from typing import Tuple, List, Sequence, Optional, Union
from torchvision import transforms
from torch import nn, Tensor
from PIL import Image
from pathlib import Path
from bs4 import BeautifulSoup as bs

import numpy as np
import numpy.typing as npt
from numpy import uint8
ImageType = npt.NDArray[uint8]
from transformers import AutoModelForObjectDetection
import torch
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from matplotlib.patches import Patch

from unitable import UnitableFullPredictor

#based on this notebook:https://github.com/NielsRogge/Transformers-Tutorials/blob/master/Table%20Transformer/Inference_with_Table_Transformer_(TATR)_for_parsing_tables.ipynb
class MaxResize(object):
    def __init__(self, max_size=800):
        self.max_size = max_size

    def __call__(self, image):
        width, height = image.size
        current_max_size = max(width, height)
        scale = self.max_size / current_max_size
        resized_image = image.resize((int(round(scale*width)), int(round(scale*height))))

        return resized_image
    
def iob(boxA, boxB):
    """
    Calculate the Intersection over Bounding Box (IoB) of two bounding boxes.
    
    Parameters:
    - boxA: list or tuple with [xmin, ymin, xmax, ymax] of the first box
    - boxB: list or tuple with [xmin, ymin, xmax, ymax] of the second box
    
    Returns:
    - iob: float, the IoB ratio
    """
    # Determine the coordinates of the intersection rectangle
    xA = max(boxA[0], boxB[0])
    yA = max(boxA[1], boxB[1])
    xB = min(boxA[2], boxB[2])
    yB = min(boxA[3], boxB[3])
    
    # Compute the area of intersection rectangle
    interWidth = max(0, xB - xA)
    interHeight = max(0, yB - yA)
    interArea = interWidth * interHeight
    
    # Compute the area of boxB (the second box)
    boxBArea = (boxB[2] - boxB[0]) * (boxB[3] - boxB[1])
    
    # Compute the Intersection over Bounding Box (IoB) ratio
    iob = interArea / float(boxBArea)
    
    return iob

class DetectionAndOcrTable2():
    #This components can take in entire pdf page as input , scan for tables and return the table in html format
    #Uses the full unitable model - different to DetectionAndOcrTable1
    def __init__(self):
        self.unitableFullPredictor = UnitableFullPredictor()

    
    @staticmethod
    def save_detection(detected_lines_images:List[ImageType], prefix = './res/test1/res_'):
        i = 0
        for img in detected_lines_images:
            pilimg = Image.fromarray(img)
            pilimg.save(prefix+str(i)+'.png')
            i=i+1
    
    @staticmethod
    # for output bounding box post-processing
    def box_cxcywh_to_xyxy(x):
        x_c, y_c, w, h = x.unbind(-1)
        b = [(x_c - 0.5 * w), (y_c - 0.5 * h), (x_c + 0.5 * w), (y_c + 0.5 * h)]
        return torch.stack(b, dim=1)

    @staticmethod
    def rescale_bboxes(out_bbox, size):
        img_w, img_h = size
        b = DetectionAndOcrTable2.box_cxcywh_to_xyxy(out_bbox)
        b = b * torch.tensor([img_w, img_h, img_w, img_h], dtype=torch.float32)
        return b

    @staticmethod
    def outputs_to_objects(outputs, img_size, id2label):
        m = outputs.logits.softmax(-1).max(-1)
        pred_labels = list(m.indices.detach().cpu().numpy())[0]
        pred_scores = list(m.values.detach().cpu().numpy())[0]
        pred_bboxes = outputs['pred_boxes'].detach().cpu()[0]
        pred_bboxes = [elem.tolist() for elem in DetectionAndOcrTable2.rescale_bboxes(pred_bboxes, img_size)]

        objects = []
        for label, score, bbox in zip(pred_labels, pred_scores, pred_bboxes):
            class_label = id2label[int(label)]
            if not class_label == 'no object':
                objects.append({'label': class_label, 'score': float(score),
                                'bbox': [float(elem) for elem in bbox]})

        return objects
    

    @staticmethod
    def visualize_detected_tables(img, det_tables, out_path=None):
        plt.imshow(img, interpolation="lanczos")
        fig = plt.gcf()
        fig.set_size_inches(20, 20)
        ax = plt.gca()

        for det_table in det_tables:
            bbox = det_table['bbox']

            if det_table['label'] == 'table':
                facecolor = (1, 0, 0.45)
                edgecolor = (1, 0, 0.45)
                alpha = 0.3
                linewidth = 2
                hatch='//////'
            elif det_table['label'] == 'table rotated':
                facecolor = (0.95, 0.6, 0.1)
                edgecolor = (0.95, 0.6, 0.1)
                alpha = 0.3
                linewidth = 2
                hatch='//////'
            else:
                continue

            rect = patches.Rectangle(bbox[:2], bbox[2]-bbox[0], bbox[3]-bbox[1], linewidth=linewidth,
                                        edgecolor='none',facecolor=facecolor, alpha=0.1)
            ax.add_patch(rect)
            rect = patches.Rectangle(bbox[:2], bbox[2]-bbox[0], bbox[3]-bbox[1], linewidth=linewidth,
                                        edgecolor=edgecolor,facecolor='none',linestyle='-', alpha=alpha)
            ax.add_patch(rect)
            rect = patches.Rectangle(bbox[:2], bbox[2]-bbox[0], bbox[3]-bbox[1], linewidth=0,
                                        edgecolor=edgecolor,facecolor='none',linestyle='-', hatch=hatch, alpha=0.2)
            ax.add_patch(rect)

        plt.xticks([], [])
        plt.yticks([], [])

