"""Prompt construction — build Responses API inputs from spec + history."""

from __future__ import annotations

from typing import Any

from omnigent.entities import (
    ConversationItem,
    FunctionCallData,
    FunctionCallOutputData,
    MessageData,
    NativeToolData,
)
from omnigent.spec import AgentSpec


def build_instructions(
    spec: AgentSpec,
    per_request_instructions: str | None,
    tool_schemas: list[dict[str, Any]],
) -> str:
    """
    Build the system instructions string from the agent's
    instructions, per-request instructions, and skill metadata.
    Passed as the ``instructions`` parameter to
    ``client.responses.create()``.

    :param spec: The parsed AgentSpec containing the agent's
        base instructions and skill definitions.
    :param per_request_instructions: Optional additional
        instructions for this specific request, appended
        after the agent's base instructions.
    :param tool_schemas: OpenAI-format tool schemas (used
        only for future skill-awareness hinting; currently
        not included in the instructions body).
    :returns: The assembled instructions string.
    """
    parts: list[str] = []

    if spec.instructions:
        parts.append(spec.instructions)

    if per_request_instructions:
        parts.append(per_request_instructions)

    # Only mention skills in the system prompt when load_skill is
    # available as a tool. Executors that handle skills natively
    # (e.g. Claude SDK with its built-in Skill tool) don't need
    # this hint — the SDK informs the model about skills itself.
    has_load_skill = any(
        schema.get("function", {}).get("name") == "load_skill" for schema in tool_schemas
    )
    if spec.skills and has_load_skill:
        skill_lines = ["Available skills (use the load_skill tool to load one):"]
        for skill in spec.skills:
            skill_lines.append(f"- {skill.name}: {skill.description}")
        parts.append("\n".join(skill_lines))

    return "\n\n".join(parts) if parts else "You are a helpful assistant."


def _strip_output_annotations(
    content: list[dict[str, Any]],
) -> list[dict[str, Any]]:
    """
    Remove ``annotations`` from ``output_text`` blocks.

    Annotations (e.g. ``file_citation``) are output metadata — they
    tell the client about files the agent produced. They are not
    input content for the LLM on subsequent turns. The text
    description itself survives and provides context.

    :param content: Content block list from a ``MessageData``.
    :returns: A new list with annotations stripped from output blocks.
    """
    result: list[dict[str, Any]] = []
    for block in content:
        if (
            isinstance(block, dict)
            and block.get("type") == "output_text"
            and "annotations" in block
        ):
            stripped = {k: v for k, v in block.items() if k != "annotations"}
            result.append(stripped)
        else:
            result.append(block)
    return result


def history_to_input_items(
    items: list[ConversationItem],
) -> list[dict[str, Any]]:
    """
    Convert persisted ConversationItems into Responses API input items.

    Each item type maps directly to a Responses API input item format:
    ``message`` → role/content pair, ``function_call`` → function call
    item, ``function_call_output`` → function call output item. This
    is simpler than Chat Completions format because function calls are
    kept as separate items rather than embedded in assistant messages.

    :param items: Persisted conversation items in chronological order.
    :returns: A list of Responses API input item dicts suitable for
        ``client.responses.create(input=...)``.
    """
    result: list[dict[str, Any]] = []

    for item in items:
        if item.type == "message":
            assert isinstance(item.data, MessageData)
            # Pass content blocks through, stripping annotations
            # from output_text blocks. Annotations are output
            # metadata (file citations) — not input content for
            # the LLM. The text description survives and gives
            # the LLM context about files it previously produced.
            content = _strip_output_annotations(item.data.content)
            result.append(
                {
                    "role": item.data.role,
                    "content": content,
                }
            )

        elif item.type == "function_call":
            assert isinstance(item.data, FunctionCallData)
            result.append(
                {
                    "type": "function_call",
                    "call_id": item.data.call_id,
                    "name": item.data.name,
                    "arguments": item.data.arguments,
                }
            )

        elif item.type == "function_call_output":
            assert isinstance(item.data, FunctionCallOutputData)
            result.append(
                {
                    "type": "function_call_output",
                    "call_id": item.data.call_id,
                    "output": item.data.output,
                }
            )

        elif item.type == "native_tool":
            assert isinstance(item.data, NativeToolData)
            # Pass the raw provider dict through as-is. The
            # Responses API accepts its own output items
            # (e.g. web_search_call) as input items.
            result.append(item.data.item)

        elif item.type == "reasoning":
            # reasoning items are not included in the LLM prompt
            # (they are output-only)
            pass

        elif item.type == "compaction":
            # compaction items are metadata, not conversation content
            # the LLM should see — they are converted to a synthetic
            # summary message pair by compaction_to_history_items()
            # before being prepended to history.
            pass

    return result