from __future__ import annotations

import io
import json
import logging
import os
from typing import Any, Dict, Optional

import numpy as np
from pydantic import (
    BaseModel,
    ConfigDict,
    Field,
    TypeAdapter,
    ValidationInfo,
    field_validator,
    model_validator,
)
from ruamel.yaml import YAML
from typing_extensions import Self

from frigate.const import REGEX_JSON
from frigate.detectors import DetectorConfig, ModelConfig
from frigate.detectors.detector_config import BaseDetectorConfig
from frigate.plus import PlusApi
from frigate.util.builtin import (
    deep_merge,
    get_ffmpeg_arg_list,
)
from frigate.util.config import (
    CURRENT_CONFIG_VERSION,
    StreamInfoRetriever,
    convert_area_to_pixels,
    find_config_file,
    get_relative_coordinates,
    migrate_frigate_config,
)
from frigate.util.image import create_mask
from frigate.util.services import auto_detect_hwaccel

from .auth import AuthConfig
from .base import FrigateBaseModel
from .camera import CameraConfig, CameraLiveConfig
from .camera.audio import AudioConfig, AudioFilterConfig
from .camera.birdseye import BirdseyeConfig
from .camera.detect import DetectConfig
from .camera.ffmpeg import FfmpegConfig
from .camera.genai import GenAIConfig, GenAIRoleEnum
from .camera.mask import ObjectMaskConfig
from .camera.motion import MotionConfig
from .camera.notification import NotificationConfig
from .camera.objects import FilterConfig, ObjectConfig
from .camera.record import RecordConfig
from .camera.review import ReviewConfig
from .camera.snapshots import SnapshotsConfig
from .camera.timestamp import TimestampStyleConfig
from .camera_group import CameraGroupConfig
from .classification import (
    AudioTranscriptionConfig,
    ClassificationConfig,
    FaceRecognitionConfig,
    LicensePlateRecognitionConfig,
    SemanticSearchConfig,
    SemanticSearchModelEnum,
)
from .database import DatabaseConfig
from .env import EnvVars
from .logger import LoggerConfig
from .mqtt import MqttConfig
from .network import NetworkingConfig
from .profile import ProfileDefinitionConfig
from .proxy import ProxyConfig
from .telemetry import TelemetryConfig
from .tls import TlsConfig
from .ui import UIConfig

__all__ = ["FrigateConfig"]

logger = logging.getLogger(__name__)

yaml = YAML()

# Pydantic field default applied when an existing config omits `detectors:`.
# Kept as cpu tflite for backwards compatibility with 0.17 configs.
DEFAULT_DETECTORS = {"cpu": {"type": "cpu"}}

# Used by the openvino branch below and rendered into the new-config YAML
# template so first-time setups default to openvino on CPU.
DEFAULT_MODEL = {
    "width": 300,
    "height": 300,
    "input_tensor": "nhwc",
    "input_pixel_format": "bgr",
    "path": "/openvino-model/ssdlite_mobilenet_v2.xml",
    "labelmap_path": "/openvino-model/coco_91cl_bkgr.txt",
}
NEW_CONFIG_DETECTORS = {"ov": {"type": "openvino", "device": "CPU"}}
DEFAULT_DETECT_DIMENSIONS = {"width": 1280, "height": 720}


def _render_default_yaml(data: dict) -> str:
    buf = io.StringIO()
    _yaml_writer = YAML()
    _yaml_writer.indent(mapping=2, sequence=4, offset=2)
    _yaml_writer.dump(data, buf)
    return buf.getvalue()


DEFAULT_CONFIG = f"""
mqtt:
  enabled: False

{_render_default_yaml({"detectors": NEW_CONFIG_DETECTORS, "model": DEFAULT_MODEL})}
cameras: {{}}  # No cameras defined, UI wizard should be used
version: {CURRENT_CONFIG_VERSION}
"""

# stream info handler
stream_info_retriever = StreamInfoRetriever()


class RuntimeMotionConfig(MotionConfig):
    """Runtime version of MotionConfig with rasterized masks."""

    rasterized_mask: np.ndarray = Field(default=None, exclude=True)

    def __init__(self, **config):
        frame_shape = config.get("frame_shape", (1, 1))

        # Store original mask dict for serialization
        original_mask = config.get("mask", {})
        if isinstance(original_mask, dict):
            # Process the new dict format - update raw_coordinates for each mask
            processed_mask = {}
            for mask_id, mask_config in original_mask.items():
                if isinstance(mask_config, dict):
                    coords = mask_config.get("coordinates", "")
                    relative_coords = get_relative_coordinates(coords, frame_shape)
                    mask_config_copy = mask_config.copy()
                    mask_config_copy["raw_coordinates"] = (
                        relative_coords if relative_coords else coords
                    )
                    mask_config_copy["coordinates"] = (
                        relative_coords if relative_coords else coords
                    )
                    processed_mask[mask_id] = mask_config_copy
                else:
                    processed_mask[mask_id] = mask_config
            config["mask"] = processed_mask
            config["raw_mask"] = processed_mask

        super().__init__(**config)

