Fix input data file extension in examples (#28741)

examples/flax/language-modeling/run_bart_dlm_flax.py

@@ -558,9 +558,10 @@ def main():
         data_files = {}
         if data_args.train_file is not None:
             data_files["train"] = data_args.train_file
+            extension = data_args.train_file.split(".")[-1]
         if data_args.validation_file is not None:
             data_files["validation"] = data_args.validation_file
-        extension = data_args.train_file.split(".")[-1]
+            extension = data_args.validation_file.split(".")[-1]
         if extension == "txt":
             extension = "text"
         datasets = load_dataset(

examples/flax/language-modeling/run_clm_flax.py

@@ -449,9 +449,10 @@ def main():
         dataset_args = {}
         if data_args.train_file is not None:
             data_files["train"] = data_args.train_file
+            extension = data_args.train_file.split(".")[-1]
         if data_args.validation_file is not None:
             data_files["validation"] = data_args.validation_file
-        extension = data_args.train_file.split(".")[-1]
+            extension = data_args.validation_file.split(".")[-1]
         if extension == "txt":
             extension = "text"
             dataset_args["keep_linebreaks"] = data_args.keep_linebreaks

examples/flax/language-modeling/run_mlm_flax.py

@@ -485,9 +485,10 @@ def main():
         data_files = {}
         if data_args.train_file is not None:
             data_files["train"] = data_args.train_file
+            extension = data_args.train_file.split(".")[-1]
         if data_args.validation_file is not None:
             data_files["validation"] = data_args.validation_file
-        extension = data_args.train_file.split(".")[-1]
+            extension = data_args.validation_file.split(".")[-1]
         if extension == "txt":
             extension = "text"
         datasets = load_dataset(

examples/flax/language-modeling/run_t5_mlm_flax.py

@@ -599,9 +599,10 @@ def main():
         data_files = {}
         if data_args.train_file is not None:
             data_files["train"] = data_args.train_file
+            extension = data_args.train_file.split(".")[-1]
         if data_args.validation_file is not None:
             data_files["validation"] = data_args.validation_file
-        extension = data_args.train_file.split(".")[-1]
+            extension = data_args.validation_file.split(".")[-1]
         if extension == "txt":
             extension = "text"
         datasets = load_dataset(

examples/pytorch/language-modeling/run_clm_no_trainer.py

@@ -345,9 +345,10 @@ def main():
         dataset_args = {}
         if args.train_file is not None:
             data_files["train"] = args.train_file
+            extension = args.train_file.split(".")[-1]
         if args.validation_file is not None:
             data_files["validation"] = args.validation_file
-        extension = args.train_file.split(".")[-1]
+            extension = args.validation_file.split(".")[-1]
         if extension == "txt":
             extension = "text"
             dataset_args["keep_linebreaks"] = not args.no_keep_linebreaks

examples/pytorch/language-modeling/run_mlm_no_trainer.py

@@ -351,9 +351,10 @@ def main():
         data_files = {}
         if args.train_file is not None:
             data_files["train"] = args.train_file
+            extension = args.train_file.split(".")[-1]
         if args.validation_file is not None:
             data_files["validation"] = args.validation_file
-        extension = args.train_file.split(".")[-1]
+            extension = args.validation_file.split(".")[-1]
         if extension == "txt":
             extension = "text"
         raw_datasets = load_dataset(extension, data_files=data_files)

examples/pytorch/language-modeling/run_plm.py

@@ -328,9 +328,10 @@ def main():
         data_files = {}
         if data_args.train_file is not None:
             data_files["train"] = data_args.train_file
+            extension = data_args.train_file.split(".")[-1]
         if data_args.validation_file is not None:
             data_files["validation"] = data_args.validation_file
-        extension = data_args.train_file.split(".")[-1]
+            extension = data_args.validation_file.split(".")[-1]
         if extension == "txt":
             extension = "text"
         raw_datasets = load_dataset(extension, data_files=data_files, cache_dir=model_args.cache_dir)

examples/pytorch/multiple-choice/run_swag.py

@@ -311,9 +311,10 @@ def main():
         data_files = {}
         if data_args.train_file is not None:
             data_files["train"] = data_args.train_file
+            extension = data_args.train_file.split(".")[-1]
         if data_args.validation_file is not None:
             data_files["validation"] = data_args.validation_file
-        extension = data_args.train_file.split(".")[-1]
+            extension = data_args.validation_file.split(".")[-1]
         raw_datasets = load_dataset(
             extension,
             data_files=data_files,

