



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  UpperCamelCase						(       _A			:				List[Any]			,							_A			:				List[Any]    )->       Any:





      """simple docstring"""





      assert isinstance(_A			,							_A    )
      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  UpperCamelCase						(       _A			:				Optional[int]			,							_A			:				Dict			,							_A			:				List[Any]    )->       List[str]:





      """simple docstring"""





      A__							=       tmp_path / "cache"
      A__							=       {"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__							=       ParquetDatasetReader(_A			,							cache_dir=_A			,							keep_in_memory=_A    ).read()
      _check_parquet_dataset(_A			,							_A    )
@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  UpperCamelCase						(       _A			:				Union[str, Any]			,							_A			:				Union[str, Any]			,							_A			:				Union[str, Any]    )->       List[str]:





      """simple docstring"""





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





      """simple docstring"""





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





      """simple docstring"""





      if issubclass(_A			,							_A    ):
            A__							=       parquet_path
      elif issubclass(_A			,							_A    ):
            A__							=       [parquet_path]
      A__							=       tmp_path / "cache"
      A__							=       {"col_1": "string", "col_2": "int64", "col_3": "float64"}
      A__							=       ParquetDatasetReader(_A			,							cache_dir=_A    ).read()
      _check_parquet_dataset(_A			,							_A    )
def  UpperCamelCase						(       _A			:				Optional[int]			,							_A			:				List[str]			,							_A			:				Tuple=("train",)    )->       List[str]:





      """simple docstring"""





      assert isinstance(_A			,							_A    )
      for split in splits:
            A__							=       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  UpperCamelCase						(       _A			:				int			,							_A			:				int			,							_A			:				Tuple    )->       Optional[Any]:





      """simple docstring"""





      A__							=       tmp_path / "cache"
      A__							=       {"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__							=       ParquetDatasetReader(
                {"train": parquet_path}			,							cache_dir=_A			,							keep_in_memory=_A    ).read()
      _check_parquet_datasetdict(_A			,							_A    )
@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  UpperCamelCase						(       _A			:				Dict			,							_A			:				Any			,							_A			:				Union[str, Any]    )->       int:





      """simple docstring"""





      A__							=       tmp_path / "cache"
      A__							=       {"col_1": "string", "col_2": "int64", "col_3": "float64"}
      A__							=       features.copy() if features else default_expected_features
      A__							=       (
          Features({feature: Value(_A    ) for feature, dtype in features.items()}    ) if features is not None else None
      )
      A__							=       ParquetDatasetReader({"train": parquet_path}			,							features=_A			,							cache_dir=_A    ).read()
      _check_parquet_datasetdict(_A			,							_A    )
@pytest.mark.parametrize("split"			,							[None, NamedSplit("train"    ), "train", "test"]    )
def  UpperCamelCase						(       _A			:				List[str]			,							_A			:				Tuple			,							_A			:				int    )->       Union[str, Any]:





      """simple docstring"""





      if split:
            A__							=       {split: parquet_path}
      else:
            A__							=       "train"
            A__							=       {"train": parquet_path, "test": parquet_path}
      A__							=       tmp_path / "cache"
      A__							=       {"col_1": "string", "col_2": "int64", "col_3": "float64"}
      A__							=       ParquetDatasetReader(_A			,							cache_dir=_A    ).read()
      _check_parquet_datasetdict(_A			,							_A			,							splits=list(path.keys()    )    )
      assert all(dataset[split].split == split for split in path.keys()    )
def  UpperCamelCase						(       _A			:				List[str]			,							_A			:				Tuple    )->       Any:





      """simple docstring"""





      A__							=       ParquetDatasetWriter(_A			,							tmp_path / "foo.parquet"    )
      assert writer.write() > 0
      A__							=       pq.ParquetFile(tmp_path / "foo.parquet"    )
      A__							=       pf.read()
      assert dataset.data.table == output_table
def  UpperCamelCase						(       _A			:				Optional[Any]			,							_A			:				Optional[Any]    )->       Dict:





      """simple docstring"""





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

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

      A__							=       ParquetDatasetReader(str(tmp_path / "foo.parquet"    )			,							streaming=_A    ).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  UpperCamelCase						(       _A			:				Optional[Any]			,							_A			:				str    )->       Tuple:





      """simple docstring"""





      assert get_writer_batch_size(_A    ) == expected










from __future__ import annotations






class      UpperCamelCase    :



	def __init__(       self			,	UpperCAmelCase__=None		):
							A__							=       data
							A__							=       None



	def __repr__(       self		):
							A__							=       []
							A__							=       self
							while temp:
													string_rep.append(F"""{temp.data}"""		)
													A__							=       temp.next
							return "->".join(UpperCAmelCase__		)





def  UpperCamelCase						(       _A			:				list    )->       Dict:





						"""simple docstring"""





						if not elements_list:
												raise Exception("The Elements List is empty"    )

						A__							=       A__							=       Node(elements_list[0]    )
						for i in range(1			,							len(_A    )    ):
												A__							=       Node(elements_list[i]    )
												A__							=       current.next
						return head
def  UpperCamelCase						(       _A			:				Node    )->       None:





						"""simple docstring"""





						if head_node is not None and isinstance(_A			,							_A    ):
												print_reverse(head_node.next    )
												print(head_node.data    )
def  UpperCamelCase						(       )->       Tuple:





						"""simple docstring"""





						from doctest import testmod

						testmod()

						A__							=       make_linked_list([14, 52, 14, 12, 43]    )
						print("Linked List:"    )
						print(_A    )
						print("Elements in Reverse:"    )
						print_reverse(_A    )


if __name__ == "__main__":
						main()





