numpy

        self.num_samples = int(math.ceil(len(self.sub_index_list) * 1.0 / self.num_replicas))
        self.total_size = self.num_samples * self.num_replicas
        print('Use MyDistributedSampler: %d, %d' % (self.num_samples, self.total_size))

    def __iter__(self):
        # deterministically shuffle based on epoch
        g = torch.Generator()
        g.manual_seed(self.epoch)
        indices = torch.randperm(len(self.sub_index_list), generator=g).tolist()

        # add extra samples to make it evenly divisible
        indices += indices[:(self.total_size - len(indices))]
        indices = self.sub_index_list[indices].tolist()
        assert len(indices) == self.total_size

        # subsample
        indices = indices[self.rank:self.total_size:self.num_replicas]
        assert len(indices) == self.num_samples

        return iter(indices)

# Once for All: Train One Network and Specialize it for Efficient Deployment
# <NAME>, <NAME>, <NAME>, <NAME>, <NAME>
# International Conference on Learning Representations (ICLR), 2020.

import numpy as np
import time
import random
import os
import math

import torch
import torchvision.transforms.functional as F
import torchvision.transforms as transforms
from torch.utils.data.distributed import DistributedSampler


class DataProvider:
    SUB_SEED = 937162211  # random seed for sampling subset
    VALID_SEED = 2147483647  # random seed for the validation set

    @staticmethod
    def name():
        """ Return name of the dataset """
        raise NotImplementedError

    @property
    def data_shape(self):
        """ Return shape as python list of one data entry """
        raise NotImplementedError

    @property
    def n_classes(self):
        """ Return `int` of num classes """
        raise NotImplementedError

    @property
    def save_path(self):
        """ local path to save the data """
        raise NotImplementedError

    @property
    def data_url(self):
        """ link to download the data """
        raise NotImplementedError

    @staticmethod
    def random_sample_valid_set(train_size, valid_size):
        assert train_size > valid_size
        g = torch.Generator()
        g.manual_seed(DataProvider.VALID_SEED)  # set random seed before sampling validation set
        rand_indexes = torch.randperm(train_size, generator=g).tolist()

        valid_indexes = rand_indexes[:valid_size]
        train_indexes = rand_indexes[valid_size:]
        return train_indexes, valid_indexes

    @staticmethod
    def labels_to_one_hot(n_classes, labels):
        new_labels = np.zeros((labels.shape[0], n_classes), dtype=np.float32)
        new_labels[range(labels.shape[0]), labels] = np.ones(labels.shape)
        return new_labels


class MyRandomResizedCrop(transforms.RandomResizedCrop):
    ACTIVE_SIZE = 224
    IMAGE_SIZE_LIST = [224]

    CONTINUOUS = False
    SYNC_DISTRIBUTED = False

    EPOCH = 0

    def __call__(self, img):
        i, j, h, w = self.get_params(img, self.scale, self.ratio)
        return F.resized_crop(
            img, i, j, h, w, (MyRandomResizedCrop.ACTIVE_SIZE, MyRandomResizedCrop.ACTIVE_SIZE), self.interpolation
        )

    @staticmethod
    def get_candidate_image_size():
        if MyRandomResizedCrop.CONTINUOUS:
            min_size = min(MyRandomResizedCrop.IMAGE_SIZE_LIST)
            max_size = max(MyRandomResizedCrop.IMAGE_SIZE_LIST)
            candidate_sizes = []
            for i in range(min_size, max_size + 1):
                if i % 4 == 0:
                    candidate_sizes.append(i)
        else:
            candidate_sizes = MyRandomResizedCrop.IMAGE_SIZE_LIST

        relative_probs = None
        return candidate_sizes, relative_probs

    @staticmethod
    def sample_image_size(batch_id):
        if MyRandomResizedCrop.SYNC_DISTRIBUTED:
            _seed = int('%d%.3d' % (batch_id, MyRandomResizedCrop.EPOCH))
        else:
            _seed = os.getpid() + time.time()
        random.seed(_seed)
        candidate_sizes, relative_probs = MyRandomResizedCrop.get_candidate_image_size()
        MyRandomResizedCrop.ACTIVE_SIZE = random.choices(candidate_sizes, weights=relative_probs)[0]


class MyDistributedSampler(DistributedSampler):
    """ Allow Subset Sampler in Distributed Training """

    def __init__(self, dataset, num_replicas=None, rank=None, sub_index_list=None):
        super(MyDistributedSampler, self).__init__(dataset, num_replicas, rank)
        self.sub_index_list = sub_index_list  # numpy

        self.num_samples = int(math.ceil(len(self.sub_index_list) * 1.0 / self.num_replicas))
        self.total_size = self.num_samples * self.num_replicas
        print('Use MyDistributedSampler: %d, %d' % (self.num_samples, self.total_size))

    def __iter__(self):
        # deterministically shuffle based on epoch
        g = torch.Generator()
        g.manual_seed(self.epoch)
        indices = torch.randperm(len(self.sub_index_list), generator=g).tolist()

        # add extra samples to make it evenly divisible
        indices += indices[:(self.total_size - len(indices))]
        indices = self.sub_index_list[indices].tolist()
        assert len(indices) == self.total_size

        # subsample
        indices = indices[self.rank:self.total_size:self.num_replicas]
        assert len(indices) == self.num_samples

        return iter(indices)

# Once for All: Train One Network and Specialize it for Efficient Deployment # <NAME>, <NAME>, <NAME>, <NAME>, <NAME> # International Conference on Learning Representations (ICLR), 2020. # random seed for sampling subset # random seed for the validation set Return name of the dataset Return shape as python list of one data entry Return `int` of num classes local path to save the data link to download the data # set random seed before sampling validation set Allow Subset Sampler in Distributed Training # numpy # deterministically shuffle based on epoch # add extra samples to make it evenly divisible # subsample