from __future__ import annotations

import asyncio
import base64
import io
import json
import logging
import mimetypes
import time
import uuid
import warnings
import wave
from difflib import get_close_matches
from operator import itemgetter
from typing import (
    Any,
    AsyncIterator,
    Callable,
    Dict,
    Iterator,
    List,
    Literal,
    Mapping,
    Optional,
    Sequence,
    Tuple,
    Type,
    Union,
    cast,
)

import filetype  # type: ignore[import]
import google.api_core

# TODO: remove ignore once the Google package is published with types
import proto  # type: ignore[import]
from google.ai.generativelanguage_v1beta import (
    GenerativeServiceAsyncClient as v1betaGenerativeServiceAsyncClient,
)
from google.ai.generativelanguage_v1beta.types import (
    Blob,
    Candidate,
    CodeExecution,
    CodeExecutionResult,
    Content,
    ExecutableCode,
    FileData,
    FunctionCall,
    FunctionDeclaration,
    FunctionResponse,
    GenerateContentRequest,
    GenerateContentResponse,
    GenerationConfig,
    Part,
    SafetySetting,
    ToolConfig,
    VideoMetadata,
)
from google.ai.generativelanguage_v1beta.types import Tool as GoogleTool
from langchain_core.callbacks.manager import (
    AsyncCallbackManagerForLLMRun,
    CallbackManagerForLLMRun,
)
from langchain_core.language_models import LanguageModelInput
from langchain_core.language_models.chat_models import BaseChatModel, LangSmithParams
from langchain_core.messages import (
    AIMessage,
    AIMessageChunk,
    BaseMessage,
    FunctionMessage,
    HumanMessage,
    SystemMessage,
    ToolMessage,
    is_data_content_block,
)
from langchain_core.messages.ai import UsageMetadata, add_usage, subtract_usage
from langchain_core.messages.tool import invalid_tool_call, tool_call, tool_call_chunk
from langchain_core.output_parsers import JsonOutputParser, PydanticOutputParser
from langchain_core.output_parsers.base import OutputParserLike
from langchain_core.output_parsers.openai_tools import (
    JsonOutputKeyToolsParser,
    PydanticToolsParser,
    parse_tool_calls,
)
from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
from langchain_core.runnables import Runnable, RunnableConfig, RunnablePassthrough
from langchain_core.tools import BaseTool
from langchain_core.utils import get_pydantic_field_names
from langchain_core.utils.function_calling import (
    convert_to_json_schema,
    convert_to_openai_tool,
)
from langchain_core.utils.pydantic import is_basemodel_subclass
from langchain_core.utils.utils import _build_model_kwargs
from pydantic import (
    BaseModel,
    ConfigDict,
    Field,
    SecretStr,
    model_validator,
)
from pydantic.v1 import BaseModel as BaseModelV1
from tenacity import (
    before_sleep_log,
    retry,
    retry_if_exception_type,
    stop_after_attempt,
    wait_exponential,
)
from typing_extensions import Self, is_typeddict

from langchain_google_genai._common import (
    GoogleGenerativeAIError,
    SafetySettingDict,
    _BaseGoogleGenerativeAI,
    get_client_info,
)
from langchain_google_genai._function_utils import (
    _dict_to_gapic_schema,
    _tool_choice_to_tool_config,
    _ToolChoiceType,
    _ToolConfigDict,
    _ToolDict,
    convert_to_genai_function_declarations,
    is_basemodel_subclass_safe,
    replace_defs_in_schema,
    tool_to_dict,
)
from langchain_google_genai._image_utils import (
    ImageBytesLoader,
    image_bytes_to_b64_string,
)

from . import _genai_extension as genaix

logger = logging.getLogger(__name__)

_allowed_params_prediction_service = ["request", "timeout", "metadata", "labels"]

_FunctionDeclarationType = Union[
    FunctionDeclaration,
    dict[str, Any],
    Callable[..., Any],
]


class ChatGoogleGenerativeAIError(GoogleGenerativeAIError):
    """
    Custom exception class for errors associated with the `Google GenAI` API.

    This exception is raised when there are specific issues related to the
    Google genai API usage in the ChatGoogleGenerativeAI class, such as unsupported
    message types or roles.
    """


def _create_retry_decorator(
    max_retries: int = 6,
    wait_exponential_multiplier: float = 2.0,
    wait_exponential_min: float = 1.0,
    wait_exponential_max: float = 60.0,
) -> Callable[[Any], Any]:
    """
    Creates and returns a preconfigured tenacity retry decorator.

    The retry decorator is configured to handle specific Google API exceptions
    such as ResourceExhausted and ServiceUnavailable. It uses an exponential
    backoff strategy for retries.

    Returns:
        Callable[[Any], Any]: A retry decorator configured for handling specific
        Google API exceptions.
    """
    return retry(
        reraise=True,
        stop=stop_after_attempt(max_retries),
        wait=wait_exponential(
            multiplier=wait_exponential_multiplier,
            min=wait_exponential_min,
            max=wait_exponential_max,
        ),
        retry=(
            retry_if_exception_type(google.api_core.exceptions.ResourceExhausted)
            | retry_if_exception_type(google.api_core.exceptions.ServiceUnavailable)
            | retry_if_exception_type(google.api_core.exceptions.GoogleAPIError)
        ),
        before_sleep=before_sleep_log(logger, logging.WARNING),
    )


def _chat_with_retry(generation_method: Callable, **kwargs: Any) -> Any:
    """
    Executes a chat generation method with retry logic using tenacity.

    This function is a wrapper that applies a retry mechanism to a provided
    chat generation function. It is useful for handling intermittent issues
    like network errors or temporary service unavailability.

    Args:
        generation_method (Callable): The chat generation method to be executed.
        **kwargs (Any): Additional keyword arguments to pass to the generation method.

    Returns:
        Any: The result from the chat generation method.
    """
    retry_decorator = _create_retry_decorator(
        max_retries=kwargs.get("max_retries", 6),
        wait_exponential_multiplier=kwargs.get("wait_exponential_multiplier", 2.0),
        wait_exponential_min=kwargs.get("wait_exponential_min", 1.0),
        wait_exponential_max=kwargs.get("wait_exponential_max", 60.0),
    )

    @retry_decorator
    def _chat_with_retry(**kwargs: Any) -> Any:
        try:
            return generation_method(**kwargs)
        except google.api_core.exceptions.FailedPrecondition as exc:
            if "location is not supported" in exc.message:
                error_msg = (
                    "Your location is not supported by google-generativeai "
                    "at the moment. Try to use ChatVertexAI LLM from "
                    "langchain_google_vertexai."
                )
                raise ValueError(error_msg)

        except google.api_core.exceptions.InvalidArgument as e:
            raise ChatGoogleGenerativeAIError(
                f"Invalid argument provided to Gemini: {e}"
            ) from e
        except google.api_core.exceptions.ResourceExhausted as e:
            # Handle quota-exceeded error with recommended retry delay
            if hasattr(e, "retry_after") and e.retry_after < kwargs.get(
                "wait_exponential_max", 60.0
            ):
                time.sleep(e.retry_after)
            raise e
        except Exception as e:
            raise e

    params = (
        {k: v for k, v in kwargs.items() if k in _allowed_params_prediction_service}
        if (request := kwargs.get("request"))
        and hasattr(request, "model")
        and "gemini" in request.model
        else kwargs
    )
    return _chat_with_retry(**params)


async def _achat_with_retry(generation_method: Callable, **kwargs: Any) -> Any:
    """
    Executes a chat generation method with retry logic using tenacity.

    This function is a wrapper that applies a retry mechanism to a provided
    chat generation function. It is useful for handling intermittent issues
    like network errors or temporary service unavailability.

    Args:
        generation_method (Callable): The chat generation method to be executed.
        **kwargs (Any): Additional keyword arguments to pass to the generation method.

