#
# SPDX-FileCopyrightText: Copyright (c) 1993-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# 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 os
import random
from io import BytesIO

import numpy as np
import requests
import torch
from PIL import Image


def preprocess_image(image):
    """
    image: torch.Tensor
    """
    w, h = image.size
    w, h = map(lambda x: x - x % 32, (w, h))  # resize to integer multiple of 32
    image = image.resize((w, h))
    image = np.array(image).astype(np.float32) / 255.0
    image = image[None].transpose(0, 3, 1, 2)
    image = torch.from_numpy(image).contiguous()
    return 2.0 * image - 1.0


def prepare_mask_and_masked_image(image, mask):
    """
    image: PIL.Image.Image
    mask: PIL.Image.Image
    """
    if isinstance(image, Image.Image):
        image = np.array(image.convert("RGB"))
    image = image[None].transpose(0, 3, 1, 2)
    image = torch.from_numpy(image).to(dtype=torch.float32).contiguous() / 127.5 - 1.0
    if isinstance(mask, Image.Image):
        mask = np.array(mask.convert("L"))
        mask = mask.astype(np.float32) / 255.0
    mask = mask[None, None]
    mask[mask < 0.5] = 0
    mask[mask >= 0.5] = 1
    mask = torch.from_numpy(mask).to(dtype=torch.float32).contiguous()

    masked_image = image * (mask < 0.5)

    return mask, masked_image


def download_image(url):
    response = requests.get(url)
    return Image.open(BytesIO(response.content)).convert("RGB")


def save_image(images, image_path_dir, image_name_prefix, image_name_suffix):
    """
    Save the generated images to png files.
    """
    for i in range(images.shape[0]):
        image_path = os.path.join(
            image_path_dir,
            f"{image_name_prefix}{i + 1}-{random.randint(1000, 9999)}-{image_name_suffix}.png",
        )
        print(f"Saving image {i+1} / {images.shape[0]} to: {image_path}")
        Image.fromarray(images[i]).save(image_path)