"""Test ChatAnthropic chat model."""

from __future__ import annotations

import asyncio
import json
import os
from base64 import b64encode
from typing import TYPE_CHECKING, Any, Literal, cast

import anthropic
import httpx
import pytest
import requests
from langchain.agents import create_agent
from langchain.agents.structured_output import ProviderStrategy
from langchain_core.callbacks import CallbackManager
from langchain_core.exceptions import OutputParserException
from langchain_core.messages import (
    AIMessage,
    AIMessageChunk,
    BaseMessage,
    BaseMessageChunk,
    HumanMessage,
    SystemMessage,
    ToolMessage,
)
from langchain_core.outputs import ChatGeneration, LLMResult
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.tools import tool

if TYPE_CHECKING:
    from collections.abc import Awaitable

    from langchain_core.language_models.chat_model_stream import (
        AsyncChatModelStream,
        ChatModelStream,
    )
from langchain_tests.utils.stream_lifecycle import assert_valid_event_stream
from pydantic import BaseModel, Field
from typing_extensions import TypedDict

from langchain_anthropic import ChatAnthropic
from langchain_anthropic._compat import _convert_from_v1_to_anthropic
from tests.unit_tests._utils import FakeCallbackHandler

MODEL_NAME = "claude-haiku-4-5-20251001"


def test_stream() -> None:
    """Test streaming tokens from Anthropic."""
    llm = ChatAnthropic(model_name=MODEL_NAME)  # type: ignore[call-arg, call-arg]

    full: BaseMessageChunk | None = None
    chunks_with_input_token_counts = 0
    chunks_with_output_token_counts = 0
    chunks_with_model_name = 0
    for token in llm.stream("I'm Pickle Rick"):
        assert isinstance(token.content, str)
        full = cast("BaseMessageChunk", token) if full is None else full + token
        assert isinstance(token, AIMessageChunk)
        if token.usage_metadata is not None:
            if token.usage_metadata.get("input_tokens"):
                chunks_with_input_token_counts += 1
            if token.usage_metadata.get("output_tokens"):
                chunks_with_output_token_counts += 1
        chunks_with_model_name += int("model_name" in token.response_metadata)
    if chunks_with_input_token_counts != 1 or chunks_with_output_token_counts != 1:
        msg = (
            "Expected exactly one chunk with input or output token counts. "
            "AIMessageChunk aggregation adds counts. Check that "
            "this is behaving properly."
        )
        raise AssertionError(
            msg,
        )
    assert chunks_with_model_name == 1
    # check token usage is populated
    assert isinstance(full, AIMessageChunk)
    assert len(full.content_blocks) == 1
    assert full.content_blocks[0]["type"] == "text"
    assert full.content_blocks[0]["text"]
    assert full.usage_metadata is not None
    assert full.usage_metadata["input_tokens"] > 0
    assert full.usage_metadata["output_tokens"] > 0
    assert full.usage_metadata["total_tokens"] > 0
    assert (
        full.usage_metadata["input_tokens"] + full.usage_metadata["output_tokens"]
        == full.usage_metadata["total_tokens"]
    )
    assert "stop_reason" in full.response_metadata
    assert "stop_sequence" in full.response_metadata
    assert "model_name" in full.response_metadata


async def test_astream() -> None:
    """Test streaming tokens from Anthropic."""
    llm = ChatAnthropic(model_name=MODEL_NAME)  # type: ignore[call-arg, call-arg]

    full: BaseMessageChunk | None = None
    chunks_with_input_token_counts = 0
    chunks_with_output_token_counts = 0
    async for token in llm.astream("I'm Pickle Rick"):
        assert isinstance(token.content, str)
        full = cast("BaseMessageChunk", token) if full is None else full + token
        assert isinstance(token, AIMessageChunk)
        if token.usage_metadata is not None:
            if token.usage_metadata.get("input_tokens"):
                chunks_with_input_token_counts += 1
            if token.usage_metadata.get("output_tokens"):
                chunks_with_output_token_counts += 1
    if chunks_with_input_token_counts != 1 or chunks_with_output_token_counts != 1:
        msg = (
            "Expected exactly one chunk with input or output token counts. "
            "AIMessageChunk aggregation adds counts. Check that "
            "this is behaving properly."
        )
        raise AssertionError(
            msg,
        )
    # check token usage is populated
    assert isinstance(full, AIMessageChunk)
    assert len(full.content_blocks) == 1
    assert full.content_blocks[0]["type"] == "text"
    assert full.content_blocks[0]["text"]
    assert full.usage_metadata is not None
    assert full.usage_metadata["input_tokens"] > 0
    assert full.usage_metadata["output_tokens"] > 0
    assert full.usage_metadata["total_tokens"] > 0
    assert (
        full.usage_metadata["input_tokens"] + full.usage_metadata["output_tokens"]
        == full.usage_metadata["total_tokens"]
    )
    assert "stop_reason" in full.response_metadata
    assert "stop_sequence" in full.response_metadata

    # Check expected raw API output
    async_client = llm._async_client
    params: dict = {
        "model": MODEL_NAME,
        "max_tokens": 1024,
        "messages": [{"role": "user", "content": "hi"}],
        "temperature": 0.0,
    }
    stream = await async_client.messages.create(**params, stream=True)
    async for event in stream:
        if event.type == "message_start":
            assert event.message.usage.input_tokens > 1
            # Different models may report different initial output token counts
            # in the message_start event. Ensure it's a positive value.
            assert event.message.usage.output_tokens >= 1
        elif event.type == "message_delta":
            assert event.usage.output_tokens >= 1
        else:
            pass


async def test_stream_usage() -> None:
    """Test usage metadata can be excluded."""
    model = ChatAnthropic(model_name=MODEL_NAME, stream_usage=False)  # type: ignore[call-arg]
    async for token in model.astream("hi"):
        assert isinstance(token, AIMessageChunk)
        assert token.usage_metadata is None


async def test_stream_usage_override() -> None:
    # check we override with kwarg
    model = ChatAnthropic(model_name=MODEL_NAME)  # type: ignore[call-arg]
    assert model.stream_usage
    async for token in model.astream("hi", stream_usage=False):
        assert isinstance(token, AIMessageChunk)
        assert token.usage_metadata is None


async def test_abatch() -> None:
    """Test streaming tokens."""
    llm = ChatAnthropic(model_name=MODEL_NAME)  # type: ignore[call-arg, call-arg]

    result = await llm.abatch(["I'm Pickle Rick", "I'm not Pickle Rick"])
    for token in result:
        assert isinstance(token.content, str)


async def test_abatch_tags() -> None:
    """Test batch tokens."""
    llm = ChatAnthropic(model_name=MODEL_NAME)  # type: ignore[call-arg, call-arg]

    result = await llm.abatch(
        ["I'm Pickle Rick", "I'm not Pickle Rick"],
        config={"tags": ["foo"]},
    )
    for token in result:
        assert isinstance(token.content, str)


async def test_async_tool_use() -> None:
    llm = ChatAnthropic(
        model=MODEL_NAME,  # type: ignore[call-arg]
    )

    llm_with_tools = llm.bind_tools(
        [
            {
                "name": "get_weather",
                "description": "Get weather report for a city",
                "input_schema": {
                    "type": "object",
                    "properties": {"location": {"type": "string"}},
                },
            },
        ],
    )
    response = await llm_with_tools.ainvoke("what's the weather in san francisco, ca")
    assert isinstance(response, AIMessage)
    assert isinstance(response.content, list)
    assert isinstance(response.tool_calls, list)
    assert len(response.tool_calls) == 1
    tool_call = response.tool_calls[0]
    assert tool_call["name"] == "get_weather"
    assert isinstance(tool_call["args"], dict)
    assert "location" in tool_call["args"]

    # Test streaming
    first = True
    chunks: list[BaseMessage | BaseMessageChunk] = []
    async for chunk in llm_with_tools.astream(
        "what's the weather in san francisco, ca",
    ):
        chunks = [*chunks, chunk]
        if first:
            gathered = chunk
            first = False
        else:
            gathered = gathered + chunk  # type: ignore[assignment]
    assert len(chunks) > 1
    assert isinstance(gathered, AIMessageChunk)
    assert isinstance(gathered.tool_call_chunks, list)
    assert len(gathered.tool_call_chunks) == 1
    tool_call_chunk = gathered.tool_call_chunks[0]
    assert tool_call_chunk["name"] == "get_weather"
    assert isinstance(tool_call_chunk["args"], str)
    assert "location" in json.loads(tool_call_chunk["args"])


def test_batch() -> None:
    """Test batch tokens."""
    llm = ChatAnthropic(model_name=MODEL_NAME)  # type: ignore[call-arg, call-arg]

    result = llm.batch(["I'm Pickle Rick", "I'm not Pickle Rick"])
    for token in result:
        assert isinstance(token.content, str)


async def test_ainvoke() -> None:
    """Test invoke tokens."""
    llm = ChatAnthropic(model_name=MODEL_NAME)  # type: ignore[call-arg, call-arg]

    result = await llm.ainvoke("I'm Pickle Rick", config={"tags": ["foo"]})
    assert isinstance(result.content, str)
    assert "model_name" in result.response_metadata


def test_invoke() -> None:
    """Test invoke tokens."""
    llm = ChatAnthropic(model_name=MODEL_NAME)  # type: ignore[call-arg, call-arg]

    result = llm.invoke("I'm Pickle Rick", config={"tags": ["foo"]})
    assert isinstance(result.content, str)


def test_system_invoke() -> None:
    """Test invoke tokens with a system message."""
    llm = ChatAnthropic(model_name=MODEL_NAME)  # type: ignore[call-arg, call-arg]

    prompt = ChatPromptTemplate.from_messages(
        [
            (
                "system",
                "You are an expert cartographer. If asked, you are a cartographer. "
                "STAY IN CHARACTER",
            ),
            ("human", "Are you a mathematician?"),
        ],
    )

    chain = prompt | llm

    result = chain.invoke({})
    assert isinstance(result.content, str)


def test_handle_empty_aimessage() -> None:
    # Anthropic can generate empty AIMessages, which are not valid unless in the last
    # message in a sequence.
    llm = ChatAnthropic(model=MODEL_NAME)
    messages = [
        HumanMessage("Hello"),
        AIMessage([]),
        HumanMessage("My name is Bob."),
    ]
    _ = llm.invoke(messages)

