# Copyright 2026 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""LLM provider, model hierarchy, fallback chaining, and configuration loaders."""

import os
from artemis.config.core.interpolator import interpolate_config_value
from pathlib import Path
from typing import Any, Literal

from pydantic import Field, BaseModel, ValidationError

from artemis.config.constants import (
    LLM_CONFIG_FILENAME,
    AgentNode,
    LLMProvider,
    LLMUtilsNode,
)
from artemis.config.paths import ROOT_DIR, get_config_path
from artemis.config.settings import settings
from artemis.utils.file import load_jsonc
from artemis.utils.logger import get_logger

logger = get_logger(__name__)


class _CyFunctionDetectorMeta(type):
    def __instancecheck__(self, instance):
        name = type(instance).__name__
        return (
            name
            in (
                "cyfunction",
                "cython_function_or_method",
                "builtin_function_or_method",
            )
            or "cyfunction" in name.lower()
        )


class CyFunctionDetector(metaclass=_CyFunctionDetectorMeta):
    """Utility type detector for Cython and C-extension functions in Pydantic."""

    pass


class LLM(BaseModel):
    """Base model representing an LLM model provider and runtime parameters."""

    model_config = {"ignored_types": (CyFunctionDetector,)}
    provider: LLMProvider = "openai"
    model: str = Field(default_factory=lambda: settings.OPENAI_MODEL)
    api_base: str | None = None
    transport: Literal["http", "websocket"] = Field(
        default_factory=lambda: settings.OPENAI_TRANSPORT
    )
    timeout: float = Field(default_factory=lambda: settings.OPENAI_TIMEOUT_SECONDS, gt=0)
    max_retries: int = Field(default_factory=lambda: settings.OPENAI_MAX_RETRIES, ge=0)
    max_tokens: int | None = Field(default=None, gt=0)
    temperature: float | None = None
    reasoning_effort: (
        Literal["default", "none", "minimal", "low", "medium", "high", "xhigh", "max"] | None
    ) = Field(default_factory=lambda: settings.OPENAI_REASONING_EFFORT)
    enable_grounding: bool | None = None

    def validate_provider(self, name: str) -> None:
        """Ensure the required API key or credentials exist in settings for this provider."""
        if self.provider == "openai":
            if not settings.OPENAI_API_KEY:
                raise Exception(f"{name} requires OPENAI_API_KEY in .env")

    def __str__(self) -> str:
        return f"{self.provider}/{self.model}"


class LLMWithFallback(LLM):
    """LLM configuration with automatic secondary fallback and timeout specs."""

    fallback: LLM
    fix_model: str | None = None

    def __str__(self) -> str:
        return f"{self.provider}/{self.model} (fallback: {self.fallback})"


def lightweight_judge_default() -> "LLMWithFallback":
    """Factory default for the lightweight judge nodes (pixel safety net and
    planner validation): the configured Responses model at temperature 0."""
    return LLMWithFallback(
        provider="openai",
        model=settings.OPENAI_MODEL,
        temperature=0.0,
        fallback=LLM(
            provider="openai",
            model=settings.OPENAI_MODEL,
            temperature=0.0,
        ),
    )


class LLMConfigUtils(BaseModel):
    """Configuration container for auxiliary utility agents/nodes."""

    model_config = {"ignored_types": (CyFunctionDetector,)}
    outputter: LLMWithFallback
    hopper: LLMWithFallback
    video_analyzer: LLMWithFallback | None = None
    object_detector: LLMWithFallback | None = None


class LLMConfig(BaseModel):
    """Comprehensive LLM configuration mapping every node to primary/fallback models."""

