from transformers import PreTrainedModel, AutoModel, AutoConfig
from .configuration_vatrpp import VATrPPConfig
import json
import os
import random
import shutil
from collections import defaultdict
import time
from datetime import timedelta
from pathlib import Path

import cv2
import numpy as np
import torch

from data.dataset import FolderDataset
from models.model import VATr
from util.loading import load_checkpoint, load_generator
from util.misc import FakeArgs
from util.text import TextGenerator
from util.vision import detect_text_bounds
from torchvision.transforms.functional import to_pil_image


def get_long_tail_chars():
    with open(f"files/longtail.txt", 'r') as f:
        chars = [c.rstrip() for c in f]

    chars.remove('')

    return chars


class VATrPP(PreTrainedModel):
    config_class = VATrPPConfig

    def __init__(self, config: VATrPPConfig) -> None:
        super().__init__(config)
        self.model = VATr(config)
        self.model.eval()

    def set_style_folder(self, style_folder, num_examples=15):
        word_lengths = None
        if os.path.exists(os.path.join(style_folder, "word_lengths.txt")):
            word_lengths = {}
            with open(os.path.join(style_folder, "word_lengths.txt"), 'r') as f:
                for line in f:
                    word, length = line.rstrip().split(",")
                    word_lengths[word] = int(length)

        self.style_dataset = FolderDataset(style_folder, num_examples=num_examples, word_lengths=word_lengths)

    @torch.no_grad()
    def generate(self, gen_text, style_imgs, align_words: bool = False, at_once: bool = False):
        style_images = style_imgs.unsqueeze(0).to(self.model.args.device)

        fake = self.create_fake_sentence(style_images, gen_text, align_words, at_once)
        return to_pil_image(fake)

    @torch.no_grad()
    def create_fake_sentence(self, style_images, text, align_words=False, at_once=False):
        text = "".join([c for c in text if c in self.model.args.alphabet])

        text = text.split() if not at_once else [text]
        gap = np.ones((32, 16))

        text_encode, len_text, encode_pos = self.model.netconverter.encode(text)
        text_encode = text_encode.to(self.model.args.device).unsqueeze(0)

        fake = self.model._generate_fakes(style_images, text_encode, len_text)
        if not at_once:
            if align_words:
                fake = self.stitch_words(fake, show_lines=False)
            else:
                fake = np.concatenate(sum([[img, gap] for img in fake], []), axis=1)[:, :-16]
        else:
            fake = fake[0]
        fake = (fake * 255).astype(np.uint8)

        return fake

    @torch.no_grad()
    def generate_batch(self, style_imgs, text):
        """
        Given a batch of style images and text, generate images using the model
        """
        device = self.model.args.device
        text_encode, _, _ = self.model.netconverter.encode(text)
        fakes, _ = self.model.netG(style_imgs.to(device), text_encode.to(device))
        return fakes

    @staticmethod
    def stitch_words(words: list, show_lines: bool = False, scale_words: bool = False):
        gap_width = 16

        bottom_lines = []
        top_lines = []
        for i in range(len(words)):
            b, t = detect_text_bounds(words[i])
            bottom_lines.append(b)
            top_lines.append(t)
            if show_lines:
                words[i] = cv2.line(words[i], (0, b), (words[i].shape[1], b), (0, 0, 1.0))
                words[i] = cv2.line(words[i], (0, t), (words[i].shape[1], t), (1.0, 0, 0))

        bottom_lines = np.array(bottom_lines, dtype=float)

        if scale_words:
            top_lines = np.array(top_lines, dtype=float)
            gaps = bottom_lines - top_lines
            target_gap = np.mean(gaps)
            scales = target_gap / gaps

            bottom_lines *= scales
            top_lines *= scales
            words = [cv2.resize(word, None, fx=scale, fy=scale) for word, scale in zip(words, scales)]

        highest = np.max(bottom_lines)
        offsets = highest - bottom_lines
        height = np.max(offsets + [word.shape[0] for word in words])

        result = np.ones((int(height), gap_width * len(words) + sum([w.shape[1] for w in words])))

        x_pos = 0
        for bottom_line, word in zip(bottom_lines, words):
            offset = int(highest - bottom_line)

            result[offset:offset + word.shape[0], x_pos:x_pos+word.shape[1]] = word

            x_pos += word.shape[1] + gap_width

        return result


AutoConfig.register("vatrpp", VATrPPConfig)
AutoModel.register(VATrPPConfig, VATrPP)