<commit_before><TestData>
  <TestDataRow>
    <comment>2 samples, each of dimension 1 (e.g., (cs.price), (cs.mileage))</comment>
    <skylineSampleSQL>SELECT * FROM cars_small cs SKYLINE OF cs.price LOW, cs.mileage LOW SAMPLE BY RANDOM_SUBSETS COUNT 2 DIMENSION 1</skylineSampleSQL>
    <entireSkylineSQL>SELECT * FROM cars_small cs SKYLINE OF cs.price LOW, cs.mileage LOW</entireSkylineSQL>
    <entireCount>6</entireCount>
  </TestDataRow>
  <TestDataRow>
    <comment>3 samples, each of dimension 2 (e.g., (cs.price, cs.mileage), (cs.mileage, cs.horsepower), (cs.price, cs.horsepower))</comment>
    <skylineSampleSQL>SELECT * FROM cars_small cs SKYLINE OF cs.price LOW, cs.mileage LOW, cs.horsepower HIGH SAMPLE BY RANDOM_SUBSETS COUNT 3 DIMENSION 2</skylineSampleSQL>
    <entireSkylineSQL>SELECT * FROM cars_small cs SKYLINE OF cs.price LOW, cs.mileage LOW, cs.horsepower HIGH</entireSkylineSQL>
    <entireCount>78</entireCount>
  </TestDataRow>
  <TestDataRow>
    <comment>3 samples, each of dimension 2 (e.g., (cs.price, cs.mileage), (cs.mileage, cs.horsepower), (cs.price, cs.horsepower))</comment>
    <skylineSampleSQL>SELECT * FROM cars cs SKYLINE OF cs.price LOW, cs.mileage LOW, cs.horsepower HIGH SAMPLE BY RANDOM_SUBSETS COUNT 3 DIMENSION 2</skylineSampleSQL>
    <entireSkylineSQL>SELECT * FROM cars cs SKYLINE OF cs.price LOW, cs.mileage LOW, cs.horsepower HIGH</entireSkylineSQL>
    <entireCount>207</entireCount>
  </TestDataRow>
  <TestDataRow>
    <comment>2 samples, each of dimension 2 (e.g., (cs.price, cs.mileage), (cs.horsepower, cs.enginesize))</comment>
    <skylineSampleSQL>SELECT * FROM cars cs SKYLINE OF cs.price LOW, cs.mileage LOW, cs.horsepower HIGH, cs.enginesize HIGH SAMPLE BY RANDOM_SUBSETS COUNT 2 DIMENSION 2</skylineSampleSQL>
    <entireSkylineSQL>SELECT * FROM cars cs SKYLINE OF cs.price LOW, cs.mileage LOW, cs.horsepower HIGH, cs.enginesize HIGH</entireSkylineSQL>
    <entireCount>353</entireCount>
  </TestDataRow>
  <TestDataRow>
    <comment>7 samples, each of dimension 3 (e.g., (cs.price, cs.mileage, cs.horsepower))</comment>
    <skylineSampleSQL>SELECT * FROM cars cs SKYLINE OF cs.price LOW, cs.mileage LOW, cs.horsepower HIGH, cs.enginesize HIGH, cs.consumption LOW, cs.cylinders HIGH, cs.seats HIGH SAMPLE BY RANDOM_SUBSETS COUNT 7 DIMENSION 3</skylineSampleSQL>
    <entireSkylineSQL>SELECT * FROM cars cs SKYLINE OF cs.price LOW, cs.mileage LOW, cs.horsepower HIGH, cs.enginesize HIGH, cs.consumption LOW, cs.cylinders HIGH, cs.seats HIGH</entireSkylineSQL>
    <entireCount>4464</entireCount>
  </TestDataRow>
  <TestDataRow>
    <comment>10 samples, each of dimension 3 (e.g., (cs.price, cs.mileage, cs.horsepower))</comment>
    <skylineSampleSQL>SELECT * FROM cars cs SKYLINE OF cs.price LOW, cs.mileage LOW, cs.horsepower HIGH, cs.enginesize HIGH, cs.consumption LOW, cs.cylinders HIGH, cs.seats HIGH, cs.doors HIGH, cs.gears HIGH SAMPLE BY RANDOM_SUBSETS COUNT 10 DIMENSION 3</skylineSampleSQL>