    # Test tool call sequence
    llm_with_tools = llm.bind_tools(
        [
            {
                "name": "get_weather",
                "description": "Get weather report for a city",
                "input_schema": {
                    "type": "object",
                    "properties": {"location": {"type": "string"}},
                },
            },
        ],
    )
    _ = llm_with_tools.invoke(
        [
            HumanMessage("What's the weather in Boston?"),
            AIMessage(
                content=[],
                tool_calls=[
                    {
                        "name": "get_weather",
                        "args": {"location": "Boston"},
                        "id": "toolu_01V6d6W32QGGSmQm4BT98EKk",
                        "type": "tool_call",
                    },
                ],
            ),
            ToolMessage(
                content="It's sunny.", tool_call_id="toolu_01V6d6W32QGGSmQm4BT98EKk"
            ),
            AIMessage([]),
            HumanMessage("Thanks!"),
        ]
    )


def test_anthropic_call() -> None:
    """Test valid call to anthropic."""
    chat = ChatAnthropic(model=MODEL_NAME)  # type: ignore[call-arg]
    message = HumanMessage(content="Hello")
    response = chat.invoke([message])
    assert isinstance(response, AIMessage)
    assert isinstance(response.content, str)


def test_anthropic_generate() -> None:
    """Test generate method of anthropic."""
    chat = ChatAnthropic(model=MODEL_NAME)  # type: ignore[call-arg]
    chat_messages: list[list[BaseMessage]] = [
        [HumanMessage(content="How many toes do dogs have?")],
    ]
    messages_copy = [messages.copy() for messages in chat_messages]
    result: LLMResult = chat.generate(chat_messages)
    assert isinstance(result, LLMResult)
    for response in result.generations[0]:
        assert isinstance(response, ChatGeneration)
        assert isinstance(response.text, str)
        assert response.text == response.message.content
    assert chat_messages == messages_copy


def test_anthropic_streaming() -> None:
    """Test streaming tokens from anthropic."""
    chat = ChatAnthropic(model=MODEL_NAME)  # type: ignore[call-arg]
    message = HumanMessage(content="Hello")
    response = chat.stream([message])
    for token in response:
        assert isinstance(token, AIMessageChunk)
        assert isinstance(token.content, str)


def test_anthropic_streaming_callback() -> None:
    """Test that streaming correctly invokes on_llm_new_token callback."""
    callback_handler = FakeCallbackHandler()
    callback_manager = CallbackManager([callback_handler])
    chat = ChatAnthropic(
        model=MODEL_NAME,  # type: ignore[call-arg]
        callbacks=callback_manager,
        verbose=True,
    )
    message = HumanMessage(content="Write me a sentence with 10 words.")
    for token in chat.stream([message]):
        assert isinstance(token, AIMessageChunk)
        assert isinstance(token.content, str)
    assert callback_handler.llm_streams > 1


async def test_anthropic_async_streaming_callback() -> None:
    """Test that streaming correctly invokes on_llm_new_token callback."""
    callback_handler = FakeCallbackHandler()
    callback_manager = CallbackManager([callback_handler])
    chat = ChatAnthropic(
        model=MODEL_NAME,  # type: ignore[call-arg]
        callbacks=callback_manager,
        verbose=True,
    )
    chat_messages: list[BaseMessage] = [
        HumanMessage(content="How many toes do dogs have?"),
    ]
    async for token in chat.astream(chat_messages):
        assert isinstance(token, AIMessageChunk)
        assert isinstance(token.content, str)
    assert callback_handler.llm_streams > 1


def test_anthropic_multimodal() -> None:
    """Test that multimodal inputs are handled correctly."""
    chat = ChatAnthropic(model=MODEL_NAME)  # type: ignore[call-arg]
    messages: list[BaseMessage] = [
        HumanMessage(
            content=[
                {
                    "type": "image_url",
                    "image_url": {
                        # langchain logo
                        "url": "data:image/jpeg;base64, ",  # noqa: E501
                    },
                },
                {"type": "text", "text": "What is this a logo for?"},
            ],
        ),
    ]
    response = chat.invoke(messages)
    assert isinstance(response, AIMessage)
    assert isinstance(response.content, str)
    num_tokens = chat.get_num_tokens_from_messages(messages)
    assert num_tokens > 0


def test_streaming() -> None:
    """Test streaming tokens from Anthropic."""
    callback_handler = FakeCallbackHandler()
    callback_manager = CallbackManager([callback_handler])

    llm = ChatAnthropic(  # type: ignore[call-arg, call-arg]
        model_name=MODEL_NAME,
        streaming=True,
        callbacks=callback_manager,
    )

    response = llm.generate([[HumanMessage(content="I'm Pickle Rick")]])
    assert callback_handler.llm_streams > 0
    assert isinstance(response, LLMResult)


async def test_astreaming() -> None:
    """Test streaming tokens from Anthropic."""
    callback_handler = FakeCallbackHandler()
    callback_manager = CallbackManager([callback_handler])

    llm = ChatAnthropic(  # type: ignore[call-arg, call-arg]
        model_name=MODEL_NAME,
        streaming=True,
        callbacks=callback_manager,
    )

    response = await llm.agenerate([[HumanMessage(content="I'm Pickle Rick")]])
    assert callback_handler.llm_streams > 0
    assert isinstance(response, LLMResult)


def test_tool_use() -> None:
    llm = ChatAnthropic(
        model="claude-sonnet-4-5-20250929",  # type: ignore[call-arg]
        temperature=0,
    )
    tool_definition = {
        "name": "get_weather",
        "description": "Get weather report for a city",
        "input_schema": {
            "type": "object",
            "properties": {"location": {"type": "string"}},
        },
    }
    llm_with_tools = llm.bind_tools([tool_definition])
    query = "how are you? what's the weather in san francisco, ca"
    response = llm_with_tools.invoke(query)
    assert isinstance(response, AIMessage)
    assert isinstance(response.content, list)
    assert isinstance(response.tool_calls, list)
    assert len(response.tool_calls) == 1
    tool_call = response.tool_calls[0]
    assert tool_call["name"] == "get_weather"
    assert isinstance(tool_call["args"], dict)
    assert "location" in tool_call["args"]

    content_blocks = response.content_blocks
    assert len(content_blocks) == 2
    assert content_blocks[0]["type"] == "text"
    assert content_blocks[0]["text"]
    assert content_blocks[1]["type"] == "tool_call"
    assert content_blocks[1]["name"] == "get_weather"
    assert content_blocks[1]["args"] == tool_call["args"]

    # Test streaming
    llm = ChatAnthropic(model="claude-sonnet-4-5-20250929")  # type: ignore[call-arg]
    llm_with_tools = llm.bind_tools([tool_definition])
    first = True
    chunks: list[BaseMessage | BaseMessageChunk] = []
    for chunk in llm_with_tools.stream(query):
        chunks = [*chunks, chunk]
        if first:
            gathered = chunk
            first = False
        else:
            gathered = gathered + chunk  # type: ignore[assignment]
        for block in chunk.content_blocks:
            assert block["type"] in ("text", "tool_call_chunk")
    assert len(chunks) > 1
    assert isinstance(gathered.content, list)
    assert len(gathered.content) == 2
    tool_use_block = None
    for content_block in gathered.content:
        assert isinstance(content_block, dict)
        if content_block["type"] == "tool_use":
            tool_use_block = content_block
            break
    assert tool_use_block is not None
    assert tool_use_block["name"] == "get_weather"
    assert "location" in json.loads(tool_use_block["partial_json"])
    assert isinstance(gathered, AIMessageChunk)
    assert isinstance(gathered.tool_calls, list)
    assert len(gathered.tool_calls) == 1
    tool_call = gathered.tool_calls[0]
    assert tool_call["name"] == "get_weather"
    assert isinstance(tool_call["args"], dict)
    assert "location" in tool_call["args"]
    assert tool_call["id"] is not None

    content_blocks = gathered.content_blocks
    assert len(content_blocks) == 2
    assert content_blocks[0]["type"] == "text"
    assert content_blocks[0]["text"]
    assert content_blocks[1]["type"] == "tool_call"
    assert content_blocks[1]["name"] == "get_weather"
    assert content_blocks[1]["args"]

    # Test passing response back to model
    stream = llm_with_tools.stream(
        [
            query,
            gathered,
            ToolMessage(content="sunny and warm", tool_call_id=tool_call["id"]),
        ],
    )
    chunks = []
    first = True
    for chunk in stream:
        chunks = [*chunks, chunk]
        if first:
            gathered = chunk
            first = False
        else:
            gathered = gathered + chunk  # type: ignore[assignment]
    assert len(chunks) > 1


def test_builtin_tools_text_editor() -> None:
    llm = ChatAnthropic(model="claude-sonnet-4-5-20250929")  # type: ignore[call-arg]
    tool = {"type": "text_editor_20250728", "name": "str_replace_based_edit_tool"}
    llm_with_tools = llm.bind_tools([tool])
    response = llm_with_tools.invoke(
        "There's a syntax error in my primes.py file. Can you help me fix it?",
    )
    assert isinstance(response, AIMessage)
    assert response.tool_calls

    content_blocks = response.content_blocks
    assert len(content_blocks) == 2
    assert content_blocks[0]["type"] == "text"
    assert content_blocks[0]["text"]
    assert content_blocks[1]["type"] == "tool_call"
    assert content_blocks[1]["name"] == "str_replace_based_edit_tool"


def test_builtin_tools_computer_use() -> None:
    """Test computer use tool integration.

    Beta header should be automatically appended based on tool type.

    This test only verifies tool call generation.
    """
    llm = ChatAnthropic(
        model="claude-sonnet-4-5-20250929",  # type: ignore[call-arg]
    )
    tool = {
        "type": "computer_20250124",
        "name": "computer",
        "display_width_px": 1024,
        "display_height_px": 768,
        "display_number": 1,
    }
    llm_with_tools = llm.bind_tools([tool])
    response = llm_with_tools.invoke(
        "Can you take a screenshot to see what's on the screen?",
    )
    assert isinstance(response, AIMessage)
    assert response.tool_calls

    content_blocks = response.content_blocks
    assert len(content_blocks) >= 2
    assert content_blocks[0]["type"] == "text"
    assert content_blocks[0]["text"]

    # Check that we have a tool_call for computer use
    tool_call_blocks = [b for b in content_blocks if b["type"] == "tool_call"]
    assert len(tool_call_blocks) >= 1
    assert tool_call_blocks[0]["name"] == "computer"

    # Verify tool call has expected action (screenshot in this case)
    tool_call = response.tool_calls[0]
    assert tool_call["name"] == "computer"
    assert "action" in tool_call["args"]
    assert tool_call["args"]["action"] == "screenshot"


class GenerateUsername(BaseModel):
    """Get a username based on someone's name and hair color."""

    name: str
    hair_color: str


def test_disable_parallel_tool_calling() -> None:
    llm = ChatAnthropic(model=MODEL_NAME)  # type: ignore[call-arg]
    llm_with_tools = llm.bind_tools([GenerateUsername], parallel_tool_calls=False)
    result = llm_with_tools.invoke(
        "Use the GenerateUsername tool to generate user names for:\n\n"
        "Sally with green hair\n"
        "Bob with blue hair",
    )
    assert isinstance(result, AIMessage)
    assert len(result.tool_calls) == 1


def test_anthropic_with_empty_text_block() -> None:
    """Anthropic SDK can return an empty text block."""

    @tool
    def type_letter(letter: str) -> str:
        """Type the given letter."""
        return "OK"

    model = ChatAnthropic(model=MODEL_NAME, temperature=0).bind_tools(  # type: ignore[call-arg]
        [type_letter],
    )

    messages = [
        SystemMessage(
            content="Repeat the given string using the provided tools. Do not write "
            "anything else or provide any explanations. For example, "
            "if the string is 'abc', you must print the "
            "letters 'a', 'b', and 'c' one at a time and in that order. ",
        ),
        HumanMessage(content="dog"),
        AIMessage(
            content=[
                {"text": "", "type": "text"},
                {
                    "id": "toolu_01V6d6W32QGGSmQm4BT98EKk",
                    "input": {"letter": "d"},
                    "name": "type_letter",
                    "type": "tool_use",
                },
            ],
            tool_calls=[
                {
                    "name": "type_letter",
                    "args": {"letter": "d"},
                    "id": "toolu_01V6d6W32QGGSmQm4BT98EKk",
                    "type": "tool_call",
                },
            ],
        ),
        ToolMessage(content="OK", tool_call_id="toolu_01V6d6W32QGGSmQm4BT98EKk"),
    ]

    model.invoke(messages)


def test_with_structured_output() -> None:
    llm = ChatAnthropic(
        model=MODEL_NAME,  # type: ignore[call-arg]
    )

    structured_llm = llm.with_structured_output(
        {
            "name": "get_weather",
            "description": "Get weather report for a city",
            "input_schema": {
                "type": "object",
                "properties": {"location": {"type": "string"}},
            },
        },
    )
    response = structured_llm.invoke("what's the weather in san francisco, ca")
    assert isinstance(response, dict)
    assert response["location"]


class Person(BaseModel):
    """Person data."""

    name: str
    age: int
    nicknames: list[str] | None


class PersonDict(TypedDict):
    """Person data as a TypedDict."""

    name: str
    age: int
    nicknames: list[str] | None


@pytest.mark.parametrize("schema", [Person, Person.model_json_schema(), PersonDict])
def test_response_format(schema: dict | type) -> None:
    model = ChatAnthropic(
        model="claude-sonnet-4-5",  # type: ignore[call-arg]
    )
    query = "Chester (a.k.a. Chet) is 100 years old."

    response = model.invoke(query, response_format=schema)
    parsed = json.loads(response.text)
    if isinstance(schema, type) and issubclass(schema, BaseModel):
        schema.model_validate(parsed)
    else:
        assert isinstance(parsed, dict)
        assert parsed["name"]
        assert parsed["age"]


@pytest.mark.vcr
def test_response_format_in_agent() -> None:
    class Weather(BaseModel):
        temperature: float
        units: str

    # no tools
    agent = create_agent(
        "anthropic:claude-sonnet-4-5", response_format=ProviderStrategy(Weather)
    )
    result = agent.invoke({"messages": [{"role": "user", "content": "75 degrees F."}]})
    assert len(result["messages"]) == 2
    parsed = json.loads(result["messages"][-1].text)
    assert Weather(**parsed) == result["structured_response"]

    # with tools
    def get_weather(location: str) -> str:
        """Get the weather at a location."""
        return "75 degrees Fahrenheit."

    agent = create_agent(
        "anthropic:claude-sonnet-4-5",
        tools=[get_weather],
        response_format=ProviderStrategy(Weather),
    )
    result = agent.invoke(
        {"messages": [{"role": "user", "content": "What's the weather in SF?"}]},
    )
    assert len(result["messages"]) == 4
    assert result["messages"][1].tool_calls
    parsed = json.loads(result["messages"][-1].text)
    assert Weather(**parsed) == result["structured_response"]


@pytest.mark.vcr
def test_strict_tool_use() -> None:
    model = ChatAnthropic(
        model="claude-sonnet-4-5",  # type: ignore[call-arg]
    )

    def get_weather(location: str, unit: Literal["C", "F"]) -> str:
        """Get the weather at a location."""
        return "75 degrees Fahrenheit."

    model_with_tools = model.bind_tools([get_weather], strict=True)

    response = model_with_tools.invoke("What's the weather in Boston, in Celsius?")
    assert response.tool_calls


def test_get_num_tokens_from_messages() -> None:
    llm = ChatAnthropic(model=MODEL_NAME)  # type: ignore[call-arg]

    # Test simple case
    messages = [
        SystemMessage(content="You are a scientist"),
        HumanMessage(content="Hello, Claude"),
    ]
    num_tokens = llm.get_num_tokens_from_messages(messages)
    assert num_tokens > 0

    # Test tool use
    @tool(parse_docstring=True)
    def get_weather(location: str) -> str:
        """Get the current weather in a given location.

        Args:
            location: The city and state, e.g. San Francisco, CA

        """
        return "Sunny"

    messages = [
        HumanMessage(content="What's the weather like in San Francisco?"),
    ]
    num_tokens = llm.get_num_tokens_from_messages(messages, tools=[get_weather])
    assert num_tokens > 0

    messages = [
        HumanMessage(content="What's the weather like in San Francisco?"),
        AIMessage(
            content=[
                {"text": "Let's see.", "type": "text"},
                {
                    "id": "toolu_01V6d6W32QGGSmQm4BT98EKk",
                    "input": {"location": "SF"},
                    "name": "get_weather",
                    "type": "tool_use",
                },
            ],
            tool_calls=[
                {
                    "name": "get_weather",
                    "args": {"location": "SF"},
                    "id": "toolu_01V6d6W32QGGSmQm4BT98EKk",
                    "type": "tool_call",
                },
            ],
        ),
        ToolMessage(content="Sunny", tool_call_id="toolu_01V6d6W32QGGSmQm4BT98EKk"),
    ]
    num_tokens = llm.get_num_tokens_from_messages(messages, tools=[get_weather])
    assert num_tokens > 0


class GetWeather(BaseModel):
    """Get the current weather in a given location."""

    location: str = Field(..., description="The city and state, e.g. San Francisco, CA")


@pytest.mark.parametrize("tool_choice", ["GetWeather", "auto", "any"])
def test_anthropic_bind_tools_tool_choice(tool_choice: str) -> None:
    chat_model = ChatAnthropic(
        model=MODEL_NAME,  # type: ignore[call-arg]
    )
    chat_model_with_tools = chat_model.bind_tools([GetWeather], tool_choice=tool_choice)
    response = chat_model_with_tools.invoke("what's the weather in ny and la")
    assert isinstance(response, AIMessage)


def test_pdf_document_input() -> None:
    url = "https://www.w3.org/WAI/ER/tests/xhtml/testfiles/resources/pdf/dummy.pdf"
    data = b64encode(requests.get(url, timeout=10).content).decode()

    result = ChatAnthropic(model=MODEL_NAME).invoke(  # type: ignore[call-arg]
        [
            HumanMessage(
                [
                    "summarize this document",
                    {
                        "type": "document",
                        "source": {
                            "type": "base64",
                            "data": data,
                            "media_type": "application/pdf",
                        },
                    },
                ],
            ),
        ],
    )
    assert isinstance(result, AIMessage)
    assert isinstance(result.content, str)
    assert len(result.content) > 0


@pytest.mark.default_cassette("test_agent_loop.yaml.gz")
@pytest.mark.vcr
@pytest.mark.parametrize("output_version", ["v0", "v1"])
def test_agent_loop(output_version: Literal["v0", "v1"]) -> None:
    @tool
    def get_weather(location: str) -> str:
        """Get the weather for a location."""
        return "It's sunny."

    llm = ChatAnthropic(model=MODEL_NAME, output_version=output_version)  # type: ignore[call-arg]
    llm_with_tools = llm.bind_tools([get_weather])
    input_message = HumanMessage("What is the weather in San Francisco, CA?")
    tool_call_message = llm_with_tools.invoke([input_message])
    assert isinstance(tool_call_message, AIMessage)
    tool_calls = tool_call_message.tool_calls
    assert len(tool_calls) == 1
    tool_call = tool_calls[0]
    tool_message = get_weather.invoke(tool_call)
    assert isinstance(tool_message, ToolMessage)
    response = llm_with_tools.invoke(
        [
            input_message,
            tool_call_message,
            tool_message,
        ]
    )
    assert isinstance(response, AIMessage)


@pytest.mark.default_cassette("test_agent_loop_streaming.yaml.gz")
@pytest.mark.vcr
@pytest.mark.parametrize(
    ("output_version", "use_v2_stream"),
    [
        ("v0", False),
        ("v1", False),
        ("v1", True),
    ],
)
def test_agent_loop_streaming(
    output_version: Literal["v0", "v1"], *, use_v2_stream: bool
) -> None:
    @tool
    def get_weather(location: str) -> str:
        """Get the weather for a location."""
        return "It's sunny."

    llm = ChatAnthropic(
        model=MODEL_NAME,
        streaming=True,
        output_version=output_version,  # type: ignore[call-arg]
    )
    llm_with_tools = llm.bind_tools([get_weather])
    input_message = HumanMessage("What is the weather in San Francisco, CA?")
    if use_v2_stream:
        tool_call_message = cast(
            "ChatModelStream",
            llm_with_tools.stream_events([input_message], version="v3"),
        ).output
    else:
        tool_call_message = llm_with_tools.invoke([input_message])
    assert isinstance(tool_call_message, AIMessage)

    tool_calls = tool_call_message.tool_calls
    assert len(tool_calls) == 1
    tool_call = tool_calls[0]
    tool_message = get_weather.invoke(tool_call)
    assert isinstance(tool_message, ToolMessage)
    if use_v2_stream:
        response = cast(
            "ChatModelStream",
            llm_with_tools.stream_events(
                [input_message, tool_call_message, tool_message],
                version="v3",
            ),
        ).output
    else:
        response = llm_with_tools.invoke(
            [
                input_message,
                tool_call_message,
                tool_message,
            ]
        )
    assert isinstance(response, AIMessage)


@pytest.mark.default_cassette("test_agent_loop_streaming.yaml.gz")
@pytest.mark.vcr
async def test_agent_loop_streaming_astream_events_v3_v1() -> None:
    """Async multi-turn through `astream_events(version="v3")`.

