import json
import uuid
import xml.etree.ElementTree as ET
from typing import List, Optional, Tuple

from letta.errors import PendingApprovalError
from letta.helpers import ToolRulesSolver
from letta.log import get_logger
from letta.schemas.agent import AgentState
from letta.schemas.letta_message import MessageType
from letta.schemas.letta_response import LettaResponse
from letta.schemas.letta_stop_reason import LettaStopReason, StopReasonType
from letta.schemas.message import Message, MessageCreate, MessageCreateBase
from letta.schemas.tool_execution_result import ToolExecutionResult
from letta.schemas.usage import LettaUsageStatistics
from letta.schemas.user import User
from letta.server.rest_api.utils import create_approval_response_message_from_input, create_input_messages
from letta.services.message_manager import MessageManager

logger = get_logger(__name__)


def _create_letta_response(
    new_in_context_messages: list[Message],
    use_assistant_message: bool,
    usage: LettaUsageStatistics,
    stop_reason: Optional[LettaStopReason] = None,
    include_return_message_types: Optional[List[MessageType]] = None,
) -> LettaResponse:
    """
    Converts the newly created/persisted messages into a LettaResponse.
    """
    # NOTE: hacky solution to avoid returning heartbeat messages and the original user message
    filter_user_messages = [m for m in new_in_context_messages if m.role != "user"]

    # Convert to Letta messages first
    response_messages = Message.to_letta_messages_from_list(
        messages=filter_user_messages, use_assistant_message=use_assistant_message, reverse=False
    )
    # Filter approval response messages
    response_messages = [m for m in response_messages if m.message_type != "approval_response_message"]

    # Apply message type filtering if specified
    if include_return_message_types is not None:
        response_messages = [msg for msg in response_messages if msg.message_type in include_return_message_types]
    if stop_reason is None:
        stop_reason = LettaStopReason(stop_reason=StopReasonType.end_turn.value)
    return LettaResponse(messages=response_messages, stop_reason=stop_reason, usage=usage)


def _prepare_in_context_messages(
    input_messages: List[MessageCreate],
    agent_state: AgentState,
    message_manager: MessageManager,
    actor: User,
) -> Tuple[List[Message], List[Message]]:
    """
    Prepares in-context messages for an agent, based on the current state and a new user input.

    Args:
        input_messages (List[MessageCreate]): The new user input messages to process.
        agent_state (AgentState): The current state of the agent, including message buffer config.
        message_manager (MessageManager): The manager used to retrieve and create messages.
        actor (User): The user performing the action, used for access control and attribution.

    Returns:
        Tuple[List[Message], List[Message]]: A tuple containing:
            - The current in-context messages (existing context for the agent).
            - The new in-context messages (messages created from the new input).
    """

    if agent_state.message_buffer_autoclear:
        # If autoclear is enabled, only include the most recent system message (usually at index 0)
        current_in_context_messages = [message_manager.get_messages_by_ids(message_ids=agent_state.message_ids, actor=actor)[0]]
    else:
        # Otherwise, include the full list of messages by ID for context
        current_in_context_messages = message_manager.get_messages_by_ids(message_ids=agent_state.message_ids, actor=actor)

    # Create a new user message from the input and store it
    new_in_context_messages = message_manager.create_many_messages(
        create_input_messages(input_messages=input_messages, agent_id=agent_state.id, timezone=agent_state.timezone, actor=actor),
        actor=actor,
    )

    return current_in_context_messages, new_in_context_messages


async def _prepare_in_context_messages_async(
    input_messages: List[MessageCreate],
    agent_state: AgentState,
    message_manager: MessageManager,
    actor: User,
) -> Tuple[List[Message], List[Message]]:
    """
    Prepares in-context messages for an agent, based on the current state and a new user input.
    Async version of _prepare_in_context_messages.

    Args:
        input_messages (List[MessageCreate]): The new user input messages to process.
        agent_state (AgentState): The current state of the agent, including message buffer config.
        message_manager (MessageManager): The manager used to retrieve and create messages.
        actor (User): The user performing the action, used for access control and attribution.

    Returns:
        Tuple[List[Message], List[Message]]: A tuple containing:
            - The current in-context messages (existing context for the agent).
            - The new in-context messages (messages created from the new input).
    """

    if agent_state.message_buffer_autoclear:
        # If autoclear is enabled, only include the most recent system message (usually at index 0)
        current_in_context_messages = [await message_manager.get_message_by_id_async(message_id=agent_state.message_ids[0], actor=actor)]
    else:
        # Otherwise, include the full list of messages by ID for context
        current_in_context_messages = await message_manager.get_messages_by_ids_async(message_ids=agent_state.message_ids, actor=actor)

    # Create a new user message from the input and store it
    new_in_context_messages = await message_manager.create_many_messages_async(
        create_input_messages(input_messages=input_messages, agent_id=agent_state.id, timezone=agent_state.timezone, actor=actor),
        actor=actor,
        project_id=agent_state.project_id,
    )

    return current_in_context_messages, new_in_context_messages


async def _prepare_in_context_messages_no_persist_async(
    input_messages: List[MessageCreateBase],
    agent_state: AgentState,
    message_manager: MessageManager,
    actor: User,
) -> Tuple[List[Message], List[Message]]:
    """
    Prepares in-context messages for an agent, based on the current state and a new user input.