    Returns:
        Any: The result from the chat generation method.
    """
    retry_decorator = _create_retry_decorator()
    from google.api_core.exceptions import InvalidArgument  # type: ignore

    @retry_decorator
    async def _achat_with_retry(**kwargs: Any) -> Any:
        try:
            return await generation_method(**kwargs)
        except InvalidArgument as e:
            # Do not retry for these errors.
            raise ChatGoogleGenerativeAIError(
                f"Invalid argument provided to Gemini: {e}"
            ) from e
        except Exception as e:
            raise e

    params = (
        {k: v for k, v in kwargs.items() if k in _allowed_params_prediction_service}
        if (request := kwargs.get("request"))
        and hasattr(request, "model")
        and "gemini" in request.model
        else kwargs
    )
    return await _achat_with_retry(**params)


def _is_lc_content_block(part: dict) -> bool:
    return "type" in part


def _is_openai_image_block(block: dict) -> bool:
    """Check if the block contains image data in OpenAI Chat Completions format."""
    if block.get("type") == "image_url":
        if (
            (set(block.keys()) <= {"type", "image_url", "detail"})
            and (image_url := block.get("image_url"))
            and isinstance(image_url, dict)
        ):
            url = image_url.get("url")
            if isinstance(url, str):
                return True
    else:
        return False

    return False


def _convert_to_parts(
    raw_content: Union[str, Sequence[Union[str, dict]]],
) -> List[Part]:
    """Converts a list of LangChain messages into a Google parts."""
    parts = []
    content = [raw_content] if isinstance(raw_content, str) else raw_content
    image_loader = ImageBytesLoader()
    for part in content:
        if isinstance(part, str):
            parts.append(Part(text=part))
        elif isinstance(part, Mapping):
            if _is_lc_content_block(part):
                if part["type"] == "text":
                    parts.append(Part(text=part["text"]))
                elif is_data_content_block(part):
                    if part["source_type"] == "url":
                        bytes_ = image_loader._bytes_from_url(part["url"])
                    elif part["source_type"] == "base64":
                        bytes_ = base64.b64decode(part["data"])
                    else:
                        raise ValueError("source_type must be url or base64.")
                    inline_data: dict = {"data": bytes_}
                    if "mime_type" in part:
                        inline_data["mime_type"] = part["mime_type"]
                    else:
                        source = cast(str, part.get("url") or part.get("data"))
                        mime_type, _ = mimetypes.guess_type(source)
                        if not mime_type:
                            kind = filetype.guess(bytes_)
                            if kind:
                                mime_type = kind.mime
                        if mime_type:
                            inline_data["mime_type"] = mime_type
                    parts.append(Part(inline_data=inline_data))
                elif part["type"] == "image_url":
                    img_url = part["image_url"]
                    if isinstance(img_url, dict):
                        if "url" not in img_url:
                            raise ValueError(
                                f"Unrecognized message image format: {img_url}"
                            )
                        img_url = img_url["url"]
                    parts.append(image_loader.load_part(img_url))
                # Handle media type like LangChain.js
                # https://github.com/langchain-ai/langchainjs/blob/e536593e2585f1dd7b0afc187de4d07cb40689ba/libs/langchain-google-common/src/utils/gemini.ts#L93-L106
                elif part["type"] == "media":
                    if "mime_type" not in part:
                        raise ValueError(f"Missing mime_type in media part: {part}")
                    mime_type = part["mime_type"]
                    media_part = Part()

                    if "data" in part:
                        media_part.inline_data = Blob(
                            data=part["data"], mime_type=mime_type
                        )
                    elif "file_uri" in part:
                        media_part.file_data = FileData(
                            file_uri=part["file_uri"], mime_type=mime_type
                        )
                    else:
                        raise ValueError(
                            f"Media part must have either data or file_uri: {part}"
                        )
                    if "video_metadata" in part:
                        metadata = VideoMetadata(part["video_metadata"])
                        media_part.video_metadata = metadata
                    parts.append(media_part)
                elif part["type"] == "executable_code":
                    if "executable_code" not in part or "language" not in part:
                        raise ValueError(
                            "Executable code part must have 'code' and 'language' "
                            f"keys, got {part}"
                        )
                    executable_code_part = Part(
                        executable_code=ExecutableCode(
                            language=part["language"], code=part["executable_code"]
                        )
                    )
                    parts.append(executable_code_part)
                elif part["type"] == "code_execution_result":
                    if "code_execution_result" not in part:
                        raise ValueError(
                            "Code execution result part must have "
                            f"'code_execution_result', got {part}"
                        )
                    if "outcome" in part:
                        outcome = part["outcome"]
                    else:
                        # Backward compatibility
                        outcome = 1  # Default to success if not specified
                    code_execution_result_part = Part(
                        code_execution_result=CodeExecutionResult(
                            output=part["code_execution_result"], outcome=outcome
                        )
                    )
                    parts.append(code_execution_result_part)
                elif part["type"] == "thinking":
                    parts.append(Part(text=part["thinking"], thought=True))
                else:
                    raise ValueError(
                        f"Unrecognized message part type: {part['type']}. Only text, "
                        f"image_url, and media types are supported."
                    )
            else:
                # Yolo
                logger.warning(
                    "Unrecognized message part format. Assuming it's a text part."
                )
                parts.append(Part(text=str(part)))
        else:
            # TODO: Maybe some of Google's native stuff
            # would hit this branch.
            raise ChatGoogleGenerativeAIError(
                "Gemini only supports text and inline_data parts."
            )
    return parts


def _convert_tool_message_to_parts(
    message: ToolMessage | FunctionMessage, name: Optional[str] = None
) -> list[Part]:
    """Converts a tool or function message to a Google part."""
    # Legacy agent stores tool name in message.additional_kwargs instead of message.name
    name = message.name or name or message.additional_kwargs.get("name")
    response: Any
    parts: list[Part] = []
    if isinstance(message.content, list):
        media_blocks = []
        other_blocks = []
        for block in message.content:
            if isinstance(block, dict) and (
                is_data_content_block(block) or _is_openai_image_block(block)
            ):
                media_blocks.append(block)
            else:
                other_blocks.append(block)
        parts.extend(_convert_to_parts(media_blocks))
        response = other_blocks

    elif not isinstance(message.content, str):
        response = message.content
    else:
        try:
            response = json.loads(message.content)
        except json.JSONDecodeError:
            response = message.content  # leave as str representation
    part = Part(
        function_response=FunctionResponse(
            name=name,
            response=(
                {"output": response} if not isinstance(response, dict) else response
            ),
        )
    )
    parts.append(part)
    return parts


def _get_ai_message_tool_messages_parts(
    tool_messages: Sequence[ToolMessage], ai_message: AIMessage
) -> list[Part]:
    """
    Finds relevant tool messages for the AI message and converts them to a single
    list of Parts.
    """
    # We are interested only in the tool messages that are part of the AI message
    tool_calls_ids = {tool_call["id"]: tool_call for tool_call in ai_message.tool_calls}
    parts = []
    for i, message in enumerate(tool_messages):
        if not tool_calls_ids:
            break
        if message.tool_call_id in tool_calls_ids:
            tool_call = tool_calls_ids[message.tool_call_id]
            message_parts = _convert_tool_message_to_parts(
                message, name=tool_call.get("name")
            )
            parts.extend(message_parts)
            # remove the id from the dict, so that we do not iterate over it again
            tool_calls_ids.pop(message.tool_call_id)
    return parts


def _parse_chat_history(
    input_messages: Sequence[BaseMessage], convert_system_message_to_human: bool = False
) -> Tuple[Optional[Content], List[Content]]:
    messages: List[Content] = []

    if convert_system_message_to_human:
        warnings.warn("Convert_system_message_to_human will be deprecated!")

    system_instruction: Optional[Content] = None
    messages_without_tool_messages = [
        message for message in input_messages if not isinstance(message, ToolMessage)
    ]
    tool_messages = [
        message for message in input_messages if isinstance(message, ToolMessage)
    ]
    for i, message in enumerate(messages_without_tool_messages):
        if isinstance(message, SystemMessage):
            system_parts = _convert_to_parts(message.content)
            if i == 0:
                system_instruction = Content(parts=system_parts)
            elif system_instruction is not None:
                system_instruction.parts.extend(system_parts)
            else:
                pass
            continue
        elif isinstance(message, AIMessage):
            role = "model"
            if message.tool_calls:
                ai_message_parts = []
                for tool_call in message.tool_calls:
                    function_call = FunctionCall(
                        {
                            "name": tool_call["name"],
                            "args": tool_call["args"],
                        }
                    )
                    ai_message_parts.append(Part(function_call=function_call))
                tool_messages_parts = _get_ai_message_tool_messages_parts(
                    tool_messages=tool_messages, ai_message=message
                )
                messages.append(Content(role=role, parts=ai_message_parts))
                messages.append(Content(role="user", parts=tool_messages_parts))
                continue
            elif raw_function_call := message.additional_kwargs.get("function_call"):
                function_call = FunctionCall(
                    {
                        "name": raw_function_call["name"],
                        "args": json.loads(raw_function_call["arguments"]),
                    }
                )
                parts = [Part(function_call=function_call)]
            else:
                parts = _convert_to_parts(message.content)
        elif isinstance(message, HumanMessage):
            role = "user"
            parts = _convert_to_parts(message.content)
            if i == 1 and convert_system_message_to_human and system_instruction:
                parts = [p for p in system_instruction.parts] + parts
                system_instruction = None
        elif isinstance(message, FunctionMessage):
            role = "user"
            parts = _convert_tool_message_to_parts(message)
        else:
            raise ValueError(
                f"Unexpected message with type {type(message)} at the position {i}."
            )

        messages.append(Content(role=role, parts=parts))
    return system_instruction, messages


# Helper function to append content consistently
def _append_to_content(
    current_content: Union[str, List[Any], None], new_item: Any
) -> Union[str, List[Any]]:
    """Appends a new item to the content, handling different initial content types."""
    if current_content is None and isinstance(new_item, str):
        return new_item
    elif current_content is None:
        return [new_item]
    elif isinstance(current_content, str):
        return [current_content, new_item]
    elif isinstance(current_content, list):
        current_content.append(new_item)
        return current_content
    else:
        # This case should ideally not be reached with proper type checking,
        # but it catches any unexpected types that might slip through.
        raise TypeError(f"Unexpected content type: {type(current_content)}")


def _parse_response_candidate(
    response_candidate: Candidate, streaming: bool = False
) -> AIMessage:
    content: Union[None, str, List[Union[str, dict]]] = None
    additional_kwargs: Dict[str, Any] = {}
    tool_calls = []
    invalid_tool_calls = []
    tool_call_chunks = []

    for part in response_candidate.content.parts:
        text: Optional[str] = None
        try:
            if hasattr(part, "text") and part.text is not None:
                text = part.text
                # Remove erroneous newline character if present
                if not streaming:
                    text = text.rstrip("\n")
        except AttributeError:
            pass

        if hasattr(part, "thought") and part.thought:
            thinking_message = {
                "type": "thinking",
                "thinking": part.text,
            }
            content = _append_to_content(content, thinking_message)
        elif text is not None and text:
            content = _append_to_content(content, text)

        if hasattr(part, "executable_code") and part.executable_code is not None:
            if part.executable_code.code and part.executable_code.language:
                code_message = {
                    "type": "executable_code",
                    "executable_code": part.executable_code.code,
                    "language": part.executable_code.language,
                }
                content = _append_to_content(content, code_message)

        if (
            hasattr(part, "code_execution_result")
            and part.code_execution_result is not None
        ):
            if part.code_execution_result.output:
                execution_result = {
                    "type": "code_execution_result",
                    "code_execution_result": part.code_execution_result.output,
                    "outcome": part.code_execution_result.outcome,
                }
                content = _append_to_content(content, execution_result)

        if part.inline_data.mime_type.startswith("audio/"):
            buffer = io.BytesIO()

            with wave.open(buffer, "wb") as wf:
                wf.setnchannels(1)
                wf.setsampwidth(2)
                # TODO: Read Sample Rate from MIME content type.
                wf.setframerate(24000)
                wf.writeframes(part.inline_data.data)

            additional_kwargs["audio"] = buffer.getvalue()

        if part.inline_data.mime_type.startswith("image/"):
            image_format = part.inline_data.mime_type[6:]
            image_message = {
                "type": "image_url",
                "image_url": {
                    "url": image_bytes_to_b64_string(
                        part.inline_data.data, image_format=image_format
                    )
                },
            }
            content = _append_to_content(content, image_message)

        if part.function_call:
            function_call = {"name": part.function_call.name}
            # dump to match other function calling llm for now
            function_call_args_dict = proto.Message.to_dict(part.function_call)["args"]
            function_call["arguments"] = json.dumps(
                {k: function_call_args_dict[k] for k in function_call_args_dict}
            )
            additional_kwargs["function_call"] = function_call

            if streaming:
                tool_call_chunks.append(
                    tool_call_chunk(
                        name=function_call.get("name"),
                        args=function_call.get("arguments"),
                        id=function_call.get("id", str(uuid.uuid4())),
                        index=function_call.get("index"),  # type: ignore
                    )
                )
            else:
                try:
                    tool_call_dict = parse_tool_calls(
                        [{"function": function_call}],
                        return_id=False,
                    )[0]
                except Exception as e:
                    invalid_tool_calls.append(
                        invalid_tool_call(
                            name=function_call.get("name"),
                            args=function_call.get("arguments"),
                            id=function_call.get("id", str(uuid.uuid4())),
                            error=str(e),
                        )
                    )
                else:
                    tool_calls.append(
                        tool_call(
                            name=tool_call_dict["name"],
                            args=tool_call_dict["args"],
                            id=tool_call_dict.get("id", str(uuid.uuid4())),
                        )
                    )
    if content is None:
        content = ""
    if any(isinstance(item, dict) and "executable_code" in item for item in content):
        warnings.warn(
            """
        ⚠️ Warning: Output may vary each run.  
        - 'executable_code': Always present.  
        - 'execution_result' & 'image_url': May be absent for some queries.  

        Validate before using in production.
"""
        )

    if streaming:
        return AIMessageChunk(
            content=cast(Union[str, List[Union[str, Dict[Any, Any]]]], content),
            additional_kwargs=additional_kwargs,
            tool_call_chunks=tool_call_chunks,
        )

    return AIMessage(
        content=cast(Union[str, List[Union[str, Dict[Any, Any]]]], content),
        additional_kwargs=additional_kwargs,
        tool_calls=tool_calls,
        invalid_tool_calls=invalid_tool_calls,
    )


def _response_to_result(
    response: GenerateContentResponse,
    stream: bool = False,
    prev_usage: Optional[UsageMetadata] = None,
) -> ChatResult:
    """Converts a PaLM API response into a LangChain ChatResult."""
    llm_output = {"prompt_feedback": proto.Message.to_dict(response.prompt_feedback)}

    # Get usage metadata
    try:
        input_tokens = response.usage_metadata.prompt_token_count
        thought_tokens = response.usage_metadata.thoughts_token_count
        output_tokens = response.usage_metadata.candidates_token_count + thought_tokens
        total_tokens = response.usage_metadata.total_token_count
        cache_read_tokens = response.usage_metadata.cached_content_token_count
        if input_tokens + output_tokens + cache_read_tokens + total_tokens > 0:
            if thought_tokens > 0:
                cumulative_usage = UsageMetadata(
                    input_tokens=input_tokens,
                    output_tokens=output_tokens,
                    total_tokens=total_tokens,
                    input_token_details={"cache_read": cache_read_tokens},
                    output_token_details={"reasoning": thought_tokens},
                )
            else:
                cumulative_usage = UsageMetadata(
                    input_tokens=input_tokens,
                    output_tokens=output_tokens,
                    total_tokens=total_tokens,
                    input_token_details={"cache_read": cache_read_tokens},
                )
            # previous usage metadata needs to be subtracted because gemini api returns
            # already-accumulated token counts with each chunk
            lc_usage = subtract_usage(cumulative_usage, prev_usage)
            if prev_usage and cumulative_usage["input_tokens"] < prev_usage.get(
                "input_tokens", 0
            ):
                # Gemini 1.5 and 2.0 return a lower cumulative count of prompt tokens
                # in the final chunk. We take this count to be ground truth because
                # it's consistent with the reported total tokens. So we need to
                # ensure this chunk compensates (the subtract_usage funcction floors
                # at zero).
                lc_usage["input_tokens"] = cumulative_usage[
                    "input_tokens"
                ] - prev_usage.get("input_tokens", 0)
        else:
            lc_usage = None
    except AttributeError:
        lc_usage = None

