from inference.mlx.sharded_inference_engine import MLXDynamicShardInferenceEngine
from inference.inference_engine import InferenceEngine
from inference.shard import Shard
from inference.tinygrad.inference import TinygradDynamicShardInferenceEngine
import numpy as np

# An inference engine should work the same for any number of Shards, as long as the Shards are continuous.
async def test_inference_engine(inference_engine: InferenceEngine, model_id: str, input_data: np.array):
    # inference_engine.reset_shard(Shard("", 0,0,0))
    resp_full, _ = await inference_engine.infer_prompt(shard=Shard(model_id=model_id, start_layer=0, end_layer=1, n_layers=2), prompt="In one word, what is the capital of USA? ")

    print("resp_full", resp_full)
    print("decoded", inference_engine.tokenizer.decode(resp_full))

    # inference_engine.reset_shard(Shard("", 0,0,0))

    # resp1, _ = await inference_engine.infer_tensor(shard=Shard(model_id=model_id, start_layer=0, end_layer=0, n_layers=2), input_data=input_data)
    # resp2, _ = await inference_engine.infer_tensor(shard=Shard(model_id=model_id, start_layer=1, end_layer=1, n_layers=2), input_data=resp1)

    # assert np.array_equal(resp_full, resp2)

import asyncio

# asyncio.run(test_inference_engine(
#     MLXDynamicShardInferenceEngine(),
#     "mlx-community/Meta-Llama-3-8B-Instruct-4bit",
#     [1234]
# ))

asyncio.run(test_inference_engine(
    TinygradDynamicShardInferenceEngine(),
    "/Users/alex/Library/Caches/tinygrad/downloads/llama3-8b-sfr",
    [1234]
))