    Mirrors `test_agent_loop_streaming` for `output_version="v1"` but
    exercises `AsyncChatModelStream` end-to-end.
    """

    @tool
    def get_weather(location: str) -> str:
        """Get the weather for a location."""
        return "It's sunny."

    llm = ChatAnthropic(
        model=MODEL_NAME,
        streaming=True,
        output_version="v1",  # type: ignore[call-arg]
    )
    llm_with_tools = llm.bind_tools([get_weather])
    input_message = HumanMessage("What is the weather in San Francisco, CA?")
    tool_call_message = await (
        await cast(
            "Awaitable[AsyncChatModelStream]",
            llm_with_tools.astream_events([input_message], version="v3"),
        )
    )
    assert isinstance(tool_call_message, AIMessage)
    tool_calls = tool_call_message.tool_calls
    assert len(tool_calls) == 1
    tool_call = tool_calls[0]
    tool_message = get_weather.invoke(tool_call)
    assert isinstance(tool_message, ToolMessage)
    response = await (
        await cast(
            "Awaitable[AsyncChatModelStream]",
            llm_with_tools.astream_events(
                [input_message, tool_call_message, tool_message],
                version="v3",
            ),
        )
    )
    assert isinstance(response, AIMessage)


@pytest.mark.default_cassette("test_citations.yaml.gz")
@pytest.mark.vcr
@pytest.mark.parametrize(
    ("output_version", "use_v2_stream"),
    [
        ("v0", False),
        ("v1", False),
        ("v1", True),
    ],
)
def test_citations(output_version: Literal["v0", "v1"], *, use_v2_stream: bool) -> None:
    llm = ChatAnthropic(model=MODEL_NAME, output_version=output_version)  # type: ignore[call-arg]
    messages = [
        {
            "role": "user",
            "content": [
                {
                    "type": "document",
                    "source": {
                        "type": "content",
                        "content": [
                            {"type": "text", "text": "The grass is green"},
                            {"type": "text", "text": "The sky is blue"},
                        ],
                    },
                    "citations": {"enabled": True},
                },
                {"type": "text", "text": "What color is the grass and sky?"},
            ],
        },
    ]
    response = llm.invoke(messages)
    assert isinstance(response, AIMessage)
    assert isinstance(response.content, list)
    if output_version == "v1":
        assert any("annotations" in block for block in response.content)
    else:
        assert any("citations" in block for block in response.content)

    # Test streaming
    full: BaseMessage
    if use_v2_stream:
        full = llm.stream_events(messages, version="v3").output
    else:
        aggregated: BaseMessageChunk | None = None
        for chunk in llm.stream(messages):
            aggregated = (
                cast("BaseMessageChunk", chunk)
                if aggregated is None
                else aggregated + chunk
            )
        assert isinstance(aggregated, AIMessageChunk)
        full = aggregated
    assert isinstance(full.content, list)
    assert not any("citation" in block for block in full.content)
    if output_version == "v1":
        assert any("annotations" in block for block in full.content)
    else:
        assert any("citations" in block for block in full.content)

    # Test pass back in
    next_message = {
        "role": "user",
        "content": "Can you comment on the citations you just made?",
    }
    _ = llm.invoke([*messages, full, next_message])


@pytest.mark.vcr
def test_thinking() -> None:
    llm = ChatAnthropic(
        model="claude-sonnet-4-5-20250929",  # type: ignore[call-arg]
        max_tokens=5_000,  # type: ignore[call-arg]
        thinking={"type": "enabled", "budget_tokens": 2_000},
    )

    input_message = {"role": "user", "content": "Hello"}
    response = llm.invoke([input_message])
    assert any("thinking" in block for block in response.content)
    for block in response.content:
        assert isinstance(block, dict)
        if block["type"] == "thinking":
            assert set(block.keys()) == {"type", "thinking", "signature"}
            assert block["thinking"]
            assert isinstance(block["thinking"], str)
            assert block["signature"]
            assert isinstance(block["signature"], str)

    # Test streaming
    full: BaseMessageChunk | None = None
    for chunk in llm.stream([input_message]):
        full = cast("BaseMessageChunk", chunk) if full is None else full + chunk
    assert isinstance(full, AIMessageChunk)
    assert isinstance(full.content, list)
    assert any("thinking" in block for block in full.content)
    for block in full.content:
        assert isinstance(block, dict)
        if block["type"] == "thinking":
            assert set(block.keys()) == {"type", "thinking", "signature", "index"}
            assert block["thinking"]
            assert isinstance(block["thinking"], str)
            assert block["signature"]
            assert isinstance(block["signature"], str)

    # Test pass back in
    next_message = {"role": "user", "content": "How are you?"}
    _ = llm.invoke([input_message, full, next_message])


@pytest.mark.default_cassette("test_thinking.yaml.gz")
@pytest.mark.vcr
@pytest.mark.parametrize("use_v2_stream", [False, True])
def test_thinking_v1(*, use_v2_stream: bool) -> None:
    llm = ChatAnthropic(
        model="claude-sonnet-4-5-20250929",  # type: ignore[call-arg]
        max_tokens=5_000,  # type: ignore[call-arg]
        thinking={"type": "enabled", "budget_tokens": 2_000},
        output_version="v1",
    )

    input_message = {"role": "user", "content": "Hello"}
    response = llm.invoke([input_message])
    assert any("reasoning" in block for block in response.content)
    for block in response.content:
        assert isinstance(block, dict)
        if block["type"] == "reasoning":
            assert set(block.keys()) == {"type", "reasoning", "extras"}
            assert block["reasoning"]
            assert isinstance(block["reasoning"], str)
            signature = block["extras"]["signature"]
            assert signature
            assert isinstance(signature, str)

    # Test streaming
    full: BaseMessage
    if use_v2_stream:
        full = llm.stream_events([input_message], version="v3").output
    else:
        aggregated: BaseMessageChunk | None = None
        for chunk in llm.stream([input_message]):
            aggregated = (
                cast(BaseMessageChunk, chunk)
                if aggregated is None
                else aggregated + chunk
            )
        assert isinstance(aggregated, AIMessageChunk)
        full = aggregated
    assert isinstance(full.content, list)
    assert any("reasoning" in block for block in full.content)
    for block in full.content:
        assert isinstance(block, dict)
        if block["type"] == "reasoning":
            assert set(block.keys()) == {"type", "reasoning", "extras", "index"}
            assert block["reasoning"]
            assert isinstance(block["reasoning"], str)
            signature = block["extras"]["signature"]
            assert signature
            assert isinstance(signature, str)

    # Test pass back in
    next_message = {"role": "user", "content": "How are you?"}
    _ = llm.invoke([input_message, full, next_message])


@pytest.mark.default_cassette("test_redacted_thinking.yaml.gz")
@pytest.mark.vcr
@pytest.mark.parametrize("output_version", ["v0", "v1"])
def test_redacted_thinking(output_version: Literal["v0", "v1"]) -> None:
    llm = ChatAnthropic(
        # It appears that Sonnet 4.5 either: isn't returning redacted thinking blocks,
        # or the magic string is broken? Retry later once 3-7 finally removed
        model="claude-3-7-sonnet-latest",  # type: ignore[call-arg]
        max_tokens=5_000,  # type: ignore[call-arg]
        thinking={"type": "enabled", "budget_tokens": 2_000},
        output_version=output_version,
    )
    query = "ANTHROPIC_MAGIC_STRING_TRIGGER_REDACTED_THINKING_46C9A13E193C177646C7398A98432ECCCE4C1253D5E2D82641AC0E52CC2876CB"  # noqa: E501
    input_message = {"role": "user", "content": query}

    response = llm.invoke([input_message])
    value = None
    for block in response.content:
        assert isinstance(block, dict)
        if block["type"] == "redacted_thinking":
            value = block
        elif (
            block["type"] == "non_standard"
            and block["value"]["type"] == "redacted_thinking"
        ):
            value = block["value"]
        else:
            pass
        if value:
            assert set(value.keys()) == {"type", "data"}
            assert value["data"]
            assert isinstance(value["data"], str)
    assert value is not None

    # Test streaming
    full: BaseMessageChunk | None = None
    for chunk in llm.stream([input_message]):
        full = cast("BaseMessageChunk", chunk) if full is None else full + chunk
    assert isinstance(full, AIMessageChunk)
    assert isinstance(full.content, list)
    value = None
    for block in full.content:
        assert isinstance(block, dict)
        if block["type"] == "redacted_thinking":
            value = block
            assert set(value.keys()) == {"type", "data", "index"}
            assert "index" in block
        elif (
            block["type"] == "non_standard"
            and block["value"]["type"] == "redacted_thinking"
        ):
            value = block["value"]
            assert isinstance(value, dict)
            assert set(value.keys()) == {"type", "data"}
            assert "index" in block
        else:
            pass
        if value:
            assert value["data"]
            assert isinstance(value["data"], str)
    assert value is not None

    # Test pass back in
    next_message = {"role": "user", "content": "What?"}
    _ = llm.invoke([input_message, full, next_message])


def test_structured_output_thinking_enabled() -> None:
    llm = ChatAnthropic(
        model="claude-sonnet-4-5-20250929",  # type: ignore[call-arg]
        max_tokens=5_000,  # type: ignore[call-arg]
        thinking={"type": "enabled", "budget_tokens": 2_000},
    )
    with pytest.warns(match="structured output"):
        structured_llm = llm.with_structured_output(GenerateUsername)
    query = "Generate a username for Sally with green hair"
    response = structured_llm.invoke(query)
    assert isinstance(response, GenerateUsername)

    with pytest.raises(OutputParserException):
        structured_llm.invoke("Hello")

    # Test streaming
    for chunk in structured_llm.stream(query):
        assert isinstance(chunk, GenerateUsername)


@pytest.mark.retry(count=3, delay=1)
def test_structured_output_thinking_force_tool_use() -> None:
    # Structured output currently relies on forced tool use, which is not supported
    # when `thinking` is enabled. When this test fails, it means that the feature
    # is supported and the workarounds in `with_structured_output` should be removed.
    client = anthropic.Anthropic()
    with pytest.raises(anthropic.BadRequestError):
        _ = client.messages.create(
            model="claude-sonnet-4-5-20250929",
            max_tokens=5_000,
            thinking={"type": "enabled", "budget_tokens": 2_000},
            tool_choice={"type": "tool", "name": "get_weather"},
            tools=[
                {
                    "name": "get_weather",
                    "description": "Get the weather at a location.",
                    "input_schema": {
                        "type": "object",
                        "properties": {
                            "location": {"type": "string"},
                        },
                        "required": ["location"],
                    },
                }
            ],
            messages=[
                {
                    "role": "user",
                    "content": "What's the weather in San Francisco?",
                }
            ],
        )


def test_effort_parameter() -> None:
    """Test that effort parameter can be passed without errors.

    Only Opus 4.5 supports currently.
    """
    llm = ChatAnthropic(
        model="claude-opus-4-5-20251101",
        effort="medium",
        max_tokens=100,
    )

    result = llm.invoke("Say hello in one sentence")

    # Verify we got a response
    assert isinstance(result.content, str)
    assert len(result.content) > 0

    # Verify response metadata is present
    assert "model_name" in result.response_metadata
    assert result.usage_metadata is not None
    assert result.usage_metadata["input_tokens"] > 0
    assert result.usage_metadata["output_tokens"] > 0


def test_reasoning_effort_parameter() -> None:
    """Test that the standard `reasoning_effort` parameter is accepted by the API."""
    llm = ChatAnthropic(
        model="claude-opus-4-5-20251101",
        reasoning_effort="medium",
        max_tokens=100,
    )

    result = llm.invoke("Say hello in one sentence")

    assert isinstance(result.content, str)
    assert len(result.content) > 0
    assert "model_name" in result.response_metadata
    assert result.usage_metadata is not None
    assert result.usage_metadata["input_tokens"] > 0
    assert result.usage_metadata["output_tokens"] > 0


def test_reasoning_effort_call_time_kwarg() -> None:
    """Test that `reasoning_effort` is accepted as a call-time kwarg."""
    llm = ChatAnthropic(model="claude-opus-4-5-20251101", max_tokens=100)

    result = llm.invoke("Say hello in one sentence", reasoning_effort="low")

    assert isinstance(result.content, str)
    assert len(result.content) > 0
    assert result.usage_metadata is not None


def test_reasoning_effort_defaults_adaptive_thinking() -> None:
    """`reasoning_effort` defaults `thinking` to adaptive on models that support it.

