# Copyright (c) Microsoft. All rights reserved.

import logging
from collections.abc import AsyncGenerator, AsyncIterable, Callable
from contextlib import AbstractAsyncContextManager
from copy import copy, deepcopy
from typing import TYPE_CHECKING, Any, Literal, TypeVar

from semantic_kernel.connectors.ai.embedding_generator_base import EmbeddingGeneratorBase
from semantic_kernel.const import METADATA_EXCEPTION_KEY
from semantic_kernel.contents.chat_history import ChatHistory
from semantic_kernel.contents.function_call_content import FunctionCallContent
from semantic_kernel.contents.function_result_content import FunctionResultContent
from semantic_kernel.contents.streaming_chat_message_content import StreamingChatMessageContent
from semantic_kernel.contents.streaming_content_mixin import StreamingContentMixin
from semantic_kernel.exceptions import (
    FunctionCallInvalidArgumentsException,
    FunctionExecutionException,
    KernelFunctionNotFoundError,
    KernelInvokeException,
    OperationCancelledException,
    TemplateSyntaxError,
)
from semantic_kernel.exceptions.kernel_exceptions import KernelServiceNotFoundError
from semantic_kernel.filters.auto_function_invocation.auto_function_invocation_context import (
    AutoFunctionInvocationContext,
)
from semantic_kernel.filters.filter_types import FilterTypes
from semantic_kernel.filters.kernel_filters_extension import (
    KernelFilterExtension,
    _rebuild_auto_function_invocation_context,
)
from semantic_kernel.functions.function_result import FunctionResult
from semantic_kernel.functions.kernel_arguments import KernelArguments
from semantic_kernel.functions.kernel_function_extension import KernelFunctionExtension
from semantic_kernel.functions.kernel_function_from_prompt import KernelFunctionFromPrompt
from semantic_kernel.functions.kernel_plugin import KernelPlugin
from semantic_kernel.kernel_types import AI_SERVICE_CLIENT_TYPE, OneOrMany, OptionalOneOrMany
from semantic_kernel.prompt_template.const import KERNEL_TEMPLATE_FORMAT_NAME
from semantic_kernel.prompt_template.prompt_template_base import PromptTemplateBase
from semantic_kernel.prompt_template.prompt_template_config import PromptTemplateConfig
from semantic_kernel.reliability.kernel_reliability_extension import KernelReliabilityExtension
from semantic_kernel.services.ai_service_selector import AIServiceSelector
from semantic_kernel.services.kernel_services_extension import KernelServicesExtension
from semantic_kernel.utils.feature_stage_decorator import experimental
from semantic_kernel.utils.naming import generate_random_ascii_name

if TYPE_CHECKING:
    from mcp.server.lowlevel.server import LifespanResultT, Server

    from semantic_kernel.connectors.ai.function_choice_behavior import FunctionChoiceBehavior
    from semantic_kernel.connectors.ai.prompt_execution_settings import PromptExecutionSettings
    from semantic_kernel.functions.kernel_function import KernelFunction

T = TypeVar("T")

TDataModel = TypeVar("TDataModel")

logger: logging.Logger = logging.getLogger(__name__)


class Kernel(KernelFilterExtension, KernelFunctionExtension, KernelServicesExtension, KernelReliabilityExtension):
    """The Kernel of Semantic Kernel.

    This is the main entry point for Semantic Kernel. It provides the ability to run
    functions and manage filters, plugins, and AI services.

    Attributes:
        function_invocation_filters: Filters applied during function invocation, from KernelFilterExtension.
        prompt_rendering_filters: Filters applied during prompt rendering, from KernelFilterExtension.
        auto_function_invocation_filters: Filters applied during auto function invocation, from KernelFilterExtension.
        plugins: A dict with the plugins registered with the Kernel, from KernelFunctionExtension.
        services: A dict with the services registered with the Kernel, from KernelServicesExtension.
        ai_service_selector: The AI service selector to be used by the kernel, from KernelServicesExtension.
    """

    def __init__(
        self,
        plugins: KernelPlugin | dict[str, KernelPlugin] | list[KernelPlugin] | None = None,
        services: (
            AI_SERVICE_CLIENT_TYPE | list[AI_SERVICE_CLIENT_TYPE] | dict[str, AI_SERVICE_CLIENT_TYPE] | None
        ) = None,
        ai_service_selector: AIServiceSelector | None = None,
        **kwargs: Any,
    ) -> None:
        """Initialize a new instance of the Kernel class.

