"""
DO Dashboard - Quality Checks Module

This module contains all quality assurance functions:
- JSON file loading and backup utilities
- Comprehensive non-regression checks with configurable rules
- Config-driven validation with Warning/Critical thresholds
- Support for special rules (New/Deleted Requests, New/Deleted Fields)
- 4-step logic for normal rules (field selection, transition matching, exception application, bloc_scope)

Note: Coherence check is not applicable for DO Dashboard since organization
counters are computed directly from request details (not from a separate API).
"""

import json
import logging
import os
import shutil

import openpyxl
from rich.console import Console
from do_dashboard_utils import get_nested_value, get_old_filename as _get_old_filename, get_config_path
from do_dashboard_constants import (
    REQUESTS_FILE_NAME,
    ORGANIZATIONS_FILE_NAME,
    OLD_FILE_SUFFIX,
    DASHBOARD_CONFIG_FILE_NAME,
    REGRESSION_CHECK_TABLE_NAME
)


# ============================================================================
# MODULE CONFIGURATION
# ============================================================================

# Debug mode: Set to True to display detailed changes for each regression check rule
debug_mode = False


def enable_debug_mode():
    """Enable debug mode to display detailed changes for each regression check rule."""
    global debug_mode
    debug_mode = True
    if console:
        console.print("[dim]DEBUG MODE enabled - detailed changes will be displayed[/dim]")


# ============================================================================
# MODULE DEPENDENCIES (injected from main module)
# ============================================================================

# Will be injected by the main module
console = None

# Regression check config is loaded on-demand via load_regression_check_config()
regression_check_config = []

# NOTE: File names and table names are imported from do_dashboard_constants.py (SINGLE SOURCE OF TRUTH):
# - REQUESTS_FILE_NAME
# - ORGANIZATIONS_FILE_NAME
# - OLD_FILE_SUFFIX
# - DASHBOARD_CONFIG_FILE_NAME
# - REGRESSION_CHECK_TABLE_NAME


def set_dependencies(console_instance):
    """
    Inject console instance from main module.

    Args:
        console_instance: Rich Console instance for formatted output
    """
    global console
    console = console_instance


# ============================================================================
# CONFIGURATION LOADING
# ============================================================================

def load_regression_check_config(console_instance=None):
    """Loads and validates the regression check configuration from the Excel file.

    Args:
        console_instance: Optional Rich Console instance. If not provided, uses global console.
    """
    global regression_check_config, console

    if console_instance:
        console = console_instance

    config_path = os.path.join(get_config_path(), DASHBOARD_CONFIG_FILE_NAME)

    try:
        workbook = openpyxl.load_workbook(config_path)
    except FileNotFoundError:
        error_msg = f"Error: Configuration file not found at: {config_path}"
        logging.critical(error_msg)
        console.print(f"[bold red]{error_msg}[/bold red]")
        raise Exception(error_msg)

    if REGRESSION_CHECK_TABLE_NAME not in workbook.sheetnames:
        error_msg = f"Error: Sheet '{REGRESSION_CHECK_TABLE_NAME}' not found in the configuration file."
        logging.critical(error_msg)
        console.print(f"[bold red]{error_msg}[/bold red]")
        raise Exception(error_msg)

    sheet = workbook[REGRESSION_CHECK_TABLE_NAME]
    headers = [cell.value for cell in sheet[1]]

    temp_config = []

    for row_index, row in enumerate(sheet.iter_rows(min_row=2, values_only=True), start=2):
        rule_config = dict(zip(headers, row))

        # Skip if ignore column contains "ignore" (case insensitive)
        ignore_value = rule_config.get("ignore")
        if ignore_value and isinstance(ignore_value, str) and "ignore" in ignore_value.lower():
            continue

        # Skip if all columns are None (empty row)
        if all(value is None for value in row):
            continue

        # Validate bloc_title and line_label
        bloc_title = rule_config.get("bloc_title")
        line_label = rule_config.get("line_label")

        if not bloc_title or not isinstance(bloc_title, str):
            continue  # Skip rows without bloc_title (header separators, etc.)

