# Copyright (c) 2025  PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import unittest
from unittest.mock import MagicMock, patch

try:
    import torch

    TORCH_AVAILABLE = True
except (ImportError, NameError, AttributeError, OSError):
    TORCH_AVAILABLE = False

from fastdeploy.benchmarks.datasets import SampleRequest
from fastdeploy.benchmarks.throughput import (
    EngineArgs,
    add_cli_args,
    get_requests,
    main,
    run_fd,
    run_fd_chat,
    run_hf,
    validate_args,
)


class TestThroughput(unittest.TestCase):
    @patch("fastdeploy.LLM")
    def test_run_fd(self, mock_llm):
        mock_llm_instance = MagicMock()
        mock_llm.return_value = mock_llm_instance
        mock_llm_instance.generate.return_value = ["output1", "output2"]
        # Mock cfg.max_model_len
        mock_cfg = MagicMock()
        mock_cfg.max_model_len = 2048
        mock_llm_instance.llm_engine.cfg = mock_cfg

        requests = [
            SampleRequest(
                no=1, prompt="test prompt", prompt_len=10, expected_output_len=20, history_QA=[], json_data=None
            )
        ]
        engine_args = EngineArgs(model="test_model")

        elapsed_time, outputs = run_fd(requests, n=1, engine_args=engine_args)
        self.assertIsInstance(elapsed_time, float)
        self.assertEqual(len(outputs), 2)

    @patch("fastdeploy.LLM")
    def test_run_fd_chat(self, mock_llm):
        mock_llm_instance = MagicMock()
        mock_llm.return_value = mock_llm_instance
        mock_llm_instance.chat.return_value = ["chat output1", "chat output2"]
        # Mock cfg.max_model_len
        mock_cfg = MagicMock()
        mock_cfg.max_model_len = 2048
        mock_llm_instance.llm_engine.cfg = mock_cfg

        requests = [
            SampleRequest(
                no=1, prompt="test chat prompt", prompt_len=10, expected_output_len=20, history_QA=[], json_data=None
            )
        ]
        engine_args = EngineArgs(model="test_model")

        elapsed_time, outputs = run_fd_chat(requests, n=1, engine_args=engine_args)
        self.assertIsInstance(elapsed_time, float)
        self.assertEqual(len(outputs), 2)

    @unittest.skipIf(not TORCH_AVAILABLE, "PyTorch is not available")
    @patch("transformers.AutoModelForCausalLM.from_pretrained")
    @patch("transformers.AutoTokenizer.from_pretrained")
    def test_run_hf(self, mock_tokenizer, mock_model):
        mock_model_instance = MagicMock()
        mock_model.return_value = mock_model_instance
        mock_model_instance.generate.return_value = torch.tensor([[1, 2, 3]]) if TORCH_AVAILABLE else None

        mock_tokenizer_instance = MagicMock()
        mock_tokenizer.return_value = mock_tokenizer_instance
        mock_tokenizer_instance.pad_token = "pad"

        requests = [
            SampleRequest(
                no=1, prompt="test hf prompt", prompt_len=10, expected_output_len=20, history_QA=[], json_data=None
            )
        ]

        elapsed_time = run_hf(
            requests,
            model="test_model",
            tokenizer=mock_tokenizer_instance,
            n=1,
            max_batch_size=4,
            trust_remote_code=True,
        )
        self.assertIsInstance(elapsed_time, float)

    @patch("fastdeploy.benchmarks.datasets.RandomDataset")
    def test_get_requests(self, mock_dataset):
        mock_dataset_instance = MagicMock()
        mock_dataset.return_value = mock_dataset_instance
        mock_dataset_instance.sample.return_value = [
            SampleRequest(no=1, prompt="test1", prompt_len=10, expected_output_len=20, history_QA=[], json_data=None),
            SampleRequest(no=2, prompt="test2", prompt_len=15, expected_output_len=25, history_QA=[], json_data=None),
        ]

        args = argparse.Namespace(
            dataset_name="random",
            dataset_path=None,
            seed=42,
            input_len=10,
            output_len=20,
            num_prompts=2,
            hf_max_batch_size=4,
            lora_path=None,
            random_range_ratio=0.0,
            prefix_len=0,
        )
        tokenizer = MagicMock()
        tokenizer.vocab_size = 10000  # 设置合理的词汇表大小
        tokenizer.num_special_tokens_to_add.return_value = 0  # 设置特殊token数量

        requests = get_requests(args, tokenizer)
        self.assertEqual(len(requests), 2)

    def test_validate_args(self):
        # Test basic validation
        args = argparse.Namespace(
            backend="fastdeploy",
            dataset_name="random",
            dataset=None,
            dataset_path=None,
            input_len=10,
            output_len=20,
            tokenizer=None,
            model="test_model",
            hf_max_batch_size=None,
            trust_remote_code=False,
            quantization=None,
        )
        validate_args(args)
        self.assertEqual(args.tokenizer, "test_model")

    def test_add_cli_args(self):
        parser = argparse.ArgumentParser()
        add_cli_args(parser)
        args = parser.parse_args([])
        self.assertEqual(args.backend, "fastdeploy")
        self.assertEqual(args.dataset_name, "random")

    @patch("fastdeploy.benchmarks.throughput.run_fd")
    @patch("fastdeploy.benchmarks.throughput.get_requests")
    @patch("transformers.AutoTokenizer.from_pretrained")
    def test_main_fastdeploy(self, mock_tokenizer, mock_get_requests, mock_run_fd):
        mock_get_requests.return_value = [
            SampleRequest(no=1, prompt="test", prompt_len=10, expected_output_len=20, history_QA=[], json_data=None)
        ]
        mock_run_fd.return_value = (1.0, ["output1", "output2"])

        parser = argparse.ArgumentParser()
        add_cli_args(parser)
        args = parser.parse_args([])
        args.backend = "fastdeploy"
        args.dataset_name = "random"
        args.dataset_path = None
        args.seed = 42
        args.input_len = 10
        args.output_len = 20
        args.num_prompts = 1
        args.tokenizer = "test_tokenizer"
        args.model = "test_model"
        args.n = 1
        args.hf_max_batch_size = None
        args.trust_remote_code = False
        args.output_json = None
        args.disable_detokenize = False
        args.tensor_parallel_size = 1

        with patch("builtins.print") as mock_print:
            main(args)
            mock_print.assert_called()

    @unittest.skipIf(not TORCH_AVAILABLE, "PyTorch is not available")
    @patch("fastdeploy.benchmarks.throughput.run_hf")
    @patch("fastdeploy.benchmarks.throughput.get_requests")
    @patch("transformers.AutoTokenizer.from_pretrained")
    @patch("transformers.AutoModelForCausalLM.from_pretrained")
    def test_main_hf(self, mock_model, mock_tokenizer, mock_get_requests, mock_run_hf):
        mock_get_requests.return_value = [
            SampleRequest(no=1, prompt="test", prompt_len=10, expected_output_len=20, history_QA=[], json_data=None)
        ]
        mock_run_hf.return_value = 1.0

        parser = argparse.ArgumentParser()
        add_cli_args(parser)
        args = parser.parse_args([])
        args.backend = "hf"
        args.dataset_name = "random"
        args.dataset_path = None
        args.seed = 42
        args.input_len = 10
        args.output_len = 20
        args.num_prompts = 1
        args.tokenizer = "test_tokenizer"
        args.model = "test_model"
        args.n = 1
        args.hf_max_batch_size = 4
        args.trust_remote_code = True
        args.output_json = None
        args.disable_detokenize = False
        args.tensor_parallel_size = 1

        with patch("builtins.print") as mock_print:
            main(args)
            mock_print.assert_called()

