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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 4 new columns ({'PitchSatisfactionScore', 'PreferredPropertyStar', 'Passport', 'CityTier'}) and 13 missing columns ({'PitchSatisfactionScore__2', 'PreferredPropertyStar__5.0', 'PitchSatisfactionScore__5', 'CityTier__3', 'ProductPitched__Standard.1', 'PreferredPropertyStar__4.0', 'Passport__1', 'CityTier__2', 'PitchSatisfactionScore__4', 'PitchSatisfactionScore__3', 'ProductPitched__King.1', 'ProductPitched__Super Deluxe.1', 'ProductPitched__Deluxe.1'}).

This happened while the csv dataset builder was generating data using

hf://datasets/huzaifa-sr/tourism-project/prepared/Xtest.csv (at revision facc013cb8b1beb1ef6d7514f75e917161df8f2b)

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 1831, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 644, 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 2272, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              Age: double
              CityTier: int64
              DurationOfPitch: double
              NumberOfPersonVisiting: int64
              NumberOfFollowups: double
              PreferredPropertyStar: double
              NumberOfTrips: double
              Passport: int64
              PitchSatisfactionScore: int64
              OwnCar: int64
              NumberOfChildrenVisiting: double
              MonthlyIncome: double
              TypeofContact__Self Enquiry: bool
              Occupation__Large Business: bool
              Occupation__Salaried: bool
              Occupation__Small Business: 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
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 3910
              to
              {'Age': Value('float64'), 'DurationOfPitch': Value('float64'), 'NumberOfPersonVisiting': Value('int64'), 'NumberOfFollowups': Value('float64'), 'NumberOfTrips': Value('float64'), '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'), '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'), 'CityTier__2': Value('bool'), 'CityTier__3': Value('bool'), 'PreferredPropertyStar__4.0': Value('bool'), 'PreferredPropertyStar__5.0': Value('bool'), 'Passport__1': Value('bool'), 'PitchSatisfactionScore__2': Value('bool'), 'PitchSatisfactionScore__3': Value('bool'), 'PitchSatisfactionScore__4': Value('bool'), 'PitchSatisfactionScore__5': Value('bool'), 'ProductPitched__Deluxe.1': Value('bool'), 'ProductPitched__King.1': Value('bool'), 'ProductPitched__Standard.1': Value('bool'), 'ProductPitched__Super Deluxe.1': 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 1456, 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 1055, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 894, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 970, 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 1702, 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 1833, 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 4 new columns ({'PitchSatisfactionScore', 'PreferredPropertyStar', 'Passport', 'CityTier'}) and 13 missing columns ({'PitchSatisfactionScore__2', 'PreferredPropertyStar__5.0', 'PitchSatisfactionScore__5', 'CityTier__3', 'ProductPitched__Standard.1', 'PreferredPropertyStar__4.0', 'Passport__1', 'CityTier__2', 'PitchSatisfactionScore__4', 'PitchSatisfactionScore__3', 'ProductPitched__King.1', 'ProductPitched__Super Deluxe.1', 'ProductPitched__Deluxe.1'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/huzaifa-sr/tourism-project/prepared/Xtest.csv (at revision facc013cb8b1beb1ef6d7514f75e917161df8f2b)
              
              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	DurationOfPitch float64	NumberOfPersonVisiting int64	NumberOfFollowups float64	NumberOfTrips float64	OwnCar int64	NumberOfChildrenVisiting float64	MonthlyIncome float64	TypeofContact__Self Enquiry bool	Occupation__Large Business bool	Occupation__Salaried bool	Occupation__Small Business 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	CityTier__2 bool	CityTier__3 bool	PreferredPropertyStar__4.0 bool	PreferredPropertyStar__5.0 bool	Passport__1 bool	PitchSatisfactionScore__2 bool	PitchSatisfactionScore__3 bool	PitchSatisfactionScore__4 bool	PitchSatisfactionScore__5 bool	ProductPitched__Deluxe.1 bool	ProductPitched__King.1 bool	ProductPitched__Standard.1 bool	ProductPitched__Super Deluxe.1 bool
34	9	2	4	4	0	0	17,979	false	false	true	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false
32	6	3	3	2	0	0	21,220	true	false	true	false	true	false	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	true	false	false	true	false	false	false
30	11	2	3	3	1	1	24,419	true	false	true	false	false	false	true	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	true	false	false	false	true	false