        if not line_label or not isinstance(line_label, str):
            error_msg = f"Error in Regression_Check config, row {row_index}: 'line_label' is mandatory when 'bloc_title' is specified."
            logging.critical(error_msg)
            console.print(f"[bold red]{error_msg}[/bold red]")
            raise Exception(error_msg)

        # Validate thresholds
        warning_threshold = rule_config.get("warning_threshold")
        critical_threshold = rule_config.get("critical_threshold")

        if warning_threshold is None or not isinstance(warning_threshold, (int, float)) or warning_threshold < 0:
            error_msg = f"Error in Regression_Check config, row {row_index}: 'warning_threshold' must be a number >= 0."
            logging.critical(error_msg)
            console.print(f"[bold red]{error_msg}[/bold red]")
            raise Exception(error_msg)

        if critical_threshold is None or not isinstance(critical_threshold, (int, float)) or critical_threshold < 0:
            error_msg = f"Error in Regression_Check config, row {row_index}: 'critical_threshold' must be a number >= 0."
            logging.critical(error_msg)
            console.print(f"[bold red]{error_msg}[/bold red]")
            raise Exception(error_msg)

        # Parse JSON fields
        for json_field in ["field_selection", "transitions"]:
            value = rule_config.get(json_field)
            if value and isinstance(value, str):
                try:
                    rule_config[json_field] = json.loads(value)
                except json.JSONDecodeError:
                    error_msg = f"Error in Regression_Check config, row {row_index}, field '{json_field}': Invalid JSON format."
                    logging.critical(error_msg)
                    console.print(f"[bold red]{error_msg}[/bold red]")
                    raise Exception(error_msg)
            elif value is None:
                rule_config[json_field] = None

        # Validate field_selection format
        field_selection = rule_config.get("field_selection")

        # Special rules that don't use field_selection
        special_rules_no_selection = ["New Fields", "Deleted Fields", "Deleted Requests"]

        if line_label not in special_rules_no_selection:
            # Standard rules and "New Requests" MUST have field_selection
            if field_selection is None:
                error_msg = f"Error in Regression_Check config, row {row_index}: 'field_selection' is mandatory for rule '{line_label}'."
                logging.critical(error_msg)
                console.print(f"[bold red]{error_msg}[/bold red]")
                raise Exception(error_msg)

            if not isinstance(field_selection, list):
                console.print(f"[yellow]⚠ Row {row_index}: 'field_selection' must be a JSON array of [action, selector] pairs, skipping rule[/yellow]")
                rule_config["_config_error"] = True
            else:
                for step_idx, step in enumerate(field_selection):
                    if not isinstance(step, list) or len(step) != 2:
                        console.print(f"[yellow]⚠ Row {row_index}: field_selection[{step_idx}] must be array of 2 elements [action, selector], skipping rule[/yellow]")
                        rule_config["_config_error"] = True
                        break

                    action, field_selector = step

                    if action not in ["include", "exclude"]:
                        console.print(f"[yellow]⚠ Row {row_index}: field_selection[{step_idx}] action must be 'include' or 'exclude', got '{action}', skipping rule[/yellow]")
                        rule_config["_config_error"] = True
                        break

                    if not isinstance(field_selector, str) or "." not in field_selector:
                        console.print(f"[yellow]⚠ Row {row_index}: field_selection[{step_idx}] selector must be string with dot notation (e.g., '*.*', 'group.*', 'group.field'), got '{field_selector}', skipping rule[/yellow]")
                        rule_config["_config_error"] = True
                        break
        else:
            if field_selection is not None and field_selection != [] and field_selection != "":
                console.print(f"[yellow]⚠ Row {row_index}: Special rule '{line_label}' should have empty field_selection, got {field_selection}[/yellow]")
                rule_config["_config_error"] = True

        # Validate bloc_scope
        bloc_scope = rule_config.get("bloc_scope")
        if bloc_scope is not None and bloc_scope not in ["all", "any"]:
            error_msg = f"Error in Regression_Check config, row {row_index}: 'bloc_scope' must be 'all' or 'any'."
            logging.critical(error_msg)
            console.print(f"[bold red]{error_msg}[/bold red]")
            raise Exception(error_msg)

        # Validate transitions format
        transitions = rule_config.get("transitions")
        config_error = False

        if transitions is not None:
            if not isinstance(transitions, list):
                console.print(f"[yellow]⚠ Row {row_index}: 'transitions' must be a JSON array, skipping this rule[/yellow]")
                config_error = True
            else:
                for step_idx, transition_step in enumerate(transitions):
                    if not isinstance(transition_step, list) or len(transition_step) != 4:
                        console.print(f"[yellow]⚠ Row {row_index}: transitions[{step_idx}] must be array of 4 elements [action, field_selector, from, to], skipping[/yellow]")
                        config_error = True
                        break

                    action, field_selector, from_val, to_val = transition_step

                    if action not in ["include", "exclude"]:
                        console.print(f"[yellow]⚠ Row {row_index}: transitions[{step_idx}] action must be 'include' or 'exclude', got '{action}', skipping[/yellow]")
                        config_error = True
                        break

                    if not isinstance(field_selector, str) or "." not in field_selector:
                        console.print(f"[yellow]⚠ Row {row_index}: transitions[{step_idx}] field_selector must be string with dot notation, got '{field_selector}', skipping[/yellow]")
                        config_error = True
                        break

        if config_error:
            rule_config["_config_error"] = True

        temp_config.append(rule_config)

    regression_check_config = temp_config
    console.print(f"Loaded {len(regression_check_config)} regression check rules.", style="green")


def run_check_only_mode(sys_argv):
    """
    Orchestrates CHECK_ONLY and CHECK_ONLY_COMPARE modes.

    - CHECK_ONLY: Full non-regression validation on existing files
    - CHECK_ONLY_COMPARE: Regression-only comparison of two specific files

    Args:
        sys_argv: sys.argv from main script (to parse command-line arguments)
    """
    global console

    if console is None:
        console = Console()

    print()

    # Detect CHECK_ONLY_COMPARE mode: --check-only <file1> <file2>
    if len(sys_argv) >= 4:
        current_file = sys_argv[2]
        old_file = sys_argv[3]

        console.print("[bold cyan]═══ CHECK ONLY COMPARE MODE ═══[/bold cyan]")
        console.print(f"Comparing two specific files:\n")
        console.print(f"  Current: [bold]{current_file}[/bold]")
        console.print(f"  Old:     [bold]{old_file}[/bold]\n")

        print()
        load_regression_check_config(console)

        print()
        has_regression_critical = run_quality_checks(
            current_requests=current_file,
            old_requests_filename=old_file
        )

        if has_regression_critical:
            console.print("[bold red]✗ CRITICAL issues detected![/bold red]")
        else:
            console.print("[bold green]✓ All checks passed successfully![/bold green]")

    else:
        console.print("[bold cyan]═══ CHECK ONLY MODE ═══[/bold cyan]")
        console.print("Running quality checks on existing data files without collecting new data.\n")

        print()
        load_regression_check_config(console)

        print()
        old_requests_file = _get_old_filename(REQUESTS_FILE_NAME, OLD_FILE_SUFFIX)
        has_regression_critical = run_quality_checks(
            current_requests=REQUESTS_FILE_NAME,
            old_requests_filename=old_requests_file
        )

        if has_regression_critical:
            console.print("[bold red]✗ CRITICAL issues detected![/bold red]")
        else:
            console.print("[bold green]✓ All checks passed successfully![/bold green]")


