from __future__ import annotations

from datetime import datetime
from typing import Any
from uuid import UUID

from pydantic import BaseModel, Field

from app.schemas.common import GeoJsonFeatureCollection


class QaProviderComparisonRequest(BaseModel):
    candidate_dataset_id: UUID
    reference_dataset_id: UUID
    iou_threshold: float = Field(default=0.5, ge=0.0, le=1.0)
    area_id: UUID | None = None


class QaProviderComparisonResult(BaseModel):
    status: str
    warnings: list[str] = Field(default_factory=list)
    # Counts of the population that was actually matched, so that
    # ``matches + false_positives == candidate_feature_count`` holds even when
    # an area filter or an unparseable geometry removed features. The ``_raw``
    # fields keep the untouched dataset totals visible next to them.
    candidate_feature_count: int
    reference_feature_count: int
    candidate_feature_count_raw: int | None = None
    reference_feature_count_raw: int | None = None
    matches: int
    false_positives: int
    false_negatives: int
    precision: float | None
    recall: float | None
    f1_score: float | None
    mean_iou: float | None
    iou_threshold: float
    unsupported_geometry: bool = False
    unsupported_geometries: list[str] = Field(default_factory=list)
    match_evidence: list[dict] = Field(default_factory=list)
    false_positive_evidence: list[dict] = Field(default_factory=list)
    false_negative_evidence: list[dict] = Field(default_factory=list)
    generated_at: datetime


class MetricRead(BaseModel):
    id: UUID
    quality_check_id: UUID | None = None
    analysis_run_id: UUID | None = None
    metric_key: str
    metric_value: float | None = None
    metric_unit: str | None = None
    label: str | None = None
    metadata_json: dict | None = None
    created_at: datetime | None = None

    model_config = {"from_attributes": True}


class QualityCheckRead(BaseModel):
    id: UUID
    project_id: UUID
    job_id: UUID | None = None
    analysis_run_id: UUID | None = None
    candidate_dataset_id: UUID | None = None
    reference_dataset_id: UUID
    check_type: str
    status: str
    score: float | None = None
    parameters_json: dict | None = None
    findings_json: dict | None = None
    created_at: datetime | None = None
    completed_at: datetime | None = None
    metrics: list[MetricRead] = Field(default_factory=list)

    model_config = {"from_attributes": True}


class QualityCheckList(BaseModel):
    items: list[QualityCheckRead]
    total: int
    limit: int
    offset: int


class QualityEvidenceResponse(BaseModel):
    quality_check_id: UUID
    project_id: UUID
    candidate_dataset_id: UUID | None = None
    reference_dataset_id: UUID
    analysis_run_id: UUID | None = None
    # The overlay is capped so a regional check stays reviewable; the counts in
    # the quality check itself are always complete.
    feature_count: int
    total_feature_count: int | None = None
    role_counts: dict[str, int] = Field(default_factory=dict)
    truncated: bool = False
    limit: int | None = None
    warnings: list[str] = Field(default_factory=list)
    geojson: GeoJsonFeatureCollection


class AnalysisQaResponse(BaseModel):
    status: str
    quality_check_id: UUID
    analysis_run_id: UUID
    reference_dataset_id: UUID
    candidate_feature_count: int
    reference_feature_count: int
    candidate_feature_count_raw: int | None = None
    reference_feature_count_raw: int | None = None
    matches: int
    false_positives: int
    false_negatives: int
    precision: float | None = None
    recall: float | None = None
    f1_score: float | None = None
    mean_iou: float | None = None
    iou_threshold: float
    warnings: list[str] = Field(default_factory=list)
    coverage: dict[str, Any] | None = None
    temporal_compatibility: dict[str, Any] | None = None
    box_to_footprint_diagnostics: dict[str, Any] | None = None
    match_evidence: list[dict[str, Any]] = Field(default_factory=list)
    false_positive_evidence: list[dict[str, Any]] = Field(default_factory=list)
    false_negative_evidence: list[dict[str, Any]] = Field(default_factory=list)