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

import os
import json
from collections.abc import Generator
from typing import Optional

import pytest

from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
                                              DeltaMessage, FunctionCall,
                                              ToolCall)
from vllm.entrypoints.openai.tool_parsers import xLAMToolParser
from vllm.transformers_utils.detokenizer_utils import detokenize_incrementally
from vllm.transformers_utils.tokenizer import AnyTokenizer, get_tokenizer
from ..utils import models_path_prefix

# Use a common model that is likely to be available
MODEL = os.path.join(models_path_prefix, "Salesforce/Llama-xLAM-2-8B-fc-r")


@pytest.fixture(scope="module")
def xlam_tokenizer():
    return get_tokenizer(tokenizer_name=MODEL)


@pytest.fixture
def xlam_tool_parser(xlam_tokenizer):
    return xLAMToolParser(xlam_tokenizer)


def assert_tool_calls(actual_tool_calls: list[ToolCall],
                      expected_tool_calls: list[ToolCall]):
    assert len(actual_tool_calls) == len(expected_tool_calls)

    for actual_tool_call, expected_tool_call in zip(actual_tool_calls,
                                                    expected_tool_calls):
        assert isinstance(actual_tool_call.id, str)
        assert len(actual_tool_call.id) > 16

        assert actual_tool_call.type == "function"
        assert actual_tool_call.function == expected_tool_call.function


def stream_delta_message_generator(
    xlam_tool_parser: xLAMToolParser,
    xlam_tokenizer: AnyTokenizer,
    model_output: str,
    request: Optional[ChatCompletionRequest] = None,
) -> Generator[DeltaMessage, None, None]:
    all_token_ids = xlam_tokenizer.encode(model_output,
                                          add_special_tokens=False)

    previous_text = ""
    previous_tokens = None
    prefix_offset = 0
    read_offset = 0
    for i, delta_token in enumerate(all_token_ids):
        delta_token_ids = [delta_token]
        previous_token_ids = all_token_ids[:i]
        current_token_ids = all_token_ids[:i + 1]

        (new_tokens, delta_text, new_prefix_offset,
         new_read_offset) = (detokenize_incrementally(
             tokenizer=xlam_tokenizer,
             all_input_ids=current_token_ids,
             prev_tokens=previous_tokens,
             prefix_offset=prefix_offset,
             read_offset=read_offset,
             skip_special_tokens=False,
             spaces_between_special_tokens=True,
         ))

        current_text = previous_text + delta_text

        delta_message = xlam_tool_parser.extract_tool_calls_streaming(
            previous_text,
            current_text,
            delta_text,
            previous_token_ids,
            current_token_ids,
            delta_token_ids,
            request=request,
        )
        if delta_message:
            yield delta_message

        previous_text = current_text
        previous_tokens = (previous_tokens +
                           new_tokens if previous_tokens else new_tokens)
        prefix_offset = new_prefix_offset
        read_offset = new_read_offset


def test_extract_tool_calls_no_tools(xlam_tool_parser):
    model_output = "This is a test"
    extracted_tool_calls = xlam_tool_parser.extract_tool_calls(
        model_output, request=None)  # type: ignore[arg-type]
    assert not extracted_tool_calls.tools_called
    assert extracted_tool_calls.tool_calls == []
    assert extracted_tool_calls.content == model_output