# ============================================================================
# FILE UTILITIES
# ============================================================================

def load_json_file(filename):
    """
    Loads a JSON file (requests, organizations, or any JSON data).
    Returns the parsed JSON data or None if file doesn't exist or error occurred.
    """
    if os.path.exists(filename):
        try:
            with open(filename, 'r', encoding='utf-8') as f:
                return json.load(f)
        except Exception as e:
            logging.warning(f"Could not load JSON file '{filename}': {e}")
            console.print(f"[yellow]⚠ Warning: Could not load JSON file '{filename}': {e}[/yellow]")
    return None


def backup_output_files():
    """
    Silently backups current output files before writing new versions.
    Called AFTER all checks pass to avoid losing history on crash.
    """
    def _backup_file_silent(source, destination):
        if os.path.exists(source):
            try:
                shutil.copy2(source, destination)
            except Exception as e:
                logging.warning(f"Could not backup {source}: {e}")

    _backup_file_silent(REQUESTS_FILE_NAME, _get_old_filename(REQUESTS_FILE_NAME, OLD_FILE_SUFFIX))
    _backup_file_silent(ORGANIZATIONS_FILE_NAME, _get_old_filename(ORGANIZATIONS_FILE_NAME, OLD_FILE_SUFFIX))


# ============================================================================
# QUALITY CHECKS ORCHESTRATION
# ============================================================================

def run_quality_checks(current_requests, old_requests_filename):
    """
    Runs non-regression quality checks on requests data.

    Note: Coherence check is not applicable for DO Dashboard since organization
    counters are computed from request details, not from a separate API.

    Args:
        current_requests: Either a filename (str) to load requests from,
                          or a list of request dictionaries (already in memory)
        old_requests_filename: Filename of old requests for regression comparison (str)

    Returns:
        has_regression_critical (bool)

    Usage:
        - Normal mode:
          run_quality_checks(
              current_requests=output_requests,       # list (in memory)
              old_requests_filename=REQUESTS_FILE_NAME  # str (current file on disk)
          )

        - Check-only mode:
          run_quality_checks(
              current_requests=REQUESTS_FILE_NAME,    # str (current file)
              old_requests_filename=get_old_filename(REQUESTS_FILE_NAME)  # str (old file)
          )
    """
    global console, regression_check_config

    if not regression_check_config:
        if console is None:
            console = Console()
        load_regression_check_config(console)

    console.print("[bold cyan]══════════════════════════════════════════════════[/bold cyan]")

    # Load current_requests if it's a filename
    if isinstance(current_requests, str):
        current_requests_data = load_json_file(current_requests)
        if current_requests_data is None:
            console.print(f"[bold red]Error: Could not load current requests from '{current_requests}'[/bold red]")
            return True
    elif isinstance(current_requests, list):
        current_requests_data = current_requests
    else:
        console.print(f"[bold red]Error: current_requests must be either a filename (str) or a list of requests[/bold red]")
        return True

    # Run non-regression check
    has_regression_critical = non_regression_check(current_requests_data, old_requests_filename)

    console.print("[bold cyan]══════════════════════════════════════════════════[/bold cyan]")
    print()

    return has_regression_critical


# ============================================================================
# NON-REGRESSION CHECK
# ============================================================================

def non_regression_check(output_requests, old_requests_filename):
    """
    Comprehensive config-driven non-regression check comparing current vs old requests.
    Uses rules from regression_check_config loaded from Excel.
    Returns True if any critical issue was found, False otherwise.

    Args:
        output_requests: Current requests data (list)
        old_requests_filename: Filename of old requests JSON file to load
    """
    console.print("\n[bold]═══ Non Regression Check ═══[/bold]\n")

    console.print(f"[dim]Loading old requests from: {old_requests_filename}[/dim]")
    old_requests = load_json_file(old_requests_filename)

    if old_requests is None:
        console.print(f"[yellow]⚠ No old requests file found at '{old_requests_filename}', skipping non-regression check[/yellow]")
        return False

    has_critical = False

    # ========== INTERNAL UTILITY FUNCTIONS ==========

    def _is_undefined(value):
        return value in [None, "", "undefined"]

    def _values_are_equal(val1, val2):
        if _is_undefined(val1) and _is_undefined(val2):
            return True
        return val1 == val2

