from typing import Union, List, Dict, Any
from dataclasses import dataclass
from enum import Enum
 
class ModalityType(Enum):
    """模态类型"""
    TEXT = "text"
    IMAGE = "image"
    AUDIO = "audio"
    VIDEO = "video"
    CODE = "code"
    STRUCTURED_DATA = "structured_data"
 
@dataclass
class MultimodalInput:
    """多模态输入"""
    modality: ModalityType
    content: Any
    metadata: Dict[str, Any] = None
 
class UnifiedMultimodalLLM:
    """统一多模态大语言模型"""
    
    def __init__(self, model_config: Dict[str, Any]):
        self.model_config = model_config
        self.supported_modalities = [
            ModalityType.TEXT,
            ModalityType.IMAGE,
            ModalityType.AUDIO,
            ModalityType.VIDEO,
            ModalityType.CODE
        ]
    
    async def process_multimodal_input(self, 
                                     inputs: List[MultimodalInput],
                                     task_description: str) -> Dict[str, Any]:
        """处理多模态输入"""
        
        # 1. 输入预处理
        processed_inputs = []
        for input_item in inputs:
            processed = await self._preprocess_input(input_item)
            processed_inputs.append(processed)
        
        # 2. 多模态编码
        encoded_representations = await self._encode_multimodal(processed_inputs)
        
        # 3. 跨模态推理
        reasoning_result = await self._cross_modal_reasoning(
            encoded_representations, 
            task_description
        )
        
        # 4. 多模态生成
        output = await self._generate_multimodal_output(reasoning_result)
        
        return output
    
    async def _preprocess_input(self, input_item: MultimodalInput) -> Dict[str, Any]:
        """预处理输入"""
        if input_item.modality == ModalityType.TEXT:
            return {
                "type": "text",
                "tokens": self._tokenize_text(input_item.content),
                "embeddings": await self._get_text_embeddings(input_item.content)
            }
        elif input_item.modality == ModalityType.IMAGE:
            return {
                "type": "image",
                "features": await self._extract_image_features(input_item.content),
                "objects": await self._detect_objects(input_item.content),
                "scene": await self._understand_scene(input_item.content)
            }
        elif input_item.modality == ModalityType.AUDIO:
            return {
                "type": "audio",
                "transcript": await self._speech_to_text(input_item.content),
                "acoustic_features": await self._extract_audio_features(input_item.content),
                "emotion": await self._detect_emotion(input_item.content)
            }
        # 其他模态的处理...
        
        return {"type": "unknown", "content": input_item.content}
    
    async def _encode_multimodal(self, processed_inputs: List[Dict[str, Any]]) -> Dict[str, Any]:
        """多模态编码"""
        # 统一表示空间编码
        unified_representations = []
        
        for input_data in processed_inputs:
            if input_data["type"] == "text":
                # 文本编码
                text_repr = await self._encode_text(input_data)
                unified_representations.append(text_repr)
            elif input_data["type"] == "image":
                # 图像编码
                image_repr = await self._encode_image(input_data)
                unified_representations.append(image_repr)
            elif input_data["type"] == "audio":
                # 音频编码
                audio_repr = await self._encode_audio(input_data)
                unified_representations.append(audio_repr)
        
        # 跨模态对齐
        aligned_representations = await self._align_modalities(unified_representations)
        
        return {
            "individual_representations": unified_representations,
            "aligned_representations": aligned_representations,
            "cross_modal_attention": await self._compute_cross_modal_attention(unified_representations)
        }
    
    async def _cross_modal_reasoning(self, encoded_data: Dict[str, Any], 
                                   task_description: str) -> Dict[str, Any]:
        """跨模态推理"""
        # 任务理解
        task_embedding = await self._encode_task(task_description)
        
        # 多模态融合推理
        reasoning_steps = []
        
        # 1. 模态内推理
        for repr_data in encoded_data["individual_representations"]:
            intra_modal_reasoning = await self._intra_modal_reasoning(repr_data, task_embedding)
            reasoning_steps.append(intra_modal_reasoning)
        
