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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 9 new columns ({'CustomerID', 'Unnamed: 0', 'ProductPitched', 'ProdTaken', 'Designation', 'MaritalStatus', 'Occupation', 'Gender', 'TypeofContact'}) and 17 missing columns ({'MaritalStatus_Married', 'Occupation_Salaried', 'ProductPitched_King', 'Designation_Manager', 'MaritalStatus_Single', 'ProductPitched_Super Deluxe', 'Occupation_Small Business', 'Designation_VP', 'Occupation_Large Business', 'Gender_Male', 'TypeofContact_Self Enquiry', 'ProductPitched_Standard', 'Gender_Female', 'Designation_Senior Manager', 'ProductPitched_Deluxe', 'Designation_Executive', 'MaritalStatus_Unmarried'}).

This happened while the csv dataset builder was generating data using

hf://datasets/chaitram/tourism-package-prediction/tourism.csv (at revision 0a104a6b43745754cd7dda3bc16b2c0bde47aba0), [/tmp/hf-datasets-cache/medium/datasets/73134276801984-config-parquet-and-info-chaitram-tourism-package--9321d7f6/hub/datasets--chaitram--tourism-package-prediction/snapshots/0a104a6b43745754cd7dda3bc16b2c0bde47aba0/Xtest.csv (origin=hf://datasets/chaitram/tourism-package-prediction@0a104a6b43745754cd7dda3bc16b2c0bde47aba0/Xtest.csv), /tmp/hf-datasets-cache/medium/datasets/73134276801984-config-parquet-and-info-chaitram-tourism-package--9321d7f6/hub/datasets--chaitram--tourism-package-prediction/snapshots/0a104a6b43745754cd7dda3bc16b2c0bde47aba0/Xtrain.csv (origin=hf://datasets/chaitram/tourism-package-prediction@0a104a6b43745754cd7dda3bc16b2c0bde47aba0/Xtrain.csv), /tmp/hf-datasets-cache/medium/datasets/73134276801984-config-parquet-and-info-chaitram-tourism-package--9321d7f6/hub/datasets--chaitram--tourism-package-prediction/snapshots/0a104a6b43745754cd7dda3bc16b2c0bde47aba0/tourism.csv (origin=hf://datasets/chaitram/tourism-package-prediction@0a104a6b43745754cd7dda3bc16b2c0bde47aba0/tourism.csv), /tmp/hf-datasets-cache/medium/datasets/73134276801984-config-parquet-and-info-chaitram-tourism-package--9321d7f6/hub/datasets--chaitram--tourism-package-prediction/snapshots/0a104a6b43745754cd7dda3bc16b2c0bde47aba0/ytest.csv (origin=hf://datasets/chaitram/tourism-package-prediction@0a104a6b43745754cd7dda3bc16b2c0bde47aba0/ytest.csv), /tmp/hf-datasets-cache/medium/datasets/73134276801984-config-parquet-and-info-chaitram-tourism-package--9321d7f6/hub/datasets--chaitram--tourism-package-prediction/snapshots/0a104a6b43745754cd7dda3bc16b2c0bde47aba0/ytrain.csv (origin=hf://datasets/chaitram/tourism-package-prediction@0a104a6b43745754cd7dda3bc16b2c0bde47aba0/ytrain.csv)]

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 "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1890, in _prepare_split_single
                  writer.write_table(table)
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 760, in write_table
                  pa_table = table_cast(pa_table, self._schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2272, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              Unnamed: 0: int64
              CustomerID: int64
              ProdTaken: int64
              Age: double
              TypeofContact: string
              CityTier: int64
              DurationOfPitch: double
              Occupation: string
              Gender: string
              NumberOfPersonVisiting: int64
              NumberOfFollowups: double
              ProductPitched: string
              PreferredPropertyStar: double
              MaritalStatus: string
              NumberOfTrips: double
              Passport: int64
              PitchSatisfactionScore: int64
              OwnCar: int64
              NumberOfChildrenVisiting: double
              Designation: string
              MonthlyIncome: double
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 2881
              to
              {'Age': Value('float64'), 'CityTier': Value('int64'), 'DurationOfPitch': Value('float64'), 'NumberOfPersonVisiting': Value('int64'), 'NumberOfFollowups': Value('float64'), 'PreferredPropertyStar': Value('float64'), 'NumberOfTrips': Value('float64'), 'Passport': Value('int64'), 'PitchSatisfactionScore': Value('int64'), 'OwnCar': Value('int64'), 'NumberOfChildrenVisiting': Value('float64'), 'MonthlyIncome': Value('float64'), 'TypeofContact_Self Enquiry': Value('bool'), 'Occupation_Large Business': Value('bool'), 'Occupation_Salaried': Value('bool'), 'Occupation_Small Business': Value('bool'), 'Gender_Female': Value('bool'), 'Gender_Male': Value('bool'), 'ProductPitched_Deluxe': Value('bool'), 'ProductPitched_King': Value('bool'), 'ProductPitched_Standard': Value('bool'), 'ProductPitched_Super Deluxe': Value('bool'), 'MaritalStatus_Married': Value('bool'), 'MaritalStatus_Single': Value('bool'), 'MaritalStatus_Unmarried': Value('bool'), 'Designation_Executive': Value('bool'), 'Designation_Manager': Value('bool'), 'Designation_Senior Manager': Value('bool'), 'Designation_VP': Value('bool')}
              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 1347, 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 980, in convert_to_parquet
                  builder.download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 884, in download_and_prepare
                  self._download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 947, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1739, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1892, 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 9 new columns ({'CustomerID', 'Unnamed: 0', 'ProductPitched', 'ProdTaken', 'Designation', 'MaritalStatus', 'Occupation', 'Gender', 'TypeofContact'}) and 17 missing columns ({'MaritalStatus_Married', 'Occupation_Salaried', 'ProductPitched_King', 'Designation_Manager', 'MaritalStatus_Single', 'ProductPitched_Super Deluxe', 'Occupation_Small Business', 'Designation_VP', 'Occupation_Large Business', 'Gender_Male', 'TypeofContact_Self Enquiry', 'ProductPitched_Standard', 'Gender_Female', 'Designation_Senior Manager', 'ProductPitched_Deluxe', 'Designation_Executive', 'MaritalStatus_Unmarried'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/chaitram/tourism-package-prediction/tourism.csv (at revision 0a104a6b43745754cd7dda3bc16b2c0bde47aba0), [/tmp/hf-datasets-cache/medium/datasets/73134276801984-config-parquet-and-info-chaitram-tourism-package--9321d7f6/hub/datasets--chaitram--tourism-package-prediction/snapshots/0a104a6b43745754cd7dda3bc16b2c0bde47aba0/Xtest.csv (origin=hf://datasets/chaitram/tourism-package-prediction@0a104a6b43745754cd7dda3bc16b2c0bde47aba0/Xtest.csv), /tmp/hf-datasets-cache/medium/datasets/73134276801984-config-parquet-and-info-chaitram-tourism-package--9321d7f6/hub/datasets--chaitram--tourism-package-prediction/snapshots/0a104a6b43745754cd7dda3bc16b2c0bde47aba0/Xtrain.csv (origin=hf://datasets/chaitram/tourism-package-prediction@0a104a6b43745754cd7dda3bc16b2c0bde47aba0/Xtrain.csv), /tmp/hf-datasets-cache/medium/datasets/73134276801984-config-parquet-and-info-chaitram-tourism-package--9321d7f6/hub/datasets--chaitram--tourism-package-prediction/snapshots/0a104a6b43745754cd7dda3bc16b2c0bde47aba0/tourism.csv (origin=hf://datasets/chaitram/tourism-package-prediction@0a104a6b43745754cd7dda3bc16b2c0bde47aba0/tourism.csv), /tmp/hf-datasets-cache/medium/datasets/73134276801984-config-parquet-and-info-chaitram-tourism-package--9321d7f6/hub/datasets--chaitram--tourism-package-prediction/snapshots/0a104a6b43745754cd7dda3bc16b2c0bde47aba0/ytest.csv (origin=hf://datasets/chaitram/tourism-package-prediction@0a104a6b43745754cd7dda3bc16b2c0bde47aba0/ytest.csv), /tmp/hf-datasets-cache/medium/datasets/73134276801984-config-parquet-and-info-chaitram-tourism-package--9321d7f6/hub/datasets--chaitram--tourism-package-prediction/snapshots/0a104a6b43745754cd7dda3bc16b2c0bde47aba0/ytrain.csv (origin=hf://datasets/chaitram/tourism-package-prediction@0a104a6b43745754cd7dda3bc16b2c0bde47aba0/ytrain.csv)]
              
              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)

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Age float64	CityTier int64	DurationOfPitch float64	NumberOfPersonVisiting int64	NumberOfFollowups float64	PreferredPropertyStar float64	NumberOfTrips float64	Passport int64	PitchSatisfactionScore int64	OwnCar int64	NumberOfChildrenVisiting float64	MonthlyIncome float64	TypeofContact_Self Enquiry bool	Occupation_Large Business bool	Occupation_Salaried bool	Occupation_Small Business bool	Gender_Female bool	Gender_Male bool	ProductPitched_Deluxe bool	ProductPitched_King bool	ProductPitched_Standard bool	ProductPitched_Super Deluxe bool	MaritalStatus_Married bool	MaritalStatus_Single bool	MaritalStatus_Unmarried bool	Designation_Executive bool	Designation_Manager bool	Designation_Senior Manager bool	Designation_VP bool
44	1	8	3	1	3	2	1	4	1	0	22,879	true	false	true	false	true	false	false	false	true	false	true	false	false	false	false	true	false
35	3	20	3	4	3	3	0	1	1	2	27,306	true	false	false	true	false	true	false	false	true	false	true	false	false	false	false	true	false
47	3	7	4	4	5	3	0	2	1	2	29,131	true	false	false	true	true	false	false	false	true	false	true	false	false	false	false	true	false
32	1	6	3	3	4	2	0	3	1	0	21,220	true	false	true	false	false	true	true	false	false	false	true	false	false	false	true	false	false
59	1	9	3	4	3	6	0	2	1	2	21,157	true	true	false	false	false	true	false	false	false	false	false	true	false	true	false	false	false
44	3	11	2	3	4	1	0	5	1	1	33,213	true	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	true
32	1	35	2	4	4	2	0	3	1	0	17,837	true	false	true	false	true	false	false	false	false	false	false	true	false	true	false	false	false
27	3	7	3	4	3	3	0	5	0	2	23,974	true	false	true	false	false	true	true	false	false	false	true	false	false	false	true	false	false
38	3	8	2	4	3	4	0	5	1	1	20,249	false	false	true	false	false	true	true	false	false	false	false	false	false	false	true	false	false
32	1	12	3	4	3	2	1	4	1	1	23,499	true	true	false	false	false	true	false	false	false	false	true	false	false	true	false	false	false
40	1	30	3	3	3	2	0	3	1	1	18,319	true	true	false	false	false	true	true	false	false	false	true	false	false	false	true	false	false
38	1	20	3	4	3	3	0	1	0	1	22,963	true	false	false	true	false	true	true	false	false	false	true	false	false	false	true	false	false
35	3	6	3	3	3	2	0	5	1	0	23,789	false	false	false	true	false	false	false	false	true	false	false	false	true	false	false	true	false
35	1	8	3	3	5	2	1	1	1	1	17,074	true	false	true	false	true	false	false	false	false	false	true	false	false	true	false	false	false
34	1	17	3	6	3	2	0	5	0	1	22,086	true	false	false	true	false	true	false	false	false	false	true	false	false	true	false	false	false
33	1	36	3	5	4	3	0	3	1	1	21,515	true	false	true	false	true	false	false	false	false	false	false	false	true	true	false	false	false