39	9	3	4	2	1	2	26,029	true	false	false	true	false	false	true	false	false	false	true	false	false	true	false	false	true	true	false	false	false	false	true	false	false	false	true	false
37	31	3	4	2	1	2	24,352	false	false	true	false	true	false	false	false	true	false	false	false	true	false	false	false	false	true	false	false	false	true	false	false	true	false	false	false
34	9	3	4	2	0	2	21,178	true	false	true	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false
27	7	4	6	5	1	3	23,042	false	false	true	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false
30	6	3	4	2	1	1	24,714	true	false	true	false	true	false	false	false	true	false	false	false	true	false	false	false	true	false	true	false	false	false	true	false	true	false	false	false
53	32	3	5	5	0	2	32,504	false	false	false	true	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	true
55	7	3	4	2	1	2	29,180	false	false	true	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	true	false
46	6	2	4	3	1	1	25,673	false	false	false	true	false	false	true	false	false	false	false	false	false	true	false	false	false	false	true	true	true	false	false	false	false	false	true	false
39	19	2	5	4	1	1	24,966	false	false	true	false	true	false	false	false	true	false	false	false	true	false	false	false	false	false	true	false	false	false	false	true	true	false	false	false
54	32	1	2	3	1	0	32,328	false	false	true	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	true	false	true	false	false	false	false	false	true
42	19	3	1	6	1	0	20,538	true	false	false	true	true	false	false	false	true	false	false	false	true	false	false	false	false	false	true	false	false	false	true	false	true	false	false	false
33	12	3	2	5	1	2	21,990	true	false	true	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false
35	6	1	4	2	1	0	17,859	true	false	false	true	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false
39	16	3	3	1	1	0	28,464	true	false	false	true	false	false	true	false	false	false	true	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	true	false
29	17	3	4	5	1	2	22,338	true	false	true	false	true	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	true	false	true	false	false	false
23	11	3	5	7	1	1	22,572	false	true	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false
37	15	2	3	2	0	0	17,326	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false
33	10	4	4	3	1	1	25,403	true	false	false	true	true	false	false	false	true	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false
33	7	4	4	3	0	2	21,634	true	false	true	false	false	false	false	false	false	false	true	true	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false
50	25	4	4	3	0	1	25,482	false	false	true	false	true	false	false	false	true	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false
42	6	2	4	1	0	0	21,062	true	false	true	false	true	false	false	false	true	false	false	false	true	false	false	false	false	false	false	true	false	true	false	false	true	false	false	false
43	33	3	4	5	0	1	31,869	false	false	false	true	false	false	true	false	true	false	false	false	false	true	false	false	false	false	true	true	false	true	false	false	false	false	true	false
36	15	3	1	2	1	0	17,810	false	false	true	false	false	false	false	false	true	false	false	true	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false
27	8	2	1	1	0	1	21,500	true	false	false	true	true	false	false	false	false	false	true	false	true	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false
29	16	4	4	3	1	2	23,931	true	false	true	false	true	false	false	false	false	false	true	false	true	false	false	false	true	false	false	false	false	true	false	false	true	false	false	false
34	12	4	5	3	0	3	21,589	true	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false
41	21	3	4	3	0	2	23,317	true	false	true	false	true	false	false	false	true	false	false	false	true	false	false	false	true	false	true	false	false	true	false	false	true	false	false	false
32	20	4	5	7	1	1	20,980	true	false	false	true	true	false	false	false	true	false	false	false	true	false	false	false	true	false	true	true	false	false	false	false	true	false	false	false
50	9	3	3	2	1	2	33,200	false	false	false	true	false	true	false	false	true	false	false	false	false	false	true	true	false	true	false	false	false	false	false	false	false	true	false	false
24	30	2	3	1	1	1	17,400	false	false	false	true	false	false	false	false	true	false	false	true	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false
43	7	3	5	2	0	1	24,740	true	false	true	false	true	false	false	false	true	false	false	false	true	false	false	false	false	false	false	true	false	true	false	false	true	false	false	false
39	16	3	3	3	1	2	20,377	true	false	false	true	true	false	false	false	true	false	false	false	true	false	false	false	false	false	true	false	false	false	false	true	true	false	false	false
