Upload permutation-groups.py with huggingface_hub

permutation-groups.py

@@ -1,6 +1,7 @@
 import datasets
 import json
 import os
+import glob
 from sympy.combinatorics import Permutation
 from sympy.combinatorics.named_groups import AlternatingGroup, SymmetricGroup
 
@@ -75,6 +76,13 @@ class PermutationGroups(datasets.GeneratorBasedBuilder):
             group_degree=7,
             group_order=2520,
         ),
+        PermutationGroupsConfig(
+            name="all",
+            description="All Permutation Composition Datasets (S3-S7 and A5-A7).",
+            group_name="All",
+            group_degree=None,
+            group_order=None,
+        ),
     ]
 
     DEFAULT_CONFIG_NAME = "s5_data"
@@ -91,37 +99,64 @@ class PermutationGroups(datasets.GeneratorBasedBuilder):
         )
 
     def _split_generators(self, dl_manager):
-        # Construct the full URL for the data folder on the Hugging Face Hub
-        remote_data_url = f"{_HOMEPAGE}/resolve/main/data/{self.config.name}"
+        # Handle the "all" configuration specially
+        if self.config.name == "all":
+            # Get all individual dataset configurations
+            all_configs = ["s3_data", "s4_data", "s5_data", "s6_data", "s7_data", 
+                          "a5_data", "a6_data", "a7_data"]
+            
+            data_files = {
+                "train": [f"data/{config}/train/*.arrow" for config in all_configs],
+                "test": [f"data/{config}/test/*.arrow" for config in all_configs],
+            }
+        else:
+            # The data is stored in nested folders: data/{config_name}/train and data/{config_name}/test
+            data_files = {
+                "train": f"data/{self.config.name}/train/*.arrow",
+                "test": f"data/{self.config.name}/test/*.arrow",
+            }
         
-        # Download the data folder
-        downloaded_path = dl_manager.download_and_extract(remote_data_url)
+        # Download the files
+        downloaded_files = dl_manager.download(data_files)
         
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
                 gen_kwargs={
-                    "filepath": os.path.join(downloaded_path, "train", "data-*.arrow"),
+                    "filepaths": downloaded_files["train"],
                     "split": "train",
                 },
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.TEST,
                 gen_kwargs={
-                    "filepath": os.path.join(downloaded_path, "test", "data-*.arrow"),
+                    "filepaths": downloaded_files["test"],
                     "split": "test",
                 },
             ),
         ]
 
-    def _generate_examples(self, filepath, split):
-        # Load the Arrow files directly
-        for i, file in enumerate(dl_manager.iter_files(filepath)):
-            # Assuming each .arrow file contains a table that can be converted to a list of dicts
-            # For simplicity, we'll load it as a Dataset and iterate
-            dataset = datasets.Dataset.from_file(file)
-            for id_, row in enumerate(dataset):
-                yield f"{split}_{i}_{id_}", {
-                    "input_sequence": row["input_sequence"],
-                    "target": row["target"],
-                }
+    def _generate_examples(self, filepaths, split):
+        # Handle both single file path and list of file paths
+        if isinstance(filepaths, str):
+            filepaths = [filepaths]
+        
+        # Generate examples from all arrow files
+        example_id = 0
+        for filepath in filepaths:
+            # Handle glob patterns
+            if "*" in filepath:
+                files = glob.glob(filepath)
+            else:
+                files = [filepath]
+            
+            for file in files:
+                if os.path.exists(file):
+                    # Load the Arrow file
+                    dataset = datasets.Dataset.from_file(file)
+                    for row in dataset:
+                        yield example_id, {
+                            "input_sequence": row["input_sequence"],
+                            "target": row["target"],
+                        }
+                        example_id += 1