






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__      (  lowercase    ,  lowercase					):

  """simple docstring"""






  assert isinstance(lowercase    ,  lowercase					)
  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__      (  lowercase    ,  lowercase    ,  lowercase					):

  """simple docstring"""






  SCREAMING_SNAKE_CASE							:   Tuple						    =     tmp_path / "cache"
  SCREAMING_SNAKE_CASE							:   str						    =     {"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							:   int						    =     ParquetDatasetReader(lowercase    ,  cache_dir=lowercase    ,  keep_in_memory=lowercase					).read()
  _check_parquet_dataset(lowercase    ,  lowercase					)


@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__      (  lowercase    ,  lowercase    ,  lowercase					):

  """simple docstring"""






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


@pytest.mark.parametrize("split"    ,  [None, NamedSplit("train"					), "train", "test"]					)
def 	lowerCamelCase__      (  lowercase    ,  lowercase    ,  lowercase					):

  """simple docstring"""






  SCREAMING_SNAKE_CASE							:   Any						    =     tmp_path / "cache"
  SCREAMING_SNAKE_CASE							:   Any						    =     {"col_1": "string", "col_2": "int64", "col_3": "float64"}
  SCREAMING_SNAKE_CASE							:   str						    =     ParquetDatasetReader(lowercase    ,  cache_dir=lowercase    ,  split=lowercase					).read()
  _check_parquet_dataset(lowercase    ,  lowercase					)
  assert dataset.split == split if split else "train"


@pytest.mark.parametrize("path_type"    ,  [str, list]					)
def 	lowerCamelCase__      (  lowercase    ,  lowercase    ,  lowercase					):

  """simple docstring"""






  if issubclass(lowercase    ,  lowercase					):
    SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     parquet_path
  elif issubclass(lowercase    ,  lowercase					):
    SCREAMING_SNAKE_CASE							:   Any						    =     [parquet_path]
  SCREAMING_SNAKE_CASE							:   str						    =     tmp_path / "cache"
  SCREAMING_SNAKE_CASE							:   str						    =     {"col_1": "string", "col_2": "int64", "col_3": "float64"}
  SCREAMING_SNAKE_CASE							:   str						    =     ParquetDatasetReader(lowercase    ,  cache_dir=lowercase					).read()
  _check_parquet_dataset(lowercase    ,  lowercase					)


def 	lowerCamelCase__      (  lowercase    ,  lowercase    ,  lowercase=("train",)					):

  """simple docstring"""






  assert isinstance(lowercase    ,  lowercase					)
  for split in splits:
    SCREAMING_SNAKE_CASE							:   Union[str, 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 	lowerCamelCase__      (  lowercase    ,  lowercase    ,  lowercase					):

  """simple docstring"""






  SCREAMING_SNAKE_CASE							:   Optional[int]						    =     tmp_path / "cache"
  SCREAMING_SNAKE_CASE							:   str						    =     {"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							:   List[Any]						    =     ParquetDatasetReader(
        {"train": parquet_path}    ,  cache_dir=lowercase    ,  keep_in_memory=lowercase					).read()
  _check_parquet_datasetdict(lowercase    ,  lowercase					)


@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__      (  lowercase    ,  lowercase    ,  lowercase					):

  """simple docstring"""






  SCREAMING_SNAKE_CASE							:   int						    =     tmp_path / "cache"
  SCREAMING_SNAKE_CASE							:   int						    =     {"col_1": "string", "col_2": "int64", "col_3": "float64"}
  SCREAMING_SNAKE_CASE							:   Optional[int]						    =     features.copy() if features else default_expected_features
  SCREAMING_SNAKE_CASE							:   Dict						    =     (
      Features({feature: Value(lowercase					) for feature, dtype in features.items()}					) if features is not None else None
  )
  SCREAMING_SNAKE_CASE							:   Tuple						    =     ParquetDatasetReader({"train": parquet_path}    ,  features=lowercase    ,  cache_dir=lowercase					).read()
  _check_parquet_datasetdict(lowercase    ,  lowercase					)


@pytest.mark.parametrize("split"    ,  [None, NamedSplit("train"					), "train", "test"]					)
def 	lowerCamelCase__      (  lowercase    ,  lowercase    ,  lowercase					):

  """simple docstring"""






  if split:
    SCREAMING_SNAKE_CASE							:   Dict						    =     {split: parquet_path}
  else:
    SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     "train"
    SCREAMING_SNAKE_CASE							:   Dict						    =     {"train": parquet_path, "test": parquet_path}
  SCREAMING_SNAKE_CASE							:   str						    =     tmp_path / "cache"
  SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     {"col_1": "string", "col_2": "int64", "col_3": "float64"}
  SCREAMING_SNAKE_CASE							:   int						    =     ParquetDatasetReader(lowercase    ,  cache_dir=lowercase					).read()
  _check_parquet_datasetdict(lowercase    ,  lowercase    ,  splits=list(path.keys()					)					)
  assert all(dataset[split].split == split for split in path.keys()					)


def 	lowerCamelCase__      (  lowercase    ,  lowercase					):

  """simple docstring"""






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


def 	lowerCamelCase__      (  lowercase    ,  lowercase					):

  """simple docstring"""






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

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

  SCREAMING_SNAKE_CASE							:   Dict						    =     ParquetDatasetReader(str(tmp_path / "foo.parquet"					)    ,  streaming=lowercase					).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__      (  lowercase    ,  lowercase					):

  """simple docstring"""






  assert get_writer_batch_size(lowercase					) == expected













"""simple docstring"""


import random
from typing import Any




def        lowercase__     (       lowercase_      )       ->		list[Any]:




      """simple docstring"""

      for _ in range(len(lowercase_      )      ):
            _UpperCamelCase   :						Dict    =							random.randint(0		,len(lowercase_      ) - 1      )
            _UpperCamelCase   :						str    =							random.randint(0		,len(lowercase_      ) - 1      )
            _UpperCamelCase,	_UpperCamelCase   :						Optional[Any]    =							data[b], data[a]
      return data


if __name__ == "__main__":
  lowerCamelCase__						= [0, 1, 2, 3, 4, 5, 6, 7]
  lowerCamelCase__						= ["python", "says", "hello", "!"]
  print("Fisher-Yates Shuffle:")
  print("List", integers, strings)
  print("FY Shuffle", fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))
