
'''simple docstring'''



def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     float:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				(num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
   # formula for sum of series
   return total
def    lowerCAmelCase_	(				)     ->     Optional[int]:




   """simple docstring"""
   print(sum_of_series(1    ,      1    ,      10						)						)


if __name__ == "__main__":
     import doctest

     doctest.testmod()








'''simple docstring'''



import pyarrow.parquet as pq
import pytest

from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Optional[int]:




   """simple docstring"""
   assert isinstance(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
   assert dataset.num_rows == 4
   assert dataset.num_columns == 3
   assert dataset.column_names == ["col_1", "col_2", "col_3"]
   for feature, expected_dtype in expected_features.items():
      assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("""keep_in_memory"""    ,      [False, True]						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     List[Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				tmp_path / """cache"""
   _SCREAMING_SNAKE_CASE      =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
   with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
      _SCREAMING_SNAKE_CASE      =				ParquetDatasetReader(SCREAMING_SNAKE_CASE_    ,      cache_dir=SCREAMING_SNAKE_CASE_    ,      keep_in_memory=SCREAMING_SNAKE_CASE_						).read()
   _check_parquet_dataset(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
@pytest.mark.parametrize(
    """features"""    ,      [
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]    ,      )
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Optional[int]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				tmp_path / """cache"""
   _SCREAMING_SNAKE_CASE      =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
   _SCREAMING_SNAKE_CASE      =				features.copy() if features else default_expected_features
   _SCREAMING_SNAKE_CASE      =				(
       Features({feature: Value(SCREAMING_SNAKE_CASE_						) for feature, dtype in features.items()}						) if features is not None else None
   )
   _SCREAMING_SNAKE_CASE      =				ParquetDatasetReader(SCREAMING_SNAKE_CASE_    ,      features=SCREAMING_SNAKE_CASE_    ,      cache_dir=SCREAMING_SNAKE_CASE_						).read()
   _check_parquet_dataset(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
@pytest.mark.parametrize("""split"""    ,      [None, NamedSplit("""train"""						), """train""", """test"""]						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     List[str]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				tmp_path / """cache"""
   _SCREAMING_SNAKE_CASE      =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
   _SCREAMING_SNAKE_CASE      =				ParquetDatasetReader(SCREAMING_SNAKE_CASE_    ,      cache_dir=SCREAMING_SNAKE_CASE_    ,      split=SCREAMING_SNAKE_CASE_						).read()
   _check_parquet_dataset(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
   assert dataset.split == split if split else "train"
@pytest.mark.parametrize("""path_type"""    ,      [str, list]						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Optional[int]:




   """simple docstring"""
   if issubclass(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						):
      _SCREAMING_SNAKE_CASE      =				parquet_path
   elif issubclass(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						):
      _SCREAMING_SNAKE_CASE      =				[parquet_path]
   _SCREAMING_SNAKE_CASE      =				tmp_path / """cache"""
   _SCREAMING_SNAKE_CASE      =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
   _SCREAMING_SNAKE_CASE      =				ParquetDatasetReader(SCREAMING_SNAKE_CASE_    ,      cache_dir=SCREAMING_SNAKE_CASE_						).read()
   _check_parquet_dataset(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_=("train",)						)     ->     List[str]:




   """simple docstring"""
   assert isinstance(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
   for split in splits:
      _SCREAMING_SNAKE_CASE      =				dataset_dict[split]
      assert dataset.num_rows == 4
      assert dataset.num_columns == 3
      assert dataset.column_names == ["col_1", "col_2", "col_3"]
      for feature, expected_dtype in expected_features.items():
         assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("""keep_in_memory"""    ,      [False, True]						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     List[Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				tmp_path / """cache"""
   _SCREAMING_SNAKE_CASE      =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
   with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
      _SCREAMING_SNAKE_CASE      =				ParquetDatasetReader(
          {"""train""": parquet_path}    ,      cache_dir=SCREAMING_SNAKE_CASE_    ,      keep_in_memory=SCREAMING_SNAKE_CASE_						).read()
   _check_parquet_datasetdict(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
@pytest.mark.parametrize(
    """features"""    ,      [
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]    ,      )
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Optional[Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				tmp_path / """cache"""
   _SCREAMING_SNAKE_CASE      =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
   _SCREAMING_SNAKE_CASE      =				features.copy() if features else default_expected_features
   _SCREAMING_SNAKE_CASE      =				(
       Features({feature: Value(SCREAMING_SNAKE_CASE_						) for feature, dtype in features.items()}						) if features is not None else None
   )
   _SCREAMING_SNAKE_CASE      =				ParquetDatasetReader({"""train""": parquet_path}    ,      features=SCREAMING_SNAKE_CASE_    ,      cache_dir=SCREAMING_SNAKE_CASE_						).read()
   _check_parquet_datasetdict(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
@pytest.mark.parametrize("""split"""    ,      [None, NamedSplit("""train"""						), """train""", """test"""]						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Optional[Any]:




   """simple docstring"""
   if split:
      _SCREAMING_SNAKE_CASE      =				{split: parquet_path}
   else:
      _SCREAMING_SNAKE_CASE      =				"""train"""
      _SCREAMING_SNAKE_CASE      =				{"""train""": parquet_path, """test""": parquet_path}
   _SCREAMING_SNAKE_CASE      =				tmp_path / """cache"""
   _SCREAMING_SNAKE_CASE      =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
   _SCREAMING_SNAKE_CASE      =				ParquetDatasetReader(SCREAMING_SNAKE_CASE_    ,      cache_dir=SCREAMING_SNAKE_CASE_						).read()
   _check_parquet_datasetdict(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      splits=list(path.keys()						)						)
   assert all(dataset[split].split == split for split in path.keys()						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Dict:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				ParquetDatasetWriter(SCREAMING_SNAKE_CASE_    ,      tmp_path / """foo.parquet"""						)
   assert writer.write() > 0
   _SCREAMING_SNAKE_CASE      =				pq.ParquetFile(tmp_path / """foo.parquet"""						)
   _SCREAMING_SNAKE_CASE      =				pf.read()
   assert dataset.data.table == output_table
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Optional[Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				str(shared_datadir / """test_image_rgb.jpg"""						)
   _SCREAMING_SNAKE_CASE      =				{"""image""": [image_path]}
   _SCREAMING_SNAKE_CASE      =				Features({"""image""": Image()}						)
   _SCREAMING_SNAKE_CASE      =				Dataset.from_dict(SCREAMING_SNAKE_CASE_    ,      features=SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE      =				ParquetDatasetWriter(SCREAMING_SNAKE_CASE_    ,      tmp_path / """foo.parquet"""						)
   assert writer.write() > 0

   _SCREAMING_SNAKE_CASE      =				Dataset.from_parquet(str(tmp_path / """foo.parquet"""						)						)
   assert dataset.features == reloaded_dataset.features

   _SCREAMING_SNAKE_CASE      =				ParquetDatasetReader(str(tmp_path / """foo.parquet"""						)    ,      streaming=SCREAMING_SNAKE_CASE_						).read()
   assert dataset.features == reloaded_iterable_dataset.features







@pytest.mark.parametrize(
    """feature, expected"""    ,      [
        (Features({"""foo""": Value("""int32"""						)}						), None),
        (Features({"""image""": Image(), """foo""": Value("""int32"""						)}						), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
        (Features({"""nested""": Sequence(Audio()						)}						), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
    ]    ,      )
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     str:




   """simple docstring"""
   assert get_writer_batch_size(SCREAMING_SNAKE_CASE_						) == expected






