"""
Code Generator Agent
====================

Specialized agent for generating BioQL code from natural language.

Uses LLM to:
- Understand user intent
- Generate BioQL-specific quantum code
- Follow best practices
- Include error handling
"""

from dataclasses import dataclass
from typing import Dict, List, Optional, Any

# Optional logging
try:
    from loguru import logger
except ImportError:
    import logging
    logger = logging.getLogger(__name__)


@dataclass
class CodeGenerationRequest:
    """Request for code generation."""
    description: str
    domain: str = "general"  # general, bioinformatics, chemistry
    complexity: str = "medium"  # simple, medium, complex
    target_backend: str = "simulator"
    shots: int = 1000
    constraints: Optional[Dict[str, Any]] = None


@dataclass
class CodeGenerationResult:
    """Result of code generation."""
    code: str
    explanation: str
    estimated_qubits: int
    estimated_depth: int
    warnings: List[str]
    metadata: Dict[str, Any]


class CodeGeneratorAgent:
    """
    Agent specialized in generating BioQL quantum code.

    Example:
        >>> agent = CodeGeneratorAgent(model="claude-3-5-sonnet")
        >>> result = agent.generate(
        ...     "Create a quantum circuit to simulate protein folding"
        ... )
        >>> print(result.code)
    """

    def __init__(self, model: str = "claude-3-5-sonnet-20241022"):
        """
        Initialize code generator agent.

        Args:
            model: LLM model to use
        """
        self.model = model
        self.bioql_context = self._load_bioql_context()

        logger.info(f"CodeGeneratorAgent initialized with {model}")

    def _load_bioql_context(self) -> str:
        """Load BioQL-specific context for LLM."""
        return """
BioQL is a quantum computing library for bioinformatics with natural language interface.

Core Functions:
- quantum(program, api_key, backend="simulator", shots=1000)
  Execute quantum program with natural language

Available Patterns (164B+ natural language variations):
- Bell States: "Create a Bell state", "Make EPR pair"
- Superposition: "Apply Hadamard", "Create superposition"
- QFT: "Run QFT on N qubits", "Apply quantum Fourier transform"
- Grover: "Search with Grover", "Quantum search"
- VQE: "Optimize with VQE", "Variational quantum eigensolver"
- Protein Folding: "Simulate protein folding", "Fold hemoglobin"
- Drug Docking: "Simulate drug binding to receptor"
- DNA Analysis: "Analyze DNA sequence", "Find DNA patterns"

Backends:
- "simulator": Local Aer simulator
- "ibm_quantum": IBM Quantum hardware
- "ionq": IonQ quantum computer

Best Practices:
1. Start with simulator for testing
2. Use appropriate number of shots (100-10000)
3. Include API key for billing
4. Handle errors gracefully
5. Validate results
"""

    def _create_prompt(self, request: CodeGenerationRequest) -> str:
        """Create specialized prompt for BioQL code generation."""
        prompt = f"""You are a BioQL quantum programming expert. Generate Python code using BioQL library.

{self.bioql_context}

User Request:
{request.description}

Domain: {request.domain}
Complexity: {request.complexity}
Backend: {request.target_backend}
Shots: {request.shots}

Requirements:
1. Write complete, runnable Python code
2. Use BioQL's natural language interface
3. Include proper error handling
4. Add comments explaining quantum operations
5. Show how to interpret results
6. Follow BioQL best practices

Generate the code now:
"""
        return prompt

    def generate(
        self,
        description: str,
        domain: str = "general",
        complexity: str = "medium",
        backend: str = "simulator",
        shots: int = 1000,
        use_local_model: bool = False
    ) -> CodeGenerationResult:
        """
        Generate BioQL code from natural language description.

        Args:
            description: What the code should do
            domain: Application domain
            complexity: Code complexity level
            backend: Quantum backend
            shots: Number of shots
            use_local_model: Use local Ollama model instead of API

        Returns:
            CodeGenerationResult with generated code

        Example:
            >>> agent = CodeGeneratorAgent()
            >>> result = agent.generate(
            ...     "Create a Bell state and measure it",
            ...     complexity="simple"
            ... )
            >>> print(result.code)
        """
        logger.info(f"Generating code for: {description}")

        request = CodeGenerationRequest(
            description=description,
            domain=domain,
            complexity=complexity,
            target_backend=backend,
            shots=shots
        )

        # Create prompt
        prompt = self._create_prompt(request)

        # Generate code (using mock for now - will integrate real LLM)
        code = self._generate_code_mock(request)

        # Analyze generated code
        estimated_qubits = self._estimate_qubits(code)
        estimated_depth = self._estimate_depth(code)
        warnings = self._analyze_warnings(code)

        result = CodeGenerationResult(
            code=code,
            explanation=f"Generated BioQL code for: {description}",
            estimated_qubits=estimated_qubits,
            estimated_depth=estimated_depth,
            warnings=warnings,
            metadata={
                "model": self.model,
                "domain": domain,
                "complexity": complexity
            }
        )

        logger.info(f"Code generated: {estimated_qubits} qubits, {estimated_depth} depth")
        return result

    def _generate_code_mock(self, request: CodeGenerationRequest) -> str:
        """Mock code generation (will be replaced with real LLM)."""

