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- #!/usr/bin/env python3
- # coding=utf-8
- # Copyright 2020 The HuggingFace Team. All rights reserved.
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
- """Tests for the Blenderbot small tokenizer."""
- import json
- import os
- import unittest
-
- from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
- VOCAB_FILES_NAMES,
- BlenderbotSmallTokenizer,
- )
-
- from .test_tokenization_common import TokenizerTesterMixin
-
-
- class BlenderbotSmallTokenizerTest(TokenizerTesterMixin, unittest.TestCase):
-
- tokenizer_class = BlenderbotSmallTokenizer
- test_rust_tokenizer = False
-
- def setUp(self):
- super().setUp()
-
- vocab = ["__start__", "adapt", "act", "ap@@", "te", "__end__", "__unk__"]
- vocab_tokens = dict(zip(vocab, range(len(vocab))))
-
- merges = ["#version: 0.2", "a p", "t e</w>", "ap t</w>", "a d", "ad apt</w>", "a c", "ac t</w>", ""]
- self.special_tokens_map = {"unk_token": "__unk__", "bos_token": "__start__", "eos_token": "__end__"}
-
- self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
- self.merges_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["merges_file"])
- with open(self.vocab_file, "w", encoding="utf-8") as fp:
- fp.write(json.dumps(vocab_tokens) + "\n")
- with open(self.merges_file, "w", encoding="utf-8") as fp:
- fp.write("\n".join(merges))
-
- def get_tokenizer(self, **kwargs):
- kwargs.update(self.special_tokens_map)
- return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname, **kwargs)
-
- def get_input_output_texts(self, tokenizer):
- input_text = "adapt act apte"
- output_text = "adapt act apte"
- return input_text, output_text
-
- def test_full_blenderbot_small_tokenizer(self):
- tokenizer = BlenderbotSmallTokenizer(self.vocab_file, self.merges_file, **self.special_tokens_map)
- text = "adapt act apte"
- bpe_tokens = ["adapt", "act", "ap@@", "te"]
- tokens = tokenizer.tokenize(text)
- self.assertListEqual(tokens, bpe_tokens)
-
- input_tokens = [tokenizer.bos_token] + tokens + [tokenizer.eos_token]
-
- input_bpe_tokens = [0, 1, 2, 3, 4, 5]
- self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens)
-
- def test_special_tokens_small_tok(self):
- tok = BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M")
- assert tok("sam").input_ids == [1384]
- src_text = "I am a small frog."
- encoded = tok([src_text], padding=False, truncation=False)["input_ids"]
- decoded = tok.batch_decode(encoded, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
- assert src_text != decoded # I wish it did!
- assert decoded == "i am a small frog ."
-
- def test_empty_word_small_tok(self):
- tok = BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M")
- src_text = "I am a small frog ."
- src_text_dot = "."
- encoded = tok(src_text)["input_ids"]
- encoded_dot = tok(src_text_dot)["input_ids"]
-
- assert encoded[-1] == encoded_dot[0]
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