"""
python3 -m unittest test_srt_endpoint.TestSRTEndpoint.test_simple_decode
python3 -m unittest test_srt_endpoint.TestSRTEndpoint.test_logprob_with_chunked_prefill
python3 -m unittest test_srt_endpoint.TestTokenizeDetokenize
"""

import json
import random
import time
import unittest
from concurrent.futures import ThreadPoolExecutor
from functools import partial
from typing import Optional

import numpy as np
import requests

from sglang.srt.sampling.custom_logit_processor import CustomLogitProcessor
from sglang.srt.utils import kill_process_tree
from sglang.test.ci.ci_register import register_amd_ci, register_cuda_ci
from sglang.test.test_utils import (
    DEFAULT_SMALL_MODEL_NAME_FOR_TEST,
    DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
    DEFAULT_URL_FOR_TEST,
    CustomTestCase,
    popen_launch_server,
    run_logprob_check,
)

register_cuda_ci(est_time=127, suite="stage-b-test-1-gpu-small")
register_amd_ci(est_time=130, suite="stage-b-test-1-gpu-small-amd")


class TestSRTEndpoint(CustomTestCase):
    @classmethod
    def setUpClass(cls):
        cls.model = DEFAULT_SMALL_MODEL_NAME_FOR_TEST
        cls.base_url = DEFAULT_URL_FOR_TEST
        cls.process = popen_launch_server(
            cls.model,
            cls.base_url,
            timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
            other_args=(
                "--enable-custom-logit-processor",
                "--mem-fraction-static",
                "0.7",
                "--cuda-graph-max-bs",
                "8",
            ),
        )

    @classmethod
    def tearDownClass(cls):
        kill_process_tree(cls.process.pid)

    def run_decode(
        self,
        return_logprob=False,
        top_logprobs_num=0,
        return_text=False,
        n=1,
        stream=False,
        batch=False,
    ):
        if batch:
            text = ["The capital of France is"]
        else:
            text = "The capital of France is"

        response = requests.post(
            self.base_url + "/generate",
            json={
                "text": text,
                "sampling_params": {
                    "temperature": 0 if n == 1 else 0.5,
                    "max_new_tokens": 16,
                    "n": n,
                },
                "stream": stream,
                "return_logprob": return_logprob,
                "top_logprobs_num": top_logprobs_num,
                "return_text_in_logprobs": return_text,
                "logprob_start_len": 0,
            },
        )
        if not stream:
            response_json = response.json()
        else:
            response_json = []
            for line in response.iter_lines():
                if line.startswith(b"data: ") and line[6:] != b"[DONE]":
                    response_json.append(json.loads(line[6:]))

        print(json.dumps(response_json, indent=2))
        print("=" * 100)

    def test_simple_decode(self):
        self.run_decode()

    def test_simple_decode_batch(self):
        self.run_decode(batch=True)

    def test_parallel_sample(self):
        self.run_decode(n=3)

    def test_parallel_sample_stream(self):
        self.run_decode(n=3, stream=True)

    def test_logprob(self):
        self.run_decode(
            return_logprob=True,
            top_logprobs_num=5,
            return_text=True,
        )

    def test_logprob_start_len(self):
        logprob_start_len = 4
        new_tokens = 4
        prompts = [
            "I have a very good idea on",
            "Today is a sunndy day and",
        ]

        response = requests.post(
            self.base_url + "/generate",
            json={
                "text": prompts,
                "sampling_params": {
                    "temperature": 0,
                    "max_new_tokens": new_tokens,
                },
                "return_logprob": True,
                "top_logprobs_num": 5,
                "return_text_in_logprobs": True,
                "logprob_start_len": logprob_start_len,
            },
        )
        response_json = response.json()
        print(json.dumps(response_json, indent=2))

        for i, res in enumerate(response_json):
            self.assertEqual(
                res["meta_info"]["prompt_tokens"],
                logprob_start_len + len(res["meta_info"]["input_token_logprobs"]),
            )
            assert prompts[i].endswith(
                "".join([x[-1] for x in res["meta_info"]["input_token_logprobs"]])
            )

            self.assertEqual(res["meta_info"]["completion_tokens"], new_tokens)
            self.assertEqual(len(res["meta_info"]["output_token_logprobs"]), new_tokens)
            self.assertEqual(
                res["text"],
                "".join([x[-1] for x in res["meta_info"]["output_token_logprobs"]]),
            )