    model_config = {"ignored_types": (CyFunctionDetector,)}
    planner: LLMWithFallback
    utils: LLMConfigUtils
    summarizer: LLMWithFallback
    operator: LLMWithFallback
    operator_summarizer: LLMWithFallback
    log_reader_sub_agent: LLMWithFallback
    log_analyzer: LLMWithFallback
    diagnoser: LLMWithFallback
    checker: LLMWithFallback
    planner_avatar: LLMWithFallback
    history_analyzer_expert: LLMWithFallback
    diagnoser_expert: LLMWithFallback
    explorer: LLMWithFallback
    history_analyzer: LLMWithFallback | None = None
    validator_pixel_safety_net: LLMWithFallback | None = None
    planner_validation: LLMWithFallback | None = None
    output_analyzer: LLMWithFallback | None = None

    def validate_providers(self) -> None:
        """Validate credentials across all configured agent nodes."""
        self.planner.validate_provider("Planner")
        self.utils.outputter.validate_provider("Outputter")
        self.utils.hopper.validate_provider("Hopper")
        if self.utils.video_analyzer:
            self.utils.video_analyzer.validate_provider("VideoAnalyzer")
        if self.utils.object_detector:
            self.utils.object_detector.validate_provider("ObjectDetector")
        self.summarizer.validate_provider("Summarizer")
        self.operator.validate_provider("Operator")
        self.operator_summarizer.validate_provider("OperatorSummarizer")
        self.log_reader_sub_agent.validate_provider("LogReaderSubAgent")
        self.log_analyzer.validate_provider("LogAnalyzer")
        self.diagnoser.validate_provider("Diagnoser")
        self.checker.validate_provider("Checker")
        self.planner_avatar.validate_provider("PlannerAvatar")
        self.history_analyzer_expert.validate_provider("HistoryAnalyzerExpert")
        self.diagnoser_expert.validate_provider("DiagnoserExpert")
        self.explorer.validate_provider("Explorer")
        if self.history_analyzer:
            self.history_analyzer.validate_provider("HistoryAnalyzer")
        if self.validator_pixel_safety_net:
            self.validator_pixel_safety_net.validate_provider("ValidatorPixelSafetyNet")
        if self.planner_validation:
            self.planner_validation.validate_provider("PlannerValidation")
        if self.output_analyzer:
            self.output_analyzer.validate_provider("OutputAnalyzer")

    def __str__(self) -> str:
        return f"""
📃 Planner: {self.planner}
🧩 Utils:
    🔽 Hopper: {self.utils.hopper}
    📝 Outputter: {self.utils.outputter}
    🎬 Video Analyzer: {self.utils.video_analyzer or "Not configured"}
    👁️ Object Detector: {self.utils.object_detector or "Not configured"}
"""

    def get_agent(self, item: AgentNode) -> LLMWithFallback:
        """Retrieve model configuration for a specific agent node with sensible defaults."""
        val = getattr(self, item)
        if val is None:
            if item == "history_analyzer":
                return self.operator
            elif item in ("validator_pixel_safety_net", "planner_validation"):
                # Both are cheap, high-frequency judges: the pixel safety net
                # runs before actions, the planner validator after every
                # milestone edit. They share one lightweight default.
                return lightweight_judge_default()
            elif item == "output_analyzer":
                return self.log_analyzer
        return val

    def get_utils(self, item: LLMUtilsNode) -> LLMWithFallback:
        """Retrieve model configuration for a specific utility node."""
        value = getattr(self.utils, item)
        if value is None:
            raise ValueError(
                f"Utils '{item}' is not configured. Please add it to your LLM "
                "config or enable it via AgentConfigBuilder."
            )
        return value


def _expand_default_into_nodes(config_dict: dict) -> dict:
    """Expand unified config format with 'default' and 'nodes' into full LLMConfig schema."""
    if "planner" in config_dict and "utils" in config_dict:
        return config_dict

    default_model_cfg = config_dict.get(
        "default",
        {
            "provider": "openai",
            "model": settings.OPENAI_MODEL,
            "fallback": {
                "provider": "openai",
                "model": settings.OPENAI_MODEL,
            },
        },
    )

    nodes_override = config_dict.get("nodes", {})

