




from __future__ import annotations

from collections.abc import Iterator
from typing import Any


class        UpperCamelCase_					:

    '''simple docstring'''






    def __init__(    self   :			Tuple      ,				UpperCAmelCase__   :			Any)		->str:





           '''simple docstring'''

           A__   				=					data
           A__   				=					None


class        UpperCamelCase_					:

    '''simple docstring'''






    def __init__(    self   :			int)		->Any:





           '''simple docstring'''

           A__   				=					None
           A__   				=					None


    def __iter__(    self   :			Union[str, Any])		->Iterator[Any]:





           '''simple docstring'''

           A__   				=					self.head
           while self.head:
                  yield node.data
                  A__   				=					node.next
                  if node == self.head:
                         break


    def __len__(    self   :			Any)		->int:





           '''simple docstring'''

           return sum(1 for _ in self)


    def __repr__(    self   :			Optional[Any])		->Optional[int]:





           '''simple docstring'''

           return "->".join(str(UpperCAmelCase__) for item in iter(self))


    def  SCREAMING_SNAKE_CASE							(    self   :			List[str]      ,				UpperCAmelCase__   :			Any)		->None:





           '''simple docstring'''

           self.insert_nth(len(self)      ,				UpperCAmelCase__)


    def  SCREAMING_SNAKE_CASE							(    self   :			List[Any]      ,				UpperCAmelCase__   :			Any)		->None:





           '''simple docstring'''

           self.insert_nth(0      ,				UpperCAmelCase__)


    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any]      ,				UpperCAmelCase__   :			int      ,				UpperCAmelCase__   :			Any)		->None:





           '''simple docstring'''

           if index < 0 or index > len(self):
                  raise IndexError('''list index out of range.''')
           A__   				=					Node(UpperCAmelCase__)
           if self.head is None:
                  A__   				=					new_node  # first node points itself
                  A__   				=					A__   				=					new_node
           elif index == 0:  # insert at head
                  A__   				=					self.head
                  A__   				=					A__   				=					new_node
           else:
                  A__   				=					self.head
                  for _ in range(index - 1):
                         A__   				=					temp.next
                  A__   				=					temp.next
                  A__   				=					new_node
                  if index == len(self) - 1:  # insert at tail
                         A__   				=					new_node


    def  SCREAMING_SNAKE_CASE							(    self   :			Tuple)		->int:





           '''simple docstring'''

           return self.delete_nth(0)


    def  SCREAMING_SNAKE_CASE							(    self   :			List[str])		->Any:





           '''simple docstring'''

           return self.delete_nth(len(self) - 1)


    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[int]      ,				UpperCAmelCase__   :			int = 0)		->Any:





           '''simple docstring'''

           if not 0 <= index < len(self):
                  raise IndexError('''list index out of range.''')
           A__   				=					self.head
           if self.head == self.tail:  # just one node
                  A__   				=					A__   				=					None
           elif index == 0:  # delete head node
                  A__   				=					self.tail.next.next
                  A__   				=					self.head.next
           else:
                  A__   				=					self.head
                  for _ in range(index - 1):
                         A__   				=					temp.next
                  A__   				=					temp.next
                  A__   				=					temp.next.next
                  if index == len(self) - 1:  # delete at tail
                         A__   				=					temp
           return delete_node.data





    def  SCREAMING_SNAKE_CASE							(    self   :			int)		->bool:





           '''simple docstring'''

           return len(self) == 0







def SCREAMING_SNAKE_CASE       ( )     -> None:



       """simple docstring"""

       A__   				=					CircularLinkedList()
       assert len(lowercase_		) == 0
       assert circular_linked_list.is_empty() is True
       assert str(lowercase_		) == ""

       try:
              circular_linked_list.delete_front()
              raise AssertionError  # This should not happen
       except IndexError:
              assert True  # This should happen

       try:
              circular_linked_list.delete_tail()
              raise AssertionError  # This should not happen
       except IndexError:
              assert True  # This should happen

       try:
              circular_linked_list.delete_nth(-1		)
              raise AssertionError
       except IndexError:
              assert True

       try:
              circular_linked_list.delete_nth(0		)
              raise AssertionError
       except IndexError:
              assert True

       assert circular_linked_list.is_empty() is True
       for i in range(5		):
              assert len(lowercase_		) == i
              circular_linked_list.insert_nth(lowercase_				,       i + 1		)
       assert str(lowercase_		) == "->".join(str(lowercase_		) for i in range(1				,       6		)		)

       circular_linked_list.insert_tail(6		)
       assert str(lowercase_		) == "->".join(str(lowercase_		) for i in range(1				,       7		)		)
       circular_linked_list.insert_head(0		)
       assert str(lowercase_		) == "->".join(str(lowercase_		) for i in range(0				,       7		)		)

       assert circular_linked_list.delete_front() == 0
       assert circular_linked_list.delete_tail() == 6
       assert str(lowercase_		) == "->".join(str(lowercase_		) for i in range(1				,       6		)		)
       assert circular_linked_list.delete_nth(2		) == 3

       circular_linked_list.insert_nth(2				,       3		)
       assert str(lowercase_		) == "->".join(str(lowercase_		) for i in range(1				,       6		)		)

       assert circular_linked_list.is_empty() is False


if __name__ == "__main__":
  import doctest

  doctest.testmod()





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 SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> Tuple:



       """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 SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_		)     -> int:



       """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(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 SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_		)     -> 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(lowercase_		) for feature, dtype in features.items()}		) if features is not None else None
       )
       A__   				=					ParquetDatasetReader(lowercase_				,       features=lowercase_				,       cache_dir=lowercase_		).read()
       _check_parquet_dataset(lowercase_				,       lowercase_		)







@pytest.mark.parametrize('''split'''				,       [None, NamedSplit('''train'''		), '''train''', '''test''']		)
def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_		)     -> str:



       """simple docstring"""

       A__   				=					tmp_path / '''cache'''
       A__   				=					{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
       A__   				=					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 SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_		)     -> List[Any]:



       """simple docstring"""

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







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_=("train",)		)     -> List[str]:



       """simple docstring"""

       assert isinstance(lowercase_				,       lowercase_		)
       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 SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_		)     -> 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(
                  {'''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 SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_		)     -> List[Any]:



       """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(lowercase_		) for feature, dtype in features.items()}		) if features is not None else None
       )
       A__   				=					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 SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_		)     -> 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(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 SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> List[str]:



       """simple docstring"""

       A__   				=					ParquetDatasetWriter(lowercase_				,       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 SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> str:



       """simple docstring"""

       A__   				=					str(shared_datadir / '''test_image_rgb.jpg'''		)
       A__   				=					{'''image''': [image_path]}
       A__   				=					Features({'''image''': Image()}		)
       A__   				=					Dataset.from_dict(lowercase_				,       features=lowercase_		)
       A__   				=					ParquetDatasetWriter(lowercase_				,       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=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 SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> Tuple:



       """simple docstring"""

       assert get_writer_batch_size(lowercase_		) == expected