        Args:
            plugins: The plugins to be used by the kernel, will be rewritten to a dict with plugin name as key
            services: The services to be used by the kernel, will be rewritten to a dict with service_id as key
            ai_service_selector: The AI service selector to be used by the kernel,
                                 default is based on order of execution settings.
            **kwargs: Additional fields to be passed to the Kernel model, these are limited to filters.
        """
        args = {
            "services": services,
            "plugins": plugins,
            **kwargs,
        }
        if ai_service_selector:
            args["ai_service_selector"] = ai_service_selector
        super().__init__(**args)

    async def invoke_stream(
        self,
        function: "KernelFunction | None" = None,
        arguments: KernelArguments | None = None,
        function_name: str | None = None,
        plugin_name: str | None = None,
        metadata: dict[str, Any] | None = None,
        return_function_results: bool = False,
        **kwargs: Any,
    ) -> AsyncGenerator[list["StreamingContentMixin"] | FunctionResult | list[FunctionResult], Any]:
        """Execute one or more stream functions.

        This will execute the functions in the order they are provided, if a list of functions is provided.
        When multiple functions are provided only the last one is streamed, the rest is executed as a pipeline.

        Args:
            function (KernelFunction): The function to execute,
                this value has precedence when supplying both this and using function_name and plugin_name,
                if this is none, function_name and plugin_name are used and cannot be None.
            arguments (KernelArguments | None): The arguments to pass to the function(s), optional
            function_name (str | None): The name of the function to execute
            plugin_name (str | None): The name of the plugin to execute
            metadata (dict[str, Any]): The metadata to pass to the function(s)
            return_function_results (bool): If True, the function results are yielded as a list[FunctionResult]
            in addition to the streaming content, otherwise only the streaming content is yielded.
            kwargs (dict[str, Any]): arguments that can be used instead of supplying KernelArguments

        Yields:
            StreamingContentMixin: The content of the stream of the last function provided.
        """
        if arguments is None:
            arguments = KernelArguments(**kwargs)
        else:
            arguments.update(kwargs)
        if not function:
            if not function_name or not plugin_name:
                raise KernelFunctionNotFoundError("No function(s) or function- and plugin-name provided")
            function = self.get_function(plugin_name, function_name)

        function_result: list[list["StreamingContentMixin"] | Any] = []

        async for stream_message in function.invoke_stream(self, arguments):
            if isinstance(stream_message, FunctionResult) and (
                exception := stream_message.metadata.get(METADATA_EXCEPTION_KEY, None)
            ):
                raise KernelInvokeException(
                    f"Error occurred while invoking function: '{function.fully_qualified_name}'"
                ) from exception
            function_result.append(stream_message)
            yield stream_message

        if return_function_results:
            output_function_result: list["StreamingContentMixin"] = []
            for result in function_result:
                for choice in result:
                    if not isinstance(choice, StreamingContentMixin):
                        continue
                    if len(output_function_result) <= choice.choice_index:
                        output_function_result.append(copy(choice))
                    else:
                        output_function_result[choice.choice_index] += choice
            yield FunctionResult(function=function.metadata, value=output_function_result)

    async def invoke(
        self,
        function: "KernelFunction | None" = None,
        arguments: KernelArguments | None = None,
        function_name: str | None = None,
        plugin_name: str | None = None,
        metadata: dict[str, Any] | None = None,
        **kwargs: Any,
    ) -> FunctionResult | None:
        """Execute a function and return the FunctionResult.

        Args:
            function (KernelFunction): The function or functions to execute,
                this value has precedence when supplying both this and using function_name and plugin_name,
                if this is none, function_name and plugin_name are used and cannot be None.
            arguments (KernelArguments): The arguments to pass to the function(s), optional
            function_name (str | None): The name of the function to execute
            plugin_name (str | None): The name of the plugin to execute
            metadata (dict[str, Any]): The metadata to pass to the function(s)
            kwargs (dict[str, Any]): arguments that can be used instead of supplying KernelArguments

        Raises:
            KernelInvokeException: If an error occurs during function invocation

        """
        if arguments is None:
            arguments = KernelArguments(**kwargs)
        else:
            arguments.update(kwargs)
        if not function:
            if not function_name or not plugin_name:
                raise KernelFunctionNotFoundError("No function, or function name and plugin name provided")
            function = self.get_function(plugin_name, function_name)

        try:
            return await function.invoke(kernel=self, arguments=arguments, metadata=metadata)
        except OperationCancelledException as exc:
            logger.info(f"Operation cancelled during function invocation. Message: {exc}")
            return None
        except Exception as exc:
            logger.error(
                "Something went wrong in function invocation. During function invocation:"
                f" '{function.fully_qualified_name}'. Error description: '{exc!s}'"
            )
            raise KernelInvokeException(
                f"Error occurred while invoking function: '{function.fully_qualified_name}'"
            ) from exc

    async def invoke_prompt(
        self,
        prompt: str,
        function_name: str | None = None,
        plugin_name: str | None = None,
        arguments: KernelArguments | None = None,
        template_format: Literal[
            "semantic-kernel",
            "handlebars",
            "jinja2",
        ] = KERNEL_TEMPLATE_FORMAT_NAME,
        prompt_template_config: PromptTemplateConfig | None = None,
        **kwargs: Any,
    ) -> FunctionResult | None:
        """Invoke a function from the provided prompt.

        Args:
            prompt (str): The prompt to use
            function_name (str): The name of the function, optional
            plugin_name (str): The name of the plugin, optional
            arguments (KernelArguments | None): The arguments to pass to the function(s), optional
            template_format (str | None): The format of the prompt template
            prompt_template_config (PromptTemplateConfig | None): The prompt template configuration
            kwargs (dict[str, Any]): arguments that can be used instead of supplying KernelArguments

        Returns:
            FunctionResult | list[FunctionResult] | None: The result of the function(s)
        """
        if arguments is None:
            arguments = KernelArguments(**kwargs)
        if not prompt:
            raise TemplateSyntaxError("The prompt is either null or empty.")

        function = KernelFunctionFromPrompt(
            function_name=function_name or generate_random_ascii_name(),
            plugin_name=plugin_name,
            prompt=prompt,
            template_format=template_format,
            prompt_template_config=prompt_template_config,
        )
        return await self.invoke(function=function, arguments=arguments)

    async def invoke_prompt_stream(
        self,
        prompt: str,
        function_name: str | None = None,
        plugin_name: str | None = None,
        arguments: KernelArguments | None = None,
        template_format: Literal[
            "semantic-kernel",
            "handlebars",
            "jinja2",
        ] = KERNEL_TEMPLATE_FORMAT_NAME,
        return_function_results: bool | None = False,
        prompt_template_config: PromptTemplateConfig | None = None,
        **kwargs: Any,
    ) -> AsyncIterable[list["StreamingContentMixin"] | FunctionResult | list[FunctionResult]]:
        """Invoke a function from the provided prompt and stream the results.

        Args:
            prompt (str): The prompt to use
            function_name (str): The name of the function, optional
            plugin_name (str): The name of the plugin, optional
            arguments (KernelArguments | None): The arguments to pass to the function(s), optional
            template_format (str | None): The format of the prompt template
            return_function_results (bool): If True, the function results are yielded as a list[FunctionResult]
            prompt_template_config (PromptTemplateConfig | None): The prompt template configuration
            kwargs (dict[str, Any]): arguments that can be used instead of supplying KernelArguments

        Returns:
            AsyncIterable[StreamingContentMixin]: The content of the stream of the last function provided.
        """
        if arguments is None:
            arguments = KernelArguments(**kwargs)
        if not prompt:
            raise TemplateSyntaxError("The prompt is either null or empty.")

