

'''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 	UpperCamelCase   (       lowercase_						:    Dict     ,					lowercase_						:    List[Any]	)		->	Any:




  '''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 	UpperCamelCase   (       lowercase_						:    str     ,					lowercase_						:    int     ,					lowercase_						:    Optional[Any]	)		->	Tuple:




  '''simple docstring'''



  lowercase									=tmp_path / '''cache'''
  lowercase									={'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    lowercase									=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 	UpperCamelCase   (       lowercase_						:    List[Any]     ,					lowercase_						:    str     ,					lowercase_						:    Any	)		->	int:




  '''simple docstring'''



  lowercase									=tmp_path / '''cache'''
  lowercase									={'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  lowercase									=features.copy() if features else default_expected_features
  lowercase									=(
      Features({feature: Value(lowercase_	) for feature, dtype in features.items()}	) if features is not None else None
  )
  lowercase									=ParquetDatasetReader(lowercase_     ,					features=lowercase_     ,					cache_dir=lowercase_	).read()
  _check_parquet_dataset(lowercase_     ,					lowercase_	)




@pytest.mark.parametrize('''split'''     ,					[None, NamedSplit('''train'''	), '''train''', '''test''']	)
def 	UpperCamelCase   (       lowercase_						:    Optional[int]     ,					lowercase_						:    List[str]     ,					lowercase_						:    List[Any]	)		->	Optional[int]:




  '''simple docstring'''



  lowercase									=tmp_path / '''cache'''
  lowercase									={'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  lowercase									=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 	UpperCamelCase   (       lowercase_						:    Optional[Any]     ,					lowercase_						:    Tuple     ,					lowercase_						:    Optional[Any]	)		->	Any:




  '''simple docstring'''



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




def 	UpperCamelCase   (       lowercase_						:    int     ,					lowercase_						:    List[Any]     ,					lowercase_						:    Any=("train",)	)		->	Optional[Any]:




  '''simple docstring'''



  assert isinstance(lowercase_     ,					lowercase_	)
  for split in splits:
    lowercase									=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   (       lowercase_						:    Dict     ,					lowercase_						:    Dict     ,					lowercase_						:    Optional[int]	)		->	int:




  '''simple docstring'''



  lowercase									=tmp_path / '''cache'''
  lowercase									={'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    lowercase									=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 	UpperCamelCase   (       lowercase_						:    Union[str, Any]     ,					lowercase_						:    List[Any]     ,					lowercase_						:    Optional[int]	)		->	List[str]:




  '''simple docstring'''



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




  '''simple docstring'''



  if split:
    lowercase									={split: parquet_path}
  else:
    lowercase									='''train'''
    lowercase									={'''train''': parquet_path, '''test''': parquet_path}
  lowercase									=tmp_path / '''cache'''
  lowercase									={'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  lowercase									=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 	UpperCamelCase   (       lowercase_						:    Optional[int]     ,					lowercase_						:    Optional[Any]	)		->	Optional[int]:




  '''simple docstring'''



  lowercase									=ParquetDatasetWriter(lowercase_     ,					tmp_path / '''foo.parquet'''	)
  assert writer.write() > 0
  lowercase									=pq.ParquetFile(tmp_path / '''foo.parquet'''	)
  lowercase									=pf.read()
  assert dataset.data.table == output_table




def 	UpperCamelCase   (       lowercase_						:    Tuple     ,					lowercase_						:    List[str]	)		->	str:




  '''simple docstring'''



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

  lowercase									=Dataset.from_parquet(str(tmp_path / '''foo.parquet'''	)	)
  assert dataset.features == reloaded_dataset.features

  lowercase									=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 	UpperCamelCase   (       lowercase_						:    str     ,					lowercase_						:    List[Any]	)		->	List[Any]:




  '''simple docstring'''



  assert get_writer_batch_size(lowercase_	) == expected



from collections.abc import Callable







class 					lowercase							:




				def __init__(   self ,  snake_case = None			):
											# Stores actual heap items.
											snake_case_     							=       []
											# Stores indexes of each item for supporting updates and deletion.
											snake_case_     							=       {}
											# Stores current size of heap.
											snake_case_     							=       0
											# Stores function used to evaluate the score of an item on which basis ordering
											# will be done.
											snake_case_     							=       key or (lambda snake_case			: x)




				def        a				(   self ,  snake_case			):
											return int((i - 1) / 2			) if i > 0 else None




				def        a				(   self ,  snake_case			):
											snake_case_     							=       int(2 * i + 1			)
											return left if 0 < left < self.size else None




				def        a				(   self ,  snake_case			):
											snake_case_     							=       int(2 * i + 2			)
											return right if 0 < right < self.size else None




				def        a				(   self ,  snake_case ,  snake_case			):
											snake_case_							,       snake_case_     							=       (
											    self.pos_map[self.arr[j][0]],
											    self.pos_map[self.arr[i][0]],
											)
											# Then swap the items in the list.
											snake_case_							,       snake_case_     							=       self.arr[j], self.arr[i]




				def        a				(   self ,  snake_case ,  snake_case			):
											return self.arr[i][1] < self.arr[j][1]




				def        a				(   self ,  snake_case			):
											snake_case_     							=       self._left(snake_case			)
											snake_case_     							=       self._right(snake_case			)
											snake_case_     							=       i

											if left is not None and not self._cmp(snake_case ,  snake_case			):
																		snake_case_     							=       left
											if right is not None and not self._cmp(snake_case ,  snake_case			):
																		snake_case_     							=       right

											return valid_parent




				def        a				(   self ,  snake_case			):
											snake_case_     							=       self._parent(snake_case			)
											while parent is not None and not self._cmp(snake_case ,  snake_case			):
																		self._swap(snake_case ,  snake_case			)
																		snake_case_							,       snake_case_     							=       parent, self._parent(snake_case			)




				def        a				(   self ,  snake_case			):
											snake_case_     							=       self._get_valid_parent(snake_case			)
											while valid_parent != index:
																		self._swap(snake_case ,  snake_case			)
																		snake_case_							,       snake_case_     							=       valid_parent, self._get_valid_parent(snake_case			)




				def        a				(   self ,  snake_case ,  snake_case			):
											if item not in self.pos_map:
																		return
											snake_case_     							=       self.pos_map[item]
											snake_case_     							=       [item, self.key(snake_case			)]
											# Make sure heap is right in both up and down direction.
											# Ideally only one of them will make any change.
											self._heapify_up(snake_case			)
											self._heapify_down(snake_case			)




				def        a				(   self ,  snake_case			):
											if item not in self.pos_map:
																		return
											snake_case_     							=       self.pos_map[item]
											del self.pos_map[item]
											snake_case_     							=       self.arr[self.size - 1]
											snake_case_     							=       index
											self.size -= 1
											# Make sure heap is right in both up and down direction. Ideally only one
											# of them will make any change- so no performance loss in calling both.
											if self.size > index:
																		self._heapify_up(snake_case			)
																		self._heapify_down(snake_case			)




				def        a				(   self ,  snake_case ,  snake_case			):
											snake_case_     							=       len(self.arr			)
											if arr_len == self.size:
																		self.arr.append([item, self.key(snake_case			)]			)
											else:
																		snake_case_     							=       [item, self.key(snake_case			)]
											snake_case_     							=       self.size
											self.size += 1
											self._heapify_up(self.size - 1			)




				def        a				(   self			):
											return self.arr[0] if self.size else None




				def        a				(   self			):
											snake_case_     							=       self.get_top()
											if top_item_tuple:
																		self.delete_item(top_item_tuple[0]			)
											return top_item_tuple





def    __lowerCamelCase      (					):







							'''simple docstring'''








if __name__ == "__main__":
					import doctest

					doctest.testmod()




