Pass datasets trust_remote_code (#31406)

* Pass datasets trust_remote_code

* Pass trust_remote_code in more tests

* Add trust_remote_dataset_code arg to some tests

* Revert "Temporarily pin datasets upper version to fix CI"

This reverts commit b7672826ca.

* Pass trust_remote_code in librispeech_asr_dummy docstrings

* Revert "Pin datasets<2.20.0 for examples"

This reverts commit 833fc17a3e.

* Pass trust_remote_code to all examples

* Revert "Add trust_remote_dataset_code arg to some tests" to research_projects

* Pass trust_remote_code to tests

* Pass trust_remote_code to docstrings

* Fix flax examples tests requirements

* Pass trust_remote_dataset_code arg to tests

* Replace trust_remote_dataset_code with trust_remote_code in one example

* Fix duplicate trust_remote_code

* Replace args.trust_remote_dataset_code with args.trust_remote_code

* Replace trust_remote_dataset_code with trust_remote_code in parser

* Replace trust_remote_dataset_code with trust_remote_code in dataclasses

* Replace trust_remote_dataset_code with trust_remote_code arg

tests/sagemaker/scripts/pytorch/run_glue_model_parallelism.py

@@ -253,7 +253,7 @@ def main():
     # download the dataset.
     if data_args.task_name is not None:
         # Downloading and loading a dataset from the hub.
-        datasets = load_dataset("glue", data_args.task_name)
+        datasets = load_dataset("nyu-mll/glue", data_args.task_name)
     else:
         # Loading a dataset from your local files.
         # CSV/JSON training and evaluation files are needed.

tests/sagemaker/scripts/tensorflow/run_tf.py

@@ -56,7 +56,7 @@ if __name__ == "__main__":
     tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path)
 
     # Load dataset
-    train_dataset, test_dataset = load_dataset("imdb", split=["train", "test"])
+    train_dataset, test_dataset = load_dataset("stanfordnlp/imdb", split=["train", "test"])
     train_dataset = train_dataset.shuffle().select(range(5000))  # smaller the size for train dataset to 5k
     test_dataset = test_dataset.shuffle().select(range(500))  # smaller the size for test dataset to 500
 

tests/sagemaker/scripts/tensorflow/run_tf_dist.py

@@ -50,7 +50,7 @@ def fit(model, loss, opt, train_dataset, epochs, train_batch_size, max_steps=Non
 
 def get_datasets(tokenizer, train_batch_size, eval_batch_size):
     # Load dataset
-    train_dataset, test_dataset = load_dataset("imdb", split=["train", "test"])
+    train_dataset, test_dataset = load_dataset("stanfordnlp/imdb", split=["train", "test"])
 
     # Preprocess train dataset
     train_dataset = train_dataset.map(