"""Log analyzer orchestrator."""

from collections import Counter, defaultdict
from dataclasses import dataclass, field
from datetime import datetime
from typing import Any, Dict, List, Optional

from loglens.parsers.base import LogFormat, ParsedLogEntry
from loglens.parsers.factory import ParserFactory
from loglens.analyzers.patterns import ErrorPattern, PatternLibrary
from loglens.analyzers.severity import SeverityClassifier, SeverityLevel


@dataclass
class AnalysisResult:
    """Result of log analysis."""
    entries: List[ParsedLogEntry] = field(default_factory=list)
    format_detected: LogFormat = LogFormat.UNKNOWN
    total_lines: int = 0
    parsed_count: int = 0
    error_count: int = 0
    warning_count: int = 0
    critical_count: int = 0
    debug_count: int = 0
    pattern_matches: Dict[str, int] = field(default_factory=dict)
    severity_breakdown: Dict[str, int] = field(default_factory=dict)
    top_errors: List[Dict[str, Any]] = field(default_factory=list)
    host_breakdown: Dict[str, int] = field(default_factory=dict)
    time_range: Optional[tuple] = None
    analysis_time: datetime = field(default_factory=datetime.now)
    suggestions: List[str] = field(default_factory=list)


class LogAnalyzer:
    """Orchestrates log parsing and analysis."""

    def __init__(self, config: Optional[Dict[str, Any]] = None):
        self.parser_factory = ParserFactory()
        self.pattern_library = PatternLibrary()
        self.severity_classifier = SeverityClassifier(
            custom_rules=config.get("severity_rules") if config else None
        )
        self.config = config or {}

    def analyze(self, lines: List[str], format: Optional[LogFormat] = None) -> AnalysisResult:
        """Analyze a list of log lines."""
        result = AnalysisResult(
            total_lines=len(lines),
            analysis_time=datetime.now()
        )

        if not lines:
            return result

        if format is None:
            format = self.parser_factory.detect_format_batch(lines)

        result.format_detected = format

        entries = self.parser_factory.parse_lines(lines, format)
        result.entries = entries
        result.parsed_count = len(entries)

        for entry in entries:
            self._analyze_entry(entry)

        self._compute_statistics(result)

        return result

    def _analyze_entry(self, entry: ParsedLogEntry) -> None:
        """Analyze a single entry."""
        message = entry.message or ""
        raw_text = entry.raw_line

        patterns = self.pattern_library.detect(raw_text)
        if patterns:
            pattern, match = patterns[0]
            entry.error_pattern = pattern.name

        severity = self.severity_classifier.classify(
            level=entry.level,
            message=message,
            pattern_match=entry.error_pattern
        )
        entry.severity = severity.value

    def _compute_statistics(self, result: AnalysisResult) -> None:
        """Compute statistics from analyzed entries."""
        severity_counts = Counter()
        pattern_counts = Counter()
        host_counts = Counter()
        timestamps = []

        for entry in result.entries:
            severity = entry.severity or "unknown"
            severity_counts[severity] += 1

            if entry.error_pattern:
                pattern_counts[entry.error_pattern] += 1

            if entry.host:
                host_counts[entry.host] += 1

            if entry.timestamp:
                timestamps.append(entry.timestamp)

        result.severity_breakdown = dict(severity_counts)
        result.pattern_matches = dict(pattern_counts)
        result.host_breakdown = dict(host_counts)

        result.critical_count = severity_counts.get("critical", 0)
        result.error_count = severity_counts.get("error", 0)
        result.warning_count = severity_counts.get("warning", 0)
        result.debug_count = severity_counts.get("debug", 0)

        if timestamps:
            result.time_range = (min(timestamps), max(timestamps))

        result.top_errors = [
            {"pattern": name, "count": count}
            for name, count in pattern_counts.most_common(10)
        ]

        result.suggestions = self._generate_suggestions(result)

    def _generate_suggestions(self, result: AnalysisResult) -> List[str]:
        """Generate suggestions based on analysis."""
        suggestions = []

        if result.critical_count > 0:
            suggestions.append(
                f"Found {result.critical_count} critical errors. "
                "Review immediately - these may indicate system failures."
            )

        if result.error_count > 10:
            suggestions.append(
                f"High error volume detected ({result.error_count} errors). "
                "Consider implementing automated alerting."
            )

        if result.pattern_matches:
            top_pattern = max(result.pattern_matches, key=result.pattern_matches.get)
            suggestions.append(
                f"Most common issue: '{top_pattern}' "
                f"({result.pattern_matches[top_pattern]} occurrences). "
                "Prioritize fixing this pattern."
            )

        if result.host_breakdown:
            top_host = max(result.host_breakdown, key=result.host_breakdown.get)
            if result.host_breakdown[top_host] > len(result.entries) * 0.5:
                suggestions.append(
                    f"Host '{top_host}' shows high error concentration. "
                    "Check this host's configuration and resources."
                )

        return suggestions

    def analyze_file(self, file_path: str, format: Optional[LogFormat] = None) -> AnalysisResult:
        """Analyze a log file."""
        with open(file_path, 'r', encoding='utf-8', errors='replace') as f:
            lines = f.readlines()

        return self.analyze(lines, format)

    def analyze_stdin(self) -> AnalysisResult:
        """Analyze from stdin."""
        import sys
        lines = sys.stdin.readlines()
        return self.analyze(lines)

    def get_pattern_info(self, pattern_name: str) -> Optional[Dict[str, Any]]:
        """Get information about a pattern."""
        for pattern in self.pattern_library.list_patterns():
            if pattern.name == pattern_name:
                return {
                    "name": pattern.name,
                    "pattern": pattern.pattern,
                    "severity": pattern.severity,
                    "description": pattern.description,
                    "suggestion": pattern.suggestion,
                    "group": pattern.group,
                    "enabled": pattern.enabled
                }
        return None

    def list_patterns_by_group(self) -> Dict[str, List[Dict[str, Any]]]:
        """List all patterns organized by group."""
        result = {}
        for group_name, patterns in self.pattern_library.list_groups().items():
            result[group_name] = [
                {
                    "name": p.name,
                    "severity": p.severity,
                    "description": p.description
                }
                for p in patterns
            ]
        return result