Lint ao (#1521)

Author: jainapurva

.github/scripts/github_utils.py

@@ -3,14 +3,12 @@
 import json
 import os
 import warnings
-
 from dataclasses import dataclass
-from typing import Any, Callable, cast, Dict, List, Optional, Tuple, Union
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union, cast
 from urllib.error import HTTPError
 from urllib.parse import quote
 from urllib.request import Request, urlopen
 
-
 GITHUB_API_URL = "https://api.github.com"
 
 

.github/scripts/gitutils.py

@@ -9,14 +9,14 @@
 from typing import (
     Any,
     Callable,
-    cast,
     Dict,
     Iterator,
     List,
     Optional,
     Tuple,
     TypeVar,
     Union,
+    cast,
 )
 
 T = TypeVar("T")
@@ -45,7 +45,7 @@ def fuzzy_list_to_dict(items: List[Tuple[str, str]]) -> Dict[str, List[str]]:
 
 
 def _check_output(items: List[str], encoding: str = "utf-8") -> str:
-    from subprocess import CalledProcessError, check_output, STDOUT
+    from subprocess import STDOUT, CalledProcessError, check_output
 
     try:
         return check_output(items, stderr=STDOUT).decode(encoding)

.github/scripts/label_utils.py

@@ -1,11 +1,10 @@
 """GitHub Label Utilities."""
 
 import json
-
 from functools import lru_cache
-from typing import Any, List, Tuple, TYPE_CHECKING, Union
+from typing import TYPE_CHECKING, Any, List, Tuple, Union
 
-from github_utils import gh_fetch_url_and_headers, GitHubComment
+from github_utils import GitHubComment, gh_fetch_url_and_headers
 
 # TODO: this is a temp workaround to avoid circular dependencies,
 #       and should be removed once GitHubPR is refactored out of trymerge script.

.github/scripts/trymerge.py

@@ -23,44 +23,41 @@
 from typing import (
     Any,
     Callable,
-    cast,
     Dict,
     Iterable,
     List,
     NamedTuple,
     Optional,
     Pattern,
     Tuple,
+    cast,
 )
 from warnings import warn
 
 import yaml
 from github_utils import (
+    GitHubComment,
     gh_fetch_json_list,
     gh_fetch_merge_base,
     gh_fetch_url,
     gh_graphql,
     gh_post_commit_comment,
     gh_post_pr_comment,
     gh_update_pr_state,
-    GitHubComment,
 )
-
 from gitutils import (
+    GitRepo,
     are_ghstack_branches_in_sync,
     get_git_remote_name,
     get_git_repo_dir,
-    GitRepo,
     patterns_to_regex,
     retries_decorator,
 )
 from label_utils import (
     gh_add_labels,
     gh_remove_label,
-    has_required_labels,
-    LABEL_ERR_MSG,
 )
-from trymerge_explainer import get_revert_message, TryMergeExplainer
+from trymerge_explainer import TryMergeExplainer, get_revert_message
 
 # labels
 MERGE_IN_PROGRESS_LABEL = "merging"
@@ -1477,7 +1474,7 @@ def checks_to_str(checks: List[Tuple[str, Optional[str]]]) -> str:
 
 
 def checks_to_markdown_bullets(
-    checks: List[Tuple[str, Optional[str], Optional[int]]]
+    checks: List[Tuple[str, Optional[str], Optional[int]]],
 ) -> List[str]:
     return [
         f"- [{c[0]}]({c[1]})" if c[1] is not None else f"- {c[0]}" for c in checks[:5]
@@ -1716,7 +1713,7 @@ def get_readable_drci_results(drci_classifications: Any) -> str:
         try:
             print(f"From Dr.CI checkrun summary: {drci_summary}")
             drci_classifications = json.loads(str(drci_summary))
-        except json.JSONDecodeError as error:
+        except json.JSONDecodeError:
             warn("Invalid Dr.CI checkrun summary")
             drci_classifications = {}
 
@@ -1887,7 +1884,6 @@ def do_revert_prs(
     dry_run: bool = False,
 ) -> None:
     # Prepare and push revert commits
-    commit_shas: List[str] = []
     for commit_sha, pr in shas_and_prs:
         revert_msg = f"\nReverted {pr.get_pr_url()} on behalf of {prefix_with_github_url(author_login)}"
         revert_msg += extra_msg

.github/scripts/trymerge_explainer.py

@@ -2,7 +2,6 @@
 import re
 from typing import List, Optional, Pattern, Tuple
 
-
 BOT_COMMANDS_WIKI = "https://github.com/pytorch/pytorch/wiki/Bot-commands"
 
 CIFLOW_LABEL = re.compile(r"^ciflow/.+")

docs/source/conf.py

@@ -22,8 +22,9 @@
 
 import os
 import sys
-from docutils.parsers import rst
+
 import pytorch_sphinx_theme
+from docutils.parsers import rst
 
 sys.path.append(os.path.abspath("."))
 
@@ -60,7 +61,7 @@
 
 ### TODO: Delete this when we have content
 suppress_warnings = [
-    'toc.unlisted',
+    "toc.unlisted",
 ]
 ###
 
@@ -169,12 +170,8 @@
 # -- A patch that prevents Sphinx from cross-referencing ivar tags -------
 # See http://stackoverflow.com/a/41184353/3343043
 
-from docutils import nodes
-from sphinx import addnodes
-from sphinx.util.docfields import TypedField
 
 from custom_directives import CustomCardEnd, CustomCardItem, CustomCardStart
-from docutils.parsers import rst
 
 rst.directives.register_directive("customcardstart", CustomCardStart)
 rst.directives.register_directive("customcarditem", CustomCardItem)

docs/source/tutorials_source/template_tutorial.py

@@ -32,8 +32,9 @@
 # -----
 #
 # Example code (the output below is generated automatically):
-# 
+#
 import torch
+
 x = torch.rand(5, 3)
 print(x)
 
@@ -48,7 +49,7 @@
 ######################################################################
 # Conclusion
 # ----------
-# 
+#
 # Summarize the steps and concepts covered. Highlight key takeaways.
 #
 # Further Reading

examples/sam2_amg_server/amg_example.py

@@ -1,57 +1,68 @@
+import cv2
+import matplotlib.pyplot as plt
 import numpy as np
 import torch
-import matplotlib.pyplot as plt
-import cv2
-import torch.utils.benchmark as benchmark
-
 from torch._inductor import config as inductorconfig
+
 inductorconfig.triton.unique_kernel_names = True
 inductorconfig.coordinate_descent_tuning = True
 inductorconfig.coordinate_descent_check_all_directions = True
 
+
 def profiler_runner(path, fn, *args, **kwargs):
     with torch.profiler.profile(
-            activities=[torch.profiler.ProfilerActivity.CPU,
-                        torch.profiler.ProfilerActivity.CUDA],
-            record_shapes=True) as prof:
+        activities=[
+            torch.profiler.ProfilerActivity.CPU,
+            torch.profiler.ProfilerActivity.CUDA,
+        ],
+        record_shapes=True,
+    ) as prof:
         result = fn(*args, **kwargs)
     print(f"Saving trace under {path}")
     prof.export_chrome_trace(path)
     return result
 
+
 def show_anns(anns):
     if len(anns) == 0:
         return
-    sorted_anns = sorted(anns, key=(lambda x: x['area']), reverse=True)
+    sorted_anns = sorted(anns, key=(lambda x: x["area"]), reverse=True)
     ax = plt.gca()
     ax.set_autoscale_on(False)
 
-    img = np.ones((sorted_anns[0]['segmentation'].shape[0], sorted_anns[0]['segmentation'].shape[1], 4))
-    img[:,:,3] = 0
+    img = np.ones(
+        (
+            sorted_anns[0]["segmentation"].shape[0],
+            sorted_anns[0]["segmentation"].shape[1],
+            4,
+        )
+    )
+    img[:, :, 3] = 0
     ms = []
     for ann in sorted_anns:
-        m = ann['segmentation']
+        m = ann["segmentation"]
         ms.append(torch.as_tensor(m))
         color_mask = np.concatenate([np.random.random(3), [0.35]])
         img[m] = color_mask
     ax.imshow(img)
     return torch.stack(ms)
 
