





import math






def 					lowerCamelCase_						(    )		->						Dict:






				"""simple docstring"""


				snake_case_						:   Dict           =  input('''Enter message: '''			)
				snake_case_						:   Optional[int]           =  int(input(f'''Enter key [2-{len(snake_case_			) - 1}]: '''			)			)
				snake_case_						:   Union[str, Any]           =  input('''Encryption/Decryption [e/d]: '''			)

				if mode.lower().startswith('''e'''			):
								snake_case_						:   int           =  encrypt_message(snake_case_			,    snake_case_			)
				elif mode.lower().startswith('''d'''			):
								snake_case_						:   str           =  decrypt_message(snake_case_			,    snake_case_			)

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






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			)		->						List[str]:






				"""simple docstring"""


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






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			)		->						Union[str, Any]:






				"""simple docstring"""


				snake_case_						:   Any           =  math.ceil(len(snake_case_			) / key			)
				snake_case_						:   Tuple           =  key
				snake_case_						:   Any           =  (num_cols * num_rows) - len(snake_case_			)
				snake_case_						:   List[str]           =  [""""""] * num_cols
				snake_case_						:   Dict           =  0
				snake_case_						:   Optional[int]           =  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)
								):
												snake_case_						:   Dict           =  0
												row += 1

				return "".join(snake_case_			)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()











from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase_    =    {'''configuration_xglm''': ['''XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XGLMConfig''']}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase_    =    ['''XGLMTokenizer''']

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase_    =    ['''XGLMTokenizerFast''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase_    =    [
	    '''XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''XGLMForCausalLM''',
	    '''XGLMModel''',
	    '''XGLMPreTrainedModel''',
	]


try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase_    =    [
	    '''FlaxXGLMForCausalLM''',
	    '''FlaxXGLMModel''',
	    '''FlaxXGLMPreTrainedModel''',
	]


try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase_    =    [
	    '''TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFXGLMForCausalLM''',
	    '''TFXGLMModel''',
	    '''TFXGLMPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_xglm import XGLMTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_xglm_fast import XGLMTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_xglm import (
		    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFXGLMForCausalLM,
		    TFXGLMModel,
		    TFXGLMPreTrainedModel,
		)


else:
	import sys

	lowerCAmelCase_    =    _LazyModule(__name__, globals()['''__file__'''], _import_structure)
