diff --git a/torchvision/datasets/samplers/clip_sampler.py b/torchvision/datasets/samplers/clip_sampler.py
index 2432a6d20de..e1a5775b3db 100644
--- a/torchvision/datasets/samplers/clip_sampler.py
+++ b/torchvision/datasets/samplers/clip_sampler.py
@@ -3,6 +3,7 @@
 from torch.utils.data import Sampler
 import torch.distributed as dist
 from torchvision.datasets.video_utils import VideoClips
+from typing import Optional, List, Iterator, Sized, Union, cast
 
 
 class DistributedSampler(Sampler):
@@ -34,7 +35,14 @@ class DistributedSampler(Sampler):
 
     """
 
-    def __init__(self, dataset, num_replicas=None, rank=None, shuffle=False, group_size=1):
+    def __init__(
+            self,
+            dataset: Sized,
+            num_replicas: Optional[int] = None,
+            rank: Optional[int] = None,
+            shuffle: bool = False,
+            group_size: int = 1,
+    ) -> None:
         if num_replicas is None:
             if not dist.is_available():
                 raise RuntimeError("Requires distributed package to be available")
@@ -60,10 +68,11 @@ def __init__(self, dataset, num_replicas=None, rank=None, shuffle=False, group_s
         self.total_size = self.num_samples * self.num_replicas
         self.shuffle = shuffle
 
-    def __iter__(self):
+    def __iter__(self) -> Iterator[int]:
         # deterministically shuffle based on epoch
         g = torch.Generator()
         g.manual_seed(self.epoch)
+        indices: Union[torch.Tensor, List[int]]
         if self.shuffle:
             indices = torch.randperm(len(self.dataset), generator=g).tolist()
         else:
@@ -89,10 +98,10 @@ def __iter__(self):
 
         return iter(indices)
 
-    def __len__(self):
+    def __len__(self) -> int:
         return self.num_samples
 
-    def set_epoch(self, epoch):
+    def set_epoch(self, epoch: int) -> None:
         self.epoch = epoch
 
 
@@ -106,14 +115,14 @@ class UniformClipSampler(Sampler):
         video_clips (VideoClips): video clips to sample from
         num_clips_per_video (int): number of clips to be sampled per video
     """
-    def __init__(self, video_clips, num_clips_per_video):
+    def __init__(self, video_clips: VideoClips, num_clips_per_video: int) -> None:
         if not isinstance(video_clips, VideoClips):
             raise TypeError("Expected video_clips to be an instance of VideoClips, "
                             "got {}".format(type(video_clips)))
         self.video_clips = video_clips
         self.num_clips_per_video = num_clips_per_video
 
-    def __iter__(self):
+    def __iter__(self) -> Iterator[int]:
         idxs = []
         s = 0
         # select num_clips_per_video for each video, uniformly spaced
@@ -130,10 +139,9 @@ def __iter__(self):
             )
             s += length
             idxs.append(sampled)
-        idxs = torch.cat(idxs).tolist()
-        return iter(idxs)
+        return iter(cast(List[int], torch.cat(idxs).tolist()))
 
-    def __len__(self):
+    def __len__(self) -> int:
         return sum(
             self.num_clips_per_video for c in self.video_clips.clips if len(c) > 0
         )
@@ -147,14 +155,14 @@ class RandomClipSampler(Sampler):
         video_clips (VideoClips): video clips to sample from
         max_clips_per_video (int): maximum number of clips to be sampled per video
     """
-    def __init__(self, video_clips, max_clips_per_video):
+    def __init__(self, video_clips: VideoClips, max_clips_per_video: int) -> None:
         if not isinstance(video_clips, VideoClips):
             raise TypeError("Expected video_clips to be an instance of VideoClips, "
                             "got {}".format(type(video_clips)))
         self.video_clips = video_clips
         self.max_clips_per_video = max_clips_per_video
 
-    def __iter__(self):
+    def __iter__(self) -> Iterator[int]:
         idxs = []
         s = 0
         # select at most max_clips_per_video for each video, randomly
@@ -164,11 +172,10 @@ def __iter__(self):
             sampled = torch.randperm(length)[:size] + s
             s += length
             idxs.append(sampled)
-        idxs = torch.cat(idxs)
+        idxs_ = torch.cat(idxs)
         # shuffle all clips randomly
-        perm = torch.randperm(len(idxs))
-        idxs = idxs[perm].tolist()
-        return iter(idxs)
+        perm = torch.randperm(len(idxs_))
+        return iter(idxs_[perm].tolist())
 
-    def __len__(self):
+    def __len__(self) -> int:
         return sum(min(len(c), self.max_clips_per_video) for c in self.video_clips.clips)