





"""simple docstring"""




import math






def    _SCREAMING_SNAKE_CASE	(			)				->None:






	'''simple docstring'''






	a   :	Tuple			=       input("Enter message: "      )
	a   :	List[Any]			=       int(input(F"""Enter key [2-{len(_lowercase      ) - 1}]: """      )      )
	a   :	Dict			=       input("Encryption/Decryption [e/d]: "      )

	if mode.lower().startswith("e"      ):
		a   :	Dict			=       encrypt_message(_lowercase					, _lowercase      )
	elif mode.lower().startswith("d"      ):
		a   :	Optional[Any]			=       decrypt_message(_lowercase					, _lowercase      )

	# Append pipe symbol (vertical bar) to identify spaces at the end.
	print(F"""Output:\n{text + '|'}"""      )






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					int					, _lowercase		:					str      )				->str:






	'''simple docstring'''






	a   :	str			=       [""] * key
	for col in range(_lowercase      ):
		a   :	Optional[Any]			=       col
		while pointer < len(_lowercase      ):
			cipher_text[col] += message[pointer]
			pointer += key
	return "".join(_lowercase      )






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					int					, _lowercase		:					str      )				->str:






	'''simple docstring'''






	a   :	int			=       math.ceil(len(_lowercase      ) / key      )
	a   :	Union[str, Any]			=       key
	a   :	List[Any]			=       (num_cols * num_rows) - len(_lowercase      )
	a   :	List[str]			=       [""] * num_cols
	a   :	int			=       0
	a   :	Any			=       0

	for symbol in message:
		plain_text[col] += symbol
		col += 1

		if (
		    (col == num_cols)
		    or (col == num_cols - 1)
		    and (row >= num_rows - num_shaded_boxes)
		):
			a   :	List[Any]			=       0
			row += 1

	return "".join(_lowercase      )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		main()








"""simple docstring"""




import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
		import torch

		from transformers.generation import (
		    MaxLengthCriteria,
		    MaxNewTokensCriteria,
		    MaxTimeCriteria,
		    StoppingCriteriaList,
		    validate_stopping_criteria,
		)


@require_torch
class  __UpperCamelCase       (		unittest.TestCase		):






							def 					__a     (     self   ,      lowerCAmelCase__   )			->				Optional[int]:
								a   :	str			=       3
								a   :	str			=       250

								a   :	List[Any]			=       ids_tensor((batch_size, length)   ,      lowerCAmelCase__   )
								a   :	Optional[Any]			=       torch.ones((batch_size, length)   ,      device=lowerCAmelCase__   ,      dtype=torch.float   ) / length
								return input_ids, scores






							def 					__a     (     self   )			->				List[Any]:
								a, a   :	str			=       self._get_tensors(5   )

								a   :	Any			=       StoppingCriteriaList(
								    [
								        MaxLengthCriteria(max_length=10   ),
								        MaxTimeCriteria(max_time=0.1   ),
								    ]   )

								self.assertFalse(criteria(lowerCAmelCase__   ,      lowerCAmelCase__   )   )

								a, a   :	str			=       self._get_tensors(9   )
								self.assertFalse(criteria(lowerCAmelCase__   ,      lowerCAmelCase__   )   )

								a, a   :	Union[str, Any]			=       self._get_tensors(10   )
								self.assertTrue(criteria(lowerCAmelCase__   ,      lowerCAmelCase__   )   )






							def 					__a     (     self   )			->				List[Any]:
								a   :	Optional[Any]			=       MaxLengthCriteria(max_length=10   )

								a, a   :	int			=       self._get_tensors(5   )
								self.assertFalse(criteria(lowerCAmelCase__   ,      lowerCAmelCase__   )   )

								a, a   :	int			=       self._get_tensors(9   )
								self.assertFalse(criteria(lowerCAmelCase__   ,      lowerCAmelCase__   )   )

								a, a   :	Union[str, Any]			=       self._get_tensors(10   )
								self.assertTrue(criteria(lowerCAmelCase__   ,      lowerCAmelCase__   )   )






							def 					__a     (     self   )			->				List[str]:
								a   :	Tuple			=       MaxNewTokensCriteria(start_length=5   ,      max_new_tokens=5   )

								a, a   :	str			=       self._get_tensors(5   )
								self.assertFalse(criteria(lowerCAmelCase__   ,      lowerCAmelCase__   )   )

								a, a   :	int			=       self._get_tensors(9   )
								self.assertFalse(criteria(lowerCAmelCase__   ,      lowerCAmelCase__   )   )

								a, a   :	int			=       self._get_tensors(10   )
								self.assertTrue(criteria(lowerCAmelCase__   ,      lowerCAmelCase__   )   )

								a   :	List[Any]			=       StoppingCriteriaList([criteria]   )
								self.assertEqual(criteria_list.max_length   ,      10   )






							def 					__a     (     self   )			->				str:
								a, a   :	Tuple			=       self._get_tensors(5   )

								a   :	str			=       MaxTimeCriteria(max_time=0.1   )
								self.assertFalse(criteria(lowerCAmelCase__   ,      lowerCAmelCase__   )   )

								a   :	Optional[int]			=       MaxTimeCriteria(max_time=0.1   ,      initial_timestamp=time.time() - 0.2   )
								self.assertTrue(criteria(lowerCAmelCase__   ,      lowerCAmelCase__   )   )






							def 					__a     (     self   )			->				str:
								validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10   )]   )   ,      10   )

								with self.assertWarns(lowerCAmelCase__   ):
									validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10   )]   )   ,      11   )

								a   :	Optional[int]			=       validate_stopping_criteria(StoppingCriteriaList()   ,      11   )

								self.assertEqual(len(lowerCAmelCase__   )   ,      1   )


