
'''simple docstring'''

def   A     (      UpperCamelCase_					:     str						,	UpperCamelCase_					:     str      )					->       str:







			'''simple docstring'''



			if not isinstance(UpperCamelCase_						,	UpperCamelCase_      ):
						raise TypeError("plaintext must be a string"      )
			if not isinstance(UpperCamelCase_						,	UpperCamelCase_      ):
						raise TypeError("key must be a string"      )

			if not plaintext:
						raise ValueError("plaintext is empty"      )
			if not key:
						raise ValueError("key is empty"      )

			key += plaintext
			lowerCAmelCase__								=							plaintext.lower()
			lowerCAmelCase__								=							key.lower()
			lowerCAmelCase__								=							0
			lowerCAmelCase__								=							0
			lowerCAmelCase__								=							""
			while plaintext_iterator < len(UpperCamelCase_      ):
						if (
						    ord(plaintext[plaintext_iterator]      ) < 97
						    or ord(plaintext[plaintext_iterator]      ) > 1_22
						):
									ciphertext += plaintext[plaintext_iterator]
									plaintext_iterator += 1
						elif ord(key[key_iterator]      ) < 97 or ord(key[key_iterator]      ) > 1_22:
									key_iterator += 1
						else:
									ciphertext += chr(
									    (
									        (ord(plaintext[plaintext_iterator]      ) - 97 + ord(key[key_iterator]      ))
									        - 97
									    )
									    % 26
									    + 97      )
									key_iterator += 1
									plaintext_iterator += 1
			return ciphertext







def   A     (      UpperCamelCase_					:     str						,	UpperCamelCase_					:     str      )					->       str:







			'''simple docstring'''



			if not isinstance(UpperCamelCase_						,	UpperCamelCase_      ):
						raise TypeError("ciphertext must be a string"      )
			if not isinstance(UpperCamelCase_						,	UpperCamelCase_      ):
						raise TypeError("key must be a string"      )

			if not ciphertext:
						raise ValueError("ciphertext is empty"      )
			if not key:
						raise ValueError("key is empty"      )

			lowerCAmelCase__								=							key.lower()
			lowerCAmelCase__								=							0
			lowerCAmelCase__								=							0
			lowerCAmelCase__								=							""
			while ciphertext_iterator < len(UpperCamelCase_      ):
						if (
						    ord(ciphertext[ciphertext_iterator]      ) < 97
						    or ord(ciphertext[ciphertext_iterator]      ) > 1_22
						):
									plaintext += ciphertext[ciphertext_iterator]
						else:
									plaintext += chr(
									    (ord(ciphertext[ciphertext_iterator]      ) - ord(key[key_iterator]      )) % 26
									    + 97      )
									key += chr(
									    (ord(ciphertext[ciphertext_iterator]      ) - ord(key[key_iterator]      )) % 26
									    + 97      )
									key_iterator += 1
						ciphertext_iterator += 1
			return plaintext


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		UpperCAmelCase__   : str					=		int(input("Type 1 to encrypt or 2 to decrypt:"))
		if operation == 1:
				UpperCAmelCase__   : int					=		input("Typeplaintext to be encrypted:\n")
				UpperCAmelCase__   : List[Any]					=		input("Type the key:\n")
				print(encrypt(plaintext, key))
		elif operation == 2:
				UpperCAmelCase__   : Any					=		input("Type the ciphertext to be decrypted:\n")
				UpperCAmelCase__   : Optional[int]					=		input("Type the key:\n")
				print(decrypt(ciphertext, key))
		decrypt("jsqqs avvwo", "coffee")







'''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     (      UpperCamelCase_					:     List[str]						,	UpperCamelCase_					:     Any      )					->       Optional[Any]:







			'''simple docstring'''



			assert isinstance(UpperCamelCase_						,	UpperCamelCase_      )
			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     (      UpperCamelCase_					:     Optional[int]						,	UpperCamelCase_					:     Optional[Any]						,	UpperCamelCase_					:     Optional[Any]      )					->       Union[str, Any]:







			'''simple docstring'''



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







@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     (      UpperCamelCase_					:     List[Any]						,	UpperCamelCase_					:     Optional[int]						,	UpperCamelCase_					:     Tuple      )					->       Union[str, Any]:







			'''simple docstring'''



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







@pytest.mark.parametrize("split"						,	[None, NamedSplit("train"      ), "train", "test"]      )
def   A     (      UpperCamelCase_					:     str						,	UpperCamelCase_					:     Dict						,	UpperCamelCase_					:     str      )					->       Any:







			'''simple docstring'''



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







@pytest.mark.parametrize("path_type"						,	[str, list]      )
def   A     (      UpperCamelCase_					:     Any						,	UpperCamelCase_					:     Tuple						,	UpperCamelCase_					:     List[str]      )					->       Optional[int]:







			'''simple docstring'''



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







def   A     (      UpperCamelCase_					:     List[str]						,	UpperCamelCase_					:     Dict						,	UpperCamelCase_					:     List[str]=("train",)      )					->       Optional[int]:







			'''simple docstring'''



			assert isinstance(UpperCamelCase_						,	UpperCamelCase_      )
			for split in splits:
						lowerCAmelCase__								=							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     (      UpperCamelCase_					:     Optional[int]						,	UpperCamelCase_					:     List[str]						,	UpperCamelCase_					:     Optional[int]      )					->       Optional[Any]:







			'''simple docstring'''



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







@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     (      UpperCamelCase_					:     str						,	UpperCamelCase_					:     List[Any]						,	UpperCamelCase_					:     Dict      )					->       Optional[int]:







			'''simple docstring'''



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







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







			'''simple docstring'''



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







def   A     (      UpperCamelCase_					:     str      )					->       List[str]:







			'''simple docstring'''



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







def   A     (      UpperCamelCase_					:     Dict						,	UpperCamelCase_					:     int      )					->       int:







			'''simple docstring'''



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

			lowerCAmelCase__								=							iter_csv_file(UpperCamelCase_      )
			lowerCAmelCase__								=							iter_csv_file(UpperCamelCase_      )

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







def   A     (      UpperCamelCase_					:     Union[str, Any]						,	UpperCamelCase_					:     Dict      )					->       List[str]:







			'''simple docstring'''



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

			lowerCAmelCase__								=							iter_csv_file(UpperCamelCase_      )
			lowerCAmelCase__								=							iter_csv_file(UpperCamelCase_      )

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







def   A     (      UpperCamelCase_					:     List[str]						,	UpperCamelCase_					:     Tuple      )					->       Any:







			'''simple docstring'''



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





