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

import os
import json
import subprocess
import tempfile

import pytest

from vllm.entrypoints.openai.run_batch import BatchRequestOutput
from ...utils import models_path_prefix

MODEL_NAME = os.path.join(models_path_prefix, "hmellor/tiny-random-LlamaForCausalLM")

# ruff: noqa: E501
INPUT_BATCH = (
    '{{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {{"model": "{0}", "messages": [{{"role": "system", "content": "You are a helpful assistant."}},{{"role": "user", "content": "Hello world!"}}],"max_tokens": 1000}}}}\n'
    '{{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {{"model": "{0}", "messages": [{{"role": "system", "content": "You are an unhelpful assistant."}},{{"role": "user", "content": "Hello world!"}}],"max_tokens": 1000}}}}\n'
    '{{"custom_id": "request-3", "method": "POST", "url": "/v1/chat/completions", "body": {{"model": "NonExistModel", "messages": [{{"role": "system", "content": "You are an unhelpful assistant."}},{{"role": "user", "content": "Hello world!"}}],"max_tokens": 1000}}}}\n'
    '{{"custom_id": "request-4", "method": "POST", "url": "/bad_url", "body": {{"model": "{0}", "messages": [{{"role": "system", "content": "You are an unhelpful assistant."}},{{"role": "user", "content": "Hello world!"}}],"max_tokens": 1000}}}}\n'
    '{{"custom_id": "request-5", "method": "POST", "url": "/v1/chat/completions", "body": {{"stream": "True", "model": "{0}", "messages": [{{"role": "system", "content": "You are an unhelpful assistant."}},{{"role": "user", "content": "Hello world!"}}],"max_tokens": 1000}}}}'
).format(MODEL_NAME)

INVALID_INPUT_BATCH = (
    '{{"invalid_field": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {{"model": "{0}", "messages": [{{"role": "system", "content": "You are a helpful assistant."}},{{"role": "user", "content": "Hello world!"}}],"max_tokens": 1000}}}}\n'
    '{{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {{"model": "{0}", "messages": [{{"role": "system", "content": "You are an unhelpful assistant."}},{{"role": "user", "content": "Hello world!"}}],"max_tokens": 1000}}}}'
).format(MODEL_NAME)

INPUT_EMBEDDING_BATCH = (
    '{"custom_id": "request-1", "method": "POST", "url": "/v1/embeddings", "body": {"model": "intfloat/multilingual-e5-small", "input": "You are a helpful assistant."}}\n'
    '{"custom_id": "request-2", "method": "POST", "url": "/v1/embeddings", "body": {"model": "intfloat/multilingual-e5-small", "input": "You are an unhelpful assistant."}}\n'
    '{"custom_id": "request-3", "method": "POST", "url": "/v1/embeddings", "body": {"model": "intfloat/multilingual-e5-small", "input": "Hello world!"}}\n'
    '{"custom_id": "request-4", "method": "POST", "url": "/v1/embeddings", "body": {"model": "NonExistModel", "input": "Hello world!"}}'
)

INPUT_SCORE_BATCH = """{"custom_id": "request-1", "method": "POST", "url": "/score", "body": {"model": "BAAI/bge-reranker-v2-m3", "queries": "What is the capital of France?", "documents": ["The capital of Brazil is Brasilia.", "The capital of France is Paris."]}}
{"custom_id": "request-2", "method": "POST", "url": "/v1/score", "body": {"model": "BAAI/bge-reranker-v2-m3", "queries": "What is the capital of France?", "documents": ["The capital of Brazil is Brasilia.", "The capital of France is Paris."]}}"""

INPUT_RERANK_BATCH = """{"custom_id": "request-1", "method": "POST", "url": "/rerank", "body": {"model": "BAAI/bge-reranker-v2-m3", "query": "What is the capital of France?", "documents": ["The capital of Brazil is Brasilia.", "The capital of France is Paris."]}}
{"custom_id": "request-2", "method": "POST", "url": "/v1/rerank", "body": {"model": "BAAI/bge-reranker-v2-m3", "query": "What is the capital of France?", "documents": ["The capital of Brazil is Brasilia.", "The capital of France is Paris."]}}
{"custom_id": "request-2", "method": "POST", "url": "/v2/rerank", "body": {"model": "BAAI/bge-reranker-v2-m3", "query": "What is the capital of France?", "documents": ["The capital of Brazil is Brasilia.", "The capital of France is Paris."]}}"""

INPUT_REASONING_BATCH = """{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "Qwen/Qwen3-0.6B", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Solve this math problem: 2+2=?"}]}}
{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "Qwen/Qwen3-0.6B", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "What is the capital of France?"}]}}"""


def test_empty_file():
    with (
        tempfile.NamedTemporaryFile("w") as input_file,
        tempfile.NamedTemporaryFile("r") as output_file,
    ):
        input_file.write("")
        input_file.flush()
        proc = subprocess.Popen(
            [
                "vllm",
                "run-batch",
                "-i",
                input_file.name,
                "-o",
                output_file.name,
                "--model",
                os.path.join(models_path_prefix, "intfloat/multilingual-e5-small"),
            ],
        )
        proc.communicate()
        proc.wait()
        assert proc.returncode == 0, f"{proc=}"

        contents = output_file.read()
        assert contents.strip() == ""


def test_completions():
    with (
        tempfile.NamedTemporaryFile("w") as input_file,
        tempfile.NamedTemporaryFile("r") as output_file,
    ):
        input_file.write(INPUT_BATCH)
        input_file.flush()
        proc = subprocess.Popen(
            [
                "vllm",
                "run-batch",
                "-i",
                input_file.name,
                "-o",
                output_file.name,
                "--model",
                MODEL_NAME,
            ],
        )
        proc.communicate()
        proc.wait()
        assert proc.returncode == 0, f"{proc=}"

        contents = output_file.read()
        for line in contents.strip().split("\n"):
            # Ensure that the output format conforms to the openai api.
            # Validation should throw if the schema is wrong.
            BatchRequestOutput.model_validate_json(line)


def test_completions_invalid_input():
    """
    Ensure that we fail when the input doesn't conform to the openai api.
    """
    with (
        tempfile.NamedTemporaryFile("w") as input_file,
        tempfile.NamedTemporaryFile("r") as output_file,
    ):
        input_file.write(INVALID_INPUT_BATCH)
        input_file.flush()
        proc = subprocess.Popen(
            [
                "vllm",
                "run-batch",
                "-i",
                input_file.name,
                "-o",
                output_file.name,
                "--model",
                MODEL_NAME,
            ],
        )
        proc.communicate()
        proc.wait()
        assert proc.returncode != 0, f"{proc=}"


def test_embeddings():
    with (
        tempfile.NamedTemporaryFile("w") as input_file,
        tempfile.NamedTemporaryFile("r") as output_file,
    ):
        input_file.write(INPUT_EMBEDDING_BATCH)
        input_file.flush()
        proc = subprocess.Popen(
            [
                "vllm",
                "run-batch",
                "-i",
                input_file.name,
                "-o",
                output_file.name,
                "--model",
                os.path.join(models_path_prefix, "intfloat/multilingual-e5-small"),
            ],
        )
        proc.communicate()
        proc.wait()
        assert proc.returncode == 0, f"{proc=}"

        contents = output_file.read()
        for line in contents.strip().split("\n"):
            # Ensure that the output format conforms to the openai api.
            # Validation should throw if the schema is wrong.
            BatchRequestOutput.model_validate_json(line)


@pytest.mark.parametrize("input_batch", [INPUT_SCORE_BATCH, INPUT_RERANK_BATCH])
def test_score(input_batch):
    with (
        tempfile.NamedTemporaryFile("w") as input_file,
        tempfile.NamedTemporaryFile("r") as output_file,
    ):
        input_file.write(input_batch)
        input_file.flush()
        proc = subprocess.Popen(
            [
                "vllm",
                "run-batch",
                "-i",
                input_file.name,
                "-o",
                output_file.name,
                "--model",
                "BAAI/bge-reranker-v2-m3",
            ],
        )
        proc.communicate()
        proc.wait()
        assert proc.returncode == 0, f"{proc=}"

        contents = output_file.read()
        for line in contents.strip().split("\n"):
            # Ensure that the output format conforms to the openai api.
            # Validation should throw if the schema is wrong.
            BatchRequestOutput.model_validate_json(line)

            # Ensure that there is no error in the response.
            line_dict = json.loads(line)
            assert isinstance(line_dict, dict)
            assert line_dict["error"] is None


def test_reasoning_parser():
    """
    Test that reasoning_parser parameter works correctly in run_batch.
    """
    with (
        tempfile.NamedTemporaryFile("w") as input_file,
        tempfile.NamedTemporaryFile("r") as output_file,
    ):
        input_file.write(INPUT_REASONING_BATCH)
        input_file.flush()
        proc = subprocess.Popen(
            [
                "vllm",
                "run-batch",
                "-i",
                input_file.name,
                "-o",
                output_file.name,
                "--model",
                "Qwen/Qwen3-0.6B",
                "--reasoning-parser",
                "qwen3",
            ],
        )
        proc.communicate()
        proc.wait()
        assert proc.returncode == 0, f"{proc=}"

        contents = output_file.read()
        for line in contents.strip().split("\n"):
            # Ensure that the output format conforms to the openai api.
            # Validation should throw if the schema is wrong.
            BatchRequestOutput.model_validate_json(line)

            # Ensure that there is no error in the response.
            line_dict = json.loads(line)
            assert isinstance(line_dict, dict)
            assert line_dict["error"] is None

            # Check that reasoning is present and not empty
            reasoning = line_dict["response"]["body"]["choices"][0]["message"][
                "reasoning"
            ]
            assert reasoning is not None
            assert len(reasoning) > 0
