//! # Automated Model Design Framework
//!
//! This module provides automated model design capabilities that can generate
//! complete model architectures based on high-level specifications and requirements.
//! It integrates with the Neural Architecture Search framework for optimization.
//!
//! ## Features
//!
//! - **Task-Based Design**: Automatically design models for specific tasks
//! - **Requirement-Driven**: Generate architectures based on performance, resource, and domain requirements
//! - **Template-Based Generation**: Use architectural templates as starting points
//! - **Constraint Satisfaction**: Ensure generated models satisfy all specified constraints
//! - **Multi-Modal Support**: Design models for text, vision, and multimodal tasks
//! - **Deployment-Aware**: Consider target deployment environment constraints
//! - **Automated Configuration**: Generate complete model configurations with hyperparameters
//!
//! ## Usage Example
//!
//! ```rust,no_run
//! use trustformers_models::automated_model_design::{
//!     ModelDesigner, DesignRequirements, TaskType, PerformanceTarget, ResourceConstraints
//! };
//! use trustformers_core::Result;
//!
//! fn main() -> Result<()> {
//!     // Define design requirements
//!     let requirements = DesignRequirements::builder()
//!         .task(TaskType::TextGeneration)
//!         .performance_target(PerformanceTarget::HighAccuracy)
//!         .resource_constraints(ResourceConstraints::mobile())
//!         .domain("scientific")
//!         .max_parameters(7_000_000_000)
//!         .build()?;
//!
//!     // Create designer and generate model
//!     let designer = ModelDesigner::new();
//!     let model_design = designer.design_model(requirements)?;
//!
//!     println!("Generated model: {}", model_design.name);
//!     println!("Architecture: {:?}", model_design.architecture);
//!     Ok(())
//! }
//! ```

use crate::neural_architecture_search::{
    Architecture, NASConfig, NeuralArchitectureSearcher, SearchSpace,
};
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::fmt;
use trustformers_core::{errors::invalid_input, Result};

/// Automated model designer
pub struct ModelDesigner {
    /// Design templates for different model families
    pub templates: HashMap<String, ArchitectureTemplate>,
    /// Constraint solver for requirement satisfaction
    pub constraint_solver: ConstraintSolver,
    /// Design patterns library
    pub patterns: DesignPatternLibrary,
}

impl ModelDesigner {
    /// Create a new model designer with default templates
    pub fn new() -> Self {
        Self {
            templates: Self::default_templates(),
            constraint_solver: ConstraintSolver::new(),
            patterns: DesignPatternLibrary::default(),
        }
    }

    /// Design a model based on requirements
    pub fn design_model(&self, requirements: DesignRequirements) -> Result<ModelDesign> {
        // Step 1: Select appropriate architecture family
        let architecture_family = self.select_architecture_family(&requirements)?;

        // Step 2: Get base template
        let template = self.templates.get(&architecture_family).ok_or_else(|| {
            invalid_input(format!(
                "No template found for architecture family: {}",
                architecture_family
            ))
        })?;

        // Step 3: Customize template based on requirements
        let customized_template = self.customize_template(template, &requirements)?;

        // Step 4: Apply design patterns
        let enhanced_design = self.apply_design_patterns(customized_template, &requirements)?;

        // Step 5: Validate and optimize
        let validated_design = self.validate_and_optimize(enhanced_design, &requirements)?;

        // Step 6: Generate final model design
        Ok(validated_design)
    }

    /// Generate model variants with different trade-offs
    pub fn generate_variants(&self, requirements: DesignRequirements) -> Result<Vec<ModelDesign>> {
        let mut variants = Vec::new();

        // Generate efficiency-optimized variant
        let mut efficiency_req = requirements.clone();
        efficiency_req.performance_target = PerformanceTarget::HighEfficiency;
        variants.push(self.design_model(efficiency_req)?);

        // Generate accuracy-optimized variant
        let mut accuracy_req = requirements.clone();
        accuracy_req.performance_target = PerformanceTarget::HighAccuracy;
        variants.push(self.design_model(accuracy_req)?);

        // Generate balanced variant
        let mut balanced_req = requirements.clone();
        balanced_req.performance_target = PerformanceTarget::Balanced;
        variants.push(self.design_model(balanced_req)?);

        Ok(variants)
    }

    /// Design a model using neural architecture search
    pub fn design_with_nas(&self, requirements: DesignRequirements) -> Result<ModelDesign> {
        // Convert requirements to NAS configuration
        let nas_config = self.requirements_to_nas_config(&requirements)?;

        // Run NAS
        let mut searcher = NeuralArchitectureSearcher::new(nas_config)?;
        let best_evaluation = searcher.search()?;

        // Convert NAS result to model design
        self.nas_result_to_model_design(best_evaluation.architecture, &requirements)
    }

    fn select_architecture_family(&self, requirements: &DesignRequirements) -> Result<String> {
        match (&requirements.task_type, &requirements.modality) {
            (TaskType::TextGeneration, Modality::Text) => Ok("decoder_transformer".to_string()),
            (TaskType::TextClassification, Modality::Text) => Ok("encoder_transformer".to_string()),
            (TaskType::Translation, Modality::Text) => {
                Ok("encoder_decoder_transformer".to_string())
            },
            (TaskType::ImageClassification, Modality::Vision) => {
                Ok("vision_transformer".to_string())
            },
            (TaskType::ImageGeneration, Modality::Vision) => {
                Ok("diffusion_transformer".to_string())
            },
            (TaskType::VisionLanguage, Modality::Multimodal) => {
                Ok("multimodal_transformer".to_string())
            },
            (TaskType::SpeechRecognition, Modality::Audio) => Ok("speech_transformer".to_string()),
            (TaskType::VideoUnderstanding, Modality::Video) => Ok("video_transformer".to_string()),
            (TaskType::Custom(_), _) => Ok("generic_transformer".to_string()),
            (TaskType::NamedEntityRecognition, Modality::Text) => {
                Ok("encoder_transformer".to_string())
            },
            (TaskType::QuestionAnswering, Modality::Text) => {
                Ok("encoder_decoder_transformer".to_string())
            },
            (TaskType::Summarization, Modality::Text) => {
                Ok("encoder_decoder_transformer".to_string())
            },
            (TaskType::ObjectDetection, Modality::Vision) => Ok("vision_transformer".to_string()),
            (TaskType::ImageSegmentation, Modality::Vision) => Ok("vision_transformer".to_string()),
            (TaskType::SpeechSynthesis, Modality::Audio) => Ok("speech_transformer".to_string()),
            // Default fallback for any other combinations
            _ => Ok("generic_transformer".to_string()),
        }
    }

    fn customize_template(
        &self,
        template: &ArchitectureTemplate,
        requirements: &DesignRequirements,
    ) -> Result<ArchitectureTemplate> {
        let mut customized = template.clone();

        // Adjust based on performance target
        match requirements.performance_target {
            PerformanceTarget::HighAccuracy => {
                customized.scale_parameters("num_layers", 1.5);
                customized.scale_parameters("hidden_size", 1.2);
                customized.set_component_choice("attention_type", "standard");
            },
            PerformanceTarget::HighEfficiency => {
                customized.scale_parameters("num_layers", 0.7);
                customized.scale_parameters("hidden_size", 0.8);
                customized.set_component_choice("attention_type", "grouped_query");
            },
            PerformanceTarget::Balanced => {
                // Keep default template values
            },
        }

        // Adjust based on resource constraints
        if let Some(ref constraints) = requirements.resource_constraints {
            if let Some(max_params) = constraints.max_parameters {
                let current_params = customized.estimate_parameters();
                if current_params > max_params {
                    let scale_factor = (max_params as f32 / current_params as f32).sqrt();
                    customized.scale_parameters("hidden_size", scale_factor);
                    customized.scale_parameters("num_layers", scale_factor.sqrt());
                }
            }

            if let Some(max_memory) = constraints.max_memory_gb {
                let current_memory = customized.estimate_memory_gb();
                if current_memory > max_memory {
                    let scale_factor = (max_memory / current_memory).sqrt();
                    customized.scale_parameters("hidden_size", scale_factor);
                }
            }
        }

        // Adjust based on domain
        if let Some(ref domain) = requirements.domain {
            match domain.as_str() {
                "code" => {
                    customized.set_component_choice("activation", "gelu");
                    customized.scale_parameters("vocab_size", 1.5); // Larger vocab for code
                },
                "scientific" => {
                    customized.set_component_choice("normalization", "rms_norm");
                    customized.scale_parameters("max_position_embeddings", 2.0);
                    // Longer documents
                },
                "legal" => {
                    customized.scale_parameters("max_position_embeddings", 4.0); // Very long documents
                    customized.set_component_choice("attention_type", "sparse");
                },
                _ => {}, // Use default for other domains
            }
        }

        Ok(customized)
    }

    fn apply_design_patterns(
        &self,
        template: ArchitectureTemplate,
        requirements: &DesignRequirements,
    ) -> Result<ArchitectureTemplate> {
        let mut enhanced = template;

        // Apply efficiency patterns if needed
        if matches!(
            requirements.performance_target,
            PerformanceTarget::HighEfficiency
        ) {
            enhanced = self.patterns.apply_efficiency_patterns(enhanced)?;
        }

        // Apply domain-specific patterns
        if let Some(ref domain) = requirements.domain {
            enhanced = self.patterns.apply_domain_patterns(enhanced, domain)?;
        }

        // Apply task-specific patterns
        enhanced = self.patterns.apply_task_patterns(enhanced, &requirements.task_type)?;

        Ok(enhanced)
    }

    fn validate_and_optimize(
        &self,
        design: ArchitectureTemplate,
        requirements: &DesignRequirements,
    ) -> Result<ModelDesign> {
        // Validate constraints
        self.constraint_solver.validate_constraints(&design, requirements)?;

        // Optimize configuration
        let optimized_config =
            self.constraint_solver.optimize_configuration(&design, requirements)?;

        // Generate final design
        Ok(ModelDesign {
            name: self.generate_model_name(&design, requirements),
            architecture: design.to_architecture()?,
            config: optimized_config,
            metadata: ModelDesignMetadata {
                task_type: requirements.task_type.clone(),
                modality: requirements.modality.clone(),
                performance_target: requirements.performance_target.clone(),
                created_at: std::time::SystemTime::now(),
                design_rationale: self.generate_design_rationale(&design, requirements),
            },
            estimated_metrics: self.estimate_model_metrics(&design, requirements)?,
        })
    }

    fn requirements_to_nas_config(&self, requirements: &DesignRequirements) -> Result<NASConfig> {
        let search_space = match requirements.task_type {
            TaskType::ImageClassification | TaskType::ImageGeneration => {
                SearchSpace::vision_transformer_space()
            },
            _ => SearchSpace::transformer_space(),
        };

        let mut objectives = Vec::new();
        match requirements.performance_target {
            PerformanceTarget::HighAccuracy => {
                objectives.push(
                    crate::neural_architecture_search::OptimizationObjective::Accuracy {
                        weight: 0.8,
                    },
                );
                objectives.push(
                    crate::neural_architecture_search::OptimizationObjective::Efficiency {
                        weight: 0.2,
                    },
                );
            },
            PerformanceTarget::HighEfficiency => {
                objectives.push(
                    crate::neural_architecture_search::OptimizationObjective::Efficiency {
                        weight: 0.7,
                    },
                );
                objectives.push(
                    crate::neural_architecture_search::OptimizationObjective::Latency {
                        weight: 0.3,
                    },
                );
            },
            PerformanceTarget::Balanced => {
                objectives.push(
                    crate::neural_architecture_search::OptimizationObjective::Accuracy {
                        weight: 0.5,
                    },
                );
                objectives.push(
                    crate::neural_architecture_search::OptimizationObjective::Efficiency {
                        weight: 0.5,
                    },
                );
            },
        }

