# /// 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

VERSION: 2.0 - Proper code extraction for both base and fine-tuned models
"""

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 (handles chat-like responses)"""
    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,
        )

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

    # Try to extract code from ```python blocks (if model generates chat-like response)
    completion = extract_python_code(raw_completion)

    # If extracted code looks like a full function, extract just the body
    if completion.strip().startswith("def "):
        lines = completion.split('\n')
        body_lines = []
        in_function = False
        for line in lines:
            if line.strip().startswith("def "):
                in_function = True
                continue
            if in_function:
                body_lines.append(line)
        if body_lines:
            completion = '\n'.join(body_lines)
    # If no code block, use raw completion
    elif completion == raw_completion.strip():
        completion = raw_completion

    # 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()