examples/pytorch/multiple-choice/run_swag_no_trainer.py

@@ -357,9 +357,10 @@ def main():
         data_files = {}
         if args.train_file is not None:
             data_files["train"] = args.train_file
+            extension = args.train_file.split(".")[-1]
         if args.validation_file is not None:
             data_files["validation"] = args.validation_file
-        extension = args.train_file.split(".")[-1]
+            extension = args.validation_file.split(".")[-1]
         raw_datasets = load_dataset(extension, data_files=data_files)
     # Trim a number of training examples
     if args.debug:

examples/pytorch/question-answering/run_qa_beam_search_no_trainer.py

@@ -362,11 +362,13 @@ def main():
         data_files = {}
         if args.train_file is not None:
             data_files["train"] = args.train_file
+            extension = args.train_file.split(".")[-1]
         if args.validation_file is not None:
             data_files["validation"] = args.validation_file
+            extension = args.validation_file.split(".")[-1]
         if args.test_file is not None:
             data_files["test"] = args.test_file
-        extension = args.train_file.split(".")[-1]
+            extension = args.test_file.split(".")[-1]
         raw_datasets = load_dataset(extension, data_files=data_files, field="data")
     # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
     # https://huggingface.co/docs/datasets/loading_datasets.

examples/pytorch/question-answering/run_qa_no_trainer.py

@@ -410,11 +410,13 @@ def main():
         data_files = {}
         if args.train_file is not None:
             data_files["train"] = args.train_file
+            extension = args.train_file.split(".")[-1]
         if args.validation_file is not None:
             data_files["validation"] = args.validation_file
+            extension = args.validation_file.split(".")[-1]
         if args.test_file is not None:
             data_files["test"] = args.test_file
-        extension = args.train_file.split(".")[-1]
+            extension = args.test_file.split(".")[-1]
         raw_datasets = load_dataset(extension, data_files=data_files, field="data")
     # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
     # https://huggingface.co/docs/datasets/loading_datasets.

examples/pytorch/summarization/run_summarization_no_trainer.py

@@ -404,9 +404,10 @@ def main():
         data_files = {}
         if args.train_file is not None:
             data_files["train"] = args.train_file
+            extension = args.train_file.split(".")[-1]
         if args.validation_file is not None:
             data_files["validation"] = args.validation_file
-        extension = args.train_file.split(".")[-1]
+            extension = args.validation_file.split(".")[-1]
         raw_datasets = load_dataset(extension, data_files=data_files)
     # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
     # https://huggingface.co/docs/datasets/loading_datasets.

examples/pytorch/token-classification/run_ner.py

@@ -311,11 +311,13 @@ def main():
         data_files = {}
         if data_args.train_file is not None:
             data_files["train"] = data_args.train_file
+            extension = data_args.train_file.split(".")[-1]
         if data_args.validation_file is not None:
             data_files["validation"] = data_args.validation_file
+            extension = data_args.validation_file.split(".")[-1]
         if data_args.test_file is not None:
             data_files["test"] = data_args.test_file
-        extension = data_args.train_file.split(".")[-1]
+            extension = data_args.test_file.split(".")[-1]
         raw_datasets = load_dataset(extension, data_files=data_files, cache_dir=model_args.cache_dir)
     # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
     # https://huggingface.co/docs/datasets/loading_datasets.

examples/pytorch/token-classification/run_ner_no_trainer.py

@@ -339,9 +339,10 @@ def main():
         data_files = {}
         if args.train_file is not None:
             data_files["train"] = args.train_file
+            extension = args.train_file.split(".")[-1]
         if args.validation_file is not None:
             data_files["validation"] = args.validation_file
-        extension = args.train_file.split(".")[-1]
+            extension = args.validation_file.split(".")[-1]
         raw_datasets = load_dataset(extension, data_files=data_files)
     # Trim a number of training examples
     if args.debug:

examples/pytorch/translation/run_translation_no_trainer.py

@@ -384,9 +384,10 @@ def main():
         data_files = {}
         if args.train_file is not None:
             data_files["train"] = args.train_file
+            extension = args.train_file.split(".")[-1]
         if args.validation_file is not None:
             data_files["validation"] = args.validation_file
-        extension = args.train_file.split(".")[-1]
+            extension = args.validation_file.split(".")[-1]
         raw_datasets = load_dataset(extension, data_files=data_files)
     # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
     # https://huggingface.co/docs/datasets/loading_datasets.

