시작하기 전에: Production에서 만난 실제 오류들
저는 3년 전 Production 환경에서 AI API 모니터링 없는 시스템을 구축한 경험이 있습니다. 그때 겪은 문제들이 이 튜토리얼을 쓰게 된 계기입니다. ConnectionError: timeout after 30s — 응답 시간 초과로 인한 서비스 장애401 Unauthorized: Invalid API key — 만료된 API 키로 인한 대량 요청 실패
QuotaExceededError: Monthly limit reached — 예상치 못한 비용 폭탄과 사용량 초과
RateLimitError: 429 Too Many Requests — 트래픽 급증 시 부하 분산 실패 이 오류들은 전부 모니터링 시스템이 없어서 미처 감지하지 못한 문제들이었습니다. 이 튜토리얼에서는 HolySheep AI 게이트웨이를 활용한 견고한 API 모니터링 아키텍처를 구축하는 방법을 설명드리겠습니다.
HolySheep AI 소개
지금 가입하여 HolySheep AI를 시작해보세요. HolySheep AI는 글로벌 AI API 게이트웨이로, 해외 신용카드 없이 로컬 결제가 가능하며 단일 API 키로 GPT-4.1, Claude Sonnet, Gemini, DeepSeek 등 모든 주요 모델을 통합 관리할 수 있습니다. 특히 비용 최적화 면에서 GPT-4.1 $8/MTok, Claude Sonnet 4.5 $15/MTok, Gemini 2.5 Flash $2.50/MTok, DeepSeek V3.2 $0.42/MTok의 경쟁력 있는 가격을 제공합니다.모니터링 아키텍처 설계
실시간 API 모니터링 시스템을 구축하기 전에 전체 아키텍처를 먼저 정의해야 합니다.┌─────────────────────────────────────────────────────────────────┐
│ AI API Monitoring Architecture │
├─────────────────────────────────────────────────────────────────┤
│ │
│ ┌──────────┐ ┌──────────────┐ ┌───────────────────────┐ │
│ │ Client │───▶│ HolySheep AI │───▶│ Target AI Models │ │
│ │ Request │ │ Gateway │ │ (GPT-4, Claude, etc) │ │
│ └──────────┘ └──────┬───────┘ └───────────────────────┘ │
│ │ │
│ ▼ │
│ ┌────────────────┐ │
│ │ Monitoring │ │
│ │ Collector │ │
│ │ (Prometheus) │ │
│ └───────┬────────┘ │
│ │ │
│ ▼ │
│ ┌────────────────┐ │
│ │ Grafana │ │
│ │ Dashboard │ │
│ └────────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────┘Python 기반 실시간 모니터링 시스템 구현
가장 먼저 HolySheep AI API를 호출하면서 동시에 메트릭을 수집하는 모니터링 래퍼를 구현하겠습니다.import httpx
import time
import logging
from datetime import datetime
from typing import Dict, Any, Optional, Callable
from dataclasses import dataclass, field
from collections import defaultdict
import threading
logger = logging.getLogger(__name__)
@dataclass
class APIMetrics:
"""API 호출 메트릭 데이터 클래스"""
request_count: int = 0
success_count: int = 0
error_count: int = 0
total_tokens: int = 0
total_latency_ms: float = 0.0
total_cost_usd: float = 0.0
error_types: Dict[str, int] = field(default_factory=lambda: defaultdict(int))
latency_history: list = field(default_factory=list)
class HolySheepMonitor:
"""
HolySheep AI API 모니터링 래퍼
실시간 메트릭 수집 및 비용 추적 기능 제공
"""
# 모델별 토큰당 비용 (USD, 2024년 기준)
MODEL_COSTS = {
"gpt-4.1": {"input": 0.000008, "output": 0.000032},
"gpt-4.1-mini": {"input": 0.0000015, "output": 0.000006},
"claude-sonnet-4.5": {"input": 0.000015, "output": 0.000075},
"claude-3-5-sonnet": {"input": 0.000003, "output": 0.000015},
"gemini-2.5-flash": {"input": 0.0000025, "output": 0.00001},
"deepseek-v3.2": {"input": 0.00000042, "output": 0.0000021},
}
def __init__(self, api_key: str, base_url: str = "https://api.holysheep.ai/v1"):
self.api_key = api_key
self.base_url = base_url
self.metrics = APIMetrics()
self._lock = threading.Lock()
# Grafana/Prometheus 호환 형식으로 메트릭 내보내기
self._metrics_endpoint = "http://localhost:9090/api/v1/push"
def _calculate_cost(self, model: str, usage: Dict[str, int]) -> float:
"""토큰 사용량 기반 비용 계산"""
costs = self.MODEL_COSTS.get(model, {"input": 0, "output": 0})
input_cost = usage.get("prompt_tokens", 0) * costs["input"]
output_cost = usage.get("completion_tokens", 0) * costs["output"]
return round(input_cost + output_cost, 6)
def _track_metrics(self, latency_ms: float, success: bool,
error_type: Optional[str] = None, usage: Optional[Dict] = None,
model: Optional[str] = None):
"""스레드 세이프하게 메트릭 업데이트"""
with self._lock:
self.metrics.request_count += 1
self.metrics.total_latency_ms += latency_ms
self.metrics.latency_history.append({
"timestamp": datetime.now().isoformat(),
"latency_ms": latency_ms,
"success": success
})
# 최근 100개만 유지 (메모리 최적화)
if len(self.metrics.latency_history) > 100:
self.metrics.latency_history = self.metrics.latency_history[-100:]
if success:
self.metrics.success_count += 1
if usage:
total_tokens = usage.get("prompt_tokens", 0) + usage.get("completion_tokens", 0)
self.metrics.total_tokens += total_tokens
if model:
self.metrics.total_cost_usd += self._calculate_cost(model, usage)
else:
self.metrics.error_count += 1
if error_type:
self.metrics.error_types[error_type] += 1
async def chat_completion(self, messages: list, model: str = "deepseek-v3.2",
**kwargs) -> Dict[str, Any]:
"""
HolySheep AI 채팅 완료 API 호출 + 모니터링
실제 지연 시간 측정 및 메트릭 수집 포함
"""
start_time = time.perf_counter()
error_type = None
try:
async with httpx.AsyncClient(timeout=60.0) as client:
response = await client.post(
f"{self.base_url}/chat/completions",
headers={
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json"
},
json={
"model": model,
"messages": messages,
**kwargs
}
)
latency_ms = (time.perf_counter() - start_time) * 1000
if response.status_code == 200:
data = response.json()
usage = data.get("usage", {})
self._track_metrics(latency_ms, True, usage=usage, model=model)
logger.info(f"[SUCCESS] {model} - Latency: {latency_ms:.2f}ms")
return data
elif response.status_code == 401:
error_type = "401_Unauthorized"
self._track_metrics(latency_ms, False, error_type=error_type)
raise PermissionError(f"Invalid API key or unauthorized access")
elif response.status_code == 429:
error_type = "429_RateLimit"
self._track_metrics(latency_ms, False, error_type=error_type)
raise RuntimeError(f"Rate limit exceeded - Retry after backoff")
elif response.status_code == 500:
error_type = "500_ServerError"
self._track_metrics(latency_ms, False, error_type=error_type)
raise RuntimeError(f"HolySheep AI server error: {response.text}")
else:
error_type = f"HTTP_{response.status_code}"
self._track_metrics(latency_ms, False, error_type=error_type)
raise RuntimeError(f"API error {response.status_code}: {response.text}")
except httpx.TimeoutException:
latency_ms = (time.perf_counter() - start_time) * 1000
error_type = "ConnectionError_Timeout"
self._track_metrics(latency_ms, False, error_type=error_type)
logger.error(f"[TIMEOUT] Request exceeded 60s timeout")
raise ConnectionError("Request timeout - Check network connectivity")
except httpx.ConnectError as e:
latency_ms = (time.perf_counter() - start_time) * 1000
error_type = "ConnectionError_Connect"
self._track_metrics(latency_ms, False, error_type=error_type)
logger.error(f"[CONNECT_ERROR] Cannot reach HolySheep AI: {e}")
raise ConnectionError(f"Cannot connect to HolySheep AI gateway: {e}")
def get_metrics_summary(self) -> Dict[str, Any]:
"""현재 메트릭 요약 반환"""
with self._lock:
avg_latency = (self.metrics.total_latency_ms / self.metrics.request_count
if self.metrics.request_count > 0 else 0)
success_rate = (self.metrics.success_count / self.metrics.request_count * 100
if self.metrics.request_count > 0 else 0)
return {
"total_requests": self.metrics.request_count,
"success_count": self.metrics.success_count,
"error_count": self.metrics.error_count,
"success_rate_percent": round(success_rate, 2),
"average_latency_ms": round(avg_latency, 2),
"total_tokens_used": self.metrics.total_tokens,
"estimated_cost_usd": round(self.metrics.total_cost_usd, 6),
"error_breakdown": dict(self.metrics.error_types),
"recent_latencies": self.metrics.latency_history[-10:]
}
사용 예시
monitor = HolySheepMonitor(api_key="YOUR_HOLYSHEEP_API_KEY")
테스트 호출
async def test_monitoring():
try:
result = await monitor.chat_completion(
messages=[{"role": "user", "content": "안녕하세요, 모니터링 테스트입니다."}],
model="deepseek-v3.2"
)
print(f"Response: {result['choices'][0]['message']['content']}")
except Exception as e:
print(f"Error: {e}")
# 메트릭 확인
metrics = monitor.get_metrics_summary()
print(f"Total Cost: ${metrics['estimated_cost_usd']}")
print(f"Success Rate: {metrics['success_rate_percent']}%")
print(f"Avg Latency: {metrics['average_latency_ms']}ms")Grafana 실시간 대시보드 구축
수집된 메트릭을 Prometheus와 Grafana를 활용하여 시각화하는 시스템을 구현하겠습니다. HolySheep AI의 모델별 비용 추적과 지연 시간 모니터링에 최적화되어 있습니다.# prometheus_metrics.py
from prometheus_client import Counter, Histogram, Gauge, push_to_gateway
import json
from datetime import datetime
Prometheus 메트릭 정의
REQUEST_COUNTER = Counter(
'holysheep_api_requests_total',
'Total number of HolySheep API requests',
['model', 'status']
)
LATENCY_HISTOGRAM = Histogram(
'holysheep_api_latency_seconds',
'API request latency in seconds',
['model', 'endpoint'],
buckets=[0.1, 0.25, 0.5, 1.0, 2.5, 5.0, 10.0]
)
TOKEN_GAUGE = Gauge(
'holysheep_api_tokens_total',
'Total tokens used',
['model', 'type'] # type: prompt, completion
)
COST_GAUGE = Gauge(
'holysheep_api_cost_usd',
'Estimated API cost in USD',
['model']
)
ERROR_COUNTER = Counter(
'holysheep_api_errors_total',
'Total API errors',
['model', 'error_type']
)
def update_prometheus_metrics(metrics_summary: dict):
"""HolySheepMonitor에서 수집한 메트릭을 Prometheus 형식으로 변환"""
for error_type, count in metrics_summary.get('error_breakdown', {}).items():
ERROR_COUNTER.labels(model='all', error_type=error_type).inc(count)
# 히스토그램 업데이트 (최근 지연 시간 기반)
for latency_record in metrics_summary.get('recent_latencies', [])[-5:]:
latency_sec = latency_record['latency_ms'] / 1000
LATENCY_HISTOGRAM.labels(model='deepseek-v3.2', endpoint='chat/completions').observe(latency_sec)
# 성공/실패 카운터 업데이트
REQUEST_COUNTER.labels(model='all', status='success').inc(metrics_summary.get('success_count', 0))
REQUEST_COUNTER.labels(model='all', status='error').inc(metrics_summary.get('error_count', 0))
# 비용 업데이트
COST_GAUGE.labels(model='all').set(metrics_summary.get('estimated_cost_usd', 0))
# 토큰 사용량 업데이트
total_tokens = metrics_summary.get('total_tokens_used', 0)
TOKEN_GAUGE.labels(model='all', type='total').set(total_tokens)
def export_to_prometheus_pushgateway():
"""Prometheus Pushgateway로 메트릭 푸시 (배치 작업용)"""
try:
push_to_gateway(
'localhost:9091',
job='holy_sheep_monitor',
grouping_key={'instance': 'production-api'}
)
print(f"[{datetime.now().isoformat()}] Metrics pushed to Pushgateway")
except Exception as e:
print(f"Failed to push metrics: {e}")
Grafana Dashboard JSON (대시보드 템플릿)
GRAFANA_DASHBOARD_JSON = {
"title": "HolySheep AI API Monitoring Dashboard",
"tags": ["holysheep", "ai-api", "monitoring"],
"timezone": "browser",
"panels": [
{
"title": "Request Success Rate",
"type": "stat",
"gridPos": {"h": 8, "w": 6, "x": 0, "y": 0},
"targets": [{
"expr": "sum(holysheep_api_requests_total{status='success'}) / sum(holysheep_api_requests_total) * 100",
"legendFormat": "Success Rate %"
}]
},
{
"title": "Average Latency (ms)",
"type": "graph",
"gridPos": {"h": 8, "w": 12, "x": 6, "y": 0},
"targets": [{
"expr": "rate(holysheep_api_latency_seconds_sum[5m]) / rate(holysheep_api_latency_seconds_count[5m]) * 1000",
"legendFormat": "Avg Latency (ms)"
}]
},
{
"title": "Total Cost (USD)",
"type": "stat",
"gridPos": {"h": 8, "w": 6, "x": 18, "y": 0},
"targets": [{
"expr": "holysheep_api_cost_usd",
"legendFormat": "Total Cost"
}]
},
{
"title": "Error Breakdown",
"type": "piechart",
"gridPos": {"h": 8, "w": 8, "x": 0, "y": 8},
"targets": [{
"expr": "sum by (error_type) (holysheep_api_errors_total)",
"legendFormat": "{{error_type}}"
}]
},
{
"title": "Token Usage Over Time",
"type": "graph",
"gridPos": {"h": 8, "w": 16, "x": 8, "y": 8},
"targets": [{
"expr": "rate(holysheep_api_tokens_total[1h])",
"legendFormat": "Tokens/hour"
}]
},
{
"title": "Latency Distribution (P50, P95, P99)",
"type": "graph",
"gridPos": {"h": 8, "w": 12, "x": 0, "y": 16},
"targets": [
{"expr": "histogram_quantile(0.50, rate(holysheep_api_latency_seconds_bucket[5m])) * 1000", "legendFormat": "P50"},
{"expr": "histogram_quantile(0.95, rate(holysheep_api_latency_seconds_bucket[5m])) * 1000", "legendFormat": "P95"},
{"expr": "histogram_quantile(0.99, rate(holysheep_api_latency_seconds_bucket[5m])) * 1000", "legendFormat": "P99"}
]
}
]
}
def create_grafana_dashboard():
"""Grafana API를 통해 대시보드 자동 생성"""
import requests
grafana_url = "http://localhost:3000"
grafana_token = "YOUR_GRAFANA_API_TOKEN"
headers = {
"Authorization": f"Bearer {grafana_token}",
"Content-Type": "application/json"
}
response = requests.post(
f"{grafana_url}/api/dashboards/db",
headers=headers,
json={"dashboard": GRAFANA_DASHBOARD_JSON, "overwrite": True}
)
if response.status_code == 200:
print(f"[SUCCESS] Dashboard created: {response.json().get('url')}")
else:
print(f"[ERROR] Failed to create dashboard: {response.text}")비용 알림 시스템 구현
예상치 못한 비용 증가를 방지하기 위한 실시간 알림 시스템을 구현합니다. HolySheep AI의 다양한 모델 가격을 기준으로 한도 설정이 가능합니다.# cost_alert.py
import asyncio
from datetime import datetime, timedelta
from dataclasses import dataclass
from typing import List, Callable, Optional
import logging
logger = logging.getLogger(__name__)
@dataclass
class CostAlert:
"""비용 알림 설정"""
threshold_usd: float
period_minutes: int
model: Optional[str] = None
callback: Optional[Callable] = None
class CostAlertManager:
"""
HolySheep AI 비용 알림 관리자
모델별, 전체 사용량 기준 알림 설정 가능
"""
# HolySheep AI 모델별 현재 가격 (참고용)
CURRENT_PRICING = {
"gpt-4.1": {"input": 8.00, "output": 8.00, "unit": "per_1M_tokens"},
"claude-sonnet-4.5": {"input": 15.00, "output": 15.00, "unit": "per_1M_tokens"},
"gemini-2.5-flash": {"input": 2.50, "output": 2.50, "unit": "per_1M_tokens"},
"deepseek-v3.2": {"input": 0.42, "output": 0.42, "unit": "per_1M_tokens"},
}
def __init__(self):
self.alerts: List[CostAlert] = []
self.cost_history: List[dict] = []
self.total_daily_cost = 0.0
self.total_monthly_cost = 0.0
self._monitoring = False
def add_alert(self, threshold: float, period_minutes: int,
model: Optional[str] = None, callback: Optional[Callable] = None):
"""알림 규칙 추가"""
alert = CostAlert(
threshold_usd=threshold,
period_minutes=period_minutes,
model=model,
callback=callback
)
self.alerts.append(alert)
logger.info(f"Added alert: ${threshold}/${period_minutes}min" +
(f" for {model}" if model else " (all models)"))
def record_cost(self, cost_usd: float, model: str, tokens_used: int):
"""비용 기록 및 알림 확인"""
timestamp = datetime.now()
self.cost_history.append({
"timestamp": timestamp,
"cost_usd": cost_usd,
"model": model,
"tokens": tokens_used
})
# 30일 이상 된 기록 삭제 (메모리 관리)
cutoff = timestamp - timedelta(days=30)
self.cost_history = [c for c in self.cost_history if c["timestamp"] > cutoff]
# 일일/월간 비용 업데이트
self.total_daily_cost += cost_usd
self.total_monthly_cost += cost_usd
# 알림 확인
self._check_alerts(cost_usd, model, timestamp)
def _check_alerts(self, current_cost: float, model: str, timestamp: datetime):
"""설정된 알림 규칙 확인"""
for alert in self.alerts:
# 모델 필터 확인
if alert.model and alert.model != model:
continue
# 기간 내 비용 합산
cutoff = timestamp - timedelta(minutes=alert.period_minutes)
period_costs = [
c["cost_usd"] for c in self.cost_history
if c["timestamp"] > cutoff
]
period_total = sum(period_costs)
if period_total >= alert.threshold_usd:
message = (
f"🚨 Cost Alert! "
f"${period_total:.2f} spent in last {alert.period_minutes}min "
f"({model})"
)
logger.warning(message)
if alert.callback:
alert.callback(message)
else:
self._default_alert_handler(message)
def _default_alert_handler(self, message: str):
"""기본 알림 처리 (실제 환경에서는 Slack/Email/PagerDuty 연동)"""
print(f"[ALERT] {message}")
# 실제 구현 예시:
# - Slack webhook 전송
# - Email 발송
# - PagerDuty incident 생성
async def start_monitoring(self, monitor, check_interval_seconds: int = 60):
"""지속적 모니터링 시작"""
self._monitoring = True
logger.info("Starting cost monitoring...")
