Fast transformers import part 1 (#9441)

* Don't import libs to check they are available

* Don't import integrations at init

* Add importlib_metdata to deps

* Remove old vars references

* Avoid syntax error

* Adapt testing utils

* Try to appease torchhub

* Add dependency

* Remove more private variables

* Fix typo

* Another typo

* Refine the tf availability test

src/transformers/trainer.py

@@ -25,7 +25,7 @@ import shutil
 import time
 import warnings
 from pathlib import Path
-from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union
 
 
 # Integrations must be imported before ML frameworks:
@@ -143,12 +143,6 @@ if is_mlflow_available():
 
     DEFAULT_CALLBACKS.append(MLflowCallback)
 
-if is_optuna_available():
-    import optuna
-
-if is_ray_tune_available():
-    from ray import tune
-
 if is_azureml_available():
     from .integrations import AzureMLCallback
 
@@ -159,6 +153,10 @@ if is_fairscale_available():
     from fairscale.optim import OSS
     from fairscale.optim.grad_scaler import ShardedGradScaler
 
+
+if TYPE_CHECKING:
+    import optuna
+
 logger = logging.get_logger(__name__)
 
 
@@ -611,15 +609,21 @@ class Trainer:
             return
         self.objective = self.compute_objective(metrics.copy())
         if self.hp_search_backend == HPSearchBackend.OPTUNA:
+            import optuna
+
             trial.report(self.objective, epoch)
             if trial.should_prune():
                 raise optuna.TrialPruned()
         elif self.hp_search_backend == HPSearchBackend.RAY:
+            from ray import tune
+
             if self.state.global_step % self.args.save_steps == 0:
                 self._tune_save_checkpoint()
             tune.report(objective=self.objective, **metrics)
 
     def _tune_save_checkpoint(self):
+        from ray import tune
+
         if not self.use_tune_checkpoints:
             return
         with tune.checkpoint_dir(step=self.state.global_step) as checkpoint_dir:
@@ -981,7 +985,12 @@ class Trainer:
         checkpoint_folder = f"{PREFIX_CHECKPOINT_DIR}-{self.state.global_step}"
 
         if self.hp_search_backend is not None and trial is not None:
-            run_id = trial.number if self.hp_search_backend == HPSearchBackend.OPTUNA else tune.get_trial_id()
+            if self.hp_search_backend == HPSearchBackend.OPTUNA:
+                run_id = trial.number
+            else:
+                from ray import tune
+
+                run_id = tune.get_trial_id()
             run_name = self.hp_name(trial) if self.hp_name is not None else f"run-{run_id}"
             output_dir = os.path.join(self.args.output_dir, run_name, checkpoint_folder)
         else: