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fix deprecation warnings (#7033)

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* fix deprecation warnings

* remove tests/test_tokenization_common.py's test_padding_to_max_length

* revert test_padding_to_max_length
This commit is contained in:
Stas Bekman
2020-09-14 04:51:19 -07:00
committed by GitHub
parent 576eec98e0
commit 4d39148419
3 changed files with 3 additions and 3 deletions
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2
src/transformers/modeling_funnel.py
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@@ -127,7 +127,7 @@ def load_tf_weights_in_funnel(model, config, tf_checkpoint_path):
skipped = False skipped = False
for m_name in name[1:]: for m_name in name[1:]:
if not isinstance(pointer, FunnelPositionwiseFFN) and re.fullmatch(r"layer_\d+", m_name): if not isinstance(pointer, FunnelPositionwiseFFN) and re.fullmatch(r"layer_\d+", m_name):
layer_index = int(re.search("layer_(\d+)", m_name).groups()[0]) layer_index = int(re.search(r"layer_(\d+)", m_name).groups()[0])
if layer_index < config.num_hidden_layers: if layer_index < config.num_hidden_layers:
block_idx = 0 block_idx = 0
while layer_index >= config.block_sizes[block_idx]: while layer_index >= config.block_sizes[block_idx]:

2
src/transformers/modeling_tf_utils.py
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@@ -699,7 +699,7 @@ class TFConv1D(tf.keras.layers.Layer):
class TFSharedEmbeddings(tf.keras.layers.Layer): class TFSharedEmbeddings(tf.keras.layers.Layer):
""" r"""
Construct shared token embeddings. Construct shared token embeddings.
The weights of the embedding layer is usually shared with the weights of the linear decoder when doing The weights of the embedding layer is usually shared with the weights of the linear decoder when doing

2
tests/test_tokenization_common.py
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@@ -156,7 +156,7 @@ class TokenizerTesterMixin:
tokenizers = self.get_tokenizers() tokenizers = self.get_tokenizers()
for tokenizer in tokenizers: for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"): with self.subTest(f"{tokenizer.__class__.__name__}"):
self.assertNotEqual(tokenizer.max_len, 42) self.assertNotEqual(tokenizer.model_max_length, 42)
# Now let's start the test # Now let's start the test
tokenizers = self.get_tokenizers() tokenizers = self.get_tokenizers()