



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


_snake_case	=