# /// script
# requires-python = ">=3.12,<3.13"  # Required for vllm==0.10.1+gptoss
# dependencies = [
#     "datasets",
#     "huggingface-hub[hf_transfer]",
#     "torch",
#     "openai-harmony",  # Official OpenAI harmony library
#     "vllm==0.10.1+gptoss",  # Specific version for GPT OSS models
#     "tqdm",
# ]
# 
# [[tool.uv.index]]
# url = "https://wheels.vllm.ai/gpt-oss/"
# 
# [[tool.uv.index]]
# url = "https://download.pytorch.org/whl/nightly/cu128"
# 
# [tool.uv]
# index-strategy = "unsafe-best-match"
# ///
"""
Generate responses with transparent reasoning using OpenAI GPT OSS models with harmony format.

This script uses the official openai_harmony library for proper message formatting
and channel parsing, as recommended in the OpenAI cookbook.

Example usage:
    # Generate haiku with reasoning
    uv run gpt_oss_vllm_harmony.py \\
        --input-dataset davanstrien/haiku_dpo \\
        --output-dataset username/haiku-reasoning \\
        --prompt-column question
    
    # Any prompt dataset with custom settings
    uv run gpt_oss_vllm_harmony.py \\
        --input-dataset username/prompts \\
        --output-dataset username/responses-with-reasoning \\
        --prompt-column prompt \\
        --reasoning-level high \\
        --max-samples 100
    
    # HF Jobs execution
    hf jobs uv run --flavor a10g-small \\
        https://huggingface.co/datasets/uv-scripts/openai-reasoning/raw/main/gpt_oss_vllm_harmony.py \\
        --input-dataset username/prompts \\
        --output-dataset username/responses-with-reasoning
"""

import argparse
import json
import logging
import os
import sys
import time
from datetime import datetime
from typing import Dict, List, Optional

from datasets import Dataset, load_dataset
from huggingface_hub import DatasetCard, get_token, login
from openai_harmony import (
    HarmonyEncodingName,
    load_harmony_encoding,
    Conversation,
    Message,
    Role,
    SystemContent,
    DeveloperContent,
)
from torch import cuda
from tqdm.auto import tqdm
from vllm import LLM, SamplingParams

# Enable HF Transfer for faster downloads
os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"

# TODO: Change logging level back to INFO after initial testing
logging.basicConfig(
    level=logging.DEBUG, format="%(asctime)s - %(levelname)s - %(message)s"
)
logger = logging.getLogger(__name__)


def check_gpu_availability() -> int:
    """Check if CUDA is available and return the number of GPUs."""
    if not cuda.is_available():
        logger.error("CUDA is not available. This script requires a GPU.")
        logger.error(
            "Please run on a machine with NVIDIA GPU or use HF Jobs with GPU flavor."
        )
        sys.exit(1)

    num_gpus = cuda.device_count()
    for i in range(num_gpus):
        gpu_name = cuda.get_device_name(i)
        gpu_memory = cuda.get_device_properties(i).total_memory / 1024**3
        logger.info(f"GPU {i}: {gpu_name} with {gpu_memory:.1f} GB memory")

    return num_gpus


def parse_harmony_messages(entries: List, prompt: str) -> Dict[str, str]:
    """
    Parse harmony message entries into think/content structure.
    
    The harmony format produces structured messages with different channels:
    - analysis: Chain of thought reasoning
    - final: User-facing response
    - commentary: Tool calls (if any)
    """
    think = ""
    content = ""
    
    # Log what we received for debugging
    logger.debug(f"[VERBOSE] Parsing {len(entries)} harmony entries")
    
    for i, entry in enumerate(entries):
        entry_dict = entry.to_dict()
        logger.debug(f"[VERBOSE] Entry {i}: {json.dumps(entry_dict, indent=2)}")
        
        # Extract content based on the message structure
        if "content" in entry_dict:
            if isinstance(entry_dict["content"], list):
                for content_item in entry_dict["content"]:
                    if content_item.get("type") == "text":
                        text = content_item.get("text", "")
                        # Determine channel based on content or metadata
                        # This is a simplified approach - adjust based on actual harmony output
                        if "analysis" in str(entry_dict).lower() or i == 0:
                            think += text + "\n"
                        else:
                            content += text + "\n"
            elif isinstance(entry_dict["content"], str):
                # Simple string content
                if i == 0:  # First message is often reasoning
                    think = entry_dict["content"]
                else:
                    content = entry_dict["content"]
    
    # Clean up whitespace
    think = think.strip()
    content = content.strip()
    
    # If we didn't parse anything, use the first entry as content
    if not think and not content and entries:
        content = str(entries[0].to_dict())
    
    return {
        "prompt": prompt,
        "think": think,
        "content": content,
        "raw_output": json.dumps([e.to_dict() for e in entries], indent=2)
    }


def create_dataset_card(
    input_dataset: str,
    model_id: str,
    prompt_column: str,
    reasoning_level: str,
    num_examples: int,
    generation_time: str,
    tensor_parallel_size: int,
    temperature: float,
    max_tokens: int,
) -> str:
    """Create a dataset card documenting the generation process."""
    return f"""---
tags:
- generated
- synthetic
- reasoning
- openai-gpt-oss
- harmony-format
---

# Generated Responses with Reasoning (Harmony Format)

This dataset contains AI-generated responses with transparent chain-of-thought reasoning using OpenAI GPT OSS models and the official harmony format.

## Generation Details

- **Source Dataset**: [{input_dataset}](https://huggingface.co/datasets/{input_dataset})
- **Model**: [{model_id}](https://huggingface.co/{model_id})
- **Reasoning Level**: {reasoning_level}
- **Number of Examples**: {num_examples:,}
- **Generation Date**: {generation_time}
- **Format**: Official OpenAI Harmony format

## Dataset Structure

Each example contains:
- `prompt`: The input prompt from the source dataset
- `think`: The model's internal reasoning process (analysis channel)
- `content`: The final response (final channel)
- `raw_output`: Complete harmony format output
- `reasoning_level`: The reasoning effort level used
- `model`: Model identifier

## Generation Script

Generated using [uv-scripts/openai-reasoning](https://huggingface.co/datasets/uv-scripts/openai-reasoning) with official harmony format.

To reproduce:
```bash
uv run gpt_oss_vllm_harmony.py \\
    --input-dataset {input_dataset} \\
    --output-dataset <your-dataset> \\
    --prompt-column {prompt_column} \\
    --model-id {model_id} \\
    --reasoning-level {reasoning_level}
```
"""


def main(
    input_dataset: str,
    output_dataset_hub_id: str,
    prompt_column: str = "prompt",
    model_id: str = "openai/gpt-oss-20b",
    reasoning_level: str = "high",
    max_samples: Optional[int] = None,
    temperature: float = 0.7,
    max_tokens: int = 512,
    gpu_memory_utilization: float = 0.90,
    tensor_parallel_size: Optional[int] = None,
    hf_token: Optional[str] = None,
):
    """
    Main generation pipeline using official harmony format.

    Args:
        input_dataset: Source dataset on Hugging Face Hub
        output_dataset_hub_id: Where to save results on Hugging Face Hub
        prompt_column: Column containing the prompts
        model_id: OpenAI GPT OSS model to use
        reasoning_level: Reasoning effort level (high/medium/low)
        max_samples: Maximum number of samples to process
        temperature: Sampling temperature
        max_tokens: Maximum tokens to generate
        gpu_memory_utilization: GPU memory utilization factor
        tensor_parallel_size: Number of GPUs to use (auto-detect if None)
        hf_token: Hugging Face authentication token
    """
    generation_start_time = datetime.now().isoformat()

    # GPU check and configuration
    num_gpus = check_gpu_availability()
    if tensor_parallel_size is None:
        tensor_parallel_size = num_gpus
        logger.info(
            f"Auto-detected {num_gpus} GPU(s), using tensor_parallel_size={tensor_parallel_size}"
        )

    # Authentication
    HF_TOKEN = hf_token or os.environ.get("HF_TOKEN") or get_token()

    if not HF_TOKEN:
        logger.error("No HuggingFace token found. Please provide token via:")
        logger.error("  1. --hf-token argument")
        logger.error("  2. HF_TOKEN environment variable")
        logger.error("  3. Run 'huggingface-cli login'")
        sys.exit(1)

    logger.info("HuggingFace token found, authenticating...")
    login(token=HF_TOKEN)

    # Initialize harmony encoding
    logger.info("Loading harmony encoding...")
    encoding = load_harmony_encoding(HarmonyEncodingName.HARMONY_GPT_OSS)
    
    # Get stop tokens from harmony
    stop_token_ids = encoding.stop_tokens_for_assistant_actions()  # Note: plural "actions"
    logger.info(f"[VERBOSE] Harmony stop token IDs: {stop_token_ids}")

    # Initialize vLLM
    logger.info(f"Loading model: {model_id}")
    logger.info("Note: vLLM will handle batching automatically for optimal throughput")
    try:
        llm = LLM(
            model=model_id,
            tensor_parallel_size=tensor_parallel_size,
            gpu_memory_utilization=gpu_memory_utilization,
            trust_remote_code=True,
            dtype="bfloat16",
        )
        logger.info("[VERBOSE] Model loaded successfully")
    except Exception as e:
        logger.error(f"Failed to load model with vLLM: {e}")
        if "mxfp4" in str(e).lower():
            logger.error("This appears to be a quantization format issue.")
            logger.error("The model uses mxfp4 quantization which requires specific support.")
        sys.exit(1)

    # Create sampling parameters
    sampling_params = SamplingParams(
        temperature=temperature,
        max_tokens=max_tokens,
        stop_token_ids=stop_token_ids,
    )
    logger.info(f"[VERBOSE] Sampling params: temp={temperature}, max_tokens={max_tokens}")

    # Load dataset
    logger.info(f"Loading dataset: {input_dataset}")
    dataset = load_dataset(input_dataset, split="train")

    # Validate prompt column
    if prompt_column not in dataset.column_names:
        logger.error(
            f"Column '{prompt_column}' not found. Available columns: {dataset.column_names}"
        )
        sys.exit(1)

    # Limit samples if requested
    if max_samples:
        dataset = dataset.select(range(min(max_samples, len(dataset))))
    total_examples = len(dataset)
    logger.info(f"Processing {total_examples:,} examples")

    # Prepare prompts using harmony format
    logger.info(f"Preparing prompts with harmony format and reasoning_level={reasoning_level}...")
    prefill_ids_list = []
    prompts = []

    for i, example in enumerate(tqdm(dataset, desc="Preparing prompts")):
        prompt_text = example[prompt_column]
        prompts.append(prompt_text)
        
        # Create harmony conversation
        # Inject reasoning level into developer message
        developer_content = DeveloperContent.new()
        if reasoning_level:
            developer_content = developer_content.with_instructions(
                f"Reasoning: {reasoning_level}"
            )
        
        convo = Conversation.from_messages([
            Message.from_role_and_content(Role.SYSTEM, SystemContent.new()),
            Message.from_role_and_content(Role.DEVELOPER, developer_content),
            Message.from_role_and_content(Role.USER, prompt_text),
        ])
        
        # Render to token IDs
        prefill_ids = encoding.render_conversation_for_completion(convo, Role.ASSISTANT)
        prefill_ids_list.append(prefill_ids)
        
        # Log first few examples
        if i < 10:
            logger.info(f"[VERBOSE] Example {i} original text: {prompt_text[:200]}...")
            logger.info(f"[VERBOSE] Example {i} prefill length: {len(prefill_ids)} tokens")

    # Generate responses with vLLM
    logger.info(f"Starting generation for {len(prefill_ids_list):,} prompts...")
    logger.info("[VERBOSE] Using prompt_token_ids for generation")
    
    start_time = time.time()
    outputs = llm.generate(
        prompt_token_ids=prefill_ids_list,
        sampling_params=sampling_params,
    )
    end_time = time.time()
    
    generation_time = end_time - start_time
    logger.info(f"\n[VERBOSE] Generation Performance Metrics:")
    logger.info(f"[VERBOSE]   - Total time: {generation_time:.2f} seconds")
    logger.info(f"[VERBOSE]   - Throughput: {len(outputs) / generation_time:.2f} prompts/second")
    logger.info(f"[VERBOSE]   - Average time per prompt: {generation_time / len(outputs):.2f} seconds")

    # Parse outputs using harmony format
    logger.info("Parsing generated outputs with harmony format...")
    results = []
    
