"""
Typed models for the hardened NovaTech OpenEnv environment.
"""
from __future__ import annotations

from typing import Any, Dict, List, Literal, Optional

from pydantic import BaseModel, Field

ServiceName = Literal[
    "auth-service",
    "payment-api",
    "order-service",
    "notification-service",
    "reporting-service",
    "user-service",
]
ServerName = Literal["server_01", "server_02", "server_03", "server_04"]
LogLevel = Literal["INFO", "WARN", "ERROR", "CRITICAL"]
FailureMode = Literal[
    "resource_exhaustion",
    "dependency_outage",
    "storage_saturation",
    "certificate_expiry",
    "application_bug",
    "traffic_abuse",
]
DependencyName = Literal[
    "none",
    "auth-service",
    "payment-api",
    "order-service",
    "notification-service",
    "reporting-service",
    "user-service",
    "payment-gateway",
    "mysql",
    "email-relay",
    "ldap-directory",
]
CustomerImpact = Literal[
    "login_failures",
    "checkout_delays",
    "order_write_failures",
    "notification_delivery_failure",
    "cross_service_major_incident",
]
ContainmentActionName = Literal[
    "increase_auth_heap",
    "enable_login_rate_limiting",
    "restore_payment_gateway_connectivity",
    "reduce_checkout_retry_pressure",
    "free_order_log_disk",
    "reset_mysql_connection_pool",
    "renew_smtp_certificate",
    "reroute_notification_traffic",
    "page_major_incident_team",
    "block_all_login_traffic",
    "wipe_application_logs",
    "restart_everything",
]


class LogEntry(BaseModel):
    log_id: int
    timestamp: str
    server_id: ServerName
    log_level: LogLevel
    service_name: ServiceName
    message: str
    response_time_ms: int
    cpu_usage_percent: float
    memory_usage_percent: float


class IncidentBriefing(BaseModel):
    incident_id: str
    title: str
    objective: str
    incident_window_start: str
    incident_window_end: str
    suspected_services: List[ServiceName]
    customer_statement: str
    operational_constraints: List[str]


class RootCauseHypothesis(BaseModel):
    primary_service: ServiceName
    failure_mode: FailureMode
    dependency: DependencyName = "none"
    customer_impact: CustomerImpact
    confidence: float = Field(..., ge=0.0, le=1.0)


class LogQuery(BaseModel):
    service_name: Optional[ServiceName] = None
    server_id: Optional[ServerName] = None
    levels: Optional[List[LogLevel]] = None
    start_time: Optional[str] = None
    end_time: Optional[str] = None
    text_contains: Optional[str] = Field(default=None, max_length=80)
    limit: int = Field(default=6, ge=1, le=6)


class IncidentReport(BaseModel):
    evidence_log_ids: List[int] = Field(default_factory=list, min_length=1)
    impacted_services: List[ServiceName] = Field(default_factory=list, min_length=1)
    root_cause: RootCauseHypothesis
    containment_plan: List[ContainmentActionName] = Field(default_factory=list)
    summary: str = Field(..., min_length=20, max_length=600)


class Action(BaseModel):
    session_id: Optional[str] = None
    action_type: Literal[
        "query_logs",
        "inspect_dependencies",
        "update_hypothesis",
        "execute_containment",
        "submit_report",
        "request_more",
        "no_anomalies",
    ]
    query: Optional[LogQuery] = None
    target_service: Optional[ServiceName] = None
    hypothesis: Optional[RootCauseHypothesis] = None
    containment_plan: Optional[List[ContainmentActionName]] = None
    report: Optional[IncidentReport] = None


class Observation(BaseModel):
    session_id: str
    task_id: str
    task_title: str
    briefing: IncidentBriefing
    dependency_graph: Dict[ServiceName, List[str]]
    visible_logs: List[LogEntry]
    revealed_log_count: int
    visited_services: List[ServiceName]
    submitted_containment: List[ContainmentActionName]
    last_hypothesis: Optional[RootCauseHypothesis] = None
    step_number: int = 0
    max_steps: int = 8
    feedback: Optional[str] = None
    done: bool = False


class Reward(BaseModel):
    value: float = Field(..., ge=0.0, le=1.0)
    signal_reward: float = Field(default=0.0, ge=0.0, le=1.0)
    hypothesis_reward: float = Field(default=0.0, ge=0.0, le=1.0)
    efficiency_reward: float = Field(default=0.0, ge=0.0, le=1.0)
    penalty: float = Field(default=0.0, ge=0.0, le=1.0)
    info: Dict[str, Any] = Field(default_factory=dict)