🔧 Processing Modules¶

class JSONSchemaGenerator:
    def __init__(self):
        self.schema_validator = PydanticValidator()
        self.template_engine = TemplateEngine()
        self.brand_manager = BrandManager()

    def generate_schema(self, ai_content, project_config):
        """Generate JSON schema from AI content and project configuration"""

        # Validate input
        validated_content = self.schema_validator.validate_ai_content(ai_content)
        validated_config = self.schema_validator.validate_project_config(project_config)

        # Build base schema
        schema = {
            "version": "1.0",
            "metadata": self.generate_metadata(validated_content, validated_config),
            "composition": self.generate_composition(validated_config),
            "assets": self.generate_assets(validated_content),
            "layers": self.generate_layers(validated_content),
            "animations": self.generate_animations(validated_content),
            "timeline": self.generate_timeline(validated_content)
        }

        # Apply brand guidelines
        schema = self.brand_manager.apply_brand_guidelines(schema, validated_config.brand_id)

        # Validate final schema
        validated_schema = self.schema_validator.validate_final_schema(schema)

        return validated_schema

    def generate_metadata(self, content, config):
        """Generate metadata section"""
        return {
            "name": content.name,
            "description": content.description,
            "created_at": datetime.now().isoformat(),
            "project_id": config.project_id,
            "brand_id": config.brand_id,
            "platform": config.target_platform,
            "duration": config.duration,
            "resolution": config.resolution
        }

    def generate_composition(self, config):
        """Generate composition settings"""
        return {
            "width": config.resolution.width,
            "height": config.resolution.height,
            "frame_rate": config.frame_rate,
            "duration": config.duration,
            "background_color": config.background_color,
            "safe_area": config.safe_area
        }

from pydantic import BaseModel, validator
from typing import List, Dict, Any, Optional

class LayerSchema(BaseModel):
    id: str
    name: str
    type: str
    z_index: int
    visible: bool = True
    opacity: float = 1.0
    transform: Dict[str, Any]
    content: Dict[str, Any]

    @validator('opacity')
    def validate_opacity(cls, v):
        if not 0 <= v <= 1:
            raise ValueError('Opacity must be between 0 and 1')
        return v

    @validator('z_index')
    def validate_z_index(cls, v):
        if v < 0:
            raise ValueError('Z-index must be non-negative')
        return v

class AnimationSchema(BaseModel):
    id: str
    layer_id: str
    property: str
    keyframes: List[Dict[str, Any]]
    easing: str = 'linear'
    duration: float

    @validator('duration')
    def validate_duration(cls, v):
        if v <= 0:
            raise ValueError('Duration must be positive')
        return v

class MotionGraphicsSchema(BaseModel):
    version: str
    metadata: Dict[str, Any]
    composition: Dict[str, Any]
    assets: List[Dict[str, Any]]
    layers: List[LayerSchema]
    animations: List[AnimationSchema]
    timeline: Dict[str, Any]

class StaticLayoutGenerator:
    def __init__(self):
        self.constraint_solver = ConstraintSolver()
        self.brand_compliance = BrandComplianceChecker()
        self.layout_engine = LayoutEngine()

    def generate_layout(self, schema, brand_guidelines):
        """Generate static layout from schema and brand guidelines"""

        # Extract layout requirements
        requirements = self.extract_layout_requirements(schema)

        # Apply brand constraints
        constraints = self.brand_compliance.generate_constraints(brand_guidelines)

        # Solve layout problem
        layout_solution = self.constraint_solver.solve_layout(
            requirements, constraints
        )

        # Generate visual layout
        visual_layout = self.layout_engine.render_layout(layout_solution)

        return visual_layout

    def extract_layout_requirements(self, schema):
        """Extract layout requirements from schema"""
        requirements = {
            "elements": [],
            "constraints": [],
            "objectives": []
        }

        for layer in schema.layers:
            element = {
                "id": layer.id,
                "type": layer.type,
                "content": layer.content,
                "preferred_position": layer.transform.get("position"),
                "size_constraints": layer.transform.get("size"),
                "priority": layer.get("priority", 1)
            }
            requirements["elements"].append(element)

        return requirements

    def solve_layout(self, requirements, constraints):
        """Solve layout using constraint programming"""
        from ortools.sat.python import cp_model

        model = cp_model.CpModel()

