The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
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 132 new columns ({'0.667', '0.657', '254.13', '254.19', '151', '254.16', '0.652', '67', '0.646', '115', '61', '224', '31', '52.1', '0.651', '0.658', '254.5', '84', '0.631', '0.633', '38', '0.648', '44', '0.661', '163', '121', '0.637', '254.4', '254.17', '0.642', '0.630', '0.626', '0.629', '0.622', '236', '0.625', '17', '254.2', '19', '254.1', '254.18', '254.20', '0.632', '0.645', '254.12', '140', '77', '57', '0.639', '254.15', '114.1', '72', '58', '0.660', '60', '0.627', '254.25', '0.638', '83.1', '0.636', '106', '0.666', '198.3', '0.620', '0.665', '0.644', '227', '0.650', '0.663', '254.10', '0.623', '185', '254.9', '67.2', '5', '248', '0.640', '254.6', '254.22', '0.628', '0.655', '0.635', '198.2', '198.4', '0.659', '254', '22', '40', '0.656', '0.621', '59', '121.1', '0.649', '0.641', '254.3', '198.6', '0.634', '0.624', '0.618', '0.653', '159', '52', '198.5', '21', '67.3', '254.21', '83', '0.664', '0.647', '233', '254.14', '254.24', '59.1', '52.2', '67.1', '0.662', '198.7', '254.26', '209', '254.23', '254.7', '133.1', '254.11', '0.619', '75', '62', '129', '254.8', '222', '203', '0.654', '0.643'}) and 132 missing columns ({'253.36', '253.34', '18.3', '80', '18.4', '253.13', '253.16', '171', '11', '43', '172.1', '1.1', '253.4', '24', '190.1', '27', '160', '2', '253.19', '11.1', '23', '244', '70', '82', '182.1', '49', '64.1', '148', '195', '226', '253.32', '213', '55', '253.22', '247.1', '253.5', '46', '2.1', '136', '241.1', '16', '253.7', '253.44', '154.2', '247', '45', '30', '253.28', '253.9', '253.37', '253.3', '253.39', '26', '190', '253.42', '16.1', '253.30', '80.1', '139', '253.29', '253.15', '39', '253.14', '172', '253.10', '253.18', '78', '253.27', '150', '253.24', '81.1', '64', '18.2', '253.38', '107', '39.1', '253.52', '253.20', '253.31', '253.26', '25', '56', '154.1', '132', '253.45', '253.46', '253.41', '225.1', '130', '253.50', '253.8', '253.21', '253.43', '175', '253.11', '93', '93.1', '253.23', '253.17', '136.1', '127', '253.12', '11.2', '186', '81', '2.2', '253.35', '253.6', '253.40', '253.2', '94', '90', '119', '253.49', '253.33', '183', '212', '135', '253.47', '253.48', '195.1', '156', '82.1', '108', '253.1', '253.53', '249.1', '253.25', '252', '154', '253.51', '201'}).
This happened while the csv dataset builder was generating data using
hf://datasets/vessl/mnist-demo-dataset/test.csv (at revision 0b82253484efd0ea576696c0d91a2b9aadbe8f34)
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
label: int64
0: int64
0.1: int64
0.2: int64
0.3: int64
0.4: int64
0.5: int64
0.6: int64
0.7: int64
0.8: int64
0.9: int64
0.10: int64
0.11: int64
0.12: int64
0.13: int64
0.14: int64
0.15: int64
0.16: int64
0.17: int64
0.18: int64
0.19: int64
0.20: int64
0.21: int64
0.22: int64
0.23: int64
0.24: int64
0.25: int64
0.26: int64
0.27: int64
0.28: int64
0.29: int64
0.30: int64
0.31: int64
0.32: int64
0.33: int64
0.34: int64
0.35: int64
0.36: int64
0.37: int64
0.38: int64
0.39: int64
0.40: int64
0.41: int64
0.42: int64
0.43: int64
0.44: int64
0.45: int64
0.46: int64
0.47: int64
0.48: int64
0.49: int64
0.50: int64
0.51: int64
0.52: int64
0.53: int64
0.54: int64
0.55: int64
0.56: int64
0.57: int64
0.58: int64
0.59: int64
0.60: int64
0.61: int64
0.62: int64
0.63: int64
0.64: int64
0.65: int64
0.66: int64
0.67: int64
0.68: int64
0.69: int64
0.70: int64
0.71: int64
0.72: int64
0.73: int64
0.74: int64
0.75: int64
0.76: int64
0.77: int64
0.78: int64
0.79: int64
0.80: int64
0.81: int64
0.82: int64
0.83: int64
0.84: int64
0.85: int64
0.86: int64
0.87: int64
0.88: int64
0.89: int64
0.90: int64
0.91: int64
0.92: int64
0.93: int64
0.94: int64
0.95: int64
0.96: int64
0.97: int64
0.98: int64
0.99: int64
0.100: int64
0.101: int64
0.102: int64
0.103: int64
0.104: int64
0.105: int64
0.106: int64
0.107: int64
0.108: int64
0.109: int64
0.110: int64
0.111: int64
0.112: int64
0.113: int64
0.114: int64
0.115: int64
0.116: int64
0.117: int64
0.118: int64
0.119: int64
0.120: int64
0.121: int64
0.122: int64
...