    Args:
        input_messages (List[MessageCreate]): The new user input messages to process.
        agent_state (AgentState): The current state of the agent, including message buffer config.
        message_manager (MessageManager): The manager used to retrieve and create messages.
        actor (User): The user performing the action, used for access control and attribution.

    Returns:
        Tuple[List[Message], List[Message]]: A tuple containing:
            - The current in-context messages (existing context for the agent).
            - The new in-context messages (messages created from the new input).
    """

    if agent_state.message_buffer_autoclear:
        # If autoclear is enabled, only include the most recent system message (usually at index 0)
        current_in_context_messages = [await message_manager.get_message_by_id_async(message_id=agent_state.message_ids[0], actor=actor)]
    else:
        # Otherwise, include the full list of messages by ID for context
        current_in_context_messages = await message_manager.get_messages_by_ids_async(message_ids=agent_state.message_ids, actor=actor)

    # Check for approval-related message validation
    if len(input_messages) == 1 and input_messages[0].type == "approval":
        # User is trying to send an approval response
        if current_in_context_messages[-1].role != "approval":
            raise ValueError(
                "Cannot process approval response: No tool call is currently awaiting approval. "
                "Please send a regular message to interact with the agent."
            )
        if input_messages[0].approval_request_id != current_in_context_messages[-1].id:
            raise ValueError(
                f"Invalid approval request ID. Expected '{current_in_context_messages[-1].id}' "
                f"but received '{input_messages[0].approval_request_id}'."
            )
        new_in_context_messages = create_approval_response_message_from_input(agent_state=agent_state, input_message=input_messages[0])
    else:
        # User is trying to send a regular message
        if current_in_context_messages[-1].role == "approval":
            raise PendingApprovalError(pending_request_id=current_in_context_messages[-1].id)

        # Create a new user message from the input but dont store it yet
        new_in_context_messages = create_input_messages(
            input_messages=input_messages, agent_id=agent_state.id, timezone=agent_state.timezone, actor=actor
        )

    return current_in_context_messages, new_in_context_messages


def serialize_message_history(messages: List[str], context: str) -> str:
    """
    Produce an XML document like:

    <memory>
      <messages>
        <message>…</message>
        <message>…</message>
        …
      </messages>
      <context>…</context>
    </memory>
    """
    root = ET.Element("memory")

    msgs_el = ET.SubElement(root, "messages")
    for msg in messages:
        m = ET.SubElement(msgs_el, "message")
        m.text = msg

    sum_el = ET.SubElement(root, "context")
    sum_el.text = context

    # ET.tostring will escape reserved chars for you
    return ET.tostring(root, encoding="unicode")


def deserialize_message_history(xml_str: str) -> Tuple[List[str], str]:
    """
    Parse the XML back into (messages, context). Raises ValueError if tags are missing.
    """
    try:
        root = ET.fromstring(xml_str)
    except ET.ParseError as e:
        raise ValueError(f"Invalid XML: {e}")

    msgs_el = root.find("messages")
    if msgs_el is None:
        raise ValueError("Missing <messages> section")

    messages = []
    for m in msgs_el.findall("message"):
        # .text may be None if empty, so coerce to empty string
        messages.append(m.text or "")

    sum_el = root.find("context")
    if sum_el is None:
        raise ValueError("Missing <context> section")
    context = sum_el.text or ""

    return messages, context


def generate_step_id():
    return f"step-{uuid.uuid4()}"


def _safe_load_tool_call_str(tool_call_args_str: str) -> dict:
    """Lenient JSON → dict with fallback to eval on assertion failure."""
    # Temp hack to gracefully handle parallel tool calling attempt, only take first one
    if "}{" in tool_call_args_str:
        tool_call_args_str = tool_call_args_str.split("}{", 1)[0] + "}"

    try:
        tool_args = json.loads(tool_call_args_str)
        if not isinstance(tool_args, dict):
            # Load it again - this is due to sometimes Anthropic returning weird json @caren
            tool_args = json.loads(tool_args)
    except json.JSONDecodeError:
        logger.error("Failed to JSON decode tool call argument string: %s", tool_call_args_str)
        tool_args = {}

    return tool_args


def _pop_heartbeat(tool_args: dict) -> bool:
    hb = tool_args.pop("request_heartbeat", False)
    return str(hb).lower() == "true" if isinstance(hb, str) else bool(hb)


def _build_rule_violation_result(tool_name: str, valid: list[str], solver: ToolRulesSolver) -> ToolExecutionResult:
    hint_lines = solver.guess_rule_violation(tool_name)
    hint_txt = ("\n** Hint: Possible rules that were violated:\n" + "\n".join(f"\t- {h}" for h in hint_lines)) if hint_lines else ""
    msg = f"[ToolConstraintError] Cannot call {tool_name}, valid tools include: {valid}.{hint_txt}"
    return ToolExecutionResult(status="error", func_return=msg)