"""The CORTEX agent loop.

Ties together the encoding layer, a model adapter and the tool registry:

    messages -> encode_messages -> model -> parse -> tool call? -> execute
             -> append tool result -> encode again -> ... -> final answer

The loop is backend-agnostic. With ``MockAdapter`` it runs on a CPU with no
downloads, which is how it is tested in this repository.
"""

from __future__ import annotations

import json
from dataclasses import dataclass, field
from typing import Any

from ..adapters.base import ModelAdapter
from ..config import EngineConfig
from ..tools.registry import ToolError, ToolRegistry


@dataclass
class ToolCallRecord:
    """One tool invocation performed during a turn."""

    name: str
    arguments: dict[str, Any]
    result: str
    ok: bool


@dataclass
class AgentTurn:
    """The complete result of one user turn."""

    content: str = ""
    reasoning: str = ""
    tool_calls: list[ToolCallRecord] = field(default_factory=list)
    rounds: int = 0
    raw_completions: list[str] = field(default_factory=list)

    @property
    def used_tools(self) -> bool:
        return bool(self.tool_calls)


def _encode(messages: list[dict[str, Any]], engine: EngineConfig) -> str:
    """Call the DeepSeek-V4 encoder, with a clear error if it is missing."""
    try:
        from encoding_dsv4 import encode_messages
    except ImportError as exc:  # pragma: no cover - environment dependent
        raise RuntimeError(
            "The encoding module is required. Add the repository's encoding/ folder "
            "to PYTHONPATH, e.g. PYTHONPATH=/workspace/project/encoding"
        ) from exc

    return encode_messages(
        messages,
        thinking_mode=engine.thinking_mode,
        reasoning_effort=engine.reasoning_effort,
    )


def _parse(text: str, engine: EngineConfig) -> dict[str, Any]:
    try:
        from encoding_dsv4 import parse_message_from_completion_text
    except ImportError as exc:  # pragma: no cover
        raise RuntimeError("The encoding module is required.") from exc

    return parse_message_from_completion_text(text, thinking_mode=engine.thinking_mode)


def _normalize_call(call: dict[str, Any]) -> tuple[str, Any]:
    """Accept both the flat and the OpenAI-nested tool-call shape.

    ``parse_message_from_completion_text`` returns OpenAI format::

        {"type": "function", "function": {"name": ..., "arguments": "<json>"}}

    while hand-written or replayed data may use ``{"name": ..., "arguments": ...}``.
    """
    if "function" in call and isinstance(call["function"], dict):
        fn = call["function"]
        return fn.get("name", ""), fn.get("arguments", "{}")
    return call.get("name", ""), call.get("arguments", {})


class CortexAgent:
    """Runs the reasoning-and-tool loop for a conversation."""

    def __init__(
        self,
        adapter: ModelAdapter,
        tools: ToolRegistry | None = None,
        engine: EngineConfig | None = None,
        system_prompt: str | None = None,
    ) -> None:
        self.adapter = adapter
        self.tools = tools if tools is not None else ToolRegistry()
        self.engine = engine or EngineConfig()
        self.system_prompt = system_prompt

    def _system_message(self) -> dict[str, Any]:
        msg: dict[str, Any] = {"role": "system", "content": self.system_prompt or ""}
        if len(self.tools):
            msg["tools"] = self.tools.to_openai_schemas()
        return msg

    def run(self, messages: list[dict[str, Any]]) -> AgentTurn:
        """Execute one turn. *messages* are OpenAI-style dicts, without the system message."""
        turn = AgentTurn()
        convo: list[dict[str, Any]] = [self._system_message(), *messages]

        for round_index in range(self.engine.max_tool_rounds):
            turn.rounds = round_index + 1
            prompt = _encode(convo, self.engine)
            result = self.adapter.generate(
                prompt,
                max_new_tokens=self.engine.max_new_tokens,
                temperature=self.engine.temperature,
            )
            turn.raw_completions.append(result.text)

            parsed = _parse(result.text, self.engine)
            turn.reasoning = parsed.get("reasoning_content") or turn.reasoning
            calls = parsed.get("tool_calls") or []

            if not calls:
                turn.content = parsed.get("content", "")
                return turn

            convo.append(
                {
                    "role": "assistant",
                    "content": parsed.get("content", ""),
                    "reasoning_content": parsed.get("reasoning_content", ""),
                    "tool_calls": calls,
                }
            )
            for call in calls:
                name, raw_args = _normalize_call(call)
                record = self._execute(name, raw_args)
                turn.tool_calls.append(record)
                convo.append({"role": "tool", "content": record.result})

        turn.content = (
            f"Limite de {self.engine.max_tool_rounds} tours d'outils atteinte "
            "sans réponse finale."
        )
        return turn

    def _execute(self, name: str, raw_args: Any) -> ToolCallRecord:
        try:
            if isinstance(raw_args, str):
                args = json.loads(raw_args) if raw_args.strip() else {}
            else:
                args = dict(raw_args or {})
        except json.JSONDecodeError:
            args = {}
            return ToolCallRecord(name, {}, f"ERROR: arguments are not valid JSON: {raw_args}", False)

        try:
            result = self.tools.call(name, args)
        except ToolError as exc:
            return ToolCallRecord(name, args, f"ERROR: {exc}", False)
        return ToolCallRecord(name, args, result, not result.startswith("ERROR:"))