        legend_elements = [Patch(facecolor=(1, 0, 0.45), edgecolor=(1, 0, 0.45),
                                    label='Table', hatch='//////', alpha=0.3),
                            Patch(facecolor=(0.95, 0.6, 0.1), edgecolor=(0.95, 0.6, 0.1),
                                    label='Table (rotated)', hatch='//////', alpha=0.3)]
        plt.legend(handles=legend_elements, bbox_to_anchor=(0.5, -0.02), loc='upper center', borderaxespad=0,
                        fontsize=10, ncol=2)
        plt.gcf().set_size_inches(10, 10)
        plt.axis('off')

        if out_path is not None:
            plt.savefig(out_path, bbox_inches='tight', dpi=150)

        return fig
    
    #For that, the TATR authors employ some padding to make sure the borders of the table are included.
    @staticmethod
    def objects_to_crops(img, tokens, objects, class_thresholds, padding=10):
        """
        Process the bounding boxes produced by the table detection model into
        cropped table images and cropped tokens.
        """

        table_crops = []
        for obj in objects:
            # abit unecessary here cause i crop them anywyas 
            if obj['score'] < class_thresholds[obj['label']]:
                print('skipping object with score', obj['score'])
                continue

            cropped_table = {}

            bbox = obj['bbox']
            bbox = [bbox[0]-padding, bbox[1]-padding, bbox[2]+padding, bbox[3]+padding]

            cropped_img = img.crop(bbox)
            
            # Add padding to the cropped image
            padded_width = cropped_img.width + 40
            padded_height = cropped_img.height +40
            
            new_img_np = np.full((padded_height, padded_width, 3), fill_value=255, dtype=np.uint8)
            y_offset = (padded_height - cropped_img.height) // 2
            x_offset = (padded_width - cropped_img.width) // 2
            new_img_np[y_offset:y_offset + cropped_img.height, x_offset:x_offset+cropped_img.width] = np.array(cropped_img)

            padded_img = Image.fromarray(new_img_np,'RGB')


            table_tokens = [token for token in tokens if iob(token['bbox'], bbox) >= 0.5]
            for token in table_tokens:
                token['bbox'] = [token['bbox'][0]-bbox[0] + padding,
                                 token['bbox'][1]-bbox[1] + padding,
                                 token['bbox'][2]-bbox[0] + padding,
                                 token['bbox'][3]-bbox[1] + padding]

            # If table is predicted to be rotated, rotate cropped image and tokens/words:
            if obj['label'] == 'table rotated':
                padded_img = padded_img.rotate(270, expand=True)
                for token in table_tokens:
                    bbox = token['bbox']
                    bbox = [padded_img.size[0]-bbox[3]-1,
                            bbox[0],
                            padded_img.size[0]-bbox[1]-1,
                            bbox[2]]
                    token['bbox'] = bbox

            cropped_table['image'] = padded_img
            cropped_table['tokens'] = table_tokens

            table_crops.append(cropped_table)

        return table_crops
    
    def predict(self,image:Image.Image,debugfolder_filename_page_name):
        
        
        """
        0. Locate the table using Table detection 
        1. Unitable 
        """

        # Step 0 : Locate the table using Table detection TODO

        #First we load a Table Transformer pre-trained for table detection. We use the "no_timm" version here to load the checkpoint with a Transformers-native backbone.
        model = AutoModelForObjectDetection.from_pretrained("microsoft/table-transformer-detection", revision="no_timm")
        device = "cuda" if torch.cuda.is_available() else "cpu"
        model.to(device)

        #Preparing the image for the model 
        detection_transform = transforms.Compose([
            MaxResize(800),
            transforms.ToTensor(),
            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
        ])
        pixel_values = detection_transform(image).unsqueeze(0)
        pixel_values = pixel_values.to(device)
        
        # Next, we forward the pixel values through the model. 
        # The model outputs logits of shape (batch_size, num_queries, num_labels + 1). The +1 is for the "no object" class.
        with torch.no_grad():
            outputs = model(pixel_values)

        # update id2label to include "no object"
        id2label = model.config.id2label
        id2label[len(model.config.id2label)] = "no object"

        #[{'label': 'table', 'score': 0.9999570846557617, 'bbox': [110.24547576904297, 73.31171417236328, 1024.609130859375, 308.7159423828125]}]
        objects = DetectionAndOcrTable2.outputs_to_objects(outputs, image.size, id2label)
        
        #Only do these for objects with score greater than 0.8 
        objects = [obj for obj in objects if obj['score'] > 0.95]

        print(objects)
        if objects: 
            fig = DetectionAndOcrTable2.visualize_detected_tables(image, objects,out_path = "./res/table_debug/table_former_detection.jpg")

            #Next, we crop the table out of the image. For that, the TATR authors employ some padding to make sure the borders of the table are included.


            tokens = []
            detection_class_thresholds = {
                "table": 0.95,
                "table rotated": 0.95,
                "no object": 10
            }
            crop_padding = 10

            
            tables_crops = DetectionAndOcrTable2.objects_to_crops(image, tokens, objects, detection_class_thresholds, padding=crop_padding)
            
            #[{'image': <PIL.Image.Image image mode=RGB size=1392x903 at 0x7F71B02BCB50>, 'tokens': []}]
            #print(tables_crops)

            #TODO: Handle the case where there are multiple tables 
            cropped_tables =[]
            for i in range (len(tables_crops)):
                cropped_table = tables_crops[i]['image'].convert("RGB")
                cropped_table.save(debugfolder_filename_page_name +"cropped_table_"+str(i)+".png")
                cropped_tables.append(cropped_table)

            print("number of cropped tables found: "+str(len(cropped_tables)))

            
            # Step 1: Unitable 
            #This take PIL Images as input 
            table_codes = self.unitableFullPredictor.predict(cropped_tables,debugfolder_filename_page_name)

        else: 
            return