233 lines
10 KiB
Python
233 lines
10 KiB
Python
# Copyright 2020-2022 Huawei Technologies Co., Ltd
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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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# ==============================================================================
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import copy
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import numpy as np
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import mindspore.dataset.text as text
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import mindspore.dataset as ds
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from mindspore.dataset.text import SentencePieceModel, to_str, SPieceTokenizerOutType
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VOCAB_FILE = "../data/dataset/test_sentencepiece/vocab.txt"
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DATA_FILE = "../data/dataset/testTokenizerData/sentencepiece_tokenizer.txt"
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def test_sentence_piece_tokenizer_callable():
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"""
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Feature: SentencePieceTokenizer
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Description: Test SentencePieceTokenizer with eager mode
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Expectation: Output is equal to the expected value
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"""
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vocab = text.SentencePieceVocab.from_file([VOCAB_FILE], 100, 0.9995, SentencePieceModel.UNIGRAM, {})
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tokenizer = text.SentencePieceTokenizer(vocab, out_type=SPieceTokenizerOutType.STRING)
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data = "123"
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assert np.array_equal(tokenizer(data), ["▁", "1", "23"])
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def test_from_vocab_to_str_unigram():
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"""
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Feature: SentencePieceTokenizer
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Description: Test SentencePieceTokenizer with UNIGRAM model
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Expectation: Output is equal to the expected value
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"""
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vocab = text.SentencePieceVocab.from_file([VOCAB_FILE], 100, 0.9995, SentencePieceModel.UNIGRAM, {})
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tokenizer = text.SentencePieceTokenizer(vocab, out_type=SPieceTokenizerOutType.STRING)
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dataset = ds.TextFileDataset(DATA_FILE, shuffle=False)
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dataset = dataset.map(operations=tokenizer)
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expect = ["▁", "I", "▁use", "▁MindSpore", "▁", "to", "▁", "t", "r", "a", "i", "n", "▁", "m", "y", "▁model", "."]
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for i in dataset.create_dict_iterator(num_epochs=1, output_numpy=True):
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ret = to_str(i["text"])
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for key, value in enumerate(ret):
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assert value == expect[key]
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def test_from_vocab_to_str_bpe():
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"""
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Feature: SentencePieceTokenizer
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Description: Test SentencePieceTokenizer with BPE model
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Expectation: Output is equal to the expected value
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"""
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vocab = text.SentencePieceVocab.from_file([VOCAB_FILE], 100, 0.9995, SentencePieceModel.BPE, {})
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tokenizer = text.SentencePieceTokenizer(vocab, out_type=SPieceTokenizerOutType.STRING)
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dataset = ds.TextFileDataset(DATA_FILE, shuffle=False)
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dataset = dataset.map(operations=tokenizer)
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expect = ["▁", "I", "▁", "u", "s", "e", "▁", "M", "in", "d", "S", "p", "or", "e", "▁t", "o", "▁t", "ra", "in", "▁m",
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"y", "▁m", "ode", "l", "."]
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for i in dataset.create_dict_iterator(num_epochs=1, output_numpy=True):
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ret = to_str(i["text"])
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for key, value in enumerate(ret):
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assert value == expect[key]
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def test_from_vocab_to_str_char():
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"""
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Feature: SentencePieceTokenizer
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Description: Test SentencePieceTokenizer with CHAR model
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Expectation: Output is equal to the expected value
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"""
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vocab = text.SentencePieceVocab.from_file([VOCAB_FILE], 100, 0.9995, SentencePieceModel.CHAR, {})
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tokenizer = text.SentencePieceTokenizer(vocab, out_type=SPieceTokenizerOutType.STRING)
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dataset = ds.TextFileDataset(DATA_FILE, shuffle=False)
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dataset = dataset.map(operations=tokenizer)
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expect = ["▁", "I", "▁", "u", "s", "e", "▁", "M", "i", "n", "d", "S", "p", "o", "r", "e", "▁", "t", "o", "▁", "t",
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"r", "a", "i", "n", "▁", "m", "y", "▁", "m", "o", "d", "e", "l", "."]