@pytest.mark.parametrize(
    ids=[
        "parallel_tool_calls",
        "single_tool_with_think_tag",
        "single_tool_with_json_code_block",
        "single_tool_with_tool_calls_tag",
        "single_tool_with_tool_call_xml_tags",
    ],
    argnames=["model_output", "expected_tool_calls", "expected_content"],
    argvalues=[
        (
            """[{"name": "get_current_weather", "arguments": {"city": "Dallas", "state": "TX", "unit": "fahrenheit"}}, {"name": "get_current_weather", "arguments": {"city": "Orlando", "state": "FL", "unit": "fahrenheit"}}]""",  # noqa: E501
            [
                ToolCall(function=FunctionCall(
                    name="get_current_weather",
                    arguments=json.dumps({
                        "city": "Dallas",
                        "state": "TX",
                        "unit": "fahrenheit",
                    }),
                )),
                ToolCall(function=FunctionCall(
                    name="get_current_weather",
                    arguments=json.dumps({
                        "city": "Orlando",
                        "state": "FL",
                        "unit": "fahrenheit",
                    }),
                )),
            ],
            None,
        ),
        (
            """<think>I'll help you with that.</think>[{"name": "get_current_weather", "arguments": {"city": "Dallas", "state": "TX", "unit": "fahrenheit"}}]""",  # noqa: E501
            [
                ToolCall(function=FunctionCall(
                    name="get_current_weather",
                    arguments=json.dumps({
                        "city": "Dallas",
                        "state": "TX",
                        "unit": "fahrenheit",
                    }),
                ))
            ],
            "<think>I'll help you with that.</think>",
        ),
        (
            """I'll help you with that.\n```json\n[{"name": "get_current_weather", "arguments": {"city": "Dallas", "state": "TX", "unit": "fahrenheit"}}]\n```""",  # noqa: E501
            [
                ToolCall(function=FunctionCall(
                    name="get_current_weather",
                    arguments=json.dumps({
                        "city": "Dallas",
                        "state": "TX",
                        "unit": "fahrenheit",
                    }),
                ))
            ],
            "I'll help you with that.",
        ),
        (
            """I'll check the weather for you.[TOOL_CALLS][{"name": "get_current_weather", "arguments": {"city": "Dallas", "state": "TX", "unit": "fahrenheit"}}]""",  # noqa: E501
            [
                ToolCall(function=FunctionCall(
                    name="get_current_weather",
                    arguments=json.dumps({
                        "city": "Dallas",
                        "state": "TX",
                        "unit": "fahrenheit",
                    }),
                ))
            ],
            "I'll check the weather for you.",
        ),
        (
            """I'll help you check the weather.<tool_call>[{"name": "get_current_weather", "arguments": {"city": "Dallas", "state": "TX", "unit": "fahrenheit"}}]</tool_call>""",  # noqa: E501
            [
                ToolCall(function=FunctionCall(
                    name="get_current_weather",
                    arguments=json.dumps({
                        "city": "Dallas",
                        "state": "TX",
                        "unit": "fahrenheit",
                    }),
                ))
            ],
            "I'll help you check the weather.",
        ),
    ],
)
def test_extract_tool_calls(xlam_tool_parser, model_output,
                            expected_tool_calls, expected_content):
    extracted_tool_calls = xlam_tool_parser.extract_tool_calls(
        model_output, request=None)  # type: ignore[arg-type]
    assert extracted_tool_calls.tools_called

    assert_tool_calls(extracted_tool_calls.tool_calls, expected_tool_calls)

    assert extracted_tool_calls.content == expected_content


@pytest.mark.parametrize(
    ids=["list_structured_tool_call"],
    argnames=["model_output", "expected_tool_calls", "expected_content"],
    argvalues=[
        (
            """[{"name": "get_current_weather", "arguments": {"city": "Seattle", "state": "WA", "unit": "celsius"}}]""",  # noqa: E501
            [
                ToolCall(function=FunctionCall(
                    name="get_current_weather",
                    arguments=json.dumps({
                        "city": "Seattle",
                        "state": "WA",
                        "unit": "celsius",
                    }),
                ))
            ],
            None,
        ),
    ],
)
def test_extract_tool_calls_list_structure(xlam_tool_parser, model_output,
                                           expected_tool_calls,
                                           expected_content):
    """Test extraction of tool calls when the model outputs a list-structured tool call."""  # noqa: E501
    extracted_tool_calls = xlam_tool_parser.extract_tool_calls(
        model_output, request=None)  # type: ignore[arg-type]
    assert extracted_tool_calls.tools_called

    assert_tool_calls(extracted_tool_calls.tool_calls, expected_tool_calls)

    assert extracted_tool_calls.content == expected_content


# Test for preprocess_model_output method
def test_preprocess_model_output(xlam_tool_parser):
    # Test with list structure
    model_output = """[{"name": "get_current_weather", "arguments": {"city": "Seattle"}}]"""  # noqa: E501
    content, potential_tool_calls = xlam_tool_parser.preprocess_model_output(
        model_output)
    assert content is None
    assert potential_tool_calls == model_output

