[Dependencies|tokenizers] Make both SentencePiece and Tokenizers optional dependencies (#7659)
* splitting fast and slow tokenizers [WIP] * [WIP] splitting sentencepiece and tokenizers dependencies * update dummy objects * add name_or_path to models and tokenizers * prefix added to file names * prefix * styling + quality * spliting all the tokenizer files - sorting sentencepiece based ones * update tokenizer version up to 0.9.0 * remove hard dependency on sentencepiece 🎉 * and removed hard dependency on tokenizers 🎉 * update conversion script * update missing models * fixing tests * move test_tokenization_fast to main tokenization tests - fix bugs * bump up tokenizers * fix bert_generation * update ad fix several tokenizers * keep sentencepiece in deps for now * fix funnel and deberta tests * fix fsmt * fix marian tests * fix layoutlm * fix squeezebert and gpt2 * fix T5 tokenization * fix xlnet tests * style * fix mbart * bump up tokenizers to 0.9.2 * fix model tests * fix tf models * fix seq2seq examples * fix tests without sentencepiece * fix slow => fast conversion without sentencepiece * update auto and bert generation tests * fix mbart tests * fix auto and common test without tokenizers * fix tests without tokenizers * clean up tests lighten up when tokenizers + sentencepiece are both off * style quality and tests fixing * add sentencepiece to doc/examples reqs * leave sentencepiece on for now * style quality split hebert and fix pegasus * WIP Herbert fast * add sample_text_no_unicode and fix hebert tokenization * skip FSMT example test for now * fix style * fix fsmt in example tests * update following Lysandre and Sylvain's comments * Update src/transformers/testing_utils.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/testing_utils.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/tokenization_utils_base.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/tokenization_utils_base.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
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@@ -18,11 +18,14 @@ import json
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import os
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import unittest
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from transformers.tokenization_openai import VOCAB_FILES_NAMES, OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
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from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
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from transformers.testing_utils import require_tokenizers
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from transformers.tokenization_openai import VOCAB_FILES_NAMES
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from .test_tokenization_common import TokenizerTesterMixin
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@require_tokenizers
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class OpenAIGPTTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
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tokenizer_class = OpenAIGPTTokenizer
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@@ -80,3 +83,47 @@ class OpenAIGPTTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
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input_tokens = tokens + ["<unk>"]
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input_bpe_tokens = [14, 15, 20]
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self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens)
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def test_padding(self, max_length=15):
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for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
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with self.subTest("{} ({})".format(tokenizer.__class__.__name__, pretrained_name)):
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tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
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# Simple input
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s = "This is a simple input"
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s2 = ["This is a simple input 1", "This is a simple input 2"]
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p = ("This is a simple input", "This is a pair")
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p2 = [
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("This is a simple input 1", "This is a simple input 2"),
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("This is a simple pair 1", "This is a simple pair 2"),
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]
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# Simple input tests
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self.assertRaises(ValueError, tokenizer_r.encode, s, max_length=max_length, padding="max_length")
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# Simple input
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self.assertRaises(ValueError, tokenizer_r.encode_plus, s, max_length=max_length, padding="max_length")
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# Simple input
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self.assertRaises(
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ValueError,
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tokenizer_r.batch_encode_plus,
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s2,
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max_length=max_length,
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padding="max_length",
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)
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# Pair input
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self.assertRaises(ValueError, tokenizer_r.encode, p, max_length=max_length, padding="max_length")
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# Pair input
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self.assertRaises(ValueError, tokenizer_r.encode_plus, p, max_length=max_length, padding="max_length")
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# Pair input
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self.assertRaises(
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ValueError,
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tokenizer_r.batch_encode_plus,
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p2,
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max_length=max_length,
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padding="max_length",
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)
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