#
# Copyright 2016 The BigDL Authors.
#
# 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
# limitations under the License.
#

# This would makes sure Python is aware there is more than one sub-package within bigdl,
# physically located elsewhere.
# Otherwise there would be module not found error in non-pip's setting as Python would
# only search the first bigdl package and end up finding only one sub-package.

import argparse
import warnings

from langchain.chains import LLMChain
from langchain_community.llms import IpexLLM
from langchain_core.prompts import PromptTemplate

warnings.filterwarnings("ignore", category=UserWarning, message=".*padding_mask.*")


def main(args):
    
    question = args.question
    model_path = args.model_path
    # Below is the prompt format for LLaMa-2 according to 
    # https://huggingface.co/meta-llama/Llama-2-7b-chat-hf
    # If you're using a different language model, 
    # please adjust the template according to its own model card.
    template = """<s>[INST] <<SYS>>\n    \n<</SYS>>\n\n{question} [/INST]"""

    prompt = PromptTemplate(template=template, input_variables=["question"])

    llm = IpexLLM.from_model_id(
        model_id=model_path,
        model_kwargs={
            "temperature": 0,
            "max_length": 64,
            "trust_remote_code": True,
        },
    )

    llm_chain = prompt | llm

    output = llm_chain.invoke(question)
    print("====output=====")
    print(output)


if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='TransformersLLM Langchain Chat Example')
    parser.add_argument('-m','--model-path', type=str, required=True,
                        help='the path to transformers model')
    parser.add_argument('-q', '--question', type=str, default='What is AI?',
                        help='qustion you want to ask.')
    args = parser.parse_args()
    
    main(args)