Project dependencies may have API risk issues

[PyDR-Tool]

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Hi, In mRASP2, inappropriate dependency versioning constraints can cause risks.

Below are the dependencies and version constraints that the project is using

subword-nmt
sacrebleu
sacremoses
kytea
six

The version constraint == will introduce the risk of dependency conflicts because the scope of dependencies is too strict. The version constraint No Upper Bound and * will introduce the risk of the missing API Error because the latest version of the dependencies may remove some APIs.

After further analysis, in this project, The version constraint of dependency sacrebleu can be changed to >=1.1.0,<=1.1.1. The version constraint of dependency sacrebleu can be changed to >=1.1.3,<=1.4.5.

The above modification suggestions can reduce the dependency conflicts as much as possible, and introduce the latest version as much as possible without calling Error in the projects.

The invocation of the current project includes all the following methods.

The calling methods from the sacrebleu

sacrebleu.corpus_bleu
sacrebleu.compute_bleu

The calling methods from the all methods

get_hypo_and_ref
numpy.array
counts.append
fairseq.utils.strip_pad
max
all_dataset_upsample_ratio.strip
fairseq.data.PrependTokenDataset
self.swap_sample
FileNotFoundError
self.tgt_dict.string
model.encoder.forward.transpose
torch.no_grad
inspect.getfullargspec
log.get
float
tqdm.tqdm
hyps.append
json.loads
torch.cat
f.read.split
self.temperature.anchor_dot_contrast.torch.div.nn.LogSoftmax.diag
cls.load_dictionary.index
eval.readlines
self.dataset.size
self.temperature.anchor_dot_contrast.torch.div.nn.LogSoftmax.diag.sum
isinstance
torch.LongTensor
_sentence_embedding
self.inference_step
fairseq.criterions.label_smoothed_cross_entropy.LabelSmoothedCrossEntropyCriterion.add_args
open.close
mask.float
cls
recover_bpe
open
hasattr
id_num.score_dict.append
self.dataset.prefetch
fairseq.data.TruncateDataset
eval.read
numpy.array.sum
src_list.append
fairseq.models.transformer.transformer_wmt_en_de_big_t2t
self.tgt_dict.pad
eval
toks.int
src_datasets.append
format
bpe_symbol.line.replace.rstrip
cls.load_dictionary.eos
fairseq.data.data_utils.infer_language_pair
torch.cat.contiguous
logging.getLogger.info
argparse.Namespace
fairseq.models.register_model_architecture
j.line.split
similarity_function
str
Exception
ValueError
self.set_epoch
open.write
mask.float.sum.unsqueeze
fairseq.data.AppendTokenDataset
fairseq.data.encoders.build_tokenizer
super.set_epoch
super.__init__
cls.load_dictionary.unk
size_ratio.dataset.len.np.ceil.astype
super
self.padding_idx.src_tokens.int.sum
numpy.argsort
super.reduce_metrics
self.padding_idx.src_tokens.int
itertools.count
os.path.join
self.padding_idx.target.int
super.build_model
generator.generate
super.valid_step
round
int
len
fairseq.data.indexed_dataset.dataset_exists
refs.append
os.path.dirname
torch.nn.LogSoftmax
toks.int.cpu
logging.getLogger
re.compile
mask.unsqueeze
self.tokenizer.decode
numpy.ceil
remove_bpe_fn
fairseq.tasks.register_task
fairseq.tasks.translation.TranslationTask.add_args
re.search.span
torch.nn.CosineSimilarity
self.dataset.num_tokens
totals.append
fairseq.utils.deprecation_warning
self.compute_loss
cls.load_dictionary
self.target_dictionary.index
prefix_tokens.to.to
split_exists
fairseq.utils.eval_bool
remove_bpe
torch.transpose
self.len.np.random.permutation.astype
getattr
fairseq.tasks.translation.load_langpair_dataset
torch.div
re.search
target.contiguous
sum_logs
fairseq.metrics.log_scalar
self.padding_idx.target.int.sum
contrast_feature.expand
numpy.random.permutation
tgt_list.append
self.dataset.__getitem__
numpy.random.RandomState
cls.load_dictionary.bos
src_tokens.size
numpy.random.RandomState.choice
load_langpair_dataset
bpe_symbol.line.replace.rstrip.replace
fairseq.data.data_utils.load_indexed_dataset
cls.load_dictionary.pad
sacrebleu.compute_bleu
fairseq.options.eval_bool
mask.float.sum
map
self.get_contrastive_loss
fairseq.data.StripTokenDataset
self.build_generator
fairseq.utils.split_paths
fairseq.data.ConcatDataset
fairseq.metrics.log_derived
decode
data.SubsampleLanguagePairDataset
model
join
parser.add_argument
id_num.hypothesis_dict.append
tgt_datasets.append
math.log
fairseq.data.plasma_utils.PlasmaArray
prefix_tokens.to.expand
self.similarity_function
all_dataset_upsample_ratio.strip.split
fairseq.data.LanguagePairDataset
id_num.pos_score_dict.append
mask.unsqueeze.encoder_output.sum
numpy.arange
fairseq.utils.item
o.write
sacrebleu.corpus_bleu
reprocess
sample.size
re.search.group
fairseq.models.transformer.transformer_wmt_en_de
fairseq.criterions.register_criterion
self._inference_with_bleu
mono_datas.append
range
anchor_feature.expand
prefix_tokens.torch.LongTensor.unsqueeze
sum
model.encoder.forward

@developer Could please help me check this issue? May I pull a request to fix it? Thank you very much.