Update run_glue for do_predict with local test data (#9442) (#9486)

* Update run_glue for do_predict with local test data (#9442)

* Update run_glue (#9442): fix comments ('files' to 'a file')

* Update run_glue (#9442): reflect the code review

* Update run_glue (#9442): auto format

* Update run_glue (#9442): reflect the code review

examples/text-classification/run_glue.py

@@ -93,6 +93,7 @@ class DataTrainingArguments:
     validation_file: Optional[str] = field(
         default=None, metadata={"help": "A csv or a json file containing the validation data."}
     )
+    test_file: Optional[str] = field(default=None, metadata={"help": "A csv or a json file containing the test data."})
 
     def __post_init__(self):
         if self.task_name is not None:
@@ -102,10 +103,12 @@ class DataTrainingArguments:
         elif self.train_file is None or self.validation_file is None:
             raise ValueError("Need either a GLUE task or a training/validation file.")
         else:
-            extension = self.train_file.split(".")[-1]
-            assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
-            extension = self.validation_file.split(".")[-1]
-            assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
+            train_extension = self.train_file.split(".")[-1]
+            assert train_extension in ["csv", "json"], "`train_file` should be a csv or a json file."
+            validation_extension = self.validation_file.split(".")[-1]
+            assert (
+                validation_extension == train_extension
+            ), "`validation_file` should have the same extension (csv or json) as `train_file`."
 
 
 @dataclass
@@ -205,16 +208,33 @@ def main():
     if data_args.task_name is not None:
         # Downloading and loading a dataset from the hub.
         datasets = load_dataset("glue", data_args.task_name)
-    elif data_args.train_file.endswith(".csv"):
+    else:
+        # Loading a dataset from your local files.
+        # CSV/JSON training and evaluation files are needed.
+        data_files = {"train": data_args.train_file, "validation": data_args.validation_file}
+
+        # Get the test dataset: you can provide your own CSV/JSON test file (see below)
+        # when you use `do_predict` without specifying a GLUE benchmark task.
+        if training_args.do_predict:
+            if data_args.test_file is not None:
+                train_extension = data_args.train_file.split(".")[-1]
+                test_extension = data_args.test_file.split(".")[-1]
+                assert (
+                    test_extension == train_extension
+                ), "`test_file` should have the same extension (csv or json) as `train_file`."
+                data_files["test"] = data_args.test_file
+            else:
+                raise ValueError("Need either a GLUE task or a test file for `do_predict`.")
+
+        for key in data_files.keys():
+            logger.info(f"load a local file for {key}: {data_files[key]}")
+
+        if data_args.train_file.endswith(".csv"):
             # Loading a dataset from local csv files
-        datasets = load_dataset(
-            "csv", data_files={"train": data_args.train_file, "validation": data_args.validation_file}
-        )
+            datasets = load_dataset("csv", data_files=data_files)
         else:
             # Loading a dataset from local json files
-        datasets = load_dataset(
-            "json", data_files={"train": data_args.train_file, "validation": data_args.validation_file}
-        )
+            datasets = load_dataset("json", data_files=data_files)
     # See more about loading any type of standard or custom dataset at
     # https://huggingface.co/docs/datasets/loading_datasets.html.
 
@@ -323,7 +343,7 @@ def main():
 
     train_dataset = datasets["train"]
     eval_dataset = datasets["validation_matched" if data_args.task_name == "mnli" else "validation"]
-    if data_args.task_name is not None:
+    if data_args.task_name is not None or data_args.test_file is not None:
         test_dataset = datasets["test_matched" if data_args.task_name == "mnli" else "test"]
 
     # Log a few random samples from the training set: