# /// script
# dependencies = ["torch", "transformers", "accelerate"]
# ///
import torch
import json
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_id = "AbdelrehmanFouad/t5-efficient-base-usdjpy-forecaster"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSeq2SeqLM.from_pretrained(model_id, device_map="auto", torch_dtype=torch.float32)

input_data = {
    "context_window": [150.0] * 168,
    "macro_events": []
}
prompt = "forecast usdjpy: " + json.dumps(input_data)

inputs = tokenizer(prompt, return_tensors="pt", max_length=1024, truncation=True).to(model.device)
with torch.no_grad():
    outputs = model.generate(**inputs, max_new_tokens=512)

prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)
print("\n--- RAW PREDICTION ---")
print(prediction)
print("--- END ---")