import pdfplumber
import re
from pdf_engine import extract_header_value, apply_value_replacement
from parser_welspun import extract_welspun_items
from parser_bkt import extract_bkt_items

# 🚀 LayoutLMv3 & Document AI Integration Imports
from transformers import LayoutLMv3Processor, LayoutLMv3ForTokenClassification
import torch
from PIL import Image
import io

def process_document_with_layoutlmv3(image_or_pdf_bytes):
    """
    ZeroGPU / GPU environment ke liye LayoutLMv3 processing function.
    Yeh document ki visual bounding boxes aur text dono ko process karta hai.
    """
    try:
        # Note: Model loading aur inference yahan ZeroGPU decorator ke antargat run hoga
        processor = LayoutLMv3Processor.from_pretrained("microsoft/layoutlmv3-base")
        # Model initialization aur inference logic yahan aage configure kiya ja sakta hai
        return True, "LayoutLMv3 processor initialized successfully."
    except Exception as e:
        return False, str(e)

def run_universal_test_suite_backend(selected_shipper, shipper_database, pdf_lines, pdf_text, test_category, target_field):
    """
    Streamlit-free backend test suite jo Gradio ke through single field ya row-by-row inspection run karta hai.
    """
    if not pdf_lines:
        return "💡 कृपया पहले इनवॉइस PDF अपलोड करें, फिर टेस्ट रन करें।"

    shipper_info = shipper_database.get(selected_shipper, {})
    header_rules = shipper_info.get("mapping_rules", {})
    item_rules = shipper_info.get("item_table_rules", {})
    assigned_parser = shipper_info.get("item_table_rule_name", "parser_welspun").strip().lower()

    if test_category == "Header Fields Rules":
        if target_field not in header_rules:
            return f"⚠️ Header Field '{target_field}' nahi mila."
            
        rule_data = header_rules[target_field]
        ky = rule_data.get("keyword", "")
        pos = rule_data.get("position", "Right (आगे)")
        cl = rule_data.get("cell", "").strip()
        m_mode = rule_data.get("match_mode", "Exact Word")
        stop_kw = rule_data.get("stop_kw", "")
        final_flt = rule_data.get("filter", "None")

        final_val = extract_header_value(pdf_lines, pdf_text, ky, pos, m_mode, stop_kw, final_flt)

        display_cell = cl if cl else 'Not Set'
        if cl and cl.isalpha():
            display_cell = f"{cl.upper()}2 (Dynamic Auto-Increment Row)"

        result_summary = (
            f"🔍 Inspection Result: {target_field}\n"
            f"----------------------------------------\n"
            f"📋 Parameters:\n"
            f"- Target Excel Cell: {display_cell}\n"
            f"- Keyword: {ky if ky else 'N/A'}\n"
            f"- Match Mode: {m_mode}\n"
            f"- Filter: {final_flt}\n\n"
            f"🎯 Extracted Value: {final_val if final_val else 'BLANK / NOT FOUND'}"
        )
        return result_summary

    else:
        if target_field not in item_rules:
            return f"⚠️ Item Field '{target_field}' nahi mila."
            
        rule_info = item_rules[target_field]
        col_letter = rule_info.get("col", "").upper()
        rule_type = rule_info.get("type", "PDF Row Item")
        rule_val = rule_info.get("rule", "")

        if "bkt" in assigned_parser:
            parsed_items = extract_bkt_items(pdf_lines)
        else:
            parsed_items = extract_welspun_items(pdf_lines, pdf_text=pdf_text)

        if not parsed_items:
            return "⚠️ इस PDF में कोई आइटम रो नहीं मिली या पार्सर से डेटा एक्सट्रेक्ट नहीं हुआ।"

        preview_lines = [f"📊 Row-by-Row Preview for Column {col_letter}:"]
        for idx, item in enumerate(parsed_items):
            excel_row_num = 2 + idx
            cell_target = f"{col_letter}{excel_row_num}"
            nums = item.get("nums", [])
            r_val_lower = str(rule_val).lower().strip()
            f_name_lower = target_field.lower().strip()
            extracted_cell_val = ""

            if rule_type == "Constant Text":
                extracted_cell_val = apply_value_replacement(rule_val, rule_val)
            elif rule_type == "Excel Cell Reference":
                extracted_cell_val = f"={rule_val}"
            elif rule_type == "Smart Detection":
                desc = item.get("description_text", "").upper()
                extracted_cell_val = "PCS" if ("PCS" in desc or "PC" in desc) else (rule_val if rule_val else "SET")
            else:
                if "igst %" in r_val_lower or "igst rate" in f_name_lower:
                    extracted_cell_val = nums[5] if len(nums) > 5 else ""
                elif "igst amt" in r_val_lower or "igst amount" in f_name_lower:
                    extracted_cell_val = nums[6] if len(nums) > 6 else ""
                elif "hs" in r_val_lower or "ritc" in f_name_lower or "hs code" in r_val_lower:
                    extracted_cell_val = item.get("hs_code", "")
                elif "description" in r_val_lower or "description" in f_name_lower:
                    extracted_cell_val = item.get("description_text", "")
                elif "dbk" in r_val_lower or "drawback" in f_name_lower or col_letter == "S":
                    extracted_cell_val = item.get("dbk_found", "")
                elif "weight" in r_val_lower or "net wt" in f_name_lower:
                    extracted_cell_val = nums[0] if len(nums) > 0 else ""
                elif "qty" in r_val_lower or "quantity" in f_name_lower:
                    extracted_cell_val = nums[1] if len(nums) > 1 else ""
                elif "rate" in r_val_lower:
                    extracted_cell_val = nums[2] if len(nums) > 2 else ""
                elif "amount" in r_val_lower or "goods value" in f_name_lower:
                    extracted_cell_val = nums[3] if len(nums) > 3 else ""
                elif "taxable" in r_val_lower:
                    extracted_cell_val = nums[4] if len(nums) > 4 else ""
                else:
                    extracted_cell_val = rule_val

                if "=" in str(rule_val):
                    extracted_cell_val = apply_value_replacement(str(extracted_cell_val), str(rule_val))

            preview_lines.append(f"Row {idx+1} | Cell: {cell_target} | Value: {extracted_cell_val}")

        return "\n".join(preview_lines)