"""simple docstring"""


import csv
import os

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.csv import CsvDatasetReader, CsvDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases







def        __a (		A     ,					A   ):






     '''simple docstring'''



     assert isinstance(_SCREAMING_SNAKE_CASE     ,					_SCREAMING_SNAKE_CASE   )
     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        __a (		A     ,					A     ,					A   ):






     '''simple docstring'''



     lowercase__								=				tmp_path / 'cache'
     lowercase__								=				{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
     with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
          lowercase__								=				CsvDatasetReader(_SCREAMING_SNAKE_CASE     ,					cache_dir=_SCREAMING_SNAKE_CASE     ,					keep_in_memory=_SCREAMING_SNAKE_CASE   ).read()
     _check_csv_dataset(_SCREAMING_SNAKE_CASE     ,					_SCREAMING_SNAKE_CASE   )







@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        __a (		A     ,					A     ,					A   ):






     '''simple docstring'''



     lowercase__								=				tmp_path / 'cache'
     # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
     lowercase__								=				{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
     lowercase__								=				features.copy() if features else default_expected_features
     lowercase__								=				(
         Features({feature: Value(_SCREAMING_SNAKE_CASE   ) for feature, dtype in features.items()}   ) if features is not None else None
     )
     lowercase__								=				CsvDatasetReader(_SCREAMING_SNAKE_CASE     ,					features=_SCREAMING_SNAKE_CASE     ,					cache_dir=_SCREAMING_SNAKE_CASE   ).read()
     _check_csv_dataset(_SCREAMING_SNAKE_CASE     ,					_SCREAMING_SNAKE_CASE   )







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






     '''simple docstring'''



     lowercase__								=				tmp_path / 'cache'
     lowercase__								=				{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
     lowercase__								=				CsvDatasetReader(_SCREAMING_SNAKE_CASE     ,					cache_dir=_SCREAMING_SNAKE_CASE     ,					split=_SCREAMING_SNAKE_CASE   ).read()
     _check_csv_dataset(_SCREAMING_SNAKE_CASE     ,					_SCREAMING_SNAKE_CASE   )
     assert dataset.split == split if split else "train"







@pytest.mark.parametrize("path_type"     ,					[str, list]   )
def        __a (		A     ,					A     ,					A   ):






     '''simple docstring'''



     if issubclass(_SCREAMING_SNAKE_CASE     ,					_SCREAMING_SNAKE_CASE   ):
          lowercase__								=				csv_path
     elif issubclass(_SCREAMING_SNAKE_CASE     ,					_SCREAMING_SNAKE_CASE   ):
          lowercase__								=				[csv_path]
     lowercase__								=				tmp_path / 'cache'
     lowercase__								=				{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
     lowercase__								=				CsvDatasetReader(_SCREAMING_SNAKE_CASE     ,					cache_dir=_SCREAMING_SNAKE_CASE   ).read()
     _check_csv_dataset(_SCREAMING_SNAKE_CASE     ,					_SCREAMING_SNAKE_CASE   )







def        __a (		A     ,					A     ,					A=("train",)   ):






     '''simple docstring'''



     assert isinstance(_SCREAMING_SNAKE_CASE     ,					_SCREAMING_SNAKE_CASE   )
     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        __a (		A     ,					A     ,					A   ):






     '''simple docstring'''



     lowercase__								=				tmp_path / 'cache'
     lowercase__								=				{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
     with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
          lowercase__								=				CsvDatasetReader({"train": csv_path}     ,					cache_dir=_SCREAMING_SNAKE_CASE     ,					keep_in_memory=_SCREAMING_SNAKE_CASE   ).read()
     _check_csv_datasetdict(_SCREAMING_SNAKE_CASE     ,					_SCREAMING_SNAKE_CASE   )







@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        __a (		A     ,					A     ,					A   ):






     '''simple docstring'''



     lowercase__								=				tmp_path / 'cache'
     # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
     lowercase__								=				{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
     lowercase__								=				features.copy() if features else default_expected_features
     lowercase__								=				(
         Features({feature: Value(_SCREAMING_SNAKE_CASE   ) for feature, dtype in features.items()}   ) if features is not None else None
     )
     lowercase__								=				CsvDatasetReader({"train": csv_path}     ,					features=_SCREAMING_SNAKE_CASE     ,					cache_dir=_SCREAMING_SNAKE_CASE   ).read()
     _check_csv_datasetdict(_SCREAMING_SNAKE_CASE     ,					_SCREAMING_SNAKE_CASE   )







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






     '''simple docstring'''



     if split:
          lowercase__								=				{split: csv_path}
     else:
          lowercase__								=				{'train': csv_path, 'test': csv_path}
     lowercase__								=				tmp_path / 'cache'
     lowercase__								=				{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
     lowercase__								=				CsvDatasetReader(_SCREAMING_SNAKE_CASE     ,					cache_dir=_SCREAMING_SNAKE_CASE   ).read()
     _check_csv_datasetdict(_SCREAMING_SNAKE_CASE     ,					_SCREAMING_SNAKE_CASE     ,					splits=list(path.keys()   )   )
     assert all(dataset[split].split == split for split in path.keys()   )







def        __a (		A   ):






     '''simple docstring'''



     with open(_SCREAMING_SNAKE_CASE     ,					encoding="utf-8"   ) as csvfile:
          yield from csv.reader(_SCREAMING_SNAKE_CASE   )







def        __a (		A     ,					A   ):






     '''simple docstring'''



     lowercase__								=				tmp_path / 'cache'
     lowercase__								=				os.path.join(_SCREAMING_SNAKE_CASE     ,					"tmp.csv"   )
     lowercase__								=				CsvDatasetReader({"train": csv_path}     ,					cache_dir=_SCREAMING_SNAKE_CASE   ).read()
     CsvDatasetWriter(dataset["train"]     ,					_SCREAMING_SNAKE_CASE     ,					num_proc=1   ).write()

     lowercase__								=				iter_csv_file(_SCREAMING_SNAKE_CASE   )
     lowercase__								=				iter_csv_file(_SCREAMING_SNAKE_CASE   )

     for rowa, rowa in zip(_SCREAMING_SNAKE_CASE     ,					_SCREAMING_SNAKE_CASE   ):
          assert rowa == rowa







def        __a (		A     ,					A   ):






     '''simple docstring'''



     lowercase__								=				tmp_path / 'cache'
     lowercase__								=				os.path.join(_SCREAMING_SNAKE_CASE     ,					"tmp.csv"   )
     lowercase__								=				CsvDatasetReader({"train": csv_path}     ,					cache_dir=_SCREAMING_SNAKE_CASE   ).read()
     CsvDatasetWriter(dataset["train"]     ,					_SCREAMING_SNAKE_CASE     ,					num_proc=2   ).write()

     lowercase__								=				iter_csv_file(_SCREAMING_SNAKE_CASE   )
     lowercase__								=				iter_csv_file(_SCREAMING_SNAKE_CASE   )

     for rowa, rowa in zip(_SCREAMING_SNAKE_CASE     ,					_SCREAMING_SNAKE_CASE   ):
          assert rowa == rowa







def        __a (		A     ,					A   ):






     '''simple docstring'''



     lowercase__								=				tmp_path / 'cache'
     lowercase__								=				os.path.join(_SCREAMING_SNAKE_CASE     ,					"tmp.csv"   )
     lowercase__								=				CsvDatasetReader({"train": csv_path}     ,					cache_dir=_SCREAMING_SNAKE_CASE   ).read()
     with pytest.raises(_SCREAMING_SNAKE_CASE   ):
          CsvDatasetWriter(dataset["train"]     ,					_SCREAMING_SNAKE_CASE     ,					num_proc=0   )





"""simple docstring"""







def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				list[int]						)    ->     tuple[int, int]:







     '''simple docstring'''


     if len(_SCREAMING_SNAKE_CASE						) == 1:
          return a[0], a[0]
     else:
          # split A into half.
          __A					:					List[str]								=		a[: len(_SCREAMING_SNAKE_CASE						) // 2]
          __A					:					Optional[Any]								=		a[len(_SCREAMING_SNAKE_CASE						) // 2 :]

          # 2 sub problems, 1/2 of original size.
          __A						,		__A					:					int								=		max_difference(_SCREAMING_SNAKE_CASE						)
          __A						,		__A					:					str								=		max_difference(_SCREAMING_SNAKE_CASE						)

          # get min of first and max of second
          # linear time
          __A					:					str								=		min(_SCREAMING_SNAKE_CASE						)
          __A					:					Optional[int]								=		max(_SCREAMING_SNAKE_CASE						)

          # 3 cases, either (small1, big1),
          # (min_first, max_second), (small2, big2)
          # constant comparisons
          if biga - smalla > max_second - min_first and biga - smalla > biga - smalla:
               return smalla, biga
          elif biga - smalla > max_second - min_first:
               return smalla, biga
          else:
               return min_first, max_second


if __name__ == "__main__":
       import doctest

       doctest.testmod()



