Remotion Video Generator

# chuk-motion/src/chuk_motion/tokens/token_manager.py """ Token Manager for individual token type management. Provides import/export and customization for: - Typography tokens (font families, sizes, weights, styles) - Color tokens (palettes, themes) - Motion tokens (springs, easings, durations, presets) """ import contextlib import json from typing import TYPE_CHECKING, Any if TYPE_CHECKING: from chuk_virtual_fs import AsyncVirtualFileSystem from .colors import COLOR_TOKENS from .motion import MOTION_TOKENS from .spacing import SPACING_TOKENS from .typography import TYPOGRAPHY_TOKENS class TokenManager: """ Manages design tokens with import/export capabilities. Allows customization and sharing of token sets across projects. """ def __init__(self, vfs: "AsyncVirtualFileSystem"): """ Initialize token manager with default tokens. Args: vfs: Virtual filesystem for file operations """ self.vfs = vfs self.custom_typography_tokens: dict[str, Any] = {} self.custom_color_tokens: dict[str, Any] = {} self.custom_motion_tokens: dict[str, Any] = {} self.custom_spacing_tokens: dict[str, Any] = {} # ======================================================================== # TYPOGRAPHY TOKEN MANAGEMENT # ======================================================================== async def export_typography_tokens( self, file_path: str | None = None, include_all: bool = True, font_families_only: bool = False, text_styles_only: bool = False, ) -> str: """ Export typography tokens to JSON file. Args: file_path: Output file path (default: typography_tokens.json) include_all: Include all typography tokens (default: True) font_families_only: Export only font families text_styles_only: Export only text styles Returns: Path to exported file or error message """ if not file_path: file_path = "typography_tokens.json" try: # Build export data export_data = {} if include_all: export_data = TYPOGRAPHY_TOKENS.model_dump() else: if font_families_only: export_data["font_families"] = TYPOGRAPHY_TOKENS.font_families.model_dump() if text_styles_only: export_data["text_styles"] = TYPOGRAPHY_TOKENS.text_styles.model_dump() # Add custom tokens if any if self.custom_typography_tokens: export_data["custom"] = self.custom_typography_tokens json_content = json.dumps(export_data, indent=2) # Write to virtual filesystem await self.vfs.write_file(file_path, json_content) return file_path except Exception as e: return f"Error exporting typography tokens: {str(e)}" async def import_typography_tokens(self, file_path: str, merge: bool = True) -> str: """ Import typography tokens from JSON file. Args: file_path: Path to JSON file merge: Merge with existing custom tokens (default: True) Returns: Success message or error """ try: # Read from virtual filesystem json_content = await self.vfs.read_text(file_path) imported_data = json.loads(json_content) # Validate structure if not isinstance(imported_data, dict): return "Error: Invalid typography token format" if merge: # Merge with existing custom tokens self.custom_typography_tokens.update(imported_data) else: # Replace custom tokens self.custom_typography_tokens = imported_data return f"Successfully imported typography tokens from {file_path}" except Exception as e: return f"Error importing typography tokens: {str(e)}" def get_typography_token( self, category: str, key: str | None = None, use_custom: bool = True ) -> Any | None: """ Get a typography token value. Args: category: Token category (font_families, font_sizes, etc.) key: Optional specific key within category use_custom: Check custom tokens first (default: True) Returns: Token value or None """ # Check custom tokens first if enabled if use_custom and category in self.custom_typography_tokens: custom_value = self.custom_typography_tokens[category] if key and isinstance(custom_value, dict): return custom_value.get(key) return custom_value # Fall back to default tokens if not hasattr(TYPOGRAPHY_TOKENS, category): return None value = getattr(TYPOGRAPHY_TOKENS, category) # Convert Pydantic model to dict for consistent access if hasattr(value, "model_dump"): value_dict = value.model_dump() if key: return value_dict.get(key) return value_dict if key and isinstance(value, dict): return value.get(key) return value # ======================================================================== # COLOR TOKEN MANAGEMENT # ======================================================================== async def export_color_tokens( self, file_path: str | None = None, theme_name: str | None = None ) -> str: """ Export color tokens to JSON file. Args: file_path: Output file path (default: color_tokens.json) theme_name: Export only specific theme (default: all themes) Returns: Path to exported file or error message """ if not file_path: file_path = "color_tokens.json" try: # Build export data if