import numpy as np
from transformers import BertTokenizer, BertModel
from sklearn.feature_extraction.text import TfidfVectorizer
import torch
from bert_score import score

def cosine_similarity(a, b):
    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))

def semantic_similarity(text1, text2):
    tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
    model = BertModel.from_pretrained('bert-base-uncased')

    inputs1 = tokenizer(text1, return_tensors='pt')
    inputs2 = tokenizer(text2, return_tensors='pt')

    with torch.no_grad():
        outputs1 = model(**inputs1)
        outputs2 = model(**inputs2)

    embedding1 = outputs1.last_hidden_state.mean(dim=1).squeeze().numpy()
    embedding2 = outputs2.last_hidden_state.mean(dim=1).squeeze().numpy()

    return cosine_similarity(embedding1, embedding2)

def lexical_similarity(text1, text2):
    vectorizer = TfidfVectorizer(stop_words=None, analyzer='char', ngram_range=(1, 3))
    tfidf_matrix = vectorizer.fit_transform([text1, text2])
    vec1 = tfidf_matrix.toarray()[0]
    vec2 = tfidf_matrix.toarray()[1]
    return cosine_similarity(vec1, vec2)

def bert_score_similarity(texts1, texts2):
    P, R, F1 = score(texts1, texts2, lang='en', verbose=False, model_type='bert-base-uncased',device=torch.device('cuda' if torch.cuda.is_available() else 'cpu'))
    return F1.item()