        # Create variables for element positions
        element_vars = {}
        for element in requirements["elements"]:
            element_vars[element["id"]] = {
                "x": model.NewIntVar(0, 1920, f"{element['id']}_x"),
                "y": model.NewIntVar(0, 1080, f"{element['id']}_y"),
                "width": model.NewIntVar(10, 1920, f"{element['id']}_width"),
                "height": model.NewIntVar(10, 1080, f"{element['id']}_height")
            }

        # Add constraints
        for constraint in constraints:
            self.add_constraint(model, element_vars, constraint)

        # Add objectives
        self.add_objectives(model, element_vars, requirements["objectives"])

        # Solve
        solver = cp_model.CpSolver()
        status = solver.Solve(model)

        if status == cp_model.OPTIMAL:
            return self.extract_solution(solver, element_vars)
        else:
            raise LayoutError("No optimal layout solution found")

class AnimationEngine:
    def __init__(self):
        self.easing_functions = EasingFunctionLibrary()
        self.keyframe_generator = KeyframeGenerator()
        self.timing_engine = TimingEngine()

    def generate_animation(self, animation_spec, layer_properties):
        """Generate animation from specification"""

        # Parse animation specification
        parsed_spec = self.parse_animation_spec(animation_spec)

        # Generate keyframes
        keyframes = self.keyframe_generator.generate_keyframes(
            parsed_spec, layer_properties
        )

        # Apply easing
        eased_keyframes = self.apply_easing(keyframes, parsed_spec.easing)

        # Optimize timing
        optimized_keyframes = self.timing_engine.optimize_timing(eased_keyframes)

        return {
            "keyframes": optimized_keyframes,
            "duration": parsed_spec.duration,
            "easing": parsed_spec.easing,
            "property": parsed_spec.property
        }

    def generate_transition(self, from_state, to_state, transition_type, duration):
        """Generate transition between two states"""

        # Analyze state differences
        changes = self.calculate_state_diff(from_state, to_state)

        # Generate intermediate keyframes
        keyframes = self.generate_intermediate_keyframes(
            changes, transition_type, duration
        )

        # Apply timing
        timed_keyframes = self.apply_timing(keyframes, duration)

        return Animation(timed_keyframes)

    def apply_easing(self, keyframes, easing_type):
        """Apply easing function to keyframes"""
        easing_func = self.easing_functions.get_easing_function(easing_type)

        eased_keyframes = []
        for i, keyframe in enumerate(keyframes):
            if i == 0:
                eased_keyframes.append(keyframe)
                continue

            prev_keyframe = keyframes[i - 1]
            progress = keyframe.time / keyframes[-1].time

            # Apply easing to progress
            eased_progress = easing_func(progress)

            # Interpolate value
            eased_value = self.interpolate_value(
                prev_keyframe.value,
                keyframe.value,
                eased_progress
            )

            eased_keyframes.append(Keyframe(
                time=keyframe.time,
                value=eased_value,
                easing=easing_type
            ))

        return eased_keyframes

class EasingFunctionLibrary:
    def __init__(self):
        self.functions = {
            'linear': self.linear_ease,
            'easeIn': self.ease_in,
            'easeOut': self.ease_out,
            'easeInOut': self.ease_in_out,
            'bounce': self.bounce_ease,
            'elastic': self.elastic_ease,
            'back': self.back_ease
        }

    def linear_ease(self, t):
        return t

    def ease_in(self, t):
        return t * t

    def ease_out(self, t):
        return 1 - (1 - t) * (1 - t)

    def ease_in_out(self, t):
        return 3 * t * t - 2 * t * t * t

    def bounce_ease(self, t):
        if t < 1/2.75:
            return 7.5625 * t * t
        elif t < 2/2.75:
            t -= 1.5/2.75
            return 7.5625 * t * t + 0.75
        elif t < 2.5/2.75:
            t -= 2.25/2.75
            return 7.5625 * t * t + 0.9375
        else:
            t -= 2.625/2.75
            return 7.5625 * t * t + 0.984375

    def elastic_ease(self, t):
        if t == 0 or t == 1:
            return t
        return -(2**(-10 * t)) * math.sin((t - 0.1) * (2 * math.pi) / 0.4) + 1

class SceneStateManager:
    def __init__(self):
        self.state_machine = StateMachine()
        self.timeline_manager = TimelineManager()
        self.element_lifecycle = ElementLifecycleManager()

    def create_scene_state(self, timestamp, duration, elements):
        """Create a scene state at specific timestamp"""

        state = SceneState(
            timestamp=timestamp,
            duration=duration,
            elements=elements,
            layout_constraints=[],
            animation_hints=[]
        )

        # Add element lifecycle information
        for element in elements:
            lifecycle_info = self.element_lifecycle.get_lifecycle_info(element)
            state.add_element_lifecycle(element.id, lifecycle_info)

        return state

    def manage_timeline(self, scene_states):
        """Manage timeline with multiple scene states"""

        timeline = Timeline()

        # Sort states by timestamp
        sorted_states = sorted(scene_states, key=lambda s: s.timestamp)

        # Create timeline segments
        for i, state in enumerate(sorted_states):
            segment = TimelineSegment(
                start_time=state.timestamp,
                duration=state.duration,
                state=state
            )

            # Add transitions
            if i > 0:
                prev_state = sorted_states[i - 1]
                transition = self.create_transition(prev_state, state)
                segment.add_transition(transition)

            timeline.add_segment(segment)

        return timeline

    def create_transition(self, from_state, to_state):
        """Create transition between two states"""

        # Analyze differences
        differences = self.analyze_state_differences(from_state, to_state)

        # Determine transition type
        transition_type = self.determine_transition_type(differences)

        # Create transition
        transition = Transition(
            from_state=from_state,
            to_state=to_state,
            type=transition_type,
            duration=self.calculate_transition_duration(differences),
            elements_affected=differences.affected_elements
        )

        return transition

class ConstraintSolver4D:
    def __init__(self):
        self.spatial_solver = SpatialConstraintSolver()
        self.temporal_solver = TemporalConstraintSolver()
        self.multi_objective_optimizer = MultiObjectiveOptimizer()

    def solve_timeline(self, states, transitions, constraints):
        """Solve 4D constraint problem (3D space + time)"""

        # Build temporal graph
        temporal_graph = self.build_temporal_graph(states, transitions)

        # Apply constraints
        for constraint in constraints:
            temporal_graph.add_constraint(constraint)

        # Multi-objective optimization
        objectives = [
            self.objective_visual_coherence,
            self.objective_communication_effectiveness,
            self.objective_technical_constraints,
            self.objective_historical_performance
        ]

        optimized_timeline = self.multi_objective_optimizer.optimize(
            temporal_graph, objectives
        )

        return optimized_timeline

    def build_temporal_graph(self, states, transitions):
        """Build temporal constraint graph"""
        graph = TemporalGraph()

        # Add state nodes
        for state in states:
            graph.add_node(state.id, {
                'timestamp': state.timestamp,
                'duration': state.duration,
                'elements': state.elements
            })

        # Add transition edges
        for transition in transitions:
            graph.add_edge(
                transition.from_state.id,
                transition.to_state.id,
                {
                    'type': transition.type,
                    'duration': transition.duration,
                    'constraints': transition.constraints
                }
            )

        return graph

    def objective_visual_coherence(self, timeline):
        """Optimize for visual coherence"""
        coherence_score = 0

        for segment in timeline.segments:
            # Calculate visual consistency
            consistency = self.calculate_visual_consistency(segment)
            coherence_score += consistency

        return coherence_score / len(timeline.segments)

    def objective_communication_effectiveness(self, timeline):
        """Optimize for communication effectiveness"""
        effectiveness_score = 0

        for segment in timeline.segments:
            # Calculate information clarity
            clarity = self.calculate_information_clarity(segment)
            effectiveness_score += clarity

        return effectiveness_score / len(timeline.segments)

class TimelineOptimizer:
    def __init__(self):
        self.graph_algorithms = GraphAlgorithms()
        self.temporal_analyzer = TemporalAnalyzer()
        self.sequence_optimizer = SequenceOptimizer()

    def optimize_timeline(self, timeline, constraints):
        """Optimize timeline using graph algorithms"""

        # Build timeline graph
        timeline_graph = self.build_timeline_graph(timeline)

        # Apply temporal constraints
        constrained_graph = self.apply_temporal_constraints(
            timeline_graph, constraints
        )

        # Find optimal sequence
        optimal_sequence = self.graph_algorithms.find_optimal_sequence(
            constrained_graph
        )

        # Optimize timing
        optimized_timing = self.optimize_timing(optimal_sequence)

        return optimized_timing

    def build_timeline_graph(self, timeline):
        """Build graph representation of timeline"""
        graph = TimelineGraph()

        # Add nodes for each timeline element
        for element in timeline.elements:
            graph.add_node(element.id, {
                'start_time': element.start_time,
                'duration': element.duration,
                'dependencies': element.dependencies,
                'priority': element.priority
            })

        # Add edges for dependencies
        for element in timeline.elements:
            for dependency in element.dependencies:
                graph.add_edge(dependency, element.id, {
                    'type': 'dependency',
                    'min_gap': 0
                })

        return graph

    def apply_temporal_constraints(self, graph, constraints):
        """Apply temporal constraints to graph"""
        constrained_graph = graph.copy()

        for constraint in constraints:
            if constraint.type == 'timing':
                self.apply_timing_constraint(constrained_graph, constraint)
            elif constraint.type == 'sequence':
                self.apply_sequence_constraint(constrained_graph, constraint)
            elif constraint.type == 'attention':
                self.apply_attention_constraint(constrained_graph, constraint)

        return constrained_graph

class QualityValidator:
    def __init__(self):
        self.brand_compliance = BrandComplianceChecker()
        self.technical_validator = TechnicalValidator()
        self.accessibility_checker = AccessibilityChecker()

    def validate_motion_graphics(self, motion_graphics):
        """Validate motion graphics quality"""

        validation_results = {
            'overall_score': 0,
            'checks': {},
            'recommendations': []
        }

        # Brand compliance check
        brand_score = self.brand_compliance.check_compliance(motion_graphics)
        validation_results['checks']['brand_compliance'] = brand_score

        # Technical quality check
        technical_score = self.technical_validator.validate_technical_quality(motion_graphics)
        validation_results['checks']['technical_quality'] = technical_score

        # Accessibility check
        accessibility_score = self.accessibility_checker.check_accessibility(motion_graphics)
        validation_results['checks']['accessibility'] = accessibility_score

        # Temporal flow check
        temporal_score = self.validate_temporal_flow(motion_graphics)
        validation_results['checks']['temporal_flow'] = temporal_score

        # Calculate overall score
        validation_results['overall_score'] = self.calculate_overall_score(validation_results['checks'])

        # Generate recommendations
        validation_results['recommendations'] = self.generate_recommendations(validation_results['checks'])

        return validation_results

    def validate_temporal_flow(self, motion_graphics):
        """Validate temporal flow and pacing"""
        score = 0

        # Check for appropriate pacing
        pacing_score = self.check_pacing(motion_graphics.timeline)
        score += pacing_score * 0.3

        # Check for visual continuity
        continuity_score = self.check_visual_continuity(motion_graphics.timeline)
        score += continuity_score * 0.3

        # Check for information density
        density_score = self.check_information_density(motion_graphics.timeline)
        score += density_score * 0.4

        return score

    def check_accessibility(self, motion_graphics):
        """Check accessibility compliance"""
        score = 0

        # Check for motion sensitivity
        motion_score = self.check_motion_sensitivity(motion_graphics)
        score += motion_score * 0.4

        # Check for epilepsy triggers
        epilepsy_score = self.check_epilepsy_triggers(motion_graphics)
        score += epilepsy_score * 0.3

        # Check for color contrast
        contrast_score = self.check_color_contrast(motion_graphics)
        score += contrast_score * 0.3

        return score