theme_name: if not hasattr(COLOR_TOKENS, theme_name): return f"Error: Theme '{theme_name}' not found" export_data = {theme_name: getattr(COLOR_TOKENS, theme_name).model_dump()} else: export_data = COLOR_TOKENS.model_dump() # Add custom tokens if any if self.custom_color_tokens: export_data["custom"] = self.custom_color_tokens json_content = json.dumps(export_data, indent=2) # Write to virtual filesystem await self.vfs.write_file(file_path, json_content) return file_path except Exception as e: return f"Error exporting color tokens: {str(e)}" async def import_color_tokens(self, file_path: str, merge: bool = True) -> str: """ Import color tokens from JSON file. Args: file_path: Path to JSON file merge: Merge with existing custom tokens (default: True) Returns: Success message or error """ try: # Read from virtual filesystem json_content = await self.vfs.read_text(file_path) imported_data = json.loads(json_content) # Validate structure if not isinstance(imported_data, dict): return "Error: Invalid color token format" if merge: self.custom_color_tokens.update(imported_data) else: self.custom_color_tokens = imported_data return f"Successfully imported color tokens from {file_path}" except Exception as e: return f"Error importing color tokens: {str(e)}" def get_color_token( self, theme_name: str, color_type: str | None = None, use_custom: bool = True ) -> Any | None: """ Get a color token value. Args: theme_name: Theme name color_type: Optional color type (primary, accent, etc.) use_custom: Check custom tokens first (default: True) Returns: Token value or None """ # Check custom tokens first if use_custom and theme_name in self.custom_color_tokens: custom_theme = self.custom_color_tokens[theme_name] if color_type: return custom_theme.get(color_type) return custom_theme # Fall back to default tokens if not hasattr(COLOR_TOKENS, theme_name): return None theme = getattr(COLOR_TOKENS, theme_name) theme_dict = theme.model_dump() if color_type: return theme_dict.get(color_type) return theme_dict # ======================================================================== # MOTION TOKEN MANAGEMENT # ======================================================================== async def export_motion_tokens( self, file_path: str | None = None, springs_only: bool = False, easings_only: bool = False, presets_only: bool = False, ) -> str: """ Export motion tokens to JSON file. Args: file_path: Output file path (default: motion_tokens.json) springs_only: Export only spring configs easings_only: Export only easing curves presets_only: Export only animation presets Returns: Path to exported file or error message """ if not file_path: file_path = "motion_tokens.json" try: # Build export data from Pydantic model export_data = {} if springs_only: export_data["spring_configs"] = MOTION_TOKENS.model_dump()["spring_configs"] elif easings_only: export_data["easing"] = MOTION_TOKENS.model_dump()["easing"] elif presets_only: # Get enter and exit transitions (animation presets) motion_dict = MOTION_TOKENS.model_dump() export_data["enter"] = motion_dict["enter"] export_data["exit"] = motion_dict["exit"] else: # Export all - convert Pydantic model to dict export_data = MOTION_TOKENS.model_dump() # Add custom tokens if any if self.custom_motion_tokens: export_data["custom"] = self.custom_motion_tokens json_content = json.dumps(export_data, indent=2) # Write to virtual filesystem await self.vfs.write_file(file_path, json_content) return file_path except Exception as e: return f"Error exporting motion tokens: {str(e)}" async def import_motion_tokens(self, file_path: str, merge: bool = True) -> str: """ Import motion tokens from JSON file. Args: file_path: Path to JSON file merge: Merge with existing custom tokens (default: True) Returns: Success message or error """ try: # Read from virtual filesystem json_content = await self.vfs.read_text(file_path) imported_data = json.loads(json_content) # Validate structure if not isinstance(imported_data, dict): return "Error: Invalid motion token format" if merge: self.custom_motion_tokens.update(imported_data) else: self.custom_motion_tokens = imported_data return f"Successfully imported motion tokens from {file_path}" except Exception as e: return f"Error importing motion tokens: {str(e)}" def get_motion_token( self, category: str, key: str | None = None, use_custom: bool = True ) -> Any | None: """ Get a motion token value. Args: category: Token category (spring_configs, easing_curves, etc.) key: Optional specific key within category use_custom: Check custom tokens first (default: True) Returns: Token value or None """ # Check custom tokens first if use_custom and category in self.custom_motion_tokens: custom_value = self.custom_motion_tokens[category] if key and isinstance(custom_value, dict): return custom_value.get(key) return custom_value # Fall