Update use_auth_token -> token in example scripts (#25167)

* pytorch examples

* tensorflow examples

* flax examples

---------

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

examples/flax/image-captioning/run_image_captioning_flax.py

@@ -475,18 +475,18 @@ def main():
         model_args.model_name_or_path,
         seed=training_args.seed,
         dtype=getattr(jnp, model_args.dtype),
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=True if model_args.use_auth_token else None,
     )
     image_processor = AutoImageProcessor.from_pretrained(
         model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=True if model_args.use_auth_token else None,
     )
     tokenizer = AutoTokenizer.from_pretrained(
         model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
         use_fast=model_args.use_fast_tokenizer,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=True if model_args.use_auth_token else None,
     )
     tokenizer.pad_token = tokenizer.convert_ids_to_tokens(model.config.pad_token_id)
 

examples/flax/language-modeling/run_bart_dlm_flax.py

@@ -576,14 +576,14 @@ def main():
             model_args.tokenizer_name,
             cache_dir=model_args.cache_dir,
             use_fast=model_args.use_fast_tokenizer,
-            use_auth_token=True if model_args.use_auth_token else None,
+            token=True if model_args.use_auth_token else None,
         )
     elif model_args.model_name_or_path:
         tokenizer = AutoTokenizer.from_pretrained(
             model_args.model_name_or_path,
             cache_dir=model_args.cache_dir,
             use_fast=model_args.use_fast_tokenizer,
-            use_auth_token=True if model_args.use_auth_token else None,
+            token=True if model_args.use_auth_token else None,
         )
     else:
         raise ValueError(
@@ -596,13 +596,13 @@ def main():
             model_args.config_name,
             cache_dir=model_args.cache_dir,
             vocab_size=len(tokenizer),
-            use_auth_token=True if model_args.use_auth_token else None,
+            token=True if model_args.use_auth_token else None,
         )
     elif model_args.model_name_or_path:
         config = BartConfig.from_pretrained(
             model_args.model_name_or_path,
             cache_dir=model_args.cache_dir,
-            use_auth_token=True if model_args.use_auth_token else None,
+            token=True if model_args.use_auth_token else None,
         )
     else:
         config = CONFIG_MAPPING[model_args.model_type]()
@@ -707,7 +707,7 @@ def main():
             config=config,
             seed=training_args.seed,
             dtype=getattr(jnp, model_args.dtype),
-            use_auth_token=True if model_args.use_auth_token else None,
+            token=True if model_args.use_auth_token else None,
         )
     else:
         config.vocab_size = len(tokenizer)

examples/flax/language-modeling/run_clm_flax.py

@@ -463,13 +463,13 @@ def main():
         config = AutoConfig.from_pretrained(
             model_args.config_name,
             cache_dir=model_args.cache_dir,
-            use_auth_token=True if model_args.use_auth_token else None,
+            token=True if model_args.use_auth_token else None,
         )
     elif model_args.model_name_or_path:
         config = AutoConfig.from_pretrained(
             model_args.model_name_or_path,
             cache_dir=model_args.cache_dir,
-            use_auth_token=True if model_args.use_auth_token else None,
+            token=True if model_args.use_auth_token else None,
         )
     else:
         config = CONFIG_MAPPING[model_args.model_type]()
@@ -480,14 +480,14 @@ def main():
             model_args.tokenizer_name,
             cache_dir=model_args.cache_dir,
             use_fast=model_args.use_fast_tokenizer,
-            use_auth_token=True if model_args.use_auth_token else None,
+            token=True if model_args.use_auth_token else None,
         )
     elif model_args.model_name_or_path:
         tokenizer = AutoTokenizer.from_pretrained(
             model_args.model_name_or_path,
             cache_dir=model_args.cache_dir,
             use_fast=model_args.use_fast_tokenizer,
-            use_auth_token=True if model_args.use_auth_token else None,
+            token=True if model_args.use_auth_token else None,
         )
     else:
         raise ValueError(
@@ -501,7 +501,7 @@ def main():
             config=config,
             seed=training_args.seed,
             dtype=getattr(jnp, model_args.dtype),
-            use_auth_token=True if model_args.use_auth_token else None,
+            token=True if model_args.use_auth_token else None,
         )
     else:
         model = FlaxAutoModelForCausalLM.from_config(

examples/flax/language-modeling/run_mlm_flax.py

@@ -495,13 +495,13 @@ def main():
         config = AutoConfig.from_pretrained(
             model_args.config_name,
             cache_dir=model_args.cache_dir,
-            use_auth_token=True if model_args.use_auth_token else None,
+            token=True if model_args.use_auth_token else None,
         )
     elif model_args.model_name_or_path:
         config = AutoConfig.from_pretrained(
             model_args.model_name_or_path,
             cache_dir=model_args.cache_dir,
-            use_auth_token=True if model_args.use_auth_token else None,
+            token=True if model_args.use_auth_token else None,
         )
     else:
         config = CONFIG_MAPPING[model_args.model_type]()
@@ -512,14 +512,14 @@ def main():
             model_args.tokenizer_name,
             cache_dir=model_args.cache_dir,
             use_fast=model_args.use_fast_tokenizer,
-            use_auth_token=True if model_args.use_auth_token else None,
+            token=True if model_args.use_auth_token else None,
         )
     elif model_args.model_name_or_path:
         tokenizer = AutoTokenizer.from_pretrained(
             model_args.model_name_or_path,
             cache_dir=model_args.cache_dir,
             use_fast=model_args.use_fast_tokenizer,
-            use_auth_token=True if model_args.use_auth_token else None,
+            token=True if model_args.use_auth_token else None,
         )
     else:
         raise ValueError(
@@ -638,7 +638,7 @@ def main():
             config=config,
             seed=training_args.seed,
             dtype=getattr(jnp, model_args.dtype),
-            use_auth_token=True if model_args.use_auth_token else None,
+            token=True if model_args.use_auth_token else None,
         )
     else:
         model = FlaxAutoModelForMaskedLM.from_config(

examples/flax/language-modeling/run_t5_mlm_flax.py

@@ -617,14 +617,14 @@ def main():
             model_args.tokenizer_name,
             cache_dir=model_args.cache_dir,
             use_fast=model_args.use_fast_tokenizer,
-            use_auth_token=True if model_args.use_auth_token else None,
+            token=True if model_args.use_auth_token else None,
         )
     elif model_args.model_name_or_path:
         tokenizer = AutoTokenizer.from_pretrained(
             model_args.model_name_or_path,
             cache_dir=model_args.cache_dir,
             use_fast=model_args.use_fast_tokenizer,
-            use_auth_token=True if model_args.use_auth_token else None,
+            token=True if model_args.use_auth_token else None,
         )
     else:
         raise ValueError(
@@ -637,13 +637,13 @@ def main():
             model_args.config_name,
             cache_dir=model_args.cache_dir,
             vocab_size=len(tokenizer),
-            use_auth_token=True if model_args.use_auth_token else None,
+            token=True if model_args.use_auth_token else None,
         )
     elif model_args.model_name_or_path:
         config = T5Config.from_pretrained(
             model_args.model_name_or_path,
             cache_dir=model_args.cache_dir,
-            use_auth_token=True if model_args.use_auth_token else None,
+            token=True if model_args.use_auth_token else None,
         )
     else:
         config = CONFIG_MAPPING[model_args.model_type]()
@@ -738,7 +738,7 @@ def main():
             config=config,
             seed=training_args.seed,
             dtype=getattr(jnp, model_args.dtype),
-            use_auth_token=True if model_args.use_auth_token else None,
+            token=True if model_args.use_auth_token else None,
         )
     else:
         config.vocab_size = len(tokenizer)

examples/flax/question-answering/run_qa.py

@@ -520,14 +520,14 @@ def main():
         model_args.config_name if model_args.config_name else model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=True if model_args.use_auth_token else None,
     )
     tokenizer = AutoTokenizer.from_pretrained(
         model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
         use_fast=True,
         revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=True if model_args.use_auth_token else None,
     )
     # endregion
 
@@ -874,7 +874,7 @@ def main():
         config=config,
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=True if model_args.use_auth_token else None,
         seed=training_args.seed,
         dtype=getattr(jnp, model_args.dtype),
     )

examples/flax/summarization/run_summarization_flax.py

@@ -503,13 +503,13 @@ def main():
         config = AutoConfig.from_pretrained(
             model_args.config_name,
             cache_dir=model_args.cache_dir,
-            use_auth_token=True if model_args.use_auth_token else None,
+            token=True if model_args.use_auth_token else None,
         )
     elif model_args.model_name_or_path:
         config = AutoConfig.from_pretrained(
             model_args.model_name_or_path,
             cache_dir=model_args.cache_dir,
-            use_auth_token=True if model_args.use_auth_token else None,
+            token=True if model_args.use_auth_token else None,
         )
     else:
         config = CONFIG_MAPPING[model_args.model_type]()
@@ -520,14 +520,14 @@ def main():
             model_args.tokenizer_name,
             cache_dir=model_args.cache_dir,
             use_fast=model_args.use_fast_tokenizer,
-            use_auth_token=True if model_args.use_auth_token else None,
+            token=True if model_args.use_auth_token else None,
         )
     elif model_args.model_name_or_path:
         tokenizer = AutoTokenizer.from_pretrained(
             model_args.model_name_or_path,
             cache_dir=model_args.cache_dir,
             use_fast=model_args.use_fast_tokenizer,
-            use_auth_token=True if model_args.use_auth_token else None,
+            token=True if model_args.use_auth_token else None,
         )
     else:
         raise ValueError(
@@ -541,7 +541,7 @@ def main():
             config=config,
             seed=training_args.seed,
             dtype=getattr(jnp, model_args.dtype),
-            use_auth_token=True if model_args.use_auth_token else None,
+            token=True if model_args.use_auth_token else None,
         )
     else:
         model = FlaxAutoModelForSeq2SeqLM.from_config(

examples/flax/text-classification/run_flax_glue.py

@@ -411,17 +411,17 @@ def main():
         model_args.model_name_or_path,
         num_labels=num_labels,
         finetuning_task=data_args.task_name,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=True if model_args.use_auth_token else None,
     )
     tokenizer = AutoTokenizer.from_pretrained(
         model_args.model_name_or_path,
         use_fast=not model_args.use_slow_tokenizer,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=True if model_args.use_auth_token else None,
     )
     model = FlaxAutoModelForSequenceClassification.from_pretrained(
         model_args.model_name_or_path,
         config=config,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=True if model_args.use_auth_token else None,
     )
 
     # Preprocessing the datasets

examples/flax/token-classification/run_flax_ner.py

@@ -490,7 +490,7 @@ def main():
         finetuning_task=data_args.task_name,
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=True if model_args.use_auth_token else None,
     )
     tokenizer_name_or_path = model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path
     if config.model_type in {"gpt2", "roberta"}:
@@ -498,7 +498,7 @@ def main():
             tokenizer_name_or_path,
             cache_dir=model_args.cache_dir,
             revision=model_args.model_revision,
-            use_auth_token=True if model_args.use_auth_token else None,
+            token=True if model_args.use_auth_token else None,
             add_prefix_space=True,
         )
     else:
@@ -506,14 +506,14 @@ def main():
             tokenizer_name_or_path,
             cache_dir=model_args.cache_dir,
             revision=model_args.model_revision,
-            use_auth_token=True if model_args.use_auth_token else None,
+            token=True if model_args.use_auth_token else None,
         )
     model = FlaxAutoModelForTokenClassification.from_pretrained(
         model_args.model_name_or_path,
         config=config,
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=True if model_args.use_auth_token else None,
     )
 