        Ok(NASConfig {
            strategy: crate::neural_architecture_search::SearchStrategy::Evolutionary,
            search_space,
            objectives,
            max_evaluations: 500,
            ..Default::default()
        })
    }

    fn nas_result_to_model_design(
        &self,
        architecture: Architecture,
        requirements: &DesignRequirements,
    ) -> Result<ModelDesign> {
        let config = HashMap::new(); // Would populate with hyperparameters

        Ok(ModelDesign {
            name: format!("NAS-{}", requirements.task_type.name()),
            architecture,
            config,
            metadata: ModelDesignMetadata {
                task_type: requirements.task_type.clone(),
                modality: requirements.modality.clone(),
                performance_target: requirements.performance_target.clone(),
                created_at: std::time::SystemTime::now(),
                design_rationale: "Generated using Neural Architecture Search".to_string(),
            },
            estimated_metrics: ModelMetrics::default(),
        })
    }

    fn generate_model_name(
        &self,
        design: &ArchitectureTemplate,
        requirements: &DesignRequirements,
    ) -> String {
        let base_name = requirements.task_type.name();
        let size_suffix = self.get_size_suffix(design);
        let domain_prefix = requirements.domain.as_deref().unwrap_or("general");

        format!("{}-{}-{}", domain_prefix, base_name, size_suffix)
    }

    fn get_size_suffix(&self, design: &ArchitectureTemplate) -> &str {
        let params = design.estimate_parameters();
        match params {
            0..=100_000_000 => "small",
            100_000_001..=1_000_000_000 => "base",
            1_000_000_001..=10_000_000_000 => "large",
            _ => "xl",
        }
    }

    fn generate_design_rationale(
        &self,
        _design: &ArchitectureTemplate,
        requirements: &DesignRequirements,
    ) -> String {
        let mut rationale = Vec::new();

        rationale.push(format!(
            "Designed for {} task",
            requirements.task_type.name()
        ));
        rationale.push(format!(
            "Optimized for {}",
            requirements.performance_target.name()
        ));

        if let Some(ref domain) = requirements.domain {
            rationale.push(format!("Specialized for {} domain", domain));
        }

        if let Some(ref constraints) = requirements.resource_constraints {
            if constraints.max_parameters.is_some() || constraints.max_memory_gb.is_some() {
                rationale.push("Resource-constrained design".to_string());
            }
        }

        rationale.join(". ")
    }

    fn estimate_model_metrics(
        &self,
        design: &ArchitectureTemplate,
        _requirements: &DesignRequirements,
    ) -> Result<ModelMetrics> {
        Ok(ModelMetrics {
            estimated_parameters: design.estimate_parameters(),
            estimated_memory_gb: design.estimate_memory_gb(),
            estimated_flops: design.estimate_flops(),
            estimated_latency_ms: design.estimate_latency_ms(),
            estimated_accuracy: design.estimate_accuracy(),
        })
    }

    fn default_templates() -> HashMap<String, ArchitectureTemplate> {
        let mut templates = HashMap::new();

        // Decoder transformer (GPT-style)
        templates.insert(
            "decoder_transformer".to_string(),
            ArchitectureTemplate::decoder_transformer(),
        );

        // Encoder transformer (BERT-style)
        templates.insert(
            "encoder_transformer".to_string(),
            ArchitectureTemplate::encoder_transformer(),
        );

        // Encoder-decoder transformer (T5-style)
        templates.insert(
            "encoder_decoder_transformer".to_string(),
            ArchitectureTemplate::encoder_decoder_transformer(),
        );

        // Vision transformer
        templates.insert(
            "vision_transformer".to_string(),
            ArchitectureTemplate::vision_transformer(),
        );

        // Multimodal transformer
        templates.insert(
            "multimodal_transformer".to_string(),
            ArchitectureTemplate::multimodal_transformer(),
        );

        templates
    }
}

impl Default for ModelDesigner {
    fn default() -> Self {
        Self::new()
    }
}

/// Requirements for automated model design
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DesignRequirements {
    /// Primary task the model will perform
    pub task_type: TaskType,
    /// Input/output modality
    pub modality: Modality,
    /// Performance optimization target
    pub performance_target: PerformanceTarget,
    /// Resource constraints
    pub resource_constraints: Option<ResourceConstraints>,
    /// Domain specialization
    pub domain: Option<String>,
    /// Maximum number of parameters
    pub max_parameters: Option<usize>,
    /// Target deployment environment
    pub deployment_environment: Option<DeploymentEnvironment>,
    /// Custom requirements
    pub custom_requirements: HashMap<String, String>,
}

impl DesignRequirements {
    pub fn builder() -> DesignRequirementsBuilder {
        DesignRequirementsBuilder::new()
    }
}

