Re-export KeyDataset. (#15645)

* Re-export `KeyDataset`.

* Update the docs locations.

docs/source/main_classes/pipelines.mdx

@@ -78,7 +78,7 @@ GPU. If it doesn't don't hesitate to create an issue.
 ```python
 import datasets
 from transformers import pipeline
-from transformers.pipelines.base import KeyDataset
+from transformers.pipelines.pt_utils import KeyDataset
 from tqdm.auto import tqdm
 
 pipe = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-base-960h", device=0)
@@ -128,7 +128,7 @@ whenever the pipeline uses its streaming ability (so when passing lists or `Data
 
 ```python
 from transformers import pipeline
-from transformers.pipelines.base import KeyDataset
+from transformers.pipelines.pt_utils import KeyDataset
 import datasets
 
 dataset = datasets.load_dataset("imdb", name="plain_text", split="unsupervised")

docs/source/quicktour.mdx

@@ -118,7 +118,7 @@ Next, load a dataset (see the 🤗 Datasets [Quick Start](https://huggingface.co
 Now you can iterate over the dataset with the pipeline. `KeyDataset` retrieves the item in the dictionary returned by the dataset:
 
 ```py
->>> from transformers.pipelines.base import KeyDataset
+>>> from transformers.pipelines.pt_utils import KeyDataset
 >>> from tqdm.auto import tqdm
 
 >>> for out in tqdm(speech_recognizer(KeyDataset(dataset, "file"))):
@@ -316,4 +316,4 @@ One particularly cool 🤗 Transformers feature is the ability to save a model a
 
 >>> tokenizer = AutoTokenizer.from_pretrained(pt_save_directory)
 >>> tf_model = TFAutoModelForSequenceClassification.from_pretrained(pt_save_directory, from_pt=True)
-```
+```

src/transformers/pipelines/base.py

@@ -49,6 +49,9 @@ if is_torch_available():
     from torch.utils.data import DataLoader, Dataset
 
     from ..models.auto.modeling_auto import AutoModel
+
+    # Re-export for backward compatibility
+    from .pt_utils import KeyDataset
 else:
     Dataset = None
     KeyDataset = None