        # 2. 跨模态推理
        cross_modal_reasoning = await self._inter_modal_reasoning(
            encoded_data["aligned_representations"],
            encoded_data["cross_modal_attention"],
            task_embedding
        )
        
        # 3. 整合推理结果
        integrated_reasoning = await self._integrate_reasoning(
            reasoning_steps,
            cross_modal_reasoning
        )
        
        return {
            "reasoning_steps": reasoning_steps,
            "cross_modal_insights": cross_modal_reasoning,
            "final_reasoning": integrated_reasoning
        }
    
    async def _generate_multimodal_output(self, reasoning_result: Dict[str, Any]) -> Dict[str, Any]:
        """生成多模态输出"""
        output = {}
        
        # 根据推理结果确定输出模态
        required_modalities = await self._determine_output_modalities(reasoning_result)
        
        for modality in required_modalities:
            if modality == ModalityType.TEXT:
                output["text"] = await self._generate_text(reasoning_result)
            elif modality == ModalityType.IMAGE:
                output["image"] = await self._generate_image(reasoning_result)
            elif modality == ModalityType.AUDIO:
                output["audio"] = await self._generate_audio(reasoning_result)
        
        return output
    
    # 辅助方法（简化实现）
    def _tokenize_text(self, text: str) -> List[str]:
        return text.split()
    
    async def _get_text_embeddings(self, text: str):
        # 文本嵌入
        return "text_embeddings"
    
    async def _extract_image_features(self, image):
        # 图像特征提取
        return "image_features"
    
    async def _speech_to_text(self, audio):
        # 语音转文本
        return "transcribed_text"
    
    # 更多辅助方法...
 
# 使用示例
multimodal_llm = UnifiedMultimodalLLM({
    "model_name": "unified-multimodal-v1",
    "max_context_length": 100000
})
 
# 多模态输入示例
inputs = [
    MultimodalInput(ModalityType.TEXT, "请分析这张图片中的内容"),
    MultimodalInput(ModalityType.IMAGE, "image_data"),
    MultimodalInput(ModalityType.AUDIO, "audio_data")
]
 
# 处理多模态任务
# result = await multimodal_llm.process_multimodal_input(
#     inputs, 
#     "综合分析图片和音频内容，生成详细报告"
# )

class AdaptiveLLM:
    """自适应学习LLM"""
    
    def __init__(self, base_model_config: Dict[str, Any]):
        self.base_model = base_model_config
        self.knowledge_graph = {}
        self.learning_history = []
        self.adaptation_strategies = []
    
    async def continuous_learning(self, new_data: List[Dict[str, Any]]):
        """持续学习"""
        
        # 1. 知识提取
        extracted_knowledge = await self._extract_knowledge(new_data)
        
        # 2. 知识验证
        validated_knowledge = await self._validate_knowledge(extracted_knowledge)
        
        # 3. 知识整合
        await self._integrate_knowledge(validated_knowledge)
        
        # 4. 模型更新
        await self._update_model_parameters(validated_knowledge)
        
        # 5. 性能评估
        performance_metrics = await self._evaluate_performance()
        
        return {
            "learned_concepts": len(validated_knowledge),
            "knowledge_graph_size": len(self.knowledge_graph),
            "performance_improvement": performance_metrics
        }
    
    async def meta_learning(self, task_distribution: List[Dict[str, Any]]):
        """元学习"""
        
        # 学习如何快速适应新任务
        adaptation_strategies = []
        
        for task_family in task_distribution:
            # 1. 任务分析
            task_patterns = await self._analyze_task_patterns(task_family)
            
            # 2. 策略学习
            strategy = await self._learn_adaptation_strategy(task_patterns)
            
            # 3. 策略验证
            validated_strategy = await self._validate_strategy(strategy, task_family)
            
            adaptation_strategies.append(validated_strategy)
        
        # 整合元学习策略
        self.adaptation_strategies = await self._integrate_strategies(adaptation_strategies)
        
        return {
            "learned_strategies": len(adaptation_strategies),
            "adaptation_efficiency": await self._measure_adaptation_efficiency()
        }
    
    async def self_reflection(self) -> Dict[str, Any]:
        """自我反思"""
        
