conftest.py

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

import pytest


@pytest.fixture
def sample_prompts():
    return [
        "Hello, my name is",
        "The president of the United States is",
        "The capital of France is",
        "The future of AI is",
    ]


@pytest.fixture
def sample_token_ids():
    return [
        [0],
        [0, 1],
        [0, 2, 1],
        [0, 3, 1, 2],
    ]


@pytest.fixture
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
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.fixture
def sample_complex_json_schema():
    return {
        "type": "object",
        "properties": {
            "score": {
                "type": "integer",
                "minimum": 0,
                "maximum": 100  # Numeric range
            },
            "grade": {
                "type": "string",
                "pattern": "^[A-D]$"  # Regex pattern
            },
            "email": {
                "type": "string",
                "pattern": "^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\\.[a-zA-Z]{2,}$"
            },
            "tags": {
                "type": "array",
                "items": {
                    "type": "string",
                    "pattern":
                    "^[a-z]{1,10}$"  # Combining length and pattern restrictions
                }
            }
        },
        "required": ["score", "grade", "email", "tags"]
    }


@pytest.fixture
def sample_definition_json_schema():
    return {
        '$defs': {
            'Step': {
                'properties': {
                    'explanation': {
                        'title': 'Explanation',
                        'type': 'string'
                    },
                    'output': {
                        'title': 'Output',
                        'type': 'string'
                    }
                },
                'required': ['explanation', 'output'],
                'title': 'Step',
                'type': 'object'
            }
        },
        'properties': {
            'steps': {
                'items': {
                    '$ref': '#/$defs/Step'
                },
                'title': 'Steps',
                'type': 'array'
            },
            'final_answer': {
                'title': 'Final Answer',
                'type': 'string'
            }
        },
        'required': ['steps', 'final_answer'],
        'title': 'MathReasoning',
        'type': 'object'
    }


@pytest.fixture
def sample_enum_json_schema():
    return {
        "type": "object",
        "properties": {
            "status": {
                "type": "string",
                "enum": ["active", "inactive",
                         "pending"]  # Literal values using enum
            },
            "priority": {
                "type": "string",
                "enum": ["low", "medium", "high", "critical"]
            },
            "category": {
                "type": "object",
                "properties": {
                    "type": {
                        "type": "string",
                        "enum": ["bug", "feature", "improvement"]
                    },
                    "severity": {
                        "type": "integer",
                        "enum": [1, 2, 3, 4,
                                 5]  # Enum can also contain numbers
                    }
                },
                "required": ["type", "severity"]
            },
            "flags": {
                "type": "array",
                "items": {
                    "type": "string",
                    "enum": ["urgent", "blocked", "needs_review", "approved"]
                }
            }
        },
        "required": ["status", "priority", "category", "flags"]
    }


@pytest.fixture
def sample_guided_choice():
    return [
        "Python", "Java", "JavaScript", "C++", "C#", "PHP", "TypeScript",
        "Ruby", "Swift", "Kotlin"
    ]


@pytest.fixture
def sample_sql_statements():
    return ("""
start: select_statement
select_statement: "SELECT" column "from" table "where" condition
column: "col_1" | "col_2"
table: "table_1" | "table_2"
condition: column "=" number
number: "1" | "2"
""")


@pytest.fixture(scope="session")
def zephyr_lora_files():
    """Download zephyr LoRA files once per test session."""
    from huggingface_hub import snapshot_download
    return snapshot_download(repo_id="typeof/zephyr-7b-beta-lora")


@pytest.fixture(scope="session")
def zephyr_lora_added_tokens_files(zephyr_lora_files):
    """Create zephyr LoRA files with added tokens once per test session."""
    import shutil
    from tempfile import TemporaryDirectory

    from transformers import AutoTokenizer

    tmp_dir = TemporaryDirectory()
    tmp_model_dir = f"{tmp_dir.name}/zephyr"
    shutil.copytree(zephyr_lora_files, tmp_model_dir)
    tokenizer = AutoTokenizer.from_pretrained("HuggingFaceH4/zephyr-7b-beta")
    # Copy tokenizer to adapter and add some unique tokens
    # 32000, 32001, 32002
    added = tokenizer.add_tokens(["vllm1", "vllm2", "vllm3"],
                                 special_tokens=True)
    assert added == 3
    tokenizer.save_pretrained(tmp_model_dir)
    yield tmp_model_dir
    tmp_dir.cleanup()