    # Test with thinking tag
    model_output = """<think>I'll help you with that.</think>[{"name": "get_current_weather", "arguments": {"city": "Seattle"}}]"""  # noqa: E501
    content, potential_tool_calls = xlam_tool_parser.preprocess_model_output(
        model_output)
    assert content == "<think>I'll help you with that.</think>"
    assert (
        potential_tool_calls ==
        '[{"name": "get_current_weather", "arguments": {"city": "Seattle"}}]')

    # Test with JSON code block
    model_output = """I'll help you with that.
```json
[{"name": "get_current_weather", "arguments": {"city": "Seattle"}}]
```"""
    content, potential_tool_calls = xlam_tool_parser.preprocess_model_output(
        model_output)
    assert content == "I'll help you with that."
    assert "get_current_weather" in potential_tool_calls

    # Test with no tool calls
    model_output = """I'll help you with that."""
    content, potential_tool_calls = xlam_tool_parser.preprocess_model_output(
        model_output)
    assert content == model_output
    assert potential_tool_calls is None


# Simulate streaming to test extract_tool_calls_streaming
def test_streaming_with_list_structure(xlam_tool_parser):
    # Reset streaming state
    xlam_tool_parser.prev_tool_calls = []
    xlam_tool_parser.current_tools_sent = []
    xlam_tool_parser.streamed_args = []
    xlam_tool_parser.current_tool_id = -1

    # Simulate receiving a message with list structure
    current_text = """[{"name": "get_current_weather", "arguments": {"city": "Seattle"}}]"""  # noqa: E501

    # First call to set up the tool
    xlam_tool_parser.extract_tool_calls_streaming(
        previous_text="",
        current_text=current_text,
        delta_text="]",
        previous_token_ids=[],
        current_token_ids=[],
        delta_token_ids=[],
        request=None,
    )

    # Make sure the tool is set up correctly
    assert (xlam_tool_parser.current_tool_id
            >= 0), "Tool index should be initialized"

    # Manually set up the state for sending the tool name
    xlam_tool_parser.current_tools_sent = [False]

    # Call to send the function name
    result = xlam_tool_parser.extract_tool_calls_streaming(
        previous_text=current_text,
        current_text=current_text,
        delta_text="",
        previous_token_ids=[],
        current_token_ids=[],
        delta_token_ids=[],
        request=None,
    )

    # Check that we get a result with the proper tool call
    if result is not None:
        assert hasattr(result, "tool_calls")
        assert len(result.tool_calls) == 1
        assert result.tool_calls[0].function.name == "get_current_weather"