0.574: int64
0.575: int64
0.576: int64
0.577: int64
0.578: int64
0.579: int64
61: int64
242: int64
254.22: int64
254.23: int64
52.2: int64
0.580: int64
0.581: int64
0.582: int64
0.583: int64
0.584: int64
0.585: int64
0.586: int64
0.587: int64
0.588: int64
0.589: int64
0.590: int64
0.591: int64
0.592: int64
0.593: int64
0.594: int64
0.595: int64
0.596: int64
0.597: int64
0.598: int64
0.599: int64
0.600: int64
0.601: int64
0.602: int64
121: int64
254.24: int64
254.25: int64
219.1: int64
40: int64
0.603: int64
0.604: int64
0.605: int64
0.606: int64
0.607: int64
0.608: int64
0.609: int64
0.610: int64
0.611: int64
0.612: int64
0.613: int64
0.614: int64
0.615: int64
0.616: int64
0.617: int64
0.618: int64
0.619: int64
0.620: int64
0.621: int64
0.622: int64
0.623: int64
0.624: int64
0.625: int64
121.1: int64
254.26: int64
207: int64
18.1: int64
0.626: int64
0.627: int64
0.628: int64
0.629: int64
0.630: int64
0.631: int64
0.632: int64
0.633: int64
0.634: int64
0.635: int64
0.636: int64
0.637: int64
0.638: int64
0.639: int64
0.640: int64
0.641: int64
0.642: int64
0.643: int64
0.644: int64
0.645: int64
0.646: int64
0.647: int64
0.648: int64
0.649: int64
0.650: int64
0.651: int64
0.652: int64
0.653: int64
0.654: int64
0.655: int64
0.656: int64
0.657: int64
0.658: int64
0.659: int64
0.660: int64
0.661: int64
0.662: int64
0.663: int64
0.664: int64
0.665: int64
0.666: int64
0.667: int64
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 83699
to
{'label': Value(dtype='int64', id=None), '0': Value(dtype='int64', id=None), '0.1': Value(dtype='int64', id=None), '0.2': Value(dtype='int64', id=None), '0.3': Value(dtype='int64', id=None), '0.4': Value(dtype='int64', id=None), '0.5': Value(dtype='int64', id=None), '0.6': Value(dtype='int64', id=None), '0.7': Value(dtype='int64', id=None), '0.8': Value(dtype='int64', id=None), '0.9': Value(dtype='int64', id=None), '0.10': Value(dtype='int64', id=None), '0.11': Value(dtype='int64', id=None), '0.12': Value(dtype='int64', id=None), '0.13': Value(dtype='int64', id=None), '0.14': Value(dtype='int64', id=None), '0.15': Value(dtype='int64', id=None), '0.16': Value(dtype='int64', id=None), '0.17': Value(dtype='int64', id=None), '0.18': Value(dtype='int64', id=None), '0.19': Value(dtype='int64', id=None), '0.20': Value(dtype='int64', id=None), '0.21': Value(dtype='int64', id=None), '0.22': Value(dtype='int64', id=None), '0.23': Value(dtype='int64', id=None), '0.24': Value(dtype='int64', id=None), '0.25': Value(dtype='int64', id=None), '0.26': Value(dtype='int64', id=None), '0.27': Value(dtype='int64', id=None), '0.28': Value(dtype='int64', id=None), '0.29': Value(dtype='int64', id=None), '0.30': Value(dtype='int64', id=None), '0.31': Value(dtype='int64', id=None), '0.32': Value(dtype='int64', id=None), '0.33': Value(dtype='int64', id=None), '0.34': Value(dtype='int64', id=None), '0.35': Value(dtype='int64', id=None), '0.36': Value(dtype='int64', id=None), '0.37': Value(dtype='int64',
...
