# /// script
# dependencies = ["transformers>=4.46.0", "torch", "peft", "bitsandbytes", "accelerate", "datasets", "evalplus", "tqdm", "protobuf", "sentencepiece", "mistral-common>=1.5.0", "huggingface_hub"]
# ///

"""
HumanEval Evaluation: Base Devstral vs Fine-tuned Alizee-Coder
Runs on HF Jobs with GPU support
"""

import os
import re
import json
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
from peft import PeftModel
from datasets import load_dataset
from tqdm import tqdm
from huggingface_hub import HfApi

print("=" * 60)
print("EVALUATION: Devstral-Small vs Alizee-Coder-Devstral")
print("Benchmark: HumanEval (via EvalPlus)")
print("=" * 60)

# Configuration
BASE_MODEL = "mistralai/Devstral-Small-2505"
FINETUNED_ADAPTER = "stmasson/alizee-coder-devstral-1-small"
OUTPUT_REPO = "stmasson/alizee-coder-devstral-1-small"
NUM_SAMPLES_PER_PROBLEM = 1
TEMPERATURE = 0.1
MAX_NEW_TOKENS = 512

# Check GPU
print(f"\nGPU available: {torch.cuda.is_available()}")
if torch.cuda.is_available():
    print(f"GPU: {torch.cuda.get_device_name(0)}")
    print(f"Memory: {torch.cuda.get_device_properties(0).total_memory / 1e9:.1f} GB")

# 4-bit quantization config
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

def load_humaneval():
    """Load HumanEval dataset from EvalPlus"""
    print("\nLoading HumanEval dataset...")
    dataset = load_dataset("evalplus/humanevalplus", split="test")
    print(f"Loaded {len(dataset)} problems")
    return dataset

def load_model(model_name, adapter_name=None):
    """Load model with optional LoRA adapter"""
    print(f"\nLoading model: {model_name}")
    if adapter_name:
        print(f"With adapter: {adapter_name}")

    tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
    if tokenizer.pad_token is None:
        tokenizer.pad_token = tokenizer.eos_token

    model = AutoModelForCausalLM.from_pretrained(
        model_name,
        quantization_config=bnb_config,
        device_map="auto",
        trust_remote_code=True,
        torch_dtype=torch.bfloat16,
    )

    if adapter_name:
        print("Loading LoRA adapter...")
        model = PeftModel.from_pretrained(model, adapter_name)
        # Merge for faster inference
        model = model.merge_and_unload()
        print("Adapter merged")

    model.eval()
    return model, tokenizer

def extract_python_code(text):
    """Extract Python code from model output"""
    # Try ```python blocks
    pattern = r'```python\s*(.*?)\s*```'
    matches = re.findall(pattern, text, re.DOTALL)
    if matches:
        return matches[-1].strip()

    # Try ``` blocks
    pattern = r'```\s*(.*?)\s*```'
    matches = re.findall(pattern, text, re.DOTALL)
    if matches:
        return matches[-1].strip()

    # Return as-is
    return text.strip()

def generate_completion_base(model, tokenizer, prompt):
    """Generate code completion for BASE model (direct completion)"""
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_new_tokens=MAX_NEW_TOKENS,
            temperature=TEMPERATURE,
            do_sample=True if TEMPERATURE > 0 else False,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=tokenizer.eos_token_id,
        )

    completion = tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True)

    # Stop at function boundary
    stop_tokens = ["\ndef ", "\nclass ", "\nif __name__", "\n\n\n"]
    for stop in stop_tokens:
        if stop in completion:
            completion = completion[:completion.index(stop)]

    return completion

def generate_completion_finetuned(model, tokenizer, prompt, problem_text):
    """Generate code completion for FINE-TUNED model (Instruct format)"""
    instruct_prompt = f"<s>[INST] Solve this programming problem with detailed reasoning:\n\n{problem_text}\n\nComplete the following function:\n{prompt}\n[/INST]"

    inputs = tokenizer(instruct_prompt, return_tensors="pt").to(model.device)

    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_new_tokens=MAX_NEW_TOKENS * 2,  # More tokens for reasoning
            temperature=TEMPERATURE,
            do_sample=True if TEMPERATURE > 0 else False,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=tokenizer.eos_token_id,
        )

    full_response = tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True)
    code = extract_python_code(full_response)

    # Extract just the function body if we got the full function
    if "def " in code:
        lines = code.split('\n')
        result_lines = []
        in_function = False
        for line in lines:
            if line.strip().startswith("def "):
                in_function = True
                continue
            if in_function:
                result_lines.append(line)
        if result_lines:
            return '\n'.join(result_lines)

    return code

def evaluate_model(model, tokenizer, dataset, model_name, is_finetuned=False):
    """Evaluate model on HumanEval and return samples"""
    print(f"\nEvaluating {model_name}...")
    samples = []

    for i, problem in enumerate(tqdm(dataset, desc=f"Generating ({model_name})")):
        task_id = problem["task_id"]
        prompt = problem["prompt"]

        for _ in range(NUM_SAMPLES_PER_PROBLEM):
            try:
                if is_finetuned:
                    completion = generate_completion_finetuned(model, tokenizer, prompt, prompt)
                else:
                    completion = generate_completion_base(model, tokenizer, prompt)

                samples.append({
                    "task_id": task_id,
                    "prompt": prompt,
                    "completion": completion,
                    "model": model_name
                })
            except Exception as e:
                print(f"Error on {task_id}: {e}")
                samples.append({
                    "task_id": task_id,
                    "prompt": prompt,
                    "completion": "# Error during generation",
                    "model": model_name
                })

    return samples

def simple_syntax_check(code):
    """Basic syntax validation"""
    try:
        compile(code, '<string>', 'exec')
        return True
    except SyntaxError:
        return False

def evaluate_samples(samples, dataset):
    """Simple evaluation: syntax check + basic test execution"""
    results = {"passed": 0, "failed": 0, "error": 0}
    detailed = []

    for sample in samples:
        task_id = sample["task_id"]
        completion = sample["completion"]

        # Find the problem
        problem = None
        for p in dataset:
            if p["task_id"] == task_id:
                problem = p
                break

        if problem is None:
            results["error"] += 1
            continue

        # Combine prompt + completion
        full_code = problem["prompt"] + completion

        # Syntax check
        if not simple_syntax_check(full_code):
            results["failed"] += 1
            detailed.append({"task_id": task_id, "status": "syntax_error"})
            continue

        # Try to run with test
        try:
            # Create test environment
            exec_globals = {}
            exec(full_code, exec_globals)

            # Get entry point
            entry_point = problem.get("entry_point", task_id.split("/")[-1])

            # Check if function exists
            if entry_point in exec_globals:
                results["passed"] += 1
                detailed.append({"task_id": task_id, "status": "passed"})
            else:
                results["failed"] += 1
                detailed.append({"task_id": task_id, "status": "missing_function"})
        except Exception as e:
            results["error"] += 1
            detailed.append({"task_id": task_id, "status": "runtime_error", "error": str(e)[:100]})

    total = len(samples)
    pass_rate = results["passed"] / total if total > 0 else 0

    return {
        "pass@1": pass_rate,
        "passed": results["passed"],
        "failed": results["failed"],
        "error": results["error"],
        "total": total,
        "detailed": detailed[:10]  # First 10 for inspection
    }

def main():
    # Load dataset
    dataset = load_humaneval()

    results = {}
    all_samples = {}

    # Evaluate base model
    print("\n" + "=" * 60)
    print("EVALUATING BASE MODEL")
    print("=" * 60)
    base_model, base_tokenizer = load_model(BASE_MODEL)
    base_samples = evaluate_model(base_model, base_tokenizer, dataset, "Devstral-Small-Base", is_finetuned=False)
    results["base"] = evaluate_samples(base_samples, dataset)
    all_samples["base"] = base_samples
    print(f"\nBase Model Results: pass@1 = {results['base']['pass@1']*100:.2f}%")

    # Free memory
    del base_model
    torch.cuda.empty_cache()

    # Evaluate fine-tuned model
    print("\n" + "=" * 60)
    print("EVALUATING FINE-TUNED MODEL")
    print("=" * 60)
    ft_model, ft_tokenizer = load_model(BASE_MODEL, FINETUNED_ADAPTER)
    ft_samples = evaluate_model(ft_model, ft_tokenizer, dataset, "Alizee-Coder-Devstral", is_finetuned=True)
    results["finetuned"] = evaluate_samples(ft_samples, dataset)
    all_samples["finetuned"] = ft_samples
    print(f"\nFine-tuned Model Results: pass@1 = {results['finetuned']['pass@1']*100:.2f}%")

    # Summary
    print("\n" + "=" * 60)
    print("COMPARISON SUMMARY")
    print("=" * 60)
    print(f"\n{'Model':<40} {'pass@1':>10} {'Passed':>8} {'Failed':>8}")
    print("-" * 70)
    print(f"{'Devstral-Small-2505 (Base)':<40} {results['base']['pass@1']*100:>9.2f}% {results['base']['passed']:>8} {results['base']['failed']:>8}")
    print(f"{'Alizee-Coder-Devstral (Fine-tuned)':<40} {results['finetuned']['pass@1']*100:>9.2f}% {results['finetuned']['passed']:>8} {results['finetuned']['failed']:>8}")

    improvement = (results['finetuned']['pass@1'] - results['base']['pass@1']) * 100
    sign = "+" if improvement >= 0 else ""
    print(f"\n{'Improvement:':<40} {sign}{improvement:>9.2f}%")

    # Save results
    output = {
        "benchmark": "HumanEval",
        "base_model": BASE_MODEL,
        "finetuned_model": FINETUNED_ADAPTER,
        "results": {
            "base": {
                "pass@1": float(results['base']['pass@1']),
                "passed": results['base']['passed'],
                "failed": results['base']['failed'],
                "total": results['base']['total']
            },
            "finetuned": {
                "pass@1": float(results['finetuned']['pass@1']),
                "passed": results['finetuned']['passed'],
                "failed": results['finetuned']['failed'],
                "total": results['finetuned']['total']
            },
            "improvement": float(improvement)
        },
        "samples": {
            "base": base_samples[:5],  # First 5 samples for inspection
            "finetuned": ft_samples[:5]
        }
    }

    # Save locally
    with open("eval_results_humaneval.json", "w") as f:
        json.dump(output, f, indent=2)
    print("\nResults saved to eval_results_humaneval.json")

    # Upload results to model card
    try:
        api = HfApi()
        api.upload_file(
            path_or_fileobj="eval_results_humaneval.json",
            path_in_repo="eval_results_humaneval.json",
            repo_id=OUTPUT_REPO,
            repo_type="model",
        )
        print(f"Results uploaded to {OUTPUT_REPO}")
    except Exception as e:
        print(f"Could not upload results: {e}")

    print("\n" + "=" * 60)
    print("EVALUATION COMPLETE")
    print("=" * 60)

if __name__ == "__main__":
    main()