Fulfill the proposed deletions

Author: saskra

src/cleanvision/dataset/utils.py

@@ -22,9 +22,13 @@ def build_dataset(
     verbose: bool = True,
 ) -> Dataset:
     if data_path:
-        return FSDataset(data_folder=data_path, storage_opts=storage_opts, verbose=verbose)
+        return FSDataset(
+            data_folder=data_path, storage_opts=storage_opts, verbose=verbose
+        )
     elif filepaths:
-        return FSDataset(filepaths=filepaths, storage_opts=storage_opts, verbose=verbose)
+        return FSDataset(
+            filepaths=filepaths, storage_opts=storage_opts, verbose=verbose
+        )
     elif hf_dataset and image_key:
         return HFDataset(hf_dataset, image_key)
     elif torchvision_dataset:

src/cleanvision/issue_managers/duplicate_issue_manager.py

@@ -126,7 +126,9 @@ def find_issues(
 
         results: List[Dict[str, Union[str, int]]] = []
         if n_jobs == 1:
-            for idx in tqdm(dataset.index, leave=verbose, desc="Computing hashes", smoothing=0):
+            for idx in tqdm(
+                dataset.index, leave=verbose, desc="Computing hashes", smoothing=0
+            ):
                 results.append(compute_hash(idx, dataset, to_compute, self.params))
         else:
             args = [
@@ -145,7 +147,10 @@ def find_issues(
                         p.imap_unordered(
                             compute_hash_wrapper, args, chunksize=chunksize
                         ),
-                        total=len(dataset), leave=verbose, desc="Computing hashes", smoothing=0,
+                        total=len(dataset),
+                        leave=verbose,
+                        desc="Computing hashes",
+                        smoothing=0,
                     )
                 )
 

src/cleanvision/issue_managers/image_property_issue_manager.py

@@ -139,7 +139,9 @@ def find_issues(
         if to_be_computed:
             results: List[Dict[str, Union[int, float, str]]] = []
             if n_jobs == 1:
-                for idx in tqdm(dataset.index, leave=verbose, desc="Computing scores", smoothing=0):
+                for idx in tqdm(
+                    dataset.index, leave=verbose, desc="Computing scores", smoothing=0
+                ):
                     results.append(
                         compute_scores(
                             idx, dataset, to_be_computed, self.image_properties
@@ -162,7 +164,10 @@ def find_issues(
                             p.imap_unordered(
                                 compute_scores_wrapper, args, chunksize=chunksize
                             ),
-                            total=len(dataset), leave=verbose, desc="Computing scores", smoothing=0,
+                            total=len(dataset),
+                            leave=verbose,
+                            desc="Computing scores",
+                            smoothing=0,
                         )
                     )
 

src/cleanvision/utils/base_issue_manager.py

@@ -1,5 +1,4 @@
 from abc import ABC, abstractmethod
-from tabnanny import verbose
 from typing import Dict, Any
 
 import pandas as pd