

"""simple docstring"""





__UpperCamelCase						:   Union[str, Any]							   =	[chr(i) for i in range(3_2, 1_2_6)]
__UpperCamelCase						:   Any							   =	list(range(len(alphabets)))
__UpperCamelCase						:   Tuple							   =	list(range(len(alphabets)))
__UpperCamelCase						:   str							   =	list(range(len(alphabets)))
__UpperCamelCase						:   int							   =	list(reversed(range(len(alphabets))))
__UpperCamelCase						:   str							   =	[]
__UpperCamelCase						:   Tuple							   =	0

def 						__SCREAMING_SNAKE_CASE						(   ):
	global gear_one_pos
	global gear_two_pos
	global gear_three_pos
	lowerCAmelCase__			:		List[Any]						       =			gear_one[0]
	gear_one.append(A_				)
	del gear_one[0]
	gear_one_pos += 1
	if gear_one_pos % int(len(A_				)				) == 0:
		lowerCAmelCase__			:		Optional[int]						       =			gear_two[0]
		gear_two.append(A_				)
		del gear_two[0]
		gear_two_pos += 1
		if gear_two_pos % int(len(A_				)				) == 0:
			lowerCAmelCase__			:		List[str]						       =			gear_three[0]
			gear_three.append(A_				)
			del gear_three[0]
			gear_three_pos += 1

def 						__SCREAMING_SNAKE_CASE						(   A_				):
	lowerCAmelCase__			:		Union[str, Any]						       =			alphabets.index(A_				)
	lowerCAmelCase__			:		Dict						       =			gear_one[target]
	lowerCAmelCase__			:		Optional[Any]						       =			gear_two[target]
	lowerCAmelCase__			:		Any						       =			gear_three[target]
	lowerCAmelCase__			:		str						       =			reflector[target]
	lowerCAmelCase__			:		Union[str, Any]						       =			gear_three.index(A_				)
	lowerCAmelCase__			:		Dict						       =			gear_two.index(A_				)
	lowerCAmelCase__			:		Optional[int]						       =			gear_one.index(A_				)
	code.append(alphabets[target]				)
	rotator()


if __name__ == "__main__":
							__UpperCamelCase						:   Tuple							   =	list(input('''Type your message:\n'''))
							while True:
														try:
																					__UpperCamelCase						:   Any							   =	int(input('''Please set token:(must be only digits)\n'''))
																					break
														except Exception as error:
																					print(error)
							for _ in range(token):
														rotator()
							for j in decode:
														engine(j)
							print('''\n''' + ''''''.join(code))
							print(
							    F'''\nYour Token is {token} please write it down.\nIf you want to decode '''
							    '''this message again you should input same digits as token!'''
							)







"""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__)




