


"""simple docstring"""







from __future__ import annotations

from math import pi

# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
lowerCAmelCase__				    =       1.0_5_4_5_7_1_8_1_7e-3_4  # unit of ℏ : J * s

lowerCAmelCase__				    =       3e8  # unit of c : m * s^-1
def  a__      (    SCREAMING_SNAKE_CASE   :   float    ,				SCREAMING_SNAKE_CASE   :   float    ,				SCREAMING_SNAKE_CASE   :   float    ):


  '''simple docstring'''







  if (force, area, distance).count(0    ) != 1:
    raise ValueError("One and only one argument must be 0"    )
  if force < 0:
    raise ValueError("Magnitude of force can not be negative"    )
  if distance < 0:
    raise ValueError("Distance can not be negative"    )
  if area < 0:
    raise ValueError("Area can not be negative"    )
  if force == 0:
    lowerCAmelCase					:					Any								=							(REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
        2_4_0 * (distance) ** 4
    )
    return {"force": force}
  elif area == 0:
    lowerCAmelCase					:					Any								=							(2_4_0 * force * (distance) ** 4) / (
        REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
    )
    return {"area": area}
  elif distance == 0:
    lowerCAmelCase					:					Dict								=							(
        (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (2_4_0 * force)
    ) ** (1 / 4)
    return {"distance": distance}
  raise ValueError("One and only one argument must be 0"    )


# Run doctest
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  a__      (    SCREAMING_SNAKE_CASE   :   str    ,				SCREAMING_SNAKE_CASE   :   Any    ):


  '''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  a__      (    SCREAMING_SNAKE_CASE   :   Any    ,				SCREAMING_SNAKE_CASE   :   List[str]    ,				SCREAMING_SNAKE_CASE   :   int    ):


  '''simple docstring'''







  lowerCAmelCase					:					List[Any]								=							tmp_path / "cache"
  lowerCAmelCase					:					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():
    lowerCAmelCase					:					str								=							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  a__      (    SCREAMING_SNAKE_CASE   :   Tuple    ,				SCREAMING_SNAKE_CASE   :   Optional[Any]    ,				SCREAMING_SNAKE_CASE   :   Tuple    ):


  '''simple docstring'''







  lowerCAmelCase					:					Optional[int]								=							tmp_path / "cache"
  lowerCAmelCase					:					Optional[Any]								=							{"col_1": "string", "col_2": "int64", "col_3": "float64"}
  lowerCAmelCase					:					Tuple								=							features.copy() if features else default_expected_features
  lowerCAmelCase					:					List[str]								=							(
      Features({feature: Value(SCREAMING_SNAKE_CASE    ) for feature, dtype in features.items()}    ) if features is not None else None
  )
  lowerCAmelCase					:					List[str]								=							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  a__      (    SCREAMING_SNAKE_CASE   :   Union[str, Any]    ,				SCREAMING_SNAKE_CASE   :   Union[str, Any]    ,				SCREAMING_SNAKE_CASE   :   Optional[int]    ):


  '''simple docstring'''







  lowerCAmelCase					:					List[Any]								=							tmp_path / "cache"
  lowerCAmelCase					:					int								=							{"col_1": "string", "col_2": "int64", "col_3": "float64"}
  lowerCAmelCase					:					Tuple								=							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  a__      (    SCREAMING_SNAKE_CASE   :   List[Any]    ,				SCREAMING_SNAKE_CASE   :   Dict    ,				SCREAMING_SNAKE_CASE   :   List[str]    ):


  '''simple docstring'''







  if issubclass(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ):
    lowerCAmelCase					:					Optional[Any]								=							parquet_path
  elif issubclass(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ):
    lowerCAmelCase					:					Dict								=							[parquet_path]
  lowerCAmelCase					:					Optional[Any]								=							tmp_path / "cache"
  lowerCAmelCase					:					Any								=							{"col_1": "string", "col_2": "int64", "col_3": "float64"}
  lowerCAmelCase					:					Optional[Any]								=							ParquetDatasetReader(SCREAMING_SNAKE_CASE    ,				cache_dir=SCREAMING_SNAKE_CASE    ).read()
  _check_parquet_dataset(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    )
def  a__      (    SCREAMING_SNAKE_CASE   :   Dict    ,				SCREAMING_SNAKE_CASE   :   List[str]    ,				SCREAMING_SNAKE_CASE   :   Tuple=("train",)    ):


  '''simple docstring'''







  assert isinstance(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    )
  for split in splits:
    lowerCAmelCase					:					List[Any]								=							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  a__      (    SCREAMING_SNAKE_CASE   :   Optional[int]    ,				SCREAMING_SNAKE_CASE   :   Tuple    ,				SCREAMING_SNAKE_CASE   :   Tuple    ):


  '''simple docstring'''







  lowerCAmelCase					:					Tuple								=							tmp_path / "cache"
  lowerCAmelCase					:					List[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():
    lowerCAmelCase					:					Optional[Any]								=							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  a__      (    SCREAMING_SNAKE_CASE   :   Optional[int]    ,				SCREAMING_SNAKE_CASE   :   Dict    ,				SCREAMING_SNAKE_CASE   :   Optional[int]    ):


  '''simple docstring'''







  lowerCAmelCase					:					Any								=							tmp_path / "cache"
  lowerCAmelCase					:					Union[str, Any]								=							{"col_1": "string", "col_2": "int64", "col_3": "float64"}
  lowerCAmelCase					:					List[Any]								=							features.copy() if features else default_expected_features
  lowerCAmelCase					:					List[Any]								=							(
      Features({feature: Value(SCREAMING_SNAKE_CASE    ) for feature, dtype in features.items()}    ) if features is not None else None
  )
  lowerCAmelCase					:					Optional[int]								=							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  a__      (    SCREAMING_SNAKE_CASE   :   str    ,				SCREAMING_SNAKE_CASE   :   Dict    ,				SCREAMING_SNAKE_CASE   :   List[str]    ):


  '''simple docstring'''







  if split:
    lowerCAmelCase					:					List[str]								=							{split: parquet_path}
  else:
    lowerCAmelCase					:					List[str]								=							"train"
    lowerCAmelCase					:					str								=							{"train": parquet_path, "test": parquet_path}
  lowerCAmelCase					:					Optional[int]								=							tmp_path / "cache"
  lowerCAmelCase					:					List[Any]								=							{"col_1": "string", "col_2": "int64", "col_3": "float64"}
  lowerCAmelCase					:					List[str]								=							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  a__      (    SCREAMING_SNAKE_CASE   :   Optional[Any]    ,				SCREAMING_SNAKE_CASE   :   Any    ):


  '''simple docstring'''







  lowerCAmelCase					:					Optional[int]								=							ParquetDatasetWriter(SCREAMING_SNAKE_CASE    ,				tmp_path / "foo.parquet"    )
  assert writer.write() > 0
  lowerCAmelCase					:					List[str]								=							pq.ParquetFile(tmp_path / "foo.parquet"    )
  lowerCAmelCase					:					Union[str, Any]								=							pf.read()
  assert dataset.data.table == output_table
def  a__      (    SCREAMING_SNAKE_CASE   :   List[Any]    ,				SCREAMING_SNAKE_CASE   :   List[str]    ):


  '''simple docstring'''







  lowerCAmelCase					:					str								=							str(shared_datadir / "test_image_rgb.jpg"    )
  lowerCAmelCase					:					List[str]								=							{"image": [image_path]}
  lowerCAmelCase					:					str								=							Features({"image": Image()}    )
  lowerCAmelCase					:					Optional[Any]								=							Dataset.from_dict(SCREAMING_SNAKE_CASE    ,				features=SCREAMING_SNAKE_CASE    )
  lowerCAmelCase					:					List[Any]								=							ParquetDatasetWriter(SCREAMING_SNAKE_CASE    ,				tmp_path / "foo.parquet"    )
  assert writer.write() > 0

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

  lowerCAmelCase					:					Optional[int]								=							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  a__      (    SCREAMING_SNAKE_CASE   :   str    ,				SCREAMING_SNAKE_CASE   :   List[str]    ):


  '''simple docstring'''







  assert get_writer_batch_size(SCREAMING_SNAKE_CASE    ) == expected