e='int64', id=None), '0.581': Value(dtype='int64', id=None), '0.582': Value(dtype='int64', id=None), '0.583': Value(dtype='int64', id=None), '0.584': Value(dtype='int64', id=None), '0.585': Value(dtype='int64', id=None), '0.586': Value(dtype='int64', id=None), '0.587': Value(dtype='int64', id=None), '0.588': Value(dtype='int64', id=None), '0.589': Value(dtype='int64', id=None), '0.590': Value(dtype='int64', id=None), '0.591': Value(dtype='int64', id=None), '0.592': Value(dtype='int64', id=None), '0.593': Value(dtype='int64', id=None), '0.594': Value(dtype='int64', id=None), '0.595': Value(dtype='int64', id=None), '0.596': Value(dtype='int64', id=None), '0.597': Value(dtype='int64', id=None), '0.598': Value(dtype='int64', id=None), '0.599': Value(dtype='int64', id=None), '0.600': Value(dtype='int64', id=None), '0.601': Value(dtype='int64', id=None), '0.602': Value(dtype='int64', id=None), '0.603': Value(dtype='int64', id=None), '0.604': Value(dtype='int64', id=None), '0.605': Value(dtype='int64', id=None), '0.606': Value(dtype='int64', id=None), '0.607': Value(dtype='int64', id=None), '0.608': Value(dtype='int64', id=None), '0.609': Value(dtype='int64', id=None), '0.610': Value(dtype='int64', id=None), '0.611': Value(dtype='int64', id=None), '0.612': Value(dtype='int64', id=None), '0.613': Value(dtype='int64', id=None), '0.614': Value(dtype='int64', id=None), '0.615': Value(dtype='int64', id=None), '0.616': Value(dtype='int64', id=None), '0.617': Value(dtype='int64', 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 1321, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 935, in convert_to_parquet
builder.download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1027, in download_and_prepare
self._download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1122, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
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 132 new columns ({'0.667', '0.657', '254.13', '254.19', '151', '254.16', '0.652', '67', '0.646', '115', '61', '224', '31', '52.1', '0.651', '0.658', '254.5', '84', '0.631', '0.633', '38', '0.648', '44', '0.661', '163', '121', '0.637', '254.4', '254.17', '0.642', '0.630', '0.626', '0.629', '0.622', '236', '0.625', '17', '254.2', '19', '254.1', '254.18', '254.20', '0.632', '0.645', '254.12', '140', '77', '57', '0.639', '254.15', '114.1', '72', '58', '0.660', '60', '0.627', '254.25', '0.638', '83.1', '0.636', '106', '0.666', '198.3', '0.620', '0.665', '0.644', '227', '0.650', '0.663', '254.10', '0.623', '185', '254.9', '67.2', '5', '248', '0.640', '254.6', '254.22', '0.628', '0.655', '0.635', '198.2', '198.4', '0.659', '254', '22', '40', '0.656', '0.621', '59', '121.1', '0.649', '0.641', '254.3', '198.6', '0.634', '0.624', '0.618', '0.653', '159', '52', '198.5', '21', '67.3', '254.21', '83', '0.664', '0.647', '233', '254.14', '254.24', '59.1', '52.2', '67.1', '0.662', '198.7', '254.26', '209', '254.23', '254.7', '133.1', '254.11', '0.619', '75', '62', '129', '254.8', '222', '203', '0.654', '0.643'}) and 