Delete data/tempofunk.py

data/tempofunk.py

@@ -1,59 +0,0 @@
-import datasets
-from io import BytesIO
-import numpy as np
-
-_TAR_FILES=[
-    "data/0000.tar",
-    "data/0001.tar",
-    "data/0002.tar",
-    "data/0003.tar",
-    "data/0004.tar",
-    "data/0005.tar",
-    "data/0006.tar",
-    "data/0007.tar",
-    "data/0008.tar",
-    "data/0009.tar",
-    ]
-
-class Food101(datasets.GeneratorBasedBuilder):
-    """Food-101 Images dataset."""
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description="TMP description",
-            homepage="google it",
-            citation="lmao",
-            license="dunno, tbh, assume the worst, k thx."
-        )
-
-    def _split_generators(self, dl_manager):
-        
-        l=[]
-        for k in _TAR_FILES:
-            archive_path = dl_manager.download(k)
-            l.append(
-                datasets.SplitGenerator(
-                name=k,
-                gen_kwargs={
-                    "npy_files": dl_manager.iter_archive(archive_path),
-                },)
-            )
-            
-        return l
-
-    def _generate_examples(self, npy_files):
-        """Generate images and labels for splits."""
-        for file_path, file_obj in npy_files:
-            # NOTE: File object is (ALREADY) opened in binary mode.
-            numpy_bytes = file_obj.read()
-            numpy_dict = np.load(BytesIO(numpy_bytes), allow_pickle=True)
-
-            reconverted_dict = {
-                "frames": numpy_dict.item().get("frames"),
-                "prompt": numpy_dict.item().get("prompt")
-            }
-
-            yield file_path, {
-                "tokenized_prompt": reconverted_dict['prompt'],
-                "video": reconverted_dict['frames']
-                }