from __future__ import annotations

import argparse
import os
import re
import sys
from datetime import datetime
from pathlib import Path

import fitz  # PyMuPDF


BASE_DIR = Path(__file__).resolve().parent
DEFAULT_SOURCE_DIR = BASE_DIR / "source"
DEFAULT_OUTPUT_DIR = BASE_DIR / "output"


def safe_dir_name(name: str) -> str:
	"""Sanitize directory name for Windows filesystem."""
	cleaned = re.sub(r"[\\/:*?\"<>|]", "_", name.strip())
	cleaned = re.sub(r"\s+", " ", cleaned)
	return cleaned[:120] if cleaned else "未识别收款人"


def extract_payee_name(text: str) -> str | None:
	"""Extract 收款人全称 from text using common label patterns."""
	if not text:
		return None

	normalized = text.replace("\u3000", " ")
	compact = re.sub(r"\s+", "", normalized)

	compact_match = re.search(
		r"收款人全称[:：]?(.+?)(别名|账号|开户行|大写金额|小写金额|用途|钞汇标志|摘要|重要提示|付款人全称|$)",
		compact,
		flags=re.IGNORECASE,
	)
	if compact_match:
		value = compact_match.group(1).strip(" :：")
		if value:
			return value

	patterns = [
		r"收款人全称\s*[:：]\s*([^\n\r]+)",
		r"收款人\s*全称\s*[:：]\s*([^\n\r]+)",
		r"收\s*款\s*人\s*全\s*称\s*[:：]?\s*([^\n\r]+)",
		r"收款人全称\s+([^\n\r]+)",
	]
	for pattern in patterns:
		match = re.search(pattern, normalized, flags=re.IGNORECASE)
		if match:
			value = match.group(1).strip()
			value = re.split(r"\s{2,}|金额|开户行|账号|日期", value)[0].strip(" :：")
			if value:
				return value

	lines = [line.strip() for line in normalized.splitlines() if line.strip()]
	for idx, line in enumerate(lines):
		if "收款人全称" in line:
			after = line.split("收款人全称", 1)[1].strip(" :：")
			if after:
				return after
			if idx + 1 < len(lines):
				candidate = lines[idx + 1].strip(" :：")
				if candidate:
					return candidate

	return None


def extract_text_via_ocr(image_path: Path) -> str:
	"""OCR fallback for scanned PDFs without embedded text."""
	try:
		import pytesseract
		from PIL import Image
	except ImportError:
		return ""

	tesseract_cmd = os.environ.get("TESSERACT_CMD", "")
	if tesseract_cmd:
		pytesseract.pytesseract.tesseract_cmd = tesseract_cmd

	try:
		with Image.open(image_path) as img:
			return pytesseract.image_to_string(img, lang="chi_sim+eng")
	except Exception:
		return ""


def resolve_io_dirs() -> tuple[Path, Path]:
	"""Resolve source/output directories from CLI, with defaults as fallback."""
	parser = argparse.ArgumentParser(description="PDF 回执拆分与按收款人分类")
	parser.add_argument("input_dir", nargs="?", help="输入目录（可选，默认 ./source）")
	parser.add_argument("output_dir", nargs="?", help="输出目录（可选，默认 ./output）")
	parser.add_argument("-i", "--input", dest="input_opt", help="输入目录")
	parser.add_argument("-o", "--output", dest="output_opt", help="输出目录")
	args = parser.parse_args()

	input_raw = args.input_opt or args.input_dir
	output_raw = args.output_opt or args.output_dir

	source_dir = Path(input_raw).expanduser().resolve() if input_raw else DEFAULT_SOURCE_DIR
	output_dir = Path(output_raw).expanduser().resolve() if output_raw else DEFAULT_OUTPUT_DIR
	return source_dir, output_dir


def ensure_dirs(source_dir: Path, output_dir: Path) -> None:
	source_dir.mkdir(parents=True, exist_ok=True)
	output_dir.mkdir(parents=True, exist_ok=True)


def get_receipt_clips(page: fitz.Page) -> list[fitz.Rect]:
	"""Infer receipt regions on one page by locating repeated receipt headers."""
	header_text = "中国建设银行网上银行电子回执"
	header_rects = page.search_for(header_text)

	if not header_rects:
		return [page.rect]

	sorted_rects = sorted(header_rects, key=lambda r: (round(r.y0, 1), r.x0))
	unique_y: list[float] = []
	for rect in sorted_rects:
		y = rect.y0
		if not unique_y or abs(y - unique_y[-1]) > 8:
			unique_y.append(y)

	bounds = page.rect
	clips: list[fitz.Rect] = []
	for idx, y in enumerate(unique_y):
		y0 = max(bounds.y0, y - 6)
		y1 = unique_y[idx + 1] - 6 if idx + 1 < len(unique_y) else bounds.y1
		if y1 - y0 > 40:
			clips.append(fitz.Rect(bounds.x0, y0, bounds.x1, y1))

	return clips or [page.rect]


def process_pdf(pdf_path: Path, output_dir: Path) -> tuple[int, int, dict[str, int]]:
	"""Split PDF pages to images and classify by payee name.