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for i in dataset.create_dict_iterator(num_epochs=1, output_numpy=True):
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ret = to_str(i["text"])
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for key, value in enumerate(ret):
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assert value == expect[key]
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def test_from_vocab_to_str_word():
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"""
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Feature: SentencePieceTokenizer
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Description: Test SentencePieceTokenizer with WORD model
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Expectation: Output is equal to the expected value
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"""
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vocab = text.SentencePieceVocab.from_file([VOCAB_FILE], 100, 0.9995, SentencePieceModel.WORD, {})
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tokenizer = text.SentencePieceTokenizer(vocab, out_type=SPieceTokenizerOutType.STRING)
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dataset = ds.TextFileDataset(DATA_FILE, shuffle=False)
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dataset = dataset.map(operations=tokenizer)
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expect = ["▁I", "▁use", "▁MindSpore", "▁to", "▁train▁my▁model."]
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for i in dataset.create_dict_iterator(num_epochs=1, output_numpy=True):
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ret = to_str(i["text"])
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for key, value in enumerate(ret):
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assert value == expect[key]
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def test_from_vocab_to_int():
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"""
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Feature: SentencePieceTokenizer
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Description: Test SentencePieceTokenizer with out_type equal to int
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Expectation: Output is equal to the expected value
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"""
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vocab = text.SentencePieceVocab.from_file([VOCAB_FILE], 100, 0.9995, SentencePieceModel.UNIGRAM, {})
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tokenizer = text.SentencePieceTokenizer(vocab, out_type=SPieceTokenizerOutType.INT)
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dataset = ds.TextFileDataset(DATA_FILE, shuffle=False)
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dataset = dataset.map(operations=tokenizer)
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expect = [3, 41, 59, 53, 3, 29, 3, 6, 12, 99, 7, 10, 3, 11, 20, 45, 19]
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for i in dataset.create_dict_iterator(num_epochs=1, output_numpy=True):
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ret = i["text"]
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for key, value in enumerate(ret):
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assert value == expect[key]
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def test_from_file_to_str():
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"""
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Feature: SentencePieceTokenizer
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Description: Test SentencePieceTokenizer with out_type equal to string
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Expectation: Output is equal to the expected value
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"""
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vocab = text.SentencePieceVocab.from_file([VOCAB_FILE], 100, 0.9995, SentencePieceModel.UNIGRAM, {})
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text.SentencePieceVocab.save_model(vocab, "./", "m.model")
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tokenizer = text.SentencePieceTokenizer("./m.model", out_type=SPieceTokenizerOutType.STRING)
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dataset = ds.TextFileDataset(DATA_FILE, shuffle=False)
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dataset = dataset.map(operations=tokenizer)
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expect = ["▁", "I", "▁use", "▁MindSpore", "▁", "to", "▁", "t", "r", "a", "i", "n", "▁", "m", "y", "▁model", "."]
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for i in dataset.create_dict_iterator(num_epochs=1, output_numpy=True):
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ret = to_str(i["text"])
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for key, value in enumerate(ret):
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assert value == expect[key]
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def test_from_file_to_int():
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"""
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Feature: SentencePieceTokenizer
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Description: Test SentencePieceTokenizer while loading vocab model from file
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Expectation: Output is equal to the expected value
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"""
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vocab = text.SentencePieceVocab.from_file([VOCAB_FILE], 100, 0.9995, SentencePieceModel.UNIGRAM, {})
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text.SentencePieceVocab.save_model(vocab, "./", "m.model")
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tokenizer = text.SentencePieceTokenizer("./m.model", out_type=SPieceTokenizerOutType.INT)
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dataset = ds.TextFileDataset(DATA_FILE, shuffle=False)
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dataset = dataset.map(operations=tokenizer)
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expect = [3, 41, 59, 53, 3, 29, 3, 6, 12, 99, 7, 10, 3, 11, 20, 45, 19]
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for i in dataset.create_dict_iterator(num_epochs=1, output_numpy=True):
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ret = i["text"]
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for key, value in enumerate(ret):
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assert value == expect[key]
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def test_build_from_dataset():
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"""
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Feature: SentencePieceTokenizer
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Description: Test SentencePieceTokenizer while loading vocab model from dataset
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Expectation: Output is equal to the expected value
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"""
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data = ds.TextFileDataset(VOCAB_FILE, shuffle=False)
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vocab = text.SentencePieceVocab.from_dataset(data, ["text"], 100, 0.9995, SentencePieceModel.UNIGRAM, {})
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tokenizer = text.SentencePieceTokenizer(vocab, out_type=SPieceTokenizerOutType.STRING)
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dataset = ds.TextFileDataset(DATA_FILE, shuffle=False)
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dataset = dataset.map(operations=tokenizer)
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expect = ["▁", "I", "▁use", "▁MindSpore", "▁", "to", "▁", "t", "r", "a", "i", "n", "▁", "m", "y", "▁model", "."]
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for i in dataset.create_dict_iterator(num_epochs=1, output_numpy=True):
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ret = to_str(i["text"])
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for key, value in enumerate(ret):
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assert value == expect[key]
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def apply_func(dataset):
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input_columns = ["text"]
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output_columns = ["text2"]
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dataset = dataset.rename(input_columns, output_columns)
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return dataset
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def zip_test(dataset):
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dataset_1 = copy.deepcopy(dataset)
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dataset_2 = copy.deepcopy(dataset)
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dataset_1 = dataset_1.apply(apply_func)
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dataset_zip = ds.zip((dataset_1, dataset_2))
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expect = ["▁", "I", "▁use", "▁MindSpore", "▁", "to", "▁", "t", "r", "a", "i", "n", "▁", "m", "y", "▁model", "."]
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for i in dataset_zip.create_dict_iterator(num_epochs=1, output_numpy=True):
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ret = to_str(i["text"])
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for key, value in enumerate(ret):
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assert value == expect[key]
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def concat_test(dataset):
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dataset_1 = copy.deepcopy(dataset)
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dataset = dataset.concat(dataset_1)
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expect = ["▁", "I", "▁use", "▁MindSpore", "▁", "to", "▁", "t", "r", "a", "i", "n", "▁", "m", "y", "▁model", "."]
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for i in dataset.create_dict_iterator(num_epochs=1, output_numpy=True):
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ret = to_str(i["text"])
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for key, value in enumerate(ret):
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assert value == expect[key]
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def test_with_zip_concat():
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"""
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Feature: SentencePieceTokenizer
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Description: Test SentencePieceTokenizer with zip and concat operations
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Expectation: Output is equal to the expected value
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"""
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data = ds.TextFileDataset(VOCAB_FILE, shuffle=False)
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vocab = text.SentencePieceVocab.from_dataset(data, ["text"], 100, 0.9995, SentencePieceModel.UNIGRAM, {})
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tokenizer = text.SentencePieceTokenizer(vocab, out_type=SPieceTokenizerOutType.STRING)
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dataset = ds.TextFileDataset(DATA_FILE, shuffle=False)
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dataset = dataset.map(operations=tokenizer, num_parallel_workers=2)
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zip_test(dataset)
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concat_test(dataset)
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if __name__ == "__main__":
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test_sentence_piece_tokenizer_callable()
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test_from_vocab_to_str_unigram()
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test_from_vocab_to_str_bpe()
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test_from_vocab_to_str_char()
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test_from_vocab_to_str_word()
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test_from_vocab_to_int()
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test_from_file_to_str()
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test_from_file_to_int()
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test_build_from_dataset()
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test_with_zip_concat()
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