        # Rasterize only enabled masks
        enabled_coords = []
        for mask_config in self.mask.values():
            if mask_config.enabled and mask_config.coordinates:
                coords = mask_config.coordinates
                if isinstance(coords, list):
                    enabled_coords.extend(coords)
                else:
                    enabled_coords.append(coords)

        if enabled_coords:
            self.rasterized_mask = create_mask(frame_shape, enabled_coords)
        else:
            empty_mask = np.zeros(frame_shape, np.uint8)
            empty_mask[:] = 255
            self.rasterized_mask = empty_mask

    model_config = ConfigDict(arbitrary_types_allowed=True, extra="ignore")


class RuntimeFilterConfig(FilterConfig):
    """Runtime version of FilterConfig with rasterized masks."""

    rasterized_mask: Optional[np.ndarray] = Field(default=None, exclude=True)

    def __init__(self, **config):
        frame_shape = config.get("frame_shape", (1, 1))

        # Store original mask dict for serialization
        original_mask = config.get("mask", {})
        if isinstance(original_mask, dict):
            # Process the new dict format - update raw_coordinates for each mask
            processed_mask = {}
            for mask_id, mask_config in original_mask.items():
                # Handle both dict and ObjectMaskConfig formats
                if hasattr(mask_config, "model_dump"):
                    # It's an ObjectMaskConfig object
                    mask_dict = mask_config.model_dump()
                    coords = mask_dict.get("coordinates", "")
                    relative_coords = get_relative_coordinates(coords, frame_shape)
                    mask_dict["raw_coordinates"] = (
                        relative_coords if relative_coords else coords
                    )
                    mask_dict["coordinates"] = (
                        relative_coords if relative_coords else coords
                    )
                    processed_mask[mask_id] = mask_dict
                elif isinstance(mask_config, dict):
                    coords = mask_config.get("coordinates", "")
                    relative_coords = get_relative_coordinates(coords, frame_shape)
                    mask_config_copy = mask_config.copy()
                    mask_config_copy["raw_coordinates"] = (
                        relative_coords if relative_coords else coords
                    )
                    mask_config_copy["coordinates"] = (
                        relative_coords if relative_coords else coords
                    )
                    processed_mask[mask_id] = mask_config_copy
                else:
                    processed_mask[mask_id] = mask_config
            config["mask"] = processed_mask
            config["raw_mask"] = processed_mask

        # Convert min_area and max_area to pixels if they're percentages
        if "min_area" in config:
            config["min_area"] = convert_area_to_pixels(config["min_area"], frame_shape)

        if "max_area" in config:
            config["max_area"] = convert_area_to_pixels(config["max_area"], frame_shape)

        super().__init__(**config)

        # Rasterize only enabled masks
        enabled_coords = []
        for mask_config in self.mask.values():
            if mask_config.enabled and mask_config.coordinates:
                coords = mask_config.coordinates
                if isinstance(coords, list):
                    enabled_coords.extend(coords)
                else:
                    enabled_coords.append(coords)

        if enabled_coords:
            self.rasterized_mask = create_mask(frame_shape, enabled_coords)
        else:
            self.rasterized_mask = None

    model_config = ConfigDict(arbitrary_types_allowed=True, extra="ignore")


class RestreamConfig(BaseModel):
    model_config = ConfigDict(extra="allow")


def verify_config_roles(camera_config: CameraConfig) -> None:
    """Verify that roles are setup in the config correctly."""
    assigned_roles = list(
        set([r for i in camera_config.ffmpeg.inputs for r in i.roles])
    )

    if camera_config.record.enabled and "record" not in assigned_roles:
        raise ValueError(
            f"Camera {camera_config.name} has record enabled, but record is not assigned to an input."
        )

    if camera_config.audio.enabled and "audio" not in assigned_roles:
        raise ValueError(
            f"Camera {camera_config.name} has audio events enabled, but audio is not assigned to an input."
        )


def verify_valid_live_stream_names(
    frigate_config: FrigateConfig, camera_config: CameraConfig
) -> ValueError | None:
    """Verify that a restream exists to use for live view."""
    for _, stream_name in camera_config.live.streams.items():
        if (
            stream_name
            not in frigate_config.go2rtc.model_dump().get("streams", {}).keys()
        ):
            return ValueError(
                f"No restream with name {stream_name} exists for camera {camera_config.name}."
            )


def verify_recording_segments_setup_with_reasonable_time(
    camera_config: CameraConfig,
) -> None:
    """Verify that recording segments are setup and segment time is not greater than 60."""
    record_args: list[str] = get_ffmpeg_arg_list(
        camera_config.ffmpeg.output_args.record
    )

    if record_args[0].startswith("preset"):
        return

    try:
        seg_arg_index = record_args.index("-segment_time")
    except ValueError:
        raise ValueError(
            f"Camera {camera_config.name} has no segment_time in \
                         recording output args, segment args are required for record."
        )

    if int(record_args[seg_arg_index + 1]) > 60:
        raise ValueError(
            f"Camera {camera_config.name} has invalid segment_time output arg, \
                         segment_time must be 60 or less."
        )


def verify_zone_objects_are_tracked(camera_config: CameraConfig) -> None:
    """Verify that user has not entered zone objects that are not in the tracking config."""
    for zone_name, zone in camera_config.zones.items():
        for obj in zone.objects:
            if obj not in camera_config.objects.track:
                raise ValueError(
                    f"Zone {zone_name} is configured to track {obj} but that object type is not added to objects -> track."
                )


def verify_required_zones_exist(camera_config: CameraConfig) -> None:
    for det_zone in camera_config.review.detections.required_zones:
        if det_zone not in camera_config.zones.keys():
            raise ValueError(
                f"Camera {camera_config.name} has a required zone for detections {det_zone} that is not defined."
            )

    for det_zone in camera_config.review.alerts.required_zones:
        if det_zone not in camera_config.zones.keys():
            raise ValueError(
                f"Camera {camera_config.name} has a required zone for alerts {det_zone} that is not defined."
            )


def verify_profile_overrides_match_base(camera_config: CameraConfig) -> None:
    """Verify that profile zone and mask IDs reference entries defined on the base camera."""
    for profile_name, profile in camera_config.profiles.items():
        if profile.zones:
            for zone_name in profile.zones:
                if zone_name not in camera_config.zones:
                    raise ValueError(
                        f"Camera '{camera_config.name}' profile '{profile_name}' defines "
                        f"zone '{zone_name}' that does not exist on the base config"
                    )

        if profile.motion and profile.motion.mask:
            for mask_name in profile.motion.mask:
                if mask_name not in camera_config.motion.mask:
                    raise ValueError(
                        f"Camera '{camera_config.name}' profile '{profile_name}' defines "
                        f"motion mask '{mask_name}' that does not exist on the base config"
                    )

        if profile.objects:
            for mask_name in profile.objects.mask or {}:
                if mask_name not in (camera_config.objects.mask or {}):
                    raise ValueError(
                        f"Camera '{camera_config.name}' profile '{profile_name}' defines "
                        f"object mask '{mask_name}' that does not exist on the base config"
                    )
            for label, filter_config in (profile.objects.filters or {}).items():
                base_filter = (camera_config.objects.filters or {}).get(label)
                profile_filter_masks = (
                    filter_config.mask if filter_config else None
                ) or {}
                base_filter_masks = (base_filter.mask if base_filter else None) or {}
                for mask_name in profile_filter_masks:
                    if mask_name not in base_filter_masks:
                        raise ValueError(
                            f"Camera '{camera_config.name}' profile '{profile_name}' defines "
                            f"object mask '{mask_name}' for '{label}' that does not exist "
                            f"on the base config"
                        )


def verify_autotrack_zones(camera_config: CameraConfig) -> ValueError | None:
    """Verify that required_zones are specified when autotracking is enabled."""
    if (
        camera_config.onvif.autotracking.enabled
        and not camera_config.onvif.autotracking.required_zones
    ):
        raise ValueError(
            f"Camera {camera_config.name} has autotracking enabled, required_zones must be set to at least one of the camera's zones."
        )


def verify_motion_and_detect(camera_config: CameraConfig) -> ValueError | None:
    """Verify that motion detection is not disabled and object detection is enabled."""
    if camera_config.detect.enabled and not camera_config.motion.enabled:
        raise ValueError(
            f"Camera {camera_config.name} has motion detection disabled and object detection enabled but object detection requires motion detection."
        )


def verify_objects_track(
    camera_config: CameraConfig, enabled_objects: list[str]
) -> None:
    """Verify that a user has not specified an object to track that is not in the labelmap."""
    valid_objects = [
        obj for obj in camera_config.objects.track if obj in enabled_objects
    ]

    if len(valid_objects) != len(camera_config.objects.track):
        invalid_objects = set(camera_config.objects.track) - set(valid_objects)
        logger.warning(
            f"{camera_config.name} is configured to track {list(invalid_objects)} objects, which are not supported by the current model."
        )
        camera_config.objects.track = valid_objects


def verify_lpr_and_face(
    frigate_config: FrigateConfig, camera_config: CameraConfig
) -> ValueError | None:
    """Verify that lpr and face are enabled at the global level if enabled at the camera level."""
    if camera_config.lpr.enabled and not frigate_config.lpr.enabled:
        raise ValueError(
            f"Camera {camera_config.name} has lpr enabled but lpr is disabled at the global level of the config. You must enable lpr at the global level."
        )
    if (
        camera_config.face_recognition.enabled
        and not frigate_config.face_recognition.enabled
    ):
        raise ValueError(
            f"Camera {camera_config.name} has face_recognition enabled but face_recognition is disabled at the global level of the config. You must enable face_recognition at the global level."
        )


class FrigateConfig(FrigateBaseModel):
    version: Optional[str] = Field(
        default=None,
        title="Current config version",
        description="Numeric or string version of the active configuration to help detect migrations or format changes.",
    )
    safe_mode: bool = Field(
        default=False,
        title="Safe mode",
        description="When enabled, start Frigate in safe mode with reduced features for troubleshooting.",
    )

    # Fields that install global state should be defined first, so that their validators run first.
    environment_vars: EnvVars = Field(
        default_factory=dict,
        title="Environment variables",
        description="Key/value pairs of environment variables to set for the Frigate process in Home Assistant OS. Non-HAOS users must use Docker environment variable configuration instead.",
    )
    logger: LoggerConfig = Field(
        default_factory=LoggerConfig,
        title="Logging",
        description="Controls default log verbosity and per-component log level overrides.",
        validate_default=True,
    )

    # Global config
    auth: AuthConfig = Field(
        default_factory=AuthConfig,
        title="Authentication",
        description="Authentication and session-related settings including cookie and rate limit options.",
    )
    database: DatabaseConfig = Field(
        default_factory=DatabaseConfig,
        title="Database",
        description="Settings for the SQLite database used by Frigate to store tracked object and recording metadata.",
    )
    go2rtc: RestreamConfig = Field(
        default_factory=RestreamConfig,
        title="go2rtc",
        description="Settings for the integrated go2rtc restreaming service used for live stream relaying and translation.",
    )
    mqtt: MqttConfig = Field(
        title="MQTT",
        description="Settings for connecting and publishing telemetry, snapshots, and event details to an MQTT broker.",
    )
    notifications: NotificationConfig = Field(
        default_factory=NotificationConfig,
        title="Notifications",
        description="Settings to enable and control notifications for all cameras; can be overridden per-camera.",
    )
    networking: NetworkingConfig = Field(
        default_factory=NetworkingConfig,
        title="Networking",
        description="Network-related settings such as IPv6 enablement for Frigate endpoints.",
    )
    proxy: ProxyConfig = Field(
        default_factory=ProxyConfig,
        title="Proxy",
        description="Settings for integrating Frigate behind a reverse proxy that passes authenticated user headers.",
    )
    telemetry: TelemetryConfig = Field(
        default_factory=TelemetryConfig,
        title="Telemetry",
        description="System telemetry and stats options including GPU and network bandwidth monitoring.",
    )
    tls: TlsConfig = Field(
        default_factory=TlsConfig,
        title="TLS",
        description="TLS settings for Frigate's web endpoints (port 8971).",
    )
    ui: UIConfig = Field(
        default_factory=UIConfig,
        title="UI",
        description="User interface preferences such as timezone, time/date formatting, and units.",
    )

    # Detector config
    detectors: Dict[str, BaseDetectorConfig] = Field(
        default=DEFAULT_DETECTORS,
        title="Detector hardware",
        description="Configuration for object detectors (CPU, GPU, ONNX backends) and any detector-specific model settings.",
    )
    model: ModelConfig = Field(
        default_factory=ModelConfig,
        title="Detection model",
        description="Settings to configure a custom object detection model and its input shape.",
    )

    # GenAI config (named provider configs: name -> GenAIConfig)
    genai: Dict[str, GenAIConfig] = Field(
        default_factory=dict,
        title="Generative AI configuration",
        description="Settings for integrated generative AI providers used to generate object descriptions and review summaries.",
    )

    # Camera config
    cameras: Dict[str, CameraConfig] = Field(title="Cameras", description="Cameras")
    audio: AudioConfig = Field(
        default_factory=AudioConfig,
        title="Audio detection",
        description="Settings for audio-based event detection for all cameras; can be overridden per-camera.",
    )
    birdseye: BirdseyeConfig = Field(
        default_factory=BirdseyeConfig,
        title="Birdseye",
        description="Settings for the Birdseye composite view that composes multiple camera feeds into a single layout.",
    )
    detect: DetectConfig = Field(
        default_factory=DetectConfig,
        title="Object Detection",
        description="Settings for the detection/detect role used to run object detection and initialize trackers.",
    )
    ffmpeg: FfmpegConfig = Field(
        default_factory=FfmpegConfig,
        title="FFmpeg",
        description="FFmpeg settings including binary path, args, hwaccel options, and per-role output args.",
    )
    live: CameraLiveConfig = Field(
        default_factory=CameraLiveConfig,
        title="Live playback",
        description="Settings to control the jsmpeg live stream resolution and quality. This does not affect restreamed cameras that use go2rtc for live view.",
    )
    motion: Optional[MotionConfig] = Field(
        default=None,
        title="Motion detection",
        description="Default motion detection settings applied to cameras unless overridden per-camera.",
    )
    objects: ObjectConfig = Field(
        default_factory=ObjectConfig,
        title="Objects",
        description="Object tracking defaults including which labels to track and per-object filters.",
    )
    record: RecordConfig = Field(
        default_factory=RecordConfig,
        title="Recording",
        description="Recording and retention settings applied to cameras unless overridden per-camera.",
    )
    review: ReviewConfig = Field(
        default_factory=ReviewConfig,
        title="Review",
        description="Settings that control alerts, detections, and GenAI review summaries used by the UI and storage.",
    )
    snapshots: SnapshotsConfig = Field(
        default_factory=SnapshotsConfig,
        title="Snapshots",
        description="Settings for API-generated snapshots of tracked objects for all cameras; can be overridden per-camera.",
    )
    timestamp_style: TimestampStyleConfig = Field(
        default_factory=TimestampStyleConfig,
        title="Timestamp style",
        description="Styling options for in-feed timestamps applied to debug view and snapshots.",
    )

    # Classification Config
    audio_transcription: AudioTranscriptionConfig = Field(
        default_factory=AudioTranscriptionConfig,
        title="Audio transcription",
        description="Settings for live and speech audio transcription used for events and live captions.",
    )
    classification: ClassificationConfig = Field(
        default_factory=ClassificationConfig,
        title="Object classification",
        description="Settings for classification models used to refine object labels or state classification.",
    )
    semantic_search: SemanticSearchConfig = Field(
        default_factory=SemanticSearchConfig,
        title="Semantic Search",
        description="Settings for Semantic Search which builds and queries object embeddings to find similar items.",
    )
    face_recognition: FaceRecognitionConfig = Field(
        default_factory=FaceRecognitionConfig,
        title="Face recognition",
        description="Settings for face detection and recognition for all cameras; can be overridden per-camera.",
    )
    lpr: LicensePlateRecognitionConfig = Field(
        default_factory=LicensePlateRecognitionConfig,
        title="License Plate Recognition",
        description="License plate recognition settings including detection thresholds, formatting, and known plates.",
    )

    camera_groups: Dict[str, CameraGroupConfig] = Field(
        default_factory=dict,
        title="Camera groups",
        description="Configuration for named camera groups used to organize cameras in the UI.",
    )

    profiles: Dict[str, ProfileDefinitionConfig] = Field(
        default_factory=dict,
        title="Profiles",
        description="Named profile definitions with friendly names. Camera profiles must reference names defined here.",
    )

    active_profile: Optional[str] = Field(
        default=None,
        title="Active profile",
        description="Currently active profile name. Runtime-only, not persisted in YAML.",
        exclude=True,
    )

    _plus_api: PlusApi

    @property
    def plus_api(self) -> PlusApi:
        return self._plus_api

    @model_validator(mode="after")
    def post_validation(self, info: ValidationInfo) -> Self:
        # Load plus api from context, if possible.
        self._plus_api = None
        if isinstance(info.context, dict):
            self._plus_api = info.context.get("plus_api")

        # Ensure self._plus_api is set, if no explicit value is provided.
        if self._plus_api is None:
            self._plus_api = PlusApi()

        # set notifications state
        self.notifications.enabled_in_config = self.notifications.enabled

        # validate genai: each role (tools, vision, embeddings) at most once
        role_to_name: dict[GenAIRoleEnum, str] = {}
        for name, genai_cfg in self.genai.items():
            for role in genai_cfg.roles:
                if role in role_to_name:
                    raise ValueError(
                        f"GenAI role '{role.value}' is assigned to both "
                        f"'{role_to_name[role]}' and '{name}'; each role must have "
                        "exactly one provider."
                    )
                role_to_name[role] = name

        # validate semantic_search.model when it is a GenAI provider name
        if (
            self.semantic_search.enabled
            and isinstance(self.semantic_search.model, str)
            and not isinstance(self.semantic_search.model, SemanticSearchModelEnum)
        ):
            if self.semantic_search.model not in self.genai:
                raise ValueError(
                    f"semantic_search.model '{self.semantic_search.model}' is not a "
                    "valid GenAI config key. Must match a key in genai config."
                )
            genai_cfg = self.genai[self.semantic_search.model]
            if GenAIRoleEnum.embeddings not in genai_cfg.roles:
                raise ValueError(
                    f"GenAI provider '{self.semantic_search.model}' must have "
                    "'embeddings' in its roles for semantic search."
                )

        # set default min_score for object attributes
        for attribute in self.model.all_attributes:
            existing = self.objects.filters.get(attribute)
            if existing is None:
                self.objects.filters[attribute] = FilterConfig(min_score=0.7)
            elif "min_score" not in existing.model_fields_set:
                existing.min_score = 0.7

        # auto detect hwaccel args
        if self.ffmpeg.hwaccel_args == "auto":
            self.ffmpeg.hwaccel_args = auto_detect_hwaccel()

        # Resolve global export hwaccel_args so it matches the per-camera
        # resolution below. Without this, every camera reads as overriding
        # record.export.hwaccel_args because the global stays "auto" while
        # the camera value gets resolved to the actual args list.
        if self.record.export.hwaccel_args == "auto":
            self.record.export.hwaccel_args = self.ffmpeg.hwaccel_args

        # Populate global audio filters from listen. Existing user-defined
        # entries for labels not in listen are preserved but unused at runtime.
        if self.audio.filters is None:
            self.audio.filters = {}

        for key in sorted(set(self.audio.listen) - self.audio.filters.keys()):
            self.audio.filters[key] = AudioFilterConfig()

        self.audio.filters = dict(sorted(self.audio.filters.items()))

        # Global config to propagate down to camera level
        global_config = self.model_dump(
            include={
                "audio": ...,
                "audio_transcription": ...,
                "birdseye": ...,
                "face_recognition": ...,
                "lpr": ...,
                "record": ...,
                "snapshots": ...,
                "live": ...,
                "objects": ...,
                "review": ...,
                "motion": ...,
                "notifications": ...,
                "detect": ...,
                "ffmpeg": ...,
                "timestamp_style": ...,
            },
            exclude_unset=True,
        )

        for key, detector in self.detectors.items():
            adapter = TypeAdapter(DetectorConfig)
            model_dict = (
                detector
                if isinstance(detector, dict)
                else detector.model_dump(warnings="none")
            )
            detector_config: BaseDetectorConfig = adapter.validate_python(model_dict)

