"""
Auto-Label Report Generator

Generates quality control reports for auto-labeling process.
"""

import json
from dataclasses import dataclass, field, asdict
from datetime import datetime
from pathlib import Path
from typing import Any


@dataclass
class FieldMatchResult:
    """Result of matching a single field."""
    field_name: str
    csv_value: str | None
    matched: bool
    score: float = 0.0
    matched_text: str | None = None
    candidate_used: str | None = None  # Which normalized variant matched
    bbox: tuple[float, float, float, float] | None = None
    page_no: int = 0
    context_keywords: list[str] = field(default_factory=list)
    error: str | None = None

    def to_dict(self) -> dict:
        """Convert to dictionary."""
        # Convert bbox to native Python floats to avoid numpy serialization issues
        bbox_list = None
        if self.bbox:
            bbox_list = [float(x) for x in self.bbox]

        return {
            'field_name': self.field_name,
            'csv_value': self.csv_value,
            'matched': self.matched,
            'score': float(self.score) if self.score else 0.0,
            'matched_text': self.matched_text,
            'candidate_used': self.candidate_used,
            'bbox': bbox_list,
            'page_no': int(self.page_no) if self.page_no else 0,
            'context_keywords': self.context_keywords,
            'error': self.error
        }


@dataclass
class AutoLabelReport:
    """Report for a single document's auto-labeling process."""
    document_id: str
    pdf_path: str | None = None
    pdf_type: str | None = None  # 'text' | 'scanned' | 'mixed'
    success: bool = False
    total_pages: int = 0
    fields_matched: int = 0
    fields_total: int = 0
    field_results: list[FieldMatchResult] = field(default_factory=list)
    annotations_generated: int = 0
    image_paths: list[str] = field(default_factory=list)
    label_paths: list[str] = field(default_factory=list)
    processing_time_ms: float = 0.0
    timestamp: str = field(default_factory=lambda: datetime.now().isoformat())
    errors: list[str] = field(default_factory=list)

    def add_field_result(self, result: FieldMatchResult) -> None:
        """Add a field matching result."""
        self.field_results.append(result)
        self.fields_total += 1
        if result.matched:
            self.fields_matched += 1

    def to_dict(self) -> dict:
        """Convert to dictionary."""
        return {
            'document_id': self.document_id,
            'pdf_path': self.pdf_path,
            'pdf_type': self.pdf_type,
            'success': self.success,
            'total_pages': self.total_pages,
            'fields_matched': self.fields_matched,
            'fields_total': self.fields_total,
            'field_results': [r.to_dict() for r in self.field_results],
            'annotations_generated': self.annotations_generated,
            'image_paths': self.image_paths,
            'label_paths': self.label_paths,
            'processing_time_ms': self.processing_time_ms,
            'timestamp': self.timestamp,
            'errors': self.errors
        }

    def to_json(self, indent: int | None = None) -> str:
        """Convert to JSON string."""
        return json.dumps(self.to_dict(), indent=indent, ensure_ascii=False)

    @property
    def match_rate(self) -> float:
        """Calculate field match rate."""
        if self.fields_total == 0:
            return 0.0
        return self.fields_matched / self.fields_total

    def get_summary(self) -> dict:
        """Get a summary of the report."""
        return {
            'document_id': self.document_id,
            'success': self.success,
            'match_rate': f"{self.match_rate:.1%}",
            'fields': f"{self.fields_matched}/{self.fields_total}",
            'annotations': self.annotations_generated,
            'errors': len(self.errors)
        }


class ReportWriter:
    """Writes auto-label reports to file with optional sharding."""

    def __init__(
        self,
        output_path: str | Path,
        max_records_per_file: int = 0
    ):
        """
        Initialize report writer.

        Args:
            output_path: Path to output JSONL file (base name if sharding)
            max_records_per_file: Max records per file (0 = no limit, single file)
        """
        self.output_path = Path(output_path)
        self.output_path.parent.mkdir(parents=True, exist_ok=True)
        self.max_records_per_file = max_records_per_file

        # Sharding state
        self._current_shard = 0
        self._records_in_current_shard = 0
        self._shard_files: list[Path] = []

    def _get_shard_path(self) -> Path:
        """Get the path for current shard."""
        if self.max_records_per_file > 0:
            base = self.output_path.stem
            suffix = self.output_path.suffix
            shard_path = self.output_path.parent / f"{base}_part{self._current_shard:03d}{suffix}"
        else:
            shard_path = self.output_path

        if shard_path not in self._shard_files:
            self._shard_files.append(shard_path)

        return shard_path

    def _check_shard_rotation(self) -> None:
        """Check if we need to rotate to a new shard file."""
        if self.max_records_per_file > 0:
            if self._records_in_current_shard >= self.max_records_per_file:
                self._current_shard += 1
                self._records_in_current_shard = 0

    def write(self, report: AutoLabelReport) -> None:
        """Append a report to the output file."""
        self._check_shard_rotation()
        shard_path = self._get_shard_path()
        with open(shard_path, 'a', encoding='utf-8') as f:
            f.write(report.to_json() + '\n')
        self._records_in_current_shard += 1

    def write_dict(self, report_dict: dict) -> None:
        """Append a report dict to the output file (for parallel processing)."""
        self._check_shard_rotation()
        shard_path = self._get_shard_path()
        with open(shard_path, 'a', encoding='utf-8') as f:
            f.write(json.dumps(report_dict, ensure_ascii=False) + '\n')
            f.flush()
        self._records_in_current_shard += 1

    def write_batch(self, reports: list[AutoLabelReport]) -> None:
        """Write multiple reports."""
        for report in reports:
            self.write(report)

    def get_shard_files(self) -> list[Path]:
        """Get list of all shard files created."""
        return self._shard_files.copy()


class ReportReader:
    """Reads auto-label reports from file(s)."""

    def __init__(self, input_path: str | Path):
        """
        Initialize report reader.

        Args:
            input_path: Path to input JSONL file or glob pattern (e.g., 'reports/*.jsonl')
        """
        self.input_path = Path(input_path)

        # Handle glob pattern
        if '*' in str(input_path) or '?' in str(input_path):
            parent = self.input_path.parent
            pattern = self.input_path.name
            self.input_paths = sorted(parent.glob(pattern))
        else:
            self.input_paths = [self.input_path]

    def read_all(self) -> list[AutoLabelReport]:
        """Read all reports from file(s)."""
        reports = []

        for input_path in self.input_paths:
            if not input_path.exists():
                continue

            with open(input_path, 'r', encoding='utf-8') as f:
                for line in f:
                    line = line.strip()
                    if not line:
                        continue

                    data = json.loads(line)
                    report = self._dict_to_report(data)
                    reports.append(report)

        return reports

    def _dict_to_report(self, data: dict) -> AutoLabelReport:
        """Convert dictionary to AutoLabelReport."""
        field_results = []
        for fr_data in data.get('field_results', []):
            bbox = tuple(fr_data['bbox']) if fr_data.get('bbox') else None
            field_results.append(FieldMatchResult(
                field_name=fr_data['field_name'],
                csv_value=fr_data.get('csv_value'),
                matched=fr_data.get('matched', False),
                score=fr_data.get('score', 0.0),
                matched_text=fr_data.get('matched_text'),
                candidate_used=fr_data.get('candidate_used'),
                bbox=bbox,
                page_no=fr_data.get('page_no', 0),
                context_keywords=fr_data.get('context_keywords', []),
                error=fr_data.get('error')
            ))

        return AutoLabelReport(
            document_id=data['document_id'],
            pdf_path=data.get('pdf_path'),
            pdf_type=data.get('pdf_type'),
            success=data.get('success', False),
            total_pages=data.get('total_pages', 0),
            fields_matched=data.get('fields_matched', 0),
            fields_total=data.get('fields_total', 0),
            field_results=field_results,
            annotations_generated=data.get('annotations_generated', 0),
            image_paths=data.get('image_paths', []),
            label_paths=data.get('label_paths', []),
            processing_time_ms=data.get('processing_time_ms', 0.0),
            timestamp=data.get('timestamp', ''),
            errors=data.get('errors', [])
        )

    def get_statistics(self) -> dict:
        """Calculate statistics from all reports."""
        reports = self.read_all()

        if not reports:
            return {'total': 0}

        successful = sum(1 for r in reports if r.success)
        total_fields_matched = sum(r.fields_matched for r in reports)
        total_fields = sum(r.fields_total for r in reports)
        total_annotations = sum(r.annotations_generated for r in reports)

        # Per-field statistics
        field_stats = {}
        for report in reports:
            for fr in report.field_results:
                if fr.field_name not in field_stats:
                    field_stats[fr.field_name] = {'matched': 0, 'total': 0, 'avg_score': 0.0}
                field_stats[fr.field_name]['total'] += 1
                if fr.matched:
                    field_stats[fr.field_name]['matched'] += 1
                    field_stats[fr.field_name]['avg_score'] += fr.score

        # Calculate averages
        for field_name, stats in field_stats.items():
            if stats['matched'] > 0:
                stats['avg_score'] /= stats['matched']
            stats['match_rate'] = stats['matched'] / stats['total'] if stats['total'] > 0 else 0

        return {
            'total': len(reports),
            'successful': successful,
            'success_rate': successful / len(reports),
            'total_fields_matched': total_fields_matched,
            'total_fields': total_fields,
            'overall_match_rate': total_fields_matched / total_fields if total_fields > 0 else 0,
            'total_annotations': total_annotations,
            'field_statistics': field_stats
        }