    generations: List[ChatGeneration] = []

    for candidate in response.candidates:
        generation_info = {}
        if candidate.finish_reason:
            generation_info["finish_reason"] = candidate.finish_reason.name
            # Add model_name in last chunk
            generation_info["model_name"] = response.model_version
        generation_info["safety_ratings"] = [
            proto.Message.to_dict(safety_rating, use_integers_for_enums=False)
            for safety_rating in candidate.safety_ratings
        ]
        try:
            if candidate.grounding_metadata:
                generation_info["grounding_metadata"] = proto.Message.to_dict(
                    candidate.grounding_metadata
                )
        except AttributeError:
            pass
        message = _parse_response_candidate(candidate, streaming=stream)
        message.usage_metadata = lc_usage
        if stream:
            generations.append(
                ChatGenerationChunk(
                    message=cast(AIMessageChunk, message),
                    generation_info=generation_info,
                )
            )
        else:
            generations.append(
                ChatGeneration(message=message, generation_info=generation_info)
            )
    if not response.candidates:
        # Likely a "prompt feedback" violation (e.g., toxic input)
        # Raising an error would be different than how OpenAI handles it,
        # so we'll just log a warning and continue with an empty message.
        logger.warning(
            "Gemini produced an empty response. Continuing with empty message\n"
            f"Feedback: {response.prompt_feedback}"
        )
        if stream:
            generations = [
                ChatGenerationChunk(
                    message=AIMessageChunk(content=""), generation_info={}
                )
            ]
        else:
            generations = [ChatGeneration(message=AIMessage(""), generation_info={})]
    return ChatResult(generations=generations, llm_output=llm_output)


def _is_event_loop_running() -> bool:
    try:
        asyncio.get_running_loop()
        return True
    except RuntimeError:
        return False


class ChatGoogleGenerativeAI(_BaseGoogleGenerativeAI, BaseChatModel):
    """`Google AI` chat models integration.

    Instantiation:
        To use, you must have either:

            1. The ``GOOGLE_API_KEY`` environment variable set with your API key, or
            2. Pass your API key using the ``google_api_key`` kwarg to the ChatGoogleGenerativeAI constructor.

        .. code-block:: python

            from langchain_google_genai import ChatGoogleGenerativeAI

            llm = ChatGoogleGenerativeAI(model="gemini-2.5-flash")
            llm.invoke("Write me a ballad about LangChain")

    Invoke:
        .. code-block:: python

            messages = [
                ("system", "Translate the user sentence to French."),
                ("human", "I love programming."),
            ]
            llm.invoke(messages)

        .. code-block:: python

            AIMessage(
                content="J'adore programmer. \\n",
                response_metadata={'prompt_feedback': {'block_reason': 0, 'safety_ratings': []}, 'finish_reason': 'STOP', 'safety_ratings': [{'category': 'HARM_CATEGORY_SEXUALLY_EXPLICIT', 'probability': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_HATE_SPEECH', 'probability': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_HARASSMENT', 'probability': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_DANGEROUS_CONTENT', 'probability': 'NEGLIGIBLE', 'blocked': False}]},
                id='run-56cecc34-2e54-4b52-a974-337e47008ad2-0',
                usage_metadata={'input_tokens': 18, 'output_tokens': 5, 'total_tokens': 23}
            )

    Stream:
        .. code-block:: python

            for chunk in llm.stream(messages):
                print(chunk)

        .. code-block:: python

            AIMessageChunk(content='J', response_metadata={'finish_reason': 'STOP', 'safety_ratings': []}, id='run-e905f4f4-58cb-4a10-a960-448a2bb649e3', usage_metadata={'input_tokens': 18, 'output_tokens': 1, 'total_tokens': 19})
            AIMessageChunk(content="'adore programmer. \\n", response_metadata={'finish_reason': 'STOP', 'safety_ratings': [{'category': 'HARM_CATEGORY_SEXUALLY_EXPLICIT', 'probability': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_HATE_SPEECH', 'probability': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_HARASSMENT', 'probability': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_DANGEROUS_CONTENT', 'probability': 'NEGLIGIBLE', 'blocked': False}]}, id='run-e905f4f4-58cb-4a10-a960-448a2bb649e3', usage_metadata={'input_tokens': 18, 'output_tokens': 5, 'total_tokens': 23})

        .. code-block:: python

            stream = llm.stream(messages)
            full = next(stream)
            for chunk in stream:
                full += chunk
            full

        .. code-block:: python

            AIMessageChunk(
                content="J'adore programmer. \\n",
                response_metadata={'finish_reason': 'STOPSTOP', 'safety_ratings': [{'category': 'HARM_CATEGORY_SEXUALLY_EXPLICIT', 'probability': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_HATE_SPEECH', 'probability': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_HARASSMENT', 'probability': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_DANGEROUS_CONTENT', 'probability': 'NEGLIGIBLE', 'blocked': False}]},
                id='run-3ce13a42-cd30-4ad7-a684-f1f0b37cdeec',
                usage_metadata={'input_tokens': 36, 'output_tokens': 6, 'total_tokens': 42}
            )

    Async:
        .. code-block:: python

            await llm.ainvoke(messages)

            # stream:
            # async for chunk in (await llm.astream(messages))

            # batch:
            # await llm.abatch([messages])

    Context Caching:
        Context caching allows you to store and reuse content (e.g., PDFs, images) for faster processing.
        The ``cached_content`` parameter accepts a cache name created via the Google Generative AI API.
        Below are two examples: caching a single file directly and caching multiple files using ``Part``.

        Single File Example:
        This caches a single file and queries it.

        .. code-block:: python

            from google import genai
            from google.genai import types
            import time
            from langchain_google_genai import ChatGoogleGenerativeAI
            from langchain_core.messages import HumanMessage

            client = genai.Client()

            # Upload file
            file = client.files.upload(file="./example_file")
            while file.state.name == 'PROCESSING':
                time.sleep(2)
                file = client.files.get(name=file.name)

            # Create cache
            model = 'models/gemini-1.5-flash-latest'
            cache = client.caches.create(
                model=model,
                config=types.CreateCachedContentConfig(
                    display_name='Cached Content',
                    system_instruction=(
                        'You are an expert content analyzer, and your job is to answer '
                        'the user\'s query based on the file you have access to.'
                    ),
                    contents=[file],
                    ttl="300s",
                )
            )

            # Query with LangChain
            llm = ChatGoogleGenerativeAI(
                model=model,
                cached_content=cache.name,
            )
            message = HumanMessage(content="Summarize the main points of the content.")
            llm.invoke([message])

        Multiple Files Example:
        This caches two files using `Part` and queries them together.

        .. code-block:: python

            from google import genai
            from google.genai.types import CreateCachedContentConfig, Content, Part
            import time
            from langchain_google_genai import ChatGoogleGenerativeAI
            from langchain_core.messages import HumanMessage

            client = genai.Client()

            # Upload files
            file_1 = client.files.upload(file="./file1")
            while file_1.state.name == 'PROCESSING':
                time.sleep(2)
                file_1 = client.files.get(name=file_1.name)

            file_2 = client.files.upload(file="./file2")
            while file_2.state.name == 'PROCESSING':
                time.sleep(2)
                file_2 = client.files.get(name=file_2.name)

            # Create cache with multiple files
            contents = [
                Content(
                    role="user",
                    parts=[
                        Part.from_uri(file_uri=file_1.uri, mime_type=file_1.mime_type),
                        Part.from_uri(file_uri=file_2.uri, mime_type=file_2.mime_type),
                    ],
                )
            ]
            model = "gemini-1.5-flash-latest"
            cache = client.caches.create(
                model=model,
                config=CreateCachedContentConfig(
                    display_name='Cached Contents',
                    system_instruction=(
                        'You are an expert content analyzer, and your job is to answer '
                        'the user\'s query based on the files you have access to.'
                    ),
                    contents=contents,
                    ttl="300s",
                )
            )

            # Query with LangChain
            llm = ChatGoogleGenerativeAI(
                model=model,
                cached_content=cache.name,
            )
            message = HumanMessage(content="Provide a summary of the key information across both files.")
            llm.invoke([message])