            # users should not set model themselves
            if detector_config.model:
                logger.warning(
                    "The model key should be specified at the root level of the config, not under detectors. The nested model key will be ignored."
                )
                detector_config.model = None

            model_config = self.model.model_dump(exclude_unset=True, warnings="none")

            if detector_config.model_path:
                model_config["path"] = detector_config.model_path

            if "path" not in model_config:
                if detector_config.type == "cpu" or detector_config.type.endswith(
                    "_tfl"
                ):
                    model_config["path"] = "/cpu_model.tflite"
                elif detector_config.type == "edgetpu":
                    model_config["path"] = "/edgetpu_model.tflite"
                elif detector_config.type == "openvino":
                    for default_key, default_value in DEFAULT_MODEL.items():
                        model_config.setdefault(default_key, default_value)

            model = ModelConfig.model_validate(model_config)
            model.check_and_load_plus_model(self.plus_api, detector_config.type)
            model.compute_model_hash()
            labelmap_objects = model.merged_labelmap.values()
            detector_config.model = model
            self.detectors[key] = detector_config

        for name, camera in self.cameras.items():
            modified_global_config = global_config.copy()

            # only populate some fields down to the camera level for specific keys
            allowed_fields_map = {
                "face_recognition": ["enabled", "min_area"],
                "lpr": ["enabled", "expire_time", "min_area", "enhancement"],
                "audio_transcription": ["enabled", "live_enabled"],
            }

            for section in allowed_fields_map:
                if section in modified_global_config:
                    modified_global_config[section] = {
                        k: v
                        for k, v in modified_global_config[section].items()
                        if k in allowed_fields_map[section]
                    }

            merged_config = deep_merge(
                camera.model_dump(exclude_unset=True), modified_global_config
            )
            camera_config: CameraConfig = CameraConfig.model_validate(
                {"name": name, **merged_config}
            )

            if camera_config.ffmpeg.hwaccel_args == "auto":
                camera_config.ffmpeg.hwaccel_args = self.ffmpeg.hwaccel_args

            # Resolve export hwaccel_args: camera export -> camera ffmpeg -> global ffmpeg
            # This allows per-camera override for exports (e.g., when camera resolution
            # exceeds hardware encoder limits)
            if camera_config.record.export.hwaccel_args == "auto":
                camera_config.record.export.hwaccel_args = (
                    camera_config.ffmpeg.hwaccel_args
                )

            for input in camera_config.ffmpeg.inputs:
                need_detect_dimensions = "detect" in input.roles and (
                    camera_config.detect.height is None
                    or camera_config.detect.width is None
                )

                if need_detect_dimensions:
                    logger.info(
                        f"detect.width and detect.height not set for {camera_config.name}, probing detect stream to determine resolution."
                    )
                    stream_info = {"width": 0, "height": 0, "fourcc": None}
                    try:
                        stream_info = stream_info_retriever.get_stream_info(
                            self.ffmpeg, input.path
                        )
                    except Exception:
                        logger.warning(
                            f"Error detecting stream parameters automatically for {input.path} Applying default values."
                        )
                        stream_info = {"width": 0, "height": 0, "fourcc": None}

                if need_detect_dimensions:
                    camera_config.detect.width = (
                        stream_info["width"]
                        if stream_info.get("width")
                        else DEFAULT_DETECT_DIMENSIONS["width"]
                    )
                    camera_config.detect.height = (
                        stream_info["height"]
                        if stream_info.get("height")
                        else DEFAULT_DETECT_DIMENSIONS["height"]
                    )

            # Warn if detect fps > 10
            if camera_config.detect.fps > 10 and camera_config.type != "lpr":
                logger.warning(
                    f"{camera_config.name} detect fps is set to {camera_config.detect.fps}. This does NOT need to match your camera's frame rate. High values could lead to reduced performance. Recommended value is 5."
                )
            if camera_config.detect.fps > 15 and camera_config.type == "lpr":
                logger.warning(
                    f"{camera_config.name} detect fps is set to {camera_config.detect.fps}. This does NOT need to match your camera's frame rate. High values could lead to reduced performance. Recommended value for LPR cameras are between 5-15."
                )

            # Default min_initialized configuration
            min_initialized = max(int(camera_config.detect.fps / 2), 2)
            if camera_config.detect.min_initialized is None:
                camera_config.detect.min_initialized = min_initialized

            # Default max_disappeared configuration
            max_disappeared = camera_config.detect.fps * 5
            if camera_config.detect.max_disappeared is None:
                camera_config.detect.max_disappeared = max_disappeared

            # Default stationary_threshold configuration
            stationary_threshold = camera_config.detect.fps * 10
            if camera_config.detect.stationary.threshold is None:
                camera_config.detect.stationary.threshold = stationary_threshold
            # default to the stationary_threshold if not defined
            if camera_config.detect.stationary.interval is None:
                camera_config.detect.stationary.interval = stationary_threshold

