"""Tests for Google structured output + tool combination (Gemini 3).

Tests the three restriction lifts for Gemini 3+:
1. NativeOutput + function tools (response_schema + function_declarations)
2. Function tools + native tools (function_declarations + native_tools)
3. Output tools + native tools (ToolOutput function_declarations + native_tools)

Also verifies that older models still raise appropriate errors.
"""

from __future__ import annotations as _annotations

import re
from collections.abc import Callable
from typing import TYPE_CHECKING

import pytest
from pydantic import BaseModel

from pydantic_ai import Agent
from pydantic_ai.capabilities import NativeTool
from pydantic_ai.exceptions import UserError
from pydantic_ai.messages import (
    ModelRequest,
    ModelResponse,
    NativeToolCallPart,
    NativeToolReturnPart,
    TextPart,
    ToolCallPart,
    ToolReturnPart,
    UserPromptPart,
)
from pydantic_ai.native_tools import WebSearchTool
from pydantic_ai.output import NativeOutput, ToolOutput
from pydantic_ai.usage import RequestUsage

from ..._inline_snapshot import snapshot
from ...conftest import IsDatetime, IsStr, try_import

with try_import() as imports_successful:
    from pydantic_ai.models.google import GoogleModel

if TYPE_CHECKING:
    GoogleModelFactory = Callable[..., GoogleModel]

pytestmark = [
    pytest.mark.skipif(not imports_successful(), reason='google-genai not installed'),
    pytest.mark.anyio,
    pytest.mark.vcr,
]


class CityLocation(BaseModel):
    city: str
    country: str


# =============================================================================
# Error tests — older models still block unsupported combinations
# =============================================================================


async def test_native_output_with_function_tools_unsupported(
    allow_model_requests: None, google_model: GoogleModelFactory
):
    m = google_model('gemini-2.5-flash')
    agent = Agent(m, output_type=NativeOutput(CityLocation))

    @agent.tool_plain
    async def get_user_country() -> str:
        return 'Mexico'  # pragma: no cover

    with pytest.raises(
        UserError,
        match=re.escape(
            'This model does not support `NativeOutput` and function tools at the same time. '
            'Use `output_type=ToolOutput(...)` instead.'
        ),
    ):
        await agent.run('What is the largest city in the user country?')


async def test_function_tools_with_builtin_tools_unsupported(
    allow_model_requests: None, google_model: GoogleModelFactory
):
    m = google_model('gemini-2.5-flash')
    agent = Agent(m, capabilities=[NativeTool(WebSearchTool())])

    @agent.tool_plain
    async def get_user_country() -> str:
        return 'Mexico'  # pragma: no cover

    with pytest.raises(
        UserError,
        match=re.escape('This model does not support function tools and built-in tools at the same time.'),
    ):
        await agent.run('What is the largest city in the user country?')


async def test_tool_output_with_builtin_tools_unsupported(allow_model_requests: None, google_model: GoogleModelFactory):
    m = google_model('gemini-2.5-flash')
    agent = Agent(m, output_type=ToolOutput(CityLocation), capabilities=[NativeTool(WebSearchTool())])

    with pytest.raises(
        UserError,
        match=re.escape(
            'This model does not support output tools and built-in tools at the same time. '
            'Use `output_type=PromptedOutput(...)` instead.'
        ),
    ):
        await agent.run('What is the largest city in Mexico?')


# =============================================================================
# VCR integration tests — Gemini 3 supports all combinations
# =============================================================================


async def test_native_output_with_function_tools(allow_model_requests: None, google_model: GoogleModelFactory):
    m = google_model('gemini-3-flash-preview')
    agent = Agent(m, output_type=NativeOutput(CityLocation))

    @agent.tool_plain
    async def get_user_country() -> str:
        return 'Mexico'

