import json
from datetime import datetime, timezone
from enum import Enum
from typing import Annotated, ClassVar, List, Literal, Optional, Union

from pydantic import BaseModel, Field, field_serializer, field_validator

from letta.schemas.letta_message_content import (
    LettaAssistantMessageContentUnion,
    LettaToolReturnContentUnion,
    LettaUserMessageContentUnion,
    get_letta_assistant_message_content_union_str_json_schema,
    get_letta_tool_return_content_union_str_json_schema,
    get_letta_user_message_content_union_str_json_schema,
)

# ---------------------------
# Letta API Messaging Schemas
# ---------------------------


class MessageReturnType(str, Enum):
    approval = "approval"
    tool = "tool"


class MessageReturn(BaseModel):
    type: MessageReturnType = Field(..., description="The message type to be created.")


class ApprovalReturn(MessageReturn):
    type: Literal[MessageReturnType.approval] = Field(default=MessageReturnType.approval, description="The message type to be created.")
    tool_call_id: str = Field(..., description="The ID of the tool call that corresponds to this approval")
    approve: bool = Field(..., description="Whether the tool has been approved")
    reason: Optional[str] = Field(None, description="An optional explanation for the provided approval status")


class ToolReturn(MessageReturn):
    type: Literal[MessageReturnType.tool] = Field(default=MessageReturnType.tool, description="The message type to be created.")
    tool_return: Union[str, List[LettaToolReturnContentUnion]] = Field(
        ...,
        description="The tool return value - either a string or list of content parts (text/image)",
        json_schema_extra=get_letta_tool_return_content_union_str_json_schema(),
    )
    status: Literal["success", "error"]
    tool_call_id: str
    stdout: Optional[List[str]] = None
    stderr: Optional[List[str]] = None


LettaMessageReturnUnion = Annotated[Union[ApprovalReturn, ToolReturn], Field(discriminator="type")]


class MessageType(str, Enum):
    system_message = "system_message"
    user_message = "user_message"
    assistant_message = "assistant_message"
    reasoning_message = "reasoning_message"
    hidden_reasoning_message = "hidden_reasoning_message"
    tool_call_message = "tool_call_message"
    tool_return_message = "tool_return_message"
    approval_request_message = "approval_request_message"
    approval_response_message = "approval_response_message"
    summary_message = "summary_message"
    event_message = "event_message"


class LettaMessage(BaseModel):
    """
    Base class for simplified Letta message response type. This is intended to be used for developers
    who want the internal monologue, tool calls, and tool returns in a simplified format that does not
    include additional information other than the content and timestamp.

    Args:
        id (str): The ID of the message
        date (datetime): The date the message was created in ISO format
        name (Optional[str]): The name of the sender of the message
        message_type (MessageType): The type of the message
        otid (Optional[str]): The offline threading id associated with this message
        sender_id (Optional[str]): The id of the sender of the message, can be an identity id or agent id
        step_id (Optional[str]): The step id associated with the message
        is_err (Optional[bool]): Whether the message is an errored message or not. Used for debugging purposes only.
    """

    id: str
    date: datetime
    name: str | None = None
    message_type: MessageType = Field(..., description="The type of the message.")
    otid: str | None = None
    sender_id: str | None = None
    step_id: str | None = None
    is_err: bool | None = None
    seq_id: int | None = None
    run_id: str | None = None

    @field_serializer("date")
    def serialize_datetime(self, dt: datetime, _info):
        """
        Remove microseconds since it seems like we're inconsistent with getting them
        TODO: figure out why we don't always get microseconds (get_utc_time() does)
        """
        if dt.tzinfo is None or dt.tzinfo.utcoffset(dt) is None:
            dt = dt.replace(tzinfo=timezone.utc)
        return dt.isoformat(timespec="seconds")

    @field_serializer("is_err", mode="wrap")
    def serialize_is_err(self, value: bool | None, handler, _info):
        """
        Only serialize is_err field when it's True (for debugging purposes).
        When is_err is None or False, this field will be excluded from the JSON output.
        """
        return handler(value) if value is True else None


class SystemMessage(LettaMessage):
    """
    A message generated by the system. Never streamed back on a response, only used for cursor pagination.