132 missing columns ({'253.36', '253.34', '18.3', '80', '18.4', '253.13', '253.16', '171', '11', '43', '172.1', '1.1', '253.4', '24', '190.1', '27', '160', '2', '253.19', '11.1', '23', '244', '70', '82', '182.1', '49', '64.1', '148', '195', '226', '253.32', '213', '55', '253.22', '247.1', '253.5', '46', '2.1', '136', '241.1', '16', '253.7', '253.44', '154.2', '247', '45', '30', '253.28', '253.9', '253.37', '253.3', '253.39', '26', '190', '253.42', '16.1', '253.30', '80.1', '139', '253.29', '253.15', '39', '253.14', '172', '253.10', '253.18', '78', '253.27', '150', '253.24', '81.1', '64', '18.2', '253.38', '107', '39.1', '253.52', '253.20', '253.31', '253.26', '25', '56', '154.1', '132', '253.45', '253.46', '253.41', '225.1', '130', '253.50', '253.8', '253.21', '253.43', '175', '253.11', '93', '93.1', '253.23', '253.17', '136.1', '127', '253.12', '11.2', '186', '81', '2.2', '253.35', '253.6', '253.40', '253.2', '94', '90', '119', '253.49', '253.33', '183', '212', '135', '253.47', '253.48', '195.1', '156', '82.1', '108', '253.1', '253.53', '249.1', '253.25', '252', '154', '253.51', '201'}).
This happened while the csv dataset builder was generating data using
hf://datasets/vessl/mnist-demo-dataset/test.csv (at revision 0b82253484efd0ea576696c0d91a2b9aadbe8f34)
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.
label
int64 | 0
int64 | 0.1
int64 | 0.2
int64 | 0.3
int64 | 0.4
int64 | 0.5
int64 | 0.6
int64 | 0.7
int64 | 0.8
int64 | 0.9
int64 | 0.10
int64 | 0.11
int64 | 0.12
int64 | 0.13
int64 | 0.14
int64 | 0.15
int64 | 0.16
int64 | 0.17
int64 | 0.18
int64 | 0.19
int64 | 0.20
int64 | 0.21
int64 | 0.22
int64 | 0.23
int64 | 0.24
int64 | 0.25
int64 | 0.26
int64 | 0.27
int64 | 0.28
int64 | 0.29
int64 | 0.30
int64 | 0.31
int64 | 0.32
int64 | 0.33
int64 | 0.34
int64 | 0.35
int64 | 0.36
int64 | 0.37
int64 | 0.38
int64 | 0.39
int64 | 0.40
int64 | 0.41
int64 | 0.42
int64 | 0.43
int64 | 0.44
int64 | 0.45
int64 | 0.46
int64 | 0.47
int64 | 0.48
int64 | 0.49
int64 | 0.50
int64 | 0.51
int64 | 0.52
int64 | 0.53
int64 | 0.54
int64 | 0.55
int64 | 0.56
int64 | 0.57
int64 | 0.58
int64 | 0.59
int64 | 0.60
int64 | 0.61
int64 | 0.62
int64 | 0.63
int64 | 0.64
int64 | 0.65
int64 | 0.66
int64 | 0.67
int64 | 0.68
int64 | 0.69
int64 | 0.70
int64 | 0.71
int64 | 0.72
int64 | 0.73
int64 | 0.74
int64 | 0.75
int64 | 0.76
int64 | 0.77
int64 | 0.78
int64 | 0.79
int64 | 0.80
int64 | 0.81
int64 | 0.82
int64 | 0.83
int64 | 0.84
int64 | 0.85
int64 | 0.86
int64 | 0.87
int64 | 0.88
int64 | 0.89
int64 | 0.90
int64 | 0.91
int64 | 0.92
int64 | 0.93
int64 | 0.94
int64 | 0.95
int64 | 0.96
int64 | 0.97
int64 | 0.98
int64 | 0.99
int64 | 0.100
int64 | 0.101
int64 | 0.102
int64 | 0.103
int64 | 0.104
int64 | 0.105
int64 | 0.106
int64 | 0.107
int64 | 0.108
int64 | 0.109
int64 | 0.110
int64 | 0.111
int64 | 0.112
int64 | 0.113
int64 | 0.114
int64 | 0.115
