

'''simple docstring'''

from __future__ import annotations

from collections.abc import Iterable, Iterator
from dataclasses import dataclass

lowercase__         =      (3, 9, -11, 0, 7, 5, 1, -1)
lowercase__         =      (4, 6, 2, 0, 8, 10, 3, -2)




@dataclass
class 	A_			:






     '''simple docstring'''



     UpperCAmelCase_						:     Optional[Any]  		=  42
     UpperCAmelCase_						:     List[str]  		=  42




class 	A_			:






     '''simple docstring'''



     def __init__(		self			:					Dict					,      lowercase_			:					List[Any]	)						->     None:
       UpperCAmelCase	:     Node | None						 =			None
       for i in sorted(__lowerCamelCase					,      reverse=__lowerCamelCase	):
         UpperCAmelCase	:     Any						 =			Node(__lowerCamelCase					,      self.head	)


     def __iter__(		self			:					Union[str, Any]	)						->     Iterator[int]:
       UpperCAmelCase	:     int						 =			self.head
       while node:
         yield node.data
         UpperCAmelCase	:     int						 =			node.next_node


     def __len__(		self			:					str	)						->     int:
       return sum(1 for _ in self	)




     def __str__(		self			:					Union[str, Any]	)						->     str:
       return " -> ".join([str(__lowerCamelCase	) for node in self]	)




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
  return SortedLinkedList(list(lowerCamelCase_     ) + list(lowerCamelCase_     )     )


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 lowercase__         =      SortedLinkedList
 print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))







'''simple docstring'''

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 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 assert isinstance(UpperCAmelCase_  ,	UpperCAmelCase_     )
 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 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     int						 =			tmp_path / 'cache'
 UpperCAmelCase	:     List[str]						 =			{'text': 'string'}
 with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
  UpperCAmelCase	:     Tuple						 =			TextDatasetReader(UpperCAmelCase_  ,	cache_dir=UpperCAmelCase_  ,	keep_in_memory=UpperCAmelCase_     ).read()
 _check_text_dataset(UpperCAmelCase_  ,	UpperCAmelCase_     )




@pytest.mark.parametrize(
    'features'  ,	[
        None,
        {'text': 'string'},
        {'text': 'int32'},
        {'text': 'float32'},
    ]  ,	)
def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     Optional[int]						 =			tmp_path / 'cache'
 UpperCAmelCase	:     List[str]						 =			{'text': 'string'}
 UpperCAmelCase	:     Optional[int]						 =			features.copy() if features else default_expected_features
 UpperCAmelCase	:     int						 =			(
     Features({feature: Value(UpperCAmelCase_     ) for feature, dtype in features.items()}     ) if features is not None else None
 )
 UpperCAmelCase	:     Union[str, Any]						 =			TextDatasetReader(UpperCAmelCase_  ,	features=UpperCAmelCase_  ,	cache_dir=UpperCAmelCase_     ).read()
 _check_text_dataset(UpperCAmelCase_  ,	UpperCAmelCase_     )




@pytest.mark.parametrize('split'  ,	[None, NamedSplit('train'     ), 'train', 'test']     )
def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     Optional[int]						 =			tmp_path / 'cache'
 UpperCAmelCase	:     Tuple						 =			{'text': 'string'}
 UpperCAmelCase	:     List[str]						 =			TextDatasetReader(UpperCAmelCase_  ,	cache_dir=UpperCAmelCase_  ,	split=UpperCAmelCase_     ).read()
 _check_text_dataset(UpperCAmelCase_  ,	UpperCAmelCase_     )
 assert dataset.split == split if split else "train"




@pytest.mark.parametrize('path_type'  ,	[str, list]     )
def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ):
 if issubclass(UpperCAmelCase_  ,	UpperCAmelCase_     ):
  UpperCAmelCase	:     Tuple						 =			text_path
 elif issubclass(UpperCAmelCase_  ,	UpperCAmelCase_     ):
  UpperCAmelCase	:     Optional[Any]						 =			[text_path]
 UpperCAmelCase	:     List[Any]						 =			tmp_path / 'cache'
 UpperCAmelCase	:     Union[str, Any]						 =			{'text': 'string'}
 UpperCAmelCase	:     List[Any]						 =			TextDatasetReader(UpperCAmelCase_  ,	cache_dir=UpperCAmelCase_     ).read()
 _check_text_dataset(UpperCAmelCase_  ,	UpperCAmelCase_     )




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_=("train",)     ):
 assert isinstance(UpperCAmelCase_  ,	UpperCAmelCase_     )
 for split in splits:
  UpperCAmelCase	:     Union[str, Any]						 =			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 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     Any						 =			tmp_path / 'cache'
 UpperCAmelCase	:     List[str]						 =			{'text': 'string'}
 with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
  UpperCAmelCase	:     int						 =			TextDatasetReader({'train': text_path}  ,	cache_dir=UpperCAmelCase_  ,	keep_in_memory=UpperCAmelCase_     ).read()
 _check_text_datasetdict(UpperCAmelCase_  ,	UpperCAmelCase_     )




@pytest.mark.parametrize(
    'features'  ,	[
        None,
        {'text': 'string'},
        {'text': 'int32'},
        {'text': 'float32'},
    ]  ,	)
def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     Union[str, Any]						 =			tmp_path / 'cache'
 # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
 UpperCAmelCase	:     Tuple						 =			{'text': 'string'}
 UpperCAmelCase	:     Union[str, Any]						 =			features.copy() if features else default_expected_features
 UpperCAmelCase	:     int						 =			(
     Features({feature: Value(UpperCAmelCase_     ) for feature, dtype in features.items()}     ) if features is not None else None
 )
 UpperCAmelCase	:     List[Any]						 =			TextDatasetReader({'train': text_path}  ,	features=UpperCAmelCase_  ,	cache_dir=UpperCAmelCase_     ).read()
 _check_text_datasetdict(UpperCAmelCase_  ,	UpperCAmelCase_     )




@pytest.mark.parametrize('split'  ,	[None, NamedSplit('train'     ), 'train', 'test']     )
def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ):
 if split:
  UpperCAmelCase	:     int						 =			{split: text_path}
 else:
  UpperCAmelCase	:     int						 =			'train'
  UpperCAmelCase	:     Any						 =			{'train': text_path, 'test': text_path}
 UpperCAmelCase	:     Dict						 =			tmp_path / 'cache'
 UpperCAmelCase	:     Any						 =			{'text': 'string'}
 UpperCAmelCase	:     List[str]						 =			TextDatasetReader(UpperCAmelCase_  ,	cache_dir=UpperCAmelCase_     ).read()
 _check_text_datasetdict(UpperCAmelCase_  ,	UpperCAmelCase_  ,	splits=list(path.keys()     )     )
 assert all(dataset[split].split == split for split in path.keys()     )