     # Preprocessing the datasets

examples/flax/vision/run_image_classification.py

@@ -338,7 +338,7 @@ def main():
             num_labels=len(train_dataset.classes),
             image_size=data_args.image_size,
             cache_dir=model_args.cache_dir,
-            use_auth_token=True if model_args.use_auth_token else None,
+            token=True if model_args.use_auth_token else None,
         )
     elif model_args.model_name_or_path:
         config = AutoConfig.from_pretrained(
@@ -346,7 +346,7 @@ def main():
             num_labels=len(train_dataset.classes),
             image_size=data_args.image_size,
             cache_dir=model_args.cache_dir,
-            use_auth_token=True if model_args.use_auth_token else None,
+            token=True if model_args.use_auth_token else None,
         )
     else:
         config = CONFIG_MAPPING[model_args.model_type]()
@@ -358,7 +358,7 @@ def main():
             config=config,
             seed=training_args.seed,
             dtype=getattr(jnp, model_args.dtype),
-            use_auth_token=True if model_args.use_auth_token else None,
+            token=True if model_args.use_auth_token else None,
         )
     else:
         model = FlaxAutoModelForImageClassification.from_config(

examples/pytorch/audio-classification/run_audio_classification.py

@@ -280,7 +280,7 @@ def main():
         return_attention_mask=model_args.attention_mask,
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=True if model_args.use_auth_token else None,
     )
 
     # `datasets` takes care of automatically loading and resampling the audio,
@@ -340,7 +340,7 @@ def main():
         finetuning_task="audio-classification",
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=True if model_args.use_auth_token else None,
     )
     model = AutoModelForAudioClassification.from_pretrained(
         model_args.model_name_or_path,
@@ -348,7 +348,7 @@ def main():
         config=config,
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=True if model_args.use_auth_token else None,
         ignore_mismatched_sizes=model_args.ignore_mismatched_sizes,
     )
 

examples/pytorch/contrastive-image-text/run_clip.py

@@ -336,14 +336,14 @@ def main():
         model_args.image_processor_name or model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=True if model_args.use_auth_token else None,
     )
 
     model = AutoModel.from_pretrained(
         model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=True if model_args.use_auth_token else None,
     )
     config = model.config
 

examples/pytorch/image-classification/run_image_classification.py

@@ -276,7 +276,7 @@ def main():
         finetuning_task="image-classification",
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=True if model_args.use_auth_token else None,
     )
     model = AutoModelForImageClassification.from_pretrained(
         model_args.model_name_or_path,
@@ -284,14 +284,14 @@ def main():
         config=config,
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=True if model_args.use_auth_token else None,
         ignore_mismatched_sizes=model_args.ignore_mismatched_sizes,
     )
     image_processor = AutoImageProcessor.from_pretrained(
         model_args.image_processor_name or model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=True if model_args.use_auth_token else None,
     )
 
     # Define torchvision transforms to be applied to each image.

examples/pytorch/image-pretraining/run_mae.py

@@ -280,7 +280,7 @@ def main():
             config=config,
             cache_dir=model_args.cache_dir,
             revision=model_args.model_revision,
-            use_auth_token=True if model_args.use_auth_token else None,
+            token=True if model_args.use_auth_token else None,
         )
     else:
         logger.info("Training new model from scratch")

examples/pytorch/image-pretraining/run_mim.py

@@ -357,7 +357,7 @@ def main():
             config=config,
             cache_dir=model_args.cache_dir,
             revision=model_args.model_revision,
-            use_auth_token=True if model_args.use_auth_token else None,
+            token=True if model_args.use_auth_token else None,
         )
     else:
         logger.info("Training new model from scratch")

examples/pytorch/image-pretraining/run_mim_no_trainer.py

@@ -497,7 +497,7 @@ def main():
             config=config,
             cache_dir=args.cache_dir,
             revision=args.model_revision,
-            use_auth_token=True if args.use_auth_token else None,
+            token=True if args.use_auth_token else None,
         )
     else:
         logger.info("Training new model from scratch")

