Allow model conversion in the pipeline allocator.

transformers/pipelines.py

@@ -385,5 +385,17 @@ def pipeline(task: str, model, config: Optional[PretrainedConfig] = None, tokeni
     targeted_task = SUPPORTED_TASKS[task]
     task, allocator = targeted_task['impl'], targeted_task['tf'] if is_tf_available() else targeted_task['pt']
 
-    model = allocator.from_pretrained(model)
+    # Special handling for model conversion
+    from_tf = model.endswith('.h5') and not is_tf_available()
+    from_pt = model.endswith('.bin') and not is_torch_available()
+
+    if from_tf:
+        logger.warning('Model might be a TensorFlow model (ending with `.h5`) but TensorFlow is not available. Trying to load the model with PyTorch.')
+    elif from_pt:
+        logger.warning('Model might be a PyTorch model (ending with `.bin`) but PyTorch is not available. Trying to load the model with Tensorflow.')
+
+    if allocator.__name__.startswith('TF'):
+        model = allocator.from_pretrained(model, config=config, from_pt=from_pt)
+    else:
+        model = allocator.from_pretrained(model, config=config, from_tf=from_tf)
     return task(model, tokenizer, **kwargs)