from __future__ import annotations

from datetime import datetime
from uuid import UUID

from pydantic import BaseModel, ConfigDict, Field

from app.schemas.detection import DetectionModelCapability


SegmentationModelCapability = DetectionModelCapability


class SegmentationModelsResponse(BaseModel):
    models: list[SegmentationModelCapability]


class SegmentationRunRequest(BaseModel):
    model_config = ConfigDict(protected_namespaces=())

    project_id: UUID
    dataset_id: UUID
    model_id: str
    confidence_threshold: float = Field(default=0.5, ge=0.0, le=1.0)
    class_filter: list[str] | None = None
    tile_manifest_path: str | None = None
    parameters_json: dict = Field(default_factory=dict)


class SegmentationQaRequest(BaseModel):
    reference_dataset_id: UUID
    iou_threshold: float = Field(default=0.5, ge=0.0, le=1.0)
    class_name: str | None = None
    min_confidence: float | None = Field(default=None, ge=0.0, le=1.0)
    # Read off the one matching pass, exactly as for detection.
    calibration_thresholds: list[float] = Field(default_factory=list, max_length=32)


class SegmentationRunResponse(BaseModel):
    model_config = ConfigDict(protected_namespaces=())

    analysis_run_id: UUID
    job_id: UUID
    project_id: UUID
    dataset_id: UUID
    model_id: str
    status: str
    segmentation_count: int
    error_code: str | None = None
    message: str


class SegmentationRunRead(BaseModel):
    model_config = ConfigDict(from_attributes=True, protected_namespaces=())

    id: UUID
    project_id: UUID
    dataset_id: UUID | None = None
    job_id: UUID | None = None
    analysis_type: str
    status: str
    model_name: str | None = None
    model_version: str | None = None
    parameters_json: dict
    result_json: dict | None = None
    error_message: str | None = None
    created_at: datetime | None = None
    started_at: datetime | None = None
    finished_at: datetime | None = None


class SegmentationRunListResponse(BaseModel):
    items: list[SegmentationRunRead]
    # ``total`` counts every run; ``items`` is the most recent page of them.
    total: int
    limit: int | None = None
    offset: int = 0
    truncated: bool = False


class SegmentationRead(BaseModel):
    model_config = ConfigDict(from_attributes=True, protected_namespaces=())

    id: UUID
    project_id: UUID
    dataset_id: UUID | None = None
    analysis_run_id: UUID | None = None
    job_id: UUID | None = None
    model_name: str
    model_version: str | None = None
    class_name: str
    confidence: float | None = None
    bbox_json: dict | None = None
    area_m2: float | None = None
    mask_path: str | None = None
    source_tile_path: str | None = None
    tile_index: int | None = None
    properties_json: dict | None = None
    provenance_json: dict | None = None
    created_at: datetime | None = None


class SegmentationListResponse(BaseModel):
    items: list[SegmentationRead]
    # ``total`` describes the complete filtered population; ``items`` is one
    # stable confidence-ranked page of it.
    total: int
    limit: int | None = None
    offset: int = 0
    truncated: bool = False