import torch
from modelscope import snapshot_download
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation import GenerationConfig
from transformers import AutoConfig

from modeling_qwen import QWenLMHeadModel

seed = 4321
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)

model_dir = snapshot_download("qwen/Qwen-1_8B-Chat")
# model_dir = "/home/colin/.cache/modelscope/hub/qwen/Qwen-1_8B-Chat"

config, kwargs = AutoConfig.from_pretrained(
    "./",
    return_unused_kwargs=True,
    trust_remote_code=True,
    code_revision=None,
    _commit_hash=None,
)
model = QWenLMHeadModel(config)

print(model)

# QWenLMHeadModel(
#   (transformer): QWenModel(
#     (wte): Embedding(151936, 2048)
#     (drop): Dropout(p=0.0, inplace=False)
#     (rotary_emb): RotaryEmbedding()
#     (h): ModuleList(
#       (0-23): 24 x QWenBlock(
#         (ln_1): RMSNorm()
#         (attn): QWenAttention(
#           (c_attn): Linear(in_features=2048, out_features=6144, bias=True)
#           (c_proj): Linear(in_features=2048, out_features=2048, bias=False)
#           (attn_dropout): Dropout(p=0.0, inplace=False)
#         )
#         (ln_2): RMSNorm()
#         (mlp): QWenMLP(
#           (w1): Linear(in_features=2048, out_features=5504, bias=False)
#           (w2): Linear(in_features=2048, out_features=5504, bias=False)
#           (c_proj): Linear(in_features=5504, out_features=2048, bias=False)
#         )
#       )
#     )
#     (ln_f): RMSNorm()
#   )
#   (lm_head): Linear(in_features=2048, out_features=151936, bias=False)
# )


tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
model = model.from_pretrained(
    model_dir, config=config, device_map="auto", trust_remote_code=True
).train()
# model.train()
# model.zero_grad()

# 可指定不同的生成长度、top_p等相关超参
# model.generation_config = GenerationConfig.from_pretrained(
#     model_dir, trust_remote_code=True
# )

# 第一轮对话
response, history, decode_tokens = model.chat(tokenizer, "你好", "莎是现代汉语的男性的名字，出自《诗经》中的“采采卷", history=None)
print(decode_tokens)
# 你好！很高兴为你提供帮助。

# 第二轮对话
# response, history = model.chat(tokenizer, "给我讲一个年轻人奋斗创业最终取得成功的故事。", history=None)
# print(response)



# <|im_start|>system
# You are a helpful assistant.<|im_end|>
# <|im_start|>user
# 你好<|im_end|>
# <|im_start|>assistant
# 莎士比亚是头一个使用“你好”这个词的文学家，他在《哈姆雷特》中写道：“你是谁？你在哪儿？
# ”他的这一段话，通常被认为是最早的使用“你好”这个词的文学记载。这句话在英国语中非常常见，
# 特别是在正式或礼貌的情况下。<|im_end|><|endoftext|>