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Datasets:
will33am
/
AVA

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will33am commited on
Commit
9766e1e
•
1 Parent(s): f500a35

update

Browse files
Files changed (3) hide show
  1. .ipynb_checkpoints/AVA-checkpoint.py +2 -5
  2. AVA.py +2 -5
  3. notebooks/Test.ipynb +63 -20
.ipynb_checkpoints/AVA-checkpoint.py CHANGED
@@ -38,9 +38,7 @@ class AVA(datasets.GeneratorBasedBuilder):
38
  def _split_generators(self, dl_manager):
39
  """Returns SplitGenerators."""
40
  archives = dl_manager.download(_DATA_URL)
41
- print("Init loading Metadata")
42
- self.DICT_METADATA = Path(self.dl_manager.download_and_extract(_BASE_HF_URL/ "metadata.pkl"))
43
- print("Finish loading Metadata")
44
  return [
45
  datasets.SplitGenerator(
46
  name=datasets.Split.TRAIN,
@@ -53,13 +51,12 @@ class AVA(datasets.GeneratorBasedBuilder):
53
 
54
  def _generate_examples(self, archives, split):
55
  """Yields examples."""
56
-
57
  idx = 0
58
  for archive in archives:
59
  for path, file in tqdm(archive):
60
  if path.endswith(".jpg"):
61
  # image filepath format: <IMAGE_FILE NAME>_<SYNSET_ID>.JPEG
62
- _id = int(os.path.splitext(b[0])[0].split('/')[-1])
63
  _metadata = self.DICT_METADATA[_id]
64
  ex = {"image": {"path": path, "bytes": file.read()},
65
  "rating_counts": _metadata[0],
 
38
  def _split_generators(self, dl_manager):
39
  """Returns SplitGenerators."""
40
  archives = dl_manager.download(_DATA_URL)
41
+ self.dict_metadata = Path(dl_manager.download_and_extract(_BASE_HF_URL/ "metadata.pkl"))
 
 
42
  return [
43
  datasets.SplitGenerator(
44
  name=datasets.Split.TRAIN,
 
51
 
52
  def _generate_examples(self, archives, split):
53
  """Yields examples."""
 
54
  idx = 0
55
  for archive in archives:
56
  for path, file in tqdm(archive):
57
  if path.endswith(".jpg"):
58
  # image filepath format: <IMAGE_FILE NAME>_<SYNSET_ID>.JPEG
59
+ _id = int(os.path.splitext(path)[0].split('/')[-1])
60
  _metadata = self.DICT_METADATA[_id]
61
  ex = {"image": {"path": path, "bytes": file.read()},
62
  "rating_counts": _metadata[0],
AVA.py CHANGED
@@ -38,9 +38,7 @@ class AVA(datasets.GeneratorBasedBuilder):
38
  def _split_generators(self, dl_manager):
39
  """Returns SplitGenerators."""
40
  archives = dl_manager.download(_DATA_URL)
41
- print("Init loading Metadata")
42
- self.DICT_METADATA = Path(self.dl_manager.download_and_extract(_BASE_HF_URL/ "metadata.pkl"))
43
- print("Finish loading Metadata")
44
  return [
45
  datasets.SplitGenerator(
46
  name=datasets.Split.TRAIN,
@@ -53,13 +51,12 @@ class AVA(datasets.GeneratorBasedBuilder):
53
 
54
  def _generate_examples(self, archives, split):
55
  """Yields examples."""
56
-
57
  idx = 0
58
  for archive in archives:
59
  for path, file in tqdm(archive):
60
  if path.endswith(".jpg"):
61
  # image filepath format: <IMAGE_FILE NAME>_<SYNSET_ID>.JPEG
62
- _id = int(os.path.splitext(b[0])[0].split('/')[-1])
63
  _metadata = self.DICT_METADATA[_id]
64
  ex = {"image": {"path": path, "bytes": file.read()},
65
  "rating_counts": _metadata[0],
 
38
  def _split_generators(self, dl_manager):
39
  """Returns SplitGenerators."""
40
  archives = dl_manager.download(_DATA_URL)
41
+ self.dict_metadata = Path(dl_manager.download_and_extract(_BASE_HF_URL/ "metadata.pkl"))
 
 
42
  return [
43
  datasets.SplitGenerator(
44
  name=datasets.Split.TRAIN,
 
51
 
52
  def _generate_examples(self, archives, split):
53
  """Yields examples."""
 
54
  idx = 0
55
  for archive in archives:
56
  for path, file in tqdm(archive):
57
  if path.endswith(".jpg"):
58
  # image filepath format: <IMAGE_FILE NAME>_<SYNSET_ID>.JPEG
59
+ _id = int(os.path.splitext(path)[0].split('/')[-1])
60
  _metadata = self.DICT_METADATA[_id]
61
  ex = {"image": {"path": path, "bytes": file.read()},
62
  "rating_counts": _metadata[0],
notebooks/Test.ipynb CHANGED
@@ -2,7 +2,7 @@
2
  "cells": [
3
  {
4
  "cell_type": "code",
5
- "execution_count": 12,
6
  "id": "aef315bf",
7
  "metadata": {},
8
  "outputs": [],
@@ -12,14 +12,14 @@
12
  },
13
  {
14
  "cell_type": "code",
15
- "execution_count": 13,
16
  "id": "c0ed6498",
17
  "metadata": {},
18
  "outputs": [
19
  {
20
  "data": {
21
  "application/vnd.jupyter.widget-view+json": {
22
- "model_id": "17adb6b0a0ff4e3badf0c2e9f635e59e",
23
  "version_major": 2,
24
  "version_minor": 0
25
  },
@@ -34,13 +34,13 @@
34
  "name": "stdout",
35
  "output_type": "stream",
36
  "text": [
37
- "Downloading and preparing dataset ava/default to /home/william/.cache/huggingface/datasets/will33am___ava/default/1.0.0/d958a88aec7fd25e3600f80e249a529586de81c93df9239313082f01b126bafe...\n"
38
  ]
39
  },
40
  {
41
  "data": {
42
  "application/vnd.jupyter.widget-view+json": {
43
- "model_id": "82c3325af59640e29247da41635555d5",
44
  "version_major": 2,
45
  "version_minor": 0
46
  },
@@ -61,12 +61,12 @@
61
  {
62
  "data": {
63
  "application/vnd.jupyter.widget-view+json": {
64
- "model_id": "8df71c64148246c79343a550b1f289c1",
65
  "version_major": 2,
66
  "version_minor": 0
67
  },
68
  "text/plain": [
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- "Computing checksums: 100%|##########| 1/1 [01:33<00:00, 93.88s/it]"
70
  ]
71
  },
72
  "metadata": {},
@@ -80,25 +80,68 @@
80
  ]
81
  },
82
  {
83
- "ename": "FileNotFoundError",
84
- "evalue": "Couldn't find file at https://huggingface.co/datasets/will33am/AVA/resolve/main/data",
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
85
  "output_type": "error",
86
  "traceback": [
87
  "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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- "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
 
 
 
 
 
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  "File \u001b[0;32m<timed exec>:1\u001b[0m\n",
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  "File \u001b[0;32m/opt/conda/envs/hugginface/lib/python3.8/site-packages/datasets/load.py:1757\u001b[0m, in \u001b[0;36mload_dataset\u001b[0;34m(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, num_proc, **config_kwargs)\u001b[0m\n\u001b[1;32m 1754\u001b[0m try_from_hf_gcs \u001b[38;5;241m=\u001b[39m path \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m _PACKAGED_DATASETS_MODULES\n\u001b[1;32m 1756\u001b[0m \u001b[38;5;66;03m# Download and prepare data\u001b[39;00m\n\u001b[0;32m-> 1757\u001b[0m \u001b[43mbuilder_instance\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdownload_and_prepare\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1758\u001b[0m \u001b[43m \u001b[49m\u001b[43mdownload_config\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdownload_config\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1759\u001b[0m \u001b[43m \u001b[49m\u001b[43mdownload_mode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdownload_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1760\u001b[0m \u001b[43m \u001b[49m\u001b[43mignore_verifications\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mignore_verifications\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1761\u001b[0m \u001b[43m \u001b[49m\u001b[43mtry_from_hf_gcs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtry_from_hf_gcs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1762\u001b[0m \u001b[43m \u001b[49m\u001b[43mnum_proc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_proc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1763\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1765\u001b[0m \u001b[38;5;66;03m# Build dataset for splits\u001b[39;00m\n\u001b[1;32m 1766\u001b[0m keep_in_memory \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 1767\u001b[0m keep_in_memory \u001b[38;5;28;01mif\u001b[39;00m keep_in_memory \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m is_small_dataset(builder_instance\u001b[38;5;241m.\u001b[39minfo\u001b[38;5;241m.\u001b[39mdataset_size)\n\u001b[1;32m 1768\u001b[0m )\n",
91
  "File \u001b[0;32m/opt/conda/envs/hugginface/lib/python3.8/site-packages/datasets/builder.py:860\u001b[0m, in \u001b[0;36mDatasetBuilder.download_and_prepare\u001b[0;34m(self, output_dir, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs)\u001b[0m\n\u001b[1;32m 858\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m num_proc \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 859\u001b[0m prepare_split_kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnum_proc\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m num_proc\n\u001b[0;32m--> 860\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_download_and_prepare\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 861\u001b[0m \u001b[43m \u001b[49m\u001b[43mdl_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdl_manager\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 862\u001b[0m \u001b[43m \u001b[49m\u001b[43mverify_infos\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverify_infos\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 863\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mprepare_split_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 864\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mdownload_and_prepare_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 865\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 866\u001b[0m \u001b[38;5;66;03m# Sync info\u001b[39;00m\n\u001b[1;32m 867\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minfo\u001b[38;5;241m.\u001b[39mdataset_size \u001b[38;5;241m=\u001b[39m \u001b[38;5;28msum\u001b[39m(split\u001b[38;5;241m.\u001b[39mnum_bytes \u001b[38;5;28;01mfor\u001b[39;00m split \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minfo\u001b[38;5;241m.\u001b[39msplits\u001b[38;5;241m.\u001b[39mvalues())\n",
92
  "File \u001b[0;32m/opt/conda/envs/hugginface/lib/python3.8/site-packages/datasets/builder.py:1611\u001b[0m, in \u001b[0;36mGeneratorBasedBuilder._download_and_prepare\u001b[0;34m(self, dl_manager, verify_infos, **prepare_splits_kwargs)\u001b[0m\n\u001b[1;32m 1610\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_download_and_prepare\u001b[39m(\u001b[38;5;28mself\u001b[39m, dl_manager, verify_infos, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mprepare_splits_kwargs):\n\u001b[0;32m-> 1611\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_download_and_prepare\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1612\u001b[0m \u001b[43m \u001b[49m\u001b[43mdl_manager\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mverify_infos\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcheck_duplicate_keys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverify_infos\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mprepare_splits_kwargs\u001b[49m\n\u001b[1;32m 1613\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n",
93
- "File \u001b[0;32m/opt/conda/envs/hugginface/lib/python3.8/site-packages/datasets/builder.py:931\u001b[0m, in \u001b[0;36mDatasetBuilder._download_and_prepare\u001b[0;34m(self, dl_manager, verify_infos, **prepare_split_kwargs)\u001b[0m\n\u001b[1;32m 929\u001b[0m split_dict \u001b[38;5;241m=\u001b[39m SplitDict(dataset_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname)\n\u001b[1;32m 930\u001b[0m split_generators_kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_split_generators_kwargs(prepare_split_kwargs)\n\u001b[0;32m--> 931\u001b[0m split_generators \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_split_generators\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdl_manager\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43msplit_generators_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 933\u001b[0m \u001b[38;5;66;03m# Checksums verification\u001b[39;00m\n\u001b[1;32m 934\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m verify_infos \u001b[38;5;129;01mand\u001b[39;00m dl_manager\u001b[38;5;241m.\u001b[39mrecord_checksums:\n",
94