    result = await agent.run('What is the largest city in the user country?')
    assert isinstance(result.output, CityLocation)
    assert result.output == snapshot(CityLocation(city='Mexico City', country='Mexico'))
    assert result.all_messages() == snapshot(
        [
            ModelRequest(
                parts=[UserPromptPart(content='What is the largest city in the user country?', timestamp=IsDatetime())],
                timestamp=IsDatetime(),
                run_id=IsStr(),
                conversation_id=IsStr(),
            ),
            ModelResponse(
                parts=[
                    ToolCallPart(
                        tool_name='get_user_country',
                        args={},
                        tool_call_id='w71i0cbt',
                        provider_name='google',
                        provider_details={'thought_signature': IsStr()},
                    )
                ],
                usage=RequestUsage(
                    input_tokens=29, output_tokens=114, details={'thoughts_tokens': 102, 'text_prompt_tokens': 29}
                ),
                model_name='gemini-3-flash-preview',
                timestamp=IsDatetime(),
                provider_name='google',
                provider_url='https://generativelanguage.googleapis.com/',
                provider_details={'finish_reason': 'STOP'},
                provider_response_id='wlP6af6uB-rBz7IP3s-7kAc',
                finish_reason='stop',
                run_id=IsStr(),
                conversation_id=IsStr(),
            ),
            ModelRequest(
                parts=[
                    ToolReturnPart(
                        tool_name='get_user_country', content='Mexico', tool_call_id='w71i0cbt', timestamp=IsDatetime()
                    )
                ],
                timestamp=IsDatetime(),
                run_id=IsStr(),
                conversation_id=IsStr(),
            ),
            ModelResponse(
                parts=[
                    TextPart(
                        content="""\
{
  "city": "Mexico City",
  "country": "Mexico"
} \
""",
                        provider_name='google',
                        provider_details={'thought_signature': IsStr()},
                    )
                ],
                usage=RequestUsage(
                    input_tokens=161, output_tokens=72, details={'thoughts_tokens': 51, 'text_prompt_tokens': 161}
                ),
                model_name='gemini-3-flash-preview',
                timestamp=IsDatetime(),
                provider_name='google',
                provider_url='https://generativelanguage.googleapis.com/',
                provider_details={'finish_reason': 'STOP'},
                provider_response_id='w1P6aZ3uJ9rTz7IPx-674Aw',
                finish_reason='stop',
                run_id=IsStr(),
                conversation_id=IsStr(),
            ),
        ]
    )


async def test_native_output_with_function_tools_stream(allow_model_requests: None, google_model: GoogleModelFactory):
    m = google_model('gemini-3-flash-preview')
    agent = Agent(m, output_type=NativeOutput(CityLocation))

    @agent.tool_plain
    async def get_user_country() -> str:
        return 'Mexico'

    async with agent.run_stream('What is the largest city in the user country?') as result:
        output = await result.get_output()
    assert isinstance(output, CityLocation)
    assert output == snapshot(CityLocation(city='Mexico City', country='Mexico'))
    assert result.all_messages() == snapshot(
        [
            ModelRequest(
                parts=[UserPromptPart(content='What is the largest city in the user country?', timestamp=IsDatetime())],
                timestamp=IsDatetime(),
                run_id=IsStr(),
                conversation_id=IsStr(),
            ),
            ModelResponse(
                parts=[
                    ToolCallPart(
                        tool_name='get_user_country',
                        args={},
                        tool_call_id='96c1su3s',
                        provider_name='google',
                        provider_details={'thought_signature': IsStr()},
                    )
                ],
                usage=RequestUsage(
                    input_tokens=29, output_tokens=81, details={'thoughts_tokens': 69, 'text_prompt_tokens': 29}
                ),
                model_name='gemini-3-flash-preview',
                timestamp=IsDatetime(),
                provider_name='google',
                provider_url='https://generativelanguage.googleapis.com/',
                provider_details={'finish_reason': 'STOP'},
                provider_response_id='g1P6aZnMMZq5qtsPj9fyuQ8',
                finish_reason='stop',
                run_id=IsStr(),
                conversation_id=IsStr(),
            ),
            ModelRequest(
                parts=[
                    ToolReturnPart(
                        tool_name='get_user_country', content='Mexico', tool_call_id='96c1su3s', timestamp=IsDatetime()
                    )
                ],
                timestamp=IsDatetime(),
                run_id=IsStr(),
                conversation_id=IsStr(),
            ),
            ModelResponse(
                parts=[
                    TextPart(
                        content="""\
{
  "city": "Mexico City",
  "country": "Mexico"
} \
""",
                        provider_name='google',
                        provider_details={'thought_signature': IsStr()},
                    )
                ],
                usage=RequestUsage(
                    input_tokens=128, output_tokens=51, details={'thoughts_tokens': 30, 'text_prompt_tokens': 128}
                ),
                model_name='gemini-3-flash-preview',
                timestamp=IsDatetime(),
                provider_name='google',
                provider_url='https://generativelanguage.googleapis.com/',
                provider_details={'finish_reason': 'STOP'},
                provider_response_id='hVP6afiZEuitz7IPypuAsQY',
                finish_reason='stop',
                run_id=IsStr(),
                conversation_id=IsStr(),
            ),
        ]
    )


async def test_native_output_with_builtin_tools_stream(allow_model_requests: None, google_model: GoogleModelFactory):
    m = google_model('gemini-3-flash-preview')
    agent = Agent(m, output_type=NativeOutput(CityLocation), capabilities=[NativeTool(WebSearchTool())])

    async with agent.run_stream('What is the largest city in Mexico?') as result:
        output = await result.get_output()
    assert isinstance(output, CityLocation)
    assert output == snapshot(CityLocation(city='Mexico City', country='Mexico'))
    assert result.all_messages() == snapshot(
        [
            ModelRequest(
                parts=[UserPromptPart(content='What is the largest city in Mexico?', timestamp=IsDatetime())],
                timestamp=IsDatetime(),
                run_id=IsStr(),
                conversation_id=IsStr(),
            ),
            ModelResponse(
                parts=[
                    NativeToolCallPart(
                        tool_name='web_search',
                        args={'queries': ['largest city in Mexico']},
                        tool_call_id='d6vd9r5q',
                        provider_name='google',
                        provider_details={'thought_signature': IsStr()},
                    ),
                    NativeToolReturnPart(
                        tool_name='web_search',
                        content={
                            'search_suggestions': IsStr(),
                        },
                        tool_call_id='d6vd9r5q',
                        timestamp=IsDatetime(),
                        provider_name='google',
                        provider_details={'thought_signature': IsStr()},
                    ),
                    TextPart(
                        content="""\
{
  "city": "Mexico City",
  "country": "Mexico"
} \
""",
                        provider_name='google',
                        provider_details={'thought_signature': IsStr()},
                    ),
                ],
                usage=RequestUsage(
                    input_tokens=87, output_tokens=78, details={'thoughts_tokens': 78, 'text_prompt_tokens': 87}
                ),
                model_name='gemini-3-flash-preview',
                timestamp=IsDatetime(),
                provider_name='google',
                provider_url='https://generativelanguage.googleapis.com/',
                provider_details={'finish_reason': 'STOP'},
                provider_response_id='hlP6ae3uJuqGz7IP-6e9iA0',
                finish_reason='stop',
                run_id=IsStr(),
                conversation_id=IsStr(),
            ),
        ]
    )


async def test_function_tools_with_builtin_tools(allow_model_requests: None, google_model: GoogleModelFactory):
    m = google_model('gemini-3-flash-preview')
    agent = Agent(m, capabilities=[NativeTool(WebSearchTool())])

    @agent.tool_plain
    async def calculator(expression: str) -> str:
        return str(eval(expression))