int64 | 0.116
int64 | 0.117
int64 | 0.118
int64 | 0.119
int64 | 0.120
int64 | 0.121
int64 | 0.122
int64 | 0.123
int64 | 0.124
int64 | 0.125
int64 | 0.126
int64 | 0.127
int64 | 0.128
int64 | 0.129
int64 | 0.130
int64 | 0.131
int64 | 0.132
int64 | 0.133
int64 | 0.134
int64 | 0.135
int64 | 0.136
int64 | 0.137
int64 | 0.138
int64 | 0.139
int64 | 0.140
int64 | 0.141
int64 | 0.142
int64 | 0.143
int64 | 0.144
int64 | 0.145
int64 | 0.146
int64 | 0.147
int64 | 0.148
int64 | 0.149
int64 | 0.150
int64 | 0.151
int64 | 3
int64 | 18
int64 | 18.1
int64 | 18.2
int64 | 126
int64 | 136
int64 | 175
int64 | 26
int64 | 166
int64 | 255
int64 | 247
int64 | 127
int64 | 0.152
int64 | 0.153
int64 | 0.154
int64 | 0.155
int64 | 0.156
int64 | 0.157
int64 | 0.158
int64 | 0.159
int64 | 0.160
int64 | 0.161
int64 | 0.162
int64 | 0.163
int64 | 30
int64 | 36
int64 | 94
int64 | 154
int64 | 170
int64 | 253
int64 | 253.1
int64 | 253.2
int64 | 253.3
int64 | 253.4
int64 | 225
int64 | 172
int64 | 253.5
int64 | 242
int64 | 195
int64 | 64
int64 | 0.164
int64 | 0.165
int64 | 0.166
int64 | 0.167
int64 | 0.168
int64 | 0.169
int64 | 0.170
int64 | 0.171
int64 | 0.172
int64 | 0.173
int64 | 0.174
int64 | 49
int64 | 238
int64 | 253.6
int64 | 253.7
int64 | 253.8
int64 | 253.9
int64 | 253.10
int64 | 253.11
int64 | 253.12
int64 | 253.13
int64 | 251
int64 | 93
int64 | 82
int64 | 82.1
int64 | 56
int64 | 39
int64 | 0.175
int64 | 0.176
int64 | 0.177
int64 | 0.178
int64 | 0.179
int64 | 0.180
int64 | 0.181
int64 | 0.182
int64 | 0.183
int64 | 0.184
int64 | 0.185
int64 | 0.186
int64 | 18.3
int64 | 219
int64 | 253.14
int64 | 253.15
int64 | 253.16
int64 | 253.17
int64 | 253.18
int64 | 198
int64 | 182
int64 | 247.1
int64 | 241
int64 | 0.187
int64 | 0.188
int64 | 0.189
int64 | 0.190
int64 | 0.191
int64 | 0.192
int64 | 0.193
int64 | 0.194
int64 | 0.195
int64 | 0.196
int64 | 0.197
int64 | 0.198
int64 | 0.199
int64 | 0.200
int64 | 0.201
int64 | 0.202
int64 | 0.203
int64 | 0.204
int64 | 80
int64 | 156
int64 | 107
int64 | 253.19
int64 | 253.20
int64 | 205
int64 | 11
int64 | 0.205
int64 | 43
int64 | 154.1
int64 | 0.206
int64 | 0.207
int64 | 0.208
int64 | 0.209
int64 | 0.210
int64 | 0.211
int64 | 0.212
int64 | 0.213
int64 | 0.214
int64 | 0.215
int64 | 0.216
int64 | 0.217
int64 | 0.218
int64 | 0.219
int64 | 0.220
int64 | 0.221
int64 | 0.222
int64 | 0.223
int64 | 0.224
int64 | 14
int64 | 1
int64 | 154.2
int64 | 253.21
int64 | 90
int64 | 0.225
int64 | 0.226
int64 | 0.227
int64 | 0.228
int64 | 0.229
int64 | 0.230
int64 | 0.231
int64 | 0.232
int64 | 0.233
int64 | 0.234
int64 | 0.235
int64 | 0.236
int64 | 0.237
int64 | 0.238
int64 | 0.239
int64 | 0.240
int64 | 0.241
int64 | 0.242
int64 | 0.243
int64 | 0.244
int64 | 0.245
int64 | 0.246
int64 | 0.247
int64 | 0.248
int64 | 0.249
int64 | 139
int64 | 253.22
int64 | 190
int64 | 2
int64 | 0.250
int64 | 0.251
int64 | 0.252
int64 | 0.253
int64 | 0.254
int64 | 0.255
int64 | 0.256
int64 | 0.257
int64 | 0.258
int64 | 0.259
int64 | 0.260
int64 | 0.261
int64 | 0.262
int64 | 0.263
int64 | 0.264
int64 | 0.265
int64 | 0.266
int64 | 0.267
int64 | 0.268
int64 | 0.269
int64 | 0.270
int64 | 0.271
int64 | 0.272
int64 | 0.273
int64 | 11.1
int64 | 190.1
int64 | 253.23
int64 | 70
int64 | 0.274
int64 | 0.275
int64 | 0.276
int64 | 0.277
int64 | 0.278
int64 | 0.279
int64 | 0.280
int64 | 0.281
int64 | 0.282
int64 | 0.283
int64 | 0.284
int64 | 0.285
int64 | 0.286
int64 | 0.287
int64 | 0.288
int64 | 0.289
int64 | 0.290
int64 | 0.291
int64 | 0.292
int64 | 0.293
int64 | 0.294
int64 | 0.295
int64 | 0.296
int64 | 0.297
int64 | 0.298
int64 | 35
int64 | 241.1
int64 | 225.1
int64 | 160
int64 | 108
int64 | 1.1
int64 | 0.299
int64 | 0.300
int64 | 0.301
int64 | 0.302
int64 | 0.303
int64 | 0.304
int64 | 0.305
int64 | 0.306
int64 | 0.307
int64 | 0.308
int64 | 0.309
int64 | 0.310
int64 | 0.311
int64 | 0.312
int64 | 0.313
int64 | 0.314
int64 | 0.315
int64 | 0.316
int64 | 0.317
int64 | 0.318
int64 | 0.319
int64 | 0.320
int64 | 0.321
int64 | 81
int64 | 240
int64 | 253.24
int64 | 253.25
int64 | 119
int64 | 25
int64 | 0.322
int64 | 0.323
int64 | 0.324
int64 | 0.325
int64 | 0.326
int64 | 0.327
int64 | 0.328
int64 | 0.329
int64 | 0.330
int64 | 0.331
int64 | 0.332
int64 | 0.333
int64 | 0.334
int64 | 0.335
int64 | 0.336
int64 | 0.337
int64 | 0.338
int64 | 0.339
int64 | 0.340
int64 | 0.341
int64 | 0.342
int64 | 0.343
int64 | 0.344
int64 | 45
int64 | 186
int64 | 253.26
int64 | 253.27
int64 | 150
int64 | 27
int64 | 0.345
int64 | 0.346
int64 | 0.347
int64 | 0.348
int64 | 0.349
int64 | 0.350
int64 | 0.351
int64 | 0.352
int64 | 0.353
int64 | 0.354
int64 | 0.355
int64 | 0.356
int64 | 0.357
int64 | 0.358
int64 | 0.359
int64 | 0.360
int64 | 0.361
int64 | 0.362
int64 | 0.363
int64 | 0.364
int64 | 0.365
int64 | 0.366
int64 | 0.367
int64 | 16
int64 | 93.1
int64 | 252
int64 | 253.28
int64 | 187
int64 | 0.368
int64 | 0.369
int64 | 0.370
int64 | 0.371
int64 | 0.372
int64 | 0.373
int64 | 0.374
int64 | 0.375
int64 | 0.376
int64 | 0.377
int64 | 0.378
int64 | 0.379
int64 | 0.380
int64 | 0.381
int64 | 0.382
int64 | 0.383
int64 | 0.384
int64 | 0.385
int64 | 0.386
int64 | 0.387
int64 | 0.388
int64 | 0.389
int64 | 0.390
int64 | 0.391
int64 | 0.392
int64 | 249
int64 | 253.29
int64 | 249.1
int64 | 64.1
int64 | 0.393
int64 | 0.394
int64 | 0.395
int64 | 0.396
int64 | 0.397
int64 | 0.398
int64 | 0.399
int64 | 0.400
int64 | 0.401
int64 | 0.402
int64 | 0.403
int64 | 0.404
int64 | 0.405
int64 | 0.406
int64 | 0.407
int64 | 0.408
int64 | 0.409
int64 | 0.410
int64 | 0.411
int64 | 0.412
int64 | 0.413
int64 | 46
int64 | 130
int64 | 183
int64 | 253.30
int64 | 253.31
int64 | 207
int64 | 2.1
int64 | 0.414
int64 | 0.415
int64 | 0.416
int64 | 0.417
int64 | 0.418
int64 | 0.419
int64 | 0.420
int64 | 0.421
int64 | 0.422
int64 | 0.423
int64 | 0.424
int64 | 0.425
int64 | 0.426
int64 | 0.427
int64 | 0.428
int64 | 0.429
int64 | 0.430
int64 | 0.431
int64 | 0.432
int64 | 39.1
int64 | 148
int64 | 229
