# /// script
# requires-python = ">=3.12"
# dependencies = [
#     "torch",
#     "torchvision",
#     "transformers",
#     "diffusers",
#     "sentence-transformers",
#     "accelerate",
#     "peft",
#     "slack-sdk",
# ]
# ///

try:
    prompt = "Give me a brief explanation of gravity in simple terms."
    messages = [
        {"role": "system", "content": "/no_think"},
        {"role": "user", "content": prompt}
    ]
    
    text = tokenizer.apply_chat_template(
        messages,
        tokenize=False,
        add_generation_prompt=True,
    )
    
    model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
    
    # Generate the output
    generated_ids = model.generate(**model_inputs, max_new_tokens=32768)
    
    # Get and decode the output
    output_ids = generated_ids[0][len(model_inputs.input_ids[0]) :]
    print(tokenizer.decode(output_ids, skip_special_tokens=True))
    with open('HuggingFaceTB_SmolLM3-3B_5.txt', 'w', encoding='utf-8') as f:
        f.write('Everything was good in HuggingFaceTB_SmolLM3-3B_5.txt')
except Exception as e:
    import os
    from slack_sdk import WebClient
    client = WebClient(token=os.environ['SLACK_TOKEN'])
    client.chat_postMessage(
        channel='#hub-model-metadata-snippets-sprint',
        text='Problem in <https://huggingface.co/datasets/model-metadata/code_execution_files/blob/main/HuggingFaceTB_SmolLM3-3B_5.txt|HuggingFaceTB_SmolLM3-3B_5.txt>',
    )

    with open('HuggingFaceTB_SmolLM3-3B_5.txt', 'a', encoding='utf-8') as f:
        import traceback
        f.write('''```CODE: 
prompt = "Give me a brief explanation of gravity in simple terms."
messages = [
    {"role": "system", "content": "/no_think"},
    {"role": "user", "content": prompt}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)

model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# Generate the output
generated_ids = model.generate(**model_inputs, max_new_tokens=32768)

# Get and decode the output
output_ids = generated_ids[0][len(model_inputs.input_ids[0]) :]
print(tokenizer.decode(output_ids, skip_special_tokens=True))
```

ERROR: 
''')
        traceback.print_exc(file=f)
    
finally:
    from huggingface_hub import upload_file
    upload_file(
        path_or_fileobj='HuggingFaceTB_SmolLM3-3B_5.txt',
        repo_id='model-metadata/code_execution_files',
        path_in_repo='HuggingFaceTB_SmolLM3-3B_5.txt',
        repo_type='dataset',
    )