train args defaulting None marked as Optional (#17156)

Co-authored-by: Dom Miketa <dmiketa@exscientia.co.uk>

src/transformers/training_args.py

@@ -582,7 +582,7 @@ class TrainingArguments:
     )
     no_cuda: bool = field(default=False, metadata={"help": "Do not use CUDA even when it is available"})
     seed: int = field(default=42, metadata={"help": "Random seed that will be set at the beginning of training."})
-    data_seed: int = field(default=None, metadata={"help": "Random seed to be used with data samplers."})
+    data_seed: Optional[int] = field(default=None, metadata={"help": "Random seed to be used with data samplers."})
     bf16: bool = field(
         default=False,
         metadata={
@@ -616,14 +616,14 @@ class TrainingArguments:
         default=False,
         metadata={"help": "Whether to use full float16 evaluation instead of 32-bit"},
     )
-    tf32: bool = field(
+    tf32: Optional[bool] = field(
         default=None,
         metadata={
             "help": "Whether to enable tf32 mode, available in Ampere and newer GPU architectures. This is an experimental API and it may change."
         },
     )
     local_rank: int = field(default=-1, metadata={"help": "For distributed training: local_rank"})
-    xpu_backend: str = field(
+    xpu_backend: Optional[str] = field(
         default=None,
         metadata={"help": "The backend to be used for distributed training on Intel XPU.", "choices": ["mpi", "ccl"]},
     )
@@ -648,7 +648,7 @@ class TrainingArguments:
     dataloader_drop_last: bool = field(
         default=False, metadata={"help": "Drop the last incomplete batch if it is not divisible by the batch size."}
     )
-    eval_steps: int = field(default=None, metadata={"help": "Run an evaluation every X steps."})
+    eval_steps: Optional[int] = field(default=None, metadata={"help": "Run an evaluation every X steps."})
     dataloader_num_workers: int = field(
         default=0,
         metadata={
@@ -770,14 +770,14 @@ class TrainingArguments:
         default=None,
         metadata={"help": "The path to a folder with a valid checkpoint for your model."},
     )
-    hub_model_id: str = field(
+    hub_model_id: Optional[str] = field(
         default=None, metadata={"help": "The name of the repository to keep in sync with the local `output_dir`."}
     )
     hub_strategy: HubStrategy = field(
         default="every_save",
         metadata={"help": "The hub strategy to use when `--push_to_hub` is activated."},
     )
-    hub_token: str = field(default=None, metadata={"help": "The token to use to push to the Model Hub."})
+    hub_token: Optional[str] = field(default=None, metadata={"help": "The token to use to push to the Model Hub."})
     hub_private_repo: bool = field(default=False, metadata={"help": "Whether the model repository is private or not."})
     gradient_checkpointing: bool = field(
         default=False,
@@ -793,13 +793,15 @@ class TrainingArguments:
         default="auto",
         metadata={"help": "Deprecated. Use half_precision_backend instead", "choices": ["auto", "amp", "apex"]},
     )
-    push_to_hub_model_id: str = field(
+    push_to_hub_model_id: Optional[str] = field(
         default=None, metadata={"help": "The name of the repository to which push the `Trainer`."}
     )
-    push_to_hub_organization: str = field(
+    push_to_hub_organization: Optional[str] = field(
         default=None, metadata={"help": "The name of the organization in with to which push the `Trainer`."}
     )
-    push_to_hub_token: str = field(default=None, metadata={"help": "The token to use to push to the Model Hub."})
+    push_to_hub_token: Optional[str] = field(
+        default=None, metadata={"help": "The token to use to push to the Model Hub."}
+    )
     _n_gpu: int = field(init=False, repr=False, default=-1)
     mp_parameters: str = field(
         default="",

src/transformers/training_args_tf.py

@@ -14,7 +14,7 @@
 
 import warnings
 from dataclasses import dataclass, field
-from typing import Tuple
+from typing import Optional, Tuple
 
 from .training_args import TrainingArguments
 from .utils import cached_property, is_tf_available, logging, tf_required
@@ -161,17 +161,17 @@ class TFTrainingArguments(TrainingArguments):
             Whether to activate the XLA compilation or not.
     """
 
-    tpu_name: str = field(
+    tpu_name: Optional[str] = field(
         default=None,
         metadata={"help": "Name of TPU"},
     )
 
-    tpu_zone: str = field(
+    tpu_zone: Optional[str] = field(
         default=None,
         metadata={"help": "Zone of TPU"},
     )
 
-    gcp_project: str = field(
+    gcp_project: Optional[str] = field(
         default=None,
         metadata={"help": "Name of Cloud TPU-enabled project"},
     )