






"""simple docstring"""



from typing import TYPE_CHECKING

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


_UpperCamelCase						:    List[str]		=						{
    'configuration_sam': [
        'SAM_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SamConfig',
        'SamMaskDecoderConfig',
        'SamPromptEncoderConfig',
        'SamVisionConfig',
    ],
    'processing_sam': ['SamProcessor'],
}


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _UpperCamelCase						:    Tuple		=						[
        'SAM_PRETRAINED_MODEL_ARCHIVE_LIST',
        'SamModel',
        'SamPreTrainedModel',
    ]
try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _UpperCamelCase						:    str		=						[
        'TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST',
        'TFSamModel',
        'TFSamPreTrainedModel',
    ]
try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _UpperCamelCase						:    int		=						['SamImageProcessor']


if TYPE_CHECKING:
    from .configuration_sam import (
        SAM_PRETRAINED_CONFIG_ARCHIVE_MAP,
        SamConfig,
        SamMaskDecoderConfig,
        SamPromptEncoderConfig,
        SamVisionConfig,
    )
    from .processing_sam import SamProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_sam import SAM_PRETRAINED_MODEL_ARCHIVE_LIST, SamModel, SamPreTrainedModel

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_sam import TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST, TFSamModel, TFSamPreTrainedModel

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .image_processing_sam import SamImageProcessor

else:
    import sys

    _UpperCamelCase						:    Optional[Any]		=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)








__a			:	Optional[Any]							     =		[chr(i) for i in range(32, 1_26)]
__a			:	Dict							     =		list(range(len(alphabets)))
__a			:	List[Any]							     =		list(range(len(alphabets)))
__a			:	Tuple							     =		list(range(len(alphabets)))
__a			:	List[Any]							     =		list(reversed(range(len(alphabets))))
__a			:	List[Any]							     =		[]
__a			:	Optional[int]							     =		0




def  a_		(							)	->			Dict:

			'''simple docstring'''


			global gear_one_pos
			global gear_two_pos
			global gear_three_pos
			UpperCamelCase_     				= gear_one[0]
			gear_one.append(__snake_case					)
			del gear_one[0]
			gear_one_pos += 1
			if gear_one_pos % int(len(__snake_case					)					) == 0:
						UpperCamelCase_     				= gear_two[0]
						gear_two.append(__snake_case					)
						del gear_two[0]
						gear_two_pos += 1
						if gear_two_pos % int(len(__snake_case					)					) == 0:
									UpperCamelCase_     				= gear_three[0]
									gear_three.append(__snake_case					)
									del gear_three[0]
									gear_three_pos += 1




def  a_		(							__snake_case					)	->			Any:

			'''simple docstring'''


			UpperCamelCase_     				= alphabets.index(__snake_case					)
			UpperCamelCase_     				= gear_one[target]
			UpperCamelCase_     				= gear_two[target]
			UpperCamelCase_     				= gear_three[target]
			UpperCamelCase_     				= reflector[target]
			UpperCamelCase_     				= gear_three.index(__snake_case					)
			UpperCamelCase_     				= gear_two.index(__snake_case					)
			UpperCamelCase_     				= gear_one.index(__snake_case					)
			code.append(alphabets[target]					)
			rotator()


if __name__ == "__main__":
				__a			:	Union[str, Any]							     =		list(input("""Type your message:\n"""))
				while True:
								try:
												__a			:	Optional[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!"""
				)