back to default tokens - use model_dump() to convert Pydantic model to dict motion_dict = MOTION_TOKENS.model_dump() if category not in motion_dict: return None value = motion_dict[category] if key and isinstance(value, dict): return value.get(key) return value # ======================================================================== # UTILITY METHODS # ======================================================================== def list_custom_tokens(self) -> dict[str, list[str]]: """ List all custom tokens. Returns: Dictionary with custom token categories and their keys """ return { "typography": list(self.custom_typography_tokens.keys()), "colors": list(self.custom_color_tokens.keys()), "motion": list(self.custom_motion_tokens.keys()), } def clear_custom_tokens(self, token_type: str | None = None) -> None: """ Clear custom tokens. Args: token_type: Type to clear (typography, colors, motion), or None for all """ if token_type == "typography" or token_type is None: self.custom_typography_tokens = {} if token_type == "colors" or token_type is None: self.custom_color_tokens = {} if token_type == "motion" or token_type is None: self.custom_motion_tokens = {} if token_type == "spacing" or token_type is None: self.custom_spacing_tokens = {} # ======================================================================== # SPACING TOKEN MANAGEMENT # ======================================================================== async def export_spacing_tokens( self, file_path: str | None = None, spacing_only: bool = False, safe_margins_only: bool = False, ) -> str: """ Export spacing tokens to JSON file. Args: file_path: Output file path (default: spacing_tokens.json) spacing_only: Export only spacing scale safe_margins_only: Export only safe margin definitions Returns: Path to exported file or error message """ if not file_path: file_path = "spacing_tokens.json" try: # Build export data from Pydantic model export_data = {} if spacing_only: export_data["spacing"] = SPACING_TOKENS.model_dump()["spacing"] elif safe_margins_only: export_data["safe_area"] = SPACING_TOKENS.model_dump()["safe_area"] else: # Export all - convert Pydantic model to dict export_data = SPACING_TOKENS.model_dump() # Add custom tokens if any if self.custom_spacing_tokens: export_data["custom"] = self.custom_spacing_tokens json_content = json.dumps(export_data, indent=2) # Write to virtual filesystem await self.vfs.write_file(file_path, json_content) return file_path except Exception as e: return f"Error exporting spacing tokens: {str(e)}" async def import_spacing_tokens(self, file_path: str, merge: bool = True) -> str: """ Import spacing tokens from JSON file. Args: file_path: Path to JSON file merge: Merge with existing custom tokens (default: True) Returns: Success message or error """ try: # Read from virtual filesystem json_content = await self.vfs.read_text(file_path) imported_data = json.loads(json_content) # Validate structure if not isinstance(imported_data, dict): return "Error: Invalid spacing token format" if merge: self.custom_spacing_tokens.update(imported_data) else: self.custom_spacing_tokens = imported_data return f"Successfully imported spacing tokens from {file_path}" except Exception as e: return f"Error importing spacing tokens: {str(e)}" def get_spacing_token( self, token_type: str, key: str | None = None, use_custom: bool = True ) -> Any | None: """ Get a spacing token value. Args: token_type: Token type (spacing, safe_margins, border_radius, etc.) key: Optional specific key within the type use_custom: Check custom tokens first (default: True) Returns: Token value or None """ # Check custom tokens first if use_custom and token_type in self.custom_spacing_tokens: custom_tokens = self.custom_spacing_tokens[token_type] if key: return custom_tokens.get(key) return custom_tokens # Fall back to default tokens - use model_dump() to convert Pydantic model to dict spacing_dict = SPACING_TOKENS.model_dump() if token_type not in spacing_dict: return None tokens = spacing_dict[token_type] if key: return tokens.get(key) return tokens async def export_all_tokens(self, output_dir: str) -> dict[str, str]: """ Export all token types to separate files. Args: output_dir: Directory to save token files Returns: Dictionary with file paths for each token type """ # Ensure directory exists in virtual filesystem # Check if directory exists first (suppress errors if it already exists) with contextlib.suppress(Exception): await self.vfs.mkdir(output_dir) results = { "typography": await self.export_typography_tokens( f"{output_dir}/typography_tokens.json" ), "colors": await self.export_color_tokens(f"{output_dir}/color_tokens.json"), "motion": await self.export_motion_tokens(f"{output_dir}/motion_tokens.json"), "spacing": await self.export_spacing_tokens(f"{output_dir}/spacing_tokens.json"), } return results