# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

import os

import pytest

from vllm.model_executor.layers.pooler import CLSPool, MeanPool, PoolingType
from vllm.model_executor.models.bert import BertEmbeddingModel
from vllm.model_executor.models.roberta import RobertaEmbeddingModel
from vllm.platforms import current_platform
from ..utils import models_path_prefix

MAX_MODEL_LEN = 128
MODEL_NAME = os.environ.get("MODEL_NAME", os.path.join(models_path_prefix, "BAAI/bge-base-en-v1.5"))
REVISION = os.environ.get("REVISION", "main")

MODEL_NAME_ROBERTA = os.environ.get("MODEL_NAME",
                                    os.path.join(models_path_prefix, "intfloat/multilingual-e5-small"))
REVISION_ROBERTA = os.environ.get("REVISION", "main")


@pytest.mark.skipif(current_platform.is_rocm(),
                    reason="Xformers backend is not supported on ROCm.")
def test_model_loading_with_params(vllm_runner):
    """
    Test parameter weight loading with tp>1.
    """
    with vllm_runner(model_name=MODEL_NAME,
                     revision=REVISION,
                     dtype="float16",
                     max_model_len=MAX_MODEL_LEN) as vllm_model:
        output = vllm_model.embed("Write a short story about a robot that"
                                  " dreams for the first time.\n")

        model_config = vllm_model.model.llm_engine.model_config
        model_tokenizer = vllm_model.model.llm_engine.tokenizer

        # asserts on the bert model config file
        assert model_config.encoder_config["max_seq_length"] == 512
        assert model_config.encoder_config["do_lower_case"]

        # asserts on the pooling config files
        assert model_config.pooler_config.pooling_type == PoolingType.CLS.name
        assert model_config.pooler_config.normalize

        # asserts on the tokenizer loaded
        assert model_tokenizer.tokenizer_id == os.path.join(models_path_prefix, "BAAI/bge-base-en-v1.5")
        assert model_tokenizer.tokenizer.model_max_length == 512

        def check_model(model):
            assert isinstance(model, BertEmbeddingModel)
            assert isinstance(model._pooler, CLSPool)

        vllm_model.apply_model(check_model)

        # assert output
        assert output


@pytest.mark.skipif(current_platform.is_rocm(),
                    reason="Xformers backend is not supported on ROCm.")
def test_roberta_model_loading_with_params(vllm_runner):
    """
    Test parameter weight loading with tp>1.
    """
    with vllm_runner(model_name=MODEL_NAME_ROBERTA,
                     revision=REVISION_ROBERTA,
                     dtype="float16",
                     max_model_len=MAX_MODEL_LEN) as vllm_model:
        output = vllm_model.embed("Write a short story about a robot that"
                                  " dreams for the first time.\n")

        model_config = vllm_model.model.llm_engine.model_config
        model_tokenizer = vllm_model.model.llm_engine.tokenizer

        # asserts on the bert model config file
        assert model_config.encoder_config["max_seq_length"] == 512
        assert not model_config.encoder_config["do_lower_case"]

        # asserts on the pooling config files
        assert model_config.pooler_config.pooling_type == PoolingType.MEAN.name
        assert model_config.pooler_config.normalize

        # asserts on the tokenizer loaded
        assert model_tokenizer.tokenizer_id == os.path.join(models_path_prefix, "intfloat/multilingual-e5-small")
        assert model_tokenizer.tokenizer.model_max_length == 512

        def check_model(model):
            assert isinstance(model, RobertaEmbeddingModel)
            assert isinstance(model._pooler, MeanPool)

        vllm_model.apply_model(check_model)

        # assert output
        assert output


@pytest.mark.skipif(current_platform.is_rocm(),
                    reason="Xformers backend is not supported on ROCm.")
def test_facebook_roberta_model_loading_with_params(vllm_runner):
    """
    Test loading roberta-base model with no lm_head.
    """
    model_name = "FacebookAI/roberta-base"
    with vllm_runner(model_name=model_name,
                     dtype="float16",
                     max_model_len=MAX_MODEL_LEN) as vllm_model:
        output = vllm_model.embed("Write a short story about a robot that"
                                  " dreams for the first time.\n")

        model_tokenizer = vllm_model.model.llm_engine.tokenizer
        assert model_tokenizer.tokenizer_id == model_name

        def check_model(model):
            assert isinstance(model, RobertaEmbeddingModel)
            assert not hasattr(model, "lm_head")
            assert isinstance(model._pooler, CLSPool)

        vllm_model.apply_model(check_model)

        assert output
