# -*- coding: utf-8 -*-
"""Examples of DeepSeek model calls.

For DeepSeek, thinking (chain-of-thought) is controlled by the
``thinking_enable`` parameter on ``deepseek-v4-flash``. The legacy
``deepseek-chat`` / ``deepseek-reasoner`` model names will be deprecated;
they correspond to the non-thinking / thinking modes of
``deepseek-v4-flash`` respectively.
"""
import asyncio
import json
import os

from pydantic import BaseModel, Field

from _utils import stream_and_collect
from agentscope.message import (
    Msg,
    TextBlock,
    ToolCallBlock,
    ToolResultBlock,
    ToolResultState,
)
from agentscope.model import DeepSeekChatModel
from agentscope.credential import DeepSeekCredential
from agentscope.tool import Toolkit, ToolChoice, FunctionTool


# ---------------------------------------------------------------------------
# Example 1: Simple user message (streaming, with chain-of-thought)
# ---------------------------------------------------------------------------


async def example_simple_call() -> None:
    """Call DeepSeek with thinking enabled on a simple text message."""
    model = DeepSeekChatModel(
        credential=DeepSeekCredential(
            api_key=os.environ["DEEPSEEK_API_KEY"],
        ),
        model="deepseek-v4-flash",
        stream=True,
        context_size=1_000_000,
        parameters=DeepSeekChatModel.Parameters(thinking_enable=True),
    )

    msgs = [
        Msg(
            name="user",
            content=[TextBlock(text="What is 1 + 1? Answer briefly.")],
            role="user",
        ),
    ]

    print("=== Simple Call ===")
    await stream_and_collect(await model(msgs))


# ---------------------------------------------------------------------------
# Example 2: Tool calling (streaming)
# ---------------------------------------------------------------------------


def get_weather(city: str) -> str:
    """Get the current weather for a city.

    Args:
        city: The city name to query the weather for.

    Returns:
        A description of the current weather.
    """
    return f"The weather in {city} is sunny and 25°C."


async def example_tool_call() -> None:
    """Call DeepSeek with thinking + tool calling enabled."""
    toolkit = Toolkit(tools=[FunctionTool(get_weather)])
    tools = await toolkit.get_tool_schemas()

    model = DeepSeekChatModel(
        credential=DeepSeekCredential(
            api_key=os.environ["DEEPSEEK_API_KEY"],
        ),
        model="deepseek-v4-flash",
        stream=True,
        context_size=1_000_000,
        parameters=DeepSeekChatModel.Parameters(thinking_enable=True),
    )

    msgs = [
        Msg(
            name="user",
            content=[TextBlock(text="What is the weather in Shenzhen?")],
            role="user",
        ),
    ]

    # First call: model decides to call a tool
    print("=== Tool Call - Round 1 ===")
    response = await stream_and_collect(
        await model(msgs, tools=tools, tool_choice=ToolChoice(mode="auto")),
    )
    print(response)

    tool_calls = [b for b in response.content if isinstance(b, ToolCallBlock)]
    if tool_calls:
        tool_result_blocks = []
        for tool_call in tool_calls:
            args = json.loads(tool_call.input)
            result = get_weather(**args)
            tool_result_blocks.append(
                ToolResultBlock(
                    id=tool_call.id,
                    name=tool_call.name,
                    output=result,
                    state=ToolResultState.SUCCESS,
                ),
            )

        assistant_msg = Msg(
            name="assistant",
            content=response.content,
            role="assistant",
        )
        tool_result_msg = Msg(
            name="tool",
            content=tool_result_blocks,
            role="assistant",
        )
        msgs = msgs + [assistant_msg, tool_result_msg]

        print("=== Tool Call - Round 2 (Final) ===")
        await stream_and_collect(await model(msgs))


# ---------------------------------------------------------------------------
# Example 3: Structured output
# ---------------------------------------------------------------------------


class MathSolution(BaseModel):
    """Structured solution to a math problem."""

    problem: str = Field(description="The original problem statement")
    answer: float = Field(description="The final numeric answer")
    steps: list[str] = Field(
        description="Step-by-step reasoning leading to the answer",
    )


async def example_structured_output() -> None:
    """Call DeepSeek with thinking enabled and force a structured output."""
    model = DeepSeekChatModel(
        credential=DeepSeekCredential(
            api_key=os.environ["DEEPSEEK_API_KEY"],
        ),
        model="deepseek-v4-flash",
        stream=True,
        context_size=1_000_000,
        parameters=DeepSeekChatModel.Parameters(thinking_enable=True),
    )

    msgs = [
        Msg(
            name="user",
            content=[
                TextBlock(
                    text=(
                        "Solve this: A train travels at 60 km/h for "
                        "2.5 hours. How far does it travel in km?"
                    ),
                ),
            ],
            role="user",
        ),
    ]

    print("=== Structured Output ===")
    response = await model.generate_structured_output(
        msgs,
        structured_model=MathSolution,
    )
    print(response.content)


if __name__ == "__main__":
    asyncio.run(example_simple_call())
    asyncio.run(example_tool_call())
    asyncio.run(example_structured_output())