469 lines
19 KiB
Python
469 lines
19 KiB
Python
# Copyright 2022 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import shutil
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import tempfile
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import unittest
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from transformers import (
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SPIECE_UNDERLINE,
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AddedToken,
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BatchEncoding,
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NllbTokenizer,
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NllbTokenizerFast,
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is_torch_available,
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)
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from transformers.models.nllb.tokenization_nllb import FAIRSEQ_LANGUAGE_CODES
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from transformers.testing_utils import (
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get_tests_dir,
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nested_simplify,
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require_sentencepiece,
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require_tokenizers,
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require_torch,
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)
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from ...test_tokenization_common import TokenizerTesterMixin
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SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model")
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if is_torch_available():
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from transformers.models.m2m_100.modeling_m2m_100 import shift_tokens_right
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EN_CODE = 256047
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RO_CODE = 256145
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@require_sentencepiece
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@require_tokenizers
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class NllbTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
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from_pretrained_id = "facebook/nllb-200-distilled-600M"
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tokenizer_class = NllbTokenizer
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rust_tokenizer_class = NllbTokenizerFast
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test_rust_tokenizer = True
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test_sentencepiece = True
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from_pretrained_kwargs = {}
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def setUp(self):
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super().setUp()
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# We have a SentencePiece fixture for testing
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tokenizer = NllbTokenizer(SAMPLE_VOCAB, keep_accents=True)
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tokenizer.save_pretrained(self.tmpdirname)
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def test_full_tokenizer(self):
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tokenizer = NllbTokenizer(SAMPLE_VOCAB, keep_accents=True)
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tokens = tokenizer.tokenize("This is a test")
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self.assertListEqual(tokens, ["▁This", "▁is", "▁a", "▁t", "est"])
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self.assertListEqual(
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tokenizer.convert_tokens_to_ids(tokens),
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[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]],
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)
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tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.")
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self.assertListEqual(
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tokens,
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[
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SPIECE_UNDERLINE + "I",
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SPIECE_UNDERLINE + "was",
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SPIECE_UNDERLINE + "b",
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"or",
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"n",
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SPIECE_UNDERLINE + "in",
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SPIECE_UNDERLINE + "",
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"9",
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"2",
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"0",
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"0",
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"0",
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",",
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SPIECE_UNDERLINE + "and",
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SPIECE_UNDERLINE + "this",
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SPIECE_UNDERLINE + "is",
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SPIECE_UNDERLINE + "f",
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"al",
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"s",
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"é",
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".",
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],
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)
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ids = tokenizer.convert_tokens_to_ids(tokens)
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self.assertListEqual(
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ids,
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[
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value + tokenizer.fairseq_offset
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for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
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],
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)
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back_tokens = tokenizer.convert_ids_to_tokens(ids)
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self.assertListEqual(
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back_tokens,
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[
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SPIECE_UNDERLINE + "I",
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SPIECE_UNDERLINE + "was",
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SPIECE_UNDERLINE + "b",
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"or",
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"n",
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SPIECE_UNDERLINE + "in",
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SPIECE_UNDERLINE + "",
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"<unk>",
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"2",
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"0",
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"0",
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"0",
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",",
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SPIECE_UNDERLINE + "and",
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SPIECE_UNDERLINE + "this",
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SPIECE_UNDERLINE + "is",
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SPIECE_UNDERLINE + "f",
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"al",
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"s",
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"<unk>",
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".",
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],
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)
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# overwrite from test_tokenization_common to speed up test
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def test_save_pretrained(self):
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self.tokenizers_list[0] = (self.rust_tokenizer_class, "hf-internal-testing/tiny-random-nllb", {})
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for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
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with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
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tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
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tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
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tmpdirname2 = tempfile.mkdtemp()
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tokenizer_r_files = tokenizer_r.save_pretrained(tmpdirname2)
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tokenizer_p_files = tokenizer_p.save_pretrained(tmpdirname2)
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# Checks it save with the same files + the tokenizer.json file for the fast one
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self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files))
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tokenizer_r_files = tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f)
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self.assertSequenceEqual(tokenizer_r_files, tokenizer_p_files)
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# Checks everything loads correctly in the same way
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tokenizer_rp = tokenizer_r.from_pretrained(tmpdirname2)
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tokenizer_pp = tokenizer_p.from_pretrained(tmpdirname2)
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# Check special tokens are set accordingly on Rust and Python
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for key in tokenizer_pp.special_tokens_map:
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self.assertTrue(hasattr(tokenizer_rp, key))
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shutil.rmtree(tmpdirname2)
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# Save tokenizer rust, legacy_format=True
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tmpdirname2 = tempfile.mkdtemp()
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tokenizer_r_files = tokenizer_r.save_pretrained(tmpdirname2, legacy_format=True)
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tokenizer_p_files = tokenizer_p.save_pretrained(tmpdirname2)
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# Checks it save with the same files
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self.assertSequenceEqual(tokenizer_r_files, tokenizer_p_files)
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# Checks everything loads correctly in the same way
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tokenizer_rp = tokenizer_r.from_pretrained(tmpdirname2)
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tokenizer_pp = tokenizer_p.from_pretrained(tmpdirname2)
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# Check special tokens are set accordingly on Rust and Python
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for key in tokenizer_pp.special_tokens_map:
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self.assertTrue(hasattr(tokenizer_rp, key))
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shutil.rmtree(tmpdirname2)
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# Save tokenizer rust, legacy_format=False
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tmpdirname2 = tempfile.mkdtemp()
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tokenizer_r_files = tokenizer_r.save_pretrained(tmpdirname2, legacy_format=False)
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tokenizer_p_files = tokenizer_p.save_pretrained(tmpdirname2)
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# Checks it saved the tokenizer.json file
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self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files))
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# Checks everything loads correctly in the same way
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tokenizer_rp = tokenizer_r.from_pretrained(tmpdirname2)
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tokenizer_pp = tokenizer_p.from_pretrained(tmpdirname2)
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# Check special tokens are set accordingly on Rust and Python
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for key in tokenizer_pp.special_tokens_map:
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self.assertTrue(hasattr(tokenizer_rp, key))
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shutil.rmtree(tmpdirname2)
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@require_torch
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def test_prepare_seq2seq_batch(self):
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if not self.test_seq2seq:
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return
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tokenizers = self.get_tokenizers()
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for tokenizer in tokenizers:
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with self.subTest(f"{tokenizer.__class__.__name__}"):
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# Longer text that will definitely require truncation.
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src_text = [
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" UN Chief Says There Is No Military Solution in Syria",
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" Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for"
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" Syria is that 'there is no military solution' to the nearly five-year conflict and more weapons"
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" will only worsen the violence and misery for millions of people.",
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]
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tgt_text = [
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"Şeful ONU declară că nu există o soluţie militară în Siria",
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"Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al"
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' Rusiei pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi'
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" că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
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]
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try:
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batch = tokenizer.prepare_seq2seq_batch(
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src_texts=src_text,
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tgt_texts=tgt_text,
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max_length=3,
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max_target_length=10,
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return_tensors="pt",
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src_lang="eng_Latn",
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tgt_lang="ron_Latn",
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)
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except NotImplementedError:
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return
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self.assertEqual(batch.input_ids.shape[1], 3)
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self.assertEqual(batch.labels.shape[1], 10)
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# max_target_length will default to max_length if not specified
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batch = tokenizer.prepare_seq2seq_batch(
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src_text, tgt_texts=tgt_text, max_length=3, return_tensors="pt"
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)
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self.assertEqual(batch.input_ids.shape[1], 3)
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self.assertEqual(batch.labels.shape[1], 3)
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batch_encoder_only = tokenizer.prepare_seq2seq_batch(
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src_texts=src_text, max_length=3, max_target_length=10, return_tensors="pt"
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)
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self.assertEqual(batch_encoder_only.input_ids.shape[1], 3)
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self.assertEqual(batch_encoder_only.attention_mask.shape[1], 3)
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self.assertNotIn("decoder_input_ids", batch_encoder_only)
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@unittest.skip("Unfortunately way too slow to build a BPE with SentencePiece.")
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def test_save_slow_from_fast_and_reload_fast(self):
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pass
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def test_special_tokens_initialization(self):
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for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
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with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
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added_tokens = [AddedToken("<special>", lstrip=True)]
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tokenizer_r = self.rust_tokenizer_class.from_pretrained(
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pretrained_name, additional_special_tokens=added_tokens, **kwargs
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)
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r_output = tokenizer_r.encode("Hey this is a <special> token")
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special_token_id = tokenizer_r.encode("<special>", add_special_tokens=False)[0]
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self.assertTrue(special_token_id in r_output)
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if self.test_slow_tokenizer:
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tokenizer_cr = self.rust_tokenizer_class.from_pretrained(
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pretrained_name,
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additional_special_tokens=added_tokens,
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**kwargs, # , from_slow=True <- unfortunately too slow to convert
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)
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tokenizer_p = self.tokenizer_class.from_pretrained(
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pretrained_name, additional_special_tokens=added_tokens, **kwargs
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)
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p_output = tokenizer_p.encode("Hey this is a <special> token")
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cr_output = tokenizer_cr.encode("Hey this is a <special> token")
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self.assertEqual(p_output, r_output)
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self.assertEqual(cr_output, r_output)
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self.assertTrue(special_token_id in p_output)
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self.assertTrue(special_token_id in cr_output)
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@unittest.skip("Need to fix this after #26538")
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def test_training_new_tokenizer(self):
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pass
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def test_new_language_codes(self):
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code1, code2 = "myv_Cyrl", "myv_Latn"
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new_codes = FAIRSEQ_LANGUAGE_CODES + [code1, code2]
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# here I create a tokenizer with the default behaviour
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tok1 = NllbTokenizer.from_pretrained("facebook/nllb-200-distilled-600M")
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# here I enhance the model's vocabulary with two new language codes
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tok2 = NllbTokenizer.from_pretrained("facebook/nllb-200-distilled-600M", additional_special_tokens=new_codes)
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# testing that the new codes can work
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self.assertEqual(len(tok2), len(tok1) + 2)
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tok2.tgt_lang = code1
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tok2.src_lang = code2
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self.assertEqual(tok2("šumbrat!").input_ids[0], tok2.convert_tokens_to_ids(code2))
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with tempfile.TemporaryDirectory() as tempdir:
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# testing that saving and loading the tokenizer preserves the new behaviour
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tok2.save_pretrained(tempdir)
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tok3 = NllbTokenizer.from_pretrained(tempdir)
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self.assertEqual(tok2.get_vocab(), tok3.get_vocab())
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tok3.src_lang = code2
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self.assertEqual(tok3("šumbrat!").input_ids[0], tok3.convert_tokens_to_ids(code2))
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# testing that saving and loading the tokenizer preserves the new behaviour
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tok2.save_pretrained(tempdir)
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tok3 = NllbTokenizer(f"{tempdir}/sentencepiece.bpe.model", additional_special_tokens=None)
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self.assertEqual(len(tok3), 256204) # legacy
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tok4 = NllbTokenizer(f"{tempdir}/sentencepiece.bpe.model", additional_special_tokens=[])
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self.assertEqual(len(tok4), 256002)
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tok5 = NllbTokenizer(f"{tempdir}/sentencepiece.bpe.model", additional_special_tokens=[code1, code2])
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self.assertEqual(len(tok5), 256004)
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@require_torch
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@require_sentencepiece
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@require_tokenizers
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class NllbDistilledIntegrationTest(unittest.TestCase):
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checkpoint_name = "facebook/nllb-200-distilled-600M"
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src_text = [
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" UN Chief Says There Is No Military Solution in Syria",
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""" Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.""",
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]
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tgt_text = [
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"Şeful ONU declară că nu există o soluţie militară în Siria",
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"Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"
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' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'
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" face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
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]
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expected_src_tokens = [
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256047,
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16297,
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134408,
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8165,
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248066,
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14734,
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950,
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1135,
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105721,
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3573,
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83,
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27352,
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108,
