#
# 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.
#

import os
import torch
import time
import argparse

from ipex_llm.transformers.npu_model import AutoModelForCausalLM
from transformers import AutoTokenizer

from transformers.utils import logging

logger = logging.get_logger(__name__)

def get_prompt(message: str, chat_history: list[tuple[str, str]],
               system_prompt: str) -> str:
    texts = [f'<s>[INST] <<SYS>>\n{system_prompt}\n<</SYS>>\n\n']
    # The first user input is _not_ stripped
    do_strip = False
    for user_input, response in chat_history:
        user_input = user_input.strip() if do_strip else user_input
        do_strip = True
        texts.append(f'{user_input} [/INST] {response.strip()} </s><s>[INST] ')
    message = message.strip() if do_strip else message
    texts.append(f'{message} [/INST]')
    return ''.join(texts)

if __name__ == "__main__":
    parser = argparse.ArgumentParser(
        description="Predict Tokens using `generate()` API for npu model"
    )
    parser.add_argument(
        "--repo-id-or-model-path",
        type=str,
        default="baichuan-inc/Baichuan2-7B-Chat",
        help="The huggingface repo id for the Baichuan2 model to be downloaded"
        ", or the path to the huggingface checkpoint folder",
    )
    parser.add_argument("--lowbit-path", type=str,
        default="",
        help="The path to the lowbit model folder, leave blank if you do not want to save. \
            If path not exists, lowbit model will be saved there. \
            Else, lowbit model will be loaded.",
    )
    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")
    parser.add_argument("--max-context-len", type=int, default=1024)
    parser.add_argument("--max-prompt-len", type=int, default=512)
    parser.add_argument("--disable-transpose-value-cache", action="store_true", default=False)
    parser.add_argument("--intra-pp", type=int, default=2)
    parser.add_argument("--inter-pp", type=int, default=2)

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

    if not args.lowbit_path or not os.path.exists(args.lowbit_path):
        model = AutoModelForCausalLM.from_pretrained(
            model_path,
            torch_dtype=torch.bfloat16,
            trust_remote_code=True,
            attn_implementation="eager",
            load_in_low_bit="sym_int4",
            optimize_model=True,
            max_context_len=args.max_context_len,
            max_prompt_len=args.max_prompt_len,
            intra_pp=args.intra_pp,
            inter_pp=args.inter_pp,
            transpose_value_cache=not args.disable_transpose_value_cache,
        )
    else:
        model = AutoModelForCausalLM.load_low_bit(
            args.lowbit_path,
            attn_implementation="eager",
            torch_dtype=torch.bfloat16,
            optimize_model=True,
            max_context_len=args.max_context_len,
            max_prompt_len=args.max_prompt_len,
            intra_pp=args.intra_pp,
            inter_pp=args.inter_pp,
            transpose_value_cache=not args.disable_transpose_value_cache,
            trust_remote_code=True,
        )

    tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

    if args.lowbit_path and not os.path.exists(args.lowbit_path):
        model.save_low_bit(args.lowbit_path)

    DEFAULT_SYSTEM_PROMPT = """\
    """

    print("-" * 80)
    print("done")
    with torch.inference_mode():
        print("finish to load")
        for i in range(5):
            prompt = get_prompt(args.prompt, [], system_prompt=DEFAULT_SYSTEM_PROMPT)
            _input_ids = tokenizer.encode(prompt, return_tensors="pt")
            print("input length:", len(_input_ids[0]))
            st = time.time()
            output = model.generate(
                _input_ids, num_beams=1, do_sample=False, max_new_tokens=args.n_predict
            )
            end = time.time()
            print(f"Inference time: {end-st} s")
            input_str = tokenizer.decode(_input_ids[0], skip_special_tokens=False)
            print("-" * 20, "Input", "-" * 20)
            print(input_str)
            output_str = tokenizer.decode(output[0], skip_special_tokens=False)
            print("-" * 20, "Output", "-" * 20)
            print(output_str)

    print("-" * 80)
    print("done")
    print("success shut down")