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import json |
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from typing import List, Tuple |
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from concurrent.futures import ProcessPoolExecutor, as_completed |
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import logging |
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from hashlib import md5 |
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from tqdm import tqdm |
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similarity_threshold = 80 |
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def remove_duplicates(conversations: List[dict]) -> List[dict]: |
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unique_ids = {} |
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unique_hashes = set() |
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with ProcessPoolExecutor() as executor: |
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futures = {executor.submit(check_unique, conversation, unique_hashes): conversation for conversation in conversations} |
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total_tasks = len(futures) |
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for future in tqdm(as_completed(futures), total=total_tasks, desc="Deduplicating", unit="conversations"): |
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is_unique, conversation = future.result() |
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if is_unique: |
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id_ = conversation.pop('id') |
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hash_ = conversation_hash(conversation) |
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unique_ids[hash_] = (id_, conversation) |
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unique_hashes.add(hash_) |
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else: |
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logging.debug(f"Duplicate found: {conversation}") |
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executor.shutdown(wait=True) |
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return [{'id': unique_ids[hash_][0], **unique_ids[hash_][1]} for hash_ in unique_hashes] |
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def check_unique(conversation: dict, unique_hashes: set) -> Tuple[bool, dict]: |
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hash_ = conversation_hash(conversation) |
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if hash_ in unique_hashes: |
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return False, conversation |
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return True, conversation |
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def conversation_hash(conversation: dict) -> str: |
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set_ = frozenset((msg['value'] for msg in conversation['conversations'])) |
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return md5(json.dumps(sorted(list(set_))).encode()).hexdigest() |
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