    Tool calling:
        .. code-block:: python

            from pydantic import BaseModel, Field


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

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


            class GetPopulation(BaseModel):
                '''Get the current population in a given location'''

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


            llm_with_tools = llm.bind_tools([GetWeather, GetPopulation])
            ai_msg = llm_with_tools.invoke(
                "Which city is hotter today and which is bigger: LA or NY?"
            )
            ai_msg.tool_calls

        .. code-block:: python

            [{'name': 'GetWeather',
              'args': {'location': 'Los Angeles, CA'},
              'id': 'c186c99f-f137-4d52-947f-9e3deabba6f6'},
             {'name': 'GetWeather',
              'args': {'location': 'New York City, NY'},
              'id': 'cebd4a5d-e800-4fa5-babd-4aa286af4f31'},
             {'name': 'GetPopulation',
              'args': {'location': 'Los Angeles, CA'},
              'id': '4f92d897-f5e4-4d34-a3bc-93062c92591e'},
             {'name': 'GetPopulation',
              'args': {'location': 'New York City, NY'},
              'id': '634582de-5186-4e4b-968b-f192f0a93678'}]

    Use Search with Gemini 2:
        .. code-block:: python

            from google.ai.generativelanguage_v1beta.types import Tool as GenAITool
            llm = ChatGoogleGenerativeAI(model="gemini-2.5-flash")
            resp = llm.invoke(
                "When is the next total solar eclipse in US?",
                tools=[GenAITool(google_search={})],
            )

    Structured output:
        .. code-block:: python

            from typing import Optional

            from pydantic import BaseModel, Field


            class Joke(BaseModel):
                '''Joke to tell user.'''

                setup: str = Field(description="The setup of the joke")
                punchline: str = Field(description="The punchline to the joke")
                rating: Optional[int] = Field(description="How funny the joke is, from 1 to 10")


            structured_llm = llm.with_structured_output(Joke)
            structured_llm.invoke("Tell me a joke about cats")

        .. code-block:: python

            Joke(
                setup='Why are cats so good at video games?',
                punchline='They have nine lives on the internet',
                rating=None
            )

    Image input:
        .. code-block:: python

            import base64
            import httpx
            from langchain_core.messages import HumanMessage

            image_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
            image_data = base64.b64encode(httpx.get(image_url).content).decode("utf-8")
            message = HumanMessage(
                content=[
                    {"type": "text", "text": "describe the weather in this image"},
                    {
                        "type": "image_url",
                        "image_url": {"url": f"data:image/jpeg;base64,{image_data}"},
                    },
                ]
            )
            ai_msg = llm.invoke([message])
            ai_msg.content

        .. code-block:: python

            'The weather in this image appears to be sunny and pleasant. The sky is a bright blue with scattered white clouds, suggesting fair weather. The lush green grass and trees indicate a warm and possibly slightly breezy day. There are no signs of rain or storms.'

    PDF input:
        .. code-block:: python

            import base64
            from langchain_core.messages import HumanMessage

            pdf_bytes = open("/path/to/your/test.pdf", 'rb').read()
            pdf_base64 = base64.b64encode(pdf_bytes).decode('utf-8')

            message = HumanMessage(
                content=[
                    {"type": "text", "text": "describe the document in a sentence"},
                    {
                        "type": "file",
                        "source_type": "base64",
                        "mime_type":"application/pdf",
                        "data": pdf_base64
                    }
                ]
            )
            ai_msg = llm.invoke([message])
            ai_msg.content

        .. code-block:: python

            'This research paper describes a system developed for SemEval-2025 Task 9, which aims to automate the detection of food hazards from recall reports, addressing the class imbalance problem by leveraging LLM-based data augmentation techniques and transformer-based models to improve performance.'

    Video input:
        .. code-block:: python

            import base64
            from langchain_core.messages import HumanMessage

            video_bytes = open("/path/to/your/video.mp4", 'rb').read()
            video_base64 = base64.b64encode(video_bytes).decode('utf-8')

            message = HumanMessage(
                content=[
                    {"type": "text", "text": "describe what's in this video in a sentence"},
                    {
                        "type": "file",
                        "source_type": "base64",
                        "mime_type": "video/mp4",
                        "data": video_base64
                    }
                ]
            )
            ai_msg = llm.invoke([message])
            ai_msg.content

        .. code-block:: python

            'Tom and Jerry, along with a turkey, engage in a chaotic Thanksgiving-themed adventure involving a corn-on-the-cob chase, maze antics, and a disastrous attempt to prepare a turkey dinner.'

        You can also pass YouTube URLs directly:

        .. code-block:: python

            from langchain_core.messages import HumanMessage

            message = HumanMessage(
                content=[
                    {"type": "text", "text": "summarize the video in 3 sentences."},
                    {
                        "type": "media",
                        "file_uri": "https://www.youtube.com/watch?v=9hE5-98ZeCg",
                        "mime_type": "video/mp4",
                    }
                ]
            )
            ai_msg = llm.invoke([message])
            ai_msg.content

        .. code-block:: python

            'The video is a demo of multimodal live streaming in Gemini 2.0. The narrator is sharing his screen in AI Studio and asks if the AI can see it. The AI then reads text that is highlighted on the screen, defines the word “multimodal,” and summarizes everything that was seen and heard.'

    Audio input:
        .. code-block:: python

            import base64
            from langchain_core.messages import HumanMessage

            audio_bytes = open("/path/to/your/audio.mp3", 'rb').read()
            audio_base64 = base64.b64encode(audio_bytes).decode('utf-8')

            message = HumanMessage(
                content=[
                    {"type": "text", "text": "summarize this audio in a sentence"},
                    {
                        "type": "file",
                        "source_type": "base64",
                        "mime_type":"audio/mp3",
                        "data": audio_base64
                    }
                ]
            )
            ai_msg = llm.invoke([message])
            ai_msg.content

        .. code-block:: python

            "In this episode of the Made by Google podcast, Stephen Johnson and Simon Tokumine discuss NotebookLM, a tool designed to help users understand complex material in various modalities, with a focus on its unexpected uses, the development of audio overviews, and the implementation of new features like mind maps and source discovery."

    File upload (URI-based):
        You can also upload files to Google's servers and reference them by URI.
        This works for PDFs, images, videos, and audio files.

        .. code-block:: python

            import time
            from google import genai
            from langchain_core.messages import HumanMessage

            client = genai.Client()

            myfile = client.files.upload(file="/path/to/your/sample.pdf")
            while myfile.state.name == "PROCESSING":
                time.sleep(2)
                myfile = client.files.get(name=myfile.name)

            message = HumanMessage(
                content=[
                    {"type": "text", "text": "What is in the document?"},
                    {
                        "type": "media",
                        "file_uri": myfile.uri,
                        "mime_type": "application/pdf",
                    },
                ]
            )
            ai_msg = llm.invoke([message])
            ai_msg.content

        .. code-block:: python

            "This research paper assesses and mitigates multi-turn jailbreak vulnerabilities in large language models using the Crescendo attack study, evaluating attack success rates and mitigation strategies like prompt hardening and LLM-as-guardrail."

    Token usage:
        .. code-block:: python

            ai_msg = llm.invoke(messages)
            ai_msg.usage_metadata

        .. code-block:: python

            {'input_tokens': 18, 'output_tokens': 5, 'total_tokens': 23}


    Response metadata
        .. code-block:: python

            ai_msg = llm.invoke(messages)
            ai_msg.response_metadata

        .. code-block:: python

            {
                'prompt_feedback': {'block_reason': 0, 'safety_ratings': []},
                'finish_reason': 'STOP',
                'safety_ratings': [{'category': 'HARM_CATEGORY_SEXUALLY_EXPLICIT', 'probability': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_HATE_SPEECH', 'probability': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_HARASSMENT', 'probability': 'NEGLIGIBLE', 'blocked': False}, {'category': 'HARM_CATEGORY_DANGEROUS_CONTENT', 'probability': 'NEGLIGIBLE', 'blocked': False}]
            }

    """  # noqa: E501

    client: Any = Field(default=None, exclude=True)  #: :meta private:
    async_client_running: Any = Field(default=None, exclude=True)  #: :meta private:
    default_metadata: Sequence[Tuple[str, str]] = Field(
        default_factory=list
    )  #: :meta private:

    convert_system_message_to_human: bool = False
    """Whether to merge any leading SystemMessage into the following HumanMessage.

