"""
Benchmarking utilities for evaluating pruned models.

This module provides tools for evaluating the performance of pruned models
on standard benchmarks like LAMBADA, BoolQ, etc.
"""

import logging
from typing import Dict, List, Any, Union, Optional
import time
import torch
from transformers import PreTrainedModel, AutoTokenizer

logger = logging.getLogger(__name__)

def time_inference(
    model: PreTrainedModel,
    tokenizer: AutoTokenizer,
    prompt: str,
    max_new_tokens: int = 100,
    num_runs: int = 5,
    warmup_runs: int = 2,
) -> Dict[str, Any]:
    """
    Measure inference time for a model.
    
    Args:
        model: Model to evaluate
        tokenizer: Tokenizer to use
        prompt: Input prompt for generation
        max_new_tokens: Maximum number of tokens to generate
        num_runs: Number of inference runs to average over
        warmup_runs: Number of initial runs to discard (for warm-up)
        
    Returns:
        Dictionary containing timing results
    """
    # Prepare input
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    
    # Warmup runs
    for _ in range(warmup_runs):
        with torch.no_grad():
            _ = model.generate(
                inputs.input_ids,
                max_new_tokens=max_new_tokens,
                do_sample=False,
            )
    
    # Timed runs
    times = []
    for _ in range(num_runs):
        start_time = time.time()
        with torch.no_grad():
            output = model.generate(
                inputs.input_ids,
                max_new_tokens=max_new_tokens,
                do_sample=False,
            )
        end_time = time.time()
        times.append(end_time - start_time)
    
    generated_tokens = output.size(1) - inputs.input_ids.size(1)
    
    return {
        "avg_time": sum(times) / len(times),
        "min_time": min(times),
        "max_time": max(times),
        "tokens_per_second": generated_tokens / (sum(times) / len(times)),
        "num_runs": num_runs,
        "generated_tokens": generated_tokens,
    }

def compare_models_inference(
    original_model: PreTrainedModel,
    pruned_model: PreTrainedModel,
    tokenizer: AutoTokenizer,
    prompts: List[str],
    max_new_tokens: int = 100,
) -> Dict[str, Any]:
    """
    Compare inference performance between original and pruned models.
    
    Args:
        original_model: Original model before pruning
        pruned_model: Model after pruning
        tokenizer: Tokenizer to use
        prompts: List of input prompts for generation
        max_new_tokens: Maximum number of tokens to generate
        
    Returns:
        Dictionary containing comparison results
    """
    # Ensure models are in eval mode
    original_model.eval()
    pruned_model.eval()
    
    original_results = []
    pruned_results = []
    
    for prompt in prompts:
        logger.info(f"Testing prompt: {prompt[:50]}...")
        
        original_timing = time_inference(
            original_model, tokenizer, prompt, max_new_tokens
        )
        original_results.append(original_timing)
        
        pruned_timing = time_inference(
            pruned_model, tokenizer, prompt, max_new_tokens
        )
        pruned_results.append(pruned_timing)
    
    # Aggregate results
    avg_original_time = sum(r["avg_time"] for r in original_results) / len(original_results)
    avg_pruned_time = sum(r["avg_time"] for r in pruned_results) / len(pruned_results)
    
    avg_original_tps = sum(r["tokens_per_second"] for r in original_results) / len(original_results)
    avg_pruned_tps = sum(r["tokens_per_second"] for r in pruned_results) / len(pruned_results)
    
    speedup = avg_original_time / avg_pruned_time if avg_pruned_time > 0 else float('inf')
    tps_improvement = (avg_pruned_tps / avg_original_tps - 1) * 100 if avg_original_tps > 0 else float('inf')
    
    return {
        "avg_original_time": avg_original_time,
        "avg_pruned_time": avg_pruned_time,
        "avg_original_tokens_per_second": avg_original_tps,
        "avg_pruned_tokens_per_second": avg_pruned_tps,
        "speedup": speedup,
        "tps_improvement_percent": tps_improvement,
        "num_prompts": len(prompts),
    }

# Note: This is a placeholder for future implementation
def evaluate_on_lm_benchmarks(
    model: PreTrainedModel,
    benchmarks: List[str] = ["lambada", "boolq"],
    num_few_shot: int = 0,
) -> Dict[str, Any]:
    """
    Placeholder for evaluating a model on language modeling benchmarks.
    
    Args:
        model: Model to evaluate
        benchmarks: List of benchmarks to evaluate on
        num_few_shot: Number of few-shot examples to use
        
    Returns:
        Dictionary containing benchmark results
    """
    logger.warning("LM benchmark evaluation not implemented yet. Will be added in a future version.")
    
    # This would use libraries like lm-eval-harness, EleutherAI's evaluation framework, etc.
    return {
        "message": "Not implemented yet",
        "benchmarks": benchmarks,
    }