Merge branch 'main' into integrate-swanlab

.circleci/config.yml

@@ -31,6 +31,14 @@ jobs:
         parallelism: 1
         steps:
             - checkout
+            - run: if [[ "$CIRCLE_PULL_REQUEST" == "" && "$CIRCLE_BRANCH" != "main" && "$CIRCLE_BRANCH" != *-release ]]; then echo "Not a PR, not the main branch and not a release branch, skip test!"; circleci-agent step halt; fi
+            - run: 'curl -L -H "Accept: application/vnd.github+json" -H "X-GitHub-Api-Version: 2022-11-28" https://api.github.com/repos/$CIRCLE_PROJECT_USERNAME/$CIRCLE_PROJECT_REPONAME/pulls/${CIRCLE_PULL_REQUEST##*/} >> github.txt'
+            - run: cat github.txt
+            - run: (python3 -c 'import json; from datetime import datetime; fp = open("github.txt"); data = json.load(fp); fp.close(); f = "%Y-%m-%dT%H:%M:%SZ"; created = datetime.strptime(data["created_at"], f); updated = datetime.strptime(data["updated_at"], f); s = (updated - created).total_seconds(); print(int(s))' || true) > elapsed.txt
+            - run: if [ "$(cat elapsed.txt)" == "" ]; then echo 60 > elapsed.txt; fi
+            - run: cat elapsed.txt
+            - run: if [ "$(cat elapsed.txt)" -lt "30" ]; then echo "PR is just opened, wait some actions from GitHub"; sleep 30; fi
+            - run: 'if grep -q "\"draft\": true," github.txt; then echo "draft mode, skip test!"; circleci-agent step halt; fi'
             - run: uv pip install -U -e .
             - run: echo 'export "GIT_COMMIT_MESSAGE=$(git show -s --format=%s)"' >> "$BASH_ENV" && source "$BASH_ENV"
             - run: mkdir -p test_preparation

.github/workflows/change_pr_to_draft.yml

@@ -0,0 +1,25 @@
+name: Change PR to draft
+
+on:
+  pull_request_target:
+    types: [opened, reopened]
+
+jobs:
+  convert_pr_to_draft:
+    runs-on: ubuntu-22.04
+    name: Convert PR to draft
+    permissions:
+      pull-requests: write
+      contents: write
+    if: github.event.pull_request.draft == false
+    steps:
+      - name: Convert PR to draft
+        shell: bash
+        env:
+          PR_NUMBER: ${{ github.event.number }}
+          GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+          REPO: ${{ github.repository }}
+        run: |
+          echo $PR_NUMBER
+          gh pr ready $PR_NUMBER --repo $REPO --undo
+          gh pr comment $PR_NUMBER --repo $REPO --body "Hi 👋, thank you for opening this pull request! The pull request is converted to draft by default. When it is ready for review, please click the \`Ready for review\` button (at the bottom of the PR page)."

src/transformers/cache_utils.py

@@ -3,7 +3,7 @@
 import json
 import os
 from dataclasses import dataclass
-from typing import Any, Dict, List, Optional, Tuple, Union
+from typing import Any, Dict, Iterable, List, Optional, Tuple, Union
 
 import torch
 from packaging import version
@@ -358,12 +358,23 @@ class DynamicCache(Cache):
         ```
     """
 
-    def __init__(self) -> None:
+    def __init__(self, _distributed_cache_data: Iterable = None) -> None:
         super().__init__()
         self._seen_tokens = 0  # Used in `generate` to keep tally of how many tokens the cache has seen
         self.key_cache: List[torch.Tensor] = []
         self.value_cache: List[torch.Tensor] = []
 
+        # `_distributed_cache_data` was originally added for compatibility with `torch.distributed` (DDP). See #36121
+        # and #36373 for more information. In a nutshell, it is `map(gather_map, zip(*caches))`, i.e. each item in the
+        # iterable contains the key and value states for a layer gathered across replicas by torch.distributed
+        # (shape=[global batch size, num_heads, seq_len, head_dim]).
+        # WARNING: `_distributed_cache_data` must be the first argument in `__init__`, otherwise we'll break
+        # compatibility. The name of the argument doesn't matter.
+        if _distributed_cache_data is not None:
+            for key_states, value_states in _distributed_cache_data:
+                self.key_cache.append(key_states)
+                self.value_cache.append(value_states)
+
     def __getitem__(self, layer_idx: int) -> List[Tuple[torch.Tensor]]:
         """
         Support for backwards-compatible `past_key_value` indexing, e.g. `past_key_value[0][0].shape[2]` to get the

src/transformers/image_utils.py

@@ -74,17 +74,6 @@
             PILImageResampling.LANCZOS: InterpolationMode.LANCZOS,
         }
 
-if is_decord_available():
-    from decord import VideoReader, cpu
-
-if is_av_available():
-    import av
-
-if is_cv2_available():
-    import cv2
-
-if is_yt_dlp_available():
-    from yt_dlp import YoutubeDL
 
 if TYPE_CHECKING:
     if is_torch_available():
@@ -608,6 +597,10 @@ def sample_indices_fn(metadata, **kwargs):
             - Numpy array of frames in RGB (shape: [num_frames, height, width, 3]).
             - `VideoMetadata` object.
     """
+    # Lazy import cv2
+    requires_backends(read_video_opencv, ["cv2"])
+    import cv2
+
     video = cv2.VideoCapture(video_path)
     total_num_frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
     video_fps = video.get(cv2.CAP_PROP_FPS)
@@ -661,6 +654,10 @@ def sample_indices_fn(metadata, **kwargs):
             - Numpy array of frames in RGB (shape: [num_frames, height, width, 3]).
             - `VideoMetadata` object.
     """
+    # Lazy import from decord
+    requires_backends(read_video_decord, ["decord"])
+    from decord import VideoReader, cpu
+
     vr = VideoReader(uri=video_path, ctx=cpu(0))  # decord has problems with gpu
     video_fps = vr.get_avg_fps()
     total_num_frames = len(vr)
@@ -700,6 +697,10 @@ def sample_indices_fn(metadata, **kwargs):
             - Numpy array of frames in RGB (shape: [num_frames, height, width, 3]).
             - `VideoMetadata` object.
     """
+    # Lazy import av
+    requires_backends(read_video_pyav, ["av"])
+    import av
+
     container = av.open(video_path)
     total_num_frames = container.streams.video[0].frames
     video_fps = container.streams.video[0].average_rate  # should we better use `av_guess_frame_rate`?
@@ -834,6 +835,10 @@ def sample_indices_fn_func(metadata, **fn_kwargs):
     if video.startswith("https://www.youtube.com") or video.startswith("http://www.youtube.com"):
         if not is_yt_dlp_available():
             raise ImportError("To load a video from YouTube url you have  to install `yt_dlp` first.")
+        # Lazy import from yt_dlp
+        requires_backends(load_video, ["yt_dlp"])
+        from yt_dlp import YoutubeDL
+
         buffer = BytesIO()
         with redirect_stdout(buffer), YoutubeDL() as f:
             f.download([video])