mirror of
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99 lines
2.9 KiB
Python
99 lines
2.9 KiB
Python
# coding: utf-8
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from __future__ import unicode_literals
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from ...attrs import TAG, DEP, HEAD
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from ...serialize.packer import Packer
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from ...serialize.bits import BitArray
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from ..util import get_doc
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import pytest
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@pytest.fixture
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def text():
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return "the dog jumped"
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@pytest.fixture
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def text_b():
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return b"the dog jumped"
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def test_serialize_char_packer(en_vocab, text_b):
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packer = Packer(en_vocab, [])
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bits = BitArray()
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bits.seek(0)
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byte_str = bytearray(text_b)
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packer.char_codec.encode(byte_str, bits)
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bits.seek(0)
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result = [b''] * len(byte_str)
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packer.char_codec.decode(bits, result)
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assert bytearray(result) == byte_str
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def test_serialize_packer_unannotated(en_tokenizer, text):
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packer = Packer(en_tokenizer.vocab, [])
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tokens = en_tokenizer(text)
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assert tokens.text_with_ws == text
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bits = packer.pack(tokens)
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result = packer.unpack(bits)
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assert result.text_with_ws == text
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def test_packer_annotated(en_vocab, text):
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heads = [1, 1, 0]
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deps = ['det', 'nsubj', 'ROOT']
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tags = ['DT', 'NN', 'VBD']
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attr_freqs = [
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(TAG, [(en_vocab.strings['NN'], 0.1),
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(en_vocab.strings['DT'], 0.2),
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(en_vocab.strings['JJ'], 0.01),
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(en_vocab.strings['VBD'], 0.05)]),
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(DEP, {en_vocab.strings['det']: 0.2,
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en_vocab.strings['nsubj']: 0.1,
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en_vocab.strings['adj']: 0.05,
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en_vocab.strings['ROOT']: 0.1}.items()),
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(HEAD, {0: 0.05, 1: 0.2, -1: 0.2, -2: 0.1, 2: 0.1}.items())
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]
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packer = Packer(en_vocab, attr_freqs)
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doc = get_doc(en_vocab, [t for t in text.split()], tags=tags, deps=deps, heads=heads)
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# assert doc.text_with_ws == text
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assert [t.tag_ for t in doc] == tags
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assert [t.dep_ for t in doc] == deps
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assert [(t.head.i-t.i) for t in doc] == heads
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bits = packer.pack(doc)
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result = packer.unpack(bits)
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# assert result.text_with_ws == text
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assert [t.tag_ for t in result] == tags
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assert [t.dep_ for t in result] == deps
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assert [(t.head.i-t.i) for t in result] == heads
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def test_packer_bad_chars(en_tokenizer):
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text = "naja gut, is eher bl\xf6d und nicht mit reddit.com/digg.com vergleichbar; vielleicht auf dem weg dahin"
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packer = Packer(en_tokenizer.vocab, [])
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doc = en_tokenizer(text)
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bits = packer.pack(doc)
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result = packer.unpack(bits)
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assert result.string == doc.string
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@pytest.mark.models
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def test_packer_bad_chars_tags(EN):
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text = "naja gut, is eher bl\xf6d und nicht mit reddit.com/digg.com vergleichbar; vielleicht auf dem weg dahin"
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tags = ['JJ', 'NN', ',', 'VBZ', 'DT', 'NN', 'JJ', 'NN', 'NN',
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'ADD', 'NN', ':', 'NN', 'NN', 'NN', 'NN', 'NN']
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tokens = EN.tokenizer(text)
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doc = get_doc(tokens.vocab, [t.text for t in tokens], tags=tags)
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byte_string = doc.to_bytes()
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result = get_doc(tokens.vocab).from_bytes(byte_string)
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assert [t.tag_ for t in result] == [t.tag_ for t in doc]
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