import torch
import sys
import math
from modelscope import snapshot_download
from transformers import AutoTokenizer
from transformers import AutoConfig

from modeling_qwen import QWenLMHeadModel
from modeling_qwen import QwenRunner
import numpy as np

import torch.nn.functional as F

from qwen_generation_utils import (
    make_context,
    decode_tokens,
)

sys.path.append("..")
from tools import show

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)

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

model = model.from_pretrained(model_dir)
if torch.cuda.device_count() > 0:
    model = model.cuda()
model = model.eval()


def Dump_tokens_list(model):
    tokens = []
    for token in range(151851):
        decoded, response, end_reason = decode_tokens(
            [token],
            tokenizer,
            raw_text_len=0,
            context_length=0,
            errors="replace",
        )
        tokens.append(str(token).zfill(7) + ": " + repr(decoded))
    show.DumpListToFile(tokens, "./temp/qwen_token_list.txt")


# Dump_tokens_list(model)


def Dump_lm_head_weight(model):
    weight = model.lm_head.weight.cpu()  # [151936,2048,]
    weight = weight.reshape(64, -1, 2048)
    for i in range(64):
        sub = weight[i].reshape(-1, 64, 32)
        show.DumpTensorToImage(sub, "./temp/lm_head_" + str(i) + "_2374_2048.png")


# Dump_lm_head_weight(model)

qk_seq = []
qk_index = None


def DumpQK(query, key, causal_mask, index):
    global qk_seq
    global qk_index
    size = query.shape[2]
    scale_factor = 1 / math.sqrt(query.size(-1))
    attn_weight = query @ key.transpose(-2, -1) * scale_factor
    attn_mask = torch.ones(causal_mask.shape, dtype=query.dtype, device=query.device)
    attn_mask.masked_fill_(causal_mask.logical_not(), float(0))
    attn_weight = attn_weight * attn_mask
    attn_weight = torch.softmax(attn_weight, dim=-1)
    attn_weight = attn_weight * attn_mask
    qk = attn_weight[0]
    # prePath = "./temp/" + "q@k_seq_" + str(size) + "_layer_" + str(index) + ".png"
    # show.DumpTensorToImage(qk, prePath, GridValue=255)
    qk_seq.append(qk)
    qk_index = size


class ResearchRunner(QwenRunner):
    def __init__(self, model):
        super().__init__(model)

    def attention(self, attention, query, key, value, causal_mask):
        query = query.permute(0, 2, 1, 3)
        key = key.permute(0, 2, 1, 3)
        value = value.permute(0, 2, 1, 3)

        DumpQK(query, key, causal_mask, attention.index)

        attn_output = F.scaled_dot_product_attention(query, key, value, attn_mask=causal_mask).transpose(1, 2)
        context_layer = attention._merge_heads(attn_output, attention.num_heads, attention.head_dim)
        attn_output = attention.c_proj(context_layer)
        return attn_output

    def prepareInput(self, tokenizer, query, query_assistant, history, system):
        start_to = [151644]
        n_to = [198]
        end_to = [151645]
        system_str = "system\nYou are a helpful assistant."
        user_str = "user\n" + query
        aassistant_str = "assistant\n" + query_assistant

        system_token = start_to + tokenizer.encode(system_str, allowed_special=set()) + end_to + n_to
        user_token = start_to + tokenizer.encode(user_str, allowed_special=set()) + end_to + n_to
        aassistant_token = start_to + tokenizer.encode(aassistant_str, allowed_special=set())

        tokens = system_token + user_token + aassistant_token
        tokens = user_token + aassistant_token
        tokens = start_to + tokenizer.encode("user\nHi你好\nassistant\n", allowed_special=set())

        return "", tokens

    def isFinish(self, next_tokens):
        global qk_seq
        finish, next = super().isFinish(next_tokens)
        if finish:
            for i, s in enumerate(qk_seq):
                prePath = "./temp/" + "q@k_layer_" + str(i) + ".png"
                show.DumpTensorToImage(s, prePath, GridValue=255)
        else:
            qk_seq = []
        return finish, next


runner = ResearchRunner(model)

# 第一轮对话
# output_ids, history, decoded = runner.Chat(tokenizer, "东南亚国家日本的首都是什么市", "日本的首都是")
# print(decoded)

output_ids, history, decoded = runner.Chat(tokenizer, "你好!!", "")
print(decoded)


tokens = []
for i, token in enumerate(output_ids):
    de = tokenizer.decode([token])
    de = str(i + 1).zfill(3) + " : " + repr(de)
    tokens.append(de)

# <|im_start|>system
# You are a helpful assistant.<|im_end|>
# <|im_start|>user
# 东南亚国家日本的首都是什么市<|im_end|>
# <|im_start|>assistant
# 日本的首都东京。<|im_end|>
# <|endoftext|>

show.DumpListToFile(tokens, "./temp/token_decode_list.txt")

# if decoded.split("\n")[-2] != """日本的首都东京。<|im_end|>""":
#     raise ()


# normal   (x - mean) / (std + eps) =>  sum(y)==0
# softmax  exp(x) / sum(exp(x))  =>  0 < y < 1   sum(y)==1