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e2b70df012
* Use isort with Black profile * isort all the things * Fix import cycles as a result of import sorting * Add DOCBIN_ALL_ATTRS type definition * Add isort to requirements * Remove isort from build dependencies check * Typo
81 lines
2.2 KiB
Python
81 lines
2.2 KiB
Python
from ...attrs import LIKE_NUM
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_numeral_suffixes = {"பத்து": "பது", "ற்று": "று", "ரத்து": "ரம்", "சத்து": "சம்"}
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_num_words = [
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"பூச்சியம்",
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"ஒரு",
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"ஒன்று",
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"இரண்டு",
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"மூன்று",
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"நான்கு",
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"ஐந்து",
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"ஆறு",
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"ஏழு",
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"எட்டு",
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"ஒன்பது",
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"பத்து",
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"பதினொன்று",
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"பன்னிரண்டு",
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"பதின்மூன்று",
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"பதினான்கு",
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"பதினைந்து",
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"பதினாறு",
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"பதினேழு",
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"பதினெட்டு",
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"பத்தொன்பது",
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"இருபது",
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"முப்பது",
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"நாற்பது",
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"ஐம்பது",
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"அறுபது",
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"எழுபது",
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"எண்பது",
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"தொண்ணூறு",
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"நூறு",
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"இருநூறு",
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"முன்னூறு",
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"நாநூறு",
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"ஐநூறு",
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"அறுநூறு",
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"எழுநூறு",
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"எண்ணூறு",
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"தொள்ளாயிரம்",
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"ஆயிரம்",
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"ஒராயிரம்",
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"லட்சம்",
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"மில்லியன்",
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"கோடி",
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"பில்லியன்",
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"டிரில்லியன்",
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]
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# 20-89 ,90-899,900-99999 and above have different suffixes
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def suffix_filter(text):
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# text without numeral suffixes
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for num_suffix in _numeral_suffixes.keys():
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length = len(num_suffix)
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if len(text) < length:
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break
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elif text.endswith(num_suffix):
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return text[:-length] + _numeral_suffixes[num_suffix]
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return text
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def like_num(text):
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text = text.replace(",", "").replace(".", "")
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if text.isdigit():
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return True
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if text.count("/") == 1:
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num, denom = text.split("/")
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if num.isdigit() and denom.isdigit():
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return True
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if text.lower() in _num_words:
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return True
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elif suffix_filter(text) in _num_words:
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return True
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return False
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LEX_ATTRS = {LIKE_NUM: like_num}
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