    all_agent_nodes = [
        "planner",
        "summarizer",
        "operator",
        "operator_summarizer",
        "log_reader_sub_agent",
        "log_analyzer",
        "diagnoser",
        "checker",
        "planner_avatar",
        "history_analyzer_expert",
        "diagnoser_expert",
        "explorer",
    ]

    all_utils_nodes = [
        "outputter",
        "hopper",
        "video_analyzer",
        "object_detector",
    ]

    result: dict[str, Any] = {}
    for node in all_agent_nodes:
        node_cfg = dict(default_model_cfg)
        if node in nodes_override:
            for k, v in nodes_override[node].items():
                if isinstance(v, dict) and isinstance(node_cfg.get(k), dict):
                    node_cfg[k] = {**node_cfg[k], **v}
                else:
                    node_cfg[k] = v
        result[node] = node_cfg

    utils_dict: dict[str, Any] = {}
    for util in all_utils_nodes:
        util_cfg = dict(default_model_cfg)
        if util in nodes_override:
            for k, v in nodes_override[util].items():
                if isinstance(v, dict) and isinstance(util_cfg.get(k), dict):
                    util_cfg[k] = {**util_cfg[k], **v}
                else:
                    util_cfg[k] = v
        utils_dict[util] = util_cfg
    result["utils"] = utils_dict

    return result


def parse_llm_config() -> LLMConfig:
    """Parse and instantiate LLMConfig from artemis.jsonc or llm-config.json."""
    config_path = None
    for candidate in ("artemis.jsonc", "artemis.json", LLM_CONFIG_FILENAME):
        try:
            config_path = get_config_path(candidate)
            break
        except FileNotFoundError:
            continue

    if not config_path:
        config_path = get_config_path(LLM_CONFIG_FILENAME, ROOT_DIR / LLM_CONFIG_FILENAME)

    try:
        with open(config_path, encoding="utf-8") as f:
            config_dict = interpolate_config_value(load_jsonc(f))
            expanded_dict = _expand_default_into_nodes(config_dict)
            return LLMConfig.model_validate(expanded_dict)
    except Exception as e:
        logger.error(f"Failed to load or parse llm config: {config_path}. Error: {e}")
        raise


def initialize_llm_config() -> LLMConfig:
    """Parse and validate credentials for LLMConfig."""
    llm_config = parse_llm_config()
    llm_config.validate_providers()
    logger.success("LLM config initialized")
    return llm_config


def get_default_llm_config() -> LLMConfig:
    """Returns default LLMConfig parsed from standard configuration file."""
    return parse_llm_config()


def deep_merge_llm_config(base: LLMConfig, overrides: dict) -> LLMConfig:
    """Recursively merge dictionary overrides into an existing LLMConfig object."""
    base_dict = base.model_dump()

    def merge(d1: dict, d2: dict) -> None:
        for k, v in d2.items():
            if k in d1 and isinstance(d1[k], dict) and isinstance(v, dict):
                merge(d1[k], v)
            else:
                d1[k] = v

    merge(base_dict, overrides)
    return LLMConfig.model_validate(base_dict)


def load_llm_config_override(path: Path | str) -> LLMConfig:
    """Load custom LLM configuration JSON/JSONC overrides on top of default configuration."""
    default_config = get_default_llm_config()

    resolved_path = Path(path)
    if not resolved_path.exists():
        try:
            resolved_path = get_config_path(str(path))
        except OSError:
            pass

    override_config_dict = {}
    if resolved_path.exists():
        logger.info(f"Loading custom LLM config from {resolved_path.resolve()}...")
        with open(resolved_path, encoding="utf-8") as f:
            override_config_dict = interpolate_config_value(load_jsonc(f))
    else:
        logger.warning(f"Custom LLM config not found at {path} - using default config")

    try:
        return deep_merge_llm_config(default_config, override_config_dict)
    except ValidationError as e:
        logger.error(f"Invalid LLM config: {e}")
        logger.info("Falling back to default config")
        return default_config