Fix checkpoint path in no_trainer scripts

examples/pytorch/image-classification/run_image_classification_no_trainer.py

@@ -465,7 +465,7 @@ def collate_fn(examples):
             path = os.path.basename(checkpoint_path)
 
         accelerator.print(f"Resumed from checkpoint: {checkpoint_path}")
-        accelerator.load_state(path)
+        accelerator.load_state(checkpoint_path)
         # Extract `epoch_{i}` or `step_{i}`
         training_difference = os.path.splitext(path)[0]
 

examples/pytorch/image-pretraining/run_mim_no_trainer.py

@@ -689,7 +689,7 @@ def preprocess_images(examples):
             path = os.path.basename(checkpoint_path)
 
         accelerator.print(f"Resumed from checkpoint: {checkpoint_path}")
-        accelerator.load_state(path)
+        accelerator.load_state(checkpoint_path)
         # Extract `epoch_{i}` or `step_{i}`
         training_difference = os.path.splitext(path)[0]
 

examples/pytorch/language-modeling/run_clm_no_trainer.py

@@ -588,7 +588,7 @@ def group_texts(examples):
             path = os.path.basename(checkpoint_path)
 
         accelerator.print(f"Resumed from checkpoint: {checkpoint_path}")
-        accelerator.load_state(path)
+        accelerator.load_state(checkpoint_path)
         # Extract `epoch_{i}` or `step_{i}`
         training_difference = os.path.splitext(path)[0]
 

examples/pytorch/language-modeling/run_mlm_no_trainer.py

@@ -625,7 +625,7 @@ def group_texts(examples):
             path = os.path.basename(checkpoint_path)
 
         accelerator.print(f"Resumed from checkpoint: {checkpoint_path}")
-        accelerator.load_state(path)
+        accelerator.load_state(checkpoint_path)
         # Extract `epoch_{i}` or `step_{i}`
         training_difference = os.path.splitext(path)[0]
 

examples/pytorch/multiple-choice/run_swag_no_trainer.py

@@ -571,7 +571,7 @@ def preprocess_function(examples):
             path = os.path.basename(checkpoint_path)
 
         accelerator.print(f"Resumed from checkpoint: {checkpoint_path}")
-        accelerator.load_state(path)
+        accelerator.load_state(checkpoint_path)
         # Extract `epoch_{i}` or `step_{i}`
         training_difference = os.path.splitext(path)[0]
 

examples/pytorch/question-answering/run_qa_beam_search_no_trainer.py

@@ -808,7 +808,7 @@ def create_and_fill_np_array(start_or_end_logits, dataset, max_len):
             path = os.path.basename(checkpoint_path)
 
         accelerator.print(f"Resumed from checkpoint: {checkpoint_path}")
-        accelerator.load_state(path)
+        accelerator.load_state(checkpoint_path)
         # Extract `epoch_{i}` or `step_{i}`
         training_difference = os.path.splitext(path)[0]
 

examples/pytorch/question-answering/run_qa_no_trainer.py

@@ -839,7 +839,7 @@ def create_and_fill_np_array(start_or_end_logits, dataset, max_len):
             path = os.path.basename(checkpoint_path)
 
         accelerator.print(f"Resumed from checkpoint: {checkpoint_path}")
-        accelerator.load_state(path)
+        accelerator.load_state(checkpoint_path)
         # Extract `epoch_{i}` or `step_{i}`
         training_difference = os.path.splitext(path)[0]
 

examples/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py

@@ -569,7 +569,7 @@ def preprocess_val(example_batch):
             path = os.path.basename(checkpoint_path)
 
         accelerator.print(f"Resumed from checkpoint: {checkpoint_path}")
-        accelerator.load_state(path)
+        accelerator.load_state(checkpoint_path)
         # Extract `epoch_{i}` or `step_{i}`
         training_difference = os.path.splitext(path)[0]
 

examples/pytorch/summarization/run_summarization_no_trainer.py

@@ -640,7 +640,7 @@ def postprocess_text(preds, labels):
             path = os.path.basename(checkpoint_path)
 
         accelerator.print(f"Resumed from checkpoint: {checkpoint_path}")
-        accelerator.load_state(path)
+        accelerator.load_state(checkpoint_path)
         # Extract `epoch_{i}` or `step_{i}`
         training_difference = os.path.splitext(path)[0]
 

examples/pytorch/text-classification/run_glue_no_trainer.py

@@ -518,7 +518,7 @@ def preprocess_function(examples):
             path = os.path.basename(checkpoint_path)
 
         accelerator.print(f"Resumed from checkpoint: {checkpoint_path}")
-        accelerator.load_state(path)
+        accelerator.load_state(checkpoint_path)
         # Extract `epoch_{i}` or `step_{i}`
         training_difference = os.path.splitext(path)[0]
 

examples/pytorch/token-classification/run_ner_no_trainer.py

@@ -678,7 +678,7 @@ def compute_metrics():
             path = os.path.basename(checkpoint_path)
 
         accelerator.print(f"Resumed from checkpoint: {checkpoint_path}")
-        accelerator.load_state(path)
+        accelerator.load_state(checkpoint_path)
         # Extract `epoch_{i}` or `step_{i}`
         training_difference = os.path.splitext(path)[0]
 

examples/pytorch/translation/run_translation_no_trainer.py

@@ -621,7 +621,7 @@ def postprocess_text(preds, labels):
             path = os.path.basename(checkpoint_path)
 
         accelerator.print(f"Resumed from checkpoint: {checkpoint_path}")
-        accelerator.load_state(path)
+        accelerator.load_state(checkpoint_path)
         # Extract `epoch_{i}` or `step_{i}`
         training_difference = os.path.splitext(path)[0]