    result = await agent.run('What is 2+2? Also search for the current weather in Tokyo.')
    assert isinstance(result.output, str)
    # This snapshot keeps one literal `search_suggestions` as
    # samples of what Gemini 3 actually returns (Google-rendered HTML chip).
    # Every other occurrence in the suite uses `IsStr()` to save disk/tokens.
    assert result.all_messages() == snapshot(
        [
            ModelRequest(
                parts=[
                    UserPromptPart(
                        content='What is 2+2? Also search for the current weather in Tokyo.', timestamp=IsDatetime()
                    )
                ],
                timestamp=IsDatetime(),
                run_id=IsStr(),
                conversation_id=IsStr(),
            ),
            ModelResponse(
                parts=[
                    ToolCallPart(
                        tool_name='calculator',
                        args={'expression': '2+2'},
                        tool_call_id='oqeiriep',
                        provider_name='google',
                        provider_details={'thought_signature': IsStr()},
                    ),
                    NativeToolCallPart(
                        tool_name='web_search',
                        args={'queries': ['current weather in Tokyo']},
                        tool_call_id='93z4z1x3',
                        provider_name='google',
                        provider_details={'thought_signature': IsStr()},
                    ),
                    NativeToolReturnPart(
                        tool_name='web_search',
                        content={
                            'search_suggestions': """\
<style>
.container {
  align-items: center;
  border-radius: 8px;
  display: flex;
  font-family: Google Sans, Roboto, sans-serif;
  font-size: 14px;
  line-height: 20px;
  padding: 8px 12px;
}
.chip {
  display: inline-block;
  border: solid 1px;
  border-radius: 16px;
  min-width: 14px;
  padding: 5px 16px;
  text-align: center;
  user-select: none;
  margin: 0 8px;
  -webkit-tap-highlight-color: transparent;
}
.carousel {
  overflow: auto;
  scrollbar-width: none;
  white-space: nowrap;
  margin-right: -12px;
}
.headline {
  display: flex;
  margin-right: 4px;
}
.gradient-container {
  position: relative;
}
.gradient {
  position: absolute;
  transform: translate(3px, -9px);
  height: 36px;
  width: 9px;
}
@media (prefers-color-scheme: light) {
  .container {
    background-color: #fafafa;
    box-shadow: 0 0 0 1px #0000000f;
  }
  .headline-label {
    color: #1f1f1f;
  }
  .chip {
    background-color: #ffffff;
    border-color: #d2d2d2;
    color: #5e5e5e;
    text-decoration: none;
  }
  .chip:hover {
    background-color: #f2f2f2;
  }
  .chip:focus {
    background-color: #f2f2f2;
  }
  .chip:active {
    background-color: #d8d8d8;
    border-color: #b6b6b6;
  }
  .logo-dark {
    display: none;
  }
  .gradient {
    background: linear-gradient(90deg, #fafafa 15%, #fafafa00 100%);
  }
}
@media (prefers-color-scheme: dark) {
  .container {
    background-color: #1f1f1f;
    box-shadow: 0 0 0 1px #ffffff26;
  }
  .headline-label {
    color: #fff;
  }
  .chip {
    background-color: #2c2c2c;
    border-color: #3c4043;
    color: #fff;
    text-decoration: none;
  }
  .chip:hover {
    background-color: #353536;
  }
  .chip:focus {
    background-color: #353536;
  }
  .chip:active {
    background-color: #464849;
    border-color: #53575b;
  }
  .logo-light {
    display: none;
  }
  .gradient {
    background: linear-gradient(90deg, #1f1f1f 15%, #1f1f1f00 100%);
  }
}
</style>
<div class="container">
  <div class="headline">
    <svg class="logo-light" width="18" height="18" viewBox="9 9 35 35" fill="none" xmlns="http://www.w3.org/2000/svg">
      <path fill-rule="evenodd" clip-rule="evenodd" d="M42.8622 27.0064C42.8622 25.7839 42.7525 24.6084 42.5487 23.4799H26.3109V30.1568H35.5897C35.1821 32.3041 33.9596 34.1222 32.1258 35.3448V39.6864H37.7213C40.9814 36.677 42.8622 32.2571 42.8622 27.0064V27.0064Z" fill="#4285F4"/>
      <path fill-rule="evenodd" clip-rule="evenodd" d="M26.3109 43.8555C30.9659 43.8555 34.8687 42.3195 37.7213 39.6863L32.1258 35.3447C30.5898 36.3792 28.6306 37.0061 26.3109 37.0061C21.8282 37.0061 18.0195 33.9811 16.6559 29.906H10.9194V34.3573C13.7563 39.9841 19.5712 43.8555 26.3109 43.8555V43.8555Z" fill="#34A853"/>