    Args:
        id (str): The ID of the message
        date (datetime): The date the message was created in ISO format
        name (Optional[str]): The name of the sender of the message
        content (str): The message content sent by the system
    """

    message_type: Literal[MessageType.system_message] = Field(default=MessageType.system_message, description="The type of the message.")
    content: str = Field(..., description="The message content sent by the system")


class UserMessage(LettaMessage):
    """
    A message sent by the user. Never streamed back on a response, only used for cursor pagination.

    Args:
        id (str): The ID of the message
        date (datetime): The date the message was created in ISO format
        name (Optional[str]): The name of the sender of the message
        content (Union[str, List[LettaUserMessageContentUnion]]): The message content sent by the user (can be a string or an array of multi-modal content parts)
    """

    message_type: Literal[MessageType.user_message] = Field(default=MessageType.user_message, description="The type of the message.")
    content: Union[str, List[LettaUserMessageContentUnion]] = Field(
        ...,
        description="The message content sent by the user (can be a string or an array of multi-modal content parts)",
        json_schema_extra=get_letta_user_message_content_union_str_json_schema(),
    )


class ReasoningMessage(LettaMessage):
    """
    Representation of an agent's internal reasoning.

    Args:
        id (str): The ID of the message
        date (datetime): The date the message was created in ISO format
        name (Optional[str]): The name of the sender of the message
        source (Literal["reasoner_model", "non_reasoner_model"]): Whether the reasoning
            content was generated natively by a reasoner model or derived via prompting
        reasoning (str): The internal reasoning of the agent
        signature (Optional[str]): The model-generated signature of the reasoning step
    """

    message_type: Literal[MessageType.reasoning_message] = Field(
        default=MessageType.reasoning_message, description="The type of the message."
    )
    source: Literal["reasoner_model", "non_reasoner_model"] = "non_reasoner_model"
    reasoning: str
    signature: Optional[str] = None


class HiddenReasoningMessage(LettaMessage):
    """
    Representation of an agent's internal reasoning where reasoning content
    has been hidden from the response.

    Args:
        id (str): The ID of the message
        date (datetime): The date the message was created in ISO format
        name (Optional[str]): The name of the sender of the message
        state (Literal["redacted", "omitted"]): Whether the reasoning
            content was redacted by the provider or simply omitted by the API
        hidden_reasoning (Optional[str]): The internal reasoning of the agent
    """

    message_type: Literal[MessageType.hidden_reasoning_message] = Field(
        default=MessageType.hidden_reasoning_message, description="The type of the message."
    )
    state: Literal["redacted", "omitted"]
    hidden_reasoning: Optional[str] = None


class ToolCall(BaseModel):
    name: str
    arguments: str
    tool_call_id: str


class ToolCallDelta(BaseModel):
    name: Optional[str] = None
    arguments: Optional[str] = None
    tool_call_id: Optional[str] = None

    def model_dump(self, *args, **kwargs):
        """
        This is a workaround to exclude None values from the JSON dump since the
        OpenAI style of returning chunks doesn't include keys with null values.
        """
        kwargs["exclude_none"] = True
        return super().model_dump(*args, **kwargs)

    def json(self, *args, **kwargs):
        return json.dumps(self.model_dump(exclude_none=True), *args, **kwargs)


class ToolCallMessage(LettaMessage):
    """
    A message representing a request to call a tool (generated by the LLM to trigger tool execution).

    Args:
        id (str): The ID of the message
        date (datetime): The date the message was created in ISO format
        name (Optional[str]): The name of the sender of the message
        tool_call (Union[ToolCall, ToolCallDelta]): The tool call
    """

    message_type: Literal[MessageType.tool_call_message] = Field(
        default=MessageType.tool_call_message, description="The type of the message."
    )
    tool_call: Union[ToolCall, ToolCallDelta] = Field(..., deprecated=True)
    tool_calls: Optional[Union[List[ToolCall], ToolCallDelta]] = None

    def model_dump(self, *args, **kwargs):
        """
        Handling for the ToolCallDelta exclude_none to work correctly
        """
        kwargs["exclude_none"] = True
        data = super().model_dump(*args, **kwargs)
        if isinstance(data.get("tool_call"), dict):
            data["tool_call"] = {k: v for k, v in data["tool_call"].items() if v is not None}
        if isinstance(data.get("tool_calls"), dict):
            data["tool_calls"] = {k: v for k, v in data["tool_calls"].items() if v is not None}
        elif isinstance(data.get("tool_calls"), list):
            data["tool_calls"] = [
                {k: v for k, v in item.items() if v is not None} if isinstance(item, dict) else item for item in data["tool_calls"]
            ]
        return data

    class Config:
        json_encoders: ClassVar[dict] = {
            ToolCallDelta: lambda v: v.model_dump(exclude_none=True),
            ToolCall: lambda v: v.model_dump(exclude_none=True),
        }

    @field_validator("tool_call", mode="before")
    @classmethod
    def validate_tool_call(cls, v):
        """
        Casts dicts into ToolCallMessage objects. Without this extra validator, Pydantic will throw
        an error if 'name' or 'arguments' are None instead of properly casting to ToolCallDelta
        instead of ToolCall.
        """
        if isinstance(v, dict):
            if "name" in v and "arguments" in v and "tool_call_id" in v:
                return ToolCall(name=v["name"], arguments=v["arguments"], tool_call_id=v["tool_call_id"])
            elif "name" in v or "arguments" in v or "tool_call_id" in v:
                return ToolCallDelta(name=v.get("name"), arguments=v.get("arguments"), tool_call_id=v.get("tool_call_id"))
            else:
                raise ValueError("tool_call must contain either 'name' or 'arguments'")
        return v


class ToolReturnMessage(LettaMessage):
    """
    A message representing the return value of a tool call (generated by Letta executing the requested tool).

    Args:
        id (str): The ID of the message
        date (datetime): The date the message was created in ISO format
        name (Optional[str]): The name of the sender of the message
        tool_return (str): The return value of the tool (deprecated, use tool_returns)
        status (Literal["success", "error"]): The status of the tool call (deprecated, use tool_returns)
        tool_call_id (str): A unique identifier for the tool call that generated this message (deprecated, use tool_returns)
        stdout (Optional[List(str)]): Captured stdout (e.g. prints, logs) from the tool invocation (deprecated, use tool_returns)
        stderr (Optional[List(str)]): Captured stderr from the tool invocation (deprecated, use tool_returns)
        tool_returns (Optional[List[ToolReturn]]): List of tool returns for multi-tool support
    """

    message_type: Literal[MessageType.tool_return_message] = Field(
        default=MessageType.tool_return_message, description="The type of the message."
    )
    tool_return: str = Field(..., deprecated=True)
    status: Literal["success", "error"] = Field(..., deprecated=True)
    tool_call_id: str = Field(..., deprecated=True)
    stdout: Optional[List[str]] = Field(None, deprecated=True)
    stderr: Optional[List[str]] = Field(None, deprecated=True)
    tool_returns: Optional[List[ToolReturn]] = None


class ApprovalRequestMessage(LettaMessage):
    """
    A message representing a request for approval to call a tool (generated by the LLM to trigger tool execution).

    Args:
        id (str): The ID of the message
        date (datetime): The date the message was created in ISO format
        name (Optional[str]): The name of the sender of the message
        tool_call (ToolCall): The tool call
    """

    message_type: Literal[MessageType.approval_request_message] = Field(
        default=MessageType.approval_request_message, description="The type of the message."
    )
    tool_call: Union[ToolCall, ToolCallDelta] = Field(
        ..., description="The tool call that has been requested by the llm to run", deprecated=True
    )
    tool_calls: Optional[Union[List[ToolCall], ToolCallDelta]] = Field(
        None, description="The tool calls that have been requested by the llm to run, which are pending approval"
    )


class ApprovalResponseMessage(LettaMessage):
    """
    A message representing a response form the user indicating whether a tool has been approved to run.

    Args:
        id (str): The ID of the message
        date (datetime): The date the message was created in ISO format
        name (Optional[str]): The name of the sender of the message
        approve: (bool) Whether the tool has been approved
        approval_request_id: The ID of the approval request
        reason: (Optional[str]) An optional explanation for the provided approval status
    """

    message_type: Literal[MessageType.approval_response_message] = Field(
        default=MessageType.approval_response_message, description="The type of the message."
    )
    approvals: Optional[List[LettaMessageReturnUnion]] = Field(default=None, description="The list of approval responses")
    approve: Optional[bool] = Field(None, description="Whether the tool has been approved", deprecated=True)
    approval_request_id: Optional[str] = Field(None, description="The message ID of the approval request", deprecated=True)
    reason: Optional[str] = Field(None, description="An optional explanation for the provided approval status", deprecated=True)


class AssistantMessage(LettaMessage):
    """
    A message sent by the LLM in response to user input. Used in the LLM context.

    Args:
        id (str): The ID of the message
        date (datetime): The date the message was created in ISO format
        name (Optional[str]): The name of the sender of the message
        content (Union[str, List[LettaAssistantMessageContentUnion]]): The message content sent by the agent (can be a string or an array of content parts)
    """

    message_type: Literal[MessageType.assistant_message] = Field(
        default=MessageType.assistant_message, description="The type of the message."
    )
    content: Union[str, List[LettaAssistantMessageContentUnion]] = Field(
        ...,
        description="The message content sent by the agent (can be a string or an array of content parts)",
        json_schema_extra=get_letta_assistant_message_content_union_str_json_schema(),
    )


class LettaPing(LettaMessage):
    """
    A ping message used as a keepalive to prevent SSE streams from timing out during long running requests.

    Args:
        id (str): The ID of the message
        date (datetime): The date the message was created in ISO format
    """

    message_type: Literal["ping"] = Field(
        "ping",
        description="The type of the message. Ping messages are a keep-alive to prevent SSE streams from timing out during long running requests.",
    )


class LettaErrorMessage(BaseModel):
    """
    Message returning any error that occurred during the agent's execution, mid SSE stream.

    Args:
        run_id (str): The ID of the run
        error_type (str): The type of error
        message (str): The error message
        detail (Optional[str]): An optional error detail
        seq_id (Optional[int]): The sequence ID for cursor-based pagination
    """

    message_type: Literal["error_message"] = "error_message"
    run_id: str
    error_type: str
    message: str
    detail: Optional[str] = None
    seq_id: Optional[int] = None


class CompactionStats(BaseModel):
    """
    Statistics about a memory compaction operation.
    """

    trigger: str = Field(..., description="What triggered the compaction (e.g., 'context_window_exceeded', 'post_step_context_check')")
    context_tokens_before: Optional[int] = Field(
        None, description="Token count before compaction (from LLM usage stats, includes full context sent to LLM)"
    )
    context_tokens_after: Optional[int] = Field(
        None, description="Token count after compaction (message tokens only, does not include tool definitions)"
    )
    context_window: int = Field(..., description="The model's context window size")
    messages_count_before: int = Field(..., description="Number of messages before compaction")
    messages_count_after: int = Field(..., description="Number of messages after compaction")


def extract_compaction_stats_from_packed_json(text_content: str) -> Optional[CompactionStats]:
    """
    Extract CompactionStats from a packed summary message JSON string.

    Args:
        text_content: The packed JSON string from summary message content

    Returns:
        CompactionStats if found and valid, None otherwise
    """
    try:
        packed_json = json.loads(text_content)
        if isinstance(packed_json, dict) and "compaction_stats" in packed_json:
            return CompactionStats(**packed_json["compaction_stats"])
    except (json.JSONDecodeError, TypeError, ValueError):
        pass
    return None


class SummaryMessage(LettaMessage):
    """
    A message representing a summary of the conversation. Sent to the LLM as a user or system message depending on the provider.
    """

    message_type: Literal["summary_message"] = "summary_message"
    summary: str
    compaction_stats: Optional[CompactionStats] = None


class EventMessage(LettaMessage):
    """
    A message for notifying the developer that an event that has occured (e.g. a compaction). Events are NOT part of the context window.
    """

    message_type: Literal["event_message"] = "event_message"
    event_type: Literal["compaction"]
    event_data: dict


# NOTE: use Pydantic's discriminated unions feature: https://docs.pydantic.dev/latest/concepts/unions/#discriminated-unions
LettaMessageUnion = Annotated[
    Union[
        SystemMessage,
        UserMessage,
        ReasoningMessage,
        HiddenReasoningMessage,
        ToolCallMessage,
        ToolReturnMessage,
        AssistantMessage,
        ApprovalRequestMessage,
        ApprovalResponseMessage,
        SummaryMessage,
        EventMessage,
    ],
    Field(discriminator="message_type"),
]


def create_letta_message_union_schema():
    return {
        "oneOf": [
            {"$ref": "#/components/schemas/SystemMessage"},
            {"$ref": "#/components/schemas/UserMessage"},
            {"$ref": "#/components/schemas/ReasoningMessage"},
            {"$ref": "#/components/schemas/HiddenReasoningMessage"},
            {"$ref": "#/components/schemas/ToolCallMessage"},
            {"$ref": "#/components/schemas/ToolReturnMessage"},
            {"$ref": "#/components/schemas/AssistantMessage"},
            {"$ref": "#/components/schemas/ApprovalRequestMessage"},
            {"$ref": "#/components/schemas/ApprovalResponseMessage"},
            {"$ref": "#/components/schemas/SummaryMessage"},
            {"$ref": "#/components/schemas/EventMessage"},
        ],
        "discriminator": {
            "propertyName": "message_type",
            "mapping": {
                "system_message": "#/components/schemas/SystemMessage",
                "user_message": "#/components/schemas/UserMessage",
                "reasoning_message": "#/components/schemas/ReasoningMessage",
                "hidden_reasoning_message": "#/components/schemas/HiddenReasoningMessage",
                "tool_call_message": "#/components/schemas/ToolCallMessage",
                "tool_return_message": "#/components/schemas/ToolReturnMessage",
                "assistant_message": "#/components/schemas/AssistantMessage",
                "approval_request_message": "#/components/schemas/ApprovalRequestMessage",
                "approval_response_message": "#/components/schemas/ApprovalResponseMessage",
                "summary_message": "#/components/schemas/SummaryMessage",
                "event_message": "#/components/schemas/EventMessage",
            },
        },
    }


def create_letta_error_message_schema():
    return {
        "properties": {
            "message_type": {
                "type": "string",
                "const": "error_message",
                "title": "Message Type",
                "description": "The type of the message.",
                "default": "error_message",
            },
            "run_id": {
                "type": "string",
                "title": "Run ID",
                "description": "The ID of the run.",
            },
            "error_type": {
                "type": "string",
                "title": "Error Type",
                "description": "The type of error.",
            },
            "message": {
                "type": "string",
                "title": "Message",
                "description": "The error message.",
            },
            "detail": {
                "type": "string",
                "title": "Detail",
                "description": "An optional error detail.",
            },
            "seq_id": {
                "type": "integer",
                "title": "Seq ID",
                "description": "The sequence ID for cursor-based pagination.",
            },
        },
        "type": "object",
        "required": ["message_type", "run_id", "error_type", "message"],
        "title": "LettaErrorMessage",
        "description": "Error messages are used to notify the client of an error that occurred during the agent's execution.",
    }


# --------------------------
# Message Update API Schemas
# --------------------------


class UpdateSystemMessage(BaseModel):
    message_type: Literal["system_message"] = "system_message"
    content: str = Field(
        ..., description="The message content sent by the system (can be a string or an array of multi-modal content parts)"
    )


class UpdateUserMessage(BaseModel):
    message_type: Literal["user_message"] = "user_message"
    content: Union[str, List[LettaUserMessageContentUnion]] = Field(
        ...,
        description="The message content sent by the user (can be a string or an array of multi-modal content parts)",
        json_schema_extra=get_letta_user_message_content_union_str_json_schema(),
    )


class UpdateReasoningMessage(BaseModel):
    reasoning: str
    message_type: Literal["reasoning_message"] = "reasoning_message"


class UpdateAssistantMessage(BaseModel):
    message_type: Literal["assistant_message"] = "assistant_message"
    content: Union[str, List[LettaAssistantMessageContentUnion]] = Field(
        ...,
        description="The message content sent by the assistant (can be a string or an array of content parts)",
        json_schema_extra=get_letta_assistant_message_content_union_str_json_schema(),
    )


LettaMessageUpdateUnion = Annotated[
    Union[UpdateSystemMessage, UpdateUserMessage, UpdateReasoningMessage, UpdateAssistantMessage],
    Field(discriminator="message_type"),
]


# ------------------------------
# Message Search Result Schemas
# ------------------------------


class SystemMessageListResult(UpdateSystemMessage):
    """System message list result with agent context.

    Shape is identical to UpdateSystemMessage but includes the owning agent_id and message id.
    """

    message_id: str = Field(
        ...,
        description="The unique identifier of the message.",
    )
    agent_id: str | None = Field(
        default=None,
        description="The unique identifier of the agent that owns the message.",
    )
    conversation_id: str | None = Field(
        default=None,
        description="The unique identifier of the conversation that the message belongs to.",
    )

    created_at: datetime = Field(..., description="The time the message was created in ISO format.")


class UserMessageListResult(UpdateUserMessage):
    """User message list result with agent context.

    Shape is identical to UpdateUserMessage but includes the owning agent_id and message id.
    """

    message_id: str = Field(
        ...,
        description="The unique identifier of the message.",
    )
    agent_id: str | None = Field(
        default=None,
        description="The unique identifier of the agent that owns the message.",
    )
    conversation_id: str | None = Field(
        default=None,
        description="The unique identifier of the conversation that the message belongs to.",
    )

    created_at: datetime = Field(..., description="The time the message was created in ISO format.")


class ReasoningMessageListResult(UpdateReasoningMessage):
    """Reasoning message list result with agent context.

    Shape is identical to UpdateReasoningMessage but includes the owning agent_id and message id.
    """

    message_id: str = Field(
        ...,
        description="The unique identifier of the message.",
    )
    agent_id: str | None = Field(
        default=None,
        description="The unique identifier of the agent that owns the message.",
    )
    conversation_id: str | None = Field(
        default=None,
        description="The unique identifier of the conversation that the message belongs to.",
    )

    created_at: datetime = Field(..., description="The time the message was created in ISO format.")


class AssistantMessageListResult(UpdateAssistantMessage):
    """Assistant message list result with agent context.

    Shape is identical to UpdateAssistantMessage but includes the owning agent_id and message id.
    """

    message_id: str = Field(
        ...,
        description="The unique identifier of the message.",
    )
    agent_id: str | None = Field(
        default=None,
        description="The unique identifier of the agent that owns the message.",
    )
    conversation_id: str | None = Field(
        default=None,
        description="The unique identifier of the conversation that the message belongs to.",
    )

    created_at: datetime = Field(..., description="The time the message was created in ISO format.")


LettaMessageSearchResult = Annotated[
    Union[
        SystemMessageListResult,
        UserMessageListResult,
        ReasoningMessageListResult,
        AssistantMessageListResult,
    ],
    Field(discriminator="message_type"),
]


# --------------------------
# Deprecated Message Schemas
# --------------------------


class LegacyFunctionCallMessage(LettaMessage):
    function_call: str


class LegacyFunctionReturn(LettaMessage):
    """
    A message representing the return value of a function call (generated by Letta executing the requested function).

    Args:
        function_return (str): The return value of the function
        status (Literal["success", "error"]): The status of the function call
        id (str): The ID of the message
        date (datetime): The date the message was created in ISO format
        function_call_id (str): A unique identifier for the function call that generated this message
        stdout (Optional[List(str)]): Captured stdout (e.g. prints, logs) from the function invocation
        stderr (Optional[List(str)]): Captured stderr from the function invocation
    """

    message_type: Literal["function_return"] = "function_return"
    function_return: str
    status: Literal["success", "error"]
    function_call_id: str
    stdout: Optional[List[str]] = None
    stderr: Optional[List[str]] = None


class LegacyInternalMonologue(LettaMessage):
    """
    Representation of an agent's internal monologue.

    Args:
        internal_monologue (str): The internal monologue of the agent
        id (str): The ID of the message
        date (datetime): The date the message was created in ISO format
    """

    message_type: Literal["internal_monologue"] = "internal_monologue"
    internal_monologue: str


LegacyLettaMessage = Union[LegacyInternalMonologue, AssistantMessage, LegacyFunctionCallMessage, LegacyFunctionReturn]