    Gemini does not support system messages; any unsupported messages will
    raise an error."""

    response_mime_type: Optional[str] = None
    """Optional. Output response mimetype of the generated candidate text. Only
    supported in Gemini 1.5 and later models.
    
    Supported mimetype:
        * ``'text/plain'``: (default) Text output.
        * ``'application/json'``: JSON response in the candidates.
        * ``'text/x.enum'``: Enum in plain text.
    
    The model also needs to be prompted to output the appropriate response
    type, otherwise the behavior is undefined. This is a preview feature.
    """

    response_schema: Optional[Dict[str, Any]] = None
    """ Optional. Enforce an schema to the output.
        The format of the dictionary should follow Open API schema.
    """

    cached_content: Optional[str] = None
    """The name of the cached content used as context to serve the prediction. 

    Note: only used in explicit caching, where users can have control over caching 
    (e.g. what content to cache) and enjoy guaranteed cost savings. Format: 
    ``cachedContents/{cachedContent}``.
    """

    model_kwargs: dict[str, Any] = Field(default_factory=dict)
    """Holds any unexpected initialization parameters."""

    def __init__(self, **kwargs: Any) -> None:
        """Needed for arg validation."""
        # Get all valid field names, including aliases
        valid_fields = set()
        for field_name, field_info in self.__class__.model_fields.items():
            valid_fields.add(field_name)
            if hasattr(field_info, "alias") and field_info.alias is not None:
                valid_fields.add(field_info.alias)

        # Check for unrecognized arguments
        for arg in kwargs:
            if arg not in valid_fields:
                suggestions = get_close_matches(arg, valid_fields, n=1)
                suggestion = (
                    f" Did you mean: '{suggestions[0]}'?" if suggestions else ""
                )
                logger.warning(
                    f"Unexpected argument '{arg}' "
                    f"provided to ChatGoogleGenerativeAI.{suggestion}"
                )
        super().__init__(**kwargs)

    model_config = ConfigDict(
        populate_by_name=True,
    )

    @property
    def lc_secrets(self) -> Dict[str, str]:
        return {"google_api_key": "GOOGLE_API_KEY"}

    @property
    def _llm_type(self) -> str:
        return "chat-google-generative-ai"

    @property
    def _supports_code_execution(self) -> bool:
        return (
            "gemini-1.5-pro" in self.model
            or "gemini-1.5-flash" in self.model
            or "gemini-2" in self.model
        )

    @classmethod
    def is_lc_serializable(self) -> bool:
        return True

    @model_validator(mode="before")
    @classmethod
    def build_extra(cls, values: dict[str, Any]) -> Any:
        """Build extra kwargs from additional params that were passed in."""
        all_required_field_names = get_pydantic_field_names(cls)
        values = _build_model_kwargs(values, all_required_field_names)
        return values

    @model_validator(mode="after")
    def validate_environment(self) -> Self:
        """Validates params and passes them to google-generativeai package."""
        if self.temperature is not None and not 0 <= self.temperature <= 2.0:
            raise ValueError("temperature must be in the range [0.0, 2.0]")

        if self.top_p is not None and not 0 <= self.top_p <= 1:
            raise ValueError("top_p must be in the range [0.0, 1.0]")

        if self.top_k is not None and self.top_k <= 0:
            raise ValueError("top_k must be positive")

        if not any(self.model.startswith(prefix) for prefix in ("models/",)):
            self.model = f"models/{self.model}"

        additional_headers = self.additional_headers or {}
        self.default_metadata = tuple(additional_headers.items())
        client_info = get_client_info(f"ChatGoogleGenerativeAI:{self.model}")
        google_api_key = None
        if not self.credentials:
            if isinstance(self.google_api_key, SecretStr):
                google_api_key = self.google_api_key.get_secret_value()
            else:
                google_api_key = self.google_api_key
        transport: Optional[str] = self.transport
        self.client = genaix.build_generative_service(
            credentials=self.credentials,
            api_key=google_api_key,
            client_info=client_info,
            client_options=self.client_options,
            transport=transport,
        )
        self.async_client_running = None
        return self

    @property
    def async_client(self) -> v1betaGenerativeServiceAsyncClient:
        google_api_key = None
        if not self.credentials:
            if isinstance(self.google_api_key, SecretStr):
                google_api_key = self.google_api_key.get_secret_value()
            else:
                google_api_key = self.google_api_key
        # NOTE: genaix.build_generative_async_service requires
        # a running event loop, which causes an error
        # when initialized inside a ThreadPoolExecutor.
        # this check ensures that async client is only initialized
        # within an asyncio event loop to avoid the error
        if not self.async_client_running and _is_event_loop_running():
            # async clients don't support "rest" transport
            # https://github.com/googleapis/gapic-generator-python/issues/1962
            transport = self.transport
            if transport == "rest":
                transport = "grpc_asyncio"
            self.async_client_running = genaix.build_generative_async_service(
                credentials=self.credentials,
                api_key=google_api_key,
                client_info=get_client_info(f"ChatGoogleGenerativeAI:{self.model}"),
                client_options=self.client_options,
                transport=transport,
            )
        return self.async_client_running

    @property
    def _identifying_params(self) -> Dict[str, Any]:
        """Get the identifying parameters."""
        return {
            "model": self.model,
            "temperature": self.temperature,
            "top_k": self.top_k,
            "n": self.n,
            "safety_settings": self.safety_settings,
            "response_modalities": self.response_modalities,
            "thinking_budget": self.thinking_budget,
            "include_thoughts": self.include_thoughts,
        }

    def invoke(
        self,
        input: LanguageModelInput,
        config: Optional[RunnableConfig] = None,
        *,
        code_execution: Optional[bool] = None,
        stop: Optional[list[str]] = None,
        **kwargs: Any,
    ) -> BaseMessage:
        """
        Enable code execution. Supported on: gemini-1.5-pro, gemini-1.5-flash,
        gemini-2.0-flash, and gemini-2.0-pro. When enabled, the model can execute
        code to solve problems.
        """

        """Override invoke to add code_execution parameter."""

        if code_execution is not None:
            if not self._supports_code_execution:
                raise ValueError(
                    f"Code execution is only supported on Gemini 1.5 Pro, \
                    Gemini 1.5 Flash, "
                    f"Gemini 2.0 Flash, and Gemini 2.0 Pro models. \
                    Current model: {self.model}"
                )
            if "tools" not in kwargs:
                code_execution_tool = GoogleTool(code_execution=CodeExecution())
                kwargs["tools"] = [code_execution_tool]

            else:
                raise ValueError(
                    "Tools are already defined.code_execution tool can't be defined"
                )

        return super().invoke(input, config, stop=stop, **kwargs)

    def _get_ls_params(
        self, stop: Optional[List[str]] = None, **kwargs: Any
    ) -> LangSmithParams:
        """Get standard params for tracing."""
        params = self._get_invocation_params(stop=stop, **kwargs)
        models_prefix = "models/"
        ls_model_name = (
            self.model[len(models_prefix) :]
            if self.model and self.model.startswith(models_prefix)
            else self.model
        )
        ls_params = LangSmithParams(
            ls_provider="google_genai",
            ls_model_name=ls_model_name,
            ls_model_type="chat",
            ls_temperature=params.get("temperature", self.temperature),
        )
        if ls_max_tokens := params.get("max_output_tokens", self.max_output_tokens):
            ls_params["ls_max_tokens"] = ls_max_tokens
        if ls_stop := stop or params.get("stop", None):
            ls_params["ls_stop"] = ls_stop
        return ls_params

    def _prepare_params(
        self,
        stop: Optional[List[str]],
        generation_config: Optional[Dict[str, Any]] = None,
        **kwargs: Any,
    ) -> GenerationConfig:
        gen_config = {
            k: v
            for k, v in {
                "candidate_count": self.n,
                "temperature": self.temperature,
                "stop_sequences": stop,
                "max_output_tokens": self.max_output_tokens,
                "top_k": self.top_k,
                "top_p": self.top_p,
                "response_modalities": self.response_modalities,
                "thinking_config": (
                    (
                        {"thinking_budget": self.thinking_budget}
                        if self.thinking_budget is not None
                        else {}
                    )
                    | (
                        {"include_thoughts": self.include_thoughts}
                        if self.include_thoughts is not None
                        else {}
                    )
                )
                if self.thinking_budget is not None or self.include_thoughts is not None
                else None,
            }.items()
            if v is not None
        }
        if generation_config:
            gen_config = {**gen_config, **generation_config}

        response_mime_type = kwargs.get("response_mime_type", self.response_mime_type)
        if response_mime_type is not None:
            gen_config["response_mime_type"] = response_mime_type

        response_schema = kwargs.get("response_schema", self.response_schema)
        if response_schema is not None:
            allowed_mime_types = ("application/json", "text/x.enum")
            if response_mime_type not in allowed_mime_types:
                error_message = (
                    "`response_schema` is only supported when "
                    f"`response_mime_type` is set to one of {allowed_mime_types}"
                )
                raise ValueError(error_message)

            gapic_response_schema = _dict_to_gapic_schema(response_schema)
            if gapic_response_schema is not None:
                gen_config["response_schema"] = gapic_response_schema
        return GenerationConfig(**gen_config)

    def _generate(
        self,
        messages: List[BaseMessage],
        stop: Optional[List[str]] = None,
        run_manager: Optional[CallbackManagerForLLMRun] = None,
        *,
        tools: Optional[Sequence[Union[_ToolDict, GoogleTool]]] = None,
        functions: Optional[Sequence[_FunctionDeclarationType]] = None,
        safety_settings: Optional[SafetySettingDict] = None,
        tool_config: Optional[Union[Dict, _ToolConfigDict]] = None,
        generation_config: Optional[Dict[str, Any]] = None,
        cached_content: Optional[str] = None,
        tool_choice: Optional[Union[_ToolChoiceType, bool]] = None,
        **kwargs: Any,
    ) -> ChatResult:
        request = self._prepare_request(
            messages,
            stop=stop,
            tools=tools,
            functions=functions,
            safety_settings=safety_settings,
            tool_config=tool_config,
            generation_config=generation_config,
            cached_content=cached_content or self.cached_content,
            tool_choice=tool_choice,
            **kwargs,
        )
        response: GenerateContentResponse = _chat_with_retry(
            request=request,
            **kwargs,
            generation_method=self.client.generate_content,
            metadata=self.default_metadata,
        )
        return _response_to_result(response)

    async def _agenerate(
        self,
        messages: List[BaseMessage],
        stop: Optional[List[str]] = None,
        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
        *,
        tools: Optional[Sequence[Union[_ToolDict, GoogleTool]]] = None,
        functions: Optional[Sequence[_FunctionDeclarationType]] = None,
        safety_settings: Optional[SafetySettingDict] = None,
        tool_config: Optional[Union[Dict, _ToolConfigDict]] = None,
        generation_config: Optional[Dict[str, Any]] = None,
        cached_content: Optional[str] = None,
        tool_choice: Optional[Union[_ToolChoiceType, bool]] = None,
        **kwargs: Any,
    ) -> ChatResult:
        if not self.async_client:
            updated_kwargs = {
                **kwargs,
                **{
                    "tools": tools,
                    "functions": functions,
                    "safety_settings": safety_settings,
                    "tool_config": tool_config,
                    "generation_config": generation_config,
                },
            }
            return await super()._agenerate(
                messages, stop, run_manager, **updated_kwargs
            )

        request = self._prepare_request(
            messages,
            stop=stop,
            tools=tools,
            functions=functions,
            safety_settings=safety_settings,
            tool_config=tool_config,
            generation_config=generation_config,
            cached_content=cached_content or self.cached_content,
            tool_choice=tool_choice,
            **kwargs,
        )
        response: GenerateContentResponse = await _achat_with_retry(
            request=request,
            **kwargs,
            generation_method=self.async_client.generate_content,
            metadata=self.default_metadata,
        )
        return _response_to_result(response)

    def _stream(
        self,
        messages: List[BaseMessage],
        stop: Optional[List[str]] = None,
        run_manager: Optional[CallbackManagerForLLMRun] = None,
        *,
        tools: Optional[Sequence[Union[_ToolDict, GoogleTool]]] = None,
        functions: Optional[Sequence[_FunctionDeclarationType]] = None,
        safety_settings: Optional[SafetySettingDict] = None,
        tool_config: Optional[Union[Dict, _ToolConfigDict]] = None,
        generation_config: Optional[Dict[str, Any]] = None,
        cached_content: Optional[str] = None,
        tool_choice: Optional[Union[_ToolChoiceType, bool]] = None,
        **kwargs: Any,
    ) -> Iterator[ChatGenerationChunk]:
        request = self._prepare_request(
            messages,
            stop=stop,
            tools=tools,
            functions=functions,
            safety_settings=safety_settings,
            tool_config=tool_config,
            generation_config=generation_config,
            cached_content=cached_content or self.cached_content,
            tool_choice=tool_choice,
            **kwargs,
        )
        response: GenerateContentResponse = _chat_with_retry(
            request=request,
            generation_method=self.client.stream_generate_content,
            **kwargs,
            metadata=self.default_metadata,
        )

        prev_usage_metadata: UsageMetadata | None = None  # cumulative usage
        for chunk in response:
            _chat_result = _response_to_result(
                chunk, stream=True, prev_usage=prev_usage_metadata
            )
            gen = cast(ChatGenerationChunk, _chat_result.generations[0])
            message = cast(AIMessageChunk, gen.message)

            prev_usage_metadata = (
                message.usage_metadata
                if prev_usage_metadata is None
                else add_usage(prev_usage_metadata, message.usage_metadata)
            )

            if run_manager:
                run_manager.on_llm_new_token(gen.text, chunk=gen)
            yield gen

    async def _astream(
        self,
        messages: List[BaseMessage],
        stop: Optional[List[str]] = None,
        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
        *,
        tools: Optional[Sequence[Union[_ToolDict, GoogleTool]]] = None,
        functions: Optional[Sequence[_FunctionDeclarationType]] = None,
        safety_settings: Optional[SafetySettingDict] = None,
        tool_config: Optional[Union[Dict, _ToolConfigDict]] = None,
        generation_config: Optional[Dict[str, Any]] = None,
        cached_content: Optional[str] = None,
        tool_choice: Optional[Union[_ToolChoiceType, bool]] = None,
        **kwargs: Any,
    ) -> AsyncIterator[ChatGenerationChunk]:
        if not self.async_client:
            updated_kwargs = {
                **kwargs,
                **{
                    "tools": tools,
                    "functions": functions,
                    "safety_settings": safety_settings,
                    "tool_config": tool_config,
                    "generation_config": generation_config,
                },
            }
            async for value in super()._astream(
                messages, stop, run_manager, **updated_kwargs
            ):
                yield value
        else:
            request = self._prepare_request(
                messages,
                stop=stop,
                tools=tools,
                functions=functions,
                safety_settings=safety_settings,
                tool_config=tool_config,
                generation_config=generation_config,
                cached_content=cached_content or self.cached_content,
                tool_choice=tool_choice,
                **kwargs,
            )
            prev_usage_metadata: UsageMetadata | None = None  # cumulative usage
            async for chunk in await _achat_with_retry(
                request=request,
                generation_method=self.async_client.stream_generate_content,
                **kwargs,
                metadata=self.default_metadata,
            ):
                _chat_result = _response_to_result(
                    chunk, stream=True, prev_usage=prev_usage_metadata
                )
                gen = cast(ChatGenerationChunk, _chat_result.generations[0])
                message = cast(AIMessageChunk, gen.message)

                prev_usage_metadata = (
                    message.usage_metadata
                    if prev_usage_metadata is None
                    else add_usage(prev_usage_metadata, message.usage_metadata)
                )

                if run_manager:
                    await run_manager.on_llm_new_token(gen.text, chunk=gen)
                yield gen

    def _prepare_request(
        self,
        messages: List[BaseMessage],
        *,
        stop: Optional[List[str]] = None,
        tools: Optional[Sequence[Union[_ToolDict, GoogleTool]]] = None,
        functions: Optional[Sequence[_FunctionDeclarationType]] = None,
        safety_settings: Optional[SafetySettingDict] = None,
        tool_config: Optional[Union[Dict, _ToolConfigDict]] = None,
        tool_choice: Optional[Union[_ToolChoiceType, bool]] = None,
        generation_config: Optional[Dict[str, Any]] = None,
        cached_content: Optional[str] = None,
        **kwargs: Any,
    ) -> Tuple[GenerateContentRequest, Dict[str, Any]]:
        if tool_choice and tool_config:
            raise ValueError(
                "Must specify at most one of tool_choice and tool_config, received "
                f"both:\n\n{tool_choice=}\n\n{tool_config=}"
            )

        formatted_tools = None
        code_execution_tool = GoogleTool(code_execution=CodeExecution())
        if tools == [code_execution_tool]:
            formatted_tools = tools
        elif tools:
            formatted_tools = [convert_to_genai_function_declarations(tools)]
        elif functions:
            formatted_tools = [convert_to_genai_function_declarations(functions)]

        filtered_messages = []
        for message in messages:
            if isinstance(message, HumanMessage) and not message.content:
                warnings.warn(
                    "HumanMessage with empty content was removed to prevent API error"
                )
            else:
                filtered_messages.append(message)
        messages = filtered_messages

        system_instruction, history = _parse_chat_history(
            messages,
            convert_system_message_to_human=self.convert_system_message_to_human,
        )
        if tool_choice:
            if not formatted_tools:
                msg = (
                    f"Received {tool_choice=} but no {tools=}. 'tool_choice' can only "
                    f"be specified if 'tools' is specified."
                )
                raise ValueError(msg)
            all_names: List[str] = []
            for t in formatted_tools:
                if hasattr(t, "function_declarations"):
                    t_with_declarations = cast(Any, t)
                    all_names.extend(
                        f.name for f in t_with_declarations.function_declarations
                    )
                elif isinstance(t, GoogleTool) and hasattr(t, "code_execution"):
                    continue
                else:
                    raise TypeError(
                        f"Tool {t} doesn't have function_declarations attribute"
                    )

            tool_config = _tool_choice_to_tool_config(tool_choice, all_names)

        formatted_tool_config = None
        if tool_config:
            formatted_tool_config = ToolConfig(
                function_calling_config=tool_config["function_calling_config"]
            )
        formatted_safety_settings = []
        if safety_settings:
            formatted_safety_settings = [
                SafetySetting(category=c, threshold=t)
                for c, t in safety_settings.items()
            ]
        request = GenerateContentRequest(
            model=self.model,
            contents=history,
            tools=formatted_tools,
            tool_config=formatted_tool_config,
            safety_settings=formatted_safety_settings,
            generation_config=self._prepare_params(
                stop,
                generation_config=generation_config,
                **kwargs,
            ),
            cached_content=cached_content,
        )
        if system_instruction:
            request.system_instruction = system_instruction

        return request

    def get_num_tokens(self, text: str) -> int:
        """Get the number of tokens present in the text.

        Useful for checking if an input will fit in a model's context window.

        Args:
            text: The string input to tokenize.

        Returns:
            The integer number of tokens in the text.
        """
        result = self.client.count_tokens(
            model=self.model, contents=[Content(parts=[Part(text=text)])]
        )
        return result.total_tokens

    def with_structured_output(
        self,
        schema: Union[Dict, Type[BaseModel]],
        method: Optional[Literal["function_calling", "json_mode"]] = "function_calling",
        *,
        include_raw: bool = False,
        **kwargs: Any,
    ) -> Runnable[LanguageModelInput, Union[Dict, BaseModel]]:
        _ = kwargs.pop("strict", None)
        if kwargs:
            raise ValueError(f"Received unsupported arguments {kwargs}")

        parser: OutputParserLike

        if method == "json_mode":
            if isinstance(schema, type) and is_basemodel_subclass(schema):
                if issubclass(schema, BaseModelV1):
                    schema_json = schema.schema()
                else:
                    schema_json = schema.model_json_schema()
                parser = PydanticOutputParser(pydantic_object=schema)
            else:
                if is_typeddict(schema):
                    schema_json = convert_to_json_schema(schema)
                elif isinstance(schema, dict):
                    schema_json = schema
                else:
                    raise ValueError(f"Unsupported schema type {type(schema)}")
                parser = JsonOutputParser()

            # Resolve refs in schema because they are not supported
            # by the Gemini API.
            schema_json = replace_defs_in_schema(schema_json)

            llm = self.bind(
                response_mime_type="application/json",
                response_schema=schema_json,
                ls_structured_output_format={
                    "kwargs": {"method": method},
                    "schema": schema_json,
                },
            )
        else:
            tool_name = _get_tool_name(schema)  # type: ignore[arg-type]
            if isinstance(schema, type) and is_basemodel_subclass_safe(schema):
                parser = PydanticToolsParser(tools=[schema], first_tool_only=True)
            else:
                parser = JsonOutputKeyToolsParser(
                    key_name=tool_name, first_tool_only=True
                )
            tool_choice = tool_name if self._supports_tool_choice else None
            try:
                llm = self.bind_tools(
                    [schema],
                    tool_choice=tool_choice,
                    ls_structured_output_format={
                        "kwargs": {"method": "function_calling"},
                        "schema": convert_to_openai_tool(schema),
                    },
                )
            except Exception:
                llm = self.bind_tools([schema], tool_choice=tool_choice)
        if include_raw:
            parser_with_fallback = RunnablePassthrough.assign(
                parsed=itemgetter("raw") | parser, parsing_error=lambda _: None
            ).with_fallbacks(
                [RunnablePassthrough.assign(parsed=lambda _: None)],
                exception_key="parsing_error",
            )
            return {"raw": llm} | parser_with_fallback
        else:
            return llm | parser

    def bind_tools(
        self,
        tools: Sequence[
            dict[str, Any] | type | Callable[..., Any] | BaseTool | GoogleTool
        ],
        tool_config: Optional[Union[Dict, _ToolConfigDict]] = None,
        *,
        tool_choice: Optional[Union[_ToolChoiceType, bool]] = None,
        **kwargs: Any,
    ) -> Runnable[LanguageModelInput, BaseMessage]:
        """Bind tool-like objects to this chat model.

        Assumes model is compatible with google-generativeAI tool-calling API.

        Args:
            tools: A list of tool definitions to bind to this chat model.
                Can be a pydantic model, callable, or BaseTool. Pydantic
                models, callables, and BaseTools will be automatically converted to
                their schema dictionary representation. Tools with Union types in
                their arguments are now supported and converted to `anyOf` schemas.
            **kwargs: Any additional parameters to pass to the
                :class:`~langchain.runnable.Runnable` constructor.
        """
        if tool_choice and tool_config:
            raise ValueError(
                "Must specify at most one of tool_choice and tool_config, received "
                f"both:\n\n{tool_choice=}\n\n{tool_config=}"
            )
        try:
            formatted_tools: list = [convert_to_openai_tool(tool) for tool in tools]  # type: ignore[arg-type]
        except Exception:
            formatted_tools = [
                tool_to_dict(convert_to_genai_function_declarations(tools))
            ]
        if tool_choice:
            kwargs["tool_choice"] = tool_choice
        elif tool_config:
            kwargs["tool_config"] = tool_config
        else:
            pass
        return self.bind(tools=formatted_tools, **kwargs)

    @property
    def _supports_tool_choice(self) -> bool:
        return (
            "gemini-1.5-pro" in self.model
            or "gemini-1.5-flash" in self.model
            or "gemini-2" in self.model
        )


def _get_tool_name(
    tool: Union[_ToolDict, GoogleTool, Dict],
) -> str:
    try:
        genai_tool = tool_to_dict(convert_to_genai_function_declarations([tool]))
        return [f["name"] for f in genai_tool["function_declarations"]][0]  # type: ignore[index]
    except ValueError as e:  # other TypedDict
        if is_typeddict(tool):
            return convert_to_openai_tool(cast(Dict, tool))["function"]["name"]
        else:
            raise e