import torch
import torch.nn.functional as F

from torch import Tensor
from transformers import AutoModel, AutoTokenizer


def last_token_pool(last_hidden_states: Tensor,
                 attention_mask: Tensor) -> Tensor:
    left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])
    if left_padding:
        return last_hidden_states[:, -1]
    else:
        sequence_lengths = attention_mask.sum(dim=1) - 1
        batch_size = last_hidden_states.shape[0]
        return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths]


def get_detailed_instruct(task_description: str, query: str) -> str:
    return f'Instruct: {task_description}\nQuery:{query}'

# Each query must come with a one-sentence instruction that describes the task
task = 'Given a web search query, retrieve relevant passages that answer the query'

queries = [
    get_detailed_instruct(task, 'What is the capital of China?'),
    get_detailed_instruct(task, 'Explain gravity')
]
# No need to add instruction for retrieval documents
documents = [
    "The capital of China is Beijing.",
    "Gravity is a force that attracts two bodies towards each other. It gives weight to physical objects and is responsible for the movement of planets around the sun."
]
input_texts = queries + documents


# We recommend enabling flash_attention_2 for better acceleration and memory saving,
# together with setting `padding_side` to "left":
model = AutoModel.from_pretrained(
    "Qwen/Qwen3-Embedding-8B", attn_implementation="flash_attention_2", device_map="auto", torch_dtype=torch.float16
)
tokenizer = AutoTokenizer.from_pretrained('Qwen/Qwen3-Embedding-8B', padding_side="left")
# The queries and documents to embed
max_length = 8192

# Tokenize the input texts
batch_dict = tokenizer(
    input_texts,
    padding=True,
    truncation=True,
    max_length=max_length,
    return_tensors="pt",
)
batch_dict.to(model.device)
with torch.no_grad():
    outputs = model(**batch_dict)
    embeddings = last_token_pool(outputs.last_hidden_state, batch_dict['attention_mask'])

    # normalize embeddings
    embeddings = F.normalize(embeddings, p=2, dim=1)
    scores = (embeddings[:2] @ embeddings[2:].T)

print(scores.tolist())
# [[0.7645568251609802, 0.14142508804798126], [0.13549736142158508, 0.5999549627304077]]