    # Track statistics
    parse_stats = {"success": 0, "empty": 0, "error": 0}

    for i, output in enumerate(tqdm(outputs, desc="Parsing outputs")):
        gen = output.outputs[0]
        text = gen.text
        output_tokens = gen.token_ids
        
        logger.debug(f"[VERBOSE] Output {i}: {len(output_tokens)} tokens, {len(text)} chars")
        
        try:
            # Parse with harmony
            entries = encoding.parse_messages_from_completion_tokens(output_tokens, Role.ASSISTANT)
            
            # Convert to our format
            parsed = parse_harmony_messages(entries, prompts[i])
            
            if parsed["think"] or parsed["content"]:
                parse_stats["success"] += 1
            else:
                parse_stats["empty"] += 1
            
            # Verbose logging for first 10 examples
            if i < 10:
                logger.info(f"\n[VERBOSE] ========== Example {i} Output ==========")
                logger.info(f"[VERBOSE] Original prompt: {prompts[i][:200]}...")
                logger.info(f"[VERBOSE] Raw text output: {text}")
                logger.info(f"[VERBOSE] Harmony entries: {len(entries)}")
                for j, entry in enumerate(entries):
                    logger.info(f"[VERBOSE] Entry {j}: {json.dumps(entry.to_dict(), indent=2)}")
                logger.info(f"[VERBOSE] Parsed think ({len(parsed['think'])} chars): {parsed['think'][:500]}...")
                logger.info(f"[VERBOSE] Parsed content ({len(parsed['content'])} chars): {parsed['content'][:500]}...")
                logger.info(f"[VERBOSE] ====================================\n")
            
        except Exception as e:
            logger.error(f"[VERBOSE] Error parsing output {i}: {e}")
            parse_stats["error"] += 1
            # Fallback: use raw text
            parsed = {
                "prompt": prompts[i],
                "think": "",
                "content": text,
                "raw_output": text
            }
        
        result = {
            "prompt": parsed["prompt"],
            "think": parsed["think"],
            "content": parsed["content"],
            "raw_output": parsed["raw_output"],
            "reasoning_level": reasoning_level,
            "model": model_id,
        }
        results.append(result)
    
    # Log parsing statistics
    logger.info(f"\n[VERBOSE] Parsing Statistics:")
    logger.info(f"[VERBOSE]   - Successfully parsed: {parse_stats['success']} ({parse_stats['success']/len(outputs)*100:.1f}%)")
    logger.info(f"[VERBOSE]   - Empty results: {parse_stats['empty']} ({parse_stats['empty']/len(outputs)*100:.1f}%)")
    logger.info(f"[VERBOSE]   - Parse errors: {parse_stats['error']} ({parse_stats['error']/len(outputs)*100:.1f}%)")

    # Create dataset
    logger.info("Creating output dataset...")
    output_dataset = Dataset.from_list(results)

    # Create dataset card
    logger.info("Creating dataset card...")
    card_content = create_dataset_card(
        input_dataset=input_dataset,
        model_id=model_id,
        prompt_column=prompt_column,
        reasoning_level=reasoning_level,
        num_examples=total_examples,
        generation_time=generation_start_time,
        tensor_parallel_size=tensor_parallel_size,
        temperature=temperature,
        max_tokens=max_tokens,
    )

    # Push to hub
    logger.info(f"Pushing dataset to: {output_dataset_hub_id}")
    output_dataset.push_to_hub(output_dataset_hub_id, token=HF_TOKEN)

    # Push dataset card
    card = DatasetCard(card_content)
    card.push_to_hub(output_dataset_hub_id, token=HF_TOKEN)

    logger.info("✅ Generation complete!")
    logger.info(
        f"Dataset available at: https://huggingface.co/datasets/{output_dataset_hub_id}"
    )
    
    # Final summary
    logger.info(f"\n[VERBOSE] ========== FINAL SUMMARY ==========")
    logger.info(f"[VERBOSE] Model: {model_id}")
    logger.info(f"[VERBOSE] Reasoning level: {reasoning_level}")
    logger.info(f"[VERBOSE] Examples processed: {total_examples}")
    logger.info(f"[VERBOSE] Temperature: {temperature}")
    logger.info(f"[VERBOSE] Max tokens: {max_tokens}")
    logger.info(f"[VERBOSE] GPU config: {tensor_parallel_size} GPU(s)")
    logger.info(f"[VERBOSE] ====================================")


if __name__ == "__main__":
    if len(sys.argv) > 1:
        parser = argparse.ArgumentParser(
            description="Generate responses with reasoning using OpenAI GPT OSS models (Harmony format)",
            formatter_class=argparse.RawDescriptionHelpFormatter,
            epilog="""
Examples:
  # Generate haiku with reasoning
  uv run gpt_oss_vllm_harmony.py \\
    --input-dataset davanstrien/haiku_dpo \\
    --output-dataset username/haiku-reasoning \\
    --prompt-column question
  
  # Any prompt dataset
  uv run gpt_oss_vllm_harmony.py \\
    --input-dataset username/prompts \\
    --output-dataset username/responses-reasoning \\
    --reasoning-level high \\
    --max-samples 100
  
  # Use larger 120B model (requires 4x L40S GPUs)
  uv run gpt_oss_vllm_harmony.py \\
    --input-dataset username/prompts \\
    --output-dataset username/responses-reasoning \\
    --model-id openai/gpt-oss-120b \\
    --tensor-parallel-size 4
            """,
        )

        parser.add_argument(
            "--input-dataset",
            type=str,
            required=True,
            help="Input dataset on Hugging Face Hub",
        )
        parser.add_argument(
            "--output-dataset",
            type=str,
            required=True,
            help="Output dataset name on Hugging Face Hub",
        )
        parser.add_argument(
            "--prompt-column",
            type=str,
            default="prompt",
            help="Column containing prompts (default: prompt)",
        )
        parser.add_argument(
            "--model-id",
            type=str,
            default="openai/gpt-oss-20b",
            help="Model to use (default: openai/gpt-oss-20b)",
        )
        parser.add_argument(
            "--reasoning-level",
            type=str,
            choices=["high", "medium", "low"],
            default="high",
            help="Reasoning effort level (default: high)",
        )
        parser.add_argument(
            "--max-samples", type=int, help="Maximum number of samples to process"
        )
        parser.add_argument(
            "--temperature",
            type=float,
            default=0.7,
            help="Sampling temperature (default: 0.7)",
        )
        parser.add_argument(
            "--max-tokens",
            type=int,
            default=512,
            help="Maximum tokens to generate (default: 512)",
        )
        parser.add_argument(
            "--gpu-memory-utilization",
            type=float,
            default=0.90,
            help="GPU memory utilization (default: 0.90)",
        )
        parser.add_argument(
            "--tensor-parallel-size",
            type=int,
            help="Number of GPUs to use (default: auto-detect)",
        )
        parser.add_argument(
            "--hf-token",
            type=str,
            help="Hugging Face token (can also use HF_TOKEN env var)",
        )

        args = parser.parse_args()

        main(
            input_dataset=args.input_dataset,
            output_dataset_hub_id=args.output_dataset,
            prompt_column=args.prompt_column,
            model_id=args.model_id,
            reasoning_level=args.reasoning_level,
            max_samples=args.max_samples,
            temperature=args.temperature,
            max_tokens=args.max_tokens,
            gpu_memory_utilization=args.gpu_memory_utilization,
            tensor_parallel_size=args.tensor_parallel_size,
            hf_token=args.hf_token,
        )
    else:
        # Show HF Jobs example when run without arguments
        print("""
OpenAI GPT OSS Reasoning Generation Script (Harmony Format)
==========================================================

This script requires arguments. For usage information:
    uv run gpt_oss_vllm_harmony.py --help

Example HF Jobs command for 20B model:
    hf jobs uv run \\
        --flavor a10g-large \\  # 20B model requires ~40GB memory
        https://huggingface.co/datasets/uv-scripts/openai-reasoning/raw/main/gpt_oss_vllm_harmony.py \\
        --input-dataset davanstrien/haiku_dpo \\
        --output-dataset username/haiku-reasoning \\
        --prompt-column question \\
        --reasoning-level high

Example HF Jobs command for 120B model:
    hf jobs uv run \\
        --flavor l40s-4x \\  # 120B model requires ~240GB memory
        https://huggingface.co/datasets/uv-scripts/openai-reasoning/raw/main/gpt_oss_vllm_harmony.py \\
        --input-dataset username/prompts \\
        --output-dataset username/responses-reasoning \\
        --model-id openai/gpt-oss-120b \\
        --reasoning-level high
        """)