
def 					__lowerCAmelCase    (    a__		,					a__						) ->						str:
				if not isinstance(a__		,					a__						):
								raise TypeError('''plaintext must be a string'''						)
				if not isinstance(a__		,					a__						):
								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
				__a    							=					plaintext.lower()
				__a    							=					key.lower()
				__a    							=					0
				__a    							=					0
				__a    							=					''''''
				while plaintext_iterator < len(a__						):
								if (
								    ord(plaintext[plaintext_iterator]						) < 97
								    or ord(plaintext[plaintext_iterator]						) > 122
								):
												ciphertext += plaintext[plaintext_iterator]
												plaintext_iterator += 1
								elif ord(key[key_iterator]						) < 97 or ord(key[key_iterator]						) > 122:
												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 					__lowerCAmelCase    (    a__		,					a__						) ->						str:
				if not isinstance(a__		,					a__						):
								raise TypeError('''ciphertext must be a string'''						)
				if not isinstance(a__		,					a__						):
								raise TypeError('''key must be a string'''						)

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

				__a    							=					key.lower()
				__a    							=					0
				__a    							=					0
				__a    							=					''''''
				while ciphertext_iterator < len(a__						):
								if (
								    ord(ciphertext[ciphertext_iterator]						) < 97
								    or ord(ciphertext[ciphertext_iterator]						) > 122
								):
												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()
					A  :			List[str]     				=				int(input('Type 1 to encrypt or 2 to decrypt:'))
					if operation == 1:
										A  :			Tuple     				=				input('Typeplaintext to be encrypted:\n')
										A  :			List[str]     				=				input('Type the key:\n')
										print(encrypt(plaintext, key))
					elif operation == 2:
										A  :			Optional[Any]     				=				input('Type the ciphertext to be decrypted:\n')
										A  :			Optional[Any]     				=				input('Type the key:\n')
										print(decrypt(ciphertext, key))
					decrypt('jsqqs avvwo', 'coffee')

# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
					import tensorflow as tf







class     __A(				TensorFormatter[Mapping, '''tf.Tensor''', Mapping]    ):




				def __init__(  self	,  _snake_case=None	,  **_snake_case       )					->   Dict:


								'''simple docstring'''




								super().__init__(features=_snake_case       )
								__a    							=					tf_tensor_kwargs
								import tensorflow as tf  # noqa: import tf at initialization




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Optional[Any]:


								'''simple docstring'''




								import tensorflow as tf

								if isinstance(_snake_case	,  _snake_case       ) and column:
												if all(
												    isinstance(_snake_case	,  tf.Tensor       ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column       ):
																return tf.stack(_snake_case       )
												elif all(
												    isinstance(_snake_case	,  (tf.Tensor, tf.RaggedTensor)       ) and x.ndim == 1 and x.dtype == column[0].dtype
												    for x in column       ):
																# only rag 1-D tensors, otherwise some dimensions become ragged even though they were consolidated
																return tf.ragged.stack(_snake_case       )

								return column




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Optional[Any]:


								'''simple docstring'''




								import tensorflow as tf

								if value is None:
												return value

								__a    							=					{}

								if isinstance(_snake_case	,  (np.number, np.ndarray)       ) and np.issubdtype(value.dtype	,  np.integer       ):
												__a    							=					{'''dtype''': tf.intaa}
								elif isinstance(_snake_case	,  (np.number, np.ndarray)       ) and np.issubdtype(value.dtype	,  np.floating       ):
												__a    							=					{'''dtype''': tf.floataa}
								elif config.PIL_AVAILABLE and "PIL" in sys.modules:
												import PIL.Image

												if isinstance(_snake_case	,  PIL.Image.Image       ):
																__a    							=					np.asarray(_snake_case       )

								return tf.convert_to_tensor(_snake_case	,  **{**default_dtype, **self.tf_tensor_kwargs}       )




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   str:


								'''simple docstring'''




								import tensorflow as tf

								# support for torch, tf, jax etc.
								if config.TORCH_AVAILABLE and "torch" in sys.modules:
												import torch

												if isinstance(_snake_case	,  torch.Tensor       ):
																return self._tensorize(data_struct.detach().cpu().numpy()[()]       )
								if hasattr(_snake_case	,  '''__array__'''       ) and not isinstance(_snake_case	,  tf.Tensor       ):
												__a    							=					data_struct.__array__()
								# support for nested types like struct of list of struct
								if isinstance(_snake_case	,  np.ndarray       ):
												if data_struct.dtype == object:  # tf tensors cannot be instantied from an array of objects
																return self._consolidate([self.recursive_tensorize(_snake_case       ) for substruct in data_struct]       )
								elif isinstance(_snake_case	,  (list, tuple)       ):
												return self._consolidate([self.recursive_tensorize(_snake_case       ) for substruct in data_struct]       )
								return self._tensorize(_snake_case       )




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tuple:


								'''simple docstring'''




								return map_nested(self._recursive_tensorize	,  _snake_case	,  map_list=_snake_case       )




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Mapping:


								'''simple docstring'''




								__a    							=					self.numpy_arrow_extractor().extract_row(_snake_case       )
								__a    							=					self.python_features_decoder.decode_row(_snake_case       )
								return self.recursive_tensorize(_snake_case       )




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   "tf.Tensor":


								'''simple docstring'''




								__a    							=					self.numpy_arrow_extractor().extract_column(_snake_case       )
								__a    							=					self.python_features_decoder.decode_column(_snake_case	,  pa_table.column_names[0]       )
								__a    							=					self.recursive_tensorize(_snake_case       )
								__a    							=					self._consolidate(_snake_case       )
								return column




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Mapping:


								'''simple docstring'''




								__a    							=					self.numpy_arrow_extractor().extract_batch(_snake_case       )
								__a    							=					self.python_features_decoder.decode_batch(_snake_case       )
								__a    							=					self.recursive_tensorize(_snake_case       )
								for column_name in batch:
												__a    							=					self._consolidate(batch[column_name]       )
								return batch