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xieyizheng
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hypernerf_custom_scenes

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The dataset generation failed because of a cast error

Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 454 new columns ({'000198', '000305', '000189', '000433', '000133', '000128', '000335', '000251', '000037', '000456', '000165', '000057', '000290', '000082', '000446', '000353', '000319', '000286', '000348', '000017', '000011', '000415', '000021', '000157', '000106', '000352', '000156', '000100', '000253', '000214', '000206', '000158', '000421', '000155', '000039', '000459', '000299', '000232', '000351', '000209', '000042', '000202', '000383', '000114', '000074', '000168', '000356', '000426', '000391', '000298', '000012', '000287', '000121', '000263', '000476', '000482', '000430', '000048', '000185', '000393', '000022', '000428', '000418', '000420', '000454', '000008', '000447', '000078', '000240', '000041', '000439', '000101', '000408', '000461', '000452', '000252', '000443', '000379', '000413', '000145', '000246', '000429', '000458', '000141', '000062', '000382', '000170', '000280', '000403', '000238', '000303', '000207', '000388', '000258', '000137', '000261', '000409', '000400', '000257', '000025', '000139', '000072', '000424', '000217', '000339', '000256', '000135', '000033', '000438', '000148', '000334', '000201', '000166', '000316', '000054', '000150', '000215', '000275', '000384', '000453', '000386', '000269', '000462', '000120', '000122', '000406', '000001', '000058', '000460', '000398', '000283', '000068', '000323', '000046', '000481', '000203', '000295', '000317', '000364', '000423', '000099', '000056', '000479', '000216', '000071', '000199', '000231', '000065', '000031', '000161',
...
 '000197', '000060', '000473', '000328', '000343', '000064', '000250', '000277', '000268', '000040', '000079', '000019', '000076', '000004', '000059', '000276', '000350', '000061', '000226', '000366', '000297', '000211', '000186', '000077', '000378', '000173', '000047', '000337', '000360', '000009', '000417', '000032', '000361', '000027', '000354', '000330', '000045', '000152', '000159', '000248', '000028', '000347', '000416', '000124', '000080', '000116', '000208', '000333', '000394', '000192', '000204', '000363', '000143', '000327', '000311', '000247', '000210', '000466', '000096', '000365', '000195', '000414', '000066', '000110', '000293', '000125', '000044', '000043', '000105', '000169', '000178', '000264', '000271', '000478', '000371', '000067', '000255', '000091', '000262', '000349', '000050', '000397', '000154', '000448', '000358', '000140', '000254', '000144', '000404', '000242', '000003', '000063', '000075', '000108', '000359', '000102', '000304', '000234', '000229', '000392', '000434', '000188', '000221', '000176', '000244', '000126', '000472', '000288', '000480', '000103', '000355', '000132', '000396', '000172', '000399', '000222', '000119', '000084', '000450', '000053', '000142', '000463', '000111', '000194', '000200', '000279', '000088', '000265', '000024', '000338', '000340', '000427', '000016', '000449', '000259', '000073', '000227', '000469', '000055', '000425', '000411', '000451', '000162', '000441', '000010', '000113', '000313', '000272', '000051', '000164'}) and 5 missing columns ({'train_ids', 'num_exemplars', 'count', 'ids', 'val_ids'}).

This happened while the json dataset builder was generating data using

zip://chess4/metadata.json::hf://datasets/xieyizheng/hypernerf_custom_scenes@265589302460bc30d2e62f6ba7ab642ecfa87f4d/chess/chess_dataset.zip