      <path fill-rule="evenodd" clip-rule="evenodd" d="M16.6559 29.8904C16.3111 28.8559 16.1074 27.7588 16.1074 26.6146C16.1074 25.4704 16.3111 24.3733 16.6559 23.3388V18.8875H10.9194C9.74388 21.2072 9.06992 23.8247 9.06992 26.6146C9.06992 29.4045 9.74388 32.022 10.9194 34.3417L15.3864 30.8621L16.6559 29.8904V29.8904Z" fill="#FBBC05"/>
      <path fill-rule="evenodd" clip-rule="evenodd" d="M26.3109 16.2386C28.85 16.2386 31.107 17.1164 32.9095 18.8091L37.8466 13.8719C34.853 11.082 30.9659 9.3736 26.3109 9.3736C19.5712 9.3736 13.7563 13.245 10.9194 18.8875L16.6559 23.3388C18.0195 19.2636 21.8282 16.2386 26.3109 16.2386V16.2386Z" fill="#EA4335"/>
    </svg>
    <svg class="logo-dark" width="18" height="18" viewBox="0 0 48 48" xmlns="http://www.w3.org/2000/svg">
      <circle cx="24" cy="23" fill="#FFF" r="22"/>
      <path d="M33.76 34.26c2.75-2.56 4.49-6.37 4.49-11.26 0-.89-.08-1.84-.29-3H24.01v5.99h8.03c-.4 2.02-1.5 3.56-3.07 4.56v.75l3.91 2.97h.88z" fill="#4285F4"/>
      <path d="M15.58 25.77A8.845 8.845 0 0 0 24 31.86c1.92 0 3.62-.46 4.97-1.31l4.79 3.71C31.14 36.7 27.65 38 24 38c-5.93 0-11.01-3.4-13.45-8.36l.17-1.01 4.06-2.85h.8z" fill="#34A853"/>
      <path d="M15.59 20.21a8.864 8.864 0 0 0 0 5.58l-5.03 3.86c-.98-2-1.53-4.25-1.53-6.64 0-2.39.55-4.64 1.53-6.64l1-.22 3.81 2.98.22 1.08z" fill="#FBBC05"/>
      <path d="M24 14.14c2.11 0 4.02.75 5.52 1.98l4.36-4.36C31.22 9.43 27.81 8 24 8c-5.93 0-11.01 3.4-13.45 8.36l5.03 3.85A8.86 8.86 0 0 1 24 14.14z" fill="#EA4335"/>
    </svg>
    <div class="gradient-container"><div class="gradient"></div></div>
  </div>
  <div class="carousel">
    <a class="chip" href="https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQGF2SINL_ZQb5jTss_bBjZfyFNuEoYkYVytXoO1ujLEsxr5DUZkvmTdETjvO8TN_n-0fc4nD7Kn6C1fXZjgxeCmA9JmY8mevhdxIdnp_qqE49LnEZ4Ru3MI4c5H8rQwa5el4crQDtqznYSB8HPBoUjNk_CJCrHNMDwjYqpBbfyMAdVjbk8R4KALRG6ql-3yO0zRjupnGIY_KZI9pQ==">current weather in Tokyo</a>
  </div>
</div>
"""
                        },
                        tool_call_id='93z4z1x3',
                        timestamp=IsDatetime(),
                        provider_name='google',
                        provider_details={'thought_signature': IsStr()},
                    ),
                ],
                usage=RequestUsage(
                    input_tokens=47, output_tokens=86, details={'thoughts_tokens': 55, 'text_prompt_tokens': 47}
                ),
                model_name='gemini-3-flash-preview',
                timestamp=IsDatetime(),
                provider_name='google',
                provider_url='https://generativelanguage.googleapis.com/',
                provider_details={'finish_reason': 'STOP'},
                provider_response_id='iVP6aaqUGp-fz7IPrb-LgA0',
                finish_reason='stop',
                run_id=IsStr(),
                conversation_id=IsStr(),
            ),
            ModelRequest(
                parts=[
                    ToolReturnPart(tool_name='calculator', content='4', tool_call_id='oqeiriep', timestamp=IsDatetime())
                ],
                timestamp=IsDatetime(),
                run_id=IsStr(),
                conversation_id=IsStr(),
            ),
            ModelResponse(
                parts=[
                    NativeToolCallPart(
                        tool_name='web_search',
                        args={'queries': ['', 'current weather in Tokyo']},
                        tool_call_id=(web_search_id := IsStr()),
                        provider_name='google',
                    ),
                    NativeToolReturnPart(
                        tool_name='web_search',
                        content=[
                            {
                                'domain': None,
                                'title': 'google.com',
                                'uri': 'https://www.google.com/search?q=weather+in+Tokyo,+JP',
                            }
                        ],
                        tool_call_id=web_search_id,
                        timestamp=IsDatetime(),
                        provider_name='google',
                    ),
                    TextPart(
                        content="""\
2 + 2 is **4**.

