# SPDX-License-Identifier: Apache-2.0

import json
import re
import weakref

import jsonschema
import pytest
from pydantic import BaseModel

from vllm.distributed import cleanup_dist_env_and_memory
from vllm.entrypoints.llm import LLM
from vllm.outputs import RequestOutput
from vllm.sampling_params import GuidedDecodingParams, SamplingParams

MODEL_NAME = "Qwen/Qwen2.5-1.5B-Instruct"
GUIDED_DECODING_BACKENDS = [
    "outlines", "lm-format-enforcer", "xgrammar", "guidance"
]


@pytest.fixture(scope="module")
def llm():
    # pytest caches the fixture so we use weakref.proxy to
    # enable garbage collection
    llm = LLM(model=MODEL_NAME, max_model_len=1024, seed=0)

    with llm.deprecate_legacy_api():
        yield weakref.proxy(llm)
        del llm
    cleanup_dist_env_and_memory()


@pytest.mark.skip_global_cleanup
@pytest.mark.parametrize("guided_decoding_backend", GUIDED_DECODING_BACKENDS)
def test_guided_regex(sample_regex, llm, guided_decoding_backend: str):
    sampling_params = SamplingParams(temperature=0.8,
                                     top_p=0.95,
                                     guided_decoding=GuidedDecodingParams(
                                         regex=sample_regex,
                                         backend=guided_decoding_backend))
    outputs = llm.generate(prompts=[
        f"Give an example IPv4 address with this regex: {sample_regex}"
    ] * 2,
                           sampling_params=sampling_params,
                           use_tqdm=True)

    assert outputs is not None
    for output in outputs:
        assert output is not None
        assert isinstance(output, RequestOutput)
        prompt = output.prompt
        generated_text = output.outputs[0].text
        print(generated_text)
        assert generated_text is not None
        assert re.fullmatch(sample_regex, generated_text) is not None
        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")


@pytest.mark.skip_global_cleanup
@pytest.mark.parametrize("guided_decoding_backend", GUIDED_DECODING_BACKENDS)
def test_guided_json_completion(sample_json_schema, llm,
                                guided_decoding_backend: str):
    sampling_params = SamplingParams(temperature=1.0,
                                     max_tokens=1000,
                                     guided_decoding=GuidedDecodingParams(
                                         json=sample_json_schema,
                                         backend=guided_decoding_backend))
    outputs = llm.generate(prompts=[
        f"Give an example JSON for an employee profile "
        f"that fits this schema: {sample_json_schema}"
    ] * 2,
                           sampling_params=sampling_params,
                           use_tqdm=True)

    assert outputs is not None

    for output in outputs:
        assert output is not None
        assert isinstance(output, RequestOutput)
        prompt = output.prompt

        generated_text = output.outputs[0].text
        assert generated_text is not None
        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
        output_json = json.loads(generated_text)
        jsonschema.validate(instance=output_json, schema=sample_json_schema)


@pytest.mark.skip_global_cleanup
@pytest.mark.parametrize("guided_decoding_backend", GUIDED_DECODING_BACKENDS)
def test_guided_complex_json_completion(sample_complex_json_schema, llm,
                                        guided_decoding_backend: str):
    sampling_params = SamplingParams(temperature=1.0,
                                     max_tokens=1000,
                                     guided_decoding=GuidedDecodingParams(
                                         json=sample_complex_json_schema,
                                         backend=guided_decoding_backend))
    outputs = llm.generate(prompts=[
        f"Give an example JSON for an assignment grade "
        f"that fits this schema: {sample_complex_json_schema}"
    ] * 2,
                           sampling_params=sampling_params,
                           use_tqdm=True)

    assert outputs is not None

    for output in outputs:
        assert output is not None
        assert isinstance(output, RequestOutput)
        prompt = output.prompt

        generated_text = output.outputs[0].text
        assert generated_text is not None
        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
        output_json = json.loads(generated_text)
        jsonschema.validate(instance=output_json,
                            schema=sample_complex_json_schema)


@pytest.mark.skip_global_cleanup
@pytest.mark.parametrize("guided_decoding_backend", GUIDED_DECODING_BACKENDS)
def test_guided_definition_json_completion(sample_definition_json_schema, llm,
                                           guided_decoding_backend: str):
    sampling_params = SamplingParams(temperature=1.0,
                                     max_tokens=1000,
                                     guided_decoding=GuidedDecodingParams(
                                         json=sample_definition_json_schema,
                                         backend=guided_decoding_backend))
    outputs = llm.generate(prompts=[
        f"Give an example JSON for solving 8x + 7 = -23 "
        f"that fits this schema: {sample_definition_json_schema}"
    ] * 2,
                           sampling_params=sampling_params,
                           use_tqdm=True)

    assert outputs is not None

    for output in outputs:
        assert output is not None
        assert isinstance(output, RequestOutput)
        prompt = output.prompt

        generated_text = output.outputs[0].text
        assert generated_text is not None
        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
        output_json = json.loads(generated_text)
        jsonschema.validate(instance=output_json,
                            schema=sample_definition_json_schema)


@pytest.mark.skip_global_cleanup
@pytest.mark.parametrize("guided_decoding_backend", GUIDED_DECODING_BACKENDS)
def test_guided_enum_json_completion(sample_enum_json_schema, llm,
                                     guided_decoding_backend: str):
    sampling_params = SamplingParams(temperature=1.0,
                                     max_tokens=1000,
                                     guided_decoding=GuidedDecodingParams(
                                         json=sample_enum_json_schema,
                                         backend=guided_decoding_backend))
    outputs = llm.generate(prompts=[
        "Create a bug report JSON that fits this schema: "
        f"{sample_enum_json_schema}. Make it for a high priority critical bug."
    ] * 2,
                           sampling_params=sampling_params,
                           use_tqdm=True)

    assert outputs is not None

    for output in outputs:
        assert output is not None
        assert isinstance(output, RequestOutput)
        prompt = output.prompt

        generated_text = output.outputs[0].text
        assert generated_text is not None
        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
        output_json = json.loads(generated_text)
        jsonschema.validate(instance=output_json,
                            schema=sample_enum_json_schema)

        # Additional assertions to verify enum values
        assert output_json["status"] in ["active", "inactive", "pending"]
        assert output_json["priority"] in ["low", "medium", "high", "critical"]
        assert output_json["category"]["type"] in [
            "bug", "feature", "improvement"
        ]
        assert output_json["category"]["severity"] in [1, 2, 3, 4, 5]
        for flag in output_json["flags"]:
            assert flag in ["urgent", "blocked", "needs_review", "approved"]


@pytest.mark.skip_global_cleanup
@pytest.mark.parametrize("guided_decoding_backend", GUIDED_DECODING_BACKENDS)
def test_guided_choice_completion(sample_guided_choice, llm,
                                  guided_decoding_backend: str):
    sampling_params = SamplingParams(temperature=0.8,
                                     top_p=0.95,
                                     guided_decoding=GuidedDecodingParams(
                                         choice=sample_guided_choice,
                                         backend=guided_decoding_backend))
    outputs = llm.generate(
        prompts="The best language for type-safe systems programming is ",
        sampling_params=sampling_params,
        use_tqdm=True)

    assert outputs is not None
    for output in outputs:
        assert output is not None
        assert isinstance(output, RequestOutput)
        prompt = output.prompt
        generated_text = output.outputs[0].text
        print(generated_text)
        assert generated_text is not None
        assert generated_text in sample_guided_choice
        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")


@pytest.mark.skip_global_cleanup
@pytest.mark.parametrize("guided_decoding_backend", GUIDED_DECODING_BACKENDS)
def test_guided_grammar(sample_sql_statements, llm,
                        guided_decoding_backend: str):
    sampling_params = SamplingParams(temperature=0.8,
                                     top_p=0.95,
                                     max_tokens=1000,
                                     guided_decoding=GuidedDecodingParams(
                                         grammar=sample_sql_statements,
                                         backend=guided_decoding_backend))
    outputs = llm.generate(
        prompts=("Generate a sql state that select col_1 from "
                 "table_1 where it is equals to 1"),
        sampling_params=sampling_params,
        use_tqdm=True,
    )

    assert outputs is not None
    for output in outputs:
        assert output is not None
        assert isinstance(output, RequestOutput)
        prompt = output.prompt

        generated_text = output.outputs[0].text
        assert generated_text is not None
        # use Lark to parse the output, and make sure it's a valid parse tree
        from lark import Lark
        parser = Lark(sample_sql_statements)
        parser.parse(generated_text)

        # remove spaces for comparison b/c we removed them in the grammar
        ground_truth = "SELECT col_1 from table_1 where col_1 = 1".replace(
            " ", "")

        assert generated_text.strip() == ground_truth

        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")


@pytest.mark.skip_global_cleanup
def test_guided_options_request_deprecation_warning(sample_regex, llm):
    sampling_params = SamplingParams(temperature=0.8, top_p=0.95)

    with pytest.warns(DeprecationWarning, match="guided_options_request"):
        llm.generate(prompts="This should fail",
                     sampling_params=sampling_params,
                     use_tqdm=True,
                     guided_options_request=dict(guided_regex=sample_regex))


@pytest.mark.skip_global_cleanup
def test_validation_against_both_guided_decoding_options(sample_regex, llm):
    sampling_params = SamplingParams(
        temperature=0.8,
        top_p=0.95,
        guided_decoding=GuidedDecodingParams(regex=sample_regex))

    with pytest.raises(ValueError, match="Cannot set both"):
        llm.generate(prompts="This should fail",
                     sampling_params=sampling_params,
                     use_tqdm=True,
                     guided_options_request=dict(guided_regex=sample_regex))


@pytest.mark.skip_global_cleanup
def test_disable_guided_decoding_fallback(sample_regex, llm):
    sampling_params = SamplingParams(temperature=0.8,
                                     top_p=0.95,
                                     guided_decoding=GuidedDecodingParams(
                                         regex=sample_regex,
                                         backend="xgrammar:no-fallback"))

    with pytest.raises(
            ValueError,
            match="xgrammar does not support regex guided decoding"):
        llm.generate(prompts="This should fail",
                     sampling_params=sampling_params,
                     use_tqdm=True)


@pytest.mark.skip_global_cleanup
@pytest.mark.parametrize("guided_decoding_backend", GUIDED_DECODING_BACKENDS)
def test_guided_json_object(llm, guided_decoding_backend: str):
    sampling_params = SamplingParams(temperature=1.0,
                                     max_tokens=100,
                                     n=2,
                                     guided_decoding=GuidedDecodingParams(
                                         json_object=True,
                                         backend=guided_decoding_backend))

    outputs = llm.generate(
        prompts=("Generate a JSON object with curly braces for a person with "
                 "name and age fields for John Smith who is 31 years old."),
        sampling_params=sampling_params,
        use_tqdm=True)

    assert outputs is not None
    for output in outputs:
        assert output is not None
        assert isinstance(output, RequestOutput)

        for i in range(2):
            generated_text = output.outputs[i].text
            print(generated_text)
            assert generated_text is not None

            # Parse to verify it is valid JSON
            parsed_json = json.loads(generated_text)
            assert isinstance(parsed_json, dict)


@pytest.mark.skip_global_cleanup
def test_json_with_any_whitespace_disabled(llm):

    class ResponseSchema(BaseModel):
        clarifying_question: str
        cost_per_serving: str
        calories: str
        type_dish_ids: str
        type_meal_ids: str
        product_ids: list[str]
        exclude_product_ids: list[str]
        allergen_ids: list[str]
        total_cooking_time: str
        kitchen_ids: str
        holiday_ids: str

    # Note: Without this setting, the response is sometimes full of `\n`
    # for some models. This option prevents that.
    guided_decoding_backend = 'xgrammar:disable-any-whitespace'

    schema = ResponseSchema.model_json_schema()
    guided_params = GuidedDecodingParams(json=schema,
                                         backend=\
                                           guided_decoding_backend)
    sampling_params = SamplingParams(max_tokens=2000,
                                     frequency_penalty=0,
                                     presence_penalty=-1.1,
                                     repetition_penalty=1.3,
                                     guided_decoding=guided_params)

    prompt = ("<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You"
              "are a helpful assistant.<|im_end|>\n<|im_start|>user\nI want a "
              "quick launch fast with $10.<|im_end|>\n<|im_start|>assistant\n")
    outputs = llm.generate(prompts=prompt,
                           sampling_params=sampling_params,
                           use_tqdm=True)

    assert outputs is not None

    for output in outputs:
        assert output is not None
        assert isinstance(output, RequestOutput)

        generated_text = output.outputs[0].text
        assert generated_text is not None
        assert "\n" not in generated_text

        # Parse to verify it is valid JSON
        parsed_json = json.loads(generated_text)
        assert isinstance(parsed_json, dict)
        jsonschema.validate(instance=parsed_json, schema=schema)