    def _apply_pipeline_step(checked_fields, action, field_selector, from_pattern, to_pattern):
        for i, field_record in enumerate(checked_fields):
            group_name, field_name, old_val, new_val, is_checked = field_record
            if not _field_selector_matches_pattern(field_selector, group_name, field_name):
                continue
            if _transition_matches(old_val, new_val, from_pattern, to_pattern):
                if action == "include":
                    checked_fields[i][4] = True
                elif action == "exclude":
                    checked_fields[i][4] = False

    def _transition_matches(old_val, new_val, expected_old, expected_new):
        if expected_old == "*undefined":
            old_matches = old_val in [None, "", "undefined"]
        elif expected_old == "*defined":
            old_matches = old_val not in [None, "", "undefined"]
        elif expected_old == "*":
            old_matches = True
        else:
            old_matches = (old_val == expected_old)

        if expected_new == "*undefined":
            new_matches = new_val in [None, "", "undefined"]
        elif expected_new == "*defined":
            new_matches = new_val not in [None, "", "undefined"]
        elif expected_new == "*":
            new_matches = True
        else:
            new_matches = (new_val == expected_new)

        return old_matches and new_matches

    def _get_status_and_style(count, warning_threshold, critical_threshold):
        nonlocal has_critical
        if count > critical_threshold:
            has_critical = True
            return "CRITICAL", "red", "✗"
        elif count > warning_threshold:
            return "WARNING", "yellow", "⚠"
        else:
            return "OK", "green", "✓"

    def _print_block_header(title, status_tuple, indent=0):
        indent_str = "  " * indent
        status, color, emoji = status_tuple
        console.print(f"{indent_str}{emoji} [{color}][bold]{title}[/bold][/{color}]")

    def _print_check_line(message, count, status_tuple, indent=1):
        indent_str = "  " * indent
        status, color, emoji = status_tuple
        console.print(f"{indent_str}{emoji} [{color}]{message}: {count}[/{color}]")

    def _calculate_block_status(line_statuses):
        if any(s[0] == "CRITICAL" for s in line_statuses):
            return ("CRITICAL", "red", "✗")
        elif any(s[0] == "WARNING" for s in line_statuses):
            return ("WARNING", "yellow", "⚠")
        else:
            return ("OK", "green", "✓")

    def _field_selector_matches_pattern(selector, group_name, field_name):
        if selector == "*.*":
            return True
        sel_group, sel_field = selector.split(".", 1)
        if sel_group != "*" and sel_group != group_name:
            return False
        if sel_field == "*":
            return True
        return sel_field == field_name

    def _apply_field_selection_pipeline(all_fields, field_selection_config):
        candidate_fields = set()
        if not field_selection_config:
            return candidate_fields
        for action, field_selector in field_selection_config:
            for group_name, field_name in all_fields:
                if _field_selector_matches_pattern(field_selector, group_name, field_name):
                    if action == "include":
                        candidate_fields.add((group_name, field_name))
                    elif action == "exclude":
                        candidate_fields.discard((group_name, field_name))
        return candidate_fields

    def _get_key_field_from_new_requests_rule(rule, new_requests_list, old_requests_list):
        if not new_requests_list or not old_requests_list:
            raise ValueError("Cannot determine key field: empty request lists")

        new_req = new_requests_list[0]
        old_req = old_requests_list[0]

        candidate_fields = _build_candidate_fields(new_req, old_req, rule.get("field_selection"))

        if not candidate_fields:
            raise ValueError(
                f"field_selection produced no candidate fields. "
                f"Config: {rule.get('field_selection')}"
            )

        for group_name, field_name in sorted(candidate_fields):
            new_val = get_nested_value(new_req, [group_name, field_name])
            old_val = get_nested_value(old_req, [group_name, field_name])
            if new_val is not None and old_val is not None:
                return field_name, group_name

        raise ValueError(
            f"No field in field_selection has values in both first new and old request. "
            f"Candidates from pipeline: {candidate_fields}. "
            f"Verify field_selection config or data has proper values."
        )