        # Template-based generation for common patterns
        templates = {
            "bell": '''from bioql import quantum

# Create a Bell state - maximally entangled 2-qubit state
result = quantum(
    "Create a Bell state",
    api_key="your_api_key_here",
    backend="{backend}",
    shots={shots}
)

print(f"Results: {{result.counts}}")
print(f"Cost: ${{result.cost_estimate:.4f}}")

# Expected results: approximately 50% |00⟩ and 50% |11⟩
# This demonstrates quantum entanglement
''',
            "qft": '''from bioql import quantum

# Quantum Fourier Transform on {n} qubits
result = quantum(
    "Run QFT on {n} qubits",
    api_key="your_api_key_here",
    backend="{backend}",
    shots={shots}
)

print(f"QFT Results: {{result.counts}}")

# QFT is used in:
# - Shor's algorithm
# - Phase estimation
# - Signal processing
''',
            "protein": '''from bioql import quantum

# Simulate protein folding using quantum optimization
result = quantum(
    "Simulate protein folding with 8 amino acids",
    api_key="your_api_key_here",
    backend="{backend}",
    shots={shots}
)

print(f"Protein conformations: {{result.counts}}")
print(f"Bio interpretation: {{result.bio_interpretation}}")

# Analyzes:
# - Energy minimization
# - Structural stability
# - Folding pathways
''',
            "drug": '''from bioql import quantum

# Drug-receptor binding simulation
result = quantum(
    "Simulate drug binding to GLP1R receptor for diabetes treatment",
    api_key="your_api_key_here",
    backend="{backend}",
    shots={shots}
)

print(f"Binding affinity: {{result.bio_interpretation}}")
print(f"Quantum states: {{result.counts}}")

# Evaluates:
# - Molecular docking
# - Binding energy
# - Drug efficacy
'''
        }

        # Simple keyword matching
        desc_lower = request.description.lower()

        if "bell" in desc_lower or "epr" in desc_lower or "entangle" in desc_lower:
            template = templates["bell"]
        elif "qft" in desc_lower or "fourier" in desc_lower:
            template = templates["qft"]
            template = template.replace("{n}", "4")  # Default 4 qubits
        elif "protein" in desc_lower or "fold" in desc_lower:
            template = templates["protein"]
        elif "drug" in desc_lower or "binding" in desc_lower or "docking" in desc_lower:
            template = templates["drug"]
        else:
            # Generic template
            template = templates["bell"]  # Default to Bell state

        # Fill in template
        code = template.format(
            backend=request.target_backend,
            shots=request.shots
        )

        return code

    def _estimate_qubits(self, code: str) -> int:
        """Estimate number of qubits from code."""
        # Simple heuristic
        if "bell" in code.lower():
            return 2
        elif "4 qubits" in code.lower():
            return 4
        elif "8 amino" in code.lower():
            return 8
        else:
            return 2

    def _estimate_depth(self, code: str) -> int:
        """Estimate circuit depth from code."""
        # Simple heuristic
        if "bell" in code.lower():
            return 2  # H + CNOT
        elif "qft" in code.lower():
            return 10  # QFT depth
        else:
            return 5

    def _analyze_warnings(self, code: str) -> List[str]:
        """Analyze code for potential issues."""
        warnings = []

        if "your_api_key_here" in code:
            warnings.append("Remember to replace 'your_api_key_here' with your actual API key")

        if "ibm_quantum" in code or "ionq" in code:
            warnings.append("Using real quantum hardware - costs will apply")

        return warnings

    def optimize(self, code: str) -> str:
        """
        Optimize generated code.

        Uses LLM to improve:
        - Performance
        - Resource usage
        - Code quality
        """
        logger.info("Optimizing generated code")

        # TODO: Implement LLM-based optimization
        # For now, return as-is
        return code

    def explain(self, code: str) -> str:
        """
        Generate detailed explanation of code.

        Uses LLM to explain:
        - What the code does
        - How it works
        - Expected results
        """
        logger.info("Generating code explanation")

        # Simple explanation for now
        return f"This BioQL code uses quantum computing to solve the specified problem."
