# SPDX-License-Identifier: Apache-2.0
"""Compare the scoring outputs of HF and vLLM models.

Run `pytest tests/models/embedding/language/test_scoring.py`.
"""
import math
import os

import pytest
import torch
import torch.nn.functional as F
from ....utils import models_path_prefix

MODELS = [
    os.path.join(models_path_prefix, "cross-encoder/ms-marco-MiniLM-L-6-v2"),  # Bert
    os.path.join(models_path_prefix, "BAAI/bge-reranker-v2-m3"),  # Roberta
]

EMBEDDING_MODELS = [
    "sentence-transformers/all-MiniLM-L12-v2",
]

TEXTS_1 = [
    "What is the capital of France?",
    "What is the capital of Germany?",
]

TEXTS_2 = [
    "The capital of France is Paris.",
    "The capital of Germany is Berlin.",
]


@pytest.fixture(scope="module", params=MODELS)
def model_name(request):
    yield request.param


@pytest.mark.parametrize("dtype", ["half"])
def test_llm_1_to_1(vllm_runner, hf_runner, model_name, dtype: str):

    text_pair = [TEXTS_1[0], TEXTS_2[0]]

    with hf_runner(model_name, dtype=dtype, is_cross_encoder=True) as hf_model:
        hf_outputs = hf_model.predict([text_pair]).tolist()

    with vllm_runner(model_name, task="score", dtype=dtype,
                     max_model_len=None) as vllm_model:
        vllm_outputs = vllm_model.score(text_pair[0], text_pair[1])

    assert len(vllm_outputs) == 1
    assert len(hf_outputs) == 1

    assert math.isclose(hf_outputs[0], vllm_outputs[0], rel_tol=0.01)


@pytest.mark.parametrize("dtype", ["half"])
def test_llm_1_to_N(vllm_runner, hf_runner, model_name, dtype: str):

    text_pairs = [
        [TEXTS_1[0], TEXTS_2[0]],
        [TEXTS_1[0], TEXTS_2[1]],
    ]

    with hf_runner(model_name, dtype=dtype, is_cross_encoder=True) as hf_model:
        hf_outputs = hf_model.predict(text_pairs).tolist()

    with vllm_runner(model_name, task="score", dtype=dtype,
                     max_model_len=None) as vllm_model:
        vllm_outputs = vllm_model.score(TEXTS_1[0], TEXTS_2)

    assert len(vllm_outputs) == 2
    assert len(hf_outputs) == 2

    assert math.isclose(hf_outputs[0], vllm_outputs[0], rel_tol=0.01)
    assert math.isclose(hf_outputs[1], vllm_outputs[1], rel_tol=0.01)


@pytest.mark.parametrize("dtype", ["half"])
def test_llm_N_to_N(vllm_runner, hf_runner, model_name, dtype: str):

    text_pairs = [
        [TEXTS_1[0], TEXTS_2[0]],
        [TEXTS_1[1], TEXTS_2[1]],
    ]

    with hf_runner(model_name, dtype=dtype, is_cross_encoder=True) as hf_model:
        hf_outputs = hf_model.predict(text_pairs).tolist()

    with vllm_runner(model_name, task="score", dtype=dtype,
                     max_model_len=None) as vllm_model:
        vllm_outputs = vllm_model.score(TEXTS_1, TEXTS_2)

    assert len(vllm_outputs) == 2
    assert len(hf_outputs) == 2

    assert math.isclose(hf_outputs[0], vllm_outputs[0], rel_tol=0.01)
    assert math.isclose(hf_outputs[1], vllm_outputs[1], rel_tol=0.01)


@pytest.fixture(scope="module", params=EMBEDDING_MODELS)
def emb_model_name(request):
    yield request.param


@pytest.mark.parametrize("dtype", ["half"])
def test_llm_1_to_1_embedding(vllm_runner, hf_runner, emb_model_name,
                              dtype: str):

    text_pair = [TEXTS_1[0], TEXTS_2[0]]

    with hf_runner(emb_model_name, dtype=dtype,
                   is_sentence_transformer=True) as hf_model:
        hf_embeddings = hf_model.encode(text_pair)
        hf_outputs = [
            F.cosine_similarity(*map(torch.tensor, hf_embeddings), dim=0)
        ]

    with vllm_runner(emb_model_name,
                     task="embed",
                     dtype=dtype,
                     max_model_len=None) as vllm_model:
        vllm_outputs = vllm_model.score(text_pair[0], text_pair[1])

    assert len(vllm_outputs) == 1
    assert len(hf_outputs) == 1

    assert math.isclose(hf_outputs[0], vllm_outputs[0], rel_tol=0.01)


@pytest.mark.parametrize("dtype", ["half"])
def test_llm_1_to_N_embedding(vllm_runner, hf_runner, emb_model_name,
                              dtype: str):

    text_pairs = [
        [TEXTS_1[0], TEXTS_2[0]],
        [TEXTS_1[0], TEXTS_2[1]],
    ]

    with hf_runner(emb_model_name, dtype=dtype,
                   is_sentence_transformer=True) as hf_model:
        hf_embeddings = [
            hf_model.encode(text_pair) for text_pair in text_pairs
        ]
        hf_outputs = [
            F.cosine_similarity(*map(torch.tensor, pair), dim=0)
            for pair in hf_embeddings
        ]

    with vllm_runner(emb_model_name,
                     task="embed",
                     dtype=dtype,
                     max_model_len=None) as vllm_model:
        vllm_outputs = vllm_model.score(TEXTS_1[0], TEXTS_2)

    assert len(vllm_outputs) == 2
    assert len(hf_outputs) == 2

    assert math.isclose(hf_outputs[0], vllm_outputs[0], rel_tol=0.01)
    assert math.isclose(hf_outputs[1], vllm_outputs[1], rel_tol=0.01)


@pytest.mark.parametrize("dtype", ["half"])
def test_llm_N_to_N_embedding(vllm_runner, hf_runner, emb_model_name,
                              dtype: str):

    text_pairs = [
        [TEXTS_1[0], TEXTS_2[0]],
        [TEXTS_1[1], TEXTS_2[1]],
    ]

    with hf_runner(emb_model_name, dtype=dtype,
                   is_sentence_transformer=True) as hf_model:
        hf_embeddings = [
            hf_model.encode(text_pair) for text_pair in text_pairs
        ]
        hf_outputs = [
            F.cosine_similarity(*map(torch.tensor, pair), dim=0)
            for pair in hf_embeddings
        ]

    with vllm_runner(emb_model_name,
                     task="embed",
                     dtype=dtype,
                     max_model_len=None) as vllm_model:
        vllm_outputs = vllm_model.score(TEXTS_1, TEXTS_2)

    assert len(vllm_outputs) == 2
    assert len(hf_outputs) == 2

    assert math.isclose(hf_outputs[0], vllm_outputs[0], rel_tol=0.01)
    assert math.isclose(hf_outputs[1], vllm_outputs[1], rel_tol=0.01)