    def test_logprob_with_chunked_prefill(self):
        """Test a long prompt that requests output logprobs will not hit OOM."""
        new_tokens = 4
        prompts = "I have a very good idea on this. " * 8000

        response = requests.post(
            self.base_url + "/generate",
            json={
                "text": prompts,
                "sampling_params": {
                    "temperature": 0,
                    "max_new_tokens": new_tokens,
                },
                "return_logprob": True,
                "logprob_start_len": -1,
                "top_logprobs_num": 5,
            },
        )
        response_json = response.json()
        # print(json.dumps(response_json, indent=2))

        res = response_json
        self.assertEqual(res["meta_info"]["completion_tokens"], new_tokens)

        # Test the number of tokens are correct
        self.assertEqual(len(res["meta_info"]["output_token_logprobs"]), new_tokens)
        self.assertEqual(len(res["meta_info"]["output_top_logprobs"]), new_tokens)

        # Test the top-1 tokens are the same as output tokens (because temp = 0.0)
        for i in range(new_tokens):
            self.assertListEqual(
                res["meta_info"]["output_token_logprobs"][i],
                res["meta_info"]["output_top_logprobs"][i][0],
            )
            self.assertEqual(len(res["meta_info"]["output_top_logprobs"][i]), 5)

    def test_logprob_match(self):
        """Test the output logprobs are close to the input logprobs if we run a prefill again."""

        def run_generate(
            prompt, return_logprob=False, max_new_tokens=512, logprob_start_len=-1
        ):

            if isinstance(prompt, str):
                prompt_kwargs = {"text": prompt}
            else:
                prompt_kwargs = {"input_ids": prompt}

            response = requests.post(
                self.base_url + "/generate",
                json={
                    **prompt_kwargs,
                    "sampling_params": {
                        "temperature": 1.0,
                        "max_new_tokens": max_new_tokens,
                        "ignore_eos": True,
                    },
                    "return_logprob": return_logprob,
                    "return_text_in_logprobs": True,
                    "logprob_start_len": logprob_start_len,
                },
            )
            return response.json()

        prompt = "I have a very good idea on how to"

        gen = run_generate(prompt, return_logprob=True, logprob_start_len=0)
        output_logprobs = np.array(
            [x[0] for x in gen["meta_info"]["output_token_logprobs"]]
        )
        num_prompts_tokens = gen["meta_info"]["prompt_tokens"]

        input_tokens = [x[1] for x in gen["meta_info"]["input_token_logprobs"]]
        output_tokens = [x[1] for x in gen["meta_info"]["output_token_logprobs"]]

        new_prompt = input_tokens + output_tokens
        score = run_generate(
            new_prompt, return_logprob=True, logprob_start_len=0, max_new_tokens=0
        )
        output_logprobs_score = np.array(
            [
                x[0]
                for x in score["meta_info"]["input_token_logprobs"][num_prompts_tokens:]
            ]
        )

        print(f"{output_logprobs[-10:]=}")
        print(f"{output_logprobs_score[-10:]=}")

        diff = np.abs(output_logprobs - output_logprobs_score)
        max_diff = np.max(diff)
        self.assertLess(max_diff, 0.35)

    def test_logprob_mixed(self):
        args = []
        temperature = 0
        # input_len, output_len, temperature, logprob_start_len, return_logprob, top_logprobs_num
        for input_len in [1000, 5000, 10000, 50000]:
            for output_len in [4, 8]:
                for logprob_start_len in [0, 500, 2500, 5000, 25000]:
                    for return_logprob in [True, False]:
                        for top_logprobs_num in [0, 5]:

                            if logprob_start_len >= input_len:
                                continue

                            args.append(
                                (
                                    input_len,
                                    output_len,
                                    temperature,
                                    logprob_start_len,
                                    return_logprob,
                                    top_logprobs_num,
                                )
                            )

        random.shuffle(args)

        func = partial(run_logprob_check, self)
        with ThreadPoolExecutor(8) as executor:
            list(executor.map(func, args))

    def test_logprob_grammar(self):
        prompts = "Question: Is Paris the Capital of France? Answer:"
        allowed_tokens = [" Yes", " No"]

        response = requests.post(
            self.base_url + "/generate",
            json={
                "text": prompts,
                "sampling_params": {
                    "temperature": 1.0,
                    "max_new_tokens": 1,
                    "regex": "( Yes| No)",
                },
                "return_logprob": True,
                "top_logprobs_num": 5,  # The grammar constraint allows all prefix tokens so we need to use a larger top_k.
                "return_text_in_logprobs": True,
            },
        )
        response_json = response.json()
        output_top_logprobs = response_json["meta_info"]["output_top_logprobs"][0]
        print(f"{output_top_logprobs=}")

        # Parse results
        # This is because the grammar constraint allows all prefix tokens
        logprobs = [None] * 2
        for i in range(len(output_top_logprobs)):
            try:
                idx = allowed_tokens.index(output_top_logprobs[i][2])
            except ValueError:
                # Not found
                continue
            logprobs[idx] = output_top_logprobs[i][0]

        self.assertTrue(all(x is not None for x in logprobs))

    def run_custom_logit_processor(self, target_token_id: Optional[int] = None):
        """Test custom logit processor with custom params.

        If target_token_id is None, the custom logit processor won't be passed in.
        """

        custom_params = {"token_id": target_token_id}

        class DeterministicLogitProcessor(CustomLogitProcessor):
            """A dummy logit processor that changes the logits to always
            sample the given token id.
            """

            def __call__(self, logits, custom_param_list):
                assert logits.shape[0] == len(custom_param_list)
                key = "token_id"

                for i, param_dict in enumerate(custom_param_list):
                    # Mask all other tokens
                    logits[i, :] = -float("inf")
                    # Assign highest probability to the specified token
                    logits[i, param_dict[key]] = 0.0
                return logits

        prompts = "Question: Is Paris the Capital of France? Answer:"

        # Base case json data to be posted to the server.
        base_json = {
            "text": prompts,
            "sampling_params": {"temperature": 0.0},
            "return_logprob": True,
        }

        # Custom json data with custom logit processor and params.
        custom_json = base_json.copy()
        # Only set the custom logit processor if target_token_id is not None.
        if target_token_id is not None:
            custom_json["custom_logit_processor"] = DeterministicLogitProcessor.to_str()
            custom_json["sampling_params"]["custom_params"] = custom_params

        custom_response = requests.post(
            self.base_url + "/generate",
            json=custom_json,
        ).json()

        output_token_logprobs = custom_response["meta_info"]["output_token_logprobs"]
        sampled_tokens = [x[1] for x in output_token_logprobs]

        # The logit processor should always sample the given token as the logits is deterministic.
        if target_token_id is not None:
            self.assertTrue(
                all(x == custom_params["token_id"] for x in sampled_tokens),
                # Print the detailed test case info if the test fails.
                f"{target_token_id=}\n{sampled_tokens=}\n{custom_response=}",
            )

    def run_stateful_custom_logit_processor(
        self, first_token_id: int | None, delay: int = 2
    ):
        """Test custom logit processor with custom params and state.

        Should sample the first `delay` tokens normally, then output first_token_id and consecutive tokens after that.
        If first_token_id is None, the custom logit processor won't be passed in.
        """
        custom_params = {"token_id": first_token_id, "delay": 2}

        class DeterministicStatefulLogitProcessor(CustomLogitProcessor):
            """A dummy logit processor that changes the logits to always
            sample the given token id.
            """

            def __call__(self, logits, custom_param_list):
                assert logits.shape[0] == len(custom_param_list)

                for i, param_dict in enumerate(custom_param_list):
                    if param_dict["delay"] > 0:
                        param_dict["delay"] -= 1
                        continue
                    if param_dict["delay"] == 0:
                        param_dict["delay"] -= 1
                        force_token = param_dict["token_id"]
                    else:
                        output_ids = param_dict["__req__"].output_ids
                        force_token = output_ids[-1] + 1
                    # Mask all other tokens
                    logits[i, :] = -float("inf")
                    # Assign highest probability to the specified token
                    logits[i, force_token] = 0.0
                return logits

        prompts = "Question: Is Paris the Capital of France? Answer:"

        # Base case json data to be posted to the server.
        base_json = {
            "text": prompts,
            "sampling_params": {"temperature": 0.0},
            "return_logprob": True,
        }