    Regression test for a model (Opus 4.7+, Sonnet 5) actually accepting the
    resulting `{"type": "adaptive", "display": "summarized"}` thinking config,
    not just that the payload is well-formed locally.
    """
    llm = ChatAnthropic(
        model="claude-opus-4-7",
        reasoning_effort="xhigh",
        max_tokens=2_000,
    )

    # A genuine multi-step problem: adaptive thinking is model-decided, and a
    # trivial prompt (e.g. "what is 3+4") may not surface a visible `thinking`
    # block even when accepted, which would make this test flaky.
    result = llm.invoke(
        "A farmer has 17 sheep. All but 9 die. Then he buys triple the number "
        "of remaining sheep, then sells half (rounding down). How many sheep "
        "does he have now? Show your reasoning step by step."
    )

    assert isinstance(result.content, list)
    assert any(
        isinstance(block, dict) and block.get("type") == "thinking"
        for block in result.content
    )
    assert result.usage_metadata is not None


def test_image_tool_calling() -> None:
    """Test tool calling with image inputs."""

    class color_picker(BaseModel):  # noqa: N801
        """Input your fav color and get a random fact about it."""

        fav_color: str

    human_content: list[dict] = [
        {
            "type": "text",
            "text": "what's your favorite color in this image",
        },
    ]
    image_url = "https://raw.githubusercontent.com/langchain-ai/docs/4d11d08b6b0e210bd456943f7a22febbd168b543/src/images/agentic-rag-output.png"
    image_data = b64encode(httpx.get(image_url, timeout=10.0).content).decode("utf-8")
    human_content.append(
        {
            "type": "image",
            "source": {
                "type": "base64",
                "media_type": "image/png",
                "data": image_data,
            },
        },
    )
    messages = [
        SystemMessage("you're a good assistant"),
        HumanMessage(human_content),  # type: ignore[arg-type]
        AIMessage(
            [
                {"type": "text", "text": "Hmm let me think about that"},
                {
                    "type": "tool_use",
                    "input": {"fav_color": "purple"},
                    "id": "foo",
                    "name": "color_picker",
                },
            ],
        ),
        HumanMessage(
            [
                {
                    "type": "tool_result",
                    "tool_use_id": "foo",
                    "content": [
                        {
                            "type": "text",
                            "text": "purple is a great pick! that's my sister's favorite color",  # noqa: E501
                        },
                    ],
                    "is_error": False,
                },
                {"type": "text", "text": "what's my sister's favorite color"},
            ],
        ),
    ]
    llm = ChatAnthropic(model=MODEL_NAME)  # type: ignore[call-arg]
    _ = llm.bind_tools([color_picker]).invoke(messages)


@pytest.mark.default_cassette("test_web_search.yaml.gz")
@pytest.mark.vcr
@pytest.mark.parametrize("output_version", ["v0", "v1"])
def test_web_search(output_version: Literal["v0", "v1"]) -> None:
    llm = ChatAnthropic(
        model=MODEL_NAME,  # type: ignore[call-arg]
        max_tokens=1024,
        output_version=output_version,
    )

    tool = {"type": "web_search_20250305", "name": "web_search", "max_uses": 1}
    llm_with_tools = llm.bind_tools([tool])

    input_message = {
        "role": "user",
        "content": [
            {
                "type": "text",
                "text": "How do I update a web app to TypeScript 5.5?",
            },
        ],
    }
    response = llm_with_tools.invoke([input_message])
    assert all(isinstance(block, dict) for block in response.content)
    block_types = {block["type"] for block in response.content}  # type: ignore[index]
    if output_version == "v0":
        assert block_types == {"text", "server_tool_use", "web_search_tool_result"}
    else:
        assert block_types == {"text", "server_tool_call", "server_tool_result"}

    # Test streaming
    full: BaseMessageChunk | None = None
    for chunk in llm_with_tools.stream([input_message]):
        assert isinstance(chunk, AIMessageChunk)
        full = chunk if full is None else full + chunk

    assert isinstance(full, AIMessageChunk)
    assert isinstance(full.content, list)
    block_types = {block["type"] for block in full.content}  # type: ignore[index]
    if output_version == "v0":
        assert block_types == {"text", "server_tool_use", "web_search_tool_result"}
    else:
        assert block_types == {"text", "server_tool_call", "server_tool_result"}

    # Test we can pass back in
    next_message = {
        "role": "user",
        "content": "Please repeat the last search, but focus on sources from 2024.",
    }
    _ = llm_with_tools.invoke(
        [input_message, full, next_message],
    )


@pytest.mark.vcr
def test_web_fetch() -> None:
    """Note: this is a beta feature.

    TODO: Update to remove beta once it's generally available.
    """
    llm = ChatAnthropic(
        model=MODEL_NAME,  # type: ignore[call-arg]
        max_tokens=1024,
        betas=["web-fetch-2025-09-10"],
    )
    tool = {"type": "web_fetch_20250910", "name": "web_fetch", "max_uses": 1}
    llm_with_tools = llm.bind_tools([tool])

    input_message = {
        "role": "user",
        "content": [
            {
                "type": "text",
                "text": "Fetch the content at https://docs.langchain.com and analyze",
            },
        ],
    }
    response = llm_with_tools.invoke([input_message])
    assert all(isinstance(block, dict) for block in response.content)
    block_types = {
        block["type"] for block in response.content if isinstance(block, dict)
    }

    # A successful fetch call should include:
    # 1. text response from the model (e.g. "I'll fetch that for you")
    # 2. server_tool_use block indicating the tool was called (using tool "web_fetch")
    # 3. web_fetch_tool_result block with the results of said fetch
    assert block_types == {"text", "server_tool_use", "web_fetch_tool_result"}

    # Verify web fetch result structure
    web_fetch_results = [
        block
        for block in response.content
        if isinstance(block, dict) and block.get("type") == "web_fetch_tool_result"
    ]
    assert len(web_fetch_results) == 1  # Since max_uses=1
    fetch_result = web_fetch_results[0]
    assert "content" in fetch_result
    assert "url" in fetch_result["content"]
    assert "retrieved_at" in fetch_result["content"]

    # Fetch with citations enabled
    tool_with_citations = tool.copy()
    tool_with_citations["citations"] = {"enabled": True}
    llm_with_citations = llm.bind_tools([tool_with_citations])

    citation_message = {
        "role": "user",
        "content": (
            "Fetch https://docs.langchain.com and provide specific quotes with "
            "citations"
        ),
    }
    citation_response = llm_with_citations.invoke([citation_message])

    citation_results = [
        block
        for block in citation_response.content
        if isinstance(block, dict) and block.get("type") == "web_fetch_tool_result"
    ]
    assert len(citation_results) == 1  # Since max_uses=1
    citation_result = citation_results[0]
    assert citation_result["content"]["content"]["citations"]["enabled"]
    text_blocks = [
        block
        for block in citation_response.content
        if isinstance(block, dict) and block.get("type") == "text"
    ]

    # Check that the response contains actual citations in the content
    has_citations = False
    for block in text_blocks:
        citations = block.get("citations", [])
        for citation in citations:
            if citation.get("type") and citation.get("start_char_index"):
                has_citations = True
                break
    assert has_citations, (
        "Expected inline citation tags in response when citations are enabled for "
        "web fetch"
    )

    # Max content tokens param
    tool_with_limit = tool.copy()
    tool_with_limit["max_content_tokens"] = 1000
    llm_with_limit = llm.bind_tools([tool_with_limit])

    limit_response = llm_with_limit.invoke([input_message])
    # Response should still work even with content limits
    assert any(
        block["type"] == "web_fetch_tool_result"
        for block in limit_response.content
        if isinstance(block, dict)
    )

    # Domains filtering (note: only one can be set at a time)
    tool_with_allowed_domains = tool.copy()
    tool_with_allowed_domains["allowed_domains"] = ["docs.langchain.com"]
    llm_with_allowed = llm.bind_tools([tool_with_allowed_domains])

    allowed_response = llm_with_allowed.invoke([input_message])
    assert any(
        block["type"] == "web_fetch_tool_result"
        for block in allowed_response.content
        if isinstance(block, dict)
    )

    # Test that a disallowed domain doesn't work
    tool_with_disallowed_domains = tool.copy()
    tool_with_disallowed_domains["allowed_domains"] = [
        "example.com"
    ]  # Not docs.langchain.com
    llm_with_disallowed = llm.bind_tools([tool_with_disallowed_domains])

    disallowed_response = llm_with_disallowed.invoke([input_message])

    # We should get an error result since the domain (docs.langchain.com) is not allowed
    disallowed_results = [
        block
        for block in disallowed_response.content
        if isinstance(block, dict) and block.get("type") == "web_fetch_tool_result"
    ]
    if disallowed_results:
        disallowed_result = disallowed_results[0]
        if disallowed_result.get("content", {}).get("type") == "web_fetch_tool_error":
            assert disallowed_result["content"]["error_code"] in [
                "invalid_url",
                "fetch_failed",
            ]

    # Blocked domains filtering
    tool_with_blocked_domains = tool.copy()
    tool_with_blocked_domains["blocked_domains"] = ["example.com"]
    llm_with_blocked = llm.bind_tools([tool_with_blocked_domains])

    blocked_response = llm_with_blocked.invoke([input_message])
    assert any(
        block["type"] == "web_fetch_tool_result"
        for block in blocked_response.content
        if isinstance(block, dict)
    )

    # Test fetching from a blocked domain fails
    blocked_domain_message = {
        "role": "user",
        "content": "Fetch https://example.com and analyze",
    }
    tool_with_blocked_example = tool.copy()
    tool_with_blocked_example["blocked_domains"] = ["example.com"]
    llm_with_blocked_example = llm.bind_tools([tool_with_blocked_example])

    blocked_domain_response = llm_with_blocked_example.invoke([blocked_domain_message])

    # Should get an error when trying to access a blocked domain
    blocked_domain_results = [
        block
        for block in blocked_domain_response.content
        if isinstance(block, dict) and block.get("type") == "web_fetch_tool_result"
    ]
    if blocked_domain_results:
        blocked_result = blocked_domain_results[0]
        if blocked_result.get("content", {}).get("type") == "web_fetch_tool_error":
            assert blocked_result["content"]["error_code"] in [
                "invalid_url",
                "fetch_failed",
            ]

    # Max uses parameter - test exceeding the limit
    multi_fetch_message = {
        "role": "user",
        "content": (
            "Fetch https://docs.langchain.com and then try to fetch "
            "https://langchain.com"
        ),
    }
    max_uses_response = llm_with_tools.invoke([multi_fetch_message])