/// Builder for design requirements
pub struct DesignRequirementsBuilder {
    requirements: DesignRequirements,
}

impl Default for DesignRequirementsBuilder {
    fn default() -> Self {
        Self::new()
    }
}

impl DesignRequirementsBuilder {
    pub fn new() -> Self {
        Self {
            requirements: DesignRequirements {
                task_type: TaskType::TextGeneration,
                modality: Modality::Text,
                performance_target: PerformanceTarget::Balanced,
                resource_constraints: None,
                domain: None,
                max_parameters: None,
                deployment_environment: None,
                custom_requirements: HashMap::new(),
            },
        }
    }

    pub fn task(mut self, task_type: TaskType) -> Self {
        self.requirements.task_type = task_type;
        self
    }

    pub fn modality(mut self, modality: Modality) -> Self {
        self.requirements.modality = modality;
        self
    }

    pub fn performance_target(mut self, target: PerformanceTarget) -> Self {
        self.requirements.performance_target = target;
        self
    }

    pub fn resource_constraints(mut self, constraints: ResourceConstraints) -> Self {
        self.requirements.resource_constraints = Some(constraints);
        self
    }

    pub fn domain(mut self, domain: &str) -> Self {
        self.requirements.domain = Some(domain.to_string());
        self
    }

    pub fn max_parameters(mut self, max_params: usize) -> Self {
        self.requirements.max_parameters = Some(max_params);
        self
    }

    pub fn deployment_environment(mut self, env: DeploymentEnvironment) -> Self {
        self.requirements.deployment_environment = Some(env);
        self
    }

    pub fn custom_requirement(mut self, key: &str, value: &str) -> Self {
        self.requirements.custom_requirements.insert(key.to_string(), value.to_string());
        self
    }

    pub fn build(self) -> Result<DesignRequirements> {
        // Validate requirements
        if let Some(ref constraints) = self.requirements.resource_constraints {
            if let (Some(max_params), Some(req_max_params)) =
                (constraints.max_parameters, self.requirements.max_parameters)
            {
                if req_max_params > max_params {
                    return Err(invalid_input(
                        format!("max_parameters conflicts with resource constraints: req: {}, constraint: {}", req_max_params, max_params)
                    ));
                }
            }
        }

        Ok(self.requirements)
    }
}

/// Task types for model design
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum TaskType {
    TextGeneration,
    TextClassification,
    NamedEntityRecognition,
    QuestionAnswering,
    Translation,
    Summarization,
    ImageClassification,
    ImageGeneration,
    ObjectDetection,
    ImageSegmentation,
    SpeechRecognition,
    SpeechSynthesis,
    VideoUnderstanding,
    VisionLanguage,
    Custom(String),
}

impl TaskType {
    pub fn name(&self) -> &str {
        match self {
            TaskType::TextGeneration => "text-generation",
            TaskType::TextClassification => "text-classification",
            TaskType::NamedEntityRecognition => "ner",
            TaskType::QuestionAnswering => "qa",
            TaskType::Translation => "translation",
            TaskType::Summarization => "summarization",
            TaskType::ImageClassification => "image-classification",
            TaskType::ImageGeneration => "image-generation",
            TaskType::ObjectDetection => "object-detection",
            TaskType::ImageSegmentation => "image-segmentation",
            TaskType::SpeechRecognition => "speech-recognition",
            TaskType::SpeechSynthesis => "speech-synthesis",
            TaskType::VideoUnderstanding => "video-understanding",
            TaskType::VisionLanguage => "vision-language",
            TaskType::Custom(name) => name,
        }
    }
}

/// Input/output modalities
#[derive(Debug, Clone, Serialize, Deserialize, Hash, Eq, PartialEq)]
pub enum Modality {
    Text,
    Vision,
    Audio,
    Video,
    Multimodal,
}

/// Performance optimization targets
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum PerformanceTarget {
    HighAccuracy,
    HighEfficiency,
    Balanced,
}

impl PerformanceTarget {
    pub fn name(&self) -> &str {
        match self {
            PerformanceTarget::HighAccuracy => "high-accuracy",
            PerformanceTarget::HighEfficiency => "high-efficiency",
            PerformanceTarget::Balanced => "balanced",
        }
    }
}

/// Resource constraints for model design
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ResourceConstraints {
    /// Maximum number of parameters
    pub max_parameters: Option<usize>,
    /// Maximum memory usage in GB
    pub max_memory_gb: Option<f32>,
    /// Maximum inference latency in milliseconds
    pub max_latency_ms: Option<f32>,
    /// Maximum energy consumption per inference
    pub max_energy_mj: Option<f32>,
    /// Minimum throughput (inferences per second)
    pub min_throughput: Option<f32>,
}

impl ResourceConstraints {
    /// Create mobile-friendly constraints
    pub fn mobile() -> Self {
        Self {
            max_parameters: Some(1_000_000_000), // 1B parameters
            max_memory_gb: Some(4.0),
            max_latency_ms: Some(100.0),
            max_energy_mj: Some(50.0),
            min_throughput: Some(10.0),
        }
    }

    /// Create edge device constraints
    pub fn edge() -> Self {
        Self {
            max_parameters: Some(100_000_000), // 100M parameters
            max_memory_gb: Some(1.0),
            max_latency_ms: Some(50.0),
            max_energy_mj: Some(10.0),
            min_throughput: Some(20.0),
        }
    }

    /// Create server/cloud constraints
    pub fn server() -> Self {
        Self {
            max_parameters: Some(100_000_000_000), // 100B parameters
            max_memory_gb: Some(80.0),
            max_latency_ms: Some(1000.0),
            max_energy_mj: None,
            min_throughput: Some(1.0),
        }
    }
}

/// Deployment environment specifications
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum DeploymentEnvironment {
    Mobile {
        os: String,
        memory_gb: f32,
    },
    Edge {
        device_type: String,
        compute_units: u32,
    },
    Cloud {
        provider: String,
        instance_type: String,
    },
    OnPremise {
        hardware_specs: HashMap<String, String>,
    },
}