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2011, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 585, in write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2302, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2256, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              000002: struct<warp_id: int64, appearance_id: int64, camera_id: int64>
                child 0, warp_id: int64
                child 1, appearance_id: int64
                child 2, camera_id: int64
              000003: struct<warp_id: int64, appearance_id: int64, camera_id: int64>
                child 0, warp_id: int64
                child 1, appearance_id: int64
                child 2, camera_id: int64
              000004: struct<warp_id: int64, appearance_id: int64, camera_id: int64>
                child 0, warp_id: int64
                child 1, appearance_id: int64
                child 2, camera_id: int64
              000005: struct<warp_id: int64, appearance_id: int64, camera_id: int64>
                child 0, warp_id: int64
                child 1, appearance_id: int64
                child 2, camera_id: int64
              000006: struct<warp_id: int64, appearance_id: int64, camera_id: int64>
                child 0, warp_id: int64
                child 1, appearance_id: int64
                child 2, camera_id: int64
              000007: struct<warp_id: int64, appearance_id: int64, camera_id: int64>
                child 0, warp_id: int64
                child 1, appearance_id: int64
                child 2, camera_id: int64
              000008: struct<warp_id: int64, appearance_id: int64, camera_id: int64>
                child 0, warp_id: int64
                child 1, appearance_id: int64
                child 2, camera_id: int64
              000009: struct<warp_id: int64, appearance_id: int64, camera_id: int64>
                child 0, warp_id: int64
                child 1, appearance_id: int64
                child 2, camera_id: int64
              000010: struct<warp_id: int64, appearance_id: int64, camera_id: int64>
                child 0, warp_id: int64
                child 1, appearance_id: int64
                child 2, camera_id: int64
              000011: struct<warp_id: int64, appearance_id: int64, camera_id: int64>
                child 0, warp_
              ...
              >
                child 0, warp_id: int64
                child 1, appearance_id: int64
                child 2, camera_id: int64
              000285: struct<warp_id: int64, appearance_id: int64, camera_id: int64>
                child 0, warp_id: int64
                child 1, appearance_id: int64
                child 2, camera_id: int64
              000308: struct<warp_id: int64, appearance_id: int64, camera_id: int64>
                child 0, warp_id: int64
                child 1, appearance_id: int64
                child 2, camera_id: int64
              000332: struct<warp_id: int64, appearance_id: int64, camera_id: int64>
                child 0, warp_id: int64
                child 1, appearance_id: int64
                child 2, camera_id: int64
              000354: struct<warp_id: int64, appearance_id: int64, camera_id: int64>
                child 0, warp_id: int64
                child 1, appearance_id: int64
                child 2, camera_id: int64
              000386: struct<warp_id: int64, appearance_id: int64, camera_id: int64>
                child 0, warp_id: int64
                child 1, appearance_id: int64
                child 2, camera_id: int64
              000408: struct<warp_id: int64, appearance_id: int64, camera_id: int64>
                child 0, warp_id: int64
                child 1, appearance_id: int64
                child 2, camera_id: int64
              000428: struct<warp_id: int64, appearance_id: int64, camera_id: int64>
                child 0, warp_id: int64
                child 1, appearance_id: int64
                child 2, camera_id: int64
              000448: struct<warp_id: int64, appearance_id: int64, camera_id: int64>
                child 0, warp_id: int64
                child 1, appearance_id: int64
                child 2, camera_id: int64
              000469: struct<warp_id: int64, appearance_id: int64, camera_id: int64>
                child 0, warp_id: int64
                child 1, appearance_id: int64
                child 2, camera_id: int64
              to
              {'count': Value(dtype='int64', id=None), 'num_exemplars': Value(dtype='int64', id=None), 'ids': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'train_ids': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'val_ids': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1316, in compute_config_parquet_and_info_response
                  parquet_operations, partial = stream_convert_to_parquet(
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 909, in stream_convert_to_parquet
                  builder._prepare_split(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1882, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2013, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 454 new columns ({'000198', '000305', '000189', '000433', '000133', '000128', '000335', '000251', '000037', '000456', '000165', '000057', '000290', '000082', '000446', '000353', '000319', '000286', '000348', '000017', '000011', '000415', '000021', '000157', '000106', '000352', '000156', '000100', '000253', '000214', '000206', '000158', '000421', '000155', '000039', '000459', '000299', '000232', '000351', '000209', '000042', '000202', '000383', '000114', '000074', '000168', '000356', '000426', '000391', '000298', '000012', '000287', '000121', '000263', '000476', '000482', '000430', '000048', '000185', '000393', '000022', '000428', '000418', '000420', '000454', '000008', '000447', '000078', '000240', '000041', '000439', '000101', '000408', '000461', '000452', '000252', '000443', '000379', '000413', '000145', '000246', '000429', '000458', '000141', '000062', '000382', '000170', '000280', '000403', '000238', '000303', '000207', '000388', '000258', '000137', '000261', '000409', '000400', '000257', '000025', '000139', '000072', '000424', '000217', '000339', '000256', '000135', '000033', '000438', '000148', '000334', '000201', '000166', '000316', '000054', '000150', '000215', '000275', '000384', '000453', '000386', '000269', '000462', '000120', '000122', '000406', '000001', '000058', '000460', '000398', '000283', '000068', '000323', '000046', '000481', '000203', '000295', '000317', '000364', '000423', '000099', '000056', '000479', '000216', '000071', '000199', '000231', '000065', '000031', '000161',
              ...
               '000197', '000060', '000473', '000328', '000343', '000064', '000250', '000277', '000268', '000040', '000079', '000019', '000076', '000004', '000059', '000276', '000350', '000061', '000226', '000366', '000297', '000211', '000186', '000077', '000378', '000173', '000047', '000337', '000360', '000009', '000417', '000032', '000361', '000027', '000354', '000330', '000045', '000152', '000159', '000248', '000028', '000347', '000416', '000124', '000080', '000116', '000208', '000333', '000394', '000192', '000204', '000363', '000143', '000327', '000311', '000247', '000210', '000466', '000096', '000365', '000195', '000414', '000066', '000110', '000293', '000125', '000044', '000043', '000105', '000169', '000178', '000264', '000271', '000478', '000371', '000067', '000255', '000091', '000262', '000349', '000050', '000397', '000154', '000448', '000358', '000140', '000254', '000144', '000404', '000242', '000003', '000063', '000075', '000108', '000359', '000102', '000304', '000234', '000229', '000392', '000434', '000188', '000221', '000176', '000244', '000126', '000472', '000288', '000480', '000103', '000355', '000132', '000396', '000172', '000399', '000222', '000119', '000084', '000450', '000053', '000142', '000463', '000111', '000194', '000200', '000279', '000088', '000265', '000024', '000338', '000340', '000427', '000016', '000449', '000259', '000073', '000227', '000469', '000055', '000425', '000411', '000451', '000162', '000441', '000010', '000113', '000313', '000272', '000051', '000164'}) and 5 missing columns ({'train_ids', 'num_exemplars', 'count', 'ids', 'val_ids'}).
              