        from semantic_kernel.functions.kernel_function_from_prompt import KernelFunctionFromPrompt

        function = KernelFunctionFromPrompt(
            function_name=function_name or generate_random_ascii_name(),
            plugin_name=plugin_name,
            prompt=prompt,
            template_format=template_format,
            prompt_template_config=prompt_template_config,
        )

        function_result: list[list["StreamingContentMixin"] | Any] = []

        async for stream_message in self.invoke_stream(function=function, arguments=arguments):
            if isinstance(stream_message, FunctionResult) and (
                exception := stream_message.metadata.get(METADATA_EXCEPTION_KEY, None)
            ):
                raise KernelInvokeException(
                    f"Error occurred while invoking function: '{function.fully_qualified_name}'"
                ) from exception
            function_result.append(stream_message)
            yield stream_message

        if return_function_results:
            output_function_result: list["StreamingContentMixin"] = []
            for result in function_result:
                for choice in result:
                    if not isinstance(choice, StreamingContentMixin):
                        continue
                    if len(output_function_result) <= choice.choice_index:
                        output_function_result.append(copy(choice))
                    else:
                        output_function_result[choice.choice_index] += choice
            yield FunctionResult(function=function.metadata, value=output_function_result)

    async def invoke_function_call(
        self,
        function_call: FunctionCallContent,
        chat_history: ChatHistory,
        *,
        arguments: "KernelArguments | None" = None,
        execution_settings: "PromptExecutionSettings | None" = None,
        function_call_count: int | None = None,
        request_index: int | None = None,
        is_streaming: bool = False,
        function_behavior: "FunctionChoiceBehavior | None" = None,
    ) -> "AutoFunctionInvocationContext | None":
        """Processes the provided FunctionCallContent and updates the chat history."""
        try:
            if function_call.name is None:
                raise FunctionExecutionException("The function name is required.")
            if function_behavior is not None and function_behavior.filters:
                allowed_functions = [
                    func.fully_qualified_name for func in self.get_list_of_function_metadata(function_behavior.filters)
                ]
                if function_call.name not in allowed_functions:
                    raise FunctionExecutionException(
                        f"Only functions: {allowed_functions} are allowed, {function_call.name} is not allowed."
                    )
            function_to_call = self.get_function(function_call.plugin_name, function_call.function_name)
        except Exception as exc:
            logger.exception(f"The function `{function_call.name}` is not part of the provided functions: {exc}.")
            frc = FunctionResultContent.from_function_call_content_and_result(
                function_call_content=function_call,
                result=(
                    f"The tool call with name `{function_call.name}` is not part of the provided tools, "
                    "please try again with a supplied tool call name and make sure to validate the name."
                ),
            )
            chat_history.add_message(message=frc.to_chat_message_content())
            return None

        args_cloned = copy(arguments) if arguments else KernelArguments()
        try:
            parsed_args = function_call.to_kernel_arguments()

            # Check for missing or unexpected parameters
            required_param_names = {
                param.name for param in function_to_call.parameters if param.name is not None and param.is_required
            }
            received_param_names = set(parsed_args or {})

            missing_params = required_param_names - received_param_names
            unexpected_params = received_param_names - {param.name for param in function_to_call.parameters}

            if missing_params or unexpected_params:
                msg_parts = []
                if missing_params:
                    msg_parts.append(f"Missing required argument(s): {sorted(missing_params)}.")
                if unexpected_params:
                    msg_parts.append(f"Received unexpected argument(s): {sorted(unexpected_params)}.")
                msg = " ".join(msg_parts) + " Please revise the arguments to match the function signature."

                logger.info(msg)
                frc = FunctionResultContent.from_function_call_content_and_result(
                    function_call_content=function_call,
                    result=msg,
                )
                chat_history.add_message(message=frc.to_chat_message_content())
                return None

            if parsed_args:
                args_cloned.update(parsed_args)
        except (FunctionCallInvalidArgumentsException, TypeError) as exc:
            logger.info(f"Received invalid arguments for function {function_call.name}: {exc}. Trying tool call again.")
            frc = FunctionResultContent.from_function_call_content_and_result(
                function_call_content=function_call,
                result="The tool call arguments are malformed. Arguments must be in JSON format. Please try again.",
            )
            chat_history.add_message(message=frc.to_chat_message_content())
            return None

