import torch
import torch.nn.functional as F
import transformers
import gradio as gr

from src.client import DistributedBloomForCausalLM

INITIAL_PEERS = [
'/ip4/193.106.95.184/tcp/31337/p2p/QmUigSxrVz9x5FR9ZYr4iRfEX2vDxihL2YZtDd7sp2eKnM',
'/ip6/193.106.95.184/tcp/21337/p2p/QmSXDXLeSMXjS4YerDrdn1zpGQaNzkZ9ogN2SoAEyAdDhs',
'/ip6/193.106.95.184/udp/21337/quic/QmSXDXLeSMXjS4YerDrdn1zpGQaNzkZ9ogN2SoAEyAdDhs',
]
tokenizer = transformers.BloomTokenizerFast.from_pretrained("bigscience/test-bloomd-6b3")
model = DistributedBloomForCausalLM.from_pretrained("bigscience/test-bloomd-6b3", initial_peers=INITIAL_PEERS, low_cpu_mem_usage=True, torch_dtype=torch.float32)

def inference(text, seq_length=1):
    input_ids = tokenizer(text, return_tensors='pt')['input_ids']
    with torch.inference_mode(), model.transformer.h.inference_session() as remote_transformer:
        for i in range(seq_length):
            h = model.transformer.word_embeddings(input_ids)
            h = model.transformer.word_embeddings_layernorm(h)

            h = remote_transformer.step(h)  # note [yozh]: this line currently freezes for 10 seconds first time only, its gonna be fixed in the nearest PR

            h = model.transformer.ln_f(h)
            h = F.linear(h, weight=model.transformer.word_embeddings.weight)  # note: this line takes a while, will also be fixed
            next_token_ix = torch.multinomial((h[0, -1] / 0.8).softmax(-1), 1)
            
            # print(end=tokenizer.decode(next_token_ix.item()))
            input_ids = next_token_ix.view(1, 1)
    return tokenizer.decode(input_ids.item())

iface = gr.Interface(fn=inference, inputs="text", outputs="text")
iface.launch()