






import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader

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 == 1
  assert dataset.column_names == ["text"]
  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       :      List[Any]							=    tmp_path / "cache"
  SCREAMING_SNAKE_CASE       :      Tuple							=    {"text": "string"}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    TextDatasetReader(lowercase						,     cache_dir=lowercase						,     keep_in_memory=lowercase				).read()
  _check_text_dataset(lowercase						,     lowercase				)


@pytest.mark.parametrize(
    "features"						,     [
        None,
        {"text": "string"},
        {"text": "int32"},
        {"text": "float32"},
    ]						,     )
def   lowerCamelCase__			(     lowercase						,     lowercase						,     lowercase				):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      Optional[Any]							=    tmp_path / "cache"
  SCREAMING_SNAKE_CASE       :      int							=    {"text": "string"}
  SCREAMING_SNAKE_CASE       :      List[Any]							=    features.copy() if features else default_expected_features
  SCREAMING_SNAKE_CASE       :      Any							=    (
      Features({feature: Value(lowercase				) for feature, dtype in features.items()}				) if features is not None else None
  )
  SCREAMING_SNAKE_CASE       :      List[Any]							=    TextDatasetReader(lowercase						,     features=lowercase						,     cache_dir=lowercase				).read()
  _check_text_dataset(lowercase						,     lowercase				)


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

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      List[Any]							=    tmp_path / "cache"
  SCREAMING_SNAKE_CASE       :      List[Any]							=    {"text": "string"}
  SCREAMING_SNAKE_CASE       :      Optional[int]							=    TextDatasetReader(lowercase						,     cache_dir=lowercase						,     split=lowercase				).read()
  _check_text_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       :      List[Any]							=    text_path
  elif issubclass(lowercase						,     lowercase				):
    SCREAMING_SNAKE_CASE       :      Any							=    [text_path]
  SCREAMING_SNAKE_CASE       :      str							=    tmp_path / "cache"
  SCREAMING_SNAKE_CASE       :      Optional[int]							=    {"text": "string"}
  SCREAMING_SNAKE_CASE       :      Dict							=    TextDatasetReader(lowercase						,     cache_dir=lowercase				).read()
  _check_text_dataset(lowercase						,     lowercase				)


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

  """simple docstring"""






  assert isinstance(lowercase						,     lowercase				)
  for split in splits:
    SCREAMING_SNAKE_CASE       :      str							=    dataset_dict[split]
    assert dataset.num_rows == 4
    assert dataset.num_columns == 1
    assert dataset.column_names == ["text"]
    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[Any]							=    tmp_path / "cache"
  SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    {"text": "string"}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    SCREAMING_SNAKE_CASE       :      Tuple							=    TextDatasetReader({"train": text_path}						,     cache_dir=lowercase						,     keep_in_memory=lowercase				).read()
  _check_text_datasetdict(lowercase						,     lowercase				)


@pytest.mark.parametrize(
    "features"						,     [
        None,
        {"text": "string"},
        {"text": "int32"},
        {"text": "float32"},
    ]						,     )
def   lowerCamelCase__			(     lowercase						,     lowercase						,     lowercase				):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      List[str]							=    tmp_path / "cache"
  # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
  SCREAMING_SNAKE_CASE       :      List[Any]							=    {"text": "string"}
  SCREAMING_SNAKE_CASE       :      List[str]							=    features.copy() if features else default_expected_features
  SCREAMING_SNAKE_CASE       :      List[str]							=    (
      Features({feature: Value(lowercase				) for feature, dtype in features.items()}				) if features is not None else None
  )
  SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    TextDatasetReader({"train": text_path}						,     features=lowercase						,     cache_dir=lowercase				).read()
  _check_text_datasetdict(lowercase						,     lowercase				)


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

  """simple docstring"""






  if split:
    SCREAMING_SNAKE_CASE       :      List[str]							=    {split: text_path}
  else:
    SCREAMING_SNAKE_CASE       :      Any							=    "train"
    SCREAMING_SNAKE_CASE       :      Optional[Any]							=    {"train": text_path, "test": text_path}
  SCREAMING_SNAKE_CASE       :      Optional[int]							=    tmp_path / "cache"
  SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    {"text": "string"}
  SCREAMING_SNAKE_CASE       :      Optional[int]							=    TextDatasetReader(lowercase						,     cache_dir=lowercase				).read()
  _check_text_datasetdict(lowercase						,     lowercase						,     splits=list(path.keys()				)				)
  assert all(dataset[split].split == split for split in path.keys()				)














def   lowerCamelCase__			(     lowercase				):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      Dict							=    [0] * len(lowercase				)
  SCREAMING_SNAKE_CASE       :      List[str]							=    []
  SCREAMING_SNAKE_CASE       :      Optional[Any]							=    []
  SCREAMING_SNAKE_CASE       :      Any							=    0

  for values in graph.values():
    for i in values:
      indegree[i] += 1

  for i in range(len(lowercase				)				):
    if indegree[i] == 0:
      queue.append(lowercase				)

  while queue:
    SCREAMING_SNAKE_CASE       :      Optional[Any]							=    queue.pop(0				)
    cnt += 1
    topo.append(lowercase				)
    for x in graph[vertex]:
      indegree[x] -= 1
      if indegree[x] == 0:
        queue.append(lowercase				)

  if cnt != len(lowercase				):
    print("Cycle exists"				)
  else:
    print(lowercase				)


# Adjacency List of Graph
snake_case    				=		{0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}

topological_sort(graph)






