SuperposPrompt_Thesis/_datasets/autoload.py

from datasets import DatasetDict
from .glue_helper import GLUEHelper, SuperGLUEHelper


class AutoLoad:
    def __init__(self, tokenizer, n_prefix_token=0, lazy_load=True):
        self.tokenizer = tokenizer
        self.n_prefix_token = n_prefix_token
        # self.lowercase = lowercase
        
        self.post_tokenizer_map = {
            'input_ids': 0,
            'attention_mask': 1,
            'token_type_ids': 0
        }
        
        load_names = [] if lazy_load else None
        self.glue_helper = GLUEHelper(load_names)
        self.superglue_helper = SuperGLUEHelper(load_names)
    
    @property
    def _is_bert(self):
        return 'bert' in self.tokenizer.name_or_path.lower()
    
    def __output_type(self):
        return_value = [
            'input_ids', 'attention_mask', 'labels'
        ]
        
        if self._is_bert:
            return return_value + ['token_type_ids']
        
        return return_value
    
    def _add_prefix(self, tokenizer_out):
        if self.n_prefix_token == 0:
            return tokenizer_out
        for special_key, pad_val in self.post_tokenizer_map.items():
            if special_key in tokenizer_out:
                for batch_item in tokenizer_out[special_key]:
                    batch_item[:0] = ([pad_val] * self.n_prefix_token)
        return tokenizer_out
    
    def map_dataset(self, dataset, input_info, output_info, task_name):
        def preprocess(input_dict_row):
            return_value = {}
            if task_name == 'wic':
                word = input_dict_row['word']
                sent1 = input_dict_row['sentence1']
                sent2 = input_dict_row['sentence2']
                slice1 = slice(input_dict_row['start1'], input_dict_row['end1'])
                slice2 = slice(input_dict_row['start2'], input_dict_row['end2'])
                
                anotate_word = lambda _sent, _slice: _sent[:_slice.start] + "** " + _sent[_slice] + " **" + _sent[_slice.stop:]
                input_dict_row['sentence1'] = anotate_word(sent1, slice1)
                input_dict_row['sentence2'] = anotate_word(sent2, slice2)
                
                return_value['sentence1'] = input_dict_row['sentence1']
                return_value['sentence2'] = input_dict_row['sentence2']
            
            if len(input_info) == 1:
                return_value['merged'] = input_dict_row[input_info[0]]
            else:
                return_value['merged'] = "".join(f"{key}: {input_dict_row[key]} " for key in input_info)
            return return_value
        
        def create_input(input_dict_rows):
            if self._is_bert:
                if len(input_info) < 3:
                    generator = (input_dict_rows[input_name] for input_name in input_info)
                else:
                    generator = [input_dict_rows['merged']]
                tokenizer_out = self.tokenizer(
                    *generator,
                    truncation=True,
                    max_length=self.tokenizer.model_max_length - self.n_prefix_token
                )
            else:  # t5 or bart multi tokens
                tokenizer_out = self.tokenizer(input_dict_rows['merged'])
                    
            return self._add_prefix(tokenizer_out)
        
        def create_output(input_dict):
            if self.tokenizer._is_seq2seq:
                tokens = self.tokenizer(output_info.int2str(input_dict['label']))
                return tokens.input_ids
            else:
                return input_dict['label']
        
        def map_function(input_dict):
            return {
                **create_input(input_dict),
                'labels': create_output(input_dict)
            }
        dataset = dataset.map(preprocess)  # pass all as one batch
        dataset = dataset.map(map_function, batched=True)  # pass all as one batch
        dataset.set_format(type='torch', columns=self.__output_type())
        return dataset

    def get_glue(self, category, task_name):
        glue_agent = {
            'glue': self.glue_helper,
            'superglue': self.superglue_helper
        }[category]

        dataset = glue_agent.get_dataset(task_name)
        
        train_ds = dataset[glue_agent.get_task_train_key(task_name)]
        valid_ds_keys = glue_agent.get_task_validation_key(task_name)
        valid_ds_dict = DatasetDict({
            key: dataset[key]
            for key in valid_ds_keys
        })
        
        kwargs = {
            'input_info': glue_agent.get_task_input(task_name),
            'output_info': glue_agent.get_task_output(task_name),
            'task_name': task_name
        }

        return {
            'name': f'{category}-{task_name}',
            'train': self.map_dataset(train_ds, **kwargs),
            'valid_dict': self.map_dataset(valid_ds_dict, **kwargs),
            'compute_metrics': glue_agent.generate_compute_metrics(task_name, text2text=self.tokenizer._is_seq2seq)
        }

    def get_and_map(self, task_name):
        category, ds_name = task_name.split(':')
        if category in ['glue', 'superglue']:
            return self.get_glue(category, ds_name)

        raise Exception("not implented")
    
    @staticmethod
    def get_task_output(full_task_name):
        category, task_name = full_task_name.split(':')
        if category in ['glue', 'superglue']:
            selected_helper = {
                'glue': GLUEHelper,
                'superglue': SuperGLUEHelper
            }[category]
            return selected_helper.get_task_output(task_name)