import json
from math import sqrt
import re
from nltk.translate.bleu_score import sentence_bleu

# gold label file
gold_fn = 'test.json'

pred_fn = 'llava-v1.5-13b.json'
gold = json.load(open(gold_fn))
pred = json.load(open(pred_fn))

sequence_match = 0
action_score = 0
total_click_penalty = 0
total_press_penalty = 0
total_write_penalty = 0
ideal_score = 0
max_click_penalty = 0
max_press_penalty = 0
max_write_penalty = 0



def get_bounds(box: dict(), cx, cy):
    for i in box:
        tl = box[i]["top_left"]
        br = box[i]["bottom_right"]
        if (tl[0]+br[0])/2 == cx and (tl[1]+br[1])/2 == cy:
            return (tl,br)
    
    assert False

    
def dynamic_dirichlet_l2_penalty(tl, br, px, py):
    
    len_x = br[0] - tl[0]
    len_y = br[1] - tl[1]
    
    cx = ( br[0] - tl[0] ) / 2
    cy = ( br[1] - tl[1] ) / 2
    
    dx = abs(cx - px) - (len_x * 0.5)
    dy = abs(cy - py) - (len_y * 0.5)
    dist = sqrt((dx * (dx > 0)) ** 2 + (dy * (dy > 0)) ** 2)
    
    mu = sqrt( len_x ** 2 + len_y ** 2)
    
    score = mu / (dist+mu)
    penalty = 1 - score
    return penalty

for idx in gold:
    
    gold_script = open(gold[idx]['task']).read().strip().split('\n')[2:]
    llm_script = pred[idx].strip().split()
    llm_script = [x for x in llm_script if x.strip().startswith('pyautogui')]
    #find extreme case values
    sample_weight = (len(gold_script)-0.9)

    ideal_score += sample_weight
    for gold_line in gold_script:
        action_type = gold_line.split("pyautogui.")[1].split("(")[0]
        if action_type == 'click' or action_type == 'rightClick' or action_type == 'moveTo' or action_type == 'dragTo':
            max_click_penalty += sample_weight/len(gold_script)
        if action_type == 'press' or action_type == 'hotkey':
            max_press_penalty += sample_weight/len(gold_script)
        if action_type == 'write':
            max_write_penalty += sample_weight/len(gold_script)
       
    seq_match_flag = 1
    click_penalty = 0
    press_penalty = 0
    write_penalty = 0
    
    # if length doesn't seq match is 0
    # llm_script = llm_script[:len(gold_script)]
    if len(llm_script) != len(gold_script):
        seq_match_flag = 0
    if seq_match_flag == 1:
        for i in range(len(gold_script)):
            gold_line = gold_script[i].strip()
            gold_action = gold_line.split('pyautogui.')[1].split('(')[0]
            pred_line = llm_script[i]
            if pred_line.startswith('pyautogui.') == False:
                seq_match_flag = 0
                break
            pred_action = pred_line.split('pyautogui.')[1].split('(')[0]
            if pred_action != gold_action:
                seq_match_flag = 0
                break
        
    # find penalties for correct and wrong sequences 
    box_path = gold[idx]['box']
    box_num = box_path.split("_")[-1].split(".json")[0]
    box_path = "_".join(box_path.split("_")[:-1])+box_num+"_boxes.json"
    box = json.load(open(box_path))

    for i in range(len(gold_script)):
        gold_line = gold_script[i].strip()
        gold_action = gold_line.split('pyautogui.')[1].split('(')[0]
        # just add the penalties
        if seq_match_flag == 0:
            if gold_action == 'click' or gold_action == 'rightClick' or gold_action == 'moveTo' or gold_action == 'dragTo':
                click_penalty += 1/len(gold_script)
            if gold_action == 'press' or gold_action == 'hotkey':
                press_penalty += 1/len(gold_script)
            if gold_action == 'write':
                write_penalty += 1/len(gold_script)
            continue   
        pred_line = llm_script[i]
        pred_action = pred_line.split('pyautogui.')[1].split('(')[0]

        # l2 penalty for click
        
        if gold_action == 'click' or gold == 'rightClick':
            # get original box bounds
            gold_cx = gold_line.split("pyautogui.")[1].split('(')[1].split(',')[0]
            gold_cy = gold_line.split("pyautogui.")[1].split('(')[1].split(',')[1].split(')')[0]
            tl, br = get_bounds(box, float(gold_cx), float(gold_cy))
            
            # get predicted point
            pred_cx = gold_line.split("pyautogui.")[1].split('(')[1].split(',')[0]
            pred_cy = gold_line.split("pyautogui.")[1].split('(')[1].split(',')[1].split(')')[0]
            
            click_penalty += (1.0/len(gold_script)) * dynamic_dirichlet_l2_penalty(tl, br, float(pred_cx), float(pred_cy))
            
        # penalty for press
        if gold_action == 'press':
            gold_key = gold_line.split("\"")[1]
            pred_key = (re.split("\"|'", pred_line))[1]
            if gold_key.strip() != pred_key.strip():
                press_penalty += 1/len(gold_script)
            
        # penalty for hotkey
        if gold_action == 'hotkey':
            gold_keys = gold_line.split("(")[1].split(")")[0].split(",")
            pred_keys = pred_line.split("(")[1].split(")")[0].split(",")
            
            gold_key_set = set([x[1:-1] for x in gold_keys if len(x)>2])
            pred_key_set = set([x[1:-1] for x in pred_keys if len(x)>2])
            if gold_key_set != pred_key_set:
                press_penalty += 1/len(gold_script)
    
    
        if gold_action == 'write':
            reference = [gold_line.split("\"")[1]]
            candidate = re.split("\"|'", pred_line)[1]
            write_penalty += (1-sentence_bleu(reference, candidate, weights=(0.5, 0.5))) / len(gold_script)
            
    sequence_match += (seq_match_flag) * sample_weight
    action_score += (max(seq_match_flag - click_penalty - press_penalty - write_penalty, 0)) * sample_weight
    if seq_match_flag:
        total_click_penalty += click_penalty  * sample_weight
        total_press_penalty += press_penalty * sample_weight
        total_write_penalty += write_penalty * sample_weight
    

print(ideal_score)   
print(f"Sequence match: {sequence_match/ideal_score}")
print(f"Action match: {action_score/ideal_score}")
    
    
print(total_click_penalty/ideal_score)
print(total_press_penalty/ideal_score)
print(total_write_penalty/ideal_score)