51	1	15	3	3	3	4	0	3	1	0	17,075	true	false	true	false	false	true	false	false	false	false	false	false	false	true	false	false	false
29	3	30	2	1	5	2	0	3	1	1	16,091	false	true	false	false	false	true	false	false	false	false	false	true	false	true	false	false	false
34	3	25	3	2	3	1	1	2	1	2	20,304	false	false	false	true	false	true	true	false	false	false	false	true	false	false	true	false	false
38	1	14	2	4	3	6	0	2	0	1	32,342	true	false	false	true	false	true	false	false	true	false	false	true	false	false	false	true	false
46	1	6	3	3	5	1	0	2	0	0	24,396	true	false	false	true	false	true	false	false	true	false	true	false	false	false	false	true	false
54	2	25	2	3	4	3	0	3	1	0	25,725	true	false	false	true	false	true	false	false	true	false	false	false	false	false	false	true	false
56	1	15	2	3	3	1	0	4	0	0	26,103	true	false	false	true	false	true	false	false	false	true	true	false	false	false	false	false	false
30	1	10	2	3	3	19	1	4	1	1	17,285	false	true	false	false	false	true	false	false	false	false	false	true	false	true	false	false	false
26	1	6	3	3	5	1	0	5	1	2	17,867	true	false	false	true	false	true	false	false	false	false	false	true	false	true	false	false	false
33	1	13	2	3	3	1	0	4	1	0	26,691	true	false	false	true	false	true	false	false	true	false	true	false	false	false	false	true	false
24	1	23	3	4	4	2	0	3	1	1	17,127	true	false	true	false	false	true	false	false	false	false	true	false	false	true	false	false	false
30	1	36	4	6	3	2	0	5	1	3	25,062	true	false	true	false	false	true	true	false	false	false	true	false	false	false	true	false	false
33	3	8	3	3	4	1	0	1	0	0	20,147	false	false	false	true	true	false	true	false	false	false	false	true	false	false	true	false	false
53	3	8	2	4	4	3	0	1	1	0	22,525	false	false	false	true	true	false	false	false	true	false	true	false	false	false	false	true	false
29	3	14	3	4	5	2	0	3	1	2	23,576	false	false	true	false	false	true	true	false	false	false	false	false	true	false	true	false	false
39	1	15	2	3	5	2	0	4	1	0	20,151	true	false	false	true	false	true	true	false	false	false	true	false	false	false	true	false	false
46	3	9	4	4	4	2	0	5	1	3	23,483	true	false	true	false	false	true	true	false	false	false	true	false	false	false	true	false	false
35	1	14	3	4	4	2	0	3	1	1	30,672	true	false	true	false	true	false	false	false	true	false	false	true	false	false	false	true	false
35	3	9	4	4	3	8	0	5	0	1	20,909	false	false	false	true	true	false	false	false	false	false	true	false	false	true	false	false	false
33	1	7	4	5	4	8	0	3	0	3	21,010	false	false	true	false	true	false	false	false	false	false	true	false	false	true	false	false	false
29	1	16	2	4	3	2	0	4	1	0	21,623	false	false	true	false	true	false	false	false	false	false	false	false	true	true	false	false	false
41	3	16	2	3	3	1	0	1	0	1	21,230	false	false	true	false	false	true	true	false	false	false	false	true	false	false	true	false	false
43	1	36	3	6	3	6	0	3	1	1	22,950	true	false	false	true	false	true	true	false	false	false	false	false	true	false	true	false	false
35	3	13	3	6	3	2	0	4	0	2	21,029	false	false	false	true	true	false	false	false	false	false	true	false	false	true	false	false	false
41	3	12	3	3	3	4	1	1	0	0	28,591	true	false	true	false	true	false	false	false	true	false	false	true	false	false	false	true	false
33	1	6	2	4	3	1	0	4	0	0	21,949	true	false	true	false	true	false	true	false	false	false	false	false	true	false	true	false	false
40	1	15	2	3	3	1	0	4	0	0	28,499	false	false	false	true	false	false	false	false	true	false	false	false	true	false	false	true	false
26	1	9	3	3	5	1	0	3	0	1	18,102	false	true	false	false	false	true	false	false	false	false	false	true	false	true	false	false	false