	Returns:
		(success_count, failed_count, per_dir_counts)
	"""
	success_count = 0
	failed_count = 0
	per_dir_counts: dict[str, int] = {}

	with fitz.open(pdf_path) as doc:
		for page_index, page in enumerate(doc, start=1):
			receipt_clips = get_receipt_clips(page)
			for receipt_index, clip in enumerate(receipt_clips, start=1):
				image_name = f"{pdf_path.stem}_p{page_index:03d}_r{receipt_index:03d}.png"
				temp_image = output_dir / image_name

				# Render one inferred receipt region as one output image.
				pix = page.get_pixmap(matrix=fitz.Matrix(2.0, 2.0), alpha=False, clip=clip)
				pix.save(temp_image)

				receipt_text = page.get_text("text", clip=clip) or ""
				payee_name = extract_payee_name(receipt_text)

				if not payee_name:
					ocr_text = extract_text_via_ocr(temp_image)
					payee_name = extract_payee_name(ocr_text)

				if not payee_name:
					payee_name = "未识别收款人"
					failed_count += 1
				else:
					success_count += 1

				target_dir = output_dir / safe_dir_name(payee_name)
				target_dir.mkdir(parents=True, exist_ok=True)
				per_dir_counts[target_dir.name] = per_dir_counts.get(target_dir.name, 0) + 1

				final_image = target_dir / image_name
				if final_image.exists():
					final_image = target_dir / f"{pdf_path.stem}_p{page_index:03d}_r{receipt_index:03d}_{os.getpid()}.png"

				temp_image.replace(final_image)

	return success_count, failed_count, per_dir_counts


def write_execution_report(report_lines: list[str]) -> Path:
	"""Write execution details into a timestamped report file under BASE_DIR."""
	timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
	report_path = BASE_DIR / f"执行结果记录_{timestamp}.txt"
	report_path.write_text("\n".join(report_lines) + "\n", encoding="utf-8")
	return report_path


def main() -> int:
	source_dir, output_dir = resolve_io_dirs()
	ensure_dirs(source_dir, output_dir)

	pdf_files = sorted(source_dir.glob("*.pdf"))
	if not pdf_files:
		print(f"未在目录中发现 PDF: {source_dir}")
		print("请将 PDF 文件放到 source 目录后重试。")
		return 0

	total_ok = 0
	total_unknown = 0
	report_lines = [
		f"执行时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}",
		f"输入目录: {source_dir}",
		f"输出目录: {output_dir}",
		"",
	]

	for pdf_file in pdf_files:
		print(f"处理文件: {pdf_file.name}")
		ok, unknown, dir_counts = process_pdf(pdf_file, output_dir)
		print(f"    识别到收款人全称的图片数: {ok}")
		print(f"    未识别收款人全称的图片数: {unknown}")
		total_ok += ok
		total_unknown += unknown

		report_lines.append(f"PDF: {pdf_file.name}")
		report_lines.append(f"  拆分目录数: {len(dir_counts)}")
		report_lines.append(f"  识别到收款人全称的图片数: {ok}")
		report_lines.append(f"  未识别收款人全称的图片数: {unknown}")
		report_lines.append("  目录明细:")
		for dir_name in sorted(dir_counts):
			report_lines.append(f"    - {dir_name}: {dir_counts[dir_name]} 张")
		report_lines.append("")

	print("\n处理完成")
	print(f"识别到收款人全称的图片数: {total_ok}")
	print(f"未识别收款人全称的图片数: {total_unknown}")
	print(f"输出目录: {output_dir}")

	report_lines.append("汇总:")
	report_lines.append(f"  识别到收款人全称的图片总数: {total_ok}")
	report_lines.append(f"  未识别收款人全称的图片总数: {total_unknown}")

	report_path = write_execution_report(report_lines)
	print(f"执行结果记录文件: {report_path}")
	return 0


if __name__ == "__main__":
	sys.exit(main())