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49486,
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2,
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]
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@classmethod
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def setUpClass(cls):
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cls.tokenizer: NllbTokenizer = NllbTokenizer.from_pretrained(
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cls.checkpoint_name, src_lang="eng_Latn", tgt_lang="ron_Latn"
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)
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cls.pad_token_id = 1
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return cls
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def test_enro_tokenizer_batch_encode_plus(self):
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ids = self.tokenizer.batch_encode_plus(self.src_text).input_ids[0]
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self.assertListEqual(self.expected_src_tokens, ids)
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def test_enro_tokenizer_decode_ignores_language_codes(self):
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self.assertIn(RO_CODE, self.tokenizer.all_special_ids)
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generated_ids = [RO_CODE, 4254, 98068, 112923, 39072, 3909, 713, 102767, 26, 17314, 35642, 14683, 33118, 2022, 66987, 2, 256047] # fmt: skip
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result = self.tokenizer.decode(generated_ids, skip_special_tokens=True)
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expected_romanian = self.tokenizer.decode(generated_ids[1:], skip_special_tokens=True)
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self.assertEqual(result, expected_romanian)
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self.assertNotIn(self.tokenizer.eos_token, result)
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def test_enro_tokenizer_truncation(self):
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src_text = ["this is gunna be a long sentence " * 20]
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assert isinstance(src_text[0], str)
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desired_max_length = 10
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ids = self.tokenizer(src_text, max_length=desired_max_length, truncation=True).input_ids[0]
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self.assertEqual(ids[-1], 2)
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self.assertEqual(ids[0], EN_CODE)
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self.assertEqual(len(ids), desired_max_length)
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def test_mask_token(self):
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self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]), [256203, 3])
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@require_torch
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def test_enro_tokenizer_prepare_batch(self):
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batch = self.tokenizer(
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self.src_text,
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text_target=self.tgt_text,
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padding=True,
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truncation=True,
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max_length=len(self.expected_src_tokens),
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return_tensors="pt",
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)
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batch["decoder_input_ids"] = shift_tokens_right(
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batch["labels"], self.tokenizer.pad_token_id, self.tokenizer.convert_tokens_to_ids("ron_Latn")
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)
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self.assertIsInstance(batch, BatchEncoding)
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self.assertEqual((2, 15), batch.input_ids.shape)
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self.assertEqual((2, 15), batch.attention_mask.shape)
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result = batch.input_ids.tolist()[0]
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self.assertListEqual(self.expected_src_tokens, result)
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self.assertEqual(RO_CODE, batch.decoder_input_ids[0, 0]) # EOS
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# Test that special tokens are reset
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self.assertEqual(self.tokenizer.prefix_tokens, [EN_CODE])
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self.assertEqual(self.tokenizer.suffix_tokens, [self.tokenizer.eos_token_id])
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def test_seq2seq_max_length(self):
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batch = self.tokenizer(self.src_text, padding=True, truncation=True, max_length=3, return_tensors="pt")
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targets = self.tokenizer(
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text_target=self.tgt_text, padding=True, truncation=True, max_length=10, return_tensors="pt"
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)
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labels = targets["input_ids"]
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batch["decoder_input_ids"] = shift_tokens_right(
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labels,
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self.tokenizer.pad_token_id,
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decoder_start_token_id=self.tokenizer.convert_tokens_to_ids(self.tokenizer.tgt_lang),
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)
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self.assertEqual(batch.input_ids.shape[1], 3)
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self.assertEqual(batch.decoder_input_ids.shape[1], 10)
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@require_torch
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def test_tokenizer_translation(self):
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inputs = self.tokenizer._build_translation_inputs(
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"A test", return_tensors="pt", src_lang="eng_Latn", tgt_lang="fra_Latn"
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)
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self.assertEqual(
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nested_simplify(inputs),
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{
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# A, test, EOS, en_XX
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"input_ids": [[256047, 70, 7356, 2]],
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"attention_mask": [[1, 1, 1, 1]],
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# ar_AR
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"forced_bos_token_id": 256057,
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},
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)
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@require_torch
|
|
def test_legacy_behaviour(self):
|
|
self.tokenizer.legacy_behaviour = True
|
|
inputs = self.tokenizer(
|
|
"UN Chief says there is no military solution in Syria", src_lang="eng_Latn", tgt_lang="fra_Latn"
|
|
)
|
|
self.assertEqual(
|
|
inputs.input_ids, [16297, 134408, 25653, 6370, 248, 254, 103929, 94995, 108, 49486, 2, 256047]
|
|
)
|
|
|
|
self.tokenizer.legacy_behaviour = False
|
|
inputs = self.tokenizer(
|
|
"UN Chief says there is no military solution in Syria", src_lang="eng_Latn", tgt_lang="fra_Latn"
|
|
)
|
|
self.assertEqual(
|
|
inputs.input_ids, [256047, 16297, 134408, 25653, 6370, 248, 254, 103929, 94995, 108, 49486, 2]
|
|
)
|