





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_					(			__a      ,       __a						)	->						List[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 	lowerCAmelCase_					(			__a      ,       __a      ,       __a						)	->						Any:







  """simple docstring"""





  lowerCamelCase__: 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__: Tuple         =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 	lowerCAmelCase_					(			__a      ,       __a      ,       __a						)	->						List[str]:







  """simple docstring"""





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





@pytest.mark.parametrize("split"      ,       [None, NamedSplit("train"						), "train", "test"]						)
def 	lowerCAmelCase_					(			__a      ,       __a      ,       __a						)	->						Any:







  """simple docstring"""





  lowerCamelCase__: Any         =tmp_path / "cache"
  lowerCamelCase__: Optional[Any]         ={"col_1": "string", "col_2": "int64", "col_3": "float64"}
  lowerCamelCase__: Optional[Any]         =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 	lowerCAmelCase_					(			__a      ,       __a      ,       __a						)	->						int:







  """simple docstring"""





  if issubclass(__a      ,       __a						):
    lowerCamelCase__: List[Any]         =parquet_path
  elif issubclass(__a      ,       __a						):
    lowerCamelCase__: str         =[parquet_path]
  lowerCamelCase__: Tuple         =tmp_path / "cache"
  lowerCamelCase__: Optional[Any]         ={"col_1": "string", "col_2": "int64", "col_3": "float64"}
  lowerCamelCase__: int         =ParquetDatasetReader(__a      ,       cache_dir=__a						).read()
  _check_parquet_dataset(__a      ,       __a						)





def 	lowerCAmelCase_					(			__a      ,       __a      ,       __a=("train",)						)	->						Dict:







  """simple docstring"""





  assert isinstance(__a      ,       __a						)
  for split in splits:
    lowerCamelCase__: Tuple         =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_					(			__a      ,       __a      ,       __a						)	->						Any:







  """simple docstring"""





  lowerCamelCase__: List[Any]         =tmp_path / "cache"
  lowerCamelCase__: Optional[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__: Tuple         =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 	lowerCAmelCase_					(			__a      ,       __a      ,       __a						)	->						Optional[Any]:







  """simple docstring"""





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







  """simple docstring"""





  if split:
    lowerCamelCase__: Any         ={split: parquet_path}
  else:
    lowerCamelCase__: int         ="train"
    lowerCamelCase__: Any         ={"train": parquet_path, "test": parquet_path}
  lowerCamelCase__: str         =tmp_path / "cache"
  lowerCamelCase__: Any         ={"col_1": "string", "col_2": "int64", "col_3": "float64"}
  lowerCamelCase__: int         =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 	lowerCAmelCase_					(			__a      ,       __a						)	->						int:







  """simple docstring"""





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





def 	lowerCAmelCase_					(			__a      ,       __a						)	->						List[str]:







  """simple docstring"""





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

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

  lowerCamelCase__: Optional[Any]         =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 	lowerCAmelCase_					(			__a      ,       __a						)	->						Optional[Any]:







  """simple docstring"""





  assert get_writer_batch_size(__a						) == expected



import functools
from typing import Any



def 		UpperCamelCase     (       _UpperCAmelCase  :   str ,			_UpperCAmelCase  :   list[str]  )   ->			bool:






						'''simple docstring'''

						if not isinstance(_UpperCAmelCase ,			_UpperCAmelCase  ) or len(_UpperCAmelCase  ) == 0:
												raise ValueError("the string should be not empty string"  )

						if not isinstance(_UpperCAmelCase ,			_UpperCAmelCase  ) or not all(
						    isinstance(_UpperCAmelCase ,			_UpperCAmelCase  ) and len(_UpperCAmelCase  ) > 0 for item in words  ):
												raise ValueError("the words should be a list of non-empty strings"  )

						# Build trie
						_lowercase       :	dict[str, Any]           =   {}
						_lowercase       :	List[Any]           =   "WORD_KEEPER"

						for word in words:
												_lowercase       :	Optional[Any]           =   trie
												for c in word:
																		if c not in trie_node:
																								_lowercase       :	List[Any]           =   {}

																		_lowercase       :	Optional[int]           =   trie_node[c]

												_lowercase       :	str           =   True

						_lowercase       :	int           =   len(_UpperCAmelCase  )

						# Dynamic programming method
						@functools.cache
						def is_breakable(_UpperCAmelCase  :   int  ) -> bool:
												if index == len_string:
																		return True

												_lowercase       :	Union[str, Any]           =   trie
												for i in range(_UpperCAmelCase ,			_UpperCAmelCase  ):
																		_lowercase       :	List[str]           =   trie_node.get(string[i] ,			_UpperCAmelCase  )

																		if trie_node is None:
																								return False

																		if trie_node.get(_UpperCAmelCase ,			_UpperCAmelCase  ) and is_breakable(i + 1  ):
																								return True

												return False

						return is_breakable(0  )


if __name__ == "__main__":
							import doctest

							doctest.testmod()




