fix issues to pass tests

.gitignore

@@ -45,4 +45,4 @@ checkpoints/
 # vim
 *.swp
 
-.vscode
+.vscode

docs/apidoc/textattack.commands.rst

@@ -12,7 +12,6 @@ Subpackages
 .. toctree::
    :maxdepth: 6
 
-   textattack.commands.training
 
 .. automodule:: textattack.commands.attack_command
    :members:

docs/apidoc/textattack.commands.training.rst

@@ -1,14 +0,0 @@
-textattack.commands.training package
-========================================
-
-
-.. automodule:: textattack.commands.training.run_training
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-
-.. automodule:: textattack.commands.training.train_args_helpers
-   :members:
-   :undoc-members:
-   :show-inheritance:

docs/apidoc/textattack.models.tokenizers.rst

@@ -1,11 +1,6 @@
 textattack.models.tokenizers package
 ====================================
 
-.. automodule:: textattack.models.tokenizers
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
 
 .. automodule:: textattack.models.tokenizers.glove_tokenizer
    :members:

docs/apidoc/textattack.rst

@@ -44,6 +44,16 @@ Complete API Reference
    :undoc-members:
    :show-inheritance:
 
+.. automodule:: textattack.training_args
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+.. automodule:: textattack.trainer
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
 .. automodule:: textattack.dataset_args
    :members:
    :undoc-members:
@@ -53,8 +63,3 @@ Complete API Reference
    :members:
    :undoc-members:
    :show-inheritance:
-
-.. automodule:: textattack.training_args
-   :members:
-   :undoc-members:
-   :show-inheritance:

examples/attack/parallel_attack.py

@@ -1,26 +0,0 @@
-import textattack
-import functools
-
-if __name__ == "__main__":
-
-    def _attack_build_fn(model_name):
-        import textattack
-        import transformers
-
-        model = transformers.AutoModelForSequenceClassification.from_pretrained(
-            model_name
-        )
-        tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)
-        model_wrapper = textattack.models.wrappers.HuggingFaceModelWrapper(
-            model, tokenizer
-        )
-
-        attack = textattack.attack_recipes.PWWSRen2019.build(model_wrapper)
-        return attack
-
-    model_name = "textattack/bert-base-uncased-imdb"
-    dataset = textattack.datasets.HuggingFaceDataset("imdb", None, split="test")
-    attack_args = textattack.AttackArgs(num_examples=50, parallel=True)
-    attack_build_fn = functools.partial(_attack_build_fn, model_name)
-    attacker = textattack.Attacker(attack_build_fn, dataset, attack_args)
-    attacker.attack_dataset()

requirements.txt

@@ -17,4 +17,4 @@ tqdm>=4.27,<4.50.0
 word2number
 num2words
 more-itertools
-PySocks!=1.5.7,>=1.5.6
+PySocks!=1.5.7,>=1.5.6

tests/test_command_line/test_train.py

@@ -1,6 +1,5 @@
 import os
 import re
-import math
 
 from helpers import run_command_and_get_result
 

textattack/commands/__init__.py

@@ -1,3 +1,11 @@
+"""
+
+TextAttack Command Package
+===========================
+
+"""
+
+
 from abc import ABC, abstractmethod
 from .textattack_command import TextAttackCommand
 from . import textattack_cli

textattack/models/helpers/word_cnn_for_classification.py

@@ -78,10 +78,7 @@ class WordCNNForClassification(nn.Module):
         if not os.path.exists(output_path):
             os.makedirs(output_path)
         state_dict = {k: v.cpu() for k, v in self.state_dict().items()}
-        torch.save(
-            state_dict,
-            os.path.join(output_path, "pytorch_model.bin")
-        )
+        torch.save(state_dict, os.path.join(output_path, "pytorch_model.bin"))
         with open(os.path.join(output_path, "config.json"), "w") as f:
             json.dump(self._config, f)
 

textattack/models/wrappers/huggingface_model_wrapper.py

@@ -15,7 +15,9 @@ class HuggingFaceModelWrapper(PyTorchModelWrapper):
     """Loads a HuggingFace ``transformers`` model and tokenizer."""
 