examples/pytorch/language-modeling/run_clm.py

@@ -415,7 +415,7 @@ def main():
             config=config,
             cache_dir=model_args.cache_dir,
             revision=model_args.model_revision,
-            use_auth_token=True if model_args.use_auth_token else None,
+            token=True if model_args.use_auth_token else None,
             torch_dtype=torch_dtype,
             low_cpu_mem_usage=model_args.low_cpu_mem_usage,
         )

examples/pytorch/language-modeling/run_mlm.py

@@ -403,7 +403,7 @@ def main():
             config=config,
             cache_dir=model_args.cache_dir,
             revision=model_args.model_revision,
-            use_auth_token=True if model_args.use_auth_token else None,
+            token=True if model_args.use_auth_token else None,
             low_cpu_mem_usage=model_args.low_cpu_mem_usage,
         )
     else:

examples/pytorch/language-modeling/run_plm.py

@@ -383,7 +383,7 @@ def main():
             config=config,
             cache_dir=model_args.cache_dir,
             revision=model_args.model_revision,
-            use_auth_token=True if model_args.use_auth_token else None,
+            token=True if model_args.use_auth_token else None,
             low_cpu_mem_usage=model_args.low_cpu_mem_usage,
         )
     else:

examples/pytorch/multiple-choice/run_swag.py

@@ -314,14 +314,14 @@ def main():
         model_args.config_name if model_args.config_name else model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=True if model_args.use_auth_token else None,
     )
     tokenizer = AutoTokenizer.from_pretrained(
         model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
         use_fast=model_args.use_fast_tokenizer,
         revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=True if model_args.use_auth_token else None,
     )
     model = AutoModelForMultipleChoice.from_pretrained(
         model_args.model_name_or_path,
@@ -329,7 +329,7 @@ def main():
         config=config,
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=True if model_args.use_auth_token else None,
     )
 
     # When using your own dataset or a different dataset from swag, you will probably need to change this.

examples/pytorch/question-answering/run_qa.py

@@ -322,14 +322,14 @@ def main():
         model_args.config_name if model_args.config_name else model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=True if model_args.use_auth_token else None,
     )
     tokenizer = AutoTokenizer.from_pretrained(
         model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
         use_fast=True,
         revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=True if model_args.use_auth_token else None,
     )
     model = AutoModelForQuestionAnswering.from_pretrained(
         model_args.model_name_or_path,
@@ -337,7 +337,7 @@ def main():
         config=config,
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=True if model_args.use_auth_token else None,
     )
 
     # Tokenizer check: this script requires a fast tokenizer.

examples/pytorch/question-answering/run_qa_beam_search.py

@@ -320,13 +320,13 @@ def main():
         model_args.config_name if model_args.config_name else model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=True if model_args.use_auth_token else None,
     )
     tokenizer = XLNetTokenizerFast.from_pretrained(
         model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=True if model_args.use_auth_token else None,
     )
     model = XLNetForQuestionAnswering.from_pretrained(
         model_args.model_name_or_path,
@@ -334,7 +334,7 @@ def main():
         config=config,
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=True if model_args.use_auth_token else None,
     )
 
     # Preprocessing the datasets.

examples/pytorch/question-answering/run_seq2seq_qa.py

@@ -367,14 +367,14 @@ def main():
         model_args.config_name if model_args.config_name else model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=True if model_args.use_auth_token else None,
     )
     tokenizer = AutoTokenizer.from_pretrained(
         model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
         use_fast=model_args.use_fast_tokenizer,
         revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=True if model_args.use_auth_token else None,
     )
     model = AutoModelForSeq2SeqLM.from_pretrained(
         model_args.model_name_or_path,
@@ -382,7 +382,7 @@ def main():
         config=config,
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=True if model_args.use_auth_token else None,
     )
 
     # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch

examples/pytorch/semantic-segmentation/run_semantic_segmentation.py

@@ -379,7 +379,7 @@ def main():
         id2label=id2label,
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=True if model_args.use_auth_token else None,
     )
     model = AutoModelForSemanticSegmentation.from_pretrained(
         model_args.model_name_or_path,
@@ -387,13 +387,13 @@ def main():
         config=config,
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=True if model_args.use_auth_token else None,
     )
     image_processor = AutoImageProcessor.from_pretrained(
         model_args.image_processor_name or model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=True if model_args.use_auth_token else None,
     )
 
