Use Python 3.9 syntax in tests (#37343)

Signed-off-by: cyy <cyyever@outlook.com>

tests/trainer/test_trainer.py

@@ -1,4 +1,3 @@
-# coding=utf-8
 # Copyright 2018 the HuggingFace Inc. team.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -4923,8 +4922,7 @@ class TrainerIntegrationWithHubTester(unittest.TestCase):
     def get_commit_history(self, repo):
         commit_logs = subprocess.run(
             "git log".split(),
-            stderr=subprocess.PIPE,
-            stdout=subprocess.PIPE,
+            capture_output=True,
             check=True,
             encoding="utf-8",
             cwd=repo,

tests/trainer/test_trainer_distributed.py

@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import Dict
 
 import numpy as np
 
@@ -148,7 +147,7 @@ if __name__ == "__main__":
     for dataset_length in [101, 40, 7]:
         dataset = DummyDataset(dataset_length)
 
-        def compute_metrics(p: EvalPrediction) -> Dict:
+        def compute_metrics(p: EvalPrediction) -> dict:
             sequential = list(range(len(dataset)))
             success = p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
             if not success and training_args.local_rank == 0:

tests/trainer/test_trainer_fsdp.py

@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import Dict
 
 from transformers import is_torch_available
 from transformers.testing_utils import (
@@ -166,7 +165,7 @@ if __name__ == "__main__":
     device = torch.device(torch.distributed.get_rank())
     model = AutoModelForCausalLM.from_pretrained(pretrained_model_name).to(device)
 
-    def compute_metrics(p: EvalPrediction) -> Dict[str, bool]:
+    def compute_metrics(p: EvalPrediction) -> dict[str, bool]:
         return {"accuracy": (p.predictions == p.label_ids).mean()}
 
     trainer = Seq2SeqTrainer(

tests/trainer/test_trainer_seq2seq.py

@@ -1,4 +1,3 @@
-# coding=utf-8
 # Copyright 2020 the HuggingFace Inc. team.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");

tests/trainer/test_trainer_tpu.py

@@ -20,7 +20,6 @@
 #
 
 import sys
-from typing import Dict
 
 from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
 from transformers.utils import logging
@@ -79,7 +78,7 @@ def main():
     for dataset_length in [1001, 256, 15]:
         dataset = DummyDataset(dataset_length)
 
-        def compute_metrics(p: EvalPrediction) -> Dict:
+        def compute_metrics(p: EvalPrediction) -> dict:
             sequential = list(range(len(dataset)))
             success = p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
             return {"success": success}

tests/trainer/test_trainer_utils.py

@@ -1,4 +1,3 @@
-# coding=utf-8
 # Copyright 2018 the HuggingFace Inc. team.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");