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import json |
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from dataclasses import dataclass |
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from datetime import datetime |
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from huggingface_hub import hf_hub_download |
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from huggingface_hub.utils._errors import EntryNotFoundError |
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from loguru import logger |
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@dataclass |
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class CompetitionInfo: |
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competition_id: str |
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autotrain_token: str |
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def __post_init__(self): |
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try: |
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config_fname = hf_hub_download( |
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repo_id=self.competition_id, |
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filename="conf", |
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use_auth_token=self.autotrain_token, |
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repo_type="dataset", |
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) |
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competition_desc = hf_hub_download( |
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repo_id=self.competition_id, |
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filename="COMPETITION_DESC.md", |
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use_auth_token=self.autotrain_token, |
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repo_type="dataset", |
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) |
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dataset_desc = hf_hub_download( |
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repo_id=self.competition_id, |
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filename="DATASET_DESC.md", |
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use_auth_token=self.autotrain_token, |
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repo_type="dataset", |
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) |
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except EntryNotFoundError: |
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raise Exception("Competition config not found. Please check the competition id.") |
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except Exception as e: |
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logger.error(e) |
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raise Exception("Hugging Face Hub is unreachable, please try again later.") |
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self.config = self.load_config(config_fname) |
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self.competition_desc = self.load_md(competition_desc) |
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self.dataset_desc = self.load_md(dataset_desc) |
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def load_md(self, md_path): |
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with open(md_path) as f: |
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md = f.read() |
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return md |
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def load_config(self, config_path): |
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with open(config_path) as f: |
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config = json.load(f) |
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return config |
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@property |
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def submission_limit(self): |
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return self.config["SUBMISSION_LIMIT"] |
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@property |
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def selection_limit(self): |
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return self.config["SELECTION_LIMIT"] |
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@property |
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def end_date(self): |
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e_d = self.config["END_DATE"] |
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return datetime.strptime(e_d, "%Y-%m-%d") |
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@property |
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def eval_higher_is_better(self): |
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hb = self.config["EVAL_HIGHER_IS_BETTER"] |
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return True if int(hb) == 1 else False |
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@property |
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def competition_description(self): |
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return self.competition_desc |
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@property |
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def competition_name(self): |
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return self.config["COMPETITION_NAME"] |
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@property |
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def submission_columns(self): |
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return self.config["SUBMISSION_COLUMNS"].split(",") |
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@property |
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def dataset_description(self): |
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return self.dataset_desc |
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