     # Define torchvision transforms to be applied to each image + target.

examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py

@@ -370,7 +370,7 @@ def main():
         model_args.config_name if model_args.config_name else model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=True if model_args.use_auth_token else None,
     )
 
     config.update({"forced_decoder_ids": model_args.forced_decoder_ids, "suppress_tokens": model_args.suppress_tokens})
@@ -383,21 +383,21 @@ def main():
         model_args.feature_extractor_name if model_args.feature_extractor_name else model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=True if model_args.use_auth_token else None,
     )
     tokenizer = AutoTokenizer.from_pretrained(
         model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
         use_fast=model_args.use_fast_tokenizer,
         revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=True if model_args.use_auth_token else None,
     )
     model = AutoModelForSpeechSeq2Seq.from_pretrained(
         model_args.model_name_or_path,
         config=config,
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=True if model_args.use_auth_token else None,
     )
 
     if model.config.decoder_start_token_id is None:

examples/pytorch/summarization/run_summarization.py

@@ -417,14 +417,14 @@ def main():
         model_args.config_name if model_args.config_name else model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=True if model_args.use_auth_token else None,
     )
     tokenizer = AutoTokenizer.from_pretrained(
         model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
         use_fast=model_args.use_fast_tokenizer,
         revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=True if model_args.use_auth_token else None,
     )
     model = AutoModelForSeq2SeqLM.from_pretrained(
         model_args.model_name_or_path,
@@ -432,7 +432,7 @@ def main():
         config=config,
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=True if model_args.use_auth_token else None,
     )
 
     # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch

examples/pytorch/text-classification/run_glue.py

@@ -361,14 +361,14 @@ def main():
         finetuning_task=data_args.task_name,
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=True if model_args.use_auth_token else None,
     )
     tokenizer = AutoTokenizer.from_pretrained(
         model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
         use_fast=model_args.use_fast_tokenizer,
         revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=True if model_args.use_auth_token else None,
     )
     model = AutoModelForSequenceClassification.from_pretrained(
         model_args.model_name_or_path,
@@ -376,7 +376,7 @@ def main():
         config=config,
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=True if model_args.use_auth_token else None,
         ignore_mismatched_sizes=model_args.ignore_mismatched_sizes,
     )
 

examples/pytorch/text-classification/run_xnli.py

@@ -278,7 +278,7 @@ def main():
         finetuning_task="xnli",
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=True if model_args.use_auth_token else None,
     )
     tokenizer = AutoTokenizer.from_pretrained(
         model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
@@ -286,7 +286,7 @@ def main():
         cache_dir=model_args.cache_dir,
         use_fast=model_args.use_fast_tokenizer,
         revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=True if model_args.use_auth_token else None,
     )
     model = AutoModelForSequenceClassification.from_pretrained(
         model_args.model_name_or_path,
@@ -294,7 +294,7 @@ def main():
         config=config,
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=True if model_args.use_auth_token else None,
         ignore_mismatched_sizes=model_args.ignore_mismatched_sizes,
     )
 

examples/pytorch/token-classification/run_ner.py

@@ -348,7 +348,7 @@ def main():
         finetuning_task=data_args.task_name,
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=True if model_args.use_auth_token else None,
     )
 
     tokenizer_name_or_path = model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path
@@ -358,7 +358,7 @@ def main():
             cache_dir=model_args.cache_dir,
             use_fast=True,
             revision=model_args.model_revision,
-            use_auth_token=True if model_args.use_auth_token else None,
+            token=True if model_args.use_auth_token else None,
             add_prefix_space=True,
         )
     else:
@@ -367,7 +367,7 @@ def main():
             cache_dir=model_args.cache_dir,
             use_fast=True,
             revision=model_args.model_revision,
-            use_auth_token=True if model_args.use_auth_token else None,
+            token=True if model_args.use_auth_token else None,
         )
 
