"""
S2 Decomposed: synthetic-query-generator
==========================================

Auto-generated by S2 Auto-Decomposer.

Original skill: learning/synthetic-query-generator
Scale structure:
  L0 (Micro): 9 operations
  L1 (Meso): 4 operations
  L2 (Macro): 2 operations
  L3 (Meta): 1 orchestrator

Model routing:
  L0 -> codegemma
  L1 -> qwen3
  L2 -> qwen3
  L3 -> deepseek_r1
"""

```python
from typing import Any, Dict, List, Optional, Tuple
from skills.s2.context_stack import ScaleLevel
from skills.s2.composer import S2Composer
import random
import string

# L0 MICRO SKILLS
def micro_retrieve_domain_information(domain: str) -> Dict[str, Any]:
    """Retrieve structured knowledge about a target domain."""
    # Simulate fetching domain info from documentation/API
    return {
        "domain": domain,
        "key_concepts": ["data structures", "algorithms", "system design"],
        "resources": ["https://example.com/docs", "FAQ.md"],
        "difficulty_levels": ["easy", "medium", "hard"]
    }

def micro_understand_query_intent(user_query: str) -> Dict[str, Any]:
    """Analyze and identify the intent of a user query."""
    intent = "general"
    if "how to" in user_query.lower():
        intent = "how_to"
    elif "what is" in user_query.lower():
        intent = "definition"
    elif "why" in user_query.lower():
        intent = "explanation"
    return {
        "intent": intent,
        "query": user_query
    }

def micro_generate_query_variation(original_text: str) -> Dict[str, Any]:
    """Create variations of a text while preserving meaning."""
    variations = [
        f"How to {original_text.replace(' ', '_')}",
        f"What is {original_text.replace(' ', '_')}",
        f"Explain {original_text.replace(' ', '_')}",
        f"Can you {original_text.replace(' ', '_')}",
        f"Steps to {original_text.replace(' ', '_')}"
    ]
    return {
        "original": original_text,
        "variations": random.sample(variations, 1)
    }

def micro_assess_query_difficulty(query_text: str) -> Dict[str, Any]:
    """Evaluate complexity level of a query."""
    keywords = {
        "easy": ["what is", "define", "list"],
        "medium": ["how to", "explain", "compare"],
        "hard": ["optimize", "design", "implement"]
    }
    
    for level, terms in keywords.items():
        if any(term in query_text.lower() for term in terms):
            return {"difficulty": level}
    return {"difficulty": "medium"}

def micro_extract_metadata_from_corpus(corpus_item_id: str) -> Dict[str, Any]:
    """Extract metadata from a corpus item."""
    # Simulate metadata retrieval
    return {
        "item_id": corpus_item_id,
        "title": f"Document {corpus_item_id}",
        "source": "internal_db",
        "tags": ["tech", "guides"],
        "difficulty": "medium"
    }

# L1 MESO SKILLS
def meso_generate_initial_query_sets(domain_info: Dict[str, Any]) -> Dict[str, Any]:
    """Create initial synthetic query sets for a domain."""
    domain = domain_info["domain"]
    key_concepts = domain_info["key_concepts"]
    
    queries = []
    for concept in key_concepts:
        # Generate variations for different intents
        for intent in ["how_to", "definition", "explanation"]:
            query = f"{intent.capitalize()} {concept}"
            queries.append({
                "query": query,
                "intent": intent,
                "difficulty": "medium"
            })
    
    return {
        "initial_queries": queries,
        "domain": domain
    }

def meso_evaluate_and_refine_queries(existing_corpus: List[Dict[str, Any]]) -> Dict[str, Any]:
    """Refine queries based on corpus validation results."""
    refined_queries = []
    for query in existing_corpus:
        # Simulate validation against corpus
        if random.random() > 0.3:  # 70% chance to keep query
            refined_queries.append(query)
    
    return {
        "refined_queries": refined_queries,
        "validation_results": "partial_success"
    }

def meso_expand_coverage_with_mesos(expansion_level: int) -> Dict[str, Any]:
    """Expand query coverage through iterative refinement."""
    expansion_results = []
    for level in range(expansion_level):
        # Simulate expansion process
        expansion_results.append({
            "level": level + 1,
            "new_queries": [f"Expanded query {i}" for i in range(5)]
        })
    
    return {
        "expansion_steps": expansion_results,
        "coverage_status": "expanded"
    }

# L2 MACRO SKILLS
def macro_expand_coverage_with_mesos(expansion_level: int) -> Dict[str, Any]:
    """Macro to expand query coverage through meso operations."""
    meso_result = meso_expand_coverage_with_mesos(expansion_level)
    return {
        "macro_result": meso_result,
        "coverage_status": "expanded"
    }

def macro_orchestrator_synthetic_query_generation(domain: str) -> Dict[str, Any]:
    """Orchestrator for synthetic query generation workflow."""
    # First retrieve domain info
    domain_info = micro_retrieve_domain_information(domain)
    
    # Generate initial queries
    initial_queries = meso_generate_initial_query_sets(domain_info)
    
    # Evaluate and refine queries
    refined_queries = meso_evaluate_and_refine_queries(initial_queries["initial_queries"])
    
    # Expand coverage
    expansion_results = macro_expand_coverage_with_mesos(3)


# Skill metadata
SKILL_METADATA = {
    "original_id": "learning/synthetic-query-generator",
    "original_name": "synthetic-query-generator",
    "scale_structure": {
    "L0": [
        "retrieve_domain_information: (domain): Retrieve structured or unstructured knowledge about a specific target domain from reliable sources like documentation, FAQs, or external APIs.",