55	6	2	3	1	1	1	34,045	true	false	false	true	false	true	false	false	false	true	false	false	false	false	true	false	false	false	true	true	false	false	false	false	false	true	false	false
33	10	3	4	3	1	1	24,887	false	false	true	false	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false
34	23	4	4	4	0	1	27,242	true	false	true	false	false	false	true	false	false	false	true	false	false	true	false	false	true	false	true	true	false	false	false	true	false	false	true	false
25	25	3	4	2	0	1	21,452	true	false	true	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false
30	24	3	3	2	1	2	17,632	true	false	true	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false
32	12	3	4	3	0	1	21,467	false	false	false	true	false	false	false	false	true	false	false	true	false	false	false	false	true	true	false	false	false	true	false	false	false	false	false	false
34	12	4	4	8	1	3	30,556	false	false	true	false	false	false	true	false	false	false	false	false	false	true	false	false	false	true	false	false	false	true	false	false	false	false	true	false
50	30	3	3	4	1	2	28,973	true	false	true	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	true
33	6	3	4	4	0	0	17,799	true	false	true	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	true	true	false	false	true	false	false	false	false	false
36	18	3	4	3	0	1	23,646	false	false	false	true	true	false	false	false	true	false	false	false	true	false	false	false	true	false	false	false	false	false	false	true	true	false	false	false
50	25	4	4	3	0	2	25,482	false	false	true	false	true	false	false	false	true	false	false	false	true	false	false	false	false	false	false	true	true	false	false	false	true	false	false	false
49	14	4	4	4	1	2	21,333	false	false	false	true	false	false	false	false	true	false	false	true	false	false	false	false	true	false	false	true	false	false	true	false	false	false	false	false
37	14	3	2	4	1	1	23,317	false	false	false	true	true	false	false	false	false	false	false	false	true	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false
30	24	3	3	2	1	0	17,632	true	false	true	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false
23	7	4	4	2	0	3	22,053	true	false	true	false	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false
34	33	3	3	3	0	0	17,311	true	false	false	true	false	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false
52	28	4	4	2	0	3	24,119	true	false	false	true	true	false	false	false	false	false	true	false	true	false	false	false	true	false	false	true	false	false	false	true	true	false	false	false
27	36	4	6	2	0	1	23,647	false	false	false	true	true	false	false	false	false	false	true	false	true	false	false	false	true	false	true	false	false	true	false	false	true	false	false	false
40	30	3	1	5	1	2	28,194	false	false	true	false	false	false	false	true	false	false	true	false	false	false	false	false	true	true	false	true	false	true	false	false	false	false	false	true
44	8	3	1	2	1	0	17,011	true	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false
27	9	3	4	8	0	1	20,720	false	false	true	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	true	true	false	false	false	true	false	false	false	false
42	12	4	5	8	1	1	20,785	false	false	true	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false
28	9	3	4	2	0	2	21,719	true	false	false	true	false	false	false	false	true	false	false	true	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false
59	12	3	5	4	1	2	29,230	true	true	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	true	false	true	false	false	false	true	false	false	true	false
40	28	3	5	5	0	2	24,798	true	false	true	false	true	false	false	false	false	false	false	false	true	false	false	false	true	false	false	true	false	false	false	false	true	false	false	false
29	7	3	4	3	0	2	21,384	false	false	true	false	false	false	false	false	true	false	false	true	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false
35	15	3	4	5	1	1	23,799	true	false	true	false	true	false	false	false	true	false	false	false	true	false	false	false	false	false	true	false	false	false	false	true	true	false	false	false
34	15	2	3	2	1	0	17,742	true	true	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false
36	10	2	4	2	1	1	20,810	true	false	true	false	true	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	true	true	false	false	false
41	16	3	4	5	1	0	32,181	false	false	true	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	true
46	6	2	4	3	1	1	25,673	false	false	false	true	false	false	true	false	true	false	false	false	false	true	false	false	false	false	true	true	false	false	false	false	false	false	true	false
27	36	3	4	7	1	1	22,984	true	false	false	true	true	false	false	false	true	false	false	false	true	false	false	false	true	false	false	false	false	false	false	true	true	false	false	false
32	27	4	2	2	1	1	21,469	false	false	true	false	false	false	false	false	true	false	false	true	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false
