Python text_encoder.SubwordTextEncoder方法代碼示例

# 需要導入模塊: from tensor2tensor.data_generators import text_encoder [as 別名]
# 或者: from tensor2tensor.data_generators.text_encoder import SubwordTextEncoder [as 別名]
def test_custom_reserved_tokens(self):
    """Test that we can pass custom reserved tokens to SubwordTextEncoder."""
    corpus = "The quick brown fox jumps over the lazy dog"
    token_counts = collections.Counter(corpus.split(" "))

    start_symbol = "<S>"
    end_symbol = "<E>"
    reserved_tokens = text_encoder.RESERVED_TOKENS + [start_symbol,
                                                      end_symbol]
    encoder = text_encoder.SubwordTextEncoder.build_to_target_size(
        10, token_counts, 2, 10, reserved_tokens=reserved_tokens)

    # Make sure that reserved tokens appear in the right places.
    self.assertEqual(encoder.decode([2]), start_symbol)
    self.assertEqual(encoder.decode([3]), end_symbol)

    # Make sure that we haven't messed up the ability to reconstruct.
    reconstructed_corpus = encoder.decode(encoder.encode(corpus))
    self.assertEqual(corpus, reconstructed_corpus) 

# 需要導入模塊: from tensor2tensor.data_generators import text_encoder [as 別名]
# 或者: from tensor2tensor.data_generators.text_encoder import SubwordTextEncoder [as 別名]
def test_encodable_when_not_in_alphabet(self):
    corpus = "the quick brown fox jumps over the lazy dog"
    token_counts = collections.Counter(corpus.split(" "))

    encoder = text_encoder.SubwordTextEncoder.build_to_target_size(
        100, token_counts, 2, 10)
    original = "This has UPPER CASE letters that are out of alphabet"

    # Early versions could have an infinite loop when breaking into subtokens
    # if there was any out-of-alphabet characters in the encoded string.
    encoded = encoder.encode(original)
    decoded = encoder.decode(encoded)

    self.assertEqual(original, decoded)
    encoded_str = "".join(encoder.all_subtoken_strings[i] for i in encoded)
    self.assertIn("\\84;", encoded_str) 

# 需要導入模塊: from tensor2tensor.data_generators import text_encoder [as 別名]
# 或者: from tensor2tensor.data_generators.text_encoder import SubwordTextEncoder [as 別名]
def test_reserved_token_chars_not_in_alphabet(self):
    corpus = "dog"
    token_counts = collections.Counter(corpus.split(" "))
    encoder1 = text_encoder.SubwordTextEncoder.build_to_target_size(
        100, token_counts, 2, 100)
    filename = os.path.join(self.test_temp_dir, "out.voc")
    encoder1.store_to_file(filename)
    encoder2 = text_encoder.SubwordTextEncoder(filename=filename)

    self.assertEqual(encoder1._alphabet, encoder2._alphabet)

    for t in text_encoder.RESERVED_TOKENS:
      for c in t:
        # Verify that encoders can encode all reserved token chars.
        encoder1.encode(c)
        encoder2.encode(c) 

# 需要導入模塊: from tensor2tensor.data_generators import text_encoder [as 別名]
# 或者: from tensor2tensor.data_generators.text_encoder import SubwordTextEncoder [as 別名]
def test_save_and_reload(self):
    corpus = "the quick brown fox jumps over the lazy dog"
    token_counts = collections.Counter(corpus.split(" "))

    # Deliberately exclude some required encoding chars from the alphabet
    # and token list, making some strings unencodable.
    encoder = text_encoder.SubwordTextEncoder.build_to_target_size(
        100, token_counts, 2, 10)

    filename = os.path.join(self.test_temp_dir, "out.voc")
    encoder.store_to_file(filename)
    new_encoder = text_encoder.SubwordTextEncoder(filename)

    self.assertEqual(encoder._alphabet, new_encoder._alphabet)
    self.assertEqual(encoder.all_subtoken_strings,
                     new_encoder.all_subtoken_strings)
    self.assertEqual(encoder._subtoken_string_to_id,
                     new_encoder._subtoken_string_to_id)
    self.assertEqual(encoder._max_subtoken_len, new_encoder._max_subtoken_len) 

# 需要導入模塊: from tensor2tensor.data_generators import text_encoder [as 別名]
# 或者: from tensor2tensor.data_generators.text_encoder import SubwordTextEncoder [as 別名]
def test_save_and_reload_no_single_quotes(self):
    corpus = "the quick brown fox jumps over the lazy dog"
    token_counts = collections.Counter(corpus.split(" "))