/// Architecture template for model generation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ArchitectureTemplate {
    /// Template name
    pub name: String,
    /// Base parameter values
    pub base_parameters: HashMap<String, i32>,
    /// Component choices
    pub component_choices: HashMap<String, String>,
    /// Scaling factors for different parameters
    pub scaling_factors: HashMap<String, f32>,
    /// Template metadata
    pub metadata: TemplateMetadata,
}

impl ArchitectureTemplate {
    pub fn decoder_transformer() -> Self {
        let mut base_parameters = HashMap::new();
        base_parameters.insert("num_layers".to_string(), 12);
        base_parameters.insert("hidden_size".to_string(), 768);
        base_parameters.insert("num_heads".to_string(), 12);
        base_parameters.insert("intermediate_size".to_string(), 3072);
        base_parameters.insert("vocab_size".to_string(), 32000);
        base_parameters.insert("max_position_embeddings".to_string(), 2048);

        let mut component_choices = HashMap::new();
        component_choices.insert("activation".to_string(), "gelu".to_string());
        component_choices.insert("attention_type".to_string(), "standard".to_string());
        component_choices.insert("normalization".to_string(), "layer_norm".to_string());
        component_choices.insert("position_encoding".to_string(), "absolute".to_string());

        Self {
            name: "Decoder Transformer".to_string(),
            base_parameters,
            component_choices,
            scaling_factors: HashMap::new(),
            metadata: TemplateMetadata {
                architecture_family: "transformer".to_string(),
                suitable_tasks: vec!["text_generation".to_string(), "causal_lm".to_string()],
                parameter_range: (100_000_000, 100_000_000_000),
            },
        }
    }

    pub fn encoder_transformer() -> Self {
        let mut base_parameters = HashMap::new();
        base_parameters.insert("num_layers".to_string(), 12);
        base_parameters.insert("hidden_size".to_string(), 768);
        base_parameters.insert("num_heads".to_string(), 12);
        base_parameters.insert("intermediate_size".to_string(), 3072);
        base_parameters.insert("vocab_size".to_string(), 30522);
        base_parameters.insert("max_position_embeddings".to_string(), 512);

        let mut component_choices = HashMap::new();
        component_choices.insert("activation".to_string(), "gelu".to_string());
        component_choices.insert("attention_type".to_string(), "standard".to_string());
        component_choices.insert("normalization".to_string(), "layer_norm".to_string());
        component_choices.insert("position_encoding".to_string(), "absolute".to_string());

        Self {
            name: "Encoder Transformer".to_string(),
            base_parameters,
            component_choices,
            scaling_factors: HashMap::new(),
            metadata: TemplateMetadata {
                architecture_family: "transformer".to_string(),
                suitable_tasks: vec![
                    "text_classification".to_string(),
                    "token_classification".to_string(),
                ],
                parameter_range: (100_000_000, 1_000_000_000),
            },
        }
    }

    pub fn encoder_decoder_transformer() -> Self {
        let mut base_parameters = HashMap::new();
        base_parameters.insert("num_layers".to_string(), 12);
        base_parameters.insert("num_decoder_layers".to_string(), 12);
        base_parameters.insert("hidden_size".to_string(), 768);
        base_parameters.insert("num_heads".to_string(), 12);
        base_parameters.insert("intermediate_size".to_string(), 2048);
        base_parameters.insert("vocab_size".to_string(), 32128);
        base_parameters.insert("max_position_embeddings".to_string(), 512);

        let mut component_choices = HashMap::new();
        component_choices.insert("activation".to_string(), "relu".to_string());
        component_choices.insert("attention_type".to_string(), "standard".to_string());
        component_choices.insert("normalization".to_string(), "rms_norm".to_string());
        component_choices.insert("position_encoding".to_string(), "relative".to_string());

        Self {
            name: "Encoder-Decoder Transformer".to_string(),
            base_parameters,
            component_choices,
            scaling_factors: HashMap::new(),
            metadata: TemplateMetadata {
                architecture_family: "transformer".to_string(),
                suitable_tasks: vec!["translation".to_string(), "summarization".to_string()],
                parameter_range: (200_000_000, 10_000_000_000),
            },
        }
    }

    pub fn vision_transformer() -> Self {
        let mut base_parameters = HashMap::new();
        base_parameters.insert("num_layers".to_string(), 12);
        base_parameters.insert("hidden_size".to_string(), 768);
        base_parameters.insert("num_heads".to_string(), 12);
        base_parameters.insert("intermediate_size".to_string(), 3072);
        base_parameters.insert("patch_size".to_string(), 16);
        base_parameters.insert("image_size".to_string(), 224);
        base_parameters.insert("num_classes".to_string(), 1000);

        let mut component_choices = HashMap::new();
        component_choices.insert("pooling".to_string(), "cls_token".to_string());
        component_choices.insert("normalization".to_string(), "layer_norm".to_string());
        component_choices.insert("activation".to_string(), "gelu".to_string());

        Self {
            name: "Vision Transformer".to_string(),
            base_parameters,
            component_choices,
            scaling_factors: HashMap::new(),
            metadata: TemplateMetadata {
                architecture_family: "vision_transformer".to_string(),
                suitable_tasks: vec!["image_classification".to_string()],
                parameter_range: (85_000_000, 600_000_000),
            },
        }
    }

    pub fn multimodal_transformer() -> Self {
        let mut base_parameters = HashMap::new();
        base_parameters.insert("num_layers".to_string(), 24);
        base_parameters.insert("hidden_size".to_string(), 1024);
        base_parameters.insert("num_heads".to_string(), 16);
        base_parameters.insert("intermediate_size".to_string(), 4096);
        base_parameters.insert("vocab_size".to_string(), 32000);
        base_parameters.insert("vision_hidden_size".to_string(), 1024);
        base_parameters.insert("vision_num_layers".to_string(), 24);

        let mut component_choices = HashMap::new();
        component_choices.insert("fusion_method".to_string(), "cross_attention".to_string());
        component_choices.insert("vision_encoder".to_string(), "clip".to_string());
        component_choices.insert("text_decoder".to_string(), "llama".to_string());

