"""Normalized Agent Trace Schema for ACO."""
from dataclasses import dataclass, field
from typing import Dict, List, Optional, Any
from datetime import datetime
import json, uuid

@dataclass
class ModelCall:
    model_id: str
    provider: str
    tier: int
    input_tokens: int = 0
    output_tokens: int = 0
    reasoning_tokens: int = 0
    cache_hit: bool = False
    latency_ms: float = 0.0
    cost: float = 0.0
    success: bool = True
    error: Optional[str] = None

@dataclass
class ToolCall:
    tool_name: str
    args: Dict[str, Any] = field(default_factory=dict)
    result: Optional[str] = None
    latency_ms: float = 0.0
    cost: float = 0.0
    success: bool = True
    cached: bool = False
    unnecessary: bool = False
    error: Optional[str] = None

@dataclass
class TraceStep:
    step_num: int
    model_call: Optional[ModelCall] = None
    tool_calls: List[ToolCall] = field(default_factory=list)
    context_size: int = 0
    context_sources: List[str] = field(default_factory=list)
    context_budget_used: float = 0.0
    cache_prefix_tokens: int = 0
    cache_suffix_tokens: int = 0
    verifier_called: bool = False
    verifier_result: Optional[str] = None
    retry_num: int = 0
    recovery_action: Optional[str] = None
    timestamp: str = field(default_factory=lambda: datetime.utcnow().isoformat())

@dataclass
class AgentTrace:
    trace_id: str = field(default_factory=lambda: str(uuid.uuid4())[:12])
    request: str = ""
    task_type: str = "unknown_ambiguous"
    difficulty: int = 3
    predicted_tier: int = 4
    steps: List[TraceStep] = field(default_factory=list)
    final_outcome: str = "unknown"
    task_success: bool = False
    total_cost: float = 0.0
    total_tokens: int = 0
    total_tool_calls: int = 0
    total_retries: int = 0
    total_verifier_calls: int = 0
    cache_hit_rate: float = 0.0
    latency_total_ms: float = 0.0
    user_correction: bool = False
    failure_tags: List[str] = field(default_factory=list)
    artifacts_created: List[str] = field(default_factory=list)
    meta_tool_used: bool = False
    early_terminated: bool = False
    escalation_occurred: bool = False
    timestamp: str = field(default_factory=lambda: datetime.utcnow().isoformat())

    def to_dict(self) -> dict:
        import dataclasses
        return dataclasses.asdict(self)

    def to_json(self) -> str:
        return json.dumps(self.to_dict(), indent=2)

    def compute_summary(self) -> dict:
        total_cost = sum(
            (s.model_call.cost if s.model_call else 0) +
            sum(tc.cost for tc in s.tool_calls)
            for s in self.steps
        )
        total_tokens = sum(
            (s.model_call.input_tokens + s.model_call.output_tokens if s.model_call else 0)
            for s in self.steps
        )
        total_tool_calls = sum(len(s.tool_calls) for s in self.steps)
        total_retries = sum(s.retry_num for s in self.steps)
        total_verifiers = sum(1 for s in self.steps if s.verifier_called)
        cache_hits = sum(1 for s in self.steps if s.model_call and s.model_call.cache_hit)
        cache_total = sum(1 for s in self.steps if s.model_call)
        return {
            "trace_id": self.trace_id,
            "task_type": self.task_type,
            "difficulty": self.difficulty,
            "predicted_tier": self.predicted_tier,
            "success": self.task_success,
            "total_cost": total_cost,
            "total_tokens": total_tokens,
            "total_tool_calls": total_tool_calls,
            "total_retries": total_retries,
            "total_verifier_calls": total_verifiers,
            "cache_hit_rate": cache_hits / max(cache_total, 1),
            "num_steps": len(self.steps),
            "outcome": self.final_outcome,
            "failure_tags": self.failure_tags,
            "meta_tool_used": self.meta_tool_used,
            "early_terminated": self.early_terminated,
        }