As for the weather in Tokyo, it is currently **cloudy** with a temperature of approximately **58°F (14°C)**. The humidity is around 71%, and there is a 30% chance of precipitation throughout the day.\
""",
                        provider_name='google',
                        provider_details={
                            'thought_signature': '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'
                        },
                    ),
                ],
                usage=RequestUsage(
                    input_tokens=132, output_tokens=183, details={'thoughts_tokens': 121, 'text_prompt_tokens': 132}
                ),
                model_name='gemini-3-flash-preview',
                timestamp=IsDatetime(),
                provider_name='google',
                provider_url='https://generativelanguage.googleapis.com/',
                provider_details={'finish_reason': 'STOP'},
                provider_response_id='i1P6af7qHca0qtsPqaSfuAI',
                finish_reason='stop',
                run_id=IsStr(),
                conversation_id=IsStr(),
            ),
        ]
    )


async def test_native_output_with_function_and_builtin_tools(
    allow_model_requests: None, google_model: GoogleModelFactory
):
    m = google_model('gemini-3-flash-preview')
    agent = Agent(m, output_type=NativeOutput(CityLocation), capabilities=[NativeTool(WebSearchTool())])

    @agent.tool_plain
    async def get_user_country() -> str:
        return 'Mexico'

    result = await agent.run('What is the largest city in the user country? Search the web to confirm.')
    assert isinstance(result.output, CityLocation)
    assert result.output == snapshot(CityLocation(city='Mexico City', country='Mexico'))
    assert result.all_messages() == snapshot(
        [
            ModelRequest(
                parts=[
                    UserPromptPart(
                        content='What is the largest city in the user country? Search the web to confirm.',
                        timestamp=IsDatetime(),
                    )
                ],
                timestamp=IsDatetime(),
                run_id=IsStr(),
                conversation_id=IsStr(),
            ),
            ModelResponse(
                parts=[
                    ToolCallPart(
                        tool_name='get_user_country',
                        args={},
                        tool_call_id='x8i00o1q',
                        provider_name='google',
                        provider_details={'thought_signature': IsStr()},
                    )
                ],
                usage=RequestUsage(
                    input_tokens=35,
                    output_tokens=71,
                    details={'thoughts_tokens': 59, 'text_prompt_tokens': 35},
                ),
                model_name='gemini-3-flash-preview',
                timestamp=IsDatetime(),
                provider_name='google',
                provider_url='https://generativelanguage.googleapis.com/',
                provider_details={'finish_reason': 'STOP'},
                provider_response_id='jVP6aY3XK8ucz7IPnO2_sQY',
                finish_reason='stop',
                run_id=IsStr(),
                conversation_id=IsStr(),
            ),
            ModelRequest(
                parts=[
                    ToolReturnPart(
                        tool_name='get_user_country', content='Mexico', tool_call_id='x8i00o1q', timestamp=IsDatetime()
                    )
                ],
                timestamp=IsDatetime(),
                run_id=IsStr(),
                conversation_id=IsStr(),
            ),
            ModelResponse(
                parts=[
                    NativeToolCallPart(
                        tool_name='web_search',
                        args={'queries': ['largest city in Mexico by population']},
                        tool_call_id='ccnih13d',
                        provider_name='google',
                        provider_details={'thought_signature': IsStr()},
                    ),
                    NativeToolReturnPart(
                        tool_name='web_search',
                        content={
                            'search_suggestions': IsStr(),
                        },
                        tool_call_id='ccnih13d',
                        timestamp=IsDatetime(),
                        provider_name='google',
                        provider_details={'thought_signature': IsStr()},
                    ),
                    TextPart(
                        content="""\
{
  "city": "Mexico City",
  "country": "Mexico"
} \
""",
                        provider_name='google',
                        provider_details={'thought_signature': IsStr()},
                    ),
                ],
                usage=RequestUsage(
                    input_tokens=526,
                    output_tokens=27,
                    details={'thoughts_tokens': 27, 'tool_use_prompt_tokens': 86, 'text_prompt_tokens': 341},
                ),
                model_name='gemini-3-flash-preview',
                timestamp=IsDatetime(),
                provider_name='google',
                provider_url='https://generativelanguage.googleapis.com/',
                provider_details={'finish_reason': 'STOP'},
                provider_response_id='jlP6abq1OuqGz7IP-6e9iA0',
                finish_reason='stop',
                run_id=IsStr(),
                conversation_id=IsStr(),
            ),
        ]
    )


async def test_native_output_with_builtin_tools(allow_model_requests: None, google_model: GoogleModelFactory):
    m = google_model('gemini-3-flash-preview')
    agent = Agent(m, output_type=NativeOutput(CityLocation), capabilities=[NativeTool(WebSearchTool())])

    result = await agent.run('What is the largest city in Mexico?')
    assert isinstance(result.output, CityLocation)
    assert result.output == snapshot(CityLocation(city='Mexico City', country='Mexico'))
    assert result.all_messages() == snapshot(
        [
            ModelRequest(
                parts=[UserPromptPart(content='What is the largest city in Mexico?', timestamp=IsDatetime())],
                timestamp=IsDatetime(),
                run_id=IsStr(),
                conversation_id=IsStr(),
            ),
            ModelResponse(
                parts=[
                    NativeToolCallPart(
                        tool_name='web_search',
                        args={'queries': ['largest city in Mexico']},
                        tool_call_id='0yzlft9k',
                        provider_name='google',
                        provider_details={'thought_signature': IsStr()},
                    ),
                    NativeToolReturnPart(
                        tool_name='web_search',
                        content={
                            'search_suggestions': IsStr(),
                        },
                        tool_call_id='0yzlft9k',
                        timestamp=IsDatetime(),
                        provider_name='google',
                        provider_details={'thought_signature': IsStr()},
                    ),
                    TextPart(
                        content="""\
{
  "city": "Mexico City",
  "country": "Mexico"
} \
""",
                        provider_name='google',
                        provider_details={'thought_signature': IsStr()},
                    ),
                ],
                usage=RequestUsage(
                    input_tokens=417, output_tokens=71, details={'thoughts_tokens': 71, 'text_prompt_tokens': 351}
                ),
                model_name='gemini-3-flash-preview',
                timestamp=IsDatetime(),
                provider_name='google',
                provider_url='https://generativelanguage.googleapis.com/',
                provider_details={'finish_reason': 'STOP'},
                provider_response_id='klP6aYiLELOLqtsP8sPnwAs',
                finish_reason='stop',
                run_id=IsStr(),
                conversation_id=IsStr(),
            ),
        ]
    )