     def __init__(self, model, tokenizer):
-        assert isinstance(model, transformers.PreTrainedModel), f"`model` must be of type `transformers.PreTrainedModel`, but got type {type(model)}."
+        assert isinstance(
+            model, transformers.PreTrainedModel
+        ), f"`model` must be of type `transformers.PreTrainedModel`, but got type {type(model)}."
         assert isinstance(
             tokenizer,
             (transformers.PreTrainedTokenizer, transformers.PreTrainedTokenizerFast),

textattack/trainer.py

@@ -81,11 +81,12 @@ class Trainer:
         if not hasattr(model_wrapper, "model"):
             raise ValueError("Cannot detect `model` in `model_wrapper`")
         else:
-            assert isinstance(model_wrapper.model, torch.nn.Module), f"`model` in `model_wrapper` must be of type `torch.nn.Module`, but got type `{type(model_wrapper.model)}`."
+            assert isinstance(
+                model_wrapper.model, torch.nn.Module
+            ), f"`model` in `model_wrapper` must be of type `torch.nn.Module`, but got type `{type(model_wrapper.model)}`."
         if not hasattr(model_wrapper, "tokenizer"):
             raise ValueError("Cannot detect `tokenizer` in `model_wrapper`")
 
-
         self.model_wrapper = model_wrapper
         self.task_type = task_type
         self.attack = attack
@@ -93,8 +94,6 @@ class Trainer:
         self.eval_dataset = eval_dataset
         self.training_args = training_args
 
-    
-
     def _generate_adversarial_examples(self, dataset, epoch, eval_mode=False):
         """Generate adversarial examples using attacker."""
         if eval_mode:

textattack/training_args.py

@@ -94,16 +94,29 @@ class TrainingArgs:
         if self.lr:
             self.learning_rate = self.lr
         assert self.num_epochs > 0, "`num_epochs` must be greater than 0."
-        assert self.num_clean_epochs >= 0, "`num_clean_epochs` must be greater than or equal to 0."
+        assert (
+            self.num_clean_epochs >= 0
+        ), "`num_clean_epochs` must be greater than or equal to 0."
         if self.early_stopping_epochs is not None:
-            assert self.early_stopping_epochs > 0, "`early_stopping_epochs` must be greater than 0."
-        assert self.attack_epoch_interval > 0, "`attack_epoch_interval` must be greater than 0."
+            assert (
+                self.early_stopping_epochs > 0
+            ), "`early_stopping_epochs` must be greater than 0."
+        assert (
+            self.attack_epoch_interval > 0
+        ), "`attack_epoch_interval` must be greater than 0."
         assert self.num_warmup_steps > 0, "`num_warmup_steps` must be greater than 0."
-        assert self.num_train_adv_examples > 0 or self.num_train_adv_examples == -1, "`num_train_adv_examples` must be greater than 0 or equal to -1."
-        assert self.num_eval_adv_examples > 0 or self.num_eval_adv_examples == -1, "`num_eval_adv_examples` must be greater than 0 or equal to -1."
-        assert self.gradient_accumulation_steps > 0, "`gradient_accumulation_steps` must be greater than 0."
-        assert self.num_clean_epochs <= self.num_epochs, f"`num_clean_epochs` cannot be greater than `num_epochs` ({self.num_clean_epochs} > {self.num_epochs})."
-
+        assert (
+            self.num_train_adv_examples > 0 or self.num_train_adv_examples == -1
+        ), "`num_train_adv_examples` must be greater than 0 or equal to -1."
+        assert (
+            self.num_eval_adv_examples > 0 or self.num_eval_adv_examples == -1
+        ), "`num_eval_adv_examples` must be greater than 0 or equal to -1."
+        assert (
+            self.gradient_accumulation_steps > 0
+        ), "`gradient_accumulation_steps` must be greater than 0."
+        assert (
+            self.num_clean_epochs <= self.num_epochs
+        ), f"`num_clean_epochs` cannot be greater than `num_epochs` ({self.num_clean_epochs} > {self.num_epochs})."
 
     @classmethod
     def add_parser_args(cls, parser):
@@ -469,7 +482,7 @@ class _CommandLineTrainingArgs:
 
     @classmethod
     def create_attack_from_args(cls, args, model_wrapper):
-        import textattack
+        import textattack  # noqa: F401
 
         assert (
             args.attack in ATTACK_RECIPE_NAMES