import os
import json
import re
import sklearn
import collections
from sklearn.metrics import f1_score,accuracy_score

def get_f1(samples):
    gold,pred = [],[]
    for sample in samples:
        if sample['gold'] in sample['pred']:
            gold.append(sample['gold'])
            pred.append(sample['gold'])
        else:
            gold.append(sample['gold'])
            pred.append(sample['pred'])
    
    micro_f1 = f1_score(gold,pred,average='micro')
    macro_f1 = f1_score(gold,pred,average='macro')

    
    print(f"Micro-F1: {micro_f1:.4f}")
    print(f"Macro-F1: {macro_f1:.4f}")
    return micro_f1, macro_f1

def get_cls_acc(samples):
    gold,pred = [],[]
    for sample in samples:
        if sample['gold'] in sample['pred']:
            gold.append(sample['gold'])
            pred.append(sample['gold'])
        else:
            gold.append(sample['gold'])
            pred.append(sample['pred'])
    
    pool = {}
    for g,p in zip(gold,pred):
        if g not in pool.keys():
            pool[g] = {'g':[],'p':[]}
        pool[g]['g'].append(g)
        pool[g]['p'].append(p)
    
    result = {}
    counter = {}
    for k,v in pool.items():
        g,p = v['g'],v['p']
        acc = accuracy_score(g,p)
        result[k] = acc
        counter[k] = len(g)
    
    result = sorted([(k,v,counter[k]) for k,v in result.items()],key=lambda x:x[2],reverse=True)

    #_result = [r for r in result if r[1] > 0]
    _result = [r for r in result][:20]
    
    import pandas as pd
    prob = [r[1] for r in result]
    number = [r[2] for r in result]
    crime = [r[0] for r in result]
    data = {'crime':crime,'prob':prob,'number':number}
    df = pd.DataFrame(data)
    df.to_excel('./output/eval/lawbench.xlsx',index=False)

    for r in result:
        k,v = r[0],r[1]
        print('{x} : {y}'.format(x=k,y=v))
    print(accuracy_score(gold,pred))

def get_bias_with_acc(samples):

    def load_result(path):
        samples = []
        samples_with_tag = []
        with open(path,'r') as f:
            for l in f.readlines():
                line = json.loads(l)
                y = line['y']
                y_ = line['pred'] if line['pred'] else ''

                y = [s[3:-4] for s in y.split(';')]
                for c in y:
                    samples.append({'gold':c,'pred':y_[3:-4]})
                    if re.search('<e>.*</e>',y_):
                        samples_with_tag.append({'gold':c,'pred':y_})
        return samples
    
    def get_acc_in_cls(samples):
        gold,pred = [],[]
        for sample in samples:
            if sample['gold'] in sample['pred']:
                gold.append(sample['gold'])
                pred.append(sample['gold'])
            else:
                gold.append(sample['gold'])
                pred.append(sample['pred'])
        pool = {}
        for g,p in zip(gold,pred):
            if g not in pool.keys():
                pool[g] = {'g':[],'p':[]}
            pool[g]['g'].append(g)
            pool[g]['p'].append(p)
    
        result = {}
        counter = {}
        for k,v in pool.items():
            g,p = v['g'],v['p']
            acc = accuracy_score(g,p)
            result[k] = acc
            counter[k] = len(g)
    
        import numpy
        result = sorted([(k,v,numpy.log(counter[k]/len(gold))) for k,v in result.items()],key=lambda x:x[2],reverse=True)

        return result

    zeroshot = load_result('')       
    oneshot = load_result('')
    cot = load_result('')
    our = load_result('')

    zeroshot = get_acc_in_cls(zeroshot)
    oneshot = get_acc_in_cls(oneshot)
    cot = get_acc_in_cls(cot)
    our = get_acc_in_cls(our)
    
    crimes = {c[0]:c[-1] for c in zeroshot}
    
    crimes_pool = []
    log = []
    acc_zero = []
    acc_one = []
    acc_cot = []
    acc_our = []
    for c,crime in crimes.items():
        crimes_pool.append(c)
        log.append(crime)
        for i,s in enumerate(zeroshot):
            if c in s[0]:
                acc_zero.append(s[1])
                break
            if i == len(zeroshot) and c not in s[0]:
                raise ValueError
        for i,s in enumerate(oneshot):
            if c in s[0]:
                acc_one.append(s[1])
                break
            if i == len(oneshot) and c not in s[0]:
                raise ValueError
        if len(acc_one) < len(acc_zero):
            acc_one.append(0)
        for i,s in enumerate(cot):
            if c in s[0]:
                acc_cot.append(s[1])
                break
            if i == len(cot) and c not in s[0]:
                raise ValueError
        for i,s in enumerate(our):
            if c in s[0]:
                acc_our.append(s[1])
                break
            if i == len(our) and c not in s[0]:
                raise ValueError

    import pandas as pd
    idx = [i for i in range(len(crimes_pool))]
    data = {'crime':crimes_pool,'prob':log,'zs':acc_zero,'os':acc_one,'ct':acc_cot,'our':acc_our,'id':idx}
    df = pd.DataFrame(data)
    df.to_excel('',index=False)
    
def get_effect_with_f1():
    
    def load_result(path):
        samples = []
        samples_with_tag = []
        with open(path,'r') as f:
            for l in f.readlines():
                line = json.loads(l)
                y = line['y']
                y_ = line['pred'] if line['pred'] else ''

                y = [s[3:-4] for s in y.split(';')]
                for c in y:
                    #crimes_counter[c] += 1
                    samples.append({'gold':c,'pred':y_[3:-4]})
                    if re.search('<e>.*</e>',y_):
                        samples_with_tag.append({'gold':c,'pred':y_})
        return samples, samples_with_tag
    
    dzeroshot,dzeroshottag = load_result('')
    dzeromi,dzeroma = get_f1(dzeroshot)
    d_zeromi,d_zeroma = get_f1(dzeroshottag)

    doneshot,doneshottag = load_result('')
    donemi,donema = get_f1(doneshot)
    d_onemi,d_onema = get_f1(doneshottag)

    dcotshot,dcotshottag = load_result('')
    dcotmi,dcotma = get_f1(dcotshot)
    d_cotmi,d_cotma = get_f1(dcotshottag)

    dourshot,dourshottag = load_result('')
    dourmi,dourma = get_f1(dourshot)
    d_ourmi,d_ourma = get_f1(dourshottag)
    
    zeroshot,zeroshottag = load_result('')
    zeromi,zeroma = get_f1(zeroshot)
    _zeromi,_zeroma = get_f1(zeroshottag)

    oneshot,oneshottag = load_result('')
    onemi,onema = get_f1(oneshot)
    _onemi,_onema = get_f1(oneshottag)

    cotshot,cotshottag = load_result('')
    cotmi,cotma = get_f1(cotshot)
    _cotmi,_cotma = get_f1(cotshottag)

    ourshot,ourshottag = load_result('')
    ourmi,ourma = get_f1(ourshot)
    _ourmi,_ourma = get_f1(ourshottag)


    # mi
    acc_zero_effect = (dzeromi - zeromi) / (dzeromi - zeromi)
    acc_one_effect = (donemi - zeromi) / (dzeromi - zeromi)
    acc_cot_effect = (dcotmi - zeromi) / (dzeromi - zeromi)
    acc_our_effect = (dourmi - zeromi) / (dzeromi - zeromi) 
        # ma
    acc_zero_effect = (dzeroma - zeroma) / (dzeroma - zeroma)
    acc_one_effect = (donema - zeroma) / (dzeroma - zeroma)
    acc_cot_effect = (dcotma - zeroma) / (dzeroma - zeroma)
    acc_our_effect = (dourma - zeroma) / (dzeroma - zeroma) 

    print(acc_zero_effect)
    print(acc_one_effect)
    print(acc_cot_effect)
    print(acc_our_effect)  

def crime_topk_acc(samples,crimes=None,name='deepseek'):
    gold,pred = [],[]
    for sample in samples:
        if sample['gold'] in sample['pred']:
            gold.append(sample['gold'])
            pred.append(sample['gold'])
        else:
            gold.append(sample['gold'])
            pred.append(sample['pred'])
    
    pool = {}
    for g,p in zip(gold,pred):
        if g not in pool.keys():
            pool[g] = {'g':[],'p':[]}
        pool[g]['g'].append(g)
        pool[g]['p'].append(p)
    
    if crimes is None:
        crimes = {k:i for i,k in enumerate(pool.keys())}
    
    #counter = {}
    result = {}
    for k,v in pool.items():
        counter = collections.Counter()
        g,p = v['g'],v['p']
        for pi in p:
            counter[pi] += 1
        for c in crimes:
            if c not in counter.keys():
                counter[c] = 0
        result[k] = [counter[c] for c in crimes]
        
    import pandas as pd
    df = pd.DataFrame(result)
    df.to_excel('./output/eval/warm_acc_{x}.xlsx'.format(x=name),index=False)

    return result,crimes


def main():
    path = ''

    def load_data(path):
        crimes_counter = collections.Counter()
        samples = []
        samples_with_tag = []
        with open(path,'r') as f:
            for l in f.readlines():
                line = json.loads(l)
                y = line['y']
                y_ = line['pred'] if line['pred'] else ''

                y = [s[3:-4] for s in y.split(';')]
                for c in y:
                    crimes_counter[c] += 1
                    samples.append({'gold':c,'pred':y_[3:-4]})
                    if re.search('<e>.*</e>',y_):
                        samples_with_tag.append({'gold':c,'pred':y_})
        return samples,samples_with_tag

    samples,samples_with_tag = load_data(path)
    print('/'.join(path.split('/')[-2:]))
    get_f1(samples)
    print('-'*30)
    get_f1(samples_with_tag)
    print('-'*30)
    get_cls_acc(samples)
    print('-'*30)
    get_bias_with_acc(samples)


if __name__ =='__main__':
    main()