        # 1. 性能分析
        performance_analysis = await self._analyze_performance_history()
        
        # 2. 错误模式识别
        error_patterns = await self._identify_error_patterns()
        
        # 3. 改进策略生成
        improvement_strategies = await self._generate_improvement_strategies(
            performance_analysis, 
            error_patterns
        )
        
        # 4. 自我修正
        corrections = await self._apply_self_corrections(improvement_strategies)
        
        return {
            "identified_weaknesses": error_patterns,
            "improvement_strategies": improvement_strategies,
            "applied_corrections": corrections
        }
 
# 使用示例
adaptive_llm = AdaptiveLLM({
    "base_model": "gpt-4",
    "learning_rate": 0.001,
    "adaptation_threshold": 0.1
})
 
# 持续学习
new_data = [
    {"text": "新的科学发现...", "domain": "science"},
    {"text": "最新技术趋势...", "domain": "technology"}
]
 
# learning_result = await adaptive_llm.continuous_learning(new_data)
# reflection_result = await adaptive_llm.self_reflection()

class ScientificResearchAssistant:
    """科学研究助手"""
    
    def __init__(self, domain_expertise: List[str]):
        self.domain_expertise = domain_expertise
        self.research_tools = {}
        self.knowledge_base = {}
    
    async def hypothesis_generation(self, research_context: Dict[str, Any]) -> List[Dict[str, Any]]:
        """假设生成"""
        
        # 1. 文献分析
        literature_insights = await self._analyze_literature(research_context)
        
        # 2. 数据模式识别
        data_patterns = await self._identify_patterns(research_context.get("data", []))
        
        # 3. 跨领域知识整合
        cross_domain_insights = await self._integrate_cross_domain_knowledge(
            research_context["domain"]
        )
        
        # 4. 假设生成
        hypotheses = await self._generate_hypotheses(
            literature_insights,
            data_patterns,
            cross_domain_insights
        )
        
        # 5. 假设评估
        evaluated_hypotheses = await self._evaluate_hypotheses(hypotheses)
        
        return evaluated_hypotheses
    
    async def experiment_design(self, hypothesis: Dict[str, Any]) -> Dict[str, Any]:
        """实验设计"""
        
        # 1. 变量识别
        variables = await self._identify_variables(hypothesis)
        
        # 2. 实验方法选择
        methods = await self._select_experimental_methods(hypothesis, variables)
        
        # 3. 样本量计算
        sample_size = await self._calculate_sample_size(hypothesis, methods)
        
        # 4. 控制条件设计
        controls = await self._design_controls(variables)
        
        # 5. 统计分析计划
        analysis_plan = await self._create_analysis_plan(hypothesis, methods)
        
        return {
            "hypothesis": hypothesis,
            "variables": variables,
            "methods": methods,
            "sample_size": sample_size,
            "controls": controls,
            "analysis_plan": analysis_plan,
            "timeline": await self._estimate_timeline(methods),
            "resources": await self._estimate_resources(methods)
        }
    
    async def result_interpretation(self, experimental_data: Dict[str, Any]) -> Dict[str, Any]:
        """结果解释"""
        
        # 1. 统计分析
        statistical_results = await self._perform_statistical_analysis(experimental_data)
        
        # 2. 效应量计算
        effect_sizes = await self._calculate_effect_sizes(experimental_data)
        
        # 3. 置信区间
        confidence_intervals = await self._calculate_confidence_intervals(experimental_data)
        
        # 4. 结果解释
        interpretation = await self._interpret_results(
            statistical_results,
            effect_sizes,
            confidence_intervals
        )
        
        # 5. 局限性分析
        limitations = await self._analyze_limitations(experimental_data)
        
        # 6. 后续研究建议
        future_directions = await self._suggest_future_research(interpretation)
        
        return {
            "statistical_results": statistical_results,
            "interpretation": interpretation,
            "limitations": limitations,
            "future_directions": future_directions,
            "confidence_level": await self._assess_confidence(interpretation)
        }
 
# 使用示例
research_assistant = ScientificResearchAssistant([
    "machine_learning", "neuroscience", "psychology"
])
 
research_context = {
    "domain": "cognitive_science",
    "research_question": "How does attention affect memory consolidation?",
    "existing_literature": ["paper1.pdf", "paper2.pdf"],
    "preliminary_data": "data.csv"
}
 
# hypotheses = await research_assistant.hypothesis_generation(research_context)
# experiment = await research_assistant.experiment_design(hypotheses[0])

class CreativeContentGenerator:
    """创意内容生成器"""
    
    def __init__(self, creative_domains: List[str]):
        self.creative_domains = creative_domains
        self.style_models = {}
        self.creativity_metrics = {}
    
    async def generate_story(self, prompt: str, style_preferences: Dict[str, Any]) -> Dict[str, Any]:
        """生成故事"""
        
        # 1. 创意构思
        creative_concepts = await self._brainstorm_concepts(prompt)
        
        # 2. 情节结构设计
        plot_structure = await self._design_plot_structure(creative_concepts)
        
        # 3. 角色创建
        characters = await self._create_characters(plot_structure)
        
        # 4. 世界观构建
        world_building = await self._build_world(plot_structure, characters)
        
        # 5. 风格适配
        style_adapted_content = await self._adapt_style(
            plot_structure, 
            characters, 
            world_building, 
            style_preferences
        )
        
        # 6. 内容生成
        story_content = await self._generate_narrative(style_adapted_content)
        
        # 7. 创意评估
        creativity_score = await self._evaluate_creativity(story_content)
        
        return {
            "story": story_content,
            "characters": characters,
            "world_building": world_building,
            "creativity_score": creativity_score,
            "style_analysis": await self._analyze_style(story_content)
        }
    
    async def collaborative_creation(self, human_input: str, ai_suggestions: List[str]) -> Dict[str, Any]:
        """协作创作"""
        
        # 1. 人类意图理解
        human_intent = await self._understand_human_intent(human_input)
        
        # 2. AI建议整合
        integrated_suggestions = await self._integrate_ai_suggestions(
            human_intent, 
            ai_suggestions
        )
        
        # 3. 创意融合
        fused_creativity = await self._fuse_human_ai_creativity(
            human_intent, 
            integrated_suggestions
        )
        
        # 4. 协作优化
        optimized_content = await self._optimize_collaboration(fused_creativity)
        
        return {
            "collaborative_content": optimized_content,
            "human_contribution": await self._measure_human_contribution(optimized_content),
            "ai_contribution": await self._measure_ai_contribution(optimized_content),
            "synergy_score": await self._calculate_synergy_score(optimized_content)
        }
 
# 使用示例
creative_generator = CreativeContentGenerator([
    "storytelling", "poetry", "screenwriting", "game_design"
])
 
story_prompt = "A world where memories can be traded like currency"
style_prefs = {
    "genre": "science_fiction",
    "tone": "philosophical",
    "length": "short_story",
    "target_audience": "adult"
}
 
# story = await creative_generator.generate_story(story_prompt, style_prefs)

class QuantumEnhancedLLM:
    """量子增强LLM"""
    
    def __init__(self, quantum_config: Dict[str, Any]):
        self.quantum_config = quantum_config
        self.classical_components = {}
        self.quantum_components = {}
    
    async def quantum_attention(self, query: Any, key: Any, value: Any) -> Any:
        """量子注意力机制"""
        
        # 1. 量子态编码
        quantum_query = await self._encode_quantum_state(query)
        quantum_key = await self._encode_quantum_state(key)
        quantum_value = await self._encode_quantum_state(value)
        
        # 2. 量子并行计算
        quantum_attention_scores = await self._quantum_parallel_attention(
            quantum_query, 
            quantum_key
        )
        
        # 3. 量子纠缠利用
        entangled_representations = await self._leverage_quantum_entanglement(
            quantum_attention_scores, 
            quantum_value
        )
        
        # 4. 量子测量
        classical_output = await self._quantum_measurement(entangled_representations)
        
        return classical_output
    
    async def quantum_optimization(self, loss_function: Any) -> Dict[str, Any]:
        """量子优化"""
        
        # 1. 量子退火
        annealed_parameters = await self._quantum_annealing(loss_function)
        
        # 2. 变分量子算法
        variational_result = await self._variational_quantum_eigensolver(loss_function)
        
        # 3. 量子近似优化
        qaoa_result = await self._quantum_approximate_optimization(loss_function)
        
        # 4. 结果整合
        optimized_parameters = await self._integrate_quantum_results(
            annealed_parameters,
            variational_result,
            qaoa_result
        )
        
        return {
            "optimized_parameters": optimized_parameters,
            "quantum_advantage": await self._measure_quantum_advantage(),
            "convergence_speed": await self._measure_convergence_speed()
        }
 
class NeuromorphicLLM:
    """神经形态LLM"""
    
    def __init__(self, neuromorphic_config: Dict[str, Any]):
        self.neuromorphic_config = neuromorphic_config
        self.spiking_networks = {}
        self.synaptic_plasticity = {}
    
    async def spiking_neural_processing(self, input_data: Any) -> Any:
        """脉冲神经网络处理"""
        
        # 1. 脉冲编码
        spike_trains = await self._encode_to_spikes(input_data)
        
        # 2. 时序动态处理
        temporal_dynamics = await self._process_temporal_dynamics(spike_trains)
        
        # 3. 突触可塑性学习
        plasticity_updates = await self._synaptic_plasticity_learning(temporal_dynamics)
        
        # 4. 脉冲解码
        output_data = await self._decode_from_spikes(temporal_dynamics)
        
        return {
            "output": output_data,
            "energy_consumption": await self._measure_energy_consumption(),
            "plasticity_changes": plasticity_updates
        }

class AIGovernanceFramework:
    """AI治理框架"""
    
    def __init__(self):
        self.ethical_principles = []
        self.compliance_rules = {}
        self.audit_mechanisms = {}
    
    async def ethical_assessment(self, ai_system: Dict[str, Any]) -> Dict[str, Any]:
        """伦理评估"""
        
        assessment_results = {}
        
        # 1. 公平性评估
        fairness_score = await self._assess_fairness(ai_system)
        assessment_results["fairness"] = fairness_score
        
        # 2. 透明性评估
        transparency_score = await self._assess_transparency(ai_system)
        assessment_results["transparency"] = transparency_score
        
        # 3. 问责性评估
        accountability_score = await self._assess_accountability(ai_system)
        assessment_results["accountability"] = accountability_score
        
        # 4. 隐私保护评估
        privacy_score = await self._assess_privacy_protection(ai_system)
        assessment_results["privacy"] = privacy_score
        
        # 5. 安全性评估
        safety_score = await self._assess_safety(ai_system)
        assessment_results["safety"] = safety_score
        
        # 6. 综合评估
        overall_score = await self._calculate_overall_ethical_score(assessment_results)
        
        return {
            "individual_scores": assessment_results,
            "overall_score": overall_score,
            "recommendations": await self._generate_ethical_recommendations(assessment_results),
            "compliance_status": await self._check_compliance(assessment_results)
        }
    
    async def bias_mitigation(self, model_outputs: List[Any], 
                            protected_attributes: List[str]) -> Dict[str, Any]:
        """偏见缓解"""
        
        # 1. 偏见检测
        detected_biases = await self._detect_biases(model_outputs, protected_attributes)
        
        # 2. 偏见量化
        bias_metrics = await self._quantify_biases(detected_biases)
        
        # 3. 缓解策略生成
        mitigation_strategies = await self._generate_mitigation_strategies(bias_metrics)
        
        # 4. 策略应用
        mitigated_outputs = await self._apply_mitigation_strategies(
            model_outputs, 
            mitigation_strategies
        )
        
        # 5. 效果验证
        mitigation_effectiveness = await self._validate_mitigation_effectiveness(
            model_outputs, 
            mitigated_outputs, 
            protected_attributes
        )
        
        return {
            "detected_biases": detected_biases,
            "mitigation_strategies": mitigation_strategies,
            "mitigated_outputs": mitigated_outputs,
            "effectiveness": mitigation_effectiveness
        }