    <entireSkylineSQL>SELECT * FROM cars cs SKYLINE OF cs.price LOW, cs.mileage LOW, cs.horsepower HIGH, cs.enginesize HIGH, cs.consumption LOW, cs.cylinders HIGH, cs.seats HIGH, cs.doors HIGH, cs.gears HIGH</entireSkylineSQL>
    <entireCount>6550</entireCount>
  </TestDataRow>
  <TestDataRow>
    <comment>10 samples, each of dimension 3 (e.g., (cs.price, cs.mileage, cs.horsepower))</comment>
    <skylineSampleSQL>SELECT cs.*, colors.name, fuels.name, bodies.name, makes.name, conditions.name FROM cars cs LEFT OUTER JOIN colors ON cs.color_id = colors.ID LEFT OUTER JOIN fuels ON cs.fuel_id = fuels.ID LEFT OUTER JOIN bodies ON cs.body_id = bodies.ID LEFT OUTER JOIN makes ON cs.make_id = makes.ID LEFT OUTER JOIN conditions ON cs.condition_id = conditions.ID SKYLINE OF cs.price LOW, cs.mileage LOW, cs.horsepower HIGH, cs.enginesize HIGH, cs.consumption LOW, cs.cylinders HIGH, cs.seats HIGH, cs.doors HIGH, cs.gears HIGH, colors.name ('red' >> 'blue' >> OTHERS EQUAL), fuels.name ('diesel' >> 'petrol' >> OTHERS EQUAL), bodies.name ('limousine' >> 'coupé' >> 'suv' >> 'minivan' >> OTHERS EQUAL), makes.name ('BMW' >> 'MERCEDES-BENZ' >> 'HUMMER' >> OTHERS EQUAL), conditions.name ('new' >> 'occasion' >> OTHERS EQUAL) SAMPLE BY RANDOM_SUBSETS COUNT 10 DIMENSION 3</skylineSampleSQL>
    <entireSkylineSQL>SELECT cs.*, colors.name, fuels.name, bodies.name, makes.name, conditions.name FROM cars cs LEFT OUTER JOIN colors ON cs.color_id = colors.ID LEFT OUTER JOIN fuels ON cs.fuel_id = fuels.ID LEFT OUTER JOIN bodies ON cs.body_id = bodies.ID LEFT OUTER JOIN makes ON cs.make_id = makes.ID LEFT OUTER JOIN conditions ON cs.condition_id = conditions.ID SKYLINE OF cs.price LOW, cs.mileage LOW, cs.horsepower HIGH, cs.enginesize HIGH, cs.consumption LOW, cs.cylinders HIGH, cs.seats HIGH, cs.doors HIGH, cs.gears HIGH, colors.name ('red' >> 'blue' >> OTHERS EQUAL), fuels.name ('diesel' >> 'petrol' >> OTHERS EQUAL), bodies.name ('limousine' >> 'coupé' >> 'suv' >> 'minivan' >> OTHERS EQUAL), makes.name ('BMW' >> 'MERCEDES-BENZ' >> 'HUMMER' >> OTHERS EQUAL), conditions.name ('new' >> 'occasion' >> OTHERS EQUAL)</entireSkylineSQL>
    <entireCount>10855</entireCount>
  </TestDataRow>
  <TestDataRow>
    <comment>15 samples, each of dimension 3 (e.g., (cs.price, cs.mileage, cs.horsepower))</comment>
    <skylineSampleSQL>SELECT cs.*, colors.name, fuels.name, bodies.name, makes.name, conditions.name FROM cars cs LEFT OUTER JOIN colors ON cs.color_id = colors.ID LEFT OUTER JOIN fuels ON cs.fuel_id = fuels.ID LEFT OUTER JOIN bodies ON cs.body_id = bodies.ID LEFT OUTER JOIN makes ON cs.make_id = makes.ID LEFT OUTER JOIN conditions ON cs.condition_id = conditions.ID SKYLINE OF cs.price LOW, cs.mileage LOW, cs.horsepower HIGH, cs.enginesize HIGH, cs.consumption LOW, cs.cylinders HIGH, cs.seats HIGH, cs.doors HIGH, cs.gears HIGH, colors.name ('red' >> 'blue' >> OTHERS EQUAL), fuels.name ('diesel' >> 'petrol' >> OTHERS EQUAL), bodies.name ('limousine' >> 'coupé' >> 'suv' >> 'minivan' >> OTHERS EQUAL), makes.name ('BMW' >> 'MERCEDES-BENZ' >> 'HUMMER' >> OTHERS EQUAL), conditions.name ('new' >> 'occasion' >> OTHERS EQUAL) SAMPLE BY RANDOM_SUBSETS COUNT 15 DIMENSION 3</skylineSampleSQL>