    # Should contain at least one fetch result and potentially an error for the second
    fetch_results = [
        block
        for block in max_uses_response.content
        if isinstance(block, dict) and block.get("type") == "web_fetch_tool_result"
    ]  # type: ignore[index]
    assert len(fetch_results) >= 1
    error_results = [
        r
        for r in fetch_results
        if r.get("content", {}).get("type") == "web_fetch_tool_error"
    ]
    if error_results:
        assert any(
            r["content"]["error_code"] == "max_uses_exceeded" for r in error_results
        )

    # Streaming
    full: BaseMessageChunk | None = None
    for chunk in llm_with_tools.stream([input_message]):
        assert isinstance(chunk, AIMessageChunk)
        full = chunk if full is None else full + chunk
    assert isinstance(full, AIMessageChunk)
    assert isinstance(full.content, list)
    block_types = {block["type"] for block in full.content if isinstance(block, dict)}
    assert block_types == {"text", "server_tool_use", "web_fetch_tool_result"}

    # Test that URLs from context can be used in follow-up
    next_message = {
        "role": "user",
        "content": "What does the site you just fetched say about models?",
    }
    follow_up_response = llm_with_tools.invoke(
        [input_message, full, next_message],
    )
    # Should work without issues since URL was already in context
    assert isinstance(follow_up_response.content, (list, str))

    # Error handling - test with an invalid URL format
    error_message = {
        "role": "user",
        "content": "Try to fetch this invalid URL: not-a-valid-url",
    }
    error_response = llm_with_tools.invoke([error_message])

    # Should handle the error gracefully
    assert isinstance(error_response.content, (list, str))

    # PDF document fetching
    pdf_message = {
        "role": "user",
        "content": (
            "Fetch this PDF: "
            "https://www.w3.org/WAI/ER/tests/xhtml/testfiles/resources/pdf/dummy.pdf "
            "and summarize its content",
        ),
    }
    pdf_response = llm_with_tools.invoke([pdf_message])

    assert any(
        block["type"] == "web_fetch_tool_result"
        for block in pdf_response.content
        if isinstance(block, dict)
    )

    # Verify PDF content structure (should have base64 data for PDFs)
    pdf_results = [
        block
        for block in pdf_response.content
        if isinstance(block, dict) and block.get("type") == "web_fetch_tool_result"
    ]
    if pdf_results:
        pdf_result = pdf_results[0]
        content = pdf_result.get("content", {})
        if content.get("content", {}).get("source", {}).get("type") == "base64":
            assert content["content"]["source"]["media_type"] == "application/pdf"
            assert "data" in content["content"]["source"]


@pytest.mark.default_cassette("test_web_fetch_v1.yaml.gz")
@pytest.mark.vcr
@pytest.mark.parametrize("output_version", ["v0", "v1"])
def test_web_fetch_v1(output_version: Literal["v0", "v1"]) -> None:
    """Test that http calls are unchanged between v0 and v1."""
    llm = ChatAnthropic(
        model=MODEL_NAME,  # type: ignore[call-arg]
        betas=["web-fetch-2025-09-10"],
        output_version=output_version,
    )

    if output_version == "v0":
        call_key = "server_tool_use"
        result_key = "web_fetch_tool_result"
    else:
        # v1
        call_key = "server_tool_call"
        result_key = "server_tool_result"

    tool = {
        "type": "web_fetch_20250910",
        "name": "web_fetch",
        "max_uses": 1,
        "citations": {"enabled": True},
    }
    llm_with_tools = llm.bind_tools([tool])

    input_message = {
        "role": "user",
        "content": [
            {
                "type": "text",
                "text": "Fetch the content at https://docs.langchain.com and analyze",
            },
        ],
    }
    response = llm_with_tools.invoke([input_message])
    assert all(isinstance(block, dict) for block in response.content)
    block_types = {block["type"] for block in response.content}  # type: ignore[index]
    assert block_types == {"text", call_key, result_key}

    # Test streaming
    full: BaseMessageChunk | None = None
    for chunk in llm_with_tools.stream([input_message]):
        assert isinstance(chunk, AIMessageChunk)
        full = chunk if full is None else full + chunk

    assert isinstance(full, AIMessageChunk)
    assert isinstance(full.content, list)
    block_types = {block["type"] for block in full.content}  # type: ignore[index]
    assert block_types == {"text", call_key, result_key}

    # Test we can pass back in
    next_message = {
        "role": "user",
        "content": "What does the site you just fetched say about models?",
    }
    _ = llm_with_tools.invoke(
        [input_message, full, next_message],
    )


@pytest.mark.default_cassette("test_code_execution_old.yaml.gz")
@pytest.mark.vcr
@pytest.mark.parametrize("output_version", ["v0", "v1"])
def test_code_execution_old(output_version: Literal["v0", "v1"]) -> None:
    """Note: this tests the `code_execution_20250522` tool, which is now legacy.

    See the `test_code_execution` test below to test the current
    `code_execution_20250825` tool.

    Migration guide: https://platform.claude.com/docs/en/agents-and-tools/tool-use/code-execution-tool#upgrade-to-latest-tool-version
    """
    llm = ChatAnthropic(
        model=MODEL_NAME,  # type: ignore[call-arg]
        betas=["code-execution-2025-05-22"],
        output_version=output_version,
    )

    tool = {"type": "code_execution_20250522", "name": "code_execution"}
    llm_with_tools = llm.bind_tools([tool])

    input_message = {
        "role": "user",
        "content": [
            {
                "type": "text",
                "text": (
                    "Calculate the mean and standard deviation of "
                    "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]"
                ),
            },
        ],
    }
    response = llm_with_tools.invoke([input_message])
    assert all(isinstance(block, dict) for block in response.content)
    block_types = {block["type"] for block in response.content}  # type: ignore[index]
    if output_version == "v0":
        assert block_types == {"text", "server_tool_use", "code_execution_tool_result"}
    else:
        assert block_types == {"text", "server_tool_call", "server_tool_result"}

    # Test streaming
    full: BaseMessageChunk | None = None
    for chunk in llm_with_tools.stream([input_message]):
        assert isinstance(chunk, AIMessageChunk)
        full = chunk if full is None else full + chunk
    assert isinstance(full, AIMessageChunk)
    assert isinstance(full.content, list)
    block_types = {block["type"] for block in full.content}  # type: ignore[index]
    if output_version == "v0":
        assert block_types == {"text", "server_tool_use", "code_execution_tool_result"}
    else:
        assert block_types == {"text", "server_tool_call", "server_tool_result"}

    # Test we can pass back in
    next_message = {
        "role": "user",
        "content": "Please add more comments to the code.",
    }
    _ = llm_with_tools.invoke(
        [input_message, full, next_message],
    )


@pytest.mark.default_cassette("test_code_execution.yaml.gz")
@pytest.mark.vcr
@pytest.mark.parametrize("output_version", ["v0", "v1"])
def test_code_execution(output_version: Literal["v0", "v1"]) -> None:
    """Note: this is a beta feature.

    TODO: Update to remove beta once generally available.
    """
    llm = ChatAnthropic(
        model=MODEL_NAME,  # type: ignore[call-arg]
        betas=["code-execution-2025-08-25"],
        output_version=output_version,
    )

    tool = {"type": "code_execution_20250825", "name": "code_execution"}
    llm_with_tools = llm.bind_tools([tool])

    input_message = {
        "role": "user",
        "content": [
            {
                "type": "text",
                "text": (
                    "Calculate the mean and standard deviation of "
                    "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]"
                ),
            },
        ],
    }
    response = llm_with_tools.invoke([input_message])
    assert all(isinstance(block, dict) for block in response.content)
    block_types = {block["type"] for block in response.content}  # type: ignore[index]
    if output_version == "v0":
        assert block_types == {
            "text",
            "server_tool_use",
            "bash_code_execution_tool_result",
        }
    else:
        assert block_types == {"text", "server_tool_call", "server_tool_result"}

    # Test streaming
    full: BaseMessageChunk | None = None
    for chunk in llm_with_tools.stream([input_message]):
        assert isinstance(chunk, AIMessageChunk)
        full = chunk if full is None else full + chunk
    assert isinstance(full, AIMessageChunk)
    assert isinstance(full.content, list)
    block_types = {block["type"] for block in full.content}  # type: ignore[index]
    if output_version == "v0":
        assert block_types == {
            "text",
            "server_tool_use",
            "bash_code_execution_tool_result",
        }
    else:
        assert block_types == {"text", "server_tool_call", "server_tool_result"}

    # Test we can pass back in
    next_message = {
        "role": "user",
        "content": "Please add more comments to the code.",
    }
    _ = llm_with_tools.invoke(
        [input_message, full, next_message],
    )


@pytest.mark.default_cassette("test_remote_mcp.yaml.gz")
@pytest.mark.vcr
@pytest.mark.parametrize("output_version", ["v0", "v1"])
def test_remote_mcp(output_version: Literal["v0", "v1"]) -> None:
    """Note: this is a beta feature.

    TODO: Update to remove beta once generally available.
    """
    mcp_servers = [
        {
            "type": "url",
            "url": "https://mcp.deepwiki.com/mcp",
            "name": "deepwiki",
            "authorization_token": "PLACEHOLDER",
        },
    ]

    llm = ChatAnthropic(
        model="claude-sonnet-4-5-20250929",  # type: ignore[call-arg]
        mcp_servers=mcp_servers,
        output_version=output_version,
    ).bind_tools([{"type": "mcp_toolset", "mcp_server_name": "deepwiki"}])

    input_message = {
        "role": "user",
        "content": [
            {
                "type": "text",
                "text": (
                    "What transport protocols does the 2025-03-26 version of the MCP "
                    "spec (modelcontextprotocol/modelcontextprotocol) support?"
                ),
            },
        ],
    }
    response = llm.invoke([input_message])
    assert all(isinstance(block, dict) for block in response.content)
    block_types = {block["type"] for block in response.content}  # type: ignore[index]
    if output_version == "v0":
        assert block_types == {"text", "mcp_tool_use", "mcp_tool_result"}
    else:
        assert block_types == {"text", "server_tool_call", "server_tool_result"}

    # Test streaming
    full: BaseMessageChunk | None = None
    for chunk in llm.stream([input_message]):
        assert isinstance(chunk, AIMessageChunk)
        full = chunk if full is None else full + chunk
    assert isinstance(full, AIMessageChunk)
    assert isinstance(full.content, list)
    assert all(isinstance(block, dict) for block in full.content)
    block_types = {block["type"] for block in full.content}  # type: ignore[index]
    if output_version == "v0":
        assert block_types == {"text", "mcp_tool_use", "mcp_tool_result"}
    else:
        assert block_types == {"text", "server_tool_call", "server_tool_result"}

    # Test we can pass back in
    next_message = {
        "role": "user",
        "content": "Please query the same tool again, but add 'please' to your query.",
    }
    _ = llm.invoke(
        [input_message, full, next_message],
    )


@pytest.mark.parametrize("block_format", ["anthropic", "standard"])
def test_files_api_image(block_format: str) -> None:
    """Note: this is a beta feature.

    TODO: Update to remove beta once generally available.
    """
    image_file_id = os.getenv("ANTHROPIC_FILES_API_IMAGE_ID")
    if not image_file_id:
        pytest.skip()
    llm = ChatAnthropic(
        model=MODEL_NAME,  # type: ignore[call-arg]
        betas=["files-api-2025-04-14"],
    )
    if block_format == "anthropic":
        block = {
            "type": "image",
            "source": {
                "type": "file",
                "file_id": image_file_id,
            },
        }
    else:
        # standard block format
        block = {
            "type": "image",
            "file_id": image_file_id,
        }
    input_message = {
        "role": "user",
        "content": [
            {"type": "text", "text": "Describe this image."},
            block,
        ],
    }
    _ = llm.invoke([input_message])


@pytest.mark.parametrize("block_format", ["anthropic", "standard"])
def test_files_api_pdf(block_format: str) -> None:
    """Note: this is a beta feature.

    TODO: Update to remove beta once generally available.
    """
    pdf_file_id = os.getenv("ANTHROPIC_FILES_API_PDF_ID")
    if not pdf_file_id:
        pytest.skip()
    llm = ChatAnthropic(
        model=MODEL_NAME,  # type: ignore[call-arg]
        betas=["files-api-2025-04-14"],
    )
    if block_format == "anthropic":
        block = {"type": "document", "source": {"type": "file", "file_id": pdf_file_id}}
    else:
        # standard block format
        block = {
            "type": "file",
            "file_id": pdf_file_id,
        }
    input_message = {
        "role": "user",
        "content": [
            {"type": "text", "text": "Describe this document."},
            block,
        ],
    }
    _ = llm.invoke([input_message])


@pytest.mark.vcr
def test_search_result_tool_message() -> None:
    """Test that we can pass a search result tool message to the model."""
    llm = ChatAnthropic(
        model=MODEL_NAME,  # type: ignore[call-arg]
    )

    @tool
    def retrieval_tool(query: str) -> list[dict]:
        """Retrieve information from a knowledge base."""
        return [
            {
                "type": "search_result",
                "title": "Leave policy",
                "source": "HR Leave Policy 2025",
                "citations": {"enabled": True},
                "content": [
                    {
                        "type": "text",
                        "text": (
                            "To request vacation days, submit a leave request form "
                            "through the HR portal. Approval will be sent by email."
                        ),
                    },
                ],
            },
        ]

    tool_call = {
        "type": "tool_call",
        "name": "retrieval_tool",
        "args": {"query": "vacation days request process"},
        "id": "toolu_abc123",
    }

    tool_message = retrieval_tool.invoke(tool_call)
    assert isinstance(tool_message, ToolMessage)
    assert isinstance(tool_message.content, list)

    messages = [
        HumanMessage("How do I request vacation days?"),
        AIMessage(
            [{"type": "text", "text": "Let me look that up for you."}],
            tool_calls=[tool_call],
        ),
        tool_message,
    ]

    result = llm.invoke(messages)
    assert isinstance(result, AIMessage)
    assert isinstance(result.content, list)
    assert any("citations" in block for block in result.content)

    assert (
        _convert_from_v1_to_anthropic(result.content_blocks, [], "anthropic")
        == result.content
    )


def test_search_result_top_level() -> None:
    llm = ChatAnthropic(
        model=MODEL_NAME,  # type: ignore[call-arg]
    )
    input_message = HumanMessage(
        [
            {
                "type": "search_result",
                "title": "Leave policy",
                "source": "HR Leave Policy 2025 - page 1",
                "citations": {"enabled": True},
                "content": [
                    {
                        "type": "text",
                        "text": (
                            "To request vacation days, submit a leave request form "
                            "through the HR portal. Approval will be sent by email."
                        ),
                    },
                ],
            },
            {
                "type": "search_result",
                "title": "Leave policy",
                "source": "HR Leave Policy 2025 - page 2",
                "citations": {"enabled": True},
                "content": [
                    {
                        "type": "text",
                        "text": "Managers have 3 days to approve a request.",
                    },
                ],
            },
            {
                "type": "text",
                "text": "How do I request vacation days?",
            },
        ],
    )
    result = llm.invoke([input_message])
    assert isinstance(result, AIMessage)
    assert isinstance(result.content, list)
    assert any("citations" in block for block in result.content)

    assert (
        _convert_from_v1_to_anthropic(result.content_blocks, [], "anthropic")
        == result.content
    )


def test_memory_tool() -> None:
    llm = ChatAnthropic(
        model="claude-sonnet-4-5-20250929",  # type: ignore[call-arg]
        betas=["context-management-2025-06-27"],
    )
    llm_with_tools = llm.bind_tools([{"type": "memory_20250818", "name": "memory"}])
    response = llm_with_tools.invoke("What are my interests?")
    assert isinstance(response, AIMessage)
    assert response.tool_calls
    assert response.tool_calls[0]["name"] == "memory"


@pytest.mark.vcr
def test_context_management() -> None:
    # TODO: update example to trigger action
    llm = ChatAnthropic(
        model="claude-sonnet-4-5-20250929",  # type: ignore[call-arg]
        betas=["context-management-2025-06-27"],
        context_management={
            "edits": [
                {
                    "type": "clear_tool_uses_20250919",
                    "trigger": {"type": "input_tokens", "value": 10},
                    "clear_at_least": {"type": "input_tokens", "value": 5},
                }
            ]
        },
        max_tokens=1024,  # type: ignore[call-arg]
    )
    llm_with_tools = llm.bind_tools(
        [{"type": "web_search_20250305", "name": "web_search"}]
    )
    input_message = {"role": "user", "content": "Search for recent developments in AI"}
    response = llm_with_tools.invoke([input_message])
    assert response.response_metadata.get("context_management")

    # Test streaming
    full: BaseMessageChunk | None = None
    for chunk in llm_with_tools.stream([input_message]):
        assert isinstance(chunk, AIMessageChunk)
        full = chunk if full is None else full + chunk
    assert isinstance(full, AIMessageChunk)
    assert full.response_metadata.get("context_management")


@pytest.mark.default_cassette("test_tool_search.yaml.gz")
@pytest.mark.vcr
@pytest.mark.parametrize("output_version", ["v0", "v1"])
def test_tool_search(output_version: str) -> None:
    """Test tool search with LangChain tools using extras parameter."""

    @tool(parse_docstring=True, extras={"defer_loading": True})
    def get_weather(location: str, unit: str = "fahrenheit") -> str:
        """Get the current weather for a location.

        Args:
            location: City name
            unit: Temperature unit (celsius or fahrenheit)
        """
        return f"The weather in {location} is sunny and 72°{unit[0].upper()}"

    @tool(parse_docstring=True, extras={"defer_loading": True})
    def search_files(query: str) -> str:
        """Search through files in the workspace.

        Args:
            query: Search query
        """
        return f"Found 3 files matching '{query}'"

    model = ChatAnthropic(
        model="claude-opus-4-5-20251101", output_version=output_version
    )

    agent = create_agent(  # type: ignore[var-annotated]
        model,
        tools=[
            {
                "type": "tool_search_tool_regex_20251119",
                "name": "tool_search_tool_regex",
            },
            get_weather,
            search_files,
        ],
    )

    # Test with actual API call
    input_message = {
        "role": "user",
        "content": "What's the weather in San Francisco? Find and use a tool.",
    }
    result = agent.invoke({"messages": [input_message]})
    first_response = result["messages"][1]
    content_types = [block["type"] for block in first_response.content]
    if output_version == "v0":
        assert content_types == [
            "text",
            "server_tool_use",
            "tool_search_tool_result",
            "text",
            "tool_use",
        ]
    else:
        # v1
        assert content_types == [
            "text",
            "server_tool_call",
            "server_tool_result",
            "text",
            "tool_call",
        ]

    answer = result["messages"][-1]
    assert not answer.tool_calls
    assert answer.text


@pytest.mark.default_cassette("test_programmatic_tool_use.yaml.gz")
@pytest.mark.vcr
@pytest.mark.parametrize("output_version", ["v0", "v1"])
def test_programmatic_tool_use(output_version: str) -> None:
    """Test programmatic tool use.

    Implicitly checks that `allowed_callers` in tool extras works.
    """

    @tool(extras={"allowed_callers": ["code_execution_20250825"]})
    def get_weather(location: str) -> str:
        """Get the weather at a location."""
        return "It's sunny."

    tools: list = [
        {"type": "code_execution_20250825", "name": "code_execution"},
        get_weather,
    ]

    model = ChatAnthropic(
        model="claude-sonnet-4-5",
        betas=["advanced-tool-use-2025-11-20"],
        reuse_last_container=True,
        output_version=output_version,
    )

    agent = create_agent(model, tools=tools)  # type: ignore[var-annotated]

    input_query = {
        "role": "user",
        "content": "What's the weather in Boston?",
    }

    result = agent.invoke({"messages": [input_query]})
    assert len(result["messages"]) == 4
    tool_call_message = result["messages"][1]
    response_message = result["messages"][-1]

    if output_version == "v0":
        server_tool_use_block = next(
            block
            for block in tool_call_message.content
            if block["type"] == "server_tool_use"
        )
        assert server_tool_use_block

        tool_use_block = next(
            block for block in tool_call_message.content if block["type"] == "tool_use"
        )
        assert "caller" in tool_use_block

        code_execution_result = next(
            block
            for block in response_message.content
            if block["type"] == "code_execution_tool_result"
        )
        assert code_execution_result["content"]["return_code"] == 0
    else:
        server_tool_call_block = next(
            block
            for block in tool_call_message.content
            if block["type"] == "server_tool_call"
        )
        assert server_tool_call_block

        tool_call_block = next(
            block for block in tool_call_message.content if block["type"] == "tool_call"
        )
        assert "caller" in tool_call_block["extras"]

        server_tool_result = next(
            block
            for block in response_message.content
            if block["type"] == "server_tool_result"
        )
        assert server_tool_result["output"]["return_code"] == 0


@pytest.mark.default_cassette("test_programmatic_tool_use_streaming.yaml.gz")
@pytest.mark.vcr
@pytest.mark.parametrize("output_version", ["v0", "v1"])
def test_programmatic_tool_use_streaming(output_version: str) -> None:
    @tool(extras={"allowed_callers": ["code_execution_20250825"]})
    def get_weather(location: str) -> str:
        """Get the weather at a location."""
        return "It's sunny."

    tools: list = [
        {"type": "code_execution_20250825", "name": "code_execution"},
        get_weather,
    ]

    model = ChatAnthropic(
        model="claude-sonnet-4-5",
        betas=["advanced-tool-use-2025-11-20"],
        reuse_last_container=True,
        streaming=True,
        output_version=output_version,
    )

    agent = create_agent(model, tools=tools)  # type: ignore[var-annotated]

    input_query = {
        "role": "user",
        "content": "What's the weather in Boston?",
    }

    result = agent.invoke({"messages": [input_query]})
    assert len(result["messages"]) == 4
    tool_call_message = result["messages"][1]
    response_message = result["messages"][-1]

    if output_version == "v0":
        server_tool_use_block = next(
            block
            for block in tool_call_message.content
            if block["type"] == "server_tool_use"
        )
        assert server_tool_use_block

        tool_use_block = next(
            block for block in tool_call_message.content if block["type"] == "tool_use"
        )
        assert "caller" in tool_use_block

        code_execution_result = next(
            block
            for block in response_message.content
            if block["type"] == "code_execution_tool_result"
        )
        assert code_execution_result["content"]["return_code"] == 0
    else:
        server_tool_call_block = next(
            block
            for block in tool_call_message.content
            if block["type"] == "server_tool_call"
        )
        assert server_tool_call_block

        tool_call_block = next(
            block for block in tool_call_message.content if block["type"] == "tool_call"
        )
        assert "caller" in tool_call_block["extras"]

        server_tool_result = next(
            block
            for block in response_message.content
            if block["type"] == "server_tool_result"
        )
        assert server_tool_result["output"]["return_code"] == 0


def test_async_shared_client() -> None:
    llm = ChatAnthropic(model=MODEL_NAME)  # type: ignore[call-arg]
    _ = asyncio.run(llm.ainvoke("Hello"))
    _ = asyncio.run(llm.ainvoke("Hello"))


def test_fine_grained_tool_streaming() -> None:
    """Test fine-grained tool streaming reduces latency for tool parameter streaming.

