import dataclasses
from typing import Any

from cua_lite.data.utils import batch_proc


# @dataclasses.dataclass
# class BaseDataInterface:

#     # -- Helpers (shared between grounding and trajectory) --
#     def _add_system_prompt(self, row: dict[str, Any], system_prompt: str | None = None) -> dict[str, Any]:
#         if system_prompt and row["messages"][0]["role"] != "system":
#             row["messages"].insert(0, {"role": "system", "content": [{"type": "text", "text": system_prompt}]})
#         return row

#     def _process_action(self, row: dict[str, Any], **kwargs) -> dict[str, Any]:
#         return row

#     # -- Grounding ---
#     def process_grounding(self, row: dict[str, Any], system_prompt: str | None = None, **kwargs) -> dict[str, Any]:
#         return self._add_system_prompt(
#             self._process_action(row, **kwargs), 
#             system_prompt=system_prompt
#         )

#     def process_grounding_batch(self, batch: dict[str, list[Any]], **kwargs) -> dict[str, list[Any]]:
#         return batch_proc(self.process_grounding, batch, **kwargs)

#     # --- Trajectory ---
#     def _process_context(self, row: dict[str, Any], **kwargs) -> dict[str, Any]:
#         return row

#     def process_trajectory(self, row: dict[str, Any], system_prompt: str | None = None, **kwargs) -> dict[str, Any]:
#         return self._add_system_prompt(
#             self._process_context(self._process_action(row, **kwargs), **kwargs), 
#             system_prompt=system_prompt
#         )

#     def process_trajectory_batch(self, batch: dict[str, list[Any]], **kwargs) -> dict[str, list[Any]]:
#         return batch_proc(self.process_trajectory, batch, **kwargs)

@dataclasses.dataclass
class BaseDataInterface:

    grounding_system_prompt: str | None = None
    trajectory_system_prompt: str | None = None

    # -- Helpers (shared between grounding and trajectory) --
    def _add_system_prompt(self, row: dict[str, Any], system_prompt: str | None = None) -> dict[str, Any]:
        if system_prompt and row["messages"][0]["role"] != "system":
            row["messages"].insert(0, {"role": "system", "content": [{"type": "text", "text": system_prompt}]})
        return row

    def _process_action(self, row: dict[str, Any], **kwargs) -> dict[str, Any]:
        # TODO: overwrite this in subclass 
        return row

    # -- Grounding ---
    def process_grounding(self, row: dict[str, Any], **kwargs) -> dict[str, Any]:
        return self._add_system_prompt(
            self._process_action(row, **kwargs), 
            system_prompt=self.grounding_system_prompt
        )

    def process_grounding_batch(self, batch: dict[str, list[Any]], **kwargs) -> dict[str, list[Any]]:
        return batch_proc(self.process_grounding, batch, **kwargs)

    # --- Trajectory ---
    def _process_context(self, row: dict[str, Any], **kwargs) -> dict[str, Any]:
        # TODO: overwrite this in subclass
        return row

    def process_trajectory(self, row: dict[str, Any], **kwargs) -> dict[str, Any]:
        return self._add_system_prompt(
            self._process_context(self._process_action(row, **kwargs), **kwargs), 
            system_prompt=self.trajectory_system_prompt
        )

    def process_trajectory_batch(self, batch: dict[str, list[Any]], **kwargs) -> dict[str, list[Any]]:
        return batch_proc(self.process_trajectory, batch, **kwargs)


@dataclasses.dataclass
class UnrolledContextDataInterface(BaseDataInterface):

    """Useful for reasoning models"""

    def process_trajectory_batch(
        self, batch: dict[str, list[Any]], **kwargs) -> dict[str, list[Any]]:
        """
        Handles 1-to-N data expansion (Unrolling conversation history).
        Note: This changes the number of rows.
        """
        messages_list = []

        for messages in batch["messages"]:
            assistant_indices = [
                i for i, msg in enumerate(messages) if msg.get("role") == "assistant"
            ]
            for assistant_index in assistant_indices:
                # Slicing includes the assistant message
                processed_messages = self.process_trajectory({"messages": messages[: assistant_index + 1]}, **kwargs)["messages"]
                messages_list.append(processed_messages)

        batch = {"messages": messages_list}

        return batch