        Self {
            name: "Multimodal Transformer".to_string(),
            base_parameters,
            component_choices,
            scaling_factors: HashMap::new(),
            metadata: TemplateMetadata {
                architecture_family: "multimodal_transformer".to_string(),
                suitable_tasks: vec![
                    "vision_language".to_string(),
                    "image_captioning".to_string(),
                ],
                parameter_range: (1_000_000_000, 70_000_000_000),
            },
        }
    }

    pub fn scale_parameters(&mut self, parameter: &str, factor: f32) {
        if let Some(value) = self.base_parameters.get_mut(parameter) {
            *value = (*value as f32 * factor) as i32;
        }
        self.scaling_factors.insert(parameter.to_string(), factor);
    }

    pub fn set_component_choice(&mut self, component: &str, choice: &str) {
        self.component_choices.insert(component.to_string(), choice.to_string());
    }

    pub fn estimate_parameters(&self) -> usize {
        let hidden_size = *self.base_parameters.get("hidden_size").unwrap_or(&768) as f64;
        let num_layers = *self.base_parameters.get("num_layers").unwrap_or(&12) as f64;
        let vocab_size = *self.base_parameters.get("vocab_size").unwrap_or(&32000) as f64;
        let intermediate_size = *self
            .base_parameters
            .get("intermediate_size")
            .unwrap_or(&((hidden_size * 4.0) as i32)) as f64;

        // Parameter estimation for transformer architectures
        let embedding_params = vocab_size * hidden_size;
        let attention_params = num_layers * (4.0 * hidden_size * hidden_size);
        let ffn_params = num_layers * (2.0 * hidden_size * intermediate_size);
        let norm_params = num_layers * 2.0 * hidden_size;

        (embedding_params + attention_params + ffn_params + norm_params) as usize
    }

    pub fn estimate_memory_gb(&self) -> f32 {
        let params = self.estimate_parameters() as f32;
        // Rough estimation: 4 bytes per parameter + activation memory
        (params * 4.0 * 2.0) / (1024.0 * 1024.0 * 1024.0)
    }

    pub fn estimate_flops(&self) -> f64 {
        let hidden_size = *self.base_parameters.get("hidden_size").unwrap_or(&768) as f64;
        let num_layers = *self.base_parameters.get("num_layers").unwrap_or(&12) as f64;
        let seq_length = 512.0; // Assumed sequence length

        // Rough FLOP estimation for transformer forward pass
        let attention_flops = num_layers * seq_length * seq_length * hidden_size;
        let ffn_flops = num_layers * seq_length * hidden_size * hidden_size * 8.0;

        attention_flops + ffn_flops
    }

    pub fn estimate_latency_ms(&self) -> f32 {
        let flops = self.estimate_flops() as f32;
        // Rough latency estimation assuming 1 TFLOP/s compute
        flops / 1e12 * 1000.0
    }

    pub fn estimate_accuracy(&self) -> f32 {
        let params = self.estimate_parameters() as f32;
        let complexity = (params / 1e9).log10().max(0.0);

        // Rough accuracy estimation based on parameter count
        0.7 + complexity * 0.1
    }

    pub fn to_architecture(&self) -> Result<Architecture> {
        let mut architecture = Architecture::new();

        // Copy dimensions
        for (key, value) in &self.base_parameters {
            architecture.dimensions.insert(key.clone(), *value);
        }

        // Copy choices
        for (key, value) in &self.component_choices {
            architecture.choices.insert(key.clone(), value.clone());
        }

        Ok(architecture)
    }
}

/// Metadata for architecture templates
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TemplateMetadata {
    pub architecture_family: String,
    pub suitable_tasks: Vec<String>,
    pub parameter_range: (usize, usize), // (min_params, max_params)
}

/// Design pattern library for architectural optimizations
#[derive(Debug, Clone)]
pub struct DesignPatternLibrary {
    efficiency_patterns: Vec<EfficiencyPattern>,
    domain_patterns: HashMap<String, Vec<DomainPattern>>,
    task_patterns: HashMap<String, Vec<TaskPattern>>,
}

impl Default for DesignPatternLibrary {
    fn default() -> Self {
        Self {
            efficiency_patterns: Self::default_efficiency_patterns(),
            domain_patterns: Self::default_domain_patterns(),
            task_patterns: Self::default_task_patterns(),
        }
    }
}

impl DesignPatternLibrary {
    fn apply_efficiency_patterns(
        &self,
        mut template: ArchitectureTemplate,
    ) -> Result<ArchitectureTemplate> {
        for pattern in &self.efficiency_patterns {
            template = pattern.apply(template)?;
        }
        Ok(template)
    }

    fn apply_domain_patterns(
        &self,
        mut template: ArchitectureTemplate,
        domain: &str,
    ) -> Result<ArchitectureTemplate> {
        if let Some(patterns) = self.domain_patterns.get(domain) {
            for pattern in patterns {
                template = pattern.apply(template)?;
            }
        }
        Ok(template)
    }

    fn apply_task_patterns(
        &self,
        mut template: ArchitectureTemplate,
        task_type: &TaskType,
    ) -> Result<ArchitectureTemplate> {
        if let Some(patterns) = self.task_patterns.get(task_type.name()) {
            for pattern in patterns {
                template = pattern.apply(template)?;
            }
        }
        Ok(template)
    }

    fn default_efficiency_patterns() -> Vec<EfficiencyPattern> {
        vec![
            EfficiencyPattern::GroupedQueryAttention,
            EfficiencyPattern::SparseAttention,
            EfficiencyPattern::LayerReduction,
        ]
    }

    fn default_domain_patterns() -> HashMap<String, Vec<DomainPattern>> {
        let mut patterns = HashMap::new();
        patterns.insert(
            "code".to_string(),
            vec![DomainPattern::CodeSpecific, DomainPattern::LongContext],
        );
        patterns.insert(
            "scientific".to_string(),
            vec![
                DomainPattern::ScientificNotation,
                DomainPattern::ExtendedVocab,
            ],
        );
        patterns
    }

    fn default_task_patterns() -> HashMap<String, Vec<TaskPattern>> {
        let mut patterns = HashMap::new();
        patterns.insert(
            "text-generation".to_string(),
            vec![TaskPattern::CausalMask, TaskPattern::RotaryEmbeddings],
        );
        patterns.insert(
            "text-classification".to_string(),
            vec![
                TaskPattern::BidirectionalAttention,
                TaskPattern::ClassificationHead,
            ],
        );
        patterns
    }
}