- "File \u001b[0;32m~/.cache/huggingface/modules/datasets_modules/datasets/will33am--AVA/d958a88aec7fd25e3600f80e249a529586de81c93df9239313082f01b126bafe/AVA.py:42\u001b[0m, in \u001b[0;36mAVA._split_generators\u001b[0;34m(self, dl_manager)\u001b[0m\n\u001b[1;32m 40\u001b[0m archives \u001b[38;5;241m=\u001b[39m dl_manager\u001b[38;5;241m.\u001b[39mdownload(_DATA_URL)\n\u001b[1;32m 41\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInit loading Metadata\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m---> 42\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mDICT_METADATA \u001b[38;5;241m=\u001b[39m Path(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdl_manager\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdownload_and_extract\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_BASE_HF_URL\u001b[49m\u001b[43m)\u001b[49m) \u001b[38;5;241m/\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmetadata.pkl\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 43\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFinish loading Metadata\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 44\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m [\n\u001b[1;32m 45\u001b[0m datasets\u001b[38;5;241m.\u001b[39mSplitGenerator(\n\u001b[1;32m 46\u001b[0m name\u001b[38;5;241m=\u001b[39mdatasets\u001b[38;5;241m.\u001b[39mSplit\u001b[38;5;241m.\u001b[39mTRAIN,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 51\u001b[0m )\n\u001b[1;32m 52\u001b[0m ]\n",
95
- "File \u001b[0;32m/opt/conda/envs/hugginface/lib/python3.8/site-packages/datasets/download/download_manager.py:468\u001b[0m, in \u001b[0;36mDownloadManager.download_and_extract\u001b[0;34m(self, url_or_urls)\u001b[0m\n\u001b[1;32m 452\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdownload_and_extract\u001b[39m(\u001b[38;5;28mself\u001b[39m, url_or_urls):\n\u001b[1;32m 453\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Download and extract given `url_or_urls`.\u001b[39;00m\n\u001b[1;32m 454\u001b[0m \n\u001b[1;32m 455\u001b[0m \u001b[38;5;124;03m Is roughly equivalent to:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 466\u001b[0m \u001b[38;5;124;03m extracted_path(s): `str`, extracted paths of given URL(s).\u001b[39;00m\n\u001b[1;32m 467\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 468\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mextract(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdownload\u001b[49m\u001b[43m(\u001b[49m\u001b[43murl_or_urls\u001b[49m\u001b[43m)\u001b[49m)\n",
96
- "File \u001b[0;32m/opt/conda/envs/hugginface/lib/python3.8/site-packages/datasets/download/download_manager.py:331\u001b[0m, in \u001b[0;36mDownloadManager.download\u001b[0;34m(self, url_or_urls)\u001b[0m\n\u001b[1;32m 328\u001b[0m download_func \u001b[38;5;241m=\u001b[39m partial(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_download, download_config\u001b[38;5;241m=\u001b[39mdownload_config)\n\u001b[1;32m 330\u001b[0m start_time \u001b[38;5;241m=\u001b[39m datetime\u001b[38;5;241m.\u001b[39mnow()\n\u001b[0;32m--> 331\u001b[0m downloaded_path_or_paths \u001b[38;5;241m=\u001b[39m \u001b[43mmap_nested\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 332\u001b[0m \u001b[43m \u001b[49m\u001b[43mdownload_func\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 333\u001b[0m \u001b[43m \u001b[49m\u001b[43murl_or_urls\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 334\u001b[0m \u001b[43m \u001b[49m\u001b[43mmap_tuple\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 335\u001b[0m \u001b[43m \u001b[49m\u001b[43mnum_proc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdownload_config\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnum_proc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 336\u001b[0m \u001b[43m \u001b[49m\u001b[43mdisable_tqdm\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mis_progress_bar_enabled\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 337\u001b[0m \u001b[43m \u001b[49m\u001b[43mdesc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mDownloading data files\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 338\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 339\u001b[0m duration \u001b[38;5;241m=\u001b[39m datetime\u001b[38;5;241m.\u001b[39mnow() \u001b[38;5;241m-\u001b[39m start_time\n\u001b[1;32m 340\u001b[0m logger\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDownloading took \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mduration\u001b[38;5;241m.\u001b[39mtotal_seconds()\u001b[38;5;250m \u001b[39m\u001b[38;5;241m/\u001b[39m\u001b[38;5;241m/\u001b[39m\u001b[38;5;250m \u001b[39m\u001b[38;5;241m60\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m min\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
97
- "File \u001b[0;32m/opt/conda/envs/hugginface/lib/python3.8/site-packages/datasets/utils/py_utils.py:436\u001b[0m, in \u001b[0;36mmap_nested\u001b[0;34m(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, parallel_min_length, types, disable_tqdm, desc)\u001b[0m\n\u001b[1;32m 434\u001b[0m \u001b[38;5;66;03m# Singleton\u001b[39;00m\n\u001b[1;32m 435\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data_struct, \u001b[38;5;28mdict\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data_struct, types):\n\u001b[0;32m--> 436\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunction\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata_struct\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 438\u001b[0m disable_tqdm \u001b[38;5;241m=\u001b[39m disable_tqdm \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m logging\u001b[38;5;241m.\u001b[39mis_progress_bar_enabled()\n\u001b[1;32m 439\u001b[0m iterable \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(data_struct\u001b[38;5;241m.\u001b[39mvalues()) \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data_struct, \u001b[38;5;28mdict\u001b[39m) \u001b[38;5;28;01melse\u001b[39;00m data_struct\n",
98
- "File \u001b[0;32m/opt/conda/envs/hugginface/lib/python3.8/site-packages/datasets/download/download_manager.py:357\u001b[0m, in \u001b[0;36mDownloadManager._download\u001b[0;34m(self, url_or_filename, download_config)\u001b[0m\n\u001b[1;32m 354\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_relative_path(url_or_filename):\n\u001b[1;32m 355\u001b[0m \u001b[38;5;66;03m# append the relative path to the base_path\u001b[39;00m\n\u001b[1;32m 356\u001b[0m url_or_filename \u001b[38;5;241m=\u001b[39m url_or_path_join(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_base_path, url_or_filename)\n\u001b[0;32m--> 357\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mcached_path\u001b[49m\u001b[43m(\u001b[49m\u001b[43murl_or_filename\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdownload_config\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdownload_config\u001b[49m\u001b[43m)\u001b[49m\n",
99
- "File \u001b[0;32m/opt/conda/envs/hugginface/lib/python3.8/site-packages/datasets/utils/file_utils.py:183\u001b[0m, in \u001b[0;36mcached_path\u001b[0;34m(url_or_filename, download_config, **download_kwargs)\u001b[0m\n\u001b[1;32m 179\u001b[0m url_or_filename \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mstr\u001b[39m(url_or_filename)\n\u001b[1;32m 181\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_remote_url(url_or_filename):\n\u001b[1;32m 182\u001b[0m \u001b[38;5;66;03m# URL, so get it from the cache (downloading if necessary)\u001b[39;00m\n\u001b[0;32m--> 183\u001b[0m output_path \u001b[38;5;241m=\u001b[39m \u001b[43mget_from_cache\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 184\u001b[0m \u001b[43m \u001b[49m\u001b[43murl_or_filename\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 185\u001b[0m \u001b[43m \u001b[49m\u001b[43mcache_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache_dir\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 186\u001b[0m \u001b[43m \u001b[49m\u001b[43mforce_download\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdownload_config\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mforce_download\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 187\u001b[0m \u001b[43m \u001b[49m\u001b[43mproxies\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdownload_config\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mproxies\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 188\u001b[0m \u001b[43m \u001b[49m\u001b[43mresume_download\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdownload_config\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresume_download\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 189\u001b[0m \u001b[43m \u001b[49m\u001b[43muser_agent\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdownload_config\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43muser_agent\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 190\u001b[0m \u001b[43m \u001b[49m\u001b[43mlocal_files_only\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdownload_config\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlocal_files_only\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 191\u001b[0m \u001b[43m \u001b[49m\u001b[43muse_etag\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdownload_config\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43muse_etag\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 192\u001b[0m \u001b[43m \u001b[49m\u001b[43mmax_retries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdownload_config\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmax_retries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 193\u001b[0m \u001b[43m \u001b[49m\u001b[43muse_auth_token\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdownload_config\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43muse_auth_token\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 194\u001b[0m \u001b[43m \u001b[49m\u001b[43mignore_url_params\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdownload_config\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mignore_url_params\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 195\u001b[0m \u001b[43m \u001b[49m\u001b[43mdownload_desc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdownload_config\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdownload_desc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 196\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 197\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mexists(url_or_filename):\n\u001b[1;32m 198\u001b[0m \u001b[38;5;66;03m# File, and it exists.\u001b[39;00m\n\u001b[1;32m 199\u001b[0m output_path \u001b[38;5;241m=\u001b[39m url_or_filename\n",
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- "File \u001b[0;32m/opt/conda/envs/hugginface/lib/python3.8/site-packages/datasets/utils/file_utils.py:530\u001b[0m, in \u001b[0;36mget_from_cache\u001b[0;34m(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token, ignore_url_params, download_desc)\u001b[0m\n\u001b[1;32m 525\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mFileNotFoundError\u001b[39;00m(\n\u001b[1;32m 526\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot find the requested files in the cached path at \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcache_path\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m and outgoing traffic has been\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 527\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m disabled. To enable file online look-ups, set \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlocal_files_only\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m to False.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 528\u001b[0m )\n\u001b[1;32m 529\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m response \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m response\u001b[38;5;241m.\u001b[39mstatus_code \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m404\u001b[39m:\n\u001b[0;32m--> 530\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mFileNotFoundError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCouldn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt find file at \u001b[39m\u001b[38;5;132;01m{\u001b[39;00murl\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 531\u001b[0m _raise_if_offline_mode_is_enabled(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTried to reach \u001b[39m\u001b[38;5;132;01m{\u001b[39;00murl\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 532\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m head_error \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
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- "\u001b[0;31mFileNotFoundError\u001b[0m: Couldn't find file at https://huggingface.co/datasets/will33am/AVA/resolve/main/data"
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  }
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  ],
@@ -110,7 +153,7 @@
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  {
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  "source": []
 
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  "cells": [
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+ "execution_count": 1,
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+ "execution_count": 2,
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  "data": {
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+ "model_id": "0379eea50d7946bc95e74b9e38393a83",
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  "version_major": 2,
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  },
 
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  "name": "stdout",
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  "output_type": "stream",
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  "text": [
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+ "Downloading and preparing dataset ava/default to /home/william/.cache/huggingface/datasets/will33am___ava/default/1.0.0/ce866a196bfdfabe8895e8b963c38dcc8fe5e85e20c47b83ea842e3459fe032a...\n"
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  ]
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  },
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  {
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  "data": {
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  "application/vnd.jupyter.widget-view+json": {
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+ "model_id": "13f90839e6834e76ad009719192229f1",
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  },
 
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  {
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  "data": {
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+ "model_id": "523195fe98504ce98071f9fd39a2b9ca",
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  },
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  "text/plain": [
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+ "Computing checksums: 100%|##########| 1/1 [01:33<00:00, 93.72s/it]"
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+ "data": {
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+ "application/vnd.jupyter.widget-view+json": {
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+ "model_id": "a7159023978844d0b3059697f046b975",
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+ "version_major": 2,
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+ "text/plain": [
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+ {
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+ "name": "stdout",
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+ "output_type": "stream",
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+ "text": [
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+ "Finish loading Metadata\n"
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+ },
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+ {
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+ "data": {
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+ "application/vnd.jupyter.widget-view+json": {
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+ "model_id": "21ade8bfc87040989558d0778b21aeed",
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+ "version_major": 2,
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+ "text/plain": [
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+ "Generating train split: 0 examples [00:00, ? examples/s]"
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+ ]
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+ },
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+ "metadata": {},
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+ },
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+ "name": "stderr",
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+ "output_type": "stream",
120
+ "text": [
121
+ "\n",
122
+ "0it [00:00, ?it/s]\u001b[A\n"
123
+ ]
124
+ },
125
+ {
126
+ "ename": "DatasetGenerationError",
127
+ "evalue": "An error occurred while generating the dataset",
128
  "output_type": "error",
129
  "traceback": [
130
  "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
131
+ "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
132
+ "File \u001b[0;32m/opt/conda/envs/hugginface/lib/python3.8/site-packages/datasets/builder.py:1570\u001b[0m, in \u001b[0;36mGeneratorBasedBuilder._prepare_split_single\u001b[0;34m(self, gen_kwargs, fpath, file_format, max_shard_size, split_info, check_duplicate_keys, job_id)\u001b[0m\n\u001b[1;32m 1569\u001b[0m _time \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[0;32m-> 1570\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key, record \u001b[38;5;129;01min\u001b[39;00m generator:\n\u001b[1;32m 1571\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m max_shard_size \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m writer\u001b[38;5;241m.\u001b[39m_num_bytes \u001b[38;5;241m>\u001b[39m max_shard_size:\n",
133
+ "File \u001b[0;32m~/.cache/huggingface/modules/datasets_modules/datasets/will33am--AVA/ce866a196bfdfabe8895e8b963c38dcc8fe5e85e20c47b83ea842e3459fe032a/AVA.py:62\u001b[0m, in \u001b[0;36mAVA._generate_examples\u001b[0;34m(self, archives, split)\u001b[0m\n\u001b[1;32m 60\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m path\u001b[38;5;241m.\u001b[39mendswith(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.jpg\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m 61\u001b[0m \u001b[38;5;66;03m# image filepath format: <IMAGE_FILE NAME>_<SYNSET_ID>.JPEG\u001b[39;00m\n\u001b[0;32m---> 62\u001b[0m _id \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mint\u001b[39m(os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39msplitext(\u001b[43mb\u001b[49m[\u001b[38;5;241m0\u001b[39m])[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39msplit(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m/\u001b[39m\u001b[38;5;124m'\u001b[39m)[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m])\n\u001b[1;32m 63\u001b[0m _metadata \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mDICT_METADATA[_id]\n",
134
+ "\u001b[0;31mNameError\u001b[0m: name 'b' is not defined",
135
+ "\nThe above exception was the direct cause of the following exception:\n",
136
+ "\u001b[0;31mDatasetGenerationError\u001b[0m Traceback (most recent call last)",
137
  "File \u001b[0;32m<timed exec>:1\u001b[0m\n",
138
  "File \u001b[0;32m/opt/conda/envs/hugginface/lib/python3.8/site-packages/datasets/load.py:1757\u001b[0m, in \u001b[0;36mload_dataset\u001b[0;34m(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, num_proc, **config_kwargs)\u001b[0m\n\u001b[1;32m 1754\u001b[0m try_from_hf_gcs \u001b[38;5;241m=\u001b[39m path \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m _PACKAGED_DATASETS_MODULES\n\u001b[1;32m 1756\u001b[0m \u001b[38;5;66;03m# Download and prepare data\u001b[39;00m\n\u001b[0;32m-> 1757\u001b[0m \u001b[43mbuilder_instance\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdownload_and_prepare\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1758\u001b[0m \u001b[43m \u001b[49m\u001b[43mdownload_config\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdownload_config\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1759\u001b[0m \u001b[43m \u001b[49m\u001b[43mdownload_mode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdownload_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1760\u001b[0m \u001b[43m \u001b[49m\u001b[43mignore_verifications\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mignore_verifications\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1761\u001b[0m \u001b[43m \u001b[49m\u001b[43mtry_from_hf_gcs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtry_from_hf_gcs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1762\u001b[0m \u001b[43m \u001b[49m\u001b[43mnum_proc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_proc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1763\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1765\u001b[0m \u001b[38;5;66;03m# Build dataset for splits\u001b[39;00m\n\u001b[1;32m 1766\u001b[0m keep_in_memory \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 1767\u001b[0m keep_in_memory \u001b[38;5;28;01mif\u001b[39;00m keep_in_memory \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m is_small_dataset(builder_instance\u001b[38;5;241m.\u001b[39minfo\u001b[38;5;241m.\u001b[39mdataset_size)\n\u001b[1;32m 1768\u001b[0m )\n",
139
  "File \u001b[0;32m/opt/conda/envs/hugginface/lib/python3.8/site-packages/datasets/builder.py:860\u001b[0m, in \u001b[0;36mDatasetBuilder.download_and_prepare\u001b[0;34m(self, output_dir, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs)\u001b[0m\n\u001b[1;32m 858\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m num_proc \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 859\u001b[0m prepare_split_kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnum_proc\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m num_proc\n\u001b[0;32m--> 860\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_download_and_prepare\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 861\u001b[0m \u001b[43m \u001b[49m\u001b[43mdl_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdl_manager\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 862\u001b[0m \u001b[43m \u001b[49m\u001b[43mverify_infos\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverify_infos\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 863\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mprepare_split_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 864\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mdownload_and_prepare_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 865\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 866\u001b[0m \u001b[38;5;66;03m# Sync info\u001b[39;00m\n\u001b[1;32m 867\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minfo\u001b[38;5;241m.\u001b[39mdataset_size \u001b[38;5;241m=\u001b[39m \u001b[38;5;28msum\u001b[39m(split\u001b[38;5;241m.\u001b[39mnum_bytes \u001b[38;5;28;01mfor\u001b[39;00m split \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minfo\u001b[38;5;241m.\u001b[39msplits\u001b[38;5;241m.\u001b[39mvalues())\n",
140
  "File \u001b[0;32m/opt/conda/envs/hugginface/lib/python3.8/site-packages/datasets/builder.py:1611\u001b[0m, in \u001b[0;36mGeneratorBasedBuilder._download_and_prepare\u001b[0;34m(self, dl_manager, verify_infos, **prepare_splits_kwargs)\u001b[0m\n\u001b[1;32m 1610\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_download_and_prepare\u001b[39m(\u001b[38;5;28mself\u001b[39m, dl_manager, verify_infos, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mprepare_splits_kwargs):\n\u001b[0;32m-> 1611\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_download_and_prepare\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1612\u001b[0m \u001b[43m \u001b[49m\u001b[43mdl_manager\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mverify_infos\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcheck_duplicate_keys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverify_infos\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mprepare_splits_kwargs\u001b[49m\n\u001b[1;32m 1613\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n",
141
+ "File \u001b[0;32m/opt/conda/envs/hugginface/lib/python3.8/site-packages/datasets/builder.py:953\u001b[0m, in \u001b[0;36mDatasetBuilder._download_and_prepare\u001b[0;34m(self, dl_manager, verify_infos, **prepare_split_kwargs)\u001b[0m\n\u001b[1;32m 949\u001b[0m split_dict\u001b[38;5;241m.\u001b[39madd(split_generator\u001b[38;5;241m.\u001b[39msplit_info)\n\u001b[1;32m 951\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 952\u001b[0m \u001b[38;5;66;03m# Prepare split will record examples associated to the split\u001b[39;00m\n\u001b[0;32m--> 953\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_prepare_split\u001b[49m\u001b[43m(\u001b[49m\u001b[43msplit_generator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mprepare_split_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 954\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 955\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m(\n\u001b[1;32m 956\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot find data file. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 957\u001b[0m \u001b[38;5;241m+\u001b[39m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmanual_download_instructions \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 958\u001b[0m \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mOriginal error:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 959\u001b[0m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mstr\u001b[39m(e)\n\u001b[1;32m 960\u001b[0m ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28mNone\u001b[39m\n",
142
+ "File \u001b[0;32m/opt/conda/envs/hugginface/lib/python3.8/site-packages/datasets/builder.py:1449\u001b[0m, in \u001b[0;36mGeneratorBasedBuilder._prepare_split\u001b[0;34m(self, split_generator, check_duplicate_keys, file_format, num_proc, max_shard_size)\u001b[0m\n\u001b[1;32m 1447\u001b[0m gen_kwargs \u001b[38;5;241m=\u001b[39m split_generator\u001b[38;5;241m.\u001b[39mgen_kwargs\n\u001b[1;32m 1448\u001b[0m job_id \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m-> 1449\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m job_id, done, content \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_prepare_split_single(\n\u001b[1;32m 1450\u001b[0m gen_kwargs\u001b[38;5;241m=\u001b[39mgen_kwargs, job_id\u001b[38;5;241m=\u001b[39mjob_id, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m_prepare_split_args\n\u001b[1;32m 1451\u001b[0m ):\n\u001b[1;32m 1452\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m done:\n\u001b[1;32m 1453\u001b[0m result \u001b[38;5;241m=\u001b[39m content\n",
143
+ "File \u001b[0;32m/opt/conda/envs/hugginface/lib/python3.8/site-packages/datasets/builder.py:1606\u001b[0m, in \u001b[0;36mGeneratorBasedBuilder._prepare_split_single\u001b[0;34m(self, gen_kwargs, fpath, file_format, max_shard_size, split_info, check_duplicate_keys, job_id)\u001b[0m\n\u001b[1;32m 1604\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(e, SchemaInferenceError) \u001b[38;5;129;01mand\u001b[39;00m e\u001b[38;5;241m.\u001b[39m__context__ \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 1605\u001b[0m e \u001b[38;5;241m=\u001b[39m e\u001b[38;5;241m.\u001b[39m__context__\n\u001b[0;32m-> 1606\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m DatasetGenerationError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAn error occurred while generating the dataset\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n\u001b[1;32m 1608\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m job_id, \u001b[38;5;28;01mTrue\u001b[39;00m, (total_num_examples, total_num_bytes, writer\u001b[38;5;241m.\u001b[39m_features, num_shards, shard_lengths)\n",
144
+ "\u001b[0;31mDatasetGenerationError\u001b[0m: An error occurred while generating the dataset"
 
 
 
 
 
145
  ]
146
  }
147
  ],
 
153
  {
154
  "cell_type": "code",
155
  "execution_count": null,
156
+ "id": "aa863a32",
157
  "metadata": {},
158
  "outputs": [],
159
  "source": []