# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
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import torch
from hqq.core.quantize import BaseQuantizeConfig
from hqq.engine.hf import HQQModelForCausalLM
from transformers import AutoTokenizer


def setup(model_id: str, weights: str, activations: str, device: torch.device, group_size: int = 64):
    if activations != "none":
        raise ValueError("Activation quantization is not supported by HQQ")
    if weights == "int4":
        quant_config = BaseQuantizeConfig(nbits=4, group_size=group_size)
    elif weights == "int8":
        quant_config = BaseQuantizeConfig(nbits=8, group_size=group_size)
    else:
        raise ValueError("HQQ only supports int4 and int8 weights.")
    # Load model
    model = HQQModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16)
    # Quantize
    model.quantize_model(quant_config=quant_config, compute_dtype=torch.float16, device=device)
    tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
    tokenizer.pad_token_id = tokenizer.eos_token_id
    tokenizer.padding_side = "left"
    return model, tokenizer