#
# 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 pytest

from bigdl.llm.transformers import AutoModelForCausalLM, AutoModel
from transformers import LlamaTokenizer, AutoTokenizer


llama_model_path = os.environ.get('LLAMA_ORIGIN_PATH')
bloom_model_path = os.environ.get('BLOOM_ORIGIN_PATH')
chatglm2_6b_model_path = os.environ.get('ORIGINAL_CHATGLM2_6B_PATH')
replit_code_model_path = os.environ.get('ORIGINAL_REPLIT_CODE_PATH')

prompt = "Once upon a time, there existed a little girl who liked to have adventures. She wanted to go to places and meet new people, and have fun"

@pytest.mark.parametrize("Model, Tokenizer, model_path, prompt", [
    (AutoModelForCausalLM, LlamaTokenizer, llama_model_path, prompt),
    (AutoModelForCausalLM, AutoTokenizer, bloom_model_path, prompt),
    (AutoModel, AutoTokenizer, chatglm2_6b_model_path, prompt),
    (AutoModelForCausalLM, AutoTokenizer, replit_code_model_path, prompt)
])
    
def test_optimize_model(Model, Tokenizer, model_path, prompt):
    tokenizer = Tokenizer.from_pretrained(model_path, trust_remote_code=True)
    input_ids = tokenizer.encode(prompt, return_tensors="pt")

    model = Model.from_pretrained(model_path,
                                load_in_4bit=True,
                                optimize_model=False,
                                trust_remote_code=True)
    logits_base_model = (model(input_ids)).logits

    model = Model.from_pretrained(model_path,
                                load_in_4bit=True,
                                optimize_model=True,
                                trust_remote_code=True)
    logits_optimized_model = (model(input_ids)).logits
    diff = abs(logits_base_model - logits_optimized_model).flatten()

    assert any(diff) is False


if __name__ == '__main__':
    pytest.main([__file__])