import torch

import gradio as gr
import torch.nn.functional as F

from transformers import BertTokenizer, GPT2LMHeadModel,PreTrainedTokenizerFast
# tokenizer = BertTokenizer.from_pretrained("supermy/poetry")
tokenizer = PreTrainedTokenizerFast(tokenizer_file="poetry-bpe.json",add_special_token=True,
    bos_token="<|endoftext|>",
    eos_token="<|endoftext|>",
    pad_token="[PAD]",
    cls_token="[CLS]",
    sep_token="[SEP]",
    unk_token="[UNK]",
    padding_side="left",
)
model = GPT2LMHeadModel.from_pretrained("supermy/poetry")
model.eval()

def top_k_top_p_filtering( logits, top_k=0, top_p=0.0, filter_value=-float('Inf') ):
    assert logits.dim() == 1
    top_k = min( top_k, logits.size(-1) )
    if top_k > 0:
        indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
        logits[indices_to_remove] = filter_value
    if top_p > 0.0:
        sorted_logits, sorted_indices = torch.sort(logits, descending=True)
        cumulative_probs = torch.cumsum( F.softmax(sorted_logits, dim=-1), dim=-1 )
        sorted_indices_to_remove = cumulative_probs > top_p
        sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
        sorted_indices_to_remove[..., 0] = 0
        indices_to_remove = sorted_indices[sorted_indices_to_remove]
        logits[indices_to_remove] = filter_value
    return logits

def generate(title, context, max_len):

    # input_ids.extend( tokenizer.encode(input_text + "-", add_special_tokens=False) )

    title_ids = tokenizer.encode(title, add_special_tokens=False)
    context_ids = tokenizer.encode(context, add_special_tokens=False)
    input_ids = title_ids + [sep_id] + context_ids
    print(input_ids) 
    cur_len = len(input_ids)
    input_len = cur_len
    last_token_id = input_ids[-1]
    input_ids = torch.tensor([input_ids], dtype=torch.long)
    
    # input_ids = [tokenizer.cls_token_id]
    # input_ids.extend( tokenizer.encode(title + "-" +context, add_special_tokens=False) )
    # input_ids = torch.tensor( [input_ids] )

    print(input_ids) 

    while True:
        outputs = model( input_ids=input_ids[:, -200:] )
        logits = outputs.logits
        next_token_logits = logits[0, -1, :]
        next_token_logits = next_token_logits / 1
        next_token_logits[unk_id] = -float('Inf')
        filtered_logits = top_k_top_p_filtering(next_token_logits, top_k=0, top_p=0.85)
        next_token_id = torch.multinomial( F.softmax(filtered_logits, dim=-1), num_samples=1 )
        input_ids = torch.cat( ( input_ids, next_token_id.unsqueeze(0) ), dim=1 )
        cur_len += 1
        word = tokenizer.convert_ids_to_tokens( next_token_id.item() )
        if cur_len >= ( input_len + max_len ) and last_token_id == 8 and next_token_id == 3:
            break
        if cur_len >= ( input_len + max_len ) and word in [".", "。", "！", "!", "?", "？", ",", "，"]:
            break
        if next_token_id == eod_id:
            break
    result = tokenizer.decode( input_ids.squeeze(0) )
    return result

if __name__ == '__main__':
    # tokenizer = BertTokenizer(vocab_file="chinese_vocab.model")
    eod_id = tokenizer.convert_tokens_to_ids("<eod>")
    sep_id = tokenizer.sep_token_id
    unk_id = tokenizer.unk_token_id
 
    
    gr.Interface(
        fn=generate,
        inputs=[
            gr.Textbox(lines=1, placeholder="输入文本标题：爱莲说", value="爱莲说",label="文本标题"),
            gr.Textbox(lines=7, placeholder="输入文本内容：水陆草木之花，可爱者甚蕃。晋陶渊明独爱菊。", value="水陆草木之花，可爱者甚蕃。晋陶渊明独爱菊。",label="初始文本"),
            "number"
            ],
        outputs=gr.Textbox(lines=15, placeholder="AI生成的文本显示在这里。",label="生成的文本")
    ).launch()