    @patch("fastdeploy.benchmarks.throughput.run_fd_chat")
    @patch("fastdeploy.benchmarks.throughput.get_requests")
    @patch("transformers.AutoTokenizer.from_pretrained")
    def test_main_fastdeploy_chat(self, mock_tokenizer, mock_get_requests, mock_run_fd_chat):
        mock_get_requests.return_value = [
            SampleRequest(no=1, prompt="test", prompt_len=10, expected_output_len=20, history_QA=[], json_data=None)
        ]
        mock_run_fd_chat.return_value = (1.0, ["output1", "output2"])

        parser = argparse.ArgumentParser()
        add_cli_args(parser)
        args = parser.parse_args([])
        args.backend = "fastdeploy-chat"
        args.dataset_name = "random"
        args.dataset_path = None
        args.seed = 42
        args.input_len = 10
        args.output_len = 20
        args.num_prompts = 1
        args.tokenizer = "test_tokenizer"
        args.model = "test_model"
        args.n = 1
        args.hf_max_batch_size = None
        args.trust_remote_code = False
        args.output_json = None
        args.disable_detokenize = False
        args.tensor_parallel_size = 1

        with patch("builtins.print") as mock_print:
            main(args)
            mock_print.assert_called()

    @patch("builtins.open")
    @patch("json.dump")
    @patch("fastdeploy.benchmarks.throughput.run_fd")
    @patch("fastdeploy.benchmarks.throughput.get_requests")
    def test_main_with_output_json(self, mock_get_requests, mock_run_fd, mock_json_dump, mock_open):
        mock_get_requests.return_value = [
            SampleRequest(no=1, prompt="test", prompt_len=10, expected_output_len=20, history_QA=[], json_data=None)
        ]
        mock_run_fd.return_value = (1.0, ["output1", "output2"])

        parser = argparse.ArgumentParser()
        add_cli_args(parser)
        args = parser.parse_args([])
        args.backend = "fastdeploy"
        args.dataset_name = "random"
        args.dataset_path = None
        args.seed = 42
        args.input_len = 10
        args.output_len = 20
        args.num_prompts = 1
        args.tokenizer = "test_tokenizer"
        args.model = "test_model"
        args.n = 1
        args.hf_max_batch_size = None
        args.trust_remote_code = False
        args.output_json = "output.json"
        args.disable_detokenize = False
        args.tensor_parallel_size = 1

        main(args)
        mock_json_dump.assert_called()

    # 新增测试用例覆盖缺失的行
    def test_validate_args_with_lora(self):
        parser = argparse.ArgumentParser()
        add_cli_args(parser)
        args = parser.parse_args([])
        args.backend = "fastdeploy"  # LoRA只支持vLLM后端
        args.dataset_name = "random"
        args.enable_lora = True
        args.lora_path = "/path/to/lora"
        args.input_len = 10
        args.output_len = 20
        args.tokenizer = "test_tokenizer"
        args.model = "test_model"
        validate_args(args)

    def test_validate_args_with_hf_backend(self):
        parser = argparse.ArgumentParser()
        add_cli_args(parser)
        args = parser.parse_args([])
        args.backend = "hf"
        args.dataset_name = "random"
        args.hf_max_batch_size = 4
        args.input_len = 10
        args.output_len = 20
        args.tokenizer = "test_tokenizer"
        args.model = "test_model"
        validate_args(args)

    def test_validate_args_with_quantization(self):
        parser = argparse.ArgumentParser()
        add_cli_args(parser)
        args = parser.parse_args([])
        args.backend = "fastdeploy"
        args.dataset_name = "random"
        args.quantization = "w4a8"
        args.input_len = 10
        args.output_len = 20
        args.tokenizer = "test_tokenizer"
        args.model = "test_model"
        validate_args(args)

    @patch("fastdeploy.benchmarks.throughput.write_to_json")
    @patch("fastdeploy.benchmarks.throughput.convert_to_pytorch_benchmark_format")
    def test_save_to_pytorch_benchmark_format(self, mock_convert, mock_write):
        args = argparse.Namespace(
            output_json="test.json",
            model="test_model",
            input_len=10,
            output_len=20,
            backend="fastdeploy",
        )
        results = {
            "elapsed_time": 1.0,
            "num_requests": 10,
            "total_num_tokens": 100,
            "requests_per_second": 10.0,
            "tokens_per_second": 100.0,
        }
        mock_convert.return_value = [{"metrics": {"requests_per_second": 10.0}}]
        from fastdeploy.benchmarks.throughput import save_to_pytorch_benchmark_format

        save_to_pytorch_benchmark_format(args, results)
        mock_write.assert_called()

    @patch("fastdeploy.benchmarks.throughput.run_fd")
    @patch("fastdeploy.benchmarks.throughput.get_requests")
    def test_main_with_disable_detokenize(self, mock_get_requests, mock_run_fd):
        mock_get_requests.return_value = [
            SampleRequest(no=1, prompt="test", prompt_len=10, expected_output_len=20, history_QA=[], json_data=None)
        ]
        mock_run_fd.return_value = (1.0, ["output1", "output2"])

        parser = argparse.ArgumentParser()
        add_cli_args(parser)
        args = parser.parse_args([])
        args.backend = "fastdeploy"
        args.dataset_name = "random"
        args.dataset_path = None
        args.seed = 42
        args.input_len = 10
        args.output_len = 20
        args.num_prompts = 1
        args.tokenizer = "test_tokenizer"
        args.model = "test_model"
        args.n = 1
        args.hf_max_batch_size = None
        args.trust_remote_code = False
        args.output_json = None
        args.disable_detokenize = True
        args.tensor_parallel_size = 1

        with patch("builtins.print") as mock_print:
            main(args)
            mock_print.assert_called()

    def test_validate_args_with_random_range_ratio(self):
        parser = argparse.ArgumentParser()
        add_cli_args(parser)
        args = parser.parse_args([])
        args.backend = "fastdeploy"
        args.dataset_name = "random"
        args.random_range_ratio = 0.5
        args.input_len = 10
        args.output_len = 20
        args.tokenizer = "test_tokenizer"
        args.model = "test_model"
        validate_args(args)

    def test_validate_args_with_prefix_len(self):
        parser = argparse.ArgumentParser()
        add_cli_args(parser)
        args = parser.parse_args([])
        args.backend = "fastdeploy"
        args.dataset_name = "random"
        args.prefix_len = 5
        args.input_len = 10
        args.output_len = 20
        args.tokenizer = "test_tokenizer"
        args.model = "test_model"
        validate_args(args)

    def test_validate_args_with_eb_dataset(self):
        parser = argparse.ArgumentParser()
        add_cli_args(parser)
        args = parser.parse_args([])
        args.backend = "fastdeploy"
        args.dataset_name = "EB"
        args.dataset_path = "/path/to/eb"
        args.tokenizer = "test_tokenizer"
        args.model = "test_model"
        validate_args(args)

    def test_validate_args_with_ebchat_dataset(self):
        parser = argparse.ArgumentParser()
        add_cli_args(parser)
        args = parser.parse_args([])
        args.backend = "fastdeploy-chat"
        args.dataset_name = "EBChat"
        args.dataset_path = "/path/to/ebchat"
        args.tokenizer = "test_tokenizer"
        args.model = "test_model"
        validate_args(args)

    def test_add_cli_args_with_all_options(self):
        parser = argparse.ArgumentParser()
        add_cli_args(parser)
        # 使用parse_known_args避免未识别参数导致的SystemExit
        args, _ = parser.parse_known_args(
            [
                "--backend",
                "fastdeploy-chat",
                "--dataset-name",
                "EBChat",
                "--dataset-path",
                "/path/to/dataset",
                "--input-len",
                "10",
                "--output-len",
                "20",
                "--n",
                "2",
                "--num-prompts",
                "50",
                "--hf-max-batch-size",
                "4",
                "--output-json",
                "output.json",
                "--disable-detokenize",
                "--lora-path",
                "/path/to/lora",
                "--prefix-len",
                "5",
                "--random-range-ratio",
                "0.5",
            ]
        )
        self.assertEqual(args.backend, "fastdeploy-chat")
        self.assertEqual(args.dataset_name, "EBChat")
        self.assertEqual(args.dataset_path, "/path/to/dataset")
        self.assertEqual(args.input_len, 10)
        self.assertEqual(args.output_len, 20)
        self.assertEqual(args.n, 2)
        self.assertEqual(args.num_prompts, 50)
        self.assertEqual(args.hf_max_batch_size, 4)
        self.assertEqual(args.output_json, "output.json")
        self.assertTrue(args.disable_detokenize)
        self.assertEqual(args.lora_path, "/path/to/lora")
        self.assertEqual(args.prefix_len, 5)
        self.assertEqual(args.random_range_ratio, 0.5)


if __name__ == "__main__":
    unittest.main()