# Copyright 2022 The OFA-Sys Team. 
# All rights reserved.
# This source code is licensed under the Apache 2.0 license 
# found in the LICENSE file in the root directory.

from io import BytesIO

import logging
import warnings
import functools

import numpy as np
import torch
import base64
from torchvision import transforms
from timm.data import create_transform
from utils.vision_helper import RandomAugment

from PIL import Image, ImageFile

from data import data_utils
from data.ofa_dataset import OFADataset

ImageFile.LOAD_TRUNCATED_IMAGES = True
ImageFile.MAX_IMAGE_PIXELS = None
Image.MAX_IMAGE_PIXELS = None

logger = logging.getLogger(__name__)
warnings.filterwarnings("ignore", "(Possibly )?corrupt EXIF data", UserWarning)

IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406)
IMAGENET_DEFAULT_STD = (0.229, 0.224, 0.225)

def collate(samples, pad_idx, eos_idx):
    if len(samples) == 0:
        return {}

    def merge(key):
        return data_utils.collate_tokens(
            [s[key] for s in samples],
            pad_idx,
            eos_idx=eos_idx,
        )

    id = np.array([s["id"] for s in samples])
    src_tokens = merge("source")
    src_lengths = torch.LongTensor([s["source"].ne(pad_idx).long().sum() for s in samples])

    patch_images = torch.stack([sample['patch_image'] for sample in samples], dim=0)
    patch_masks = torch.cat([sample['patch_mask'] for sample in samples])

    conf = None
    if samples[0].get("conf", None) is not None:
        conf = torch.cat([s['conf'] for s in samples], dim=0)

    ref_dict = None
    if samples[0].get("ref_dict", None) is not None:
        ref_dict = np.array([s['ref_dict'] for s in samples])

    constraint_masks = None
    if samples[0].get("constraint_mask", None) is not None:
        constraint_masks = merge("constraint_mask")

    prev_output_tokens = None
    target = None
    if samples[0].get("target", None) is not None:
        target = merge("target")
        tgt_lengths = torch.LongTensor(
            [s["target"].ne(pad_idx).long().sum() for s in samples]
        )
        ntokens = tgt_lengths.sum().item()

        if samples[0].get("prev_output_tokens", None) is not None:
            prev_output_tokens = merge("prev_output_tokens")
    else:
        ntokens = src_lengths.sum().item()

    batch = {
        "id": id,
        "nsentences": len(samples),
        "ntokens": ntokens,
        "net_input": {
            "src_tokens": src_tokens,
            "src_lengths": src_lengths,
            "patch_images": patch_images,
            "patch_masks": patch_masks,
            "prev_output_tokens": prev_output_tokens
        },
        "conf": conf,
        "ref_dict": ref_dict,
        "constraint_masks": constraint_masks,
        "target": target,
    }

    return batch


class ImageClassifyDataset(OFADataset):
    def __init__(
        self,
        split,
        dataset,
        bpe,
        src_dict,
        tgt_dict=None,
        max_src_length=128,
        max_tgt_length=30,
        patch_image_size=224,
        constraint_trie=None,
        imagenet_default_mean_and_std=False
    ):
        super().__init__(split, dataset, bpe, src_dict, tgt_dict)
        self.max_src_length = max_src_length
        self.max_tgt_length = max_tgt_length
        self.patch_image_size = patch_image_size

        self.constraint_trie = constraint_trie

        if imagenet_default_mean_and_std:
            mean = IMAGENET_DEFAULT_MEAN
            std = IMAGENET_DEFAULT_STD
        else:
            mean = [0.5, 0.5, 0.5]
            std = [0.5, 0.5, 0.5]

        if self.split != 'train':
            self.patch_resize_transform = transforms.Compose([
                lambda image: image.convert("RGB"),
                transforms.Resize([patch_image_size, patch_image_size], interpolation=Image.BICUBIC),
                transforms.ToTensor(),
                transforms.Normalize(mean=mean, std=std),
            ])
            logger.info("val split, do not use random augmentation.")
        else:
            self.patch_resize_transform = create_transform(
                input_size=patch_image_size,
                is_training=True,
                color_jitter=0.4,
                auto_augment='rand-m9-mstd0.5-inc1',
                interpolation='bicubic',
                re_prob=0.25,
                re_mode='pixel',
                re_count=1,
                mean=mean,
                std=std,
            )
            self.patch_resize_transform = transforms.Compose(functools.reduce(lambda x, y:x + y, [
                [lambda image: image.convert("RGB"),],
                self.patch_resize_transform.transforms[:2],
                [self.patch_resize_transform.transforms[2]],
                [RandomAugment(2, 7, isPIL=True, augs=['Identity', 'AutoContrast', 'Equalize', 'Brightness', 'Sharpness', 'ShearX', 'ShearY', 'TranslateX', 'TranslateY', 'Rotate']), ],
                self.patch_resize_transform.transforms[3:],
            ]))
            logger.info("train split, use random augmentation.")

    def __getitem__(self, index):
        image, label_name = self.dataset[index]

        image = Image.open(BytesIO(base64.urlsafe_b64decode(image)))
        patch_image = self.patch_resize_transform(image)
        patch_mask = torch.tensor([True])

        src_item = self.encode_text(' what does the image describe?')
        tgt_item = self.encode_text(" {}".format(label_name))
        ref_dict = {label_name: 1.0}

        src_item = torch.cat([self.bos_item, src_item, self.eos_item])
        target_item = torch.cat([tgt_item, self.eos_item])
        prev_output_item = torch.cat([self.bos_item, tgt_item])

        example = {
            "id": index,
            "source": src_item,
            "patch_image": patch_image,
            "patch_mask": patch_mask,
            "target": target_item,
            "prev_output_tokens": prev_output_item,
            "ref_dict": ref_dict,
        }
        if self.constraint_trie is not None:
            constraint_mask = torch.zeros((len(prev_output_item), len(self.tgt_dict))).bool()
            for i in range(len(prev_output_item)):
                constraint_prefix_token = prev_output_item[:i+1].tolist()
                constraint_nodes = self.constraint_trie.get_next_layer(constraint_prefix_token)
                constraint_mask[i][constraint_nodes] = True
            example["constraint_mask"] = constraint_mask
        return example

    def collater(self, samples, pad_to_length=None):
        """Merge a list of samples to form a mini-batch.
        Args:
            samples (List[dict]): samples to collate
        Returns:
            dict: a mini-batch containing the data of the task
        """
        return collate(samples, pad_idx=self.pad, eos_idx=self.eos)