     model = AutoModelForTokenClassification.from_pretrained(
@@ -376,7 +376,7 @@ def main():
         config=config,
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=True if model_args.use_auth_token else None,
         ignore_mismatched_sizes=model_args.ignore_mismatched_sizes,
     )
 

examples/pytorch/translation/run_translation.py

@@ -366,14 +366,14 @@ def main():
         model_args.config_name if model_args.config_name else model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=True if model_args.use_auth_token else None,
     )
     tokenizer = AutoTokenizer.from_pretrained(
         model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
         use_fast=model_args.use_fast_tokenizer,
         revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=True if model_args.use_auth_token else None,
     )
     model = AutoModelForSeq2SeqLM.from_pretrained(
         model_args.model_name_or_path,
@@ -381,7 +381,7 @@ def main():
         config=config,
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=True if model_args.use_auth_token else None,
     )
 
     # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch

examples/tensorflow/contrastive-image-text/run_clip.py

@@ -362,14 +362,14 @@ def main():
             model_args.image_processor_name or model_args.model_name_or_path,
             cache_dir=model_args.cache_dir,
             revision=model_args.model_revision,
-            use_auth_token=True if model_args.use_auth_token else None,
+            token=True if model_args.use_auth_token else None,
         )
         with training_args.strategy.scope():
             model = TFAutoModel.from_pretrained(
                 model_args.model_name_or_path,
                 cache_dir=model_args.cache_dir,
                 revision=model_args.model_revision,
-                use_auth_token=True if model_args.use_auth_token else None,
+                token=True if model_args.use_auth_token else None,
             )
     else:
         # Load image_processor, in this script we only use this to get the mean and std for normalization.
@@ -377,14 +377,14 @@ def main():
             model_args.image_processor_name or model_args.vision_model_name_or_path,
             cache_dir=model_args.cache_dir,
             revision=model_args.model_revision,
-            use_auth_token=True if model_args.use_auth_token else None,
+            token=True if model_args.use_auth_token else None,
         )
         with training_args.strategy.scope():
             model = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
                 vision_model_name_or_path=model_args.vision_model_name_or_path,
                 text_model_name_or_path=model_args.text_model_name_or_path,
                 cache_dir=model_args.cache_dir,
-                use_auth_token=True if model_args.use_auth_token else None,
+                token=True if model_args.use_auth_token else None,
             )
     config = model.config
 

examples/tensorflow/image-classification/run_image_classification.py

@@ -309,13 +309,13 @@ def main():
         finetuning_task="image-classification",
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=True if model_args.use_auth_token else None,
     )
     image_processor = AutoImageProcessor.from_pretrained(
         model_args.image_processor_name or model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=True if model_args.use_auth_token else None,
     )
 
     # If we don't have a validation split, split off a percentage of train as validation.
@@ -435,7 +435,7 @@ def main():
             from_pt=bool(".bin" in model_path),
             cache_dir=model_args.cache_dir,
             revision=model_args.model_revision,
-            use_auth_token=True if model_args.use_auth_token else None,
+            token=True if model_args.use_auth_token else None,
             ignore_mismatched_sizes=model_args.ignore_mismatched_sizes,
         )
         num_replicas = training_args.strategy.num_replicas_in_sync

examples/tensorflow/multiple-choice/run_swag.py

@@ -335,14 +335,14 @@ def main():
         config_path,
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=True if model_args.use_auth_token else None,
     )
     tokenizer = AutoTokenizer.from_pretrained(
         model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
         use_fast=model_args.use_fast_tokenizer,
         revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=True if model_args.use_auth_token else None,
     )
     # endregion
 
