import torch
from transformers import AutoModel, AutoTokenizer, LlamaForCausalLM

# Load the Llama 2 checkpoint
llama2_checkpoint_path = "/mnt/data/bpop/multilinguality_tower/extended-models/llama-2-7b-hf-merged-multi-32k-meaninit"
llama2_tokenizer = AutoTokenizer.from_pretrained(llama2_checkpoint_path)
llama2_model = LlamaForCausalLM.from_pretrained(llama2_checkpoint_path)

# Load the original Llama 2 model
original_llama2_model_name = "/mnt/data_2/cache/models--meta-llama--Llama-2-7b-hf/snapshots/6fdf2e60f86ff2481f2241aaee459f85b5b0bbb9"
original_llama2_tokenizer = AutoTokenizer.from_pretrained(original_llama2_model_name)
original_llama2_model = LlamaForCausalLM.from_pretrained(original_llama2_model_name)

# Compare the weights of the embedding and output layers
llama2_embedding_weights = llama2_model.get_input_embeddings().weight
original_llama2_embedding_weights = original_llama2_model.get_input_embeddings().weight

for (name1, param1), (name2, param2) in zip(
    llama2_model.named_parameters(), original_llama2_model.named_parameters()
):
    try:
        if not torch.allclose(param1, param2, atol=1e-7):
            print(f"Different weights in {name1} and {name2}")
        else:
            print(f"Same weights in {name1} and {name2}")
    except:
        print(f"Couldn't do allclose for layer {name1}")

a = 1