from __future__ import annotations

import json
from pathlib import Path
from typing import Any

from pydantic import BaseModel, Field


class ModelsConfig(BaseModel):
    orchestrator_path: str = "models/llama.gguf"
    coder_path: str = "models/xcoder.gguf"
    critic_path: str = "models/gemma.gguf"
    embeddings_path: str = "models/all-MiniLM-L6-v2"
    inference: dict[str, Any] = Field(default_factory=dict)
    thinker: dict[str, Any] = Field(default_factory=dict)
    json_compiler: dict[str, Any] = Field(default_factory=dict)
    orchestrator: dict[str, Any] = Field(default_factory=dict)
    coder: dict[str, Any] = Field(default_factory=dict)
    critic: dict[str, Any] = Field(default_factory=dict)
    sys_util: dict[str, Any] = Field(default_factory=dict)
    embeddings: dict[str, Any] = Field(default_factory=dict)


class PromptsConfig(BaseModel):
    orchestration_prompt: str = ""
    planning_prompt: str = ""
    coder_prompt: str = ""
    critic_prompt: str = ""


class PermissionsConfig(BaseModel):
    dangerous_commands: dict[str, str] = Field(default_factory=dict)
    sensitive_paths: list[str] = Field(default_factory=list)
    default_approval_behavior: str = "ask_always"


class RuntimeConfig(BaseModel):
    step_timeout_ms: int = 30_000
    task_timeout_ms: int = 300_000
    planner_retry_limit: int = 2
    tool_retry_limit: int = 1
    replan_limit: int = 1
    max_execution_steps: int = 20
    retrieval_top_k: int = 5
    max_context_tokens: int = 8192
    context_budgets: dict[str, int] = Field(default_factory=lambda: {
        "system": 512,
        "task": 512,
        "memory": 2048,
        "execution": 2048,
        "tools": 1024,
        "safety": 512,
    })
    reserve_for_generation_pct: int = 25
    orchestrator_retry_limit: int = 2
    intent_classifier: str = "thinker"
    memory_thresholds: dict[str, float] = Field(default_factory=dict)
    critic_fallback_policy: str = "continue_without_critic"
    checkpoint_policy: dict[str, Any] = Field(default_factory=dict)
    event_retention_policy: dict[str, Any] = Field(default_factory=dict)
    streaming_settings: dict[str, Any] = Field(default_factory=dict)
    debug: bool = False
    debug_orchestrator_log_length: int = 500
    json_fix_retry_limit: int = 2
    json_fix_use_sys_util: bool = True


class AppConfig(BaseModel):
    models: ModelsConfig
    prompts: PromptsConfig
    permissions: PermissionsConfig
    runtime: RuntimeConfig


def _load_json(path: Path) -> dict[str, Any]:
    with path.open("r", encoding="utf-8") as handle:
        return json.load(handle)


def load_app_config(config_dir: str | Path) -> AppConfig:
    config_path = Path(config_dir)
    return AppConfig(
        models=ModelsConfig.model_validate(_load_json(config_path / "models.json")),
        prompts=PromptsConfig.model_validate(_load_json(config_path / "prompts.json")),
        permissions=PermissionsConfig.model_validate(_load_json(config_path / "permissions.json")),
        runtime=RuntimeConfig.model_validate(_load_json(config_path / "runtime.json")),
    )