-image = cv2.imread('dog.jpg')
+
+image = cv2.imread("dog.jpg")
 image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
 
 
 # from segment_anything_fast import sam_model_registry, sam_model_fast_registry, SamAutomaticMaskGenerator
-# 
+#
 # sam_checkpoint = "checkpoints/sam_vit_h_4b8939.pth"
 # model_type = "vit_h"
 device = "cuda"
-# 
+#
 # sam = sam_model_fast_registry[model_type](checkpoint=sam_checkpoint)
 # sam.to(device=device)
 
-from sam2.build_sam import build_sam2
 from sam2.automatic_mask_generator import SAM2AutomaticMaskGenerator
+from sam2.build_sam import build_sam2
 
 sam2_checkpoint = "checkpoints/sam2_hiera_large.pt"
 model_cfg = "sam2_hiera_l.yaml"
@@ -66,7 +77,7 @@ def show_anns(anns):
 ## TODO: Implement mIoU to allow approximations.
 # torch.set_float32_matmul_precision('high')
 # torch.autocast("cuda", dtype=torch.bfloat16).__enter__()
-## 
+##
 
 ## TODO: Using CUDA graphs can cause numerical differences?
 mask_generator.predictor.model.image_encoder = torch.compile(
@@ -93,24 +104,26 @@ def show_anns(anns):
 )
 
 # with torch.backends.cuda.sdp_kernel(enable_cudnn=False): #, enable_math=False, enable_mem_efficient=False):
-with torch.backends.cuda.sdp_kernel(enable_cudnn=True): #, enable_math=False, enable_mem_efficient=False):
+with torch.backends.cuda.sdp_kernel(
+    enable_cudnn=True
+):  # , enable_math=False, enable_mem_efficient=False):
     # Run thrice for warmup
     masks = mask_generator.generate(image)
     masks = mask_generator.generate(image)
     masks = mask_generator.generate(image)
-    
+
     # Save an example
-    plt.figure(figsize=(image.shape[1]/100., image.shape[0]/100.), dpi=100)
+    plt.figure(figsize=(image.shape[1] / 100.0, image.shape[0] / 100.0), dpi=100)
     plt.imshow(image)
     ms = show_anns(masks)
     ms_ref = torch.load("dog_mask_fast.pt")
     torch.testing.assert_allclose(ms, ms_ref)
     print("Masks match reference")
     # # torch.save(ms, "dog_mask_fast.pt")
-    plt.axis('off')
+    plt.axis("off")
     plt.tight_layout()
-    plt.savefig('dog_mask_fast.png', format='png')
-    
+    plt.savefig("dog_mask_fast.png", format="png")
+
     # Benchmark
     torch.cuda.synchronize()
     start_event = torch.cuda.Event(enable_timing=True)
@@ -120,14 +133,18 @@ def show_anns(anns):
         masks = mask_generator.generate(image)
     end_event.record()
     torch.cuda.synchronize()
-    print(start_event.elapsed_time(end_event) / 10.)
-    
+    print(start_event.elapsed_time(end_event) / 10.0)
+
     # Save a GPU trace
-    profiler_runner(f"amg_example_trace.json.gz", mask_generator.generate, image)
-    
+    profiler_runner("amg_example_trace.json.gz", mask_generator.generate, image)
+
     # Write out memory usage
     max_memory_allocated_bytes = torch.cuda.max_memory_allocated()
     _, total_memory = torch.cuda.mem_get_info()
-    max_memory_allocated_percentage = int(100 * (max_memory_allocated_bytes / total_memory))
+    max_memory_allocated_percentage = int(
+        100 * (max_memory_allocated_bytes / total_memory)
+    )
     max_memory_allocated_bytes = max_memory_allocated_bytes >> 20
-    print(f"memory(MiB): {max_memory_allocated_bytes} memory(%): {max_memory_allocated_percentage}")
+    print(
+        f"memory(MiB): {max_memory_allocated_bytes} memory(%): {max_memory_allocated_percentage}"
+    )