/// Efficiency optimization patterns
#[derive(Debug, Clone)]
pub enum EfficiencyPattern {
    GroupedQueryAttention,
    SparseAttention,
    LayerReduction,
    ParameterSharing,
}

impl EfficiencyPattern {
    pub fn apply(&self, mut template: ArchitectureTemplate) -> Result<ArchitectureTemplate> {
        match self {
            EfficiencyPattern::GroupedQueryAttention => {
                template.set_component_choice("attention_type", "grouped_query");
            },
            EfficiencyPattern::SparseAttention => {
                template.set_component_choice("attention_type", "sparse");
            },
            EfficiencyPattern::LayerReduction => {
                template.scale_parameters("num_layers", 0.8);
            },
            EfficiencyPattern::ParameterSharing => {
                // Would implement parameter sharing logic
            },
        }
        Ok(template)
    }
}

/// Domain-specific optimization patterns
#[derive(Debug, Clone)]
pub enum DomainPattern {
    CodeSpecific,
    ScientificNotation,
    LegalDocument,
    MedicalTerminology,
    LongContext,
    ExtendedVocab,
}

impl DomainPattern {
    pub fn apply(&self, mut template: ArchitectureTemplate) -> Result<ArchitectureTemplate> {
        match self {
            DomainPattern::CodeSpecific => {
                template.set_component_choice("activation", "gelu");
                template.scale_parameters("vocab_size", 1.2);
            },
            DomainPattern::ScientificNotation => {
                template.set_component_choice("normalization", "rms_norm");
            },
            DomainPattern::LegalDocument => {
                template.scale_parameters("max_position_embeddings", 4.0);
            },
            DomainPattern::MedicalTerminology => {
                template.scale_parameters("vocab_size", 1.5);
            },
            DomainPattern::LongContext => {
                template.scale_parameters("max_position_embeddings", 2.0);
                template.set_component_choice("attention_type", "sparse");
            },
            DomainPattern::ExtendedVocab => {
                template.scale_parameters("vocab_size", 1.3);
            },
        }
        Ok(template)
    }
}

/// Task-specific optimization patterns
#[derive(Debug, Clone)]
pub enum TaskPattern {
    CausalMask,
    BidirectionalAttention,
    RotaryEmbeddings,
    ClassificationHead,
    GenerationHead,
    CrossAttention,
}

impl TaskPattern {
    pub fn apply(&self, mut template: ArchitectureTemplate) -> Result<ArchitectureTemplate> {
        match self {
            TaskPattern::CausalMask => {
                // Would set causal masking in attention
            },
            TaskPattern::BidirectionalAttention => {
                // Would enable bidirectional attention
            },
            TaskPattern::RotaryEmbeddings => {
                template.set_component_choice("position_encoding", "rotary");
            },
            TaskPattern::ClassificationHead => {
                // Would add classification head configuration
            },
            TaskPattern::GenerationHead => {
                // Would add generation head configuration
            },
            TaskPattern::CrossAttention => {
                template.set_component_choice("attention_type", "cross_attention");
            },
        }
        Ok(template)
    }
}

/// Constraint solver for requirement satisfaction
#[derive(Debug, Clone)]
pub struct ConstraintSolver {
    #[allow(dead_code)]
    tolerance: f32,
}

impl ConstraintSolver {
    pub fn new() -> Self {
        Self { tolerance: 0.1 }
    }

    pub fn validate_constraints(
        &self,
        template: &ArchitectureTemplate,
        requirements: &DesignRequirements,
    ) -> Result<()> {
        // Check parameter constraints
        if let Some(max_params) = requirements.max_parameters {
            let current_params = template.estimate_parameters();
            if current_params > max_params {
                return Err(invalid_input(format!(
                    "Model has {} parameters, maximum allowed: {}",
                    current_params, max_params
                )));
            }
        }

        // Check resource constraints
        if let Some(ref constraints) = requirements.resource_constraints {
            if let Some(max_memory) = constraints.max_memory_gb {
                let current_memory = template.estimate_memory_gb();
                if current_memory > max_memory {
                    return Err(invalid_input(format!(
                        "Model requires {:.1}GB memory, maximum allowed: {:.1}GB",
                        current_memory, max_memory
                    )));
                }
            }

            if let Some(max_latency) = constraints.max_latency_ms {
                let current_latency = template.estimate_latency_ms();
                if current_latency > max_latency {
                    return Err(invalid_input(format!(
                        "Model has {:.1}ms latency, maximum allowed: {:.1}ms",
                        current_latency, max_latency
                    )));
                }
            }
        }

        Ok(())
    }

    pub fn optimize_configuration(
        &self,
        _template: &ArchitectureTemplate,
        _requirements: &DesignRequirements,
    ) -> Result<HashMap<String, String>> {
        // Would implement constraint optimization
        let mut config = HashMap::new();
        config.insert("learning_rate".to_string(), "1e-4".to_string());
        config.insert("batch_size".to_string(), "32".to_string());
        config.insert("warmup_steps".to_string(), "1000".to_string());
        Ok(config)
    }
}

impl Default for ConstraintSolver {
    fn default() -> Self {
        Self::new()
    }
}