    def _build_requests_dict(requests_list, key_field, field_group):
        result = {}
        for request in requests_list:
            key = get_nested_value(request, [field_group, key_field])
            if key:
                result[key] = request
        return result

    def _matches_transition(old_val, new_val, transitions_config):
        if transitions_config is None:
            return False
        for transition in transitions_config:
            expected_old, expected_new = transition
            if _transition_matches(old_val, new_val, expected_old, expected_new):
                return True
        return False

    def _process_special_rule(rule, line_label, new_dict, old_dict):
        if line_label == "New Requests":
            return len(set(new_dict.keys()) - set(old_dict.keys()))
        elif line_label == "Deleted Requests":
            return len(set(old_dict.keys()) - set(new_dict.keys()))
        else:
            return 0

    def _process_new_deleted_fields(line_label, new_dict, old_dict):
        field_counts = {}
        common_keys = sorted(set(new_dict.keys()) & set(old_dict.keys()))

        for key in common_keys:
            new_req = new_dict[key]
            old_req = old_dict[key]

            all_groups = sorted(set(new_req.keys()) | set(old_req.keys()))

            for group_name in all_groups:
                new_group = new_req.get(group_name, {})
                old_group = old_req.get(group_name, {})

                if not isinstance(new_group, dict):
                    new_group = {}
                if not isinstance(old_group, dict):
                    old_group = {}

                new_fields = set(new_group.keys())
                old_fields = set(old_group.keys())

                if line_label == "New Fields":
                    changed_fields = sorted(new_fields - old_fields)
                elif line_label == "Deleted Fields":
                    changed_fields = sorted(old_fields - new_fields)
                else:
                    changed_fields = []

                for field_name in changed_fields:
                    qualified_name = f"{group_name}.{field_name}"
                    field_counts[qualified_name] = field_counts.get(qualified_name, 0) + 1

        return sorted(field_counts.items(), key=lambda x: (-x[1], x[0]))

    def _build_candidate_fields(new_req, old_req, field_selection_config):
        common_groups = sorted(set(new_req.keys()) & set(old_req.keys()))
        all_available_fields = []

        for group_name in common_groups:
            new_group = new_req.get(group_name, {})
            old_group = old_req.get(group_name, {})

            if not isinstance(new_group, dict):
                new_group = {}
            if not isinstance(old_group, dict):
                old_group = {}

            common_field_names = sorted(set(new_group.keys()) & set(old_group.keys()))
            for field_name in common_field_names:
                all_available_fields.append((group_name, field_name))

        if not field_selection_config:
            return []

        candidate_fields = _apply_field_selection_pipeline(all_available_fields, field_selection_config)
        return sorted(candidate_fields, key=lambda x: (x[0], x[1]))

    def _process_rule(rule, new_dict, old_dict):
        if rule.get("_config_error"):
            return 0, []

        field_selection_config = rule.get("field_selection")
        bloc_scope = rule.get("bloc_scope") or "any"

        common_keys = sorted(set(new_dict.keys()) & set(old_dict.keys()))
        matching_requests_count = 0
        details_list = []

        for key in common_keys:
            new_req = new_dict[key]
            old_req = old_dict[key]

            candidate_fields = _build_candidate_fields(new_req, old_req, field_selection_config)

            if not candidate_fields:
                continue

            all_fields_list = []
            changed_fields = []

            for group_name, field_name in candidate_fields:
                new_val = get_nested_value(new_req, [group_name, field_name])
                old_val = get_nested_value(old_req, [group_name, field_name])

                field_has_changed = not _values_are_equal(old_val, new_val)
                if field_has_changed:
                    changed_fields.append((group_name, field_name))
                    all_fields_list.append([group_name, field_name, old_val, new_val, False])

            transitions_config = rule.get("transitions", [])
            if transitions_config and isinstance(transitions_config, list):
                for action, field_selector, from_val, to_val in transitions_config:
                    _apply_pipeline_step(all_fields_list, action, field_selector, from_val, to_val)

            checked_fields = [(f[0], f[1], f[2], f[3]) for f in all_fields_list if f[4]]

            inclusion_matches = False
            if bloc_scope == "all":
                if len(changed_fields) > 0 and len(checked_fields) == len(changed_fields):
                    inclusion_matches = True
            else:  # bloc_scope == "any"
                if len(checked_fields) > 0:
                    inclusion_matches = True

            if inclusion_matches:
                matching_requests_count += 1
                if debug_mode and checked_fields:
                    field_changes = [(f"{gn}.{fn}", ov, nv) for gn, fn, ov, nv in checked_fields]
                    details_list.append((key, field_changes))

        return matching_requests_count, details_list

    # ========== MAIN LOGIC ==========

    key_field = None
    field_group = None

    for rule in regression_check_config:
        if rule.get("line_label") == "New Requests":
            try:
                key_field, field_group = _get_key_field_from_new_requests_rule(
                    rule,
                    output_requests,
                    old_requests
                )
                break
            except ValueError as e:
                console.print(f"[bold red]Error determining key field: {e}[/bold red]")
                return True

    if not key_field:
        console.print("[bold red]Error: 'New Requests' rule not found or has no valid field_selection[/bold red]")
        return True

    console.print(f"[dim]Using key field: {field_group}.{key_field}[/dim]\n")

    new_dict = _build_requests_dict(output_requests, key_field, field_group)
    old_dict = _build_requests_dict(old_requests, key_field, field_group)

    # Group rules by bloc_title, preserving order of first appearance
    blocs = {}
    bloc_order = []
    for rule in regression_check_config:
        bloc_title = rule["bloc_title"]
        if bloc_title not in blocs:
            blocs[bloc_title] = []
            bloc_order.append(bloc_title)
        blocs[bloc_title].append(rule)

    for bloc_title in bloc_order:
        rules = blocs[bloc_title]
        line_results = []

        for rule in rules:
            line_label = rule["line_label"]

            if line_label in ["New Requests", "Deleted Requests"]:
                count = _process_special_rule(rule, line_label, new_dict, old_dict)
                line_results.append((line_label, count, None, "simple"))

            elif line_label in ["New Fields", "Deleted Fields"]:
                field_list = _process_new_deleted_fields(line_label, new_dict, old_dict)
                count = len(field_list)
                line_results.append((line_label, count, field_list, "fields"))

            else:
                count, details = _process_rule(rule, new_dict, old_dict)
                line_results.append((line_label, count, details, "details"))

        # Calculate status for each line
        line_results_with_status = []
        for line_label, count, data, result_type in line_results:
            rule = next(r for r in rules if r["line_label"] == line_label)
            warning_threshold = rule["warning_threshold"]
            critical_threshold = rule["critical_threshold"]
            status_tuple = _get_status_and_style(count, warning_threshold, critical_threshold)
            line_results_with_status.append((line_label, count, data, result_type, status_tuple))

        bloc_status = _calculate_block_status([result[4] for result in line_results_with_status])
        _print_block_header(bloc_title, bloc_status, indent=0)

        for line_label, count, data, result_type, status_tuple in line_results_with_status:
            should_display = (bloc_title == "Structure") or (status_tuple[0] != "OK")

            if should_display:
                if result_type == "fields":
                    _print_check_line(line_label, count, status_tuple, indent=1)
                    for field_name, request_count in data:
                        console.print(f"    {field_name} ({request_count} requests)")

                elif result_type == "details":
                    _print_check_line(line_label, count, status_tuple, indent=1)
                    if debug_mode and data and len(data) > 0:
                        for request_key, field_changes in data:
                            console.print(f"      [dim]{key_field}: {request_key}[/dim]")
                            for qualified_field, old_val, new_val in field_changes:
                                old_display = f"'{old_val}'" if isinstance(old_val, str) else str(old_val)
                                new_display = f"'{new_val}'" if isinstance(new_val, str) else str(new_val)
                                console.print(f"        - {qualified_field}: {old_display} → {new_display}")

                else:
                    _print_check_line(line_label, count, status_tuple, indent=1)

        console.print()

    return has_critical