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Remove dependency on pytest for running tests (#2055)

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* Switch to plain unittest for skipping slow tests.

Add a RUN_SLOW environment variable for running them.

* Switch to plain unittest for PyTorch dependency.

* Switch to plain unittest for TensorFlow dependency.

* Avoid leaking open files in the test suite.

This prevents spurious warnings when running tests.

* Fix unicode warning on Python 2 when running tests.

The warning was:

    UnicodeWarning: Unicode equal comparison failed to convert both arguments to Unicode - interpreting them as being unequal

* Support running PyTorch tests on a GPU.

Reverts 27e015bd.

* Tests no longer require pytest.

* Make tests pass on cuda
This commit is contained in:
Aymeric Augustin
2019-12-06 19:57:38 +01:00
committed by Julien Chaumond
parent e4679cddce
commit 35401fe50f
50 changed files with 344 additions and 231 deletions
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13
transformers/tokenization_utils.py
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@@ -347,7 +347,7 @@ class PreTrainedTokenizer(object):
"We assumed '{}' was a path or url to a directory containing vocabulary files "
"named {} but couldn't find such vocabulary files at this path or url.".format(
pretrained_model_name_or_path, ', '.join(s3_models),
pretrained_model_name_or_path,
pretrained_model_name_or_path,
list(cls.vocab_files_names.values())))
# Get files from url, cache, or disk depending on the case
@@ -382,7 +382,8 @@ class PreTrainedTokenizer(object):
# Did we saved some inputs and kwargs to reload ?
tokenizer_config_file = resolved_vocab_files.pop('tokenizer_config_file', None)
if tokenizer_config_file is not None:
init_kwargs = json.load(open(tokenizer_config_file, encoding="utf-8"))
with open(tokenizer_config_file, encoding="utf-8") as tokenizer_config_handle:
init_kwargs = json.load(tokenizer_config_handle)
saved_init_inputs = init_kwargs.pop('init_inputs', ())
if not init_inputs:
init_inputs = saved_init_inputs
@@ -407,7 +408,8 @@ class PreTrainedTokenizer(object):
if args_name not in init_kwargs:
init_kwargs[args_name] = file_path
if special_tokens_map_file is not None:
special_tokens_map = json.load(open(special_tokens_map_file, encoding="utf-8"))
with open(special_tokens_map_file, encoding="utf-8") as special_tokens_map_handle:
special_tokens_map = json.load(special_tokens_map_handle)
for key, value in special_tokens_map.items():
if key not in init_kwargs:
init_kwargs[key] = value
@@ -421,7 +423,8 @@ class PreTrainedTokenizer(object):
# Add supplementary tokens.
if added_tokens_file is not None:
added_tok_encoder = json.load(open(added_tokens_file, encoding="utf-8"))
with open(added_tokens_file, encoding="utf-8") as added_tokens_handle:
added_tok_encoder = json.load(added_tokens_handle)
added_tok_decoder = {v:k for k, v in added_tok_encoder.items()}
tokenizer.added_tokens_encoder.update(added_tok_encoder)
tokenizer.added_tokens_decoder.update(added_tok_decoder)
@@ -937,7 +940,7 @@ class PreTrainedTokenizer(object):
logger.warning("Token indices sequence length is longer than the specified maximum sequence length "
"for this model ({} > {}). Running this sequence through the model will result in "
"indexing errors".format(len(ids), self.max_len))
return encoded_inputs
def truncate_sequences(self, ids, pair_ids=None, num_tokens_to_remove=0, truncation_strategy='longest_first', stride=0):