[Trainer] Undo #29896 (#30129)

* Undo

* Use tokenizer

* Undo data collator

docs/source/ko/tasks/image_classification.md

@@ -321,7 +321,7 @@ food["test"].set_transform(preprocess_val)
 ...     data_collator=data_collator,
 ...     train_dataset=food["train"],
 ...     eval_dataset=food["test"],
-...     image_processor=image_processor,
+...     tokenizer=image_processor,
 ...     compute_metrics=compute_metrics,
 ... )
 
@@ -417,7 +417,7 @@ TensorFlow에서 모델을 미세 조정하려면 다음 단계를 따르세요:
 >>> metric_callback = KerasMetricCallback(metric_fn=compute_metrics, eval_dataset=tf_eval_dataset)
 >>> push_to_hub_callback = PushToHubCallback(
 ...     output_dir="food_classifier",
-...     image_processor=image_processor,
+...     tokenizer=image_processor,
 ...     save_strategy="no",
 ... )
 >>> callbacks = [metric_callback, push_to_hub_callback]

docs/source/ko/tasks/object_detection.md

@@ -366,7 +366,7 @@ DatasetDict({
 ...     args=training_args,
 ...     data_collator=collate_fn,
 ...     train_dataset=cppe5["train"],
-...     image_processor=image_processor,
+...     tokenizer=image_processor,
 ... )
 
 >>> trainer.train()

docs/source/ko/tasks/semantic_segmentation.md

@@ -424,7 +424,7 @@ TensorFlow에서 모델을 미세 조정하려면 다음 단계를 따르세요:
 ...     metric_fn=compute_metrics, eval_dataset=tf_eval_dataset, batch_size=batch_size, label_cols=["labels"]
 ... )
 
->>> push_to_hub_callback = PushToHubCallback(output_dir="scene_segmentation", image_processor=image_processor)
+>>> push_to_hub_callback = PushToHubCallback(output_dir="scene_segmentation", tokenizer=image_processor)
 
 >>> callbacks = [metric_callback, push_to_hub_callback]
 ```

docs/source/ko/tasks/video_classification.md

@@ -411,7 +411,7 @@ def compute_metrics(eval_pred):
 ...     args,
 ...     train_dataset=train_dataset,
 ...     eval_dataset=val_dataset,
-...     image_processor=image_processor,
+...     tokenizer=image_processor,
 ...     compute_metrics=compute_metrics,
 ...     data_collator=collate_fn,
 ... )