41	1	25	2	3	5	3	0	1	0	0	18,072	true	false	true	false	false	true	true	false	false	false	true	false	false	false	true	false	false
37	1	17	2	3	3	2	1	3	0	1	27,185	false	false	true	false	false	true	false	false	true	false	true	false	false	false	false	true	false
31	3	13	2	4	3	4	0	4	1	1	17,329	true	false	true	false	false	true	false	false	false	false	true	false	false	true	false	false	false
45	3	8	3	6	4	8	0	3	0	2	21,040	true	false	true	false	false	true	true	false	false	false	false	true	false	false	true	false	false
33	1	9	3	3	5	2	1	5	1	2	18,348	false	false	true	false	false	true	false	false	false	false	false	true	false	true	false	false	false
33	1	9	4	4	4	3	0	4	0	1	21,048	true	false	false	true	true	false	false	false	false	false	false	false	false	true	false	false	false
33	1	14	3	3	3	3	1	3	0	2	21,388	true	false	true	false	false	true	true	false	false	false	false	false	true	false	true	false	false
30	3	18	2	3	3	1	0	2	1	0	21,577	true	true	false	false	true	false	true	false	false	false	false	false	true	false	true	false	false
42	1	25	2	2	3	7	1	3	1	1	17,759	false	false	false	true	false	true	false	false	false	false	true	false	false	true	false	false	false
46	1	8	2	3	3	7	0	5	1	0	32,861	true	false	true	false	false	true	false	false	false	true	true	false	false	false	false	false	false
51	1	16	4	4	3	6	0	5	1	3	21,058	true	false	true	false	false	true	false	false	false	false	true	false	false	true	false	false	false
30	1	8	2	5	3	3	0	1	1	0	21,091	true	false	true	false	true	false	true	false	false	false	false	true	false	false	true	false	false
37	1	25	3	3	3	6	0	5	0	1	22,366	false	false	true	false	false	true	false	false	false	false	false	false	false	true	false	false	false
28	2	6	2	3	3	2	0	4	0	1	17,706	false	false	true	false	false	true	false	false	false	false	true	false	false	true	false	false	false
42	1	12	2	3	5	1	0	3	1	0	28,348	true	false	false	true	false	true	false	false	true	false	true	false	false	false	false	true	false
44	1	10	2	3	4	1	0	2	1	0	20,933	true	false	false	true	false	true	true	false	false	false	false	true	false	false	true	false	false
39	1	9	3	5	4	3	0	1	1	1	21,118	false	false	false	true	true	false	false	false	false	false	false	true	false	true	false	false	false
42	1	23	2	2	5	4	1	2	0	0	21,545	true	false	true	false	true	false	true	false	false	false	false	false	true	false	true	false	false
39	1	28	2	3	5	2	1	5	1	1	25,880	false	false	false	true	false	false	false	false	true	false	false	false	true	false	false	true	false
28	1	6	2	5	3	1	0	3	1	0	21,674	false	false	true	false	true	false	true	false	false	false	false	false	false	false	true	false	false
43	1	20	3	3	5	7	0	5	1	1	32,159	true	false	true	false	false	true	false	false	false	true	true	false	false	false	false	false	false
45	1	22	4	4	3	3	0	3	0	2	26,656	true	false	false	true	true	false	false	false	true	false	false	false	false	false	false	true	false
53	1	13	4	4	5	5	1	4	1	2	24,255	true	true	false	false	false	true	true	false	false	false	true	false	false	false	true	false	false
42	1	16	4	4	5	4	0	1	0	1	20,916	true	false	true	false	false	true	false	false	false	false	true	false	false	true	false	false	false
36	1	33	3	3	3	7	0	3	1	0	20,237	true	false	false	true	false	true	true	false	false	false	false	false	false	false	true	false	false
22	1	7	4	5	4	3	1	5	0	3	20,748	true	true	false	false	true	false	false	false	false	false	false	true	false	true	false	false	false
37	1	12	4	4	4	2	0	2	0	3	24,592	true	false	true	false	false	true	true	false	false	false	false	false	true	false	true	false	false
30	3	20	3	4	4	7	0	3	0	2	24,443	false	true	false	false	false	false	true	false	false	false	false	false	true	false	true	false	false
