import torch
from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler

class StableDiffusionGenerator:
    def __init__(self, model_id: str = "runwayml/stable-diffusion-v1-5"):
        self.device = "cuda" if torch.cuda.is_available() else "cpu"
        
        # Load pipeline with optimized scheduler
        self.pipeline = StableDiffusionPipeline.from_pretrained(
            model_id,
            torch_dtype=torch.float16 if self.device == "cuda" else torch.float32,
            safety_checker=None,
            requires_safety_checker=False
        )
        
        # Optimize for inference speed
        self.pipeline.scheduler = DPMSolverMultistepScheduler.from_config(
            self.pipeline.scheduler.config
        )
        self.pipeline = self.pipeline.to(self.device)
        
        # Enable memory efficient attention
        if hasattr(self.pipeline, "enable_attention_slicing"):
            self.pipeline.enable_attention_slicing()
    
    def generate_image(
        self,
        prompt: str,
        negative_prompt: str = "blurry, low quality, distorted",
        width: int = 512,
        height: int = 512,
        num_inference_steps: int = 20,
        guidance_scale: float = 7.5,
        seed: int = None
    ) -> Image:
        """Generate image with advanced parameter control"""
        
        if seed is not None:
            generator = torch.Generator(device=self.device).manual_seed(seed)
        else:
            generator = None
            
        with torch.autocast(self.device):
            result = self.pipeline(
                prompt=prompt,
                negative_prompt=negative_prompt,
                width=width,
                height=height,
                num_inference_steps=num_inference_steps,
                guidance_scale=guidance_scale,
                generator=generator
            )
            
        return result.images[0]
    
    def generate_with_controlnet(
        self,
        prompt: str,
        control_image: Image,
        controlnet_conditioning_scale: float = 1.0
    ) -> Image:
        """Generate with structural control using ControlNet"""
        from diffusers import ControlNetModel, StableDiffusionControlNetPipeline
        
        # Load ControlNet for edge detection
        controlnet = ControlNetModel.from_pretrained(
            "lllyasviel/sd-controlnet-canny",
            torch_dtype=torch.float16
        )
        
        control_pipeline = StableDiffusionControlNetPipeline.from_pretrained(
            "runwayml/stable-diffusion-v1-5",
            controlnet=controlnet,
            torch_dtype=torch.float16
        ).to(self.device)
        
        return control_pipeline(
            prompt=prompt,
            image=control_image,
            controlnet_conditioning_scale=controlnet_conditioning_scale
        ).images[0]

import cv2
import numpy as np
from PIL import Image
from diffusers import StableVideoDiffusionPipeline

class VideoGenerationEngine:
    def __init__(self):
        self.svd_pipeline = StableVideoDiffusionPipeline.from_pretrained(
            "stabilityai/stable-video-diffusion-img2vid-xt",
            torch_dtype=torch.float16,
            variant="fp16"
        ).to("cuda")
        
        # Optimize memory usage
        self.svd_pipeline.enable_model_cpu_offload()
    
    def generate_video_from_image(
        self,
        initial_image: Image,
        motion_bucket_id: int = 127,
        fps: int = 7,
        num_frames: int = 25,
        decode_chunk_size: int = 8
    ) -> List[Image]:
        """Generate video sequence from initial frame"""
        
        # Resize image to optimal dimensions
        image = initial_image.resize((1024, 576))
        
        generator = torch.manual_seed(42)
        
        frames = self.svd_pipeline(
            image,
            decode_chunk_size=decode_chunk_size,
            generator=generator,
            motion_bucket_id=motion_bucket_id,
            noise_aug_strength=0.1,
            num_frames=num_frames
        ).frames[0]
        
        return frames
    
    def create_video_file(
        self,
        frames: List[Image],
        output_path: str,
        fps: int = 7
    ):
        """Export frames to video file"""
        
        # Convert PIL images to numpy arrays
        frame_arrays = [
            cv2.cvtColor(np.array(frame), cv2.COLOR_RGB2BGR)
            for frame in frames
        ]
        
        height, width, layers = frame_arrays[0].shape
        
        # Create video writer
        fourcc = cv2.VideoWriter_fourcc(*'mp4v')
        video_writer = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
        
        for frame in frame_arrays:
            video_writer.write(frame)
            
        video_writer.release()

# Advanced video generation with temporal conditioning
class TemporalVideoGenerator:
    def __init__(self):
        self.text_to_video_pipeline = self.load_text2video_pipeline()
    
    def load_text2video_pipeline(self):
        """Load text-to-video generation pipeline"""
        # Placeholder for advanced T2V models like VideoCrafter or AnimateDiff
        pass
    
    def generate_with_motion_control(
        self,
        prompt: str,
        motion_trajectory: np.ndarray,
        camera_movement: str = "static",
        style_reference: Image = None
    ) -> List[Image]:
        """Generate video with explicit motion and camera control"""
        
        motion_conditioning = self.encode_motion_trajectory(motion_trajectory)
        camera_conditioning = self.encode_camera_movement(camera_movement)
        
        # Advanced conditioning for temporal consistency
        temporal_conditioning = {
            "motion_vectors": motion_conditioning,
            "camera_parameters": camera_conditioning,
            "style_reference": self.encode_style_reference(style_reference) if style_reference else None
        }
        
        return self.text_to_video_pipeline(
            prompt=prompt,
            temporal_conditioning=temporal_conditioning,
            num_frames=60,
            fps=24
        )

import torch
import torchaudio
from audiocraft.models import MusicGen
from audiocraft.data.audio import audio_write

class MusicGenerationStudio:
    def __init__(self):
        # Load MusicGen model
        self.musicgen_model = MusicGen.get_pretrained('musicgen-melody-large')
        self.sample_rate = self.musicgen_model.sample_rate
        
    def generate_music_from_text(
        self,
        descriptions: List[str],
        duration: int = 30,
        temperature: float = 1.0,
        top_k: int = 250,
        top_p: float = 0.0
    ) -> torch.Tensor:
        """Generate music from text descriptions"""
        
        self.musicgen_model.set_generation_params(
            duration=duration,
            temperature=temperature,
            top_k=top_k,
            top_p=top_p
        )
        
        # Generate audio tensors
        audio_tensors = self.musicgen_model.generate(descriptions)
        
        return audio_tensors
    
    def generate_with_melody_conditioning(
        self,
        descriptions: List[str],
        melody_audio: torch.Tensor,
        melody_sample_rate: int,
        duration: int = 30
    ) -> torch.Tensor:
        """Generate music conditioned on input melody"""
        
        # Resample melody if necessary
        if melody_sample_rate != self.sample_rate:
            resampler = torchaudio.transforms.Resample(
                melody_sample_rate, self.sample_rate
            )
            melody_audio = resampler(melody_audio)
        
        # Generate with melody conditioning
        self.musicgen_model.set_generation_params(duration=duration)
        
        audio_tensors = self.musicgen_model.generate_with_chroma(
            descriptions=descriptions,
            melody_wavs=melody_audio,
            melody_sample_rate=self.sample_rate
        )
        
        return audio_tensors
    
    def export_audio(
        self,
        audio_tensor: torch.Tensor,
        output_path: str,
        strategy: str = "loudness"
    ):
        """Export generated audio with normalization"""
        
        # Apply audio normalization
        if strategy == "loudness":
            audio_tensor = audio_tensor / audio_tensor.abs().max()
        elif strategy == "peak":
            audio_tensor = torch.clamp(audio_tensor, -1.0, 1.0)
            
        audio_write(
            output_path,
            audio_tensor.cpu(),
            self.sample_rate,
            strategy=strategy
        )

# Advanced audio manipulation and style transfer
class AudioStyleTransfer:
    def __init__(self):
        self.riffusion_pipeline = self.load_riffusion_pipeline()
    
    def load_riffusion_pipeline(self):
        """Load Riffusion spectrogram-based audio generation"""
        from diffusers import StableDiffusionPipeline
        
        return StableDiffusionPipeline.from_pretrained(
            "riffusion/riffusion-model-v1",
            torch_dtype=torch.float16
        ).to("cuda")
    
    def generate_audio_from_spectrogram(
        self,
        prompt: str,
        negative_prompt: str = "low quality, distorted",
        num_inference_steps: int = 50
    ) -> np.ndarray:
        """Generate audio via spectrogram synthesis"""
        
        # Generate spectrogram image
        spectrogram_image = self.riffusion_pipeline(
            prompt=prompt,
            negative_prompt=negative_prompt,
            num_inference_steps=num_inference_steps
        ).images[0]
        
        # Convert spectrogram back to audio
        audio_array = self.spectrogram_to_audio(spectrogram_image)
        
        return audio_array
    
    def spectrogram_to_audio(self, spectrogram_image: Image) -> np.ndarray:
        """Convert spectrogram image back to audio waveform"""
        # Implementation would involve STFT inversion
        # This is a simplified placeholder
        pass

from openai import OpenAI
import anthropic
from typing import List, Dict, Any

class ContentGenerationSuite:
    def __init__(self):
        self.openai_client = OpenAI()
        self.anthropic_client = anthropic.Anthropic()
        
    async def generate_multi_format_content(
        self,
        topic: str,
        target_formats: List[str],
        audience: str,
        tone: str = "professional"
    ) -> Dict[str, str]:
        """Generate content across multiple formats for a single topic"""
        
        content_variants = {}
        
        format_prompts = {
            "blog_post": self.create_blog_prompt(topic, audience, tone),
            "social_media": self.create_social_media_prompt(topic, audience, tone),
            "email_newsletter": self.create_email_prompt(topic, audience, tone),
            "video_script": self.create_video_script_prompt(topic, audience, tone),
            "technical_documentation": self.create_technical_doc_prompt(topic, audience)
        }
        
        for format_type in target_formats:
            if format_type in format_prompts:
                content = await self.generate_content_variant(
                    format_prompts[format_type],
                    format_type
                )
                content_variants[format_type] = content
                
        return content_variants
    
    def create_blog_prompt(self, topic: str, audience: str, tone: str) -> str:
        return f"""
        Write a comprehensive blog post about {topic} for {audience}.
        
        Requirements:
        - Tone: {tone}
        - Length: 1500-2000 words
        - Include practical examples and actionable insights
        - Structure with clear headings and subheadings
        - Include a compelling introduction and conclusion
        - Optimize for SEO with natural keyword integration
        
        Focus on providing genuine value while maintaining readability.
        """
    
    def create_social_media_prompt(self, topic: str, audience: str, tone: str) -> str:
        return f"""
        Create a social media content series about {topic} for {audience}.
        
        Generate:
        - 1 LinkedIn post (professional, 150-300 words)
        - 3 Twitter/X threads (conversational, engaging)
        - 1 Instagram caption (visual-focused, with hashtags)
        - 1 TikTok script (short-form, energetic)
        
        Tone: {tone}
        Include relevant hashtags and call-to-actions.
        Optimize for engagement and shareability.
        """
    
    async def generate_content_variant(
        self,
        prompt: str,
        format_type: str
    ) -> str:
        """Generate content using appropriate model for format"""
        
        # Route to optimal model based on content type
        if format_type in ["technical_documentation", "blog_post"]:
            # Use Claude for long-form, analytical content
            response = await self.anthropic_client.messages.create(
                model="claude-3-sonnet-20240229",
                max_tokens=4000,
                messages=[{"role": "user", "content": prompt}]
            )
            return response.content[0].text
            
        else:
            # Use GPT-4 for creative and marketing content
            response = await self.openai_client.chat.completions.create(
                model="gpt-4-turbo-preview",
                messages=[{"role": "user", "content": prompt}],
                max_tokens=3000,
                temperature=0.7
            )
            return response.choices[0].message.content

# Advanced content optimization and personalization
class ContentOptimizationEngine:
    def __init__(self):
        self.style_analyzer = self.load_style_analysis_model()
        self.engagement_predictor = self.load_engagement_model()
    
    async def optimize_content_for_audience(
        self,
        base_content: str,
        audience_segments: List[Dict[str, Any]],
        performance_metrics: Dict[str, float]
    ) -> Dict[str, str]:
        """Generate audience-specific content variants"""
        
        optimized_variants = {}
        
        for segment in audience_segments:
            # Analyze optimal style for segment
            style_parameters = await self.analyze_segment_preferences(segment)
            
            # Generate segment-specific variant
            optimization_prompt = f"""
            Adapt the following content for this specific audience:
            
            Original Content: {base_content}
            
            Target Audience: {segment['demographics']}
            Preferred Style: {style_parameters['writing_style']}
            Engagement Patterns: {style_parameters['engagement_preferences']}
            Platform: {segment['primary_platform']}
            
            Maintain core message while optimizing for this audience's preferences.
            """
            
            variant = await self.generate_optimized_variant(optimization_prompt)
            optimized_variants[segment['segment_id']] = variant
            
        return optimized_variants
    
    async def analyze_segment_preferences(
        self,
        segment: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Analyze audience segment for content optimization"""
        
        # Placeholder for audience analysis logic
        # Would integrate with analytics platforms and user behavior data
        return {
            "writing_style": "conversational",
            "engagement_preferences": "visual_heavy",
            "optimal_length": "medium_form",
            "tone_preference": "friendly_professional"
        }

import requests
from PIL import Image, ImageDraw, ImageFont
import io
from typing import Tuple, List

class AIDesignStudio:
    def __init__(self):
        self.image_generator = StableDiffusionGenerator()
        self.style_transfer = self.load_style_transfer_model()
        
    async def generate_brand_assets(
        self,
        brand_description: str,
        asset_types: List[str],
        color_palette: List[str],
        style_guidelines: Dict[str, Any]
    ) -> Dict[str, List[Image]]:
        """Generate comprehensive brand asset suite"""
        
        brand_assets = {}
        
        # Generate base style prompts
        style_prompt = self.create_brand_style_prompt(
            brand_description, color_palette, style_guidelines
        )
        
        asset_generators = {
            "logo_concepts": self.generate_logo_concepts,
            "social_media_templates": self.generate_social_templates,
            "marketing_materials": self.generate_marketing_assets,
            "web_graphics": self.generate_web_graphics,
            "print_materials": self.generate_print_assets
        }
        
        for asset_type in asset_types:
            if asset_type in asset_generators:
                assets = await asset_generators[asset_type](
                    style_prompt, style_guidelines
                )
                brand_assets[asset_type] = assets
                
        return brand_assets
    
    def create_brand_style_prompt(
        self,
        brand_description: str,
        color_palette: List[str],
        guidelines: Dict[str, Any]
    ) -> str:
        """Create comprehensive style prompt for brand consistency"""
        
        color_description = ", ".join(color_palette)
        
        return f"""
        Brand Identity Design:
        
        Brand Description: {brand_description}
        Color Palette: {color_description}
        Style: {guidelines.get('visual_style', 'modern, clean')}
        Target Audience: {guidelines.get('target_audience', 'general')}
        Industry: {guidelines.get('industry', 'technology')}
        
        Design Requirements:
        - Professional and memorable
        - Scalable across different media
        - Consistent with brand values
        - Modern typography and layout
        - High contrast and readability
        """
    
    async def generate_logo_concepts(
        self,
        style_prompt: str,
        guidelines: Dict[str, Any]
    ) -> List[Image]:
        """Generate multiple logo concept variations"""
        
        logo_prompts = [
            f"{style_prompt}, minimalist logo design, vector style, white background",
            f"{style_prompt}, geometric logo, abstract symbol, professional",
            f"{style_prompt}, typography-based logo, elegant lettering, sophisticated",
            f"{style_prompt}, iconic symbol, memorable mark, scalable design"
        ]
        
        logo_concepts = []
        for prompt in logo_prompts:
            logo = await self.image_generator.generate_image(
                prompt=prompt,
                width=512,
                height=512,
                guidance_scale=8.0
            )
            logo_concepts.append(logo)
            
        return logo_concepts
    
    async def create_design_variations(
        self,
        base_design: Image,
        variation_count: int = 5,
        variation_strength: float = 0.3
    ) -> List[Image]:
        """Generate design variations from base concept"""
        
        variations = []
        
        for i in range(variation_count):
            # Use img2img generation for variations
            variation_prompt = "professional design, maintain style, slight variation"
            
            variation = await self.image_generator.generate_image(
                prompt=variation_prompt,
                init_image=base_design,
                strength=variation_strength,
                guidance_scale=7.0
            )
            variations.append(variation)
            
        return variations

# Advanced layout generation and typography
class LayoutGenerationEngine:
    def __init__(self):
        self.layout_model = self.load_layout_model()
        self.typography_analyzer = self.load_typography_model()
    
    async def generate_responsive_layouts(
        self,
        content_structure: Dict[str, Any],
        device_targets: List[str],
        design_system: Dict[str, Any]
    ) -> Dict[str, Dict]:
        """Generate responsive layouts for multiple devices"""
        
        responsive_layouts = {}
        
        for device in device_targets:
            device_constraints = self.get_device_constraints(device)
            
            layout_prompt = f"""
            Generate responsive layout for {device}:
            
            Content Structure: {content_structure}
            Device Constraints: {device_constraints}
            Design System: {design_system}
            
            Requirements:
            - Optimal information hierarchy
            - Accessibility compliance
            - Performance optimization
            - Brand consistency
            """
            
            layout = await self.layout_model.generate_layout(layout_prompt)
            responsive_layouts[device] = layout
            
        return responsive_layouts
    
    def get_device_constraints(self, device: str) -> Dict[str, Any]:
        """Get device-specific design constraints"""
        
        constraints = {
            "mobile": {
                "viewport_width": "320-414px",
                "touch_targets": "44px minimum",
                "vertical_layout": True,
                "thumb_zones": "bottom_third_optimal"
            },
            "tablet": {
                "viewport_width": "768-1024px",
                "interaction_mode": "touch_and_stylus",
                "orientation_support": "both",
                "content_density": "medium"
            },
            "desktop": {
                "viewport_width": "1200px+",
                "interaction_mode": "mouse_keyboard",
                "multi_column": True,
                "content_density": "high"
            }
        }
        
        return constraints.get(device, {})

class MultimodalCreativeStudio:
    def __init__(self):
        self.text_to_image = StableDiffusionGenerator()
        self.text_to_music = MusicGenerationStudio()
        self.text_to_video = VideoGenerationEngine()
        self.content_generator = ContentGenerationSuite()
        
    async def create_multimedia_campaign(
        self,
        campaign_brief: str,
        target_outputs: List[str],
        brand_guidelines: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Generate cohesive multimedia campaign across all formats"""
        
        # Generate master creative brief
        creative_brief = await self.generate_creative_brief(
            campaign_brief, brand_guidelines
        )
        
        campaign_assets = {}
        
        # Parallel generation across media types
        generation_tasks = []
        
        if "visual" in target_outputs:
            generation_tasks.append(
                self.generate_visual_assets(creative_brief, brand_guidelines)
            )
            
        if "audio" in target_outputs:
            generation_tasks.append(
                self.generate_audio_assets(creative_brief, brand_guidelines)
            )
            
        if "video" in target_outputs:
            generation_tasks.append(
                self.generate_video_assets(creative_brief, brand_guidelines)
            )
            
        if "content" in target_outputs:
            generation_tasks.append(
                self.generate_content_assets(creative_brief, brand_guidelines)
            )
        
        # Execute all generation tasks
        results = await asyncio.gather(*generation_tasks)
        
        # Combine results into cohesive campaign
        for result in results:
            campaign_assets.update(result)
            
        return {
            "creative_brief": creative_brief,
            "campaign_assets": campaign_assets,
            "brand_consistency_score": self.evaluate_brand_consistency(
                campaign_assets, brand_guidelines
            )
        }
    
    async def generate_creative_brief(
        self,
        campaign_brief: str,
        brand_guidelines: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Generate comprehensive creative brief for cross-modal consistency"""
        
        brief_prompt = f"""
        Create a comprehensive creative brief for multimedia campaign:
        
        Campaign Objective: {campaign_brief}
        Brand Guidelines: {brand_guidelines}
        
        Generate:
        - Core message and theme
        - Visual style direction
        - Audio/music direction  
        - Tone and voice guidelines
        - Color palette and typography
        - Key emotional beats
        - Technical specifications
        
        Ensure consistency across all media formats.
        """
        
        creative_brief = await self.content_generator.generate_content_variant(
            brief_prompt, "creative_brief"
        )
        
        return self.parse_creative_brief(creative_brief)
    
    def evaluate_brand_consistency(
        self,
        campaign_assets: Dict[str, Any],
        brand_guidelines: Dict[str, Any]
    ) -> float:
        """Evaluate brand consistency across generated assets"""
        
        # Placeholder for brand consistency analysis
        # Would involve computer vision, audio analysis, and text analysis
        consistency_scores = []
        
        # Visual consistency analysis
        if "visual_assets" in campaign_assets:
            visual_score = self.analyze_visual_consistency(
                campaign_assets["visual_assets"], brand_guidelines
            )
            consistency_scores.append(visual_score)
        
        # Audio consistency analysis  
        if "audio_assets" in campaign_assets:
            audio_score = self.analyze_audio_consistency(
                campaign_assets["audio_assets"], brand_guidelines
            )
            consistency_scores.append(audio_score)
        
        # Content consistency analysis
        if "content_assets" in campaign_assets:
            content_score = self.analyze_content_consistency(
                campaign_assets["content_assets"], brand_guidelines
            )
            consistency_scores.append(content_score)
        
        return sum(consistency_scores) / len(consistency_scores) if consistency_scores else 0.0