        # Custom json data with custom logit processor and params.
        custom_json = base_json.copy()
        # Only set the custom logit processor if target_token_id is not None.
        if first_token_id is not None:
            custom_json["custom_logit_processor"] = (
                DeterministicStatefulLogitProcessor().to_str()
            )
            custom_json["sampling_params"]["custom_params"] = custom_params

        custom_response = requests.post(
            self.base_url + "/generate",
            json=custom_json,
        ).json()

        output_token_logprobs = custom_response["meta_info"]["output_token_logprobs"]
        sampled_tokens = [x[1] for x in output_token_logprobs]
        # The logit processor should always sample the given token as the logits is deterministic.
        if first_token_id is not None:
            self.assertTrue(
                all(
                    x == custom_params["token_id"] + k
                    for k, x in enumerate(sampled_tokens[custom_params["delay"] :])
                ),
                # Print the detailed test case info if the test fails.
                f"{first_token_id=}\n{sampled_tokens=}\n{custom_response=}",
            )

    def test_custom_logit_processor(self):
        """Test custom logit processor with a single request."""
        self.run_custom_logit_processor(target_token_id=5)

    def test_custom_logit_processor_batch_mixed(self):
        """Test a batch of requests mixed of requests with and without custom logit processor."""
        target_token_ids = list(range(32)) + [None] * 16
        random.shuffle(target_token_ids)
        with ThreadPoolExecutor(len(target_token_ids)) as executor:
            list(executor.map(self.run_custom_logit_processor, target_token_ids))

    @unittest.skip("Skip this test because this feature has a bug. See comments below.")
    def test_stateful_custom_logit_processor(self):
        """Test custom logit processor with a single request."""

        """
        NOTE: This feature has a race condition bug.
        This line https://github.com/sgl-project/sglang/blob/ef8ec07b2ce4c70c2a33ec5acda4ce529bc3cda4/test/srt/test_srt_endpoint.py#L395-L396 can be accessed by two concurrent threads at the same time. The access order is not guaranteed.
        In sglang, we use two python threads to overlap the GPU computation and CPU scheduling.
        Thread 1 (the CPU scheduling thread) will update the `param_dict["__req__"].output_ids`.
        Thread 2 (the GPU computation thread) will call `DeterministicStatefulLogitProcessor` because sampling is considered as GPU computation.
        We can fix this by moving the call of DeterministicStatefulLogitProcessor to the CPU scheduling thread.
        """

        self.run_stateful_custom_logit_processor(first_token_id=5)

    @unittest.skip("Skip this test because this feature has a bug. See comments above.")
    def test_stateful_custom_logit_processor_batch_mixed(self):
        """Test a batch of requests mixed of requests with and without custom logit processor."""
        target_token_ids = list(range(32)) + [None] * 16
        random.shuffle(target_token_ids)
        with ThreadPoolExecutor(len(target_token_ids)) as executor:
            list(
                executor.map(self.run_stateful_custom_logit_processor, target_token_ids)
            )

    def test_cache_tokens(self):
        for _ in range(2):
            time.sleep(1)
            response = requests.post(self.base_url + "/flush_cache")
            assert response.status_code == 200

        def send_and_check_cached_tokens(input_ids):
            response = requests.post(
                self.base_url + "/generate",
                json={
                    "input_ids": list(input_ids),
                    "sampling_params": {
                        "max_new_tokens": 1,
                    },
                },
            )
            response_json = response.json()
            return response_json["meta_info"]["cached_tokens"]

        self.assertEqual(send_and_check_cached_tokens(range(0, 100)), 0)
        self.assertEqual(send_and_check_cached_tokens(range(0, 10000)), 100)
        self.assertEqual(send_and_check_cached_tokens(range(0, 10000)), 9999)
        self.assertEqual(send_and_check_cached_tokens(range(0, 1000)), 999)
        self.assertEqual(send_and_check_cached_tokens(range(0, 11000)), 10000)

    def test_get_server_info(self):
        response = requests.get(self.base_url + "/server_info")
        response_json = response.json()

        max_total_num_tokens = response_json["max_total_num_tokens"]
        self.assertIsInstance(max_total_num_tokens, int)

        version = response_json["version"]
        self.assertIsInstance(version, str)

    def test_logit_bias(self):
        """Test that a very high logit bias forces sampling of a specific token."""
        # Choose a token ID to bias (using 5 as an example)
        target_token_id = 60704  # Paris for meta-llama/Llama-3.2-1B-Instruct, DEFAULT_SMALL_MODEL_NAME_FOR_TEST
        logit_bias = {str(target_token_id): 100.0}  # Very high positive bias

        response = requests.post(
            self.base_url + "/generate",
            json={
                "text": "The capital of France is",
                "sampling_params": {
                    "temperature": 1.0,  # Use high temperature to encourage exploration
                    "max_new_tokens": 4,
                    "logit_bias": logit_bias,
                },
                "return_logprob": True,
            },
        )
        response_json = response.json()

        # Extract the sampled token IDs from the output
        output_token_logprobs = response_json["meta_info"]["output_token_logprobs"]
        sampled_tokens = [x[1] for x in output_token_logprobs]

        # Verify that all sampled tokens are the target token
        self.assertTrue(
            all(x == target_token_id for x in sampled_tokens),
            f"Expected all tokens to be {target_token_id}, but got {sampled_tokens}",
        )

    def test_forbidden_token(self):
        """Test that a forbidden token (very negative logit bias) doesn't appear in the output."""
        # Choose a token ID to forbid (using 10 as an example)
        forbidden_token_id = 23994  # rice for meta-llama/Llama-3.2-1B-Instruct, DEFAULT_SMALL_MODEL_NAME_FOR_TEST
        logit_bias = {
            str(forbidden_token_id): -100.0
        }  # Very negative bias to forbid the token

        response = requests.post(
            self.base_url + "/generate",
            json={
                "text": "Only output 'rice' exactly like this, in lowercase ONLY: rice",
                "sampling_params": {
                    "temperature": 1.0,  # Use high temperature to encourage diverse output
                    "max_new_tokens": 50,  # Generate enough tokens to likely include numbers
                    "logit_bias": logit_bias,
                },
                "return_logprob": True,
            },
        )
        response_json = response.json()

        # Extract the sampled token IDs from the output
        output_token_logprobs = response_json["meta_info"]["output_token_logprobs"]
        sampled_tokens = [x[1] for x in output_token_logprobs]

        # Verify that the forbidden token doesn't appear in the output
        self.assertNotIn(
            forbidden_token_id,
            sampled_tokens,
            f"Expected forbidden token {forbidden_token_id} not to be present, but it was found",
        )

    def test_logit_bias_isolation(self):
        """Test that logit_bias applied to one request doesn't affect other requests in batch."""
        # Choose a token ID to bias in first request only
        biased_token_id = 60704  # Paris for meta-llama/Llama-3.2-1B-Instruct, DEFAULT_SMALL_MODEL_NAME_FOR_TEST

        # Prepare batch requests - one with logit_bias and one without
        requests_data = [
            {
                "text": "The capital of France is",
                "sampling_params": {
                    "temperature": 1.0,
                    "max_new_tokens": 4,
                    "logit_bias": {str(biased_token_id): 100.0},  # Strong bias
                },
                "return_logprob": True,
            },
            {
                "text": "The capital of France is",
                "sampling_params": {
                    "temperature": 1.0,
                    "max_new_tokens": 4,
                },
                "return_logprob": True,
            },
        ]

        # Send both requests
        responses = []
        for req in requests_data:
            response = requests.post(self.base_url + "/generate", json=req)
            responses.append(response.json())

        # Extract token IDs from each response
        biased_tokens = [
            x[1] for x in responses[0]["meta_info"]["output_token_logprobs"]
        ]
        unbiased_tokens = [
            x[1] for x in responses[1]["meta_info"]["output_token_logprobs"]
        ]

        # Verify first response contains only biased tokens
        self.assertTrue(
            all(x == biased_token_id for x in biased_tokens),
            f"Expected all tokens to be {biased_token_id} in first response, but got {biased_tokens}",
        )

        # Verify second response contains at least some different tokens
        # (We can't guarantee exactly what tokens will be generated, but they shouldn't all be the biased token)
        self.assertTrue(
            any(x != biased_token_id for x in unbiased_tokens),
            f"Expected some tokens to be different from {biased_token_id} in second response, but got {unbiased_tokens}",
        )

    def test_get_server_info_concurrent(self):
        """Make sure the concurrent get_server_info doesn't crash the server."""
        tp = ThreadPoolExecutor(max_workers=30)

        def s():
            server_info = requests.get(self.base_url + "/server_info")
            server_info.json()

        futures = []
        for _ in range(4):
            futures.append(tp.submit(s))

        for f in futures:
            f.result()


# -------------------------------------------------------------------------
#    /tokenize & /detokenize Test Class: TestTokenizeDetokenize
# -------------------------------------------------------------------------


class TestTokenizeDetokenize(CustomTestCase):
    @classmethod
    def setUpClass(cls):
        cls.model = DEFAULT_SMALL_MODEL_NAME_FOR_TEST
        cls.base_url = DEFAULT_URL_FOR_TEST
        cls.tokenize_url = f"{cls.base_url}/tokenize"
        cls.detokenize_url = f"{cls.base_url}/detokenize"
        cls.session = requests.Session()
        cls.process = popen_launch_server(
            cls.model,
            cls.base_url,
            timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
        )

    @classmethod
    def tearDownClass(cls):
        kill_process_tree(cls.process.pid)
        cls.session.close()

    def _post_json(self, url, payload):
        r = self.session.post(url, json=payload)
        r.raise_for_status()
        return r.json()

    def test_tokenize_various_inputs(self):
        single = "Hello SGLang world! 123 😊, ಪರ್ವತದ ಮೇಲೆ ಹಿಮ."
        multi = ["First sentence.", "Second, with 中文."]
        scenarios = [
            {"prompt": single, "add_special_tokens": True},
            {"prompt": single, "add_special_tokens": False},
            {"prompt": multi, "add_special_tokens": True},
            {"prompt": multi, "add_special_tokens": False},
            {"prompt": "", "add_special_tokens": False},
        ]
        for case in scenarios:
            payload = {"model": self.model, "prompt": case["prompt"]}
            if "add_special_tokens" in case:
                payload["add_special_tokens"] = case["add_special_tokens"]
            resp = self._post_json(self.tokenize_url, payload)
            tokens = resp["tokens"]
            count = resp["count"]
            self.assertIsInstance(tokens, list)
            if not tokens:
                self.assertEqual(count, 0)
            else:
                if isinstance(tokens[0], list):
                    total = sum(len(t) for t in tokens)
                    expected = sum(count) if isinstance(count, list) else count
                else:
                    total = len(tokens)
                    expected = count
                self.assertEqual(total, expected)

    def test_tokenize_invalid_type(self):
        r = self.session.post(
            self.tokenize_url, json={"model": self.model, "prompt": 12345}
        )
        self.assertEqual(r.status_code, 400)

    def test_detokenize_roundtrip(self):
        text = "Verify detokenization round trip. यह डिटोकेनाइजेशन है"
        t0 = self._post_json(
            self.tokenize_url,
            {"model": self.model, "prompt": text, "add_special_tokens": False},
        )["tokens"]
        t1 = self._post_json(
            self.tokenize_url,
            {"model": self.model, "prompt": text, "add_special_tokens": True},
        )["tokens"]
        cases = [
            {"tokens": t0, "skip_special_tokens": True, "expected": text},
            {"tokens": t1, "skip_special_tokens": True, "expected": text},
            {"tokens": t1, "skip_special_tokens": False, "expected": None},
            {"tokens": [], "skip_special_tokens": True, "expected": ""},
        ]
        for case in cases:
            payload = {"model": self.model, "tokens": case["tokens"]}
            if "skip_special_tokens" in case:
                payload["skip_special_tokens"] = case["skip_special_tokens"]
            resp = self._post_json(self.detokenize_url, payload)
            text_out = resp["text"]
            if case["expected"] is not None:
                self.assertEqual(text_out, case["expected"])
            else:
                self.assertIsInstance(text_out, str)

    def test_detokenize_invalid_tokens(self):
        r = self.session.post(
            self.detokenize_url, json={"model": self.model, "tokens": ["a", "b"]}
        )
        self.assertEqual(r.status_code, 400)
        r2 = self.session.post(
            self.detokenize_url, json={"model": self.model, "tokens": [1, -1, 2]}
        )
        self.assertEqual(r2.status_code, 500)


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