#
# 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 code is copied from llama2 example test, and added performance test
import torch
import time
import argparse

from bigdl.llm.transformers import AutoModelForCausalLM
from transformers import LlamaTokenizer


import os
benchmark_util_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..')
import sys
sys.path.append(benchmark_util_path)
from benchmark_util import BenchmarkWrapper

# you could tune the prompt based on your own model,
# here the prompt tuning refers to https://huggingface.co/georgesung/llama2_7b_chat_uncensored#prompt-style
LLAMA2_PROMPT_FORMAT = """### HUMAN:
{prompt}

### RESPONSE:
"""

if __name__ == '__main__':

    parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for Llama2 model')
    parser.add_argument('--repo-id-or-model-path', type=str, default="meta-llama/Llama-2-7b-chat-hf",
                        help='The huggingface repo id for the Llama2 (e.g. `meta-llama/Llama-2-7b-chat-hf` and `meta-llama/Llama-2-13b-chat-hf`) to be downloaded'
                                ', or the path to the huggingface checkpoint folder')
    parser.add_argument('--prompt', type=str, default="What is AI?",
                        help='Prompt to infer')
    parser.add_argument('--n-predict', type=int, default=32,
                        help='Max tokens to predict')

    args = parser.parse_args()
    model_path = args.repo_id_or_model_path

    # Load model in 4 bit,
    # which convert the relevant layers in the model into INT4 format
    model = AutoModelForCausalLM.from_pretrained(model_path,
                                                    load_in_4bit=True,
                                                    trust_remote_code=True)


    model = BenchmarkWrapper(model, do_print=False)

    # Load tokenizer
    tokenizer = LlamaTokenizer.from_pretrained(model_path, trust_remote_code=True)

    # Generate predicted tokens
    with torch.inference_mode():
        prompt = LLAMA2_PROMPT_FORMAT.format(prompt=args.prompt)
        input_ids = tokenizer.encode(prompt, return_tensors="pt")
        st = time.time()
        # if your selected model is capable of utilizing previous key/value attentions
        # to enhance decoding speed, but has `"use_cache": false` in its model config,
        # it is important to set `use_cache=True` explicitly in the `generate` function
        # to obtain optimal performance with BigDL-LLM INT4 optimizations
        output = model.generate(input_ids,
                                max_new_tokens=args.n_predict)
        end = time.time()
        output_str = tokenizer.decode(output[0], skip_special_tokens=True)
        print(f'Inference time: {end-st} s')
        print('-'*20, 'Prompt', '-'*20)
        print(prompt)
        print('-'*20, 'Output', '-'*20)
        print(output_str)
        
        assert "AI is a term" in output_str, "output is not as expected, the correctness may be wrong."
        llama2_baseline = os.getenv('LLAMA2_BASELINE')
        if llama2_baseline is None:
            print('baseline is not set, skipping baseline validation')
        else:
            llama2_baseline = float(llama2_baseline)
            ratio = model.rest_cost_mean / llama2_baseline
            assert ratio < 1.1, f"performance did not meet baseline, the cost is {(ratio - 1) * 100}% higher than the baseline"