import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
from accelerate import infer_auto_device_map, init_empty_weights, dispatch_model

model_path = "Crystalcareai/Quiet-Star-Custom"

n_ahead = 8
n_ahead_talk = 4
merged_talk_heads = True

model = AutoModelForCausalLM.from_pretrained(model_path,
                                             max_thoughts=n_ahead + n_ahead_talk + 1,
                                             merged_talk_heads=merged_talk_heads,
                                             merged_lm_and_talk_heads=False,
                                             merged_lm_and_think_heads=True,
                                             use_concat_talk_head=True,
                                             use_shallow_think=True,
                                             use_shallow_talk=False,
                                             use_complex_think_head=False,
                                             use_complex_talk_head=True,
                                             use_weighted_talk_head=True,
                                             trust_remote_code=True,
                                             torch_dtype=torch.bfloat16,
                                             device_map="auto",
                                             )

model.eval()

tokenizer = AutoTokenizer.from_pretrained(model_path)
model.tokenizer = tokenizer  # Set the tokenizer attribute of the model

streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=False)

# Convert prompt to tokens
prompt_template = "[INST] {prompt} [/INST]"
prompt = "It is not always easy to see who is related to whom -- and in which ways. The following argument pertains to this question: To begin with, Lesley is a close friend of Fernando. Moreover, being a close friend of Fernando or a schoolmate of Lowell is sufficient for being a great-grandfather of Leroy. It follows that Lesley is a great-grandfather of Leroy. Is the argument, given the explicitly stated premises, deductively valid or invalid?"

input_ids = tokenizer(
    prompt_template.format(prompt=prompt),
    return_tensors='pt'
).input_ids.to(model.device)

attention_mask = torch.ones_like(input_ids)

max_length = 256

output_ids, _ = model.generate(input_ids, attention_mask=attention_mask, max_length=max_length, streamer=streamer)

print(tokenizer.decode(output_ids[0], skip_special_tokens=False))