async def test_tool_output_with_builtin_tools(allow_model_requests: None, google_model: GoogleModelFactory):
    m = google_model('gemini-3-flash-preview')
    agent = Agent(m, output_type=ToolOutput(CityLocation), capabilities=[NativeTool(WebSearchTool())])

    result = await agent.run('What is the largest city in Mexico?')
    assert isinstance(result.output, CityLocation)
    assert result.output == snapshot(CityLocation(city='Mexico City', country='Mexico'))
    assert result.all_messages() == snapshot(
        [
            ModelRequest(
                parts=[UserPromptPart(content='What is the largest city in Mexico?', timestamp=IsDatetime())],
                timestamp=IsDatetime(),
                run_id=IsStr(),
                conversation_id=IsStr(),
            ),
            ModelResponse(
                parts=[
                    NativeToolCallPart(
                        tool_name='web_search',
                        args={'queries': ['largest city in Mexico by population']},
                        tool_call_id='jtmvhz2z',
                        provider_name='google',
                        provider_details={'thought_signature': IsStr()},
                    ),
                    NativeToolReturnPart(
                        tool_name='web_search',
                        content={
                            'search_suggestions': IsStr(),
                        },
                        tool_call_id='jtmvhz2z',
                        timestamp=IsDatetime(),
                        provider_name='google',
                        provider_details={'thought_signature': IsStr()},
                    ),
                    ToolCallPart(
                        tool_name='final_result',
                        args={'city': 'Mexico City', 'country': 'Mexico'},
                        tool_call_id='yylqxldm',
                        provider_name='google',
                        provider_details={'thought_signature': IsStr()},
                    ),
                ],
                usage=RequestUsage(
                    input_tokens=279, output_tokens=31, details={'thoughts_tokens': 31, 'text_prompt_tokens': 176}
                ),
                model_name='gemini-3-flash-preview',
                timestamp=IsDatetime(),
                provider_name='google',
                provider_url='https://generativelanguage.googleapis.com/',
                provider_details={'finish_reason': 'STOP'},
                provider_response_id='lFP6aZiqNKbXz7IPz-j6gAc',
                finish_reason='stop',
                run_id=IsStr(),
                conversation_id=IsStr(),
            ),
            ModelRequest(
                parts=[
                    ToolReturnPart(
                        tool_name='final_result',
                        content='Final result processed.',
                        tool_call_id='yylqxldm',
                        timestamp=IsDatetime(),
                    )
                ],
                timestamp=IsDatetime(),
                run_id=IsStr(),
                conversation_id=IsStr(),
            ),
        ]
    )


async def test_auto_mode_with_function_and_builtin_tools(allow_model_requests: None, google_model: GoogleModelFactory):
    m = google_model('gemini-3-flash-preview')
    agent = Agent(m, output_type=CityLocation, capabilities=[NativeTool(WebSearchTool())])

    @agent.tool_plain
    async def get_user_country() -> str:
        return 'Mexico'

    result = await agent.run('What is the largest city in the user country?')
    assert isinstance(result.output, CityLocation)
    assert result.output == snapshot(CityLocation(city='Mexico City', country='Mexico'))
    assert result.all_messages() == snapshot(
        [
            ModelRequest(
                parts=[UserPromptPart(content='What is the largest city in the user country?', timestamp=IsDatetime())],
                timestamp=IsDatetime(),
                run_id=IsStr(),
                conversation_id=IsStr(),
            ),
            ModelResponse(
                parts=[
                    ToolCallPart(
                        tool_name='get_user_country',
                        args={},
                        tool_call_id='zi06h2mp',
                        provider_name='google',
                        provider_details={'thought_signature': IsStr()},
                    )
                ],
                usage=RequestUsage(
                    input_tokens=85,
                    output_tokens=72,
                    details={'thoughts_tokens': 60, 'text_prompt_tokens': 85},
                ),
                model_name='gemini-3-flash-preview',
                timestamp=IsDatetime(),
                provider_name='google',
                provider_url='https://generativelanguage.googleapis.com/',
                provider_details={'finish_reason': 'STOP'},
                provider_response_id='l1P6af_FOMXVz7IPi8y54Aw',
                finish_reason='stop',
                run_id=IsStr(),
                conversation_id=IsStr(),
            ),
            ModelRequest(
                parts=[
                    ToolReturnPart(
                        tool_name='get_user_country', content='Mexico', tool_call_id='zi06h2mp', timestamp=IsDatetime()
                    )
                ],
                timestamp=IsDatetime(),
                run_id=IsStr(),
                conversation_id=IsStr(),
            ),
            ModelResponse(
                parts=[
                    NativeToolCallPart(
                        tool_name='web_search',
                        args={'queries': ['largest city in Mexico by population']},
                        tool_call_id='4f244mfi',
                        provider_name='google',
                        provider_details={'thought_signature': IsStr()},
                    ),
                    NativeToolReturnPart(
                        tool_name='web_search',
                        content={
                            'search_suggestions': IsStr(),
                        },
                        tool_call_id='4f244mfi',
                        timestamp=IsDatetime(),
                        provider_name='google',
                        provider_details={'thought_signature': IsStr()},
                    ),
                    ToolCallPart(
                        tool_name='final_result',
                        args={'city': 'Mexico City', 'country': 'Mexico'},
                        tool_call_id='4jh89wlf',
                        provider_name='google',
                        provider_details={'thought_signature': IsStr()},
                    ),
                ],
                usage=RequestUsage(
                    input_tokens=510,
                    output_tokens=18,
                    details={'thoughts_tokens': 18, 'tool_use_prompt_tokens': 78, 'text_prompt_tokens': 301},
                ),
                model_name='gemini-3-flash-preview',
                timestamp=IsDatetime(),
                provider_name='google',
                provider_url='https://generativelanguage.googleapis.com/',
                provider_details={'finish_reason': 'STOP'},
                provider_response_id='mVP6adOKCunUz7IP5vXVqQ0',
                finish_reason='stop',
                run_id=IsStr(),
                conversation_id=IsStr(),
            ),
            ModelRequest(
                parts=[
                    ToolReturnPart(
                        tool_name='final_result',
                        content='Final result processed.',
                        tool_call_id='4jh89wlf',
                        timestamp=IsDatetime(),
                    )
                ],
                timestamp=IsDatetime(),
                run_id=IsStr(),
                conversation_id=IsStr(),
            ),
        ]
    )


# =============================================================================
# Gemini 2 fallback behavior
# =============================================================================


async def test_auto_output_mode_with_builtin_tools_falls_back(
    allow_model_requests: None, google_model: GoogleModelFactory
):
    """Gemini 2.5 with auto output mode + builtin tools silently converts to prompted output."""
    m = google_model('gemini-2.5-flash')
    agent = Agent(m, output_type=CityLocation, capabilities=[NativeTool(WebSearchTool())])
    result = await agent.run('What is the largest city in Mexico?')
    assert isinstance(result.output, CityLocation)
    assert result.output == snapshot(CityLocation(city='Mexico City', country='Mexico'))
    assert result.all_messages() == snapshot(
        [
            ModelRequest(
                parts=[UserPromptPart(content='What is the largest city in Mexico?', timestamp=IsDatetime())],
                timestamp=IsDatetime(),
                run_id=IsStr(),
                conversation_id=IsStr(),
            ),
            ModelResponse(
                parts=[
                    NativeToolCallPart(
                        tool_name='web_search',
                        args={'queries': ['largest city in Mexico']},
                        tool_call_id=IsStr(),
                        provider_name='google',
                    ),
                    NativeToolReturnPart(
                        tool_name='web_search',
                        content=None,
                        tool_call_id=IsStr(),
                        timestamp=IsDatetime(),
                        provider_name='google',
                    ),
                    TextPart(content='{"city": "Mexico City", "country": "Mexico"}'),
                ],
                usage=RequestUsage(
                    input_tokens=217,
                    output_tokens=82,
                    details={
                        'thoughts_tokens': 54,
                        'tool_use_prompt_tokens': 132,
                        'text_prompt_tokens': 85,
                        'text_tool_use_prompt_tokens': 132,
                    },
                ),
                model_name='gemini-2.5-flash',
                timestamp=IsDatetime(),
                provider_name='google',
                provider_url='https://generativelanguage.googleapis.com/',
                provider_details={'finish_reason': 'STOP'},
                provider_response_id='nFP6aZ_4HPuU6dkP-uetwA0',
                finish_reason='stop',
                run_id=IsStr(),
                conversation_id=IsStr(),
            ),
        ]
    )