    Fine-grained tool streaming enables Claude to stream tool parameter values.

    https://platform.claude.com/docs/en/agents-and-tools/tool-use/fine-grained-tool-streaming
    """
    llm = ChatAnthropic(
        model=MODEL_NAME,  # type: ignore[call-arg]
        temperature=0,
        betas=["fine-grained-tool-streaming-2025-05-14"],
    )

    # Define a tool that requires a longer text parameter
    tool_definition = {
        "name": "write_document",
        "description": "Write a document with the given content",
        "input_schema": {
            "type": "object",
            "properties": {
                "title": {"type": "string", "description": "Document title"},
                "content": {
                    "type": "string",
                    "description": "The full document content",
                },
            },
            "required": ["title", "content"],
        },
    }

    llm_with_tools = llm.bind_tools([tool_definition])
    query = (
        "Write a document about the benefits of streaming APIs. "
        "Include at least 3 paragraphs."
    )

    # Test streaming with fine-grained tool streaming
    first = True
    chunks: list[BaseMessage | BaseMessageChunk] = []
    tool_call_chunks = []

    for chunk in llm_with_tools.stream(query):
        chunks.append(chunk)
        if first:
            gathered = chunk
            first = False
        else:
            gathered = gathered + chunk  # type: ignore[assignment]

        # Collect tool call chunks
        tool_call_chunks.extend(
            [
                block
                for block in chunk.content_blocks
                if block["type"] == "tool_call_chunk"
            ]
        )

    # Verify we got chunks
    assert len(chunks) > 1

    # Verify final message has tool call
    assert isinstance(gathered, AIMessageChunk)
    assert isinstance(gathered.tool_calls, list)
    assert len(gathered.tool_calls) >= 1

    # Find the write_document tool call
    write_doc_call = None
    for tool_call in gathered.tool_calls:
        if tool_call["name"] == "write_document":
            write_doc_call = tool_call
            break

    assert write_doc_call is not None, "write_document tool call not found"
    assert isinstance(write_doc_call["args"], dict)
    assert "title" in write_doc_call["args"]
    assert "content" in write_doc_call["args"]
    assert (
        len(write_doc_call["args"]["content"]) > 100
    )  # Should have substantial content

    # Verify tool_call_chunks were received
    # With fine-grained streaming, we should get tool call chunks
    assert len(tool_call_chunks) > 0

    # Verify content_blocks in final message
    content_blocks = gathered.content_blocks
    assert len(content_blocks) >= 1

    # Should have at least one tool_call block
    tool_call_blocks = [b for b in content_blocks if b["type"] == "tool_call"]
    assert len(tool_call_blocks) >= 1

    write_doc_block = None
    for block in tool_call_blocks:
        if block["name"] == "write_document":
            write_doc_block = block
            break

    assert write_doc_block is not None
    assert write_doc_block["name"] == "write_document"
    assert "args" in write_doc_block


@pytest.mark.vcr
def test_compaction() -> None:
    """Test the compaction beta feature."""
    llm = ChatAnthropic(
        model="claude-opus-4-6",  # type: ignore[call-arg]
        betas=["compact-2026-01-12"],
        max_tokens=4096,
        context_management={
            "edits": [
                {
                    "type": "compact_20260112",
                    "trigger": {"type": "input_tokens", "value": 50000},
                    "pause_after_compaction": True,
                }
            ]
        },
    )

    input_message = {
        "role": "user",
        "content": f"Generate a one-sentence summary of this:\n\n{'a' * 100000}",
    }
    messages: list = [input_message]

    first_response = llm.invoke(messages)
    messages.append(first_response)

    second_message = {
        "role": "user",
        "content": f"Generate a one-sentence summary of this:\n\n{'b' * 100000}",
    }
    messages.append(second_message)

    second_response = llm.invoke(messages)
    messages.append(second_response)

    content_blocks = second_response.content_blocks
    compaction_block = next(
        (block for block in content_blocks if block["type"] == "non_standard"),
        None,
    )
    assert compaction_block
    assert compaction_block["value"].get("type") == "compaction"

    third_message = {
        "role": "user",
        "content": "What are we talking about?",
    }
    messages.append(third_message)
    third_response = llm.invoke(messages)
    content_blocks = third_response.content_blocks
    assert [block["type"] for block in content_blocks] == ["text"]


@pytest.mark.vcr
def test_compaction_streaming() -> None:
    """Test the compaction beta feature."""
    llm = ChatAnthropic(
        model="claude-opus-4-6",  # type: ignore[call-arg]
        betas=["compact-2026-01-12"],
        max_tokens=4096,
        context_management={
            "edits": [
                {
                    "type": "compact_20260112",
                    "trigger": {"type": "input_tokens", "value": 50000},
                    "pause_after_compaction": False,
                }
            ]
        },
        streaming=True,
    )

    input_message = {
        "role": "user",
        "content": f"Generate a one-sentence summary of this:\n\n{'a' * 100000}",
    }
    messages: list = [input_message]

    first_response = llm.invoke(messages)
    messages.append(first_response)

    second_message = {
        "role": "user",
        "content": f"Generate a one-sentence summary of this:\n\n{'b' * 100000}",
    }
    messages.append(second_message)

    second_response = llm.invoke(messages)
    messages.append(second_response)

    content_blocks = second_response.content_blocks
    compaction_block = next(
        (block for block in content_blocks if block["type"] == "non_standard"),
        None,
    )
    assert compaction_block
    assert compaction_block["value"].get("type") == "compaction"

    third_message = {
        "role": "user",
        "content": "What are we talking about?",
    }
    messages.append(third_message)
    third_response = llm.invoke(messages)
    content_blocks = third_response.content_blocks
    assert [block["type"] for block in content_blocks] == ["text"]


class _Person(BaseModel):
    """A person with a name and age."""

    name: str = Field(description="The person's name")
    age: int = Field(description="The person's age in years")


def _stable_blocks(blocks: Any) -> list[dict[str, Any]]:
    """Drop fields that vary between API calls so blocks can be compared.

    Tool-call ids, wire indices, and provider extras are not path- or call-
    stable; strip them so the comparison targets the semantic content.
    """
    volatile = {"id", "index", "extras"}
    return [{k: v for k, v in b.items() if k not in volatile} for b in blocks]


@pytest.mark.default_cassette("test_streaming_tool_call_v1_v2_parity.yaml.gz")
@pytest.mark.vcr
def test_streaming_tool_call_v1_v2_parity() -> None:
    """`AIMessage` parity between `stream()` and `stream_events(version="v3")` output.

    Runs the same forced-tool-call prompt through both the legacy chunk
    stream (reduced with `AIMessageChunk.__add__`) and the `stream_events(version="v3")`
    bridge path on a `v1`-output `ChatAnthropic`, then compares the
    resulting messages on path-independent invariants:

    - tool call name and args (ids vary between calls and are ignored)
    - exactly one tool call, no invalid tool calls
    - `content_blocks` (the v1 projection, stripped of volatile fields)
    - a valid tool-use `finish_reason`

    The v2 path is additionally validated against the full protocol
    lifecycle via `assert_valid_event_stream`.
    """
    llm = ChatAnthropic(
        model=MODEL_NAME,
        output_version="v1",  # type: ignore[call-arg]
    )
    with_tool = llm.bind_tools(
        [_Person],
        tool_choice={"type": "tool", "name": "_Person"},
    )
    prompt = "Extract: Erick is 27 years old."

    v1_full: AIMessageChunk | None = None
    for chunk in with_tool.stream(prompt):
        assert isinstance(chunk, AIMessageChunk)
        v1_full = chunk if v1_full is None else v1_full + chunk
    assert isinstance(v1_full, AIMessageChunk)

    stream = cast("ChatModelStream", with_tool.stream_events(prompt, version="v3"))
    events = list(stream)
    assert_valid_event_stream(events)
    v2_message = stream.output
    assert isinstance(v2_message, AIMessage)

    assert len(v1_full.tool_calls) == len(v2_message.tool_calls) == 1
    assert not v1_full.invalid_tool_calls
    assert not v2_message.invalid_tool_calls

    v1_tc = v1_full.tool_calls[0]
    v2_tc = v2_message.tool_calls[0]
    assert v1_tc["name"] == v2_tc["name"] == "_Person"
    assert v1_tc["args"] == v2_tc["args"] == {"name": "Erick", "age": 27}

    v1_blocks = _stable_blocks(v1_full.content_blocks)
    v2_blocks = _stable_blocks(v2_message.content_blocks)
    assert v1_blocks == v2_blocks
    assert v1_blocks == [
        {
            "type": "tool_call",
            "name": "_Person",
            "args": {"name": "Erick", "age": 27},
        }
    ]

    # The compat bridge passes the provider's raw terminal reason through
    # unchanged — Anthropic surfaces it under `stop_reason` on both paths.
    # Accept either key on both sides rather than asserting a specific
    # normalization that the bridge does not perform.
    v1_finish = v1_full.response_metadata.get(
        "finish_reason"
    ) or v1_full.response_metadata.get("stop_reason")
    v2_finish = v2_message.response_metadata.get(
        "finish_reason"
    ) or v2_message.response_metadata.get("stop_reason")
    assert v1_finish is not None
    assert v2_finish is not None
    assert any(k in v1_finish for k in ("tool_use", "tool_calls", "stop"))
    assert any(k in v2_finish for k in ("tool_use", "tool_calls", "stop"))
