# -*- coding: utf-8 -*-
"""
Summarization by mT5 model
"""
from transformers import T5Tokenizer, T5ForConditionalGeneration
from typing import List


class mT5Summarizer:
    def __init__(
            self,
            model_size: str = "small",
            num_beams: int = 4,
            no_repeat_ngram_size: int = 2,
            min_length: int = 30,
            max_length: int = 100,
            skip_special_tokens: bool = True):
        self.model = T5ForConditionalGeneration.from_pretrained('google/mt5-%s' % model_size)
        self.tokenizer = T5Tokenizer.from_pretrained('google/mt5-%s' % model_size)
        self.num_beams = num_beams
        self.no_repeat_ngram_size = no_repeat_ngram_size
        self.min_length = min_length
        self.max_length = max_length
        self.skip_special_tokens = skip_special_tokens

    def summarize(self, text: str) -> List[str]:
        preprocess_text = text.strip().replace("\n", "")
        t5_prepared_Text = "summarize: "+preprocess_text
        tokenized_text = self.tokenizer.encode(
            t5_prepared_Text,
            return_tensors="pt"
        )
        summary_ids = self.model.generate(
            tokenized_text,
            num_beams=self.num_beams,
            no_repeat_ngram_size=self.no_repeat_ngram_size,
            min_length=self.min_length,
            max_length=self.max_length,
            early_stopping=True
        )
        output = self.tokenizer.decode(
            summary_ids[0],
            skip_special_tokens=self.skip_special_tokens
        )
        return output

@@ -486,32 +486,45 @@
 from_pretrained(
+%0A            
 'google/mt5-%25s' 
@@ -527,32 +527,41 @@
 %25s' %25 model_size
+%0A        
 )%0A        self.t
@@ -599,16 +599,29 @@
 trained(
+%0A            
 'google/
@@ -640,16 +640,25 @@
 del_size
+%0A        
 )%0A      

Update mt5.py