        "understand_query_intent: (user_intent): Analyze and identify the underlying purpose of a user-provided query string (e.g., 'how to change password', 'what is S2 framework').",
        "generate_query_variation: (original_text: str) -> str: Create slight modifications or alternative phrasings for an existing text snippet, preserving its meaning.",
        "assess_query_difficulty: (query_text: str) -> int/difficulty_level: Evaluate the complexity level of a query (e.g., 'easy', 'medium', 'hard') based on factors like domain specificity and ambiguity.",
        "extract_metadata_from_corpus: (corpus_item_id: str) -> dict: Pull relevant metadata associated with a specific retrieval corpus item (potential target document/record).",
        "generate_initial_query_sets: (domain_info): Combine retrieving domain information, understanding multiple intents, and generating variations for each to create the first batch of synthetic queries.",
        "evaluate_and_refine_queries: (existing_corpus: List[Document]): Compose understanding query intent, assessing difficulty, and potentially re-generating variations (using `generate_query_variation`) based on corpus validation results.",
        "expand_coverage_with_mesos: (expansion_level: int): Bundle the process of identifying coverage gaps via a composed operation (`evaluate_and_refine_queries`), generating new query sets by intent and difficulty level using `generate_initial_query_sets`, validating them against an existing corpus, and storing the output with metadata.",
        "orchestrator_synthetic_query_generation: (domain: str): Coordinate the entire process of expanding coverage for a given domain. This involves triggering the L2 macro (`expand_coverage_with_mesos`) multiple times or adjusting parameters based on feedback loops from corpus validation, managing dependencies between macros and ensuring retrieval systems are tested comprehensively using the generated queries."
    ],
    "L1": [
        "generate_initial_query_sets: (domain_info): Combine retrieving domain information, understanding multiple intents, and generating variations for each to create the first batch of synthetic queries.",
        "evaluate_and_refine_queries: (existing_corpus: List[Document]): Compose understanding query intent, assessing difficulty, and potentially re-generating variations (using `generate_query_variation`) based on corpus validation results.",
        "expand_coverage_with_mesos: (expansion_level: int): Bundle the process of identifying coverage gaps via a composed operation (`evaluate_and_refine_queries`), generating new query sets by intent and difficulty level using `generate_initial_query_sets`, validating them against an existing corpus, and storing the output with metadata.",
        "orchestrator_synthetic_query_generation: (domain: str): Coordinate the entire process of expanding coverage for a given domain. This involves triggering the L2 macro (`expand_coverage_with_mesos`) multiple times or adjusting parameters based on feedback loops from corpus validation, managing dependencies between macros and ensuring retrieval systems are tested comprehensively using the generated queries."
    ],
    "L2": [
        "expand_coverage_with_mesos: (expansion_level: int): Bundle the process of identifying coverage gaps via a composed operation (`evaluate_and_refine_queries`), generating new query sets by intent and difficulty level using `generate_initial_query_sets`, validating them against an existing corpus, and storing the output with metadata.",
        "orchestrator_synthetic_query_generation: (domain: str): Coordinate the entire process of expanding coverage for a given domain. This involves triggering the L2 macro (`expand_coverage_with_mesos`) multiple times or adjusting parameters based on feedback loops from corpus validation, managing dependencies between macros and ensuring retrieval systems are tested comprehensively using the generated queries."
    ],
    "L3": [
        "learning_synthetic_query_generator_orchestrator"
    ]
},
    "skill_tree": {
    "learning/synthetic_query_generator/orchestrator": [
        "learning/synthetic_query_generator/expand_coverage_with_mesos",
        "learning/synthetic_query_generator/orchestrator_synthetic_query_generation"
    ],
    "learning/synthetic_query_generator/expand_coverage_with_mesos": [
        "learning/synthetic_query_generator/retrieve_domain_information",
        "learning/synthetic_query_generator/understand_query_intent",
        "learning/synthetic_query_generator/generate_query_variation"
    ],
    "learning/synthetic_query_generator/orchestrator_synthetic_query_generation": [
        "learning/synthetic_query_generator/retrieve_domain_information",
        "learning/synthetic_query_generator/understand_query_intent",
        "learning/synthetic_query_generator/generate_query_variation"
    ],
    "learning/synthetic_query_generator/generate_initial_query_sets": [
        "learning/synthetic_query_generator/retrieve_domain_information",
        "learning/synthetic_query_generator/understand_query_intent",
        "learning/synthetic_query_generator/generate_query_variation"
    ],
    "learning/synthetic_query_generator/evaluate_and_refine_queries": [
        "learning/synthetic_query_generator/retrieve_domain_information",
        "learning/synthetic_query_generator/understand_query_intent",
        "learning/synthetic_query_generator/generate_query_variation"
    ]
},
    "model_routing": {
    "L0": "codegemma",
    "L1": "qwen3",
    "L2": "qwen3",
    "L3": "deepseek_r1"
},
}