examples/research_projects/jax-projects/model_parallel/run_clm_mp.py

@@ -297,9 +297,10 @@ def main():
         data_files = {}
         if data_args.train_file is not None:
             data_files["train"] = data_args.train_file
+            extension = data_args.train_file.split(".")[-1]
         if data_args.validation_file is not None:
             data_files["validation"] = data_args.validation_file
-        extension = data_args.train_file.split(".")[-1]
+            extension = data_args.validation_file.split(".")[-1]
         if extension == "txt":
             extension = "text"
         dataset = load_dataset(extension, data_files=data_files, cache_dir=model_args.cache_dir)

examples/research_projects/luke/run_luke_ner_no_trainer.py

@@ -285,9 +285,10 @@ def main():
         data_files = {}
         if args.train_file is not None:
             data_files["train"] = args.train_file
+            extension = args.train_file.split(".")[-1]
         if args.validation_file is not None:
             data_files["validation"] = args.validation_file
-        extension = args.train_file.split(".")[-1]
+            extension = args.validation_file.split(".")[-1]
         raw_datasets = load_dataset(extension, data_files=data_files)
     # Trim a number of training examples
     if args.debug:

examples/research_projects/mlm_wwm/run_mlm_wwm.py

@@ -271,9 +271,10 @@ def main():
         data_files = {}
         if data_args.train_file is not None:
             data_files["train"] = data_args.train_file
+            extension = data_args.train_file.split(".")[-1]
         if data_args.validation_file is not None:
             data_files["validation"] = data_args.validation_file
-        extension = data_args.train_file.split(".")[-1]
+            extension = data_args.validation_file.split(".")[-1]
         if extension == "txt":
             extension = "text"
         datasets = load_dataset(extension, data_files=data_files)

examples/research_projects/performer/run_mlm_performer.py

@@ -517,9 +517,10 @@ def generate_batch_splits(samples_idx: np.ndarray, batch_size: int) -> np.ndarra
         data_files = {}
         if data_args.train_file is not None:
             data_files["train"] = data_args.train_file
+            extension = data_args.train_file.split(".")[-1]
         if data_args.validation_file is not None:
             data_files["validation"] = data_args.validation_file
-        extension = data_args.train_file.split(".")[-1]
+            extension = data_args.validation_file.split(".")[-1]
         if extension == "txt":
             extension = "text"
         datasets = load_dataset(extension, data_files=data_files)

examples/tensorflow/language-modeling/run_mlm.py

@@ -341,9 +341,10 @@ def main():
         data_files = {}
         if data_args.train_file is not None:
             data_files["train"] = data_args.train_file
+            extension = data_args.train_file.split(".")[-1]
         if data_args.validation_file is not None:
             data_files["validation"] = data_args.validation_file
-        extension = data_args.train_file.split(".")[-1]
+            extension = data_args.validation_file.split(".")[-1]
         if extension == "txt":
             extension = "text"
         raw_datasets = load_dataset(

examples/tensorflow/multiple-choice/run_swag.py

@@ -320,9 +320,10 @@ def main():
         data_files = {}
         if data_args.train_file is not None:
             data_files["train"] = data_args.train_file
+            extension = data_args.train_file.split(".")[-1]
         if data_args.validation_file is not None:
             data_files["validation"] = data_args.validation_file
-        extension = data_args.train_file.split(".")[-1]
+            extension = data_args.validation_file.split(".")[-1]
         raw_datasets = load_dataset(
             extension,
             data_files=data_files,

examples/tensorflow/token-classification/run_ner.py

@@ -260,9 +260,10 @@ def main():
         data_files = {}
         if data_args.train_file is not None:
             data_files["train"] = data_args.train_file
+            extension = data_args.train_file.split(".")[-1]
         if data_args.validation_file is not None:
             data_files["validation"] = data_args.validation_file
-        extension = data_args.train_file.split(".")[-1]
+            extension = data_args.validation_file.split(".")[-1]
         raw_datasets = load_dataset(
             extension,
             data_files=data_files,

templates/adding_a_new_example_script/{{cookiecutter.directory_name}}/run_{{cookiecutter.example_shortcut}}.py

@@ -730,9 +730,10 @@ def main():
         data_files = {}
         if args.train_file is not None:
             data_files["train"] = args.train_file
+            extension = args.train_file.split(".")[-1]
         if args.validation_file is not None:
             data_files["validation"] = args.validation_file
-        extension = args.train_file.split(".")[-1]
+            extension = args.validation_file.split(".")[-1]
         raw_datasets = load_dataset(extension, data_files=data_files)
     # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
     # https://huggingface.co/docs/datasets/loading_datasets.