




"""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 				__lowerCAmelCase					(					__lowerCAmelCase			:		Dict  ,	__lowerCAmelCase			:		str						)    ->       Optional[int]:
 assert isinstance(__lowerCAmelCase  ,	__lowerCAmelCase						)
 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					(					__lowerCAmelCase			:		int  ,	__lowerCAmelCase			:		Tuple  ,	__lowerCAmelCase			:		Any						)    ->       List[Any]:
 _UpperCamelCase      :	str					      =  tmp_path / "cache"
 _UpperCamelCase      :	Tuple					      =  {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
 with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
  _UpperCamelCase      :	str					      =  CsvDatasetReader(__lowerCAmelCase  ,	cache_dir=__lowerCAmelCase  ,	keep_in_memory=__lowerCAmelCase						).read()
 _check_csv_dataset(__lowerCAmelCase  ,	__lowerCAmelCase						)






@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					(					__lowerCAmelCase			:		Optional[Any]  ,	__lowerCAmelCase			:		str  ,	__lowerCAmelCase			:		Dict						)    ->       Dict:
 _UpperCamelCase      :	Any					      =  tmp_path / "cache"
 # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
 _UpperCamelCase      :	List[str]					      =  {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
 _UpperCamelCase      :	List[str]					      =  features.copy() if features else default_expected_features
 _UpperCamelCase      :	Optional[Any]					      =  (
     Features({feature: Value(__lowerCAmelCase						) for feature, dtype in features.items()}						) if features is not None else None
 )
 _UpperCamelCase      :	int					      =  CsvDatasetReader(__lowerCAmelCase  ,	features=__lowerCAmelCase  ,	cache_dir=__lowerCAmelCase						).read()
 _check_csv_dataset(__lowerCAmelCase  ,	__lowerCAmelCase						)






@pytest.mark.parametrize("split"  ,	[None, NamedSplit("train"						), "train", "test"]						)
def 				__lowerCAmelCase					(					__lowerCAmelCase			:		int  ,	__lowerCAmelCase			:		Optional[Any]  ,	__lowerCAmelCase			:		List[Any]						)    ->       List[str]:
 _UpperCamelCase      :	Any					      =  tmp_path / "cache"
 _UpperCamelCase      :	Union[str, Any]					      =  {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
 _UpperCamelCase      :	List[str]					      =  CsvDatasetReader(__lowerCAmelCase  ,	cache_dir=__lowerCAmelCase  ,	split=__lowerCAmelCase						).read()
 _check_csv_dataset(__lowerCAmelCase  ,	__lowerCAmelCase						)
 assert dataset.split == split if split else "train"






@pytest.mark.parametrize("path_type"  ,	[str, list]						)
def 				__lowerCAmelCase					(					__lowerCAmelCase			:		int  ,	__lowerCAmelCase			:		Optional[Any]  ,	__lowerCAmelCase			:		str						)    ->       List[str]:
 if issubclass(__lowerCAmelCase  ,	__lowerCAmelCase						):
  _UpperCamelCase      :	Any					      =  csv_path
 elif issubclass(__lowerCAmelCase  ,	__lowerCAmelCase						):
  _UpperCamelCase      :	Union[str, Any]					      =  [csv_path]
 _UpperCamelCase      :	Optional[int]					      =  tmp_path / "cache"
 _UpperCamelCase      :	Union[str, Any]					      =  {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
 _UpperCamelCase      :	Tuple					      =  CsvDatasetReader(__lowerCAmelCase  ,	cache_dir=__lowerCAmelCase						).read()
 _check_csv_dataset(__lowerCAmelCase  ,	__lowerCAmelCase						)






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		Optional[int]  ,	__lowerCAmelCase			:		List[Any]  ,	__lowerCAmelCase			:		str=("train",)						)    ->       Tuple:
 assert isinstance(__lowerCAmelCase  ,	__lowerCAmelCase						)
 for split in splits:
  _UpperCamelCase      :	List[str]					      =  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					(					__lowerCAmelCase			:		List[str]  ,	__lowerCAmelCase			:		int  ,	__lowerCAmelCase			:		Dict						)    ->       Tuple:
 _UpperCamelCase      :	int					      =  tmp_path / "cache"
 _UpperCamelCase      :	Tuple					      =  {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
 with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
  _UpperCamelCase      :	Optional[int]					      =  CsvDatasetReader({"train": csv_path}  ,	cache_dir=__lowerCAmelCase  ,	keep_in_memory=__lowerCAmelCase						).read()
 _check_csv_datasetdict(__lowerCAmelCase  ,	__lowerCAmelCase						)






@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					(					__lowerCAmelCase			:		str  ,	__lowerCAmelCase			:		Tuple  ,	__lowerCAmelCase			:		Optional[int]						)    ->       List[str]:
 _UpperCamelCase      :	Any					      =  tmp_path / "cache"
 # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
 _UpperCamelCase      :	Any					      =  {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
 _UpperCamelCase      :	int					      =  features.copy() if features else default_expected_features
 _UpperCamelCase      :	Tuple					      =  (
     Features({feature: Value(__lowerCAmelCase						) for feature, dtype in features.items()}						) if features is not None else None
 )
 _UpperCamelCase      :	Tuple					      =  CsvDatasetReader({"train": csv_path}  ,	features=__lowerCAmelCase  ,	cache_dir=__lowerCAmelCase						).read()
 _check_csv_datasetdict(__lowerCAmelCase  ,	__lowerCAmelCase						)






@pytest.mark.parametrize("split"  ,	[None, NamedSplit("train"						), "train", "test"]						)
def 				__lowerCAmelCase					(					__lowerCAmelCase			:		Any  ,	__lowerCAmelCase			:		Dict  ,	__lowerCAmelCase			:		int						)    ->       str:
 if split:
  _UpperCamelCase      :	Optional[int]					      =  {split: csv_path}
 else:
  _UpperCamelCase      :	Dict					      =  {"train": csv_path, "test": csv_path}
 _UpperCamelCase      :	Any					      =  tmp_path / "cache"
 _UpperCamelCase      :	Dict					      =  {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
 _UpperCamelCase      :	Union[str, Any]					      =  CsvDatasetReader(__lowerCAmelCase  ,	cache_dir=__lowerCAmelCase						).read()
 _check_csv_datasetdict(__lowerCAmelCase  ,	__lowerCAmelCase  ,	splits=list(path.keys()						)						)
 assert all(dataset[split].split == split for split in path.keys()						)






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		List[str]						)    ->       List[Any]:
 with open(__lowerCAmelCase  ,	encoding="utf-8"						) as csvfile:
  yield from csv.reader(__lowerCAmelCase						)






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		List[Any]  ,	__lowerCAmelCase			:		List[str]						)    ->       Tuple:
 _UpperCamelCase      :	List[Any]					      =  tmp_path / "cache"
 _UpperCamelCase      :	str					      =  os.path.join(__lowerCAmelCase  ,	"tmp.csv"						)
 _UpperCamelCase      :	int					      =  CsvDatasetReader({"train": csv_path}  ,	cache_dir=__lowerCAmelCase						).read()
 CsvDatasetWriter(dataset["train"]  ,	__lowerCAmelCase  ,	num_proc=1						).write()

 _UpperCamelCase      :	str					      =  iter_csv_file(__lowerCAmelCase						)
 _UpperCamelCase      :	Optional[int]					      =  iter_csv_file(__lowerCAmelCase						)

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






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		Dict  ,	__lowerCAmelCase			:		Optional[Any]						)    ->       Tuple:
 _UpperCamelCase      :	List[str]					      =  tmp_path / "cache"
 _UpperCamelCase      :	str					      =  os.path.join(__lowerCAmelCase  ,	"tmp.csv"						)
 _UpperCamelCase      :	Tuple					      =  CsvDatasetReader({"train": csv_path}  ,	cache_dir=__lowerCAmelCase						).read()
 CsvDatasetWriter(dataset["train"]  ,	__lowerCAmelCase  ,	num_proc=2						).write()

 _UpperCamelCase      :	Union[str, Any]					      =  iter_csv_file(__lowerCAmelCase						)
 _UpperCamelCase      :	str					      =  iter_csv_file(__lowerCAmelCase						)

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






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		Optional[Any]  ,	__lowerCAmelCase			:		Union[str, Any]						)    ->       Any:
 _UpperCamelCase      :	Optional[int]					      =  tmp_path / "cache"
 _UpperCamelCase      :	str					      =  os.path.join(__lowerCAmelCase  ,	"tmp.csv"						)
 _UpperCamelCase      :	int					      =  CsvDatasetReader({"train": csv_path}  ,	cache_dir=__lowerCAmelCase						).read()
 with pytest.raises(__lowerCAmelCase						):
  CsvDatasetWriter(dataset["train"]  ,	__lowerCAmelCase  ,	num_proc=0						)









"""simple docstring"""
import math






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		Tuple						)    ->       Any:
 _UpperCamelCase      :	str					      =  0
 if n % 2 == 0:
  count += 1
  while n % 2 == 0:
   _UpperCamelCase      :	List[Any]					      =  int(n / 2						)
    # the n input value must be odd so that
    # we can skip one element (ie i += 2)

 _UpperCamelCase      :	Tuple					      =  3

 while i <= int(math.sqrt(__lowerCAmelCase						)						):
  if n % i == 0:
   count += 1
   while n % i == 0:
    _UpperCamelCase      :	Any					      =  int(n / i						)
  _UpperCamelCase      :	Optional[Any]					      =  i + 2

 # this condition checks the prime
 # number n is greater than 2

 if n > 2:
  count += 1
 return count


if __name__ == "__main__":
    _SCREAMING_SNAKE_CASE						 =     5_1_2_4_2_1_8_3
    print(f'The number of distinct prime factors is/are {exact_prime_factor_count(n)}')
    print(f'The value of log(log(n)) is {math.log(math.log(n)):.4f}')



