"""
 Copyright (c) 2022, salesforce.com, inc.
 All rights reserved.
 SPDX-License-Identifier: BSD-3-Clause
 For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause
"""

import os
from collections import OrderedDict

from minigpt4.datasets.datasets.base_dataset import BaseDataset
from PIL import Image
import random


class __DisplMixin:
    def displ_item(self, index):
        sample, ann = self.__getitem__(index), self.annotation[index]

        return OrderedDict(
            {
                "file": ann["image"],
                "caption": ann["caption"],
                "image": sample["image"],
            }
        )


class CaptionDataset(BaseDataset, __DisplMixin):
    def __init__(self, vis_processor, text_processor, vis_root, ann_paths):
        """
        vis_root (string): Root directory of images (e.g. coco/images/)
        ann_root (string): directory to store the annotation file
        """
        super().__init__(vis_processor, text_processor, vis_root, ann_paths)

        self.img_ids = {}
        n = 0
        for ann in self.annotation:
            img_id = ann["image_id"]
            if img_id not in self.img_ids.keys():
                self.img_ids[img_id] = n
                n += 1

    def __getitem__(self, index):

        # TODO this assumes image input, not general enough
        ann = self.annotation[index]

        img_file = '{:0>12}.jpg'.format(ann["image_id"])
        image_path = os.path.join(self.vis_root, img_file)
        image = Image.open(image_path).convert("RGB")

        image = self.vis_processor(image)
        caption = self.text_processor(ann["caption"])

        return {
            "image": image,
            "text_input": caption,
            "image_id": self.img_ids[ann["image_id"]],
        }



class COCOCaptionDataset(BaseDataset, __DisplMixin):
    def __init__(self, vis_processor, text_processor, vis_root, ann_paths):
        """
        vis_root (string): Root directory of images (e.g. coco/images/)
        ann_root (string): directory to store the annotation file
        """
        super().__init__(vis_processor, text_processor, vis_root, ann_paths)

        self.img_ids = {}
        n = 0

        self.filter_anntation = []
        
        for ann in self.annotation:
            if "train" in ann["image"]:
                self.filter_anntation.append(ann)
        self.annotation = self.filter_anntation

        for ann in self.annotation:
            img_id = ann["image_id"]
            if img_id not in self.img_ids.keys():
                self.img_ids[img_id] = n
                n += 1

        self.instruction_pool = [
            'Briefly describe this image.',
            'Provide a concise depiction of this image.',
            'Present a short description of this image.',
            'Summarize this image in a few words.',
            'A short image caption:',
            'A short image description:',
            'A photo of ',
            'An image that shows ',
            'Write a short description for the image. ',
            'Write a description for the photo.',
            'Provide a description of what is presented in the photo.',
            'Briefly describe the content of the image.',
            'Can you briefly explain what you see in the image?',
            'Could you use a few words to describe what you perceive in the photo?',
            'Please provide a short depiction of the picture.',
            'Using language, provide a short account of the image.',
            'Use a few words to illustrate what is happening in the picture.',
        ]
    def __getitem__(self, index):

        # TODO this assumes image input, not general enough
        ann = self.annotation[index]

        # img_file = '{:0>12}.jpg'.format(ann["image_id"])
        img_file = ann["image"].split("/")[-1]
        image_path = os.path.join(self.vis_root, img_file)
        image = Image.open(image_path).convert("RGB")

        image = self.vis_processor(image)
        caption = self.text_processor(ann["caption"])

        instruction = random.choice(self.instruction_pool)
        instruction = "<Img><ImageHere></Img> [caption] {} ".format(instruction)

        return {
            "image": image,
            "answer": caption,
            "instruction_input": instruction,
        }

class CaptionEvalDataset(BaseDataset, __DisplMixin):
    def __init__(self, vis_processor, text_processor, vis_root, ann_paths):
        """
        vis_root (string): Root directory of images (e.g. coco/images/)
        ann_root (string): directory to store the annotation file
        split (string): val or test
        """
        super().__init__(vis_processor, text_processor, vis_root, ann_paths)

    def __getitem__(self, index):

        ann = self.annotation[index]

        image_path = os.path.join(self.vis_root, ann["image"])
        image = Image.open(image_path).convert("RGB")

        image = self.vis_processor(image)

        return {
            "image": image,
            "image_id": ann["image_id"],
            "instance_id": ann["instance_id"],
        }
