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import ast |
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import logging |
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import os |
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import sys |
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from dataclasses import dataclass, field |
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import pandas as pd |
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from tqdm import tqdm |
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from typing import Dict, List, Optional, Tuple |
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from datasets import load_dataset |
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from transformers import ( |
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HfArgumentParser, |
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) |
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logger = logging.getLogger(__name__) |
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@dataclass |
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class DataArguments: |
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""" |
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Arguments to which dataset we are going to set up. |
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""" |
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output_dir: str = field( |
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default=".", |
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metadata={"help": "The output directory where the config will be written."}, |
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) |
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dataset_name: str = field( |
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default=None, |
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metadata={"help": "The name of the dataset to use (via the datasets library)."} |
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) |
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dataset_data_dir: Optional[str] = field( |
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default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} |
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) |
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cache_dir: Optional[str] = field( |
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default=None, |
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metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}, |
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) |
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def main(): |
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parser = HfArgumentParser([DataArguments]) |
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if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): |
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data_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))[0] |
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else: |
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data_args = parser.parse_args_into_dataclasses()[0] |
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logging.basicConfig( |
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format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", |
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datefmt="%m/%d/%Y %H:%M:%S", |
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handlers=[logging.StreamHandler(sys.stdout)], |
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) |
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logger.setLevel(logging.INFO) |
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logger.info(f"Preparing the dataset") |
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if data_args.dataset_name is not None: |
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dataset = load_dataset( |
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data_args.dataset_name, |
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data_dir=data_args.dataset_data_dir, |
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cache_dir=data_args.cache_dir |
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) |
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else: |
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dataset = load_dataset( |
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data_args.dataset_name, |
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cache_dir=data_args.cache_dir |
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) |
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def cleaning(text, item_type="ner"): |
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return text |
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def recipe_preparation(item_dict): |
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requirements = ["ner", "ingredients", "steps"] |
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constraints = [3, 3, 10] |
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if not all([ |
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True if requirements[i] in item_dict and len(item_dict[requirements[i]].split()) > constraints[i] else False |
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for i in range(len(requirements)) |
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]): |
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return None |
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ner = cleaning(item_dict["ner"], "ner") |
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ingredients = cleaning(item_dict["ingredients"], "ingredients") |
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steps = cleaning(item_dict["steps"], "steps") |
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return { |
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"inputs": ner, |
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"targets": f"{ingredients}<sep>{steps}" |
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} |
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for subset in dataset.keys(): |
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data_dict = [] |
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for item in tqdm(dataset[subset], position=0, total=len(dataset[subset])): |
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item = recipe_preparation(item) |
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if item: |
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data_dict.append(item) |
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data_df = pd.DataFrame(data_dict) |
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logger.info(f"Preparation of [{subset}] set consists of {len(data_df)} records!") |
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output_path = os.path.join(data_args.output_dir, f"{subset}.csv") |
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os.makedirs(os.path.dirname(output_path), exist_ok=True) |
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data_df.to_csv(output_path, sep="\t", encoding="utf-8", index=False) |
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logger.info(f"Data saved here {output_path}") |
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if __name__ == '__main__': |
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main() |
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