36	1	18	4	5	5	4	1	5	1	3	28,562	false	false	false	true	false	true	false	false	true	false	true	false	false	false	false	true	false
40	1	10	2	3	3	2	0	5	0	1	34,033	true	false	false	true	true	false	false	true	false	false	false	false	false	false	false	false	true
51	1	14	2	5	3	3	0	2	0	1	25,650	false	false	true	false	false	true	false	false	true	false	false	false	true	false	false	true	false
39	3	7	3	5	5	6	0	3	0	2	21,536	true	false	true	false	false	true	false	false	false	false	false	false	true	true	false	false	false
43	1	18	2	4	4	2	0	3	0	1	29,336	true	false	true	false	false	true	false	false	false	true	true	false	false	false	false	false	false
35	1	10	3	3	3	2	0	4	0	0	16,951	true	false	true	false	false	true	false	false	false	false	true	false	false	true	false	false	false
40	1	9	4	4	3	2	0	2	1	2	29,616	false	true	false	false	true	false	false	false	true	false	false	true	false	false	false	true	false
27	3	17	3	4	3	3	0	1	0	1	23,362	true	false	false	true	false	true	true	false	false	false	false	false	true	false	true	false	false
26	1	8	2	3	5	7	1	5	1	0	17,042	false	false	true	false	false	true	false	false	false	false	false	false	false	true	false	false	false
43	3	32	3	3	3	2	1	2	0	0	31,959	false	false	true	false	false	true	false	false	false	true	false	false	false	false	false	false	false
32	1	18	4	4	5	3	1	2	0	3	25,511	true	false	false	true	false	true	true	false	false	false	false	false	false	false	true	false	false
35	1	12	3	5	5	4	0	2	0	1	30,309	true	false	false	true	true	false	false	false	true	false	false	true	false	false	false	true	false
34	1	11	3	5	4	8	0	4	0	2	21,300	true	false	false	true	true	false	false	false	false	false	true	false	false	true	false	false	false
31	1	14	2	4	4	2	0	4	0	1	16,261	true	false	true	false	true	false	false	false	false	false	false	true	false	true	false	false	false
35	3	16	4	4	3	3	0	1	0	1	24,392	true	false	true	false	true	false	true	false	false	false	true	false	false	false	true	false	false
42	3	16	3	6	3	2	0	5	1	2	24,829	false	false	true	false	false	true	false	false	false	true	true	false	false	false	false	false	false
34	1	14	2	3	5	4	0	5	1	1	20,121	true	false	true	false	true	false	true	false	false	false	true	false	false	false	true	false	false
34	1	9	3	4	5	2	0	3	1	1	21,385	true	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	false
34	1	13	2	3	4	1	0	3	1	0	26,994	true	false	true	false	false	false	false	false	true	false	false	false	true	false	false	true	false
39	1	36	3	4	3	5	0	2	0	2	24,939	true	true	false	false	false	true	true	false	false	false	false	false	false	false	true	false	false
29	1	12	3	4	3	3	1	1	0	1	22,119	true	true	false	false	false	true	false	false	false	false	false	false	true	true	false	false	false
35	1	8	2	3	3	3	0	3	0	1	20,762	false	false	false	true	false	true	true	false	false	false	true	false	false	false	true	false	false
26	3	10	2	4	3	2	1	2	1	1	20,828	true	false	false	true	false	true	true	false	false	false	false	true	false	false	true	false	false
37	1	10	3	4	3	7	0	2	1	1	21,513	true	false	true	false	true	false	false	false	false	false	true	false	false	true	false	false	false
35	1	16	4	4	5	6	0	3	0	2	24,024	false	false	true	false	false	true	true	false	false	false	true	false	false	false	true	false	false
40	1	9	3	4	3	2	0	3	1	1	30,847	false	false	true	false	false	true	false	false	false	true	true	false	false	false	false	false	false
33	3	11	2	3	3	2	1	2	1	0	17,851	true	false	false	true	true	false	false	false	false	false	false	true	false	true	false	false	false
38	3	15	3	4	4	1	0	4	0	0	17,899	true	false	false	true	false	true	false	false	false	false	false	false	false	true	false	false	false

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