@pytest.mark.parametrize(
    ids=[
        "parallel_tool_calls",
        "single_tool_with_think_tag",
        "single_tool_with_json_code_block",
        "single_tool_with_tool_calls_tag",
        "single_tool_with_tool_call_xml_tags",
    ],
    argnames=["model_output", "expected_tool_calls", "expected_content"],
    argvalues=[
        (
            """[{"name": "get_current_weather", "arguments": {"city": "Dallas", "state": "TX", "unit": "fahrenheit"}}, {"name": "get_current_weather", "arguments": {"city": "Orlando", "state": "FL", "unit": "fahrenheit"}}]""",  # noqa: E501
            [
                ToolCall(function=FunctionCall(
                    name="get_current_weather",
                    arguments=json.dumps({
                        "city": "Dallas",
                        "state": "TX",
                        "unit": "fahrenheit",
                    }),
                )),
                ToolCall(function=FunctionCall(
                    name="get_current_weather",
                    arguments=json.dumps({
                        "city": "Orlando",
                        "state": "FL",
                        "unit": "fahrenheit",
                    }),
                )),
            ],
            "",
        ),
        (
            """<think>I'll help you with that.</think>[{"name": "get_current_weather", "arguments": {"city": "Dallas", "state": "TX", "unit": "fahrenheit"}}]""",  # noqa: E501
            [
                ToolCall(function=FunctionCall(
                    name="get_current_weather",
                    arguments=json.dumps({
                        "city": "Dallas",
                        "state": "TX",
                        "unit": "fahrenheit",
                    }),
                ))
            ],
            "<think>I'll help you with that.</think>",
        ),
        (
            """```json\n[{"name": "get_current_weather", "arguments": {"city": "Dallas", "state": "TX", "unit": "fahrenheit"}}]\n```""",  # noqa: E501
            [
                ToolCall(function=FunctionCall(
                    name="get_current_weather",
                    arguments=json.dumps({
                        "city": "Dallas",
                        "state": "TX",
                        "unit": "fahrenheit",
                    }),
                ))
            ],
            "",
        ),
        (
            """[TOOL_CALLS][{"name": "get_current_weather", "arguments": {"city": "Dallas", "state": "TX", "unit": "fahrenheit"}}]""",  # noqa: E501
            [
                ToolCall(function=FunctionCall(
                    name="get_current_weather",
                    arguments=json.dumps({
                        "city": "Dallas",
                        "state": "TX",
                        "unit": "fahrenheit",
                    }),
                ))
            ],
            "",
        ),
        (
            """I can help with that.<tool_call>[{"name": "get_current_weather", "arguments": {"city": "Dallas", "state": "TX", "unit": "fahrenheit"}}]</tool_call>""",  # noqa: E501
            [
                ToolCall(function=FunctionCall(
                    name="get_current_weather",
                    arguments=json.dumps({
                        "city": "Dallas",
                        "state": "TX",
                        "unit": "fahrenheit",
                    }),
                ))
            ],
            "I can help with that.",
        ),
    ],
)
def test_extract_tool_calls_streaming_incremental(
    xlam_tool_parser,
    xlam_tokenizer,
    model_output,
    expected_tool_calls,
    expected_content,
):
    """Verify the XLAM Parser streaming behavior by verifying each chunk is as expected."""  # noqa: E501
    request = ChatCompletionRequest(model=MODEL, messages=[], tools=[])

    chunks = []
    for delta_message in stream_delta_message_generator(
            xlam_tool_parser, xlam_tokenizer, model_output, request):
        chunks.append(delta_message)

    # Should have multiple chunks
    assert len(chunks) >= 3

    # Should have a chunk with tool header (id, name, type) for the first tool call # noqa: E501
    header_found = False
    expected_first_tool = expected_tool_calls[0]
    for chunk in chunks:
        if chunk.tool_calls and chunk.tool_calls[0].id:
            header_found = True
            assert (chunk.tool_calls[0].function.name ==
                    expected_first_tool.function.name)
            assert chunk.tool_calls[0].type == "function"
            # Arguments may be empty initially or None
            if chunk.tool_calls[0].function.arguments is not None:
                # If present, should be empty string initially
                assert chunk.tool_calls[0].function.arguments == ""
            break
    assert header_found

    # Should have chunks with incremental arguments
    arg_chunks = []
    for chunk in chunks:
        if (chunk.tool_calls and chunk.tool_calls[0].function.arguments
                and chunk.tool_calls[0].function.arguments != ""
                and chunk.tool_calls[0].index ==
                0  # Only collect arguments from the first tool call
            ):
            arg_chunks.append(chunk.tool_calls[0].function.arguments)

    # Arguments should be streamed incrementally
    assert len(arg_chunks) > 1

    # Concatenated arguments should form valid JSON for the first tool call
    full_args = "".join(arg_chunks)
    parsed_args = json.loads(full_args)
    expected_args = json.loads(expected_first_tool.function.arguments)
    assert parsed_args == expected_args
