from __future__ import annotations

from dataclasses import asdict, dataclass, field
from typing import Any


@dataclass
class CanonicalIncident:
    title: str
    description_text: str
    cluster_id: str = ''
    clinic_links: list[str] = field(default_factory=list)
    grafana_links: list[str] = field(default_factory=list)
    time_window: str = ''
    l2_assignee: str = 'N/A'
    assignee_or_l3: str = 'N/A'

    def to_dict(self) -> dict[str, Any]:
        return asdict(self)


@dataclass
class ReplayInputs:
    used_fields: list[str] = field(default_factory=list)
    excluded_fields: list[str] = field(default_factory=list)
    notes: str = ''

    def to_dict(self) -> dict[str, Any]:
        return asdict(self)


@dataclass
class ReplayReadyCase:
    incident_id: str
    source: str
    initial_symptom: str
    canonical_incident: CanonicalIncident
    replay_inputs: ReplayInputs

    def to_dict(self) -> dict[str, Any]:
        payload = asdict(self)
        return payload


@dataclass
class ReplayContract:
    profile: str
    allowed_mismatch_types: list[str] = field(default_factory=list)
    allowed_entry_modes: list[str] = field(default_factory=list)
    allowed_reference_files: list[str] = field(default_factory=list)
    skill_conclusion_fields: list[str] = field(default_factory=list)
    merge_readiness_values: list[str] = field(default_factory=list)

    def to_dict(self) -> dict[str, Any]:
        return asdict(self)


@dataclass
class SkillConclusion:
    entry_mode: str = ''
    activated_subcapabilities: list[str] = field(default_factory=list)
    reference_files_consulted: list[str] = field(default_factory=list)
    is_tikv_side: str = ''
    scope: str = ''
    path: str = ''
    earliest_proven_bottleneck: str = ''
    key_evidence: list[str] = field(default_factory=list)
    missing_evidence: list[str] = field(default_factory=list)
    uncertainty_statement: str = ''
    provisional_conclusion: str = ''

    def to_dict(self) -> dict[str, Any]:
        return asdict(self)


@dataclass
class AnalysisWindow:
    label: str = ''
    start_time: str = ''
    end_time: str = ''
    summary: str = ''
    inferred_path: str = ''
    inferred_bottleneck: str = ''
    evidence: list[str] = field(default_factory=list)

    def to_dict(self) -> dict[str, Any]:
        return asdict(self)


@dataclass
class FinalHumanConclusion:
    status: str = ''
    owner_final_comment: str = ''
    structured_conclusion: str = ''
    evidence_references: list[str] = field(default_factory=list)
    all_comment_summary: str = ''
    analysis_progression: list[str] = field(default_factory=list)
    supplementary_evidence: list[str] = field(default_factory=list)
    analysis_windows: list[AnalysisWindow] = field(default_factory=list)

    def to_dict(self) -> dict[str, Any]:
        return asdict(self)


@dataclass
class WindowReview:
    label: str = ''
    start_time: str = ''
    end_time: str = ''
    human_summary: str = ''
    skill_summary: str = ''
    assessment: str = ''
    notes: list[str] = field(default_factory=list)

    def to_dict(self) -> dict[str, Any]:
        return asdict(self)


@dataclass
class CaseReview:
    incident_id: str
    source: str
    initial_symptom: str
    canonical_incident: CanonicalIncident
    replay_inputs: ReplayInputs
    skill_conclusion: SkillConclusion = field(default_factory=SkillConclusion)
    final_human_conclusion: FinalHumanConclusion = field(default_factory=FinalHumanConclusion)
    match_level: str = ''
    mismatch_type: list[str] = field(default_factory=list)
    comparison_notes: list[str] = field(default_factory=list)
    improvement_suggestions: list[str] = field(default_factory=list)
    window_reviews: list[WindowReview] = field(default_factory=list)

    def to_dict(self) -> dict[str, Any]:
        return asdict(self)


@dataclass
class ComparisonContract:
    profile: str
    allowed_match_levels: list[str] = field(default_factory=list)
    allowed_mismatch_types: list[str] = field(default_factory=list)
    allowed_human_conclusion_statuses: list[str] = field(default_factory=list)
    skill_conclusion_fields: list[str] = field(default_factory=list)

    def to_dict(self) -> dict[str, Any]:
        return asdict(self)