38	26	4	4	6	0	2	21,700	true	false	true	false	false	false	false	false	true	false	false	true	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false
34	29	4	4	2	0	1	24,824	false	false	false	true	true	false	false	false	true	false	false	false	true	false	false	false	true	true	false	false	false	false	false	false	true	false	false	false
51	11	2	3	2	1	1	29,026	true	false	true	false	false	false	false	true	true	false	false	false	false	false	false	true	false	true	false	true	false	true	false	false	false	false	false	true
40	8	2	4	1	1	1	17,342	true	false	false	true	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false
49	13	2	4	1	1	0	25,965	true	false	true	false	false	false	true	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	false
48	16	4	4	6	1	1	20,783	true	false	true	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false
29	26	2	3	3	1	0	21,931	true	false	false	true	true	false	false	false	true	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false
25	31	3	4	2	1	2	21,078	false	false	false	true	false	false	false	false	true	false	false	true	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false
35	23	3	3	4	0	2	23,966	true	false	true	false	true	false	false	false	true	false	false	false	true	false	false	false	true	false	true	true	false	true	false	false	true	false	false	false
30	17	3	5	3	1	1	26,946	true	false	false	true	true	false	false	false	true	false	false	false	true	false	false	false	true	true	false	true	false	false	false	true	true	false	false	false
35	29	2	4	4	1	0	20,916	true	false	true	false	true	false	false	false	true	false	false	false	true	false	false	false	false	false	false	true	false	false	true	false	true	false	false	false
36	8	3	3	5	1	0	17,543	true	false	true	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false
50	5	2	3	5	0	1	34,331	true	false	false	true	false	true	false	false	true	false	false	false	false	false	true	false	true	false	false	true	false	false	false	true	false	true	false	false
44	32	4	5	7	1	2	29,476	true	false	false	true	false	false	true	false	true	false	false	false	false	true	false	false	true	false	false	false	false	false	true	false	false	false	true	false
38	8	2	3	1	1	0	22,351	true	false	false	true	false	false	true	false	false	false	true	false	false	true	false	false	true	true	false	false	false	false	true	false	false	false	true	false
37	14	4	4	4	0	3	20,691	true	false	true	false	false	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false
32	9	4	5	5	0	2	25,088	true	false	true	false	true	false	false	false	false	false	false	false	true	false	false	true	false	false	true	false	false	true	false	false	true	false	false	false
42	17	3	4	2	0	2	24,908	false	false	true	false	true	false	false	false	false	false	true	false	true	false	false	false	true	false	false	false	true	false	false	false	true	false	false	false
50	34	3	2	2	1	2	18,221	true	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false
25	14	3	4	3	0	1	21,564	false	false	true	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false
19	15	2	3	2	0	0	17,552	true	false	true	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false
41	17	4	5	4	0	1	28,383	true	false	false	true	false	false	true	false	true	false	false	false	false	true	false	false	true	true	false	false	false	false	true	false	false	false	true	false
47	25	3	4	7	1	1	29,205	false	false	false	true	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	true	false
32	27	3	4	3	1	1	25,610	false	false	false	true	true	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	true	false	false	false	true	false	false	false
44	34	2	1	4	1	1	28,320	true	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	true	true	false	false	false	false	false	false	true
51	15	3	4	2	1	1	22,553	true	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false
37	7	2	4	2	0	0	21,474	true	false	true	false	true	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false
36	7	4	5	3	0	3	21,128	true	false	false	true	false	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false
30	15	4	6	3	1	2	20,797	true	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	true	false	true	false	false	false	false	false	false
43	21	4	5	2	1	1	24,922	true	false	false	true	true	false	false	false	false	false	true	false	true	false	false	false	true	false	false	false	false	true	false	false	true	false	false	false
28	9	4	4	3	0	2	23,156	true	false	true	false	true	false	false	false	false	false	true	false	true	false	false	false	true	false	false	true	false	false	true	false	true	false	false	false
33	9	3	5	6	0	2	20,854	true	true	false	false	true	false	false	false	false	true	false	false	true	false	false	false	false	false	true	false	false	false	true	false	true	false	false	false

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