    # Deliberately exclude some required encoding chars from the alphabet
    # and token list, making some strings unencodable.
    encoder = text_encoder.SubwordTextEncoder.build_to_target_size(
        100, token_counts, 2, 10)

    filename = os.path.join(self.test_temp_dir, "out.voc")
    encoder.store_to_file(filename, add_single_quotes=False)
    new_encoder = text_encoder.SubwordTextEncoder(filename)

    self.assertEqual(encoder._alphabet, new_encoder._alphabet)
    self.assertEqual(encoder.all_subtoken_strings,
                     new_encoder.all_subtoken_strings)
    self.assertEqual(encoder._subtoken_string_to_id,
                     new_encoder._subtoken_string_to_id)
    self.assertEqual(encoder._max_subtoken_len, new_encoder._max_subtoken_len) 

# 需要導入模塊: from tensor2tensor.data_generators import text_encoder [as 別名]
# 或者: from tensor2tensor.data_generators.text_encoder import SubwordTextEncoder [as 別名]
def test_build_from_generator(self):

    corpus = "The quick brown fox jumps over the lazy dog"

    def gen():
      for _ in range(3):
        yield corpus

    start_symbol = "<S>"
    end_symbol = "<E>"
    reserved_tokens = text_encoder.RESERVED_TOKENS + [start_symbol,
                                                      end_symbol]
    encoder = text_encoder.SubwordTextEncoder.build_from_generator(
        gen(), 10, reserved_tokens=reserved_tokens)

    # Make sure that reserved tokens appear in the right places.
    self.assertEqual(encoder.decode([2]), start_symbol)
    self.assertEqual(encoder.decode([3]), end_symbol)

    self.assertEqual("hi%s" % start_symbol,
                     encoder.decode(encoder.encode("hi") + [2]))

    # Make sure that we haven't messed up the ability to reconstruct.
    reconstructed_corpus = encoder.decode(encoder.encode(corpus))
    self.assertEqual(corpus, reconstructed_corpus) 

# 需要導入模塊: from tensor2tensor.data_generators import text_encoder [as 別名]
# 或者: from tensor2tensor.data_generators.text_encoder import SubwordTextEncoder [as 別名]
def _get_or_generate_vocab(tmp_dir, vocab_filename, vocab_size):
  """Read or create vocabulary."""
  vocab_filepath = os.path.join(tmp_dir, vocab_filename)
  print('Vocab file written to: ' + vocab_filepath)

  if tf.gfile.Exists(vocab_filepath):
    gs = text_encoder.SubwordTextEncoder(vocab_filepath)
    return gs
  example_file = os.path.join(tmp_dir, _EXAMPLES_FILE)
  gs = text_encoder.SubwordTextEncoder()
  token_counts = tokenizer.corpus_token_counts(
      example_file, corpus_max_lines=1000000)
  gs = gs.build_to_target_size(
      vocab_size, token_counts, min_val=1, max_val=1e3)
  gs.store_to_file(vocab_filepath)
  return gs 

# 需要導入模塊: from tensor2tensor.data_generators import text_encoder [as 別名]
# 或者: from tensor2tensor.data_generators.text_encoder import SubwordTextEncoder [as 別名]
def vocab_type(self):
    """What kind of vocabulary to use.

    `VocabType`s:
      * `SUBWORD`: `SubwordTextEncoder`, an invertible wordpiece vocabulary.
        Must provide `self.approx_vocab_size`. Generates the vocabulary based on
        the training data. To limit the number of samples the vocab generation
        looks at, override `self.max_samples_for_vocab`. Recommended and
        default.
      * `CHARACTER`: `ByteTextEncoder`, encode raw bytes.
      * `TOKEN`: `TokenTextEncoder`, vocabulary based on a file. Must provide a
        vocabulary file yourself (`TokenTextEncoder.store_to_file`) because one
        will not be generated for you. The vocab file should be stored in
        `data_dir/` with the name specified by `self.vocab_filename`.

    Returns:
      VocabType constant
    """
    return VocabType.SUBWORD 

# 需要導入模塊: from tensor2tensor.data_generators import text_encoder [as 別名]
# 或者: from tensor2tensor.data_generators.text_encoder import SubwordTextEncoder [as 別名]
def get_or_create_vocab(self, data_dir, tmp_dir, force_get=False):
    if self.vocab_type == VocabType.CHARACTER:
      encoder = text_encoder.ByteTextEncoder()
    elif self.vocab_type == VocabType.SUBWORD:
      if force_get:
        vocab_filepath = os.path.join(data_dir, self.vocab_filename)
        encoder = text_encoder.SubwordTextEncoder(vocab_filepath)
      else:
        encoder = generator_utils.get_or_generate_vocab_inner(
            data_dir, self.vocab_filename, self.approx_vocab_size,
            self.generate_text_for_vocab(data_dir, tmp_dir),
            max_subtoken_length=self.max_subtoken_length,
            reserved_tokens=(
                text_encoder.RESERVED_TOKENS + self.additional_reserved_tokens))
    elif self.vocab_type == VocabType.TOKEN:
      vocab_filename = os.path.join(data_dir, self.vocab_filename)
      encoder = text_encoder.TokenTextEncoder(vocab_filename,
                                              replace_oov=self.oov_token)
    else:
      raise ValueError(
          "Unrecognized VocabType: %s" % str(self.vocab_type))
    return encoder 

# 需要導入模塊: from tensor2tensor.data_generators import text_encoder [as 別名]
# 或者: from tensor2tensor.data_generators.text_encoder import SubwordTextEncoder [as 別名]
def main(unused_argv):
  if FLAGS.corpus_filepattern and FLAGS.vocab_filepattern:
    raise ValueError(
        'Must only provide one of --corpus_filepattern or --vocab_filepattern')

  elif FLAGS.corpus_filepattern:
    token_counts = tokenizer.corpus_token_counts(
        FLAGS.corpus_filepattern,
        FLAGS.corpus_max_lines,
        split_on_newlines=FLAGS.split_on_newlines)

  elif FLAGS.vocab_filepattern:
    token_counts = tokenizer.vocab_token_counts(FLAGS.vocab_filepattern,
                                                FLAGS.corpus_max_lines)

  else:
    raise ValueError(
        'Must provide one of --corpus_filepattern or --vocab_filepattern')

  encoder = text_encoder.SubwordTextEncoder()
  encoder.build_from_token_counts(token_counts, FLAGS.min_count,
                                  FLAGS.num_iterations)
  encoder.store_to_file(FLAGS.output_filename) 

# 需要導入模塊: from tensor2tensor.data_generators import text_encoder [as 別名]
# 或者: from tensor2tensor.data_generators.text_encoder import SubwordTextEncoder [as 別名]
def get_encoder_from_vocab(vocab_filepath):
  """Get encoder from vocab file.

  If vocab is not found in output dir, it will be copied there by
  copy_vocab_to_output_dir to clarify the vocab used to generate the data.

  Args:
    vocab_filepath: path to vocab, either local or cns

  Returns:
    A SubwordTextEncoder vocabulary object. None if the output_parallel_text
    is set.
  """
  if not tf.gfile.Exists(vocab_filepath):
    raise ValueError("Vocab file does not exist: {}.".format(vocab_filepath))

  tf.logging.info("Found vocab file: %s", vocab_filepath)
  encoder = text_encoder.SubwordTextEncoder(vocab_filepath)
  return encoder 

# 需要導入模塊: from tensor2tensor.data_generators import text_encoder [as 別名]
# 或者: from tensor2tensor.data_generators.text_encoder import SubwordTextEncoder [as 別名]
def test_load_from_file(self):
    # Test a vocab file with words not wrapped with single quotes
    encoder = text_encoder.SubwordTextEncoder()
    correct_vocab = ["the", "and", "of"]
    vocab = io.StringIO("the\n"
                        "and\n"
                        "of\n")
    encoder._load_from_file_object(vocab)
    self.assertAllEqual(encoder.all_subtoken_strings, correct_vocab)

    # Test a vocab file with words wrapped in single quotes
    encoder = text_encoder.SubwordTextEncoder()
    vocab = io.StringIO("\"the\"\n"
                        "\"and\"\n"
                        "\"of\"\n")
    encoder._load_from_file_object(vocab)
    self.assertAllEqual(encoder.all_subtoken_strings, correct_vocab) 

# 需要導入模塊: from tensor2tensor.data_generators import text_encoder [as 別名]
# 或者: from tensor2tensor.data_generators.text_encoder import SubwordTextEncoder [as 別名]
def get_or_create_vocab(self, data_dir, tmp_dir, force_get=False):
    if self.vocab_type == VocabType.CHARACTER:
      encoder = text_encoder.ByteTextEncoder()
    elif self.vocab_type == VocabType.SUBWORD:
      if force_get:
        vocab_filepath = os.path.join(data_dir, self.vocab_filename)
        encoder = text_encoder.SubwordTextEncoder(vocab_filepath)
      else:
        other_problem = self.use_vocab_from_other_problem
        if other_problem:
          return other_problem.get_or_create_vocab(data_dir, tmp_dir, force_get)
        encoder = generator_utils.get_or_generate_vocab_inner(
            data_dir, self.vocab_filename, self.approx_vocab_size,
            self.generate_text_for_vocab(data_dir, tmp_dir),
            max_subtoken_length=self.max_subtoken_length,
            reserved_tokens=(
                text_encoder.RESERVED_TOKENS + self.additional_reserved_tokens))
    elif self.vocab_type == VocabType.TOKEN:
      vocab_filename = os.path.join(data_dir, self.vocab_filename)
      encoder = text_encoder.TokenTextEncoder(vocab_filename,
                                              replace_oov=self.oov_token)
    else:
      raise ValueError(
          "Unrecognized VocabType: %s" % str(self.vocab_type))
    return encoder