from .base_prompter import BasePrompter
from transformers import AutoTokenizer
import os, torch
import ftfy
import html
import string
import regex as re


def basic_clean(text):
    text = ftfy.fix_text(text)
    text = html.unescape(html.unescape(text))
    return text.strip()


def whitespace_clean(text):
    text = re.sub(r'\s+', ' ', text)
    text = text.strip()
    return text


def canonicalize(text, keep_punctuation_exact_string=None):
    text = text.replace('_', ' ')
    if keep_punctuation_exact_string:
        text = keep_punctuation_exact_string.join(
            part.translate(str.maketrans('', '', string.punctuation))
            for part in text.split(keep_punctuation_exact_string))
    else:
        text = text.translate(str.maketrans('', '', string.punctuation))
    text = text.lower()
    text = re.sub(r'\s+', ' ', text)
    return text.strip()


class HuggingfaceTokenizer:

    def __init__(self, name, seq_len=None, clean=None, **kwargs):
        assert clean in (None, 'whitespace', 'lower', 'canonicalize')
        self.name = name
        self.seq_len = seq_len
        self.clean = clean

        # init tokenizer
        self.tokenizer = AutoTokenizer.from_pretrained(name, **kwargs)
        self.vocab_size = self.tokenizer.vocab_size

    def __call__(self, sequence, **kwargs):
        return_mask = kwargs.pop('return_mask', False)

        # arguments
        _kwargs = {'return_tensors': 'pt'}
        if self.seq_len is not None:
            _kwargs.update({
                'padding': 'max_length',
                'truncation': True,
                'max_length': self.seq_len
            })
        _kwargs.update(**kwargs)

        # tokenization
        if isinstance(sequence, str):
            sequence = [sequence]
        if self.clean:
            sequence = [self._clean(u) for u in sequence]
        ids = self.tokenizer(sequence, **_kwargs)

        # output
        if return_mask:
            return ids.input_ids, ids.attention_mask
        else:
            return ids.input_ids

    def _clean(self, text):
        if self.clean == 'whitespace':
            text = whitespace_clean(basic_clean(text))
        elif self.clean == 'lower':
            text = whitespace_clean(basic_clean(text)).lower()
        elif self.clean == 'canonicalize':
            text = canonicalize(basic_clean(text))
        return text


class WanPrompter(BasePrompter):

    def __init__(self, tokenizer_path=None, text_len=512):
        super().__init__()
        self.text_len = text_len
        self.text_encoder = None
        self.fetch_tokenizer(tokenizer_path)
        
    def fetch_tokenizer(self, tokenizer_path=None):
        if tokenizer_path is not None:
            self.tokenizer = HuggingfaceTokenizer(name=tokenizer_path, seq_len=self.text_len, clean='whitespace')

    def fetch_models(self, text_encoder=None):
        self.text_encoder = text_encoder

    def encode_prompt(self, prompt, positive=True, device="cuda"):
        prompt = self.process_prompt(prompt, positive=positive)
        
        ids, mask = self.tokenizer(prompt, return_mask=True, add_special_tokens=True)
        ids = ids.to(device)
        mask = mask.to(device)
        seq_lens = mask.gt(0).sum(dim=1).long()
        prompt_emb = self.text_encoder(ids, mask)
        # Zero the tail of each sample by its valid length so a shorter sample
        # in a batch is not truncated to the longest sample's length.
        for i, v in enumerate(seq_lens.tolist()):
            prompt_emb[i, v:] = 0
        return prompt_emb