




def 			__magic_name__				( A		:					str,  A		:					int   ):






			'''simple docstring'''







			a    =			[[] for _ in range(A   )]
			a    =			key - 1

			if key <= 0:
						raise ValueError("Height of grid can't be 0 or negative"   )
			if key == 1 or len(A   ) <= key:
						return input_string

			for position, character in enumerate(A   ):
						a    =			position % (lowest * 2)  # puts it in bounds
						a    =			min(A,  lowest * 2 - num   )  # creates zigzag pattern
						temp_grid[num].append(A   )
			a    =			["".join(A   ) for row in temp_grid]
			a    =			"".join(A   )

			return output_string




def 			__magic_name__				( A		:					str,  A		:					int   ):






			'''simple docstring'''







			a    =			[]
			a    =			key - 1

			if key <= 0:
						raise ValueError("Height of grid can't be 0 or negative"   )
			if key == 1:
						return input_string

			a    =			[[] for _ in range(A   )]  # generates template
			for position in range(len(A   )   ):
						a    =			position % (lowest * 2)  # puts it in bounds
						a    =			min(A,  lowest * 2 - num   )  # creates zigzag pattern
						temp_grid[num].append("*"   )

			a    =			0
			for row in temp_grid:  # fills in the characters
						a    =			input_string[counter : counter + len(A   )]
						grid.append(list(A   )   )
						counter += len(A   )

			a    =			""  # reads as zigzag
			for position in range(len(A   )   ):
						a    =			position % (lowest * 2)  # puts it in bounds
						a    =			min(A,  lowest * 2 - num   )  # creates zigzag pattern
						output_string += grid[num][0]
						grid[num].pop(0   )
			return output_string




def 			__magic_name__				( A		:					str   ):






			'''simple docstring'''







			a    =			{}
			for key_guess in range(1,  len(A   )   ):  # tries every key
						a    =			decrypt(A,  A   )
			return results


if __name__ == "__main__":
		import doctest

		doctest.testmod()








from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE__   :			List[Any]												=    {
    "configuration_resnet": ["RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "ResNetConfig", "ResNetOnnxConfig"]
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :			int												=    [
					    "RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "ResNetForImageClassification",
					    "ResNetModel",
					    "ResNetPreTrainedModel",
					    "ResNetBackbone",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :			List[Any]												=    [
					    "TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TFResNetForImageClassification",
					    "TFResNetModel",
					    "TFResNetPreTrainedModel",
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :			Union[str, Any]												=    [
					    "FlaxResNetForImageClassification",
					    "FlaxResNetModel",
					    "FlaxResNetPreTrainedModel",
					]

if TYPE_CHECKING:
					from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_resnet import (
										    RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ResNetBackbone,
										    ResNetForImageClassification,
										    ResNetModel,
										    ResNetPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_resnet import (
										    TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFResNetForImageClassification,
										    TFResNetModel,
										    TFResNetPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel


else:
					import sys

					SCREAMING_SNAKE_CASE__   :			Any												=    _LazyModule(__name__, globals()["__file__"], _import_structure)