            # set config pre-value
            camera_config.enabled_in_config = camera_config.enabled
            camera_config.audio.enabled_in_config = camera_config.audio.enabled
            camera_config.audio_transcription.enabled_in_config = (
                camera_config.audio_transcription.enabled
            )
            camera_config.record.enabled_in_config = camera_config.record.enabled
            camera_config.notifications.enabled_in_config = (
                camera_config.notifications.enabled
            )
            camera_config.onvif.autotracking.enabled_in_config = (
                camera_config.onvif.autotracking.enabled
            )
            camera_config.review.alerts.enabled_in_config = (
                camera_config.review.alerts.enabled
            )
            camera_config.review.detections.enabled_in_config = (
                camera_config.review.detections.enabled
            )
            camera_config.objects.genai.enabled_in_config = (
                camera_config.objects.genai.enabled
            )
            camera_config.review.genai.enabled_in_config = (
                camera_config.review.genai.enabled
            )

            if camera_config.audio.filters is None:
                camera_config.audio.filters = {}

            for key in sorted(
                set(camera_config.audio.listen) - camera_config.audio.filters.keys()
            ):
                camera_config.audio.filters[key] = AudioFilterConfig()

            camera_config.audio.filters = dict(
                sorted(camera_config.audio.filters.items())
            )

            # Add default filters
            object_keys = camera_config.objects.track
            if camera_config.objects.filters is None:
                camera_config.objects.filters = {}
            object_keys = object_keys - camera_config.objects.filters.keys()
            for key in object_keys:
                camera_config.objects.filters[key] = FilterConfig()

            # Process global object masks to set raw_coordinates
            if camera_config.objects.mask:
                processed_global_masks = {}
                for mask_id, mask_config in camera_config.objects.mask.items():
                    if mask_config:
                        coords = mask_config.coordinates
                        relative_coords = get_relative_coordinates(
                            coords,
                            camera_config.frame_shape,
                            camera_name=camera_config.name,
                        )
                        # Create a new ObjectMaskConfig with raw_coordinates set
                        processed_global_masks[mask_id] = ObjectMaskConfig(
                            friendly_name=mask_config.friendly_name,
                            enabled=mask_config.enabled,
                            coordinates=relative_coords if relative_coords else coords,
                            raw_coordinates=relative_coords
                            if relative_coords
                            else coords,
                            enabled_in_config=mask_config.enabled,
                        )
                    else:
                        processed_global_masks[mask_id] = mask_config
                camera_config.objects.mask = processed_global_masks
                camera_config.objects.raw_mask = processed_global_masks

            # Apply global object masks and convert masks to numpy array
            for object, filter in camera_config.objects.filters.items():
                # Set enabled_in_config for per-object masks before processing
                for mask_config in filter.mask.values():
                    if mask_config:
                        mask_config.enabled_in_config = mask_config.enabled

                # Merge global object masks with per-object filter masks
                merged_mask = dict(filter.mask)  # Copy filter-specific masks

                # Add global object masks if they exist
                if camera_config.objects.mask:
                    for mask_id, mask_config in camera_config.objects.mask.items():
                        # Use a global prefix to avoid key collisions
                        global_mask_id = f"global_{mask_id}"
                        merged_mask[global_mask_id] = mask_config

                # Set runtime filter to create masks
                camera_config.objects.filters[object] = RuntimeFilterConfig(
                    frame_shape=camera_config.frame_shape,
                    mask=merged_mask,
                    **filter.model_dump(
                        exclude_unset=True, exclude={"mask", "raw_mask"}
                    ),
                )

            # Set enabled_in_config for motion masks to match config file state BEFORE creating RuntimeMotionConfig
            if camera_config.motion:
                camera_config.motion.enabled_in_config = camera_config.motion.enabled
                for mask_config in camera_config.motion.mask.values():
                    if mask_config:
                        mask_config.enabled_in_config = mask_config.enabled

            # Convert motion configuration
            if camera_config.motion is None:
                camera_config.motion = RuntimeMotionConfig(
                    frame_shape=camera_config.frame_shape
                )
            else:
                camera_config.motion = RuntimeMotionConfig(
                    frame_shape=camera_config.frame_shape,
                    **camera_config.motion.model_dump(exclude_unset=True),
                )

            # generate zone contours
            if len(camera_config.zones) > 0:
                for zone in camera_config.zones.values():
                    if zone.filters:
                        for object_name, filter_config in zone.filters.items():
                            zone.filters[object_name] = RuntimeFilterConfig(
                                frame_shape=camera_config.frame_shape,
                                **filter_config.model_dump(exclude_unset=True),
                            )

                    zone.generate_contour(camera_config.frame_shape)

                # Set enabled_in_config for zones to match config file state
                for zone in camera_config.zones.values():
                    zone.enabled_in_config = zone.enabled

            # Set live view stream if none is set
            if not camera_config.live.streams:
                camera_config.live.streams = {name: name}

