refactor(wandb): consolidate import (#5044)

src/transformers/trainer.py

@@ -21,7 +21,7 @@ from tqdm.auto import tqdm, trange
 from .data.data_collator import DataCollator, default_data_collator
 from .modeling_utils import PreTrainedModel
 from .optimization import AdamW, get_linear_schedule_with_warmup
-from .trainer_utils import PREFIX_CHECKPOINT_DIR, EvalPrediction, PredictionOutput, TrainOutput
+from .trainer_utils import PREFIX_CHECKPOINT_DIR, EvalPrediction, PredictionOutput, TrainOutput, is_wandb_available
 from .training_args import TrainingArguments, is_torch_tpu_available
 
 
@@ -59,22 +59,9 @@ def is_tensorboard_available():
     return _has_tensorboard
 
 
-try:
+if is_wandb_available():
     import wandb
 
-    wandb.ensure_configured()
-    if wandb.api.api_key is None:
-        _has_wandb = False
-        wandb.termwarn("W&B installed but not logged in.  Run `wandb login` or set the WANDB_API_KEY env variable.")
-    else:
-        _has_wandb = False if os.getenv("WANDB_DISABLED") else True
-except (ImportError, AttributeError):
-    _has_wandb = False
-
-
-def is_wandb_available():
-    return _has_wandb
-
 
 logger = logging.getLogger(__name__)
 

src/transformers/trainer_tf.py

@@ -10,26 +10,13 @@ import tensorflow as tf
 
 from .modeling_tf_utils import TFPreTrainedModel
 from .optimization_tf import GradientAccumulator, create_optimizer
-from .trainer_utils import PREFIX_CHECKPOINT_DIR, EvalPrediction, PredictionOutput
+from .trainer_utils import PREFIX_CHECKPOINT_DIR, EvalPrediction, PredictionOutput, is_wandb_available
 from .training_args_tf import TFTrainingArguments
 
 
-try:
+if is_wandb_available():
     import wandb
 
-    wandb.ensure_configured()
-    if wandb.api.api_key is None:
-        _has_wandb = False
-        wandb.termwarn("W&B installed but not logged in.  Run `wandb login` or set the WANDB_API_KEY env variable.")
-    else:
-        _has_wandb = False if os.getenv("WANDB_DISABLED") else True
-except (ImportError, AttributeError):
-    _has_wandb = False
-
-
-def is_wandb_available():
-    return _has_wandb
-
 
 logger = logging.getLogger(__name__)
 

src/transformers/trainer_utils.py

+import os
 from typing import Dict, NamedTuple, Optional
 
 import numpy as np
 
 
+try:
+    import wandb
+
+    wandb.ensure_configured()
+    if wandb.api.api_key is None:
+        _has_wandb = False
+        wandb.termwarn("W&B installed but not logged in.  Run `wandb login` or set the WANDB_API_KEY env variable.")
+    else:
+        _has_wandb = False if os.getenv("WANDB_DISABLED") else True
+except (ImportError, AttributeError):
+    _has_wandb = False
+
+
+def is_wandb_available():
+    return _has_wandb
+
+
 class EvalPrediction(NamedTuple):
     """
     Evaluation output (always contains labels), to be used