/// Final model design output
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ModelDesign {
    /// Model name
    pub name: String,
    /// Generated architecture
    pub architecture: Architecture,
    /// Hyperparameter configuration
    pub config: HashMap<String, String>,
    /// Design metadata
    pub metadata: ModelDesignMetadata,
    /// Estimated performance metrics
    pub estimated_metrics: ModelMetrics,
}

/// Metadata for model design
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ModelDesignMetadata {
    pub task_type: TaskType,
    pub modality: Modality,
    pub performance_target: PerformanceTarget,
    pub created_at: std::time::SystemTime,
    pub design_rationale: String,
}

/// Estimated model performance metrics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ModelMetrics {
    pub estimated_parameters: usize,
    pub estimated_memory_gb: f32,
    pub estimated_flops: f64,
    pub estimated_latency_ms: f32,
    pub estimated_accuracy: f32,
}

impl Default for ModelMetrics {
    fn default() -> Self {
        Self {
            estimated_parameters: 0,
            estimated_memory_gb: 0.0,
            estimated_flops: 0.0,
            estimated_latency_ms: 0.0,
            estimated_accuracy: 0.0,
        }
    }
}

impl fmt::Display for ModelDesign {
    fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
        write!(
            f,
            "ModelDesign {{ name: {}, parameters: {}, memory: {:.1}GB, latency: {:.1}ms }}",
            self.name,
            self.estimated_metrics.estimated_parameters,
            self.estimated_metrics.estimated_memory_gb,
            self.estimated_metrics.estimated_latency_ms
        )
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_design_requirements_builder() {
        let requirements = DesignRequirements::builder()
            .task(TaskType::TextClassification)
            .performance_target(PerformanceTarget::HighAccuracy)
            .domain("scientific")
            .max_parameters(1_000_000_000)
            .build()
            .unwrap();

        assert!(matches!(
            requirements.task_type,
            TaskType::TextClassification
        ));
        assert!(matches!(
            requirements.performance_target,
            PerformanceTarget::HighAccuracy
        ));
        assert_eq!(requirements.domain, Some("scientific".to_string()));
        assert_eq!(requirements.max_parameters, Some(1_000_000_000));
    }

    #[test]
    fn test_model_designer_creation() {
        let designer = ModelDesigner::new();
        assert!(!designer.templates.is_empty());
        assert!(designer.templates.contains_key("decoder_transformer"));
        assert!(designer.templates.contains_key("encoder_transformer"));
    }

    #[test]
    fn test_architecture_template_estimation() {
        let template = ArchitectureTemplate::decoder_transformer();

        let params = template.estimate_parameters();
        assert!(params > 100_000_000); // Should be reasonable for base model

        let memory = template.estimate_memory_gb();
        assert!(memory > 0.5 && memory < 10.0); // Reasonable memory usage

        let flops = template.estimate_flops();
        assert!(flops > 1e9); // Should require significant computation
    }

    #[test]
    fn test_template_scaling() {
        let mut template = ArchitectureTemplate::decoder_transformer();
        let original_hidden_size = *template.base_parameters.get("hidden_size").unwrap();

        template.scale_parameters("hidden_size", 1.5);
        let new_hidden_size = *template.base_parameters.get("hidden_size").unwrap();

        assert_eq!(new_hidden_size, (original_hidden_size as f32 * 1.5) as i32);
    }

    #[test]
    fn test_resource_constraints() {
        let mobile_constraints = ResourceConstraints::mobile();
        assert_eq!(mobile_constraints.max_parameters, Some(1_000_000_000));
        assert_eq!(mobile_constraints.max_memory_gb, Some(4.0));

        let edge_constraints = ResourceConstraints::edge();
        assert_eq!(edge_constraints.max_parameters, Some(100_000_000));
        assert_eq!(edge_constraints.max_memory_gb, Some(1.0));
    }

    #[test]
    fn test_model_design_flow() {
        let requirements = DesignRequirements::builder()
            .task(TaskType::TextGeneration)
            .performance_target(PerformanceTarget::Balanced)
            .resource_constraints(ResourceConstraints::mobile())
            .build()
            .unwrap();

        let designer = ModelDesigner::new();
        let design = designer.design_model(requirements).unwrap();

        assert!(!design.name.is_empty());
        assert!(!design.architecture.dimensions.is_empty());
        assert!(design.estimated_metrics.estimated_parameters > 0);
    }

    #[test]
    fn test_constraint_validation() {
        let solver = ConstraintSolver::new();
        let template = ArchitectureTemplate::decoder_transformer();

        let requirements = DesignRequirements::builder()
            .task(TaskType::TextGeneration)
            .max_parameters(10_000) // Very small limit
            .build()
            .unwrap();

        // Should fail due to parameter constraint
        assert!(solver.validate_constraints(&template, &requirements).is_err());
    }

    #[test]
    fn test_design_pattern_application() {
        let patterns = DesignPatternLibrary::default();
        let template = ArchitectureTemplate::decoder_transformer();

        let enhanced = patterns.apply_efficiency_patterns(template).unwrap();
        // Check if efficiency patterns were applied
        assert!(enhanced.component_choices.contains_key("attention_type"));
    }

    #[test]
    fn test_task_type_names() {
        assert_eq!(TaskType::TextGeneration.name(), "text-generation");
        assert_eq!(TaskType::ImageClassification.name(), "image-classification");
        assert_eq!(
            TaskType::Custom("custom-task".to_string()).name(),
            "custom-task"
        );
    }

    #[test]
    fn test_architecture_conversion() {
        let template = ArchitectureTemplate::vision_transformer();
        let architecture = template.to_architecture().unwrap();

        assert!(!architecture.dimensions.is_empty());
        assert!(!architecture.choices.is_empty());
        assert!(architecture.dimensions.contains_key("num_layers"));
        assert!(architecture.choices.contains_key("pooling"));
    }
}