@@ -428,7 +428,7 @@ def main():
             config=config,
             cache_dir=model_args.cache_dir,
             revision=model_args.model_revision,
-            use_auth_token=True if model_args.use_auth_token else None,
+            token=True if model_args.use_auth_token else None,
         )
 
         num_replicas = training_args.strategy.num_replicas_in_sync

examples/tensorflow/question-answering/run_qa.py

@@ -338,14 +338,14 @@ def main():
         model_args.config_name if model_args.config_name else model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=True if model_args.use_auth_token else None,
     )
     tokenizer = AutoTokenizer.from_pretrained(
         model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
         use_fast=True,
         revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=True if model_args.use_auth_token else None,
     )
     # endregion
 
@@ -625,7 +625,7 @@ def main():
             config=config,
             cache_dir=model_args.cache_dir,
             revision=model_args.model_revision,
-            use_auth_token=True if model_args.use_auth_token else None,
+            token=True if model_args.use_auth_token else None,
         )
         if training_args.do_train:
             training_dataset = model.prepare_tf_dataset(

examples/tensorflow/summarization/run_summarization.py

@@ -388,14 +388,14 @@ def main():
         model_args.config_name if model_args.config_name else model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=True if model_args.use_auth_token else None,
     )
     tokenizer = AutoTokenizer.from_pretrained(
         model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
         use_fast=model_args.use_fast_tokenizer,
         revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=True if model_args.use_auth_token else None,
     )
 
     prefix = data_args.source_prefix if data_args.source_prefix is not None else ""
@@ -513,7 +513,7 @@ def main():
             config=config,
             cache_dir=model_args.cache_dir,
             revision=model_args.model_revision,
-            use_auth_token=True if model_args.use_auth_token else None,
+            token=True if model_args.use_auth_token else None,
         )
 
         # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch

examples/tensorflow/text-classification/run_glue.py

@@ -284,14 +284,14 @@ def main():
         finetuning_task=data_args.task_name,
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=True if model_args.use_auth_token else None,
     )
     tokenizer = AutoTokenizer.from_pretrained(
         model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
         use_fast=model_args.use_fast_tokenizer,
         revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=True if model_args.use_auth_token else None,
     )
     # endregion
 
@@ -374,7 +374,7 @@ def main():
             config=config,
             cache_dir=model_args.cache_dir,
             revision=model_args.model_revision,
-            use_auth_token=True if model_args.use_auth_token else None,
+            token=True if model_args.use_auth_token else None,
         )
         # endregion
 

examples/tensorflow/text-classification/run_text_classification.py

@@ -301,20 +301,20 @@ def main():
             num_labels=num_labels,
             cache_dir=model_args.cache_dir,
             revision=model_args.model_revision,
-            use_auth_token=True if model_args.use_auth_token else None,
+            token=True if model_args.use_auth_token else None,
         )
     else:
         config = AutoConfig.from_pretrained(
             config_path,
             cache_dir=model_args.cache_dir,
             revision=model_args.model_revision,
-            use_auth_token=True if model_args.use_auth_token else None,
+            token=True if model_args.use_auth_token else None,
         )
     tokenizer = AutoTokenizer.from_pretrained(
         model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=True if model_args.use_auth_token else None,
     )
     # endregion
 
@@ -402,7 +402,7 @@ def main():
             config=config,
             cache_dir=model_args.cache_dir,
             revision=model_args.model_revision,
-            use_auth_token=True if model_args.use_auth_token else None,
+            token=True if model_args.use_auth_token else None,
         )
         # endregion
 

examples/tensorflow/translation/run_translation.py

@@ -352,14 +352,14 @@ def main():
         model_args.config_name if model_args.config_name else model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=True if model_args.use_auth_token else None,
     )
     tokenizer = AutoTokenizer.from_pretrained(
         model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
         use_fast=model_args.use_fast_tokenizer,
         revision=model_args.model_revision,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=True if model_args.use_auth_token else None,
     )
 
     prefix = data_args.source_prefix if data_args.source_prefix is not None else ""
@@ -466,7 +466,7 @@ def main():
             config=config,
             cache_dir=model_args.cache_dir,
             revision=model_args.model_revision,
-            use_auth_token=True if model_args.use_auth_token else None,
+            token=True if model_args.use_auth_token else None,
         )
 
         # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch