import torch.nn.functional as F

import torch
from torch import Tensor
from transformers import AutoTokenizer, AutoModel

import numpy as np
import pandas as pd


def average_pool(last_hidden_states: Tensor,
                 attention_mask: Tensor) -> Tensor:
    last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
    return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]


paper_df = pd.read_csv('anlp2024.tsv', names=["pid", "title"], sep="\t")
assert len(paper_df) == 599

# paper_df の title 列にあるテキストをリストに変換した上で、各文字列の戦闘に "passage: " をそれぞれ付け加えて input_texts とする
input_texts = [f"passage: {title}" for title in paper_df["title"].tolist()]
assert input_texts[0] == "passage: 市況コメント生成のための少数事例選択"
assert input_texts[-1] == "passage: Event-Centered Prompting for Text Style Transfer"


tokenizer = AutoTokenizer.from_pretrained('intfloat/multilingual-e5-large')
model = AutoModel.from_pretrained('intfloat/multilingual-e5-large')

# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt')

with torch.no_grad():
    outputs = model(**batch_dict)
    embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
    embeddings = F.normalize(embeddings, p=2, dim=1)

    assert embeddings.shape == (599, 1024)

    np.savez("anlp2024", embeddings.detach().numpy().copy())