ChristianLempa_Boilerplates/cli/core/collection.py

from __future__ import annotations

from collections import defaultdict
from typing import Any, Dict, List, Optional, Set, Union
import logging

from .variable import Variable
from .section import VariableSection

logger = logging.getLogger(__name__)


class VariableCollection:
  """Manages variables grouped by sections and builds Jinja context."""

  def __init__(self, spec: dict[str, Any]) -> None:
    """Initialize VariableCollection from a specification dictionary.
    
    Args:
        spec: Dictionary containing the complete variable specification structure
              Expected format (as used in compose.py):
              {
                "section_key": {
                  "title": "Section Title",
                  "prompt": "Optional prompt text",
                  "toggle": "optional_toggle_var_name", 
                  "description": "Optional description",
                  "vars": {
                    "var_name": {
                      "description": "Variable description",
                      "type": "str",
                      "default": "default_value",
                      ...
                    }
                  }
                }
              }
    """
    if not isinstance(spec, dict):
      raise ValueError("Spec must be a dictionary")
    
    self._sections: Dict[str, VariableSection] = {}
    # NOTE: The _variable_map provides a flat, O(1) lookup for any variable by its name,
    # avoiding the need to iterate through sections. It stores references to the same
    # Variable objects contained in the _set structure.
    self._variable_map: Dict[str, Variable] = {}
    self._initialize_sections(spec)
    # Validate dependencies after all sections are loaded
    self._validate_dependencies()

  def _initialize_sections(self, spec: dict[str, Any]) -> None:
    """Initialize sections from the spec."""
    for section_key, section_data in spec.items():
      if not isinstance(section_data, dict):
        continue
      
      section = self._create_section(section_key, section_data)
      # Guard against None from empty YAML sections (vars: with no content)
      vars_data = section_data.get("vars") or {}
      self._initialize_variables(section, vars_data)
      self._sections[section_key] = section
    
    # Validate all variable names are unique across sections
    self._validate_unique_variable_names()

  def _create_section(self, key: str, data: dict[str, Any]) -> VariableSection:
    """Create a VariableSection from data."""
    section_init_data = {
      "key": key,
      "title": data.get("title", key.replace("_", " ").title()),
      "description": data.get("description"),
      "toggle": data.get("toggle"),
      "required": data.get("required", key == "general"),
      "needs": data.get("needs")
    }
    return VariableSection(section_init_data)

  def _initialize_variables(self, section: VariableSection, vars_data: dict[str, Any]) -> None:
    """Initialize variables for a section."""
    # Guard against None from empty YAML sections
    if vars_data is None:
      vars_data = {}
    
    for var_name, var_data in vars_data.items():
      var_init_data = {"name": var_name, **var_data}
      variable = Variable(var_init_data)
      section.variables[var_name] = variable
      # NOTE: Populate the direct lookup map for efficient access.
      self._variable_map[var_name] = variable
    
    # Validate toggle variable after all variables are added
    self._validate_section_toggle(section)
    # TODO: Add more section-level validation:
    #   - Validate that required sections have at least one non-toggle variable
    #   - Validate that enum variables have non-empty options lists
    #   - Validate that variable names follow naming conventions (e.g., lowercase_with_underscores)
    #   - Validate that default values are compatible with their type definitions

  def _validate_unique_variable_names(self) -> None:
    """Validate that all variable names are unique across all sections."""
    var_to_sections: Dict[str, List[str]] = defaultdict(list)
    
    # Build mapping of variable names to sections
    for section_key, section in self._sections.items():
      for var_name in section.variables:
        var_to_sections[var_name].append(section_key)
    
    # Find duplicates and format error
    duplicates = {var: sections for var, sections in var_to_sections.items() if len(sections) > 1}
    
    if duplicates:
      errors = ["Variable names must be unique across all sections, but found duplicates:"]
      errors.extend(f"  - '{var}' appears in sections: {', '.join(secs)}" for var, secs in sorted(duplicates.items()))
      errors.append("\nPlease rename variables to be unique or consolidate them into a single section.")
      error_msg = "\n".join(errors)
      logger.error(error_msg)
      raise ValueError(error_msg)
  
  def _validate_section_toggle(self, section: VariableSection) -> None:
    """Validate that toggle variable is of type bool if it exists.
    
    If the toggle variable doesn't exist (e.g., filtered out), removes the toggle.
    
    Args:
        section: The section to validate
        
    Raises:
        ValueError: If toggle variable exists but is not boolean type
    """
    if not section.toggle:
      return
    
    toggle_var = section.variables.get(section.toggle)
    if not toggle_var:
      # Toggle variable doesn't exist (e.g., was filtered out) - remove toggle metadata
      section.toggle = None
      return
    
    if toggle_var.type != "bool":
      raise ValueError(
        f"Section '{section.key}' toggle variable '{section.toggle}' must be type 'bool', "
        f"but is type '{toggle_var.type}'"
      )
  
  def _validate_dependencies(self) -> None:
    """Validate section dependencies for cycles and missing references.
    
    Raises:
        ValueError: If circular dependencies or missing section references are found
    """
    # Check for missing dependencies
    for section_key, section in self._sections.items():
      for dep in section.needs:
        if dep not in self._sections:
          raise ValueError(
            f"Section '{section_key}' depends on '{dep}', but '{dep}' does not exist"
          )
    
    # Check for circular dependencies using depth-first search
    visited = set()
    rec_stack = set()
    
    def has_cycle(section_key: str) -> bool:
      visited.add(section_key)
      rec_stack.add(section_key)
      
      section = self._sections[section_key]
      for dep in section.needs:
        if dep not in visited:
          if has_cycle(dep):
            return True
        elif dep in rec_stack:
          raise ValueError(
            f"Circular dependency detected: '{section_key}' depends on '{dep}', "
            f"which creates a cycle"
          )
      
      rec_stack.remove(section_key)
      return False
    
    for section_key in self._sections:
      if section_key not in visited:
        has_cycle(section_key)
  
  def is_section_satisfied(self, section_key: str) -> bool:
    """Check if all dependencies for a section are satisfied.
    
    A dependency is satisfied if:
    1. The dependency section exists
    2. The dependency section is enabled (if it has a toggle)
    
    Args:
        section_key: The key of the section to check
        
    Returns:
        True if all dependencies are satisfied, False otherwise
    """
    section = self._sections.get(section_key)
    if not section:
      return False
    
    # No dependencies = always satisfied
    if not section.needs:
      return True
    
    # Check each dependency
    for dep_key in section.needs:
      dep_section = self._sections.get(dep_key)
      if not dep_section:
        logger.warning(f"Section '{section_key}' depends on missing section '{dep_key}'")
        return False
      
      # Check if dependency is enabled
      if not dep_section.is_enabled():
        logger.debug(f"Section '{section_key}' dependency '{dep_key}' is disabled")
        return False
    
    return True

  def sort_sections(self) -> None:
    """Sort sections with the following priority:
    
    1. Dependencies come before dependents (topological sort)
    2. Required sections first (in their original order)
    3. Enabled sections with satisfied dependencies next (in their original order)
    4. Disabled sections or sections with unsatisfied dependencies last (in their original order)
    
    This maintains the original ordering within each group while organizing
    sections logically for display and user interaction, and ensures that
    sections are prompted in the correct dependency order.
    """
    # First, perform topological sort to respect dependencies
    sorted_keys = self._topological_sort()
    
    # Then apply priority sorting within dependency groups
    section_items = [(key, self._sections[key]) for key in sorted_keys]
    
    # Define sort key: (priority, original_index)
    # Priority: 0 = required, 1 = enabled with satisfied dependencies, 2 = disabled or unsatisfied dependencies
    def get_sort_key(item_with_index):
      index, (key, section) = item_with_index
      if section.required:
        priority = 0
      elif section.is_enabled() and self.is_section_satisfied(key):
        priority = 1
      else:
        priority = 2
      return (priority, index)
    
    # Sort with original index to maintain order within each priority group
    # Note: This preserves the topological order from earlier
    sorted_items = sorted(
      enumerate(section_items),
      key=get_sort_key
    )
    
    # Rebuild _sections dict in new order
    self._sections = {key: section for _, (key, section) in sorted_items}
  
  def _topological_sort(self) -> List[str]:
    """Perform topological sort on sections based on dependencies using Kahn's algorithm."""
    in_degree = {key: len(section.needs) for key, section in self._sections.items()}
    queue = [key for key, degree in in_degree.items() if degree == 0]
    queue.sort(key=lambda k: list(self._sections.keys()).index(k))  # Preserve original order
    result = []
    
    while queue:
      current = queue.pop(0)
      result.append(current)
      
      # Update in-degree for dependent sections
      for key, section in self._sections.items():
        if current in section.needs:
          in_degree[key] -= 1
          if in_degree[key] == 0:
            queue.append(key)
    
    # Fallback to original order if cycle detected
    if len(result) != len(self._sections):
      logger.warning("Topological sort incomplete - using original order")
      return list(self._sections.keys())
    
    return result

  def get_sections(self) -> Dict[str, VariableSection]:
    """Get all sections in the collection."""
    return self._sections.copy()
  
  def get_section(self, key: str) -> Optional[VariableSection]:
    """Get a specific section by its key."""
    return self._sections.get(key)
  
  def has_sections(self) -> bool:
    """Check if the collection has any sections."""
    return bool(self._sections)

  def get_all_values(self) -> dict[str, Any]:
    """Get all variable values as a dictionary."""
    # NOTE: Uses _variable_map for O(1) access
    return {name: var.convert(var.value) for name, var in self._variable_map.items()}
  
  def get_satisfied_values(self) -> dict[str, Any]:
    """Get variable values only from sections with satisfied dependencies.
    
    This respects both toggle states and section dependencies, ensuring that:
    - Variables from disabled sections (toggle=false) are excluded
    - Variables from sections with unsatisfied dependencies are excluded
    
    Returns:
        Dictionary of variable names to values for satisfied sections only
    """
    satisfied_values = {}
    
    for section_key, section in self._sections.items():
      # Skip sections with unsatisfied dependencies
      if not self.is_section_satisfied(section_key):
        logger.debug(f"Excluding variables from section '{section_key}' - dependencies not satisfied")
        continue
      
      # Skip disabled sections (toggle check)
      if not section.is_enabled():
        logger.debug(f"Excluding variables from section '{section_key}' - section is disabled")
        continue
      
      # Include all variables from this satisfied section
      for var_name, variable in section.variables.items():
        satisfied_values[var_name] = variable.convert(variable.value)
    
    return satisfied_values

  def get_sensitive_variables(self) -> Dict[str, Any]:
    """Get only the sensitive variables with their values."""
    return {name: var.value for name, var in self._variable_map.items() if var.sensitive and var.value}

  def apply_defaults(self, defaults: dict[str, Any], origin: str = "cli") -> list[str]:
    """Apply default values to variables, updating their origin.
    
    Args:
        defaults: Dictionary mapping variable names to their default values
        origin: Source of these defaults (e.g., 'config', 'cli')
        
    Returns:
        List of variable names that were successfully updated
    """
    # NOTE: This method uses the _variable_map for a significant performance gain,
    # as it allows direct O(1) lookup of variables instead of iterating
    # through all sections to find a match.
    successful = []
    errors = []
    
    for var_name, value in defaults.items():
      try:
        variable = self._variable_map.get(var_name)
        if not variable:
          logger.warning(f"Variable '{var_name}' not found in template")
          continue
        
        # Store original value before overriding (for display purposes)
        # Only store if this is the first time config is being applied
        if origin == "config" and not hasattr(variable, '_original_stored'):
          variable.original_value = variable.value
          variable._original_stored = True
        
        # Convert and set the new value
        converted_value = variable.convert(value)
        variable.value = converted_value
        
        # Set origin to the current source (not a chain)
        variable.origin = origin
        
        successful.append(var_name)
          
      except ValueError as e:
        error_msg = f"Invalid value for '{var_name}': {value} - {e}"
        errors.append(error_msg)
        logger.error(error_msg)
    
    if errors:
      logger.warning(f"Some defaults failed to apply: {'; '.join(errors)}")
    
    return successful
  
  def validate_all(self) -> None:
    """Validate all variables in the collection, skipping disabled and unsatisfied sections."""
    errors: list[str] = []

    for section_key, section in self._sections.items():
      # Skip sections with unsatisfied dependencies or disabled via toggle
      if not self.is_section_satisfied(section_key) or not section.is_enabled():
        logger.debug(f"Skipping validation for section '{section_key}'")
        continue

      # Validate each variable in the section
      for var_name, variable in section.variables.items():
        try:
          # Skip autogenerated variables when empty
          if variable.autogenerated and not variable.value:
            continue
          
          # Check required fields
          if variable.value is None:
            if variable.is_required():
              errors.append(f"{section.key}.{var_name} (required - no default provided)")
            continue

          # Validate typed value
          typed = variable.convert(variable.value)
          if variable.type not in ("bool",) and not typed:
            msg = f"{section.key}.{var_name}"
            errors.append(f"{msg} (required - cannot be empty)" if variable.is_required() else f"{msg} (empty)")

        except ValueError as e:
          errors.append(f"{section.key}.{var_name} (invalid format: {e})")

    if errors:
      error_msg = "Variable validation failed: " + ", ".join(errors)
      logger.error(error_msg)
      raise ValueError(error_msg)

  def merge(self, other_spec: Union[Dict[str, Any], 'VariableCollection'], origin: str = "override") -> 'VariableCollection':
    """Merge another spec or VariableCollection into this one with precedence tracking.
    
    OPTIMIZED: Works directly on objects without dict conversions for better performance.
    
    The other spec/collection has higher precedence and will override values in self.
    Creates a new VariableCollection with merged data.
    
    Args:
        other_spec: Either a spec dictionary or another VariableCollection to merge
        origin: Origin label for variables from other_spec (e.g., 'template', 'config')
        
    Returns:
        New VariableCollection with merged data
        
    Example:
        module_vars = VariableCollection(module_spec)
        template_vars = module_vars.merge(template_spec, origin='template')
        # Variables from template_spec override module_spec
        # Origins tracked: 'module' or 'module -> template'
    """
    # Convert dict to VariableCollection if needed (only once)
    if isinstance(other_spec, dict):
      other = VariableCollection(other_spec)
    else:
      other = other_spec
    
    # Create new collection without calling __init__ (optimization)
    merged = VariableCollection.__new__(VariableCollection)
    merged._sections = {}
    merged._variable_map = {}
    
    # First pass: clone sections from self
    for section_key, self_section in self._sections.items():
      if section_key in other._sections:
        # Section exists in both - will merge
        merged._sections[section_key] = self._merge_sections(
          self_section, 
          other._sections[section_key], 
          origin
        )
      else:
        # Section only in self - clone it
        merged._sections[section_key] = self_section.clone()
    
    # Second pass: add sections that only exist in other
    for section_key, other_section in other._sections.items():
      if section_key not in merged._sections:
        # New section from other - clone with origin update
        merged._sections[section_key] = other_section.clone(origin_update=origin)
    
    # Rebuild variable map for O(1) lookups
    for section in merged._sections.values():
      for var_name, variable in section.variables.items():
        merged._variable_map[var_name] = variable
    
    return merged
  
  def _merge_sections(self, self_section: VariableSection, other_section: VariableSection, origin: str) -> VariableSection:
    """Merge two sections, with other_section taking precedence."""
    merged_section = self_section.clone()
    
    # Update section metadata from other (other takes precedence)
    for attr in ('title', 'description', 'toggle'):
      if getattr(other_section, attr):
        setattr(merged_section, attr, getattr(other_section, attr))
    
    merged_section.required = other_section.required
    if other_section.needs:
      merged_section.needs = other_section.needs.copy()
    
    # Merge variables
    for var_name, other_var in other_section.variables.items():
      if var_name in merged_section.variables:
        # Variable exists in both - merge with other taking precedence
        self_var = merged_section.variables[var_name]
        
        # Build update dict with ONLY explicitly provided fields from other
        update = {'origin': origin}
        field_map = {
          'type': other_var.type,
          'description': other_var.description,
          'prompt': other_var.prompt,
          'options': other_var.options,
          'sensitive': other_var.sensitive,
          'extra': other_var.extra
        }
        
        # Add fields that were explicitly provided and have values
        for field, value in field_map.items():
          if field in other_var._explicit_fields and value:
            update[field] = value
        
        # Special handling for value/default
        if ('value' in other_var._explicit_fields or 'default' in other_var._explicit_fields) and other_var.value is not None:
          update['value'] = other_var.value
        
        merged_section.variables[var_name] = self_var.clone(update=update)
      else:
        # New variable from other - clone with origin
        merged_section.variables[var_name] = other_var.clone(update={'origin': origin})
    
    return merged_section
  
  def filter_to_used(self, used_variables: Set[str], keep_sensitive: bool = True) -> 'VariableCollection':
    """Filter collection to only variables that are used (or sensitive).
    
    OPTIMIZED: Works directly on objects without dict conversions for better performance.
    
    Creates a new VariableCollection containing only the variables in used_variables.
    Sections with no remaining variables are removed.
    
    Args:
        used_variables: Set of variable names that are actually used
        keep_sensitive: If True, also keep sensitive variables even if not in used set
        
    Returns:
        New VariableCollection with filtered variables
        
    Example:
        all_vars = VariableCollection(spec)
        used_vars = all_vars.filter_to_used({'var1', 'var2', 'var3'})
        # Only var1, var2, var3 (and any sensitive vars) remain
    """
    # Create new collection without calling __init__ (optimization)
    filtered = VariableCollection.__new__(VariableCollection)
    filtered._sections = {}
    filtered._variable_map = {}
    
    # Filter each section
    for section_key, section in self._sections.items():
      # Create a new section with same metadata
      filtered_section = VariableSection({
        'key': section.key,
        'title': section.title,
        'description': section.description,
        'toggle': section.toggle,
        'required': section.required,
        'needs': section.needs.copy() if section.needs else None,
      })
      
      # Clone only the variables that should be included
      for var_name, variable in section.variables.items():
        # Include if used OR if sensitive (and keep_sensitive is True)
        should_include = (
          var_name in used_variables or 
          (keep_sensitive and variable.sensitive)
        )
        
        if should_include:
          filtered_section.variables[var_name] = variable.clone()
      
      # Only add section if it has variables
      if filtered_section.variables:
        filtered._sections[section_key] = filtered_section
        # Add variables to map
        for var_name, variable in filtered_section.variables.items():
          filtered._variable_map[var_name] = variable
    
    return filtered
  
  def get_all_variable_names(self) -> Set[str]:
    """Get set of all variable names across all sections.
    
    Returns:
        Set of all variable names
    """
    return set(self._variable_map.keys())