            # generate the ffmpeg commands
            camera_config.create_ffmpeg_cmds()
            self.cameras[name] = camera_config

            verify_config_roles(camera_config)
            verify_valid_live_stream_names(self, camera_config)
            verify_recording_segments_setup_with_reasonable_time(camera_config)
            verify_zone_objects_are_tracked(camera_config)
            verify_required_zones_exist(camera_config)
            verify_profile_overrides_match_base(camera_config)
            verify_autotrack_zones(camera_config)
            verify_motion_and_detect(camera_config)
            verify_objects_track(camera_config, labelmap_objects)
            verify_lpr_and_face(self, camera_config)

        # Validate camera profiles reference top-level profile definitions
        for cam_name, cam_config in self.cameras.items():
            for profile_name in cam_config.profiles:
                if profile_name not in self.profiles:
                    raise ValueError(
                        f"Camera '{cam_name}' references profile '{profile_name}' "
                        f"which is not defined in the top-level 'profiles' section"
                    )

        # set names on classification configs
        for name, config in self.classification.custom.items():
            config.name = name

        self.objects.parse_all_objects(self.cameras)
        self.model.create_colormap(sorted(self.objects.all_objects))
        self.model.check_and_load_plus_model(self.plus_api)

        # Check audio transcription and audio detection requirements
        if self.audio_transcription.enabled:
            # If audio transcription is enabled globally, at least one camera must have audio detection enabled
            if not any(camera.audio.enabled for camera in self.cameras.values()):
                raise ValueError(
                    "Audio transcription is enabled globally, but no cameras have audio detection enabled. At least one camera must have audio detection enabled."
                )
        else:
            # If audio transcription is disabled globally, check each camera with audio_transcription enabled
            for camera in self.cameras.values():
                if camera.audio_transcription.enabled and not camera.audio.enabled:
                    raise ValueError(
                        f"Camera {camera.name} has audio transcription enabled, but audio detection is not enabled for this camera. Audio detection must be enabled for cameras with audio transcription when it is disabled globally."
                    )

        # Validate auth roles against cameras
        camera_names = set(self.cameras.keys())

        for role, allowed_cameras in self.auth.roles.items():
            invalid_cameras = [
                cam for cam in allowed_cameras if cam not in camera_names
            ]
            if invalid_cameras:
                logger.warning(
                    f"Role '{role}' references non-existent cameras: {invalid_cameras}. "
                )

        return self

    @field_validator("cameras")
    @classmethod
    def ensure_zones_and_cameras_have_different_names(cls, v: Dict[str, CameraConfig]):
        zones = [zone for camera in v.values() for zone in camera.zones.keys()]
        for zone in zones:
            if zone in v.keys():
                raise ValueError("Zones cannot share names with cameras")
        return v

    @classmethod
    def load(cls, **kwargs):
        """Loads the Frigate config file, runs migrations, and creates the config object."""
        config_path = find_config_file()

        # No configuration file found, create one.
        new_config = False
        if not os.path.isfile(config_path):
            logger.info("No config file found, saving default config")
            config_path = config_path
            new_config = True
        else:
            # Check if the config file needs to be migrated.
            migrate_frigate_config(config_path)

        # Finally, load the resulting configuration file.
        with open(config_path, "a+" if new_config else "r") as f:
            # Only write the default config if the opened file is non-empty. This can happen as
            # a race condition. It's extremely unlikely, but eh. Might as well check it.
            if new_config and f.tell() == 0:
                f.write(DEFAULT_CONFIG)
                logger.info(
                    "Created default config file, see the getting started docs for configuration: https://docs.frigate.video/guides/getting_started"
                )

            f.seek(0)
            return FrigateConfig.parse(f, **kwargs)

    @classmethod
    def parse(cls, config, *, is_json=None, safe_load=False, **context):
        # If config is a file, read its contents.
        if hasattr(config, "read"):
            fname = getattr(config, "name", None)
            config = config.read()

            # Try to guess the value of is_json from the file extension.
            if is_json is None and fname:
                _, ext = os.path.splitext(fname)
                if ext in (".yaml", ".yml"):
                    is_json = False
                elif ext == ".json":
                    is_json = True

        # At this point, try to sniff the config string, to guess if it is json or not.
        if is_json is None:
            is_json = REGEX_JSON.match(config) is not None

        # Parse the config into a dictionary.
        if is_json:
            config = json.load(config)
        else:
            config = yaml.load(config)

        # load minimal Frigate config after the full config did not validate
        if safe_load:
            safe_config = {"safe_mode": True, "cameras": {}, "mqtt": {"enabled": False}}

            # copy over auth and proxy config in case auth needs to be enforced
            safe_config["auth"] = config.get("auth", {})
            safe_config["proxy"] = config.get("proxy", {})

            # copy over database config for auth and so a new db is not created
            safe_config["database"] = config.get("database", {})

            return cls.parse_object(safe_config, **context)

        # Validate and return the config dict.
        return cls.parse_object(config, **context)

    @classmethod
    def parse_yaml(cls, config_yaml, **context):
        return cls.parse(config_yaml, is_json=False, **context)

    @classmethod
    def parse_object(
        cls, obj: Any, *, plus_api: Optional[PlusApi] = None, install: bool = False
    ):
        return cls.model_validate(
            obj, context={"plus_api": plus_api, "install": install}
        )