    <entireSkylineSQL>SELECT cs.*, colors.name, fuels.name, bodies.name, makes.name, conditions.name FROM cars cs LEFT OUTER JOIN colors ON cs.color_id = colors.ID LEFT OUTER JOIN fuels ON cs.fuel_id = fuels.ID LEFT OUTER JOIN bodies ON cs.body_id = bodies.ID LEFT OUTER JOIN makes ON cs.make_id = makes.ID LEFT OUTER JOIN conditions ON cs.condition_id = conditions.ID SKYLINE OF cs.price LOW, cs.mileage LOW, cs.horsepower HIGH, cs.enginesize HIGH, cs.consumption LOW, cs.cylinders HIGH, cs.seats HIGH, cs.doors HIGH, cs.gears HIGH, colors.name ('red' >> 'blue' >> OTHERS EQUAL), fuels.name ('diesel' >> 'petrol' >> OTHERS EQUAL), bodies.name ('limousine' >> 'coupé' >> 'suv' >> 'minivan' >> OTHERS EQUAL), makes.name ('BMW' >> 'MERCEDES-BENZ' >> 'HUMMER' >> OTHERS EQUAL), conditions.name ('new' >> 'occasion' >> OTHERS EQUAL)</entireSkylineSQL>
    <entireCount>10855</entireCount>
  </TestDataRow>
</TestData><commit_msg>fixed unit test after merge<commit_after><TestData>
  <TestDataRow>
    <comment>2 samples, each of dimension 1 (e.g., (cs.price), (cs.mileage))</comment>
    <skylineSampleSQL>SELECT * FROM cars_small cs SKYLINE OF cs.price LOW, cs.mileage LOW SAMPLE BY RANDOM_SUBSETS COUNT 2 DIMENSION 1</skylineSampleSQL>
    <entireSkylineSQL>SELECT * FROM cars_small cs SKYLINE OF cs.price LOW, cs.mileage LOW</entireSkylineSQL>
    <entireCount>7</entireCount>
  </TestDataRow>
  <TestDataRow>
    <comment>3 samples, each of dimension 2 (e.g., (cs.price, cs.mileage), (cs.mileage, cs.horsepower), (cs.price, cs.horsepower))</comment>
    <skylineSampleSQL>SELECT * FROM cars_small cs SKYLINE OF cs.price LOW, cs.mileage LOW, cs.horsepower HIGH SAMPLE BY RANDOM_SUBSETS COUNT 3 DIMENSION 2</skylineSampleSQL>
    <entireSkylineSQL>SELECT * FROM cars_small cs SKYLINE OF cs.price LOW, cs.mileage LOW, cs.horsepower HIGH</entireSkylineSQL>
    <entireCount>90</entireCount>
  </TestDataRow>
  <TestDataRow>
    <comment>3 samples, each of dimension 2 (e.g., (cs.price, cs.mileage), (cs.mileage, cs.horsepower), (cs.price, cs.horsepower))</comment>
    <skylineSampleSQL>SELECT * FROM cars cs SKYLINE OF cs.price LOW, cs.mileage LOW, cs.horsepower HIGH SAMPLE BY RANDOM_SUBSETS COUNT 3 DIMENSION 2</skylineSampleSQL>
    <entireSkylineSQL>SELECT * FROM cars cs SKYLINE OF cs.price LOW, cs.mileage LOW, cs.horsepower HIGH</entireSkylineSQL>
    <entireCount>207</entireCount>
  </TestDataRow>
  <TestDataRow>
    <comment>2 samples, each of dimension 2 (e.g., (cs.price, cs.mileage), (cs.horsepower, cs.enginesize))</comment>
    <skylineSampleSQL>SELECT * FROM cars cs SKYLINE OF cs.price LOW, cs.mileage LOW, cs.horsepower HIGH, cs.enginesize HIGH SAMPLE BY RANDOM_SUBSETS COUNT 2 DIMENSION 2</skylineSampleSQL>
    <entireSkylineSQL>SELECT * FROM cars cs SKYLINE OF cs.price LOW, cs.mileage LOW, cs.horsepower HIGH, cs.enginesize HIGH</entireSkylineSQL>
    <entireCount>353</entireCount>
  </TestDataRow>
  <TestDataRow>
    <comment>7 samples, each of dimension 3 (e.g., (cs.price, cs.mileage, cs.horsepower))</comment>
    <skylineSampleSQL>SELECT * FROM cars cs SKYLINE OF cs.price LOW, cs.mileage LOW, cs.horsepower HIGH, cs.enginesize HIGH, cs.consumption LOW, cs.cylinders HIGH, cs.seats HIGH SAMPLE BY RANDOM_SUBSETS COUNT 7 DIMENSION 3</skylineSampleSQL>
    <entireSkylineSQL>SELECT * FROM cars cs SKYLINE OF cs.price LOW, cs.mileage LOW, cs.horsepower HIGH, cs.enginesize HIGH, cs.consumption LOW, cs.cylinders HIGH, cs.seats HIGH</entireSkylineSQL>
    <entireCount>4464</entireCount>
  </TestDataRow>
  <TestDataRow>
    <comment>10 samples, each of dimension 3 (e.g., (cs.price, cs.mileage, cs.horsepower))</comment>
    <skylineSampleSQL>SELECT * FROM cars cs SKYLINE OF cs.price LOW, cs.mileage LOW, cs.horsepower HIGH, cs.enginesize HIGH, cs.consumption LOW, cs.cylinders HIGH, cs.seats HIGH, cs.doors HIGH, cs.gears HIGH SAMPLE BY RANDOM_SUBSETS COUNT 10 DIMENSION 3</skylineSampleSQL>
    <entireSkylineSQL>SELECT * FROM cars cs SKYLINE OF cs.price LOW, cs.mileage LOW, cs.horsepower HIGH, cs.enginesize HIGH, cs.consumption LOW, cs.cylinders HIGH, cs.seats HIGH, cs.doors HIGH, cs.gears HIGH</entireSkylineSQL>
    <entireCount>6550</entireCount>
  </TestDataRow>
  <TestDataRow>
    <comment>10 samples, each of dimension 3 (e.g., (cs.price, cs.mileage, cs.horsepower))</comment>
    <skylineSampleSQL>SELECT cs.*, colors.name, fuels.name, bodies.name, makes.name, conditions.name FROM cars cs LEFT OUTER JOIN colors ON cs.color_id = colors.ID LEFT OUTER JOIN fuels ON cs.fuel_id = fuels.ID LEFT OUTER JOIN bodies ON cs.body_id = bodies.ID LEFT OUTER JOIN makes ON cs.make_id = makes.ID LEFT OUTER JOIN conditions ON cs.condition_id = conditions.ID SKYLINE OF cs.price LOW, cs.mileage LOW, cs.horsepower HIGH, cs.enginesize HIGH, cs.consumption LOW, cs.cylinders HIGH, cs.seats HIGH, cs.doors HIGH, cs.gears HIGH, colors.name ('red' >> 'blue' >> OTHERS EQUAL), fuels.name ('diesel' >> 'petrol' >> OTHERS EQUAL), bodies.name ('limousine' >> 'coupé' >> 'suv' >> 'minivan' >> OTHERS EQUAL), makes.name ('BMW' >> 'MERCEDES-BENZ' >> 'HUMMER' >> OTHERS EQUAL), conditions.name ('new' >> 'occasion' >> OTHERS EQUAL) SAMPLE BY RANDOM_SUBSETS COUNT 10 DIMENSION 3</skylineSampleSQL>
    <entireSkylineSQL>SELECT cs.*, colors.name, fuels.name, bodies.name, makes.name, conditions.name FROM cars cs LEFT OUTER JOIN colors ON cs.color_id = colors.ID LEFT OUTER JOIN fuels ON cs.fuel_id = fuels.ID LEFT OUTER JOIN bodies ON cs.body_id = bodies.ID LEFT OUTER JOIN makes ON cs.make_id = makes.ID LEFT OUTER JOIN conditions ON cs.condition_id = conditions.ID SKYLINE OF cs.price LOW, cs.mileage LOW, cs.horsepower HIGH, cs.enginesize HIGH, cs.consumption LOW, cs.cylinders HIGH, cs.seats HIGH, cs.doors HIGH, cs.gears HIGH, colors.name ('red' >> 'blue' >> OTHERS EQUAL), fuels.name ('diesel' >> 'petrol' >> OTHERS EQUAL), bodies.name ('limousine' >> 'coupé' >> 'suv' >> 'minivan' >> OTHERS EQUAL), makes.name ('BMW' >> 'MERCEDES-BENZ' >> 'HUMMER' >> OTHERS EQUAL), conditions.name ('new' >> 'occasion' >> OTHERS EQUAL)</entireSkylineSQL>
    <entireCount>10855</entireCount>
  </TestDataRow>
  <TestDataRow>
    <comment>15 samples, each of dimension 3 (e.g., (cs.price, cs.mileage, cs.horsepower))</comment>
    <skylineSampleSQL>SELECT cs.*, colors.name, fuels.name, bodies.name, makes.name, conditions.name FROM cars cs LEFT OUTER JOIN colors ON cs.color_id = colors.ID LEFT OUTER JOIN fuels ON cs.fuel_id = fuels.ID LEFT OUTER JOIN bodies ON cs.body_id = bodies.ID LEFT OUTER JOIN makes ON cs.make_id = makes.ID LEFT OUTER JOIN conditions ON cs.condition_id = conditions.ID SKYLINE OF cs.price LOW, cs.mileage LOW, cs.horsepower HIGH, cs.enginesize HIGH, cs.consumption LOW, cs.cylinders HIGH, cs.seats HIGH, cs.doors HIGH, cs.gears HIGH, colors.name ('red' >> 'blue' >> OTHERS EQUAL), fuels.name ('diesel' >> 'petrol' >> OTHERS EQUAL), bodies.name ('limousine' >> 'coupé' >> 'suv' >> 'minivan' >> OTHERS EQUAL), makes.name ('BMW' >> 'MERCEDES-BENZ' >> 'HUMMER' >> OTHERS EQUAL), conditions.name ('new' >> 'occasion' >> OTHERS EQUAL) SAMPLE BY RANDOM_SUBSETS COUNT 15 DIMENSION 3</skylineSampleSQL>
    <entireSkylineSQL>SELECT cs.*, colors.name, fuels.name, bodies.name, makes.name, conditions.name FROM cars cs LEFT OUTER JOIN colors ON cs.color_id = colors.ID LEFT OUTER JOIN fuels ON cs.fuel_id = fuels.ID LEFT OUTER JOIN bodies ON cs.body_id = bodies.ID LEFT OUTER JOIN makes ON cs.make_id = makes.ID LEFT OUTER JOIN conditions ON cs.condition_id = conditions.ID SKYLINE OF cs.price LOW, cs.mileage LOW, cs.horsepower HIGH, cs.enginesize HIGH, cs.consumption LOW, cs.cylinders HIGH, cs.seats HIGH, cs.doors HIGH, cs.gears HIGH, colors.name ('red' >> 'blue' >> OTHERS EQUAL), fuels.name ('diesel' >> 'petrol' >> OTHERS EQUAL), bodies.name ('limousine' >> 'coupé' >> 'suv' >> 'minivan' >> OTHERS EQUAL), makes.name ('BMW' >> 'MERCEDES-BENZ' >> 'HUMMER' >> OTHERS EQUAL), conditions.name ('new' >> 'occasion' >> OTHERS EQUAL)</entireSkylineSQL>
    <entireCount>10855</entireCount>
  </TestDataRow>
</TestData>
{'fraction_non_alphanumeric': 0.12895959453751935, 'fraction_numerical': 0.00915106293115585, 'mean_word_length': 4.744844318641326, 'pattern_counts': {'":': 0, '<': 167, '<?xml version=': 0, '>': 375, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '5900771', 'n_tokens_mistral': 5510, 'n_tokens_neox': 5101, 'n_words': 1451}