        num_required_func_params = len([param for param in function_to_call.parameters if param.is_required])
        if parsed_args is None or len(parsed_args) < num_required_func_params:
            msg = (
                f"There are `{num_required_func_params}` tool call arguments required and "
                f"only `{len(parsed_args) if parsed_args is not None else 0}` received. The required arguments are: "
                f"{[param.name for param in function_to_call.parameters if param.is_required]}. "
                "Please provide the required arguments and try again."
            )
            logger.info(msg)
            frc = FunctionResultContent.from_function_call_content_and_result(
                function_call_content=function_call,
                result=msg,
            )
            chat_history.add_message(message=frc.to_chat_message_content())
            return None

        logger.info(f"Calling {function_call.name} function with args: {function_call.arguments}")

        _rebuild_auto_function_invocation_context()
        invocation_context = AutoFunctionInvocationContext(
            function=function_to_call,
            kernel=self,
            arguments=args_cloned,
            is_streaming=is_streaming,
            chat_history=chat_history,
            function_call_content=function_call,
            execution_settings=execution_settings,
            function_result=FunctionResult(function=function_to_call.metadata, value=None),
            function_count=function_call_count or 0,
            request_sequence_index=request_index or 0,
        )
        if function_call.index is not None:
            invocation_context.function_sequence_index = function_call.index

        stack = self.construct_call_stack(
            filter_type=FilterTypes.AUTO_FUNCTION_INVOCATION,
            inner_function=self._inner_auto_function_invoke_handler,
        )
        await stack(invocation_context)

        # Snapshot the tool's return value so later mutations don't leak back
        if invocation_context.function_result and invocation_context.function_result.value is not None:
            invocation_context.function_result.value = deepcopy(invocation_context.function_result.value)

        frc = FunctionResultContent.from_function_call_content_and_result(
            function_call_content=function_call,
            result=invocation_context.function_result,
        )
        is_streaming = any(isinstance(message, StreamingChatMessageContent) for message in chat_history.messages)
        message = frc.to_streaming_chat_message_content() if is_streaming else frc.to_chat_message_content()

        chat_history.add_message(message=message)

        return invocation_context if invocation_context.terminate else None

    async def _inner_auto_function_invoke_handler(self, context: AutoFunctionInvocationContext):
        """Inner auto function invocation handler."""
        try:
            result = await context.function.invoke(
                context.kernel,
                context.arguments,
                metadata=context.function_call_content.metadata | context.function_call_content.to_dict()
                if context.function_call_content
                else {},
            )
            if result:
                context.function_result = result
        except Exception as exc:
            logger.exception(f"Error invoking function {context.function.fully_qualified_name}: {exc}.")
            value = f"An error occurred while invoking the function {context.function.fully_qualified_name}: {exc}"
            if context.function_result is not None:
                context.function_result.value = value
            else:
                context.function_result = FunctionResult(function=context.function.metadata, value=value)
            return

    async def add_embedding_to_object(
        self,
        inputs: OneOrMany[TDataModel],
        field_to_embed: str,
        field_to_store: str,
        execution_settings: dict[str, "PromptExecutionSettings"],
        container_mode: bool = False,
        cast_function: Callable[[list[float]], Any] | None = None,
        **kwargs: Any,
    ):
        """Gather all fields to embed, batch the embedding generation and store."""
        contents: list[Any] = []
        dict_like = (getter := getattr(inputs, "get", False)) and callable(getter)
        list_of_dicts: bool = False
        if container_mode:
            contents = inputs[field_to_embed].tolist()  # type: ignore
        elif isinstance(inputs, list):
            list_of_dicts = (getter := getattr(inputs[0], "get", False)) and callable(getter)
            for record in inputs:
                if list_of_dicts:
                    contents.append(record.get(field_to_embed))  # type: ignore
                else:
                    contents.append(getattr(record, field_to_embed))
        else:
            if dict_like:
                contents.append(inputs.get(field_to_embed))  # type: ignore
            else:
                contents.append(getattr(inputs, field_to_embed))
        vectors = None
        service: EmbeddingGeneratorBase | None = None
        for service_id, settings in execution_settings.items():
            service = self.get_service(service_id, type=EmbeddingGeneratorBase)  # type: ignore
            if service:
                vectors = await service.generate_raw_embeddings(texts=contents, settings=settings, **kwargs)  # type: ignore
                break
        if not service:
            raise KernelServiceNotFoundError("No service found to generate embeddings.")
        if vectors is None:
            raise KernelInvokeException("No vectors were generated.")
        if cast_function:
            vectors = [cast_function(vector) for vector in vectors]
        if container_mode:
            inputs[field_to_store] = vectors  # type: ignore
            return
        if isinstance(inputs, list):
            for record, vector in zip(inputs, vectors):
                if list_of_dicts:
                    record[field_to_store] = vector  # type: ignore
                else:
                    setattr(record, field_to_store, vector)
            return
        if dict_like:
            inputs[field_to_store] = vectors[0]  # type: ignore
            return
        setattr(inputs, field_to_store, vectors[0])