              This happened while the json dataset builder was generating data using
              
              zip://chess4/metadata.json::hf://datasets/xieyizheng/hypernerf_custom_scenes@265589302460bc30d2e62f6ba7ab642ecfa87f4d/chess/chess_dataset.zip
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

count int64	num_exemplars int64	ids sequence	train_ids sequence	val_ids sequence	000002 dict	000003 dict	000004 dict	000005 dict	000006 dict	000007 dict	000008 dict	000009 dict	000010 dict	000011 dict	000012 dict	000013 dict	000014 dict	000015 dict	000016 dict	000017 dict	000018 dict	000019 dict	000020 dict	000022 dict	000023 dict	000024 dict	000025 dict	000026 dict	000027 dict	000028 dict	000029 dict	000030 dict	000031 dict	000032 dict	000033 dict	000034 dict	000035 dict	000036 dict	000037 dict	000038 dict	000039 dict	000040 dict	000042 dict	000043 dict	000044 dict	000045 dict	000046 dict	000047 dict	000048 dict	000049 dict	000050 dict	000051 dict	000052 dict	000053 dict	000054 dict	000055 dict	000056 dict	000057 dict	000058 dict	000059 dict	000060 dict	000062 dict	000063 dict	000064 dict	000065 dict	000066 dict	000067 dict	000068 dict	000069 dict	000070 dict	000071 dict	000072 dict	000073 dict	000074 dict	000075 dict	000076 dict	000077 dict	000078 dict	000079 dict	000080 dict	000082 dict	000083 dict	000084 dict	000085 dict	000086 dict	000087 dict	000088 dict	000089 dict	000090 dict	000091 dict	000092 dict	000093 dict	000094 dict	000095 dict	000096 dict	000097 dict	000098 dict	000099 dict	000100 dict	000102 dict	000103 dict	000104 dict	000105 dict	000106 dict	000107 dict	000108 dict	000109 dict	000110 dict	000111 dict	000112 dict	000113 dict	000114 dict	000115 dict	000116 dict	000117 dict	000118 dict	000119 dict	000120 dict	000122 dict	000123 dict	000124 dict	000125 dict	000126 dict	000127 dict	000128 dict	000129 dict	000130 dict	000131 dict	000132 dict	000133 dict	000134 dict	000135 dict	000136 dict	000137 dict	000138 dict	000139 dict	000140 dict	000142 dict	000143 dict	000144 dict	000145 dict	000146 dict	000147 dict	000148 dict	000149 dict	000150 dict	000151 dict	000152 dict	000154 dict	000155 dict	000156 dict	000157 dict	000158 dict	000159 dict	000160 dict	000161 dict	000163 dict	000164 dict	000165 dict	000166 dict	000167 dict	000168 dict	000169 dict	000170 dict	000171 dict	000172 dict	000173 dict	000174 dict	000175 dict	000176 dict	000177 dict	000178 dict	000179 dict	000180 dict	000181 dict	000183 dict	000184 dict	000185 dict	000186 dict	000187 dict	000188 dict	000189 dict	000190 dict	000191 dict	000192 dict	000193 dict	000194 dict	000195 dict	000196 dict	000197 dict	000198 dict	000199 dict	000200 dict	000201 dict	000203 dict	000204 dict	000205 dict	000206 dict	000207 dict	000208 dict	000209 dict	000210 dict	000211 dict	000212 dict	000213 dict	000214 dict	000215 dict	000216 dict	000217 dict	000218 dict	000219 dict	000220 dict	000221 dict	000223 dict	000224 dict	000225 dict	000226 dict	000227 dict	000228 dict	000229 dict	000230 dict	000231 dict	000232 dict	000233 dict	000234 dict	000235 dict	000236 dict	000237 dict	000238 dict	000239 dict	000240 dict	000241 dict	000243 dict	000244 dict	000245 dict	000246 dict	000247 dict	000248 dict	000249 dict	000250 dict	000251 dict	000252 dict	000253 dict	000254 dict	000255 dict	000256 dict	000257 dict	000258 dict	000259 dict	000260 dict	000261 dict	000263 dict	000264 dict	000265 dict	000266 dict	000268 dict	000269 dict	000270 dict	000271 dict	000272 dict	000273 dict	000274 dict	000275 dict	000276 dict	000277 dict	000278 dict	000279 dict	000280 dict	000281 dict	000283 dict	