

import random
import sys

__UpperCamelCase						:   Tuple							   =	'''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''

def 						__SCREAMING_SNAKE_CASE						(   ):
	lowerCAmelCase__			:		List[str]						       =			input('''Enter message: '''				)
	lowerCAmelCase__			:		Optional[int]						       =			'''LFWOAYUISVKMNXPBDCRJTQEGHZ'''
	lowerCAmelCase__			:		List[Any]						       =			input('''Encrypt/Decrypt [e/d]: '''				)

	check_valid_key(A_				)

	if resp.lower().startswith('''e'''				):
		lowerCAmelCase__			:		Tuple						       =			'''encrypt'''
		lowerCAmelCase__			:		Union[str, Any]						       =			encrypt_message(A_		,  A_				)
	elif resp.lower().startswith('''d'''				):
		lowerCAmelCase__			:		Dict						       =			'''decrypt'''
		lowerCAmelCase__			:		str						       =			decrypt_message(A_		,  A_				)

	print(f'\n{mode.title()}ion: \n{translated}'				)

def 						__SCREAMING_SNAKE_CASE						(   A_				):
	lowerCAmelCase__			:		str						       =			list(A_				)
	lowerCAmelCase__			:		int						       =			list(A_				)
	key_list.sort()
	letters_list.sort()

	if key_list != letters_list:
		sys.exit('''Error in the key or symbol set.'''				)

def 						__SCREAMING_SNAKE_CASE						(   A_		,  A_				):
	return translate_message(A_		,  A_		,  '''encrypt'''				)

def 						__SCREAMING_SNAKE_CASE						(   A_		,  A_				):
	return translate_message(A_		,  A_		,  '''decrypt'''				)

def 						__SCREAMING_SNAKE_CASE						(   A_		,  A_		,  A_				):
	lowerCAmelCase__			:		str						       =			''''''
	lowerCAmelCase__			:		List[str]						       =			LETTERS
	lowerCAmelCase__			:		Optional[int]						       =			key

	if mode == "decrypt":
		lowerCAmelCase__			:		int						       =			chars_b, chars_a

	for symbol in message:
		if symbol.upper() in chars_a:
			lowerCAmelCase__			:		int						       =			chars_a.find(symbol.upper()				)
			if symbol.isupper():
				translated += chars_b[sym_index].upper()
			else:
				translated += chars_b[sym_index].lower()
		else:
			translated += symbol

	return translated

def 						__SCREAMING_SNAKE_CASE						(   ):
	lowerCAmelCase__			:		Optional[int]						       =			list(A_				)
	random.shuffle(A_				)
	return "".join(A_				)


if __name__ == "__main__":
							main()







"""simple docstring"""





from typing import TYPE_CHECKING

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


__UpperCamelCase						:   Union[str, Any]							   =	{
    '''configuration_layoutlm''': ['''LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LayoutLMConfig''', '''LayoutLMOnnxConfig'''],
    '''tokenization_layoutlm''': ['''LayoutLMTokenizer'''],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase						:   Union[str, Any]							   =	['''LayoutLMTokenizerFast''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase						:   Dict							   =	[
							    '''LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''LayoutLMForMaskedLM''',
							    '''LayoutLMForSequenceClassification''',
							    '''LayoutLMForTokenClassification''',
							    '''LayoutLMForQuestionAnswering''',
							    '''LayoutLMModel''',
							    '''LayoutLMPreTrainedModel''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase						:   str							   =	[
							    '''TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''TFLayoutLMForMaskedLM''',
							    '''TFLayoutLMForSequenceClassification''',
							    '''TFLayoutLMForTokenClassification''',
							    '''TFLayoutLMForQuestionAnswering''',
							    '''TFLayoutLMMainLayer''',
							    '''TFLayoutLMModel''',
							    '''TFLayoutLMPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
							from .tokenization_layoutlm import LayoutLMTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_layoutlm import (
														    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    LayoutLMForMaskedLM,
														    LayoutLMForQuestionAnswering,
														    LayoutLMForSequenceClassification,
														    LayoutLMForTokenClassification,
														    LayoutLMModel,
														    LayoutLMPreTrainedModel,
														)
							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_layoutlm import (
														    TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFLayoutLMForMaskedLM,
														    TFLayoutLMForQuestionAnswering,
														    TFLayoutLMForSequenceClassification,
														    TFLayoutLMForTokenClassification,
														    TFLayoutLMMainLayer,
														    TFLayoutLMModel,
														    TFLayoutLMPreTrainedModel,
														)

else:
							import sys

							__UpperCamelCase						:   int							   =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