    def clone(self) -> "Kernel":
        """Clone the kernel instance to create a new one that may be mutated without affecting the current instance.

        The current instance is not mutated by this operation.

        Note: The same service clients are used in the new instance, so if you mutate the service clients
        in the new instance, the original instance will be affected as well.

        New lists of plugins and filters are created. It will not affect the original lists when the new instance
        is mutated. A new `ai_service_selector` is created. It will not affect the original instance when the new
        instance is mutated.

        Important: Plugins are cloned without deep-copying their underlying callable methods. This avoids attempting
        to pickle/clone unpickleable objects (e.g., async generators), which can be present when plugins wrap async
        context managers such as MCP client sessions. Function metadata is deep-copied while callables are shared.
        """
        # Safely clone plugins by copying function metadata while retaining callable references.
        # This avoids deepcopying bound methods that may reference unpickleable async components.
        new_plugins: dict[str, KernelPlugin] = {}
        for plugin_name, plugin in self.plugins.items():
            cloned_plugin = KernelPlugin(name=plugin.name, description=plugin.description)
            # Using KernelPlugin.add will copy functions via KernelFunction.function_copy(),
            # which deep-copies metadata but keeps callables shallow.
            cloned_plugin.add(plugin.functions)
            new_plugins[plugin_name] = cloned_plugin

        return Kernel(
            plugins=new_plugins,
            # Shallow copy of the services, as they are not serializable
            services={k: v for k, v in self.services.items()},
            ai_service_selector=deepcopy(self.ai_service_selector),
            function_invocation_filters=deepcopy(self.function_invocation_filters),
            prompt_rendering_filters=deepcopy(self.prompt_rendering_filters),
            auto_function_invocation_filters=deepcopy(self.auto_function_invocation_filters),
        )

    @experimental
    def as_mcp_server(
        self,
        prompts: list[PromptTemplateBase] | None = None,
        server_name: str = "Semantic Kernel MCP Server",
        version: str | None = None,
        instructions: str | None = None,
        lifespan: Callable[["Server[LifespanResultT]"], AbstractAsyncContextManager["LifespanResultT"]] | None = None,
        excluded_functions: OptionalOneOrMany[str] = None,
        **kwargs: Any,
    ) -> "Server":
        """Create a MCP server from this kernel.

        This function automatically creates a MCP server from a kernel instance, it uses the provided arguments to
        configure the server and expose functions as tools and prompts, see the mcp documentation for more details.

        By default, all functions are exposed as Tools, you can control this by
        using use the `excluded_functions` argument.
        These need to be set to the function name, without the plugin_name.

        Args:
            kernel: The kernel instance to use.
            prompts: A list of prompt templates to expose as prompts.
            server_name: The name of the server.
            version: The version of the server.
            instructions: The instructions to use for the server.
            lifespan: The lifespan of the server.
            excluded_functions: The list of function names to exclude from the server.
                if None, no functions will be excluded.
            kwargs: Any extra arguments to pass to the server creation.

        Returns:
            The MCP server instance, it is a instance of
            mcp.server.lowlevel.Server

        """
        from semantic_kernel.connectors.mcp import create_mcp_server_from_kernel

        return create_mcp_server_from_kernel(
            kernel=self,
            prompts=prompts,
            server_name=server_name,
            version=version,
            instructions=instructions,
            lifespan=lifespan,
            excluded_functions=excluded_functions,
            **kwargs,
        )