while self._monitoring:
try:
metrics = monitor.get_metrics_summary()
current_cost = metrics.get("estimated_cost_usd", 0)
# 비용 기록 (실제 환경에서는 모델별 분할 필요)
self.record_cost(current_cost, "all", metrics.get("total_tokens_used", 0))
# 비용 요약 로깅
logger.info(
f"[COST UPDATE] Daily: ${self.total_daily_cost:.4f} | "
f"Monthly: ${self.total_monthly_cost:.4f} | "
f"Requests: {metrics['total_requests']}"
)
await asyncio.sleep(check_interval_seconds)
except Exception as e:
logger.error(f"Monitoring error: {e}")
await asyncio.sleep(check_interval_seconds)
def stop_monitoring(self):
"""모니터링 중지"""
self._monitoring = False
logger.info("Cost monitoring stopped")
def get_cost_breakdown(self) -> dict:
"""모델별 비용 분석 반환"""
model_costs = {}
for record in self.cost_history:
model = record["model"]
if model not in model_costs:
model_costs[model] = {"cost": 0, "tokens": 0, "requests": 0}
model_costs[model]["cost"] += record["cost_usd"]
model_costs[model]["tokens"] += record["tokens"]
model_costs[model]["requests"] += 1
return {
"total_daily_cost": round(self.total_daily_cost, 4),
"total_monthly_cost": round(self.total_monthly_cost, 4),
"model_breakdown": model_costs,
"current_pricing": self.CURRENT_PRICING
}
사용 예시
async def cost_monitoring_example():
# HolySheep Monitor 초기화
monitor = HolySheepMonitor(api_key="YOUR_HOLYSHEEP_API_KEY")
# 비용 알림 관리자 설정
alert_manager = CostAlertManager()
# 알림 규칙 추가
alert_manager.add_alert(
threshold=10.0, # 10달러
period_minutes=60, # 1시간 내
callback=lambda msg: print(f"🔔 {msg}")
)
alert_manager.add_alert(
threshold=50.0, # 50달러
period_minutes=1440, # 1일 내 (하루 한도)
callback=lambda msg: print(f"🚨 {msg}")
)
# 모니터링 시작
await alert_manager.start_monitoring(monitor, check_interval_seconds=30)
비용 최적화 권장사항 출력
def print_cost_optimization_tips():
print("""
╔════════════════════════════════════════════════════════════╗
║ HolySheep AI 비용 최적화 권장사항 ║
╠════════════════════════════════════════════════════════════╣
║ 모델 선택 전략: ║
║ • 간단한 작업: DeepSeek V3.2 ($0.42/MTok) - 95% 절감 ║
║ • 복잡한 작업: Gemini 2.5 Flash ($2.50/MTok) - 균형 ║
║ • 최고 품질: Claude Sonnet 4.5 ($15/MTok) - 프리미엄 ║
╠════════════════════════════════════════════════════════════╣
║ 실제 비용 비교 (1M 토큰 처리 시): ║
║ • DeepSeek V3.2: $0.42 (입력+출력 평균) ║
║ • Gemini 2.5 Flash: $6.25 ║
║ • Claude Sonnet 4.5: $45.00 ║
║ • GPT-4.1: $24.00 ║
╠════════════════════════════════════════════════════════════╣
║ 최적화 팁: ║
║ 1. 배치 처리로 요청 수 최소화 ║
║ 2. 캐싱으로 중복 호출 방지 ║
║ 3. 적절한 max_tokens 설정 ║
║ 4. 모델 级amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp