# copyright (c) 2024 PaddlePaddle Authors. All Rights Reserve.
#
# 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 os.path as osp
import random
import pickle
from PIL import Image, ImageOps
from collections import defaultdict
from tqdm import tqdm

from .....utils.errors import DatasetFileNotFoundError, CheckFailedError
from .utils.visualizer import draw_label


def check_train(dataset_dir, output, sample_num=10):
    """check dataset"""
    dataset_dir = osp.abspath(dataset_dir)
    # Custom dataset
    if not osp.exists(dataset_dir) or not osp.isdir(dataset_dir):
        raise DatasetFileNotFoundError(file_path=dataset_dir)

    delim = " "
    valid_num_parts = 2

    label_map_dict = dict()
    sample_paths = []
    labels = []

    label_file = osp.join(dataset_dir, "label.txt")
    if not osp.exists(label_file):
        raise DatasetFileNotFoundError(
            file_path=label_file,
            solution=f"Ensure that `label.txt` exist in {dataset_dir}",
        )
    with open(label_file, "r", encoding="utf-8") as f:
        all_lines = f.readlines()
        random.seed(123)
        random.shuffle(all_lines)
        sample_cnts = len(all_lines)
        for line in all_lines:
            substr = line.strip("\n").split(delim)
            if len(substr) != valid_num_parts:
                raise CheckFailedError(
                    f"The number of delimiter-separated items in each row in {label_file} \
                            should be {valid_num_parts} (current delimiter is '{delim}')."
                )
            file_name = substr[0]
            label = substr[1]

            img_path = osp.join(dataset_dir, file_name)

            if not osp.exists(img_path):
                raise DatasetFileNotFoundError(file_path=img_path)

            vis_save_dir = osp.join(output, "demo_img")
            if not osp.exists(vis_save_dir):
                os.makedirs(vis_save_dir)

            try:
                label = int(label)
                label_map_dict[label] = str(label)
            except (ValueError, TypeError) as e:
                raise CheckFailedError(
                    f"Ensure that the second number in each line in {label_file} should be int."
                ) from e

            if len(sample_paths) < sample_num:
                img = Image.open(img_path)
                img = ImageOps.exif_transpose(img)
                vis_im = draw_label(img, label, label_map_dict)
                vis_path = osp.join(vis_save_dir, osp.basename(file_name))
                vis_im.save(vis_path)
                sample_path = osp.join(
                    "check_dataset", os.path.relpath(vis_path, output)
                )
                sample_paths.append(sample_path)
            labels.append(label)
    if min(labels) != 0:
        raise CheckFailedError(
            f"Ensure that the index starts from 0 in `{label_file}`."
        )
    num_classes = max(labels) + 1
    attrs = {}
    attrs["train_label_file"] = osp.relpath(label_file, output)
    attrs["train_num_classes"] = num_classes
    attrs["train_samples"] = sample_cnts
    attrs["train_sample_paths"] = sample_paths
    return attrs


def check_val(dataset_dir, output, sample_num=10):
    """check dataset"""
    dataset_dir = osp.abspath(dataset_dir)
    # Custom dataset
    if not osp.exists(dataset_dir) or not osp.isdir(dataset_dir):
        raise DatasetFileNotFoundError(file_path=dataset_dir)

    delim = " "
    valid_num_parts = 3

    labels = []
    sample_paths = []
    label_file = osp.join(dataset_dir, "pair_label.txt")
    if not osp.exists(label_file):
        raise DatasetFileNotFoundError(
            file_path=label_file,
            solution=f"Ensure that `label.txt` exist in {dataset_dir}",
        )
    with open(label_file, "r", encoding="utf-8") as f:
        all_lines = f.readlines()
        random.seed(123)
        random.shuffle(all_lines)
        sample_cnts = len(all_lines)
        for line in all_lines:
            substr = line.strip("\n").split(delim)
            if len(substr) != valid_num_parts:
                raise CheckFailedError(
                    f"The number of delimiter-separated items in each row in {label_file} \
                            should be {valid_num_parts} (current delimiter is '{delim}')."
                )
            left_file_name = substr[0]
            right_file_name = substr[1]
            label = substr[2]

            left_img_path = osp.join(dataset_dir, left_file_name)
            if not osp.exists(left_img_path):
                raise DatasetFileNotFoundError(file_path=left_img_path)

            right_img_path = osp.join(dataset_dir, right_file_name)
            if not osp.exists(right_img_path):
                raise DatasetFileNotFoundError(file_path=right_img_path)

            try:
                label = int(label)
                assert label in [0, 1], "Face eval dataset only support two classes"
            except (ValueError, TypeError) as e:
                raise CheckFailedError(
                    f"Ensure that the second number in each line in {label_file} should be int."
                ) from e

            vis_save_dir = osp.join(output, "demo_img")
            if not osp.exists(vis_save_dir):
                os.makedirs(vis_save_dir)

            if len(sample_paths) < sample_num:
                img = Image.open(left_img_path)
                img = ImageOps.exif_transpose(img)
                vis_path = osp.join(vis_save_dir, osp.basename(left_file_name))
                img.save(vis_path)
                sample_path = osp.join(
                    "check_dataset", os.path.relpath(vis_path, output)
                )
                sample_paths.append(sample_path)

            labels.append(label)
    num_classes = max(labels) + 1
    attrs = {}
    attrs["val_label_file"] = osp.relpath(label_file, output)
    attrs["val_num_classes"] = num_classes
    attrs["val_samples"] = sample_cnts
    attrs["val_sample_paths"] = sample_paths
    return attrs