




"""simple docstring"""







import string





def 							lowercase					(snake_case__   : str )     ->  str:


				'''simple docstring'''






				lowerCAmelCase  =				""""""
				for i in sequence:
								lowerCAmelCase  =				ord(snake_case__ )
								if 65 <= extract <= 90:
												output += chr(155 - extract )
								elif 97 <= extract <= 122:
												output += chr(219 - extract )
								else:
												output += i
				return output





def 							lowercase					(snake_case__   : str )     ->  str:


				'''simple docstring'''






				lowerCAmelCase  =				string.ascii_letters
				lowerCAmelCase  =				string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
				return "".join(
				    letters_reversed[letters.index(snake_case__ )] if c in letters else c for c in sequence )





def 							lowercase					()     ->  None:


				'''simple docstring'''






				from timeit import timeit

				print("""Running performance benchmarks...""" )
				lowerCAmelCase  =				"""from string import printable ; from __main__ import atbash, atbash_slow"""
				print(f'''> atbash_slow(): {timeit('atbash_slow(printable)'      ,       setup=snake_case__ )} seconds''' )
				print(f'''>      atbash(): {timeit('atbash(printable)'      ,       setup=snake_case__ )} seconds''' )


if __name__ == "__main__":
		for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
				print(f"""{example} encrypted in atbash: {atbash(example)}""")
		benchmark()











"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a      =							{
    'configuration_time_series_transformer': [
        'TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'TimeSeriesTransformerConfig',
    ],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a      =							[
		    'TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TimeSeriesTransformerForPrediction',
		    'TimeSeriesTransformerModel',
		    'TimeSeriesTransformerPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_time_series_transformer import (
		    TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    TimeSeriesTransformerConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_time_series_transformer import (
				    TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TimeSeriesTransformerForPrediction,
				    TimeSeriesTransformerModel,
				    TimeSeriesTransformerPreTrainedModel,
				)

else:
		import sys

		a      =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





