#
# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
# This file is a part of the vllm-ascend project.
# Adapted from vllm/tests/entrypoints/llm/test_guided_generate.py
# Copyright 2023 The vLLM team.
#
# 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 json
from typing import Any

import jsonschema
import pytest
import regex as re
from vllm.outputs import RequestOutput
from vllm.sampling_params import SamplingParams, StructuredOutputsParams

from tests.e2e.conftest import VllmRunner

MODEL_NAME = "Qwen/Qwen3-0.6B"

GuidedDecodingBackend = ["xgrammar", "guidance", "outlines"]


@pytest.fixture(scope="module")
def sample_regex():
    return (
        r"((25[0-5]|(2[0-4]|1\d|[1-9]|)\d)\.){3}"
        r"(25[0-5]|(2[0-4]|1\d|[1-9]|)\d)"
    )


@pytest.fixture(scope="module")
def sample_json_schema():
    return {
        "type": "object",
        "properties": {
            "name": {"type": "string"},
            "age": {"type": "integer"},
            "skills": {"type": "array", "items": {"type": "string", "maxLength": 10}, "minItems": 3},
            "work_history": {
                "type": "array",
                "items": {
                    "type": "object",
                    "properties": {
                        "company": {"type": "string"},
                        "duration": {"type": "number"},
                        "position": {"type": "string"},
                    },
                    "required": ["company", "position"],
                },
            },
        },
        "required": ["name", "age", "skills", "work_history"],
    }


@pytest.mark.parametrize("guided_decoding_backend", GuidedDecodingBackend)
def test_guided_json_completion(guided_decoding_backend: str, sample_json_schema):
    runner_kwargs: dict[str, Any] = {}
    sampling_params = SamplingParams(
        temperature=1.0, max_tokens=500, structured_outputs=StructuredOutputsParams(json=sample_json_schema)
    )
    runner_kwargs = {
        "cudagraph_capture_sizes": [1, 2, 4, 8],
        "seed": 0,
        "structured_outputs_config": {"backend": guided_decoding_backend},
    }
    with VllmRunner(MODEL_NAME, **runner_kwargs) as vllm_model:
        prompts = [f"Give an example JSON for an employee profile that fits this schema: {sample_json_schema}"] * 2
        inputs = vllm_model.get_inputs(prompts)
        outputs = vllm_model.model.generate(inputs, sampling_params=sampling_params)

        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.parametrize("guided_decoding_backend", GuidedDecodingBackend)
def test_guided_regex(guided_decoding_backend: str, sample_regex):
    if guided_decoding_backend == "outlines":
        pytest.skip("Outlines doesn't support regex-based guided decoding.")
    runner_kwargs: dict[str, Any] = {}
    sampling_params = SamplingParams(
        temperature=0.8, top_p=0.95, structured_outputs=StructuredOutputsParams(regex=sample_regex)
    )
    runner_kwargs = {
        "cudagraph_capture_sizes": [1, 2, 4, 8],
        "seed": 0,
        "structured_outputs_config": {"backend": guided_decoding_backend},
    }

    with VllmRunner(MODEL_NAME, **runner_kwargs) as vllm_model:
        prompts = [f"Give an example IPv4 address with this regex: {sample_regex}"] * 2
        inputs = vllm_model.get_inputs(prompts)
        outputs = vllm_model.model.generate(inputs, sampling_params=sampling_params)
        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(".*", generated_text) is not None
            print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