int64 | 253.32
int64 | 253.33
int64 | 253.34
int64 | 250
int64 | 182.1
int64 | 0.433
int64 | 0.434
int64 | 0.435
int64 | 0.436
int64 | 0.437
int64 | 0.438
int64 | 0.439
int64 | 0.440
int64 | 0.441
int64 | 0.442
int64 | 0.443
int64 | 0.444
int64 | 0.445
int64 | 0.446
int64 | 0.447
int64 | 0.448
int64 | 0.449
int64 | 0.450
int64 | 24
int64 | 114
int64 | 221
int64 | 253.35
int64 | 253.36
int64 | 253.37
int64 | 253.38
int64 | 201
int64 | 78
int64 | 0.451
int64 | 0.452
int64 | 0.453
int64 | 0.454
int64 | 0.455
int64 | 0.456
int64 | 0.457
int64 | 0.458
int64 | 0.459
int64 | 0.460
int64 | 0.461
int64 | 0.462
int64 | 0.463
int64 | 0.464
int64 | 0.465
int64 | 0.466
int64 | 0.467
int64 | 23
int64 | 66
int64 | 213
int64 | 253.39
int64 | 253.40
int64 | 253.41
int64 | 253.42
int64 | 198.1
int64 | 81.1
int64 | 2.2
int64 | 0.468
int64 | 0.469
int64 | 0.470
int64 | 0.471
int64 | 0.472
int64 | 0.473
int64 | 0.474
int64 | 0.475
int64 | 0.476
int64 | 0.477
int64 | 0.478
int64 | 0.479
int64 | 0.480
int64 | 0.481
int64 | 0.482
int64 | 0.483
int64 | 18.4
int64 | 171
int64 | 219.1
int64 | 253.43
int64 | 253.44
int64 | 253.45
int64 | 253.46
int64 | 195.1
int64 | 80.1
int64 | 9
int64 | 0.484
int64 | 0.485
int64 | 0.486
int64 | 0.487
int64 | 0.488
int64 | 0.489
int64 | 0.490
int64 | 0.491
int64 | 0.492
int64 | 0.493
int64 | 0.494
int64 | 0.495
int64 | 0.496
int64 | 0.497
int64 | 0.498
int64 | 0.499
int64 | 55
int64 | 172.1
int64 | 226
int64 | 253.47
int64 | 253.48
int64 | 253.49
int64 | 253.50
int64 | 244
int64 | 133
int64 | 11.2
int64 | 0.500
int64 | 0.501
int64 | 0.502
int64 | 0.503
int64 | 0.504
int64 | 0.505
int64 | 0.506
int64 | 0.507
int64 | 0.508
int64 | 0.509
int64 | 0.510
int64 | 0.511
int64 | 0.512
int64 | 0.513
int64 | 0.514
int64 | 0.515
int64 | 0.516
int64 | 0.517
int64 | 136.1
int64 | 253.51
int64 | 253.52
int64 | 253.53
int64 | 212
int64 | 135
int64 | 132
int64 | 16.1
int64 | 0.518
int64 | 0.519
int64 | 0.520
int64 | 0.521
int64 | 0.522
int64 | 0.523
int64 | 0.524
int64 | 0.525
int64 | 0.526
int64 | 0.527
int64 | 0.528
int64 | 0.529
int64 | 0.530
int64 | 0.531
int64 | 0.532
int64 | 0.533
int64 | 0.534
int64 | 0.535
int64 | 0.536
int64 | 0.537
int64 | 0.538
int64 | 0.539
int64 | 0.540
int64 | 0.541
int64 | 0.542
int64 | 0.543
int64 | 0.544
int64 | 0.545
int64 | 0.546
int64 | 0.547
int64 | 0.548
int64 | 0.549
int64 | 0.550
int64 | 0.551
int64 | 0.552
int64 | 0.553
int64 | 0.554
int64 | 0.555
int64 | 0.556
int64 | 0.557
int64 | 0.558
int64 | 0.559
int64 | 0.560
int64 | 0.561
int64 | 0.562
int64 | 0.563
int64 | 0.564
int64 | 0.565
int64 | 0.566
int64 | 0.567
int64 | 0.568
int64 | 0.569
int64 | 0.570
int64 | 0.571
int64 | 0.572
int64 | 0.573
int64 | 0.574
int64 | 0.575
int64 | 0.576
int64 | 0.577
int64 | 0.578
int64 | 0.579
int64 | 0.580
int64 | 0.581
int64 | 0.582
int64 | 0.583
int64 | 0.584
int64 | 0.585
int64 | 0.586
int64 | 0.587
int64 | 0.588
int64 | 0.589
int64 | 0.590
int64 | 0.591
int64 | 0.592
int64 | 0.593
int64 | 0.594
int64 | 0.595
int64 | 0.596
int64 | 0.597
int64 | 0.598
int64 | 0.599
int64 | 0.600
int64 | 0.601
int64 | 0.602
int64 | 0.603
int64 | 0.604
int64 | 0.605
int64 | 0.606
int64 | 0.607
int64 | 0.608
int64 | 0.609
int64 | 0.610
int64 | 0.611
int64 | 0.612
int64 | 0.613
int64 | 0.614
int64 | 0.615
int64 | 0.616
int64 | 0.617
int64 |
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0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 51 | 159 | 253 | 159 | 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 48 | 238 | 252 | 252 | 252 | 237 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 54 | 227 | 253 | 252 | 239 | 233 | 252 | 57 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 10 | 60 | 224 | 252 | 253 | 252 | 202 | 84 | 252 | 253 | 122 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 163 | 252 | 252 | 252 | 253 | 252 | 252 | 96 | 189 | 253 | 167 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 51 | 238 | 253 | 253 | 190 | 114 | 253 | 228 | 47 | 79 | 255 | 168 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 48 | 238 | 252 | 252 | 179 | 12 | 75 | 121 | 21 | 0 | 0 | 253 | 243 | 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 38 | 165 | 253 | 233 | 208 | 84 | 0 | 0 | 0 | 0 | 0 | 0 | 253 | 252 | 165 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | 178 | 252 | 240 | 71 | 19 | 28 | 0 | 0 | 0 | 0 | 0 | 0 | 253 | 252 | 195 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 57 | 252 | 252 | 63 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 253 | 252 | 195 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 198 | 253 | 190 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 255 | 253 | 196 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 76 | 246 | 252 | 112 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 253 | 252 | 148 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 85 | 252 | 230 | 25 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | 135 | 253 | 186 | 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 85 | 252 | 223 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | 131 | 252 | 225 | 71 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 85 | 252 | 145 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 48 | 165 | 252 | 173 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 86 | 253 | 225 | 0 | 0 | 0 | 0 | 0 | 0 | 114 | 238 | 253 | 162 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 85 | 252 | 249 | 146 | 48 | 29 | 85 | 178 | 225 | 253 | 223 | 167 | 56 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 85 | 252 | 252 | 252 | 229 | 215 | 252 | 252 | 252 | 196 | 130 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 28 | 199 | 252 | 252 | 253 | 252 | 252 | 233 | 145 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 25 | 128 | 252 | 253 | 252 | 141 | 37 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
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