000286 dict	000287 dict	000288 dict	000290 dict	000291 dict	000292 dict	000293 dict	000294 dict	000295 dict	000297 dict	000298 dict	000299 dict	000300 dict	000302 dict	000303 dict	000304 dict	000305 dict	000306 dict	000307 dict	000309 dict	000310 dict	000311 dict	000313 dict	000314 dict	000315 dict	000316 dict	000317 dict	000319 dict	000321 dict	000322 dict	000323 dict	000324 dict	000325 dict	000326 dict	000327 dict	000328 dict	000330 dict	000331 dict	000333 dict	000334 dict	000335 dict	000336 dict	000337 dict	000338 dict	000339 dict	000340 dict	000343 dict	000344 dict	000345 dict	000346 dict	000347 dict	000348 dict	000349 dict	000350 dict	000351 dict	000352 dict	000353 dict	000355 dict	000356 dict	000357 dict	000358 dict	000359 dict	000360 dict	000361 dict	000362 dict	000363 dict	000364 dict	000365 dict	000366 dict	000371 dict	000378 dict	000379 dict	000381 dict	000382 dict	000383 dict	000384 dict	000387 dict	000388 dict	000390 dict	000391 dict	000392 dict	000393 dict	000394 dict	000396 dict	000397 dict	000398 dict	000399 dict	000400 dict	000401 dict	000402 dict	000403 dict	000404 dict	000405 dict	000406 dict	000407 dict	000409 dict	000410 dict	000411 dict	000412 dict	000413 dict	000414 dict	000415 dict	000416 dict	000417 dict	000418 dict	000419 dict	000420 dict	000421 dict	000422 dict	000423 dict	000424 dict	000425 dict	000426 dict	000427 dict	000429 dict	000430 dict	000431 dict	000432 dict	000433 dict	000434 dict	000435 dict	000436 dict	000437 dict	000438 dict	000439 dict	000440 dict	000441 dict	000442 dict	000443 dict	000444 dict	000445 dict	000446 dict	000447 dict	000449 dict	000450 dict	000451 dict	000452 dict	000453 dict	000454 dict	000455 dict	000456 dict	000457 dict	000458 dict	000459 dict	000460 dict	000461 dict	000462 dict	000463 dict	000465 dict	000466 dict	000467 dict	000468 dict	000470 dict	000471 dict	000472 dict	000473 dict	000474 dict	000475 dict	000476 dict	000477 dict	000478 dict	000479 dict	000480 dict	000481 dict	000482 dict	000001 dict	000021 dict	000041 dict	000061 dict	000081 dict	000101 dict	000121 dict	000141 dict	000162 dict	000182 dict	000202 dict	000222 dict	000242 dict	000262 dict	000285 dict	000308 dict	000332 dict	000354 dict	000386 dict	000408 dict	000428 dict	000448 dict	000469 dict	scale float64	center sequence	bbox sequence	near float64	far float64	orientation sequence	position sequence	focal_length float64	principal_point sequence	skew float64	pixel_aspect_ratio float64	radial_distortion sequence	tangential_distortion sequence	image_size sequence
454	431	[ "000001", "000002", "000003", "000004", "000005", "000006", "000007", "000008", "000009", "000010", "000011", "000012", "000013", "000014", "000015", "000016", "000017", "000018", "000019", "000020", "000021", "000022", "000023", "000024", "000025", "000026", "000027", "000028", "000029", "000030", "000031", "000032", "000033", "000034", "000035", "000036", "000037", "000038", "000039", "000040", "000041", "000042", "000043", "000044", "000045", "000046", "000047", "000048", "000049", "000050", "000051", "000052", "000053", "000054", "000055", "000056", "000057", "000058", "000059", "000060", "000061", "000062", "000063", "000064", "000065", "000066", "000067", "000068", "000069", "000070", "000071", "000072", "000073", "000074", "000075", "000076", "000077", "000078", "000079", "000080", "000081", "000082", "000083", "000084", "000085", "000086", "000087", "000088", "000089", "000090", "000091", "000092", "000093", "000094", "000095", "000096", "000097", "000098", "000099", 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