# Copyright (c) 2023, NVIDIA CORPORATION.  All rights reserved.
#
# 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 __future__ import annotations

import json
import math
from pathlib import Path
from typing import Any

import numpy as np
import torch
from einops import rearrange
from PIL import Image
from torch.utils.data import Dataset

try:
    import torchvision

    TORCHVISION_AVAILABLE = True
except (ImportError, ModuleNotFoundError):
    TORCHVISION_AVAILABLE = False


class EditDataset(Dataset):
    def __init__(
        self,
        path: str,
        split: str = "train",
        splits: tuple[float, float, float] = (0.95, 0.04, 0.01),
        min_resize_res: int = 256,
        max_resize_res: int = 256,
        crop_res: int = 256,
        flip_prob: float = 0.0,
    ):
        assert split in ("train", "val", "test")
        assert sum(splits) == 1
        self.path = path
        self.min_resize_res = min_resize_res
        self.max_resize_res = max_resize_res
        self.crop_res = crop_res
        self.flip_prob = flip_prob

        with open(Path(self.path, "seeds.json")) as f:
            self.seeds = json.load(f)

        split_0, split_1 = {
            "train": (0.0, splits[0]),
            "val": (splits[0], splits[0] + splits[1]),
            "test": (splits[0] + splits[1], 1.0),
        }[split]

        idx_0 = math.floor(split_0 * len(self.seeds))
        idx_1 = math.floor(split_1 * len(self.seeds))
        self.seeds = self.seeds[idx_0:idx_1]

    def __len__(self) -> int:
        return len(self.seeds)

    def __getitem__(self, i: int) -> dict[str, Any]:
        name, seeds = self.seeds[i]
        propt_dir = Path(self.path, name)
        seed = seeds[torch.randint(0, len(seeds), ()).item()]
        with open(propt_dir.joinpath("prompt.json")) as fp:
            prompt = json.load(fp)["edit"]

        image_0 = Image.open(propt_dir.joinpath(f"{seed}_0.jpg"))
        image_1 = Image.open(propt_dir.joinpath(f"{seed}_1.jpg"))

        resize_res = torch.randint(self.min_resize_res, self.max_resize_res + 1, ()).item()
        image_0 = image_0.resize((resize_res, resize_res), Image.Resampling.LANCZOS)
        image_1 = image_1.resize((resize_res, resize_res), Image.Resampling.LANCZOS)

        image_0 = rearrange(2 * torch.tensor(np.array(image_0)).float() / 255 - 1, "h w c -> c h w")
        image_1 = rearrange(2 * torch.tensor(np.array(image_1)).float() / 255 - 1, "h w c -> c h w")

        assert TORCHVISION_AVAILABLE, "Torchvision imports failed but they are required."
        crop = torchvision.transforms.RandomCrop(self.crop_res)
        flip = torchvision.transforms.RandomHorizontalFlip(float(self.flip_prob))
        image_0, image_1 = flip(crop(torch.cat((image_0, image_1)))).chunk(2)

        return dict(edited=image_1, edit=dict(c_concat=image_0, c_crossattn=prompt))


class EditDatasetEval(Dataset):
    def __init__(
        self, path: str, split: str = "train", splits: tuple[float, float, float] = (0.9, 0.05, 0.05), res: int = 256,
    ):
        assert split in ("train", "val", "test")
        assert sum(splits) == 1
        self.path = path
        self.res = res

        with open(Path(self.path, "seeds.json")) as f:
            self.seeds = json.load(f)

        split_0, split_1 = {
            "train": (0.0, splits[0]),
            "val": (splits[0], splits[0] + splits[1]),
            "test": (splits[0] + splits[1], 1.0),
        }[split]

        idx_0 = math.floor(split_0 * len(self.seeds))
        idx_1 = math.floor(split_1 * len(self.seeds))
        self.seeds = self.seeds[idx_0:idx_1]

    def __len__(self) -> int:
        return len(self.seeds)

    def __getitem__(self, i: int) -> dict[str, Any]:
        name, seeds = self.seeds[i]
        propt_dir = Path(self.path, name)
        seed = seeds[torch.randint(0, len(seeds), ()).item()]
        with open(propt_dir.joinpath("prompt.json")) as fp:
            prompt = json.load(fp)
            edit = prompt["edit"]
            input_prompt = prompt["input"]
            output_prompt = prompt["output"]

        image_0 = Image.open(propt_dir.joinpath(f"{seed}_0.jpg"))

        reize_res = torch.randint(self.res, self.res + 1, ()).item()
        image_0 = image_0.resize((reize_res, reize_res), Image.Resampling.LANCZOS)

        image_0 = rearrange(2 * torch.tensor(np.array(image_0)).float() / 255 - 1, "h w c -> c h w")

        return dict(image_0=image_0, input_prompt=input_prompt, edit=edit, output_prompt=output_prompt)