# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the Apache License, Version 2.0
# found in the LICENSE file in the root directory of this source tree.

import csv
from enum import Enum
import logging
import os
from typing import Callable, List, Optional, Tuple, Union
import random
import numpy as np

from .extended import ExtendedVisionDataset


logger = logging.getLogger("dinov2")
_Target = int


class _Split(Enum):
    TRAIN = "train"
    VAL = "val"
    TEST = "test"  # NOTE: torchvision does not support the test split

    @property
    def length(self) -> int:
        split_lengths = {
            _Split.TRAIN: 50000,
            _Split.VAL: 50_00,
            _Split.TEST: 100_00,
        }
        return split_lengths[self]

    def get_dirname(self, class_id: Optional[str] = None) -> str:
        return self.value if class_id is None else os.path.join(self.value, class_id)

    def get_image_relpath(self, actual_index: int, class_id: Optional[str] = None) -> str:
        dirname = self.get_dirname(class_id)
        if self == _Split.TRAIN:
            basename = f"{class_id}_{actual_index}"
        else:  # self in (_Split.VAL, _Split.TEST):
            basename = f"ILSVRC2012_{self.value}_{actual_index:08d}"
        return os.path.join(dirname, basename + self.img_format)

    def parse_image_relpath(self, image_relpath: str) -> Tuple[str, int]:
        assert self != _Split.TEST
        dirname, filename = os.path.split(image_relpath)
        class_id = os.path.split(dirname)[-1]
        basename, _ = os.path.splitext(filename)
        actual_index = int(basename.split("_")[-1])
        return class_id, actual_index


class ImageNet(ExtendedVisionDataset):
    Target = Union[_Target]
    Split = Union[_Split]

    def __init__(
        self,
        *,
        split: "ImageNet.Split",
        root: str,
        extra: str,
        transforms: Optional[Callable] = None,
        transform: Optional[Callable] = None,
        target_transform: Optional[Callable] = None,
    ) -> None:
        super().__init__(root, transforms, transform, target_transform)
        self._extra_root = extra
        self._split = split

        self._entries = None
        self._class_ids = None
        self._class_names = None
        # self.curriculum = {}
        # for i in range(200):
        #     if i < 20:
        #         self.curriculum[i] = 0
        #     elif i >= 20 and i < 30:
        #         self.curriculum[i] = 0.2
        #     elif i >= 30 and i < 40:
        #         self.curriculum[i] = 0.4
        #     elif i >= 40 and i < 50:
        #         self.curriculum[i] = 0.6
        #     elif i >= 50 and i < 60:
        #         self.curriculum[i] = 0.8
        #     elif i >= 60:
        #         self.curriculum[i] = 1.0


    @property
    def split(self) -> "ImageNet.Split":
        return self._split

    def _get_extra_full_path(self, extra_path: str) -> str:
        return os.path.join(self._extra_root, extra_path)

    def _load_extra(self, extra_path: str) -> np.ndarray:
        extra_full_path = self._get_extra_full_path(extra_path)
        return np.load(extra_full_path, mmap_mode="r")

    def _save_extra(self, extra_array: np.ndarray, extra_path: str) -> None:
        extra_full_path = self._get_extra_full_path(extra_path)
        os.makedirs(self._extra_root, exist_ok=True)
        np.save(extra_full_path, extra_array)
    
    #def set_epoch(self, epoch):
    #    self.epoch_number = epoch 

    @property
    def _entries_path(self) -> str:
        return f"entries-{self._split.value.upper()}.npy"

    @property
    def _class_ids_path(self) -> str:
        return f"class-ids-{self._split.value.upper()}.npy"

    @property
    def _class_names_path(self) -> str:
        return f"class-names-{self._split.value.upper()}.npy"

    def _get_entries(self) -> np.ndarray:
        if self._entries is None:
            self._entries = self._load_extra(self._entries_path)
        assert self._entries is not None
        return self._entries

    def _get_class_ids(self) -> np.ndarray:
        if self._split == _Split.TEST:
            assert False, "Class IDs are not available in TEST split"
        if self._class_ids is None:
            self._class_ids = self._load_extra(self._class_ids_path)
        assert self._class_ids is not None
        return self._class_ids

    def _get_class_names(self) -> np.ndarray:
        if self._split == _Split.TEST:
            assert False, "Class names are not available in TEST split"
        if self._class_names is None:
            self._class_names = self._load_extra(self._class_names_path)
        assert self._class_names is not None
        return self._class_names

    def find_class_id(self, class_index: int) -> str:
        class_ids = self._get_class_ids()
        return str(class_ids[class_index])

    def find_class_name(self, class_index: int) -> str:
        class_names = self._get_class_names()
        return str(class_names[class_index])

    def get_image_data(self, index: int) -> bytes:
        entries = self._get_entries()
        actual_index = entries[index]["actual_index"]

        class_id = self.get_class_id(index)

        #draw = random.random()
        image_relpath = self.split.get_image_relpath(actual_index, class_id)
        #if draw < self.curriculum[self.epoch_number]:
        image_full_path = os.path.join(self.root, image_relpath)
        #else:
        denoised_image_full_path = os.path.join(f"noisy_mini-imagenet-{os.getenv('NOISE_TYPE')}-denoised", image_relpath)
        with open(image_full_path, mode="rb") as f:
            image_data = f.read()
        with open(denoised_image_full_path, mode="rb") as f_d:
            denoised_image_data = f_d.read()
        return image_data, denoised_image_data
    # def get_image_data(self, index: int) -> bytes:
    #     entries = self._get_entries()
    #     actual_index = entries[index]["actual_index"]

    #     class_id = self.get_class_id(index)

    #     #draw = random.random()
    #     image_relpath = self.split.get_image_relpath(actual_index, class_id)
    #     #if draw < self.curriculum[self.epoch_number]:
    #     image_full_path = os.path.join(self.root, image_relpath)
    #     #else:
    #     #    image_full_path = os.path.join("noisy_mini-imagenet-gauss100-denoised", image_relpath)
    #     with open(image_full_path, mode="rb") as f:
    #         image_data = f.read()
    #     return image_data

    def get_target(self, index: int) -> Optional[Target]:
        entries = self._get_entries()
        class_index = entries[index]["class_index"]
        return None if self.split == _Split.TEST else int(class_index)

    def get_targets(self) -> Optional[np.ndarray]:
        entries = self._get_entries()
        return None if self.split == _Split.TEST else entries["class_index"]

    def get_class_id(self, index: int) -> Optional[str]:
        entries = self._get_entries()
        class_id = entries[index]["class_id"]
        return None if self.split == _Split.TEST else str(class_id)

    def get_class_name(self, index: int) -> Optional[str]:
        entries = self._get_entries()
        class_name = entries[index]["class_name"]
        return None if self.split == _Split.TEST else str(class_name)

    def __len__(self) -> int:
        entries = self._get_entries()
        assert len(entries) == self.split.length
        return len(entries)

    def _load_labels(self, labels_path: str) -> List[Tuple[str, str]]:
        labels_full_path = os.path.join(self.root, labels_path)
        labels = []

        try:
            with open(labels_full_path, "r") as f:
                reader = csv.reader(f)
                for row in reader:
                    class_id, class_name = row
                    labels.append((class_id, class_name))
        except OSError as e:
            raise RuntimeError(f'can not read labels file "{labels_full_path}"') from e

        return labels

    def _dump_entries(self) -> None:
        split = self.split
        if split == ImageNet.Split.TEST:
            dataset = None
            sample_count = split.length
            max_class_id_length, max_class_name_length = 0, 0
        else:
            labels_path = "labels.txt"
            logger.info(f'loading labels from "{labels_path}"')
            labels = self._load_labels(labels_path)

            # NOTE: Using torchvision ImageFolder for consistency
            from torchvision.datasets import ImageFolder

            dataset_root = os.path.join(self.root, split.get_dirname())
            dataset = ImageFolder(dataset_root)
            sample_count = len(dataset)
            max_class_id_length, max_class_name_length = -1, -1
            for sample in dataset.samples:
                _, class_index = sample
                class_id, class_name = labels[class_index]
                max_class_id_length = max(len(class_id), max_class_id_length)
                max_class_name_length = max(len(class_name), max_class_name_length)

        dtype = np.dtype(
            [
                ("actual_index", "<u4"),
                ("class_index", "<u4"),
                ("class_id", f"U{max_class_id_length}"),
                ("class_name", f"U{max_class_name_length}"),
            ]
        )
        entries_array = np.empty(sample_count, dtype=dtype)

        if split == ImageNet.Split.TEST:
            old_percent = -1
            for index in range(sample_count):
                percent = 100 * (index + 1) // sample_count
                if percent > old_percent:
                    logger.info(f"creating entries: {percent}%")
                    old_percent = percent

                actual_index = index + 1
                class_index = np.uint32(-1)
                class_id, class_name = "", ""
                entries_array[index] = (actual_index, class_index, class_id, class_name)
        else:
            class_names = {class_id: class_name for class_id, class_name in labels}

            assert dataset
            old_percent = -1
            for index in range(sample_count):
                percent = 100 * (index + 1) // sample_count
                if percent > old_percent:
                    logger.info(f"creating entries: {percent}%")
                    old_percent = percent

                image_full_path, class_index = dataset.samples[index]
                image_relpath = os.path.relpath(image_full_path, self.root)
                class_id, actual_index = split.parse_image_relpath(image_relpath)
                class_name = class_names[class_id]
                entries_array[index] = (actual_index, class_index, class_id, class_name)

        logger.info(f'saving entries to "{self._entries_path}"')
        self._save_extra(entries_array, self._entries_path)

    def _dump_class_ids_and_names(self) -> None:
        split = self.split
        if split == ImageNet.Split.TEST:
            return

        entries_array = self._load_extra(self._entries_path)

        max_class_id_length, max_class_name_length, max_class_index = -1, -1, -1
        for entry in entries_array:
            class_index, class_id, class_name = (
                entry["class_index"],
                entry["class_id"],
                entry["class_name"],
            )
            max_class_index = max(int(class_index), max_class_index)
            max_class_id_length = max(len(str(class_id)), max_class_id_length)
            max_class_name_length = max(len(str(class_name)), max_class_name_length)

        class_count = max_class_index + 1
        class_ids_array = np.empty(class_count, dtype=f"U{max_class_id_length}")
        class_names_array = np.empty(class_count, dtype=f"U{max_class_name_length}")
        for entry in entries_array:
            class_index, class_id, class_name = (
                entry["class_index"],
                entry["class_id"],
                entry["class_name"],
            )
            class_ids_array[class_index] = class_id
            class_names_array[class_index] = class_name

        logger.info(f'saving class IDs to "{self._class_ids_path}"')
        self._save_extra(class_ids_array, self._class_ids_path)

        logger.info(f'saving class names to "{self._class_names_path}"')
        self._save_extra(class_names_array, self._class_names_path)

    def dump_extra(self) -> None:
        self._dump_entries()
        self._dump_class_ids_and_names()