


'''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_efficientformer""": [
        """EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """EfficientFormerConfig""",
    ]
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCAmelCase				: Tuple							    =				["""EfficientFormerImageProcessor"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCAmelCase				: Tuple							    =				[
	    """EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """EfficientFormerForImageClassification""",
	    """EfficientFormerForImageClassificationWithTeacher""",
	    """EfficientFormerModel""",
	    """EfficientFormerPreTrainedModel""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCAmelCase				: Union[str, Any]							    =				[
	    """TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFEfficientFormerForImageClassification""",
	    """TFEfficientFormerForImageClassificationWithTeacher""",
	    """TFEfficientFormerModel""",
	    """TFEfficientFormerPreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_efficientformer import EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientFormerConfig

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .image_processing_efficientformer import EfficientFormerImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_efficientformer import (
		    EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    EfficientFormerForImageClassification,
		    EfficientFormerForImageClassificationWithTeacher,
		    EfficientFormerModel,
		    EfficientFormerPreTrainedModel,
		)
	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_efficientformer import (
		    TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFEfficientFormerForImageClassification,
		    TFEfficientFormerForImageClassificationWithTeacher,
		    TFEfficientFormerModel,
		    TFEfficientFormerPreTrainedModel,
		)

else:
	import sys

	_UpperCAmelCase				: Dict							    =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





'''simple docstring'''

from string import ascii_uppercase

_UpperCAmelCase				: Optional[int]							    =				{char: i for i, char in enumerate(ascii_uppercase)}
_UpperCAmelCase				: Optional[int]							    =				dict(enumerate(ascii_uppercase))

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase):
						__lowerCAmelCase							       =				len(lowerCamelCase)
						__lowerCAmelCase							       =				0
						while True:
												if x == i:
																		__lowerCAmelCase							       =				0
												if len(lowerCamelCase) == len(lowerCamelCase):
																		break
												key += key[i]
												i += 1
						return key

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase):
						__lowerCAmelCase							       =				''''''
						__lowerCAmelCase							       =				0
						for letter in message:
												if letter == " ":
																		cipher_text += " "
												else:
																		__lowerCAmelCase							       =				(dicta[letter] - dicta[key_new[i]]) % 2_6
																		i += 1
																		cipher_text += dicta[x]
						return cipher_text

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase):
						__lowerCAmelCase							       =				''''''
						__lowerCAmelCase							       =				0
						for letter in cipher_text:
												if letter == " ":
																		or_txt += " "
												else:
																		__lowerCAmelCase							       =				(dicta[letter] + dicta[key_new[i]] + 2_6) % 2_6
																		i += 1
																		or_txt += dicta[x]
						return or_txt

def 							__magic_name__(						):
						__lowerCAmelCase							       =				'''THE GERMAN ATTACK'''
						__lowerCAmelCase							       =				'''SECRET'''
						__lowerCAmelCase							       =				generate_key(lowerCamelCase,				lowerCamelCase)
						__lowerCAmelCase							       =				cipher_text(lowerCamelCase,				lowerCamelCase)
						print(F"""Encrypted Text = {s}""")
						print(F"""Original Text = {original_text(lowerCamelCase,				lowerCamelCase)}""")


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()

