Fix sneaky torch dependency in TF example (#22804)

examples/tensorflow/image-classification/run_image_classification.py

@@ -34,7 +34,7 @@ from PIL import Image
 
 import transformers
 from transformers import (
-    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
+    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
     AutoConfig,
     AutoImageProcessor,
     DefaultDataCollator,
@@ -58,7 +58,7 @@ check_min_version("4.29.0.dev0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/image-classification/requirements.txt")
 
-MODEL_CONFIG_CLASSES = list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
+MODEL_CONFIG_CLASSES = list(TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
 MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
 
 
@@ -262,11 +262,6 @@ def main():
         transformers.utils.logging.set_verbosity_info()
         transformers.utils.logging.enable_default_handler()
         transformers.utils.logging.enable_explicit_format()
-    # Log on each process the small summary:
-    logger.warning(
-        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
-        + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
-    )
     logger.info(f"Training/evaluation parameters {training_args}")
 
     # region Dataset and labels