




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_	(     snake_case_,snake_case_     ):
      assert isinstance(snake_case_,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_	(     snake_case_,snake_case_,snake_case_     ):
      _A							:     Optional[int]			=						tmp_path / """cache"""
      _A							:     Optional[int]			=						{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
      with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
            _A							:     Dict			=						ParquetDatasetReader(snake_case_,cache_dir=snake_case_,keep_in_memory=snake_case_     ).read()
      _check_parquet_dataset(snake_case_,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_	(     snake_case_,snake_case_,snake_case_     ):
      _A							:     Union[str, Any]			=						tmp_path / """cache"""
      _A							:     Optional[Any]			=						{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
      _A							:     Optional[int]			=						features.copy() if features else default_expected_features
      _A							:     Tuple			=						(
          Features({feature: Value(snake_case_     ) for feature, dtype in features.items()}     ) if features is not None else None
      )
      _A							:     Optional[int]			=						ParquetDatasetReader(snake_case_,features=snake_case_,cache_dir=snake_case_     ).read()
      _check_parquet_dataset(snake_case_,snake_case_     )







@pytest.mark.parametrize("""split""",[None, NamedSplit("""train"""     ), """train""", """test"""]     )
def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_     ):
      _A							:     Dict			=						tmp_path / """cache"""
      _A							:     int			=						{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
      _A							:     Optional[int]			=						ParquetDatasetReader(snake_case_,cache_dir=snake_case_,split=snake_case_     ).read()
      _check_parquet_dataset(snake_case_,snake_case_     )
      assert dataset.split == split if split else "train"







@pytest.mark.parametrize("""path_type""",[str, list]     )
def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_     ):
      if issubclass(snake_case_,snake_case_     ):
            _A							:     str			=						parquet_path
      elif issubclass(snake_case_,snake_case_     ):
            _A							:     List[Any]			=						[parquet_path]
      _A							:     Tuple			=						tmp_path / """cache"""
      _A							:     List[str]			=						{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
      _A							:     str			=						ParquetDatasetReader(snake_case_,cache_dir=snake_case_     ).read()
      _check_parquet_dataset(snake_case_,snake_case_     )







def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_=("train",)     ):
      assert isinstance(snake_case_,snake_case_     )
      for split in splits:
            _A							:     str			=						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_	(     snake_case_,snake_case_,snake_case_     ):
      _A							:     Optional[Any]			=						tmp_path / """cache"""
      _A							:     Any			=						{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
      with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
            _A							:     List[str]			=						ParquetDatasetReader(
                {"""train""": parquet_path},cache_dir=snake_case_,keep_in_memory=snake_case_     ).read()
      _check_parquet_datasetdict(snake_case_,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_	(     snake_case_,snake_case_,snake_case_     ):
      _A							:     Union[str, Any]			=						tmp_path / """cache"""
      _A							:     Any			=						{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
      _A							:     str			=						features.copy() if features else default_expected_features
      _A							:     List[Any]			=						(
          Features({feature: Value(snake_case_     ) for feature, dtype in features.items()}     ) if features is not None else None
      )
      _A							:     Optional[int]			=						ParquetDatasetReader({"""train""": parquet_path},features=snake_case_,cache_dir=snake_case_     ).read()
      _check_parquet_datasetdict(snake_case_,snake_case_     )







@pytest.mark.parametrize("""split""",[None, NamedSplit("""train"""     ), """train""", """test"""]     )
def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_     ):
      if split:
            _A							:     List[str]			=						{split: parquet_path}
      else:
            _A							:     List[Any]			=						"""train"""
            _A							:     Union[str, Any]			=						{"""train""": parquet_path, """test""": parquet_path}
      _A							:     str			=						tmp_path / """cache"""
      _A							:     Dict			=						{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
      _A							:     Union[str, Any]			=						ParquetDatasetReader(snake_case_,cache_dir=snake_case_     ).read()
      _check_parquet_datasetdict(snake_case_,snake_case_,splits=list(path.keys()     )     )
      assert all(dataset[split].split == split for split in path.keys()     )







def 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      _A							:     Dict			=						ParquetDatasetWriter(snake_case_,tmp_path / """foo.parquet"""     )
      assert writer.write() > 0
      _A							:     List[str]			=						pq.ParquetFile(tmp_path / """foo.parquet"""     )
      _A							:     Dict			=						pf.read()
      assert dataset.data.table == output_table







def 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      _A							:     int			=						str(shared_datadir / """test_image_rgb.jpg"""     )
      _A							:     Any			=						{"""image""": [image_path]}
      _A							:     Union[str, Any]			=						Features({"""image""": Image()}     )
      _A							:     Union[str, Any]			=						Dataset.from_dict(snake_case_,features=snake_case_     )
      _A							:     str			=						ParquetDatasetWriter(snake_case_,tmp_path / """foo.parquet"""     )
      assert writer.write() > 0

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

      _A							:     int			=						ParquetDatasetReader(str(tmp_path / """foo.parquet"""     ),streaming=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_	(     snake_case_,snake_case_     ):
      assert get_writer_batch_size(snake_case_     ) == expected







def 				lowerCAmelCase_	(     snake_case_     ):
      if n_term == "":
            return []
      _A							:     list			=						[]
      for temp in range(int(snake_case_     )     ):
            series.append(f'''1/{temp + 1}''' if series else """1"""     )
      return series


if __name__ == "__main__":
  _snake_case										=       input("Enter the last number (nth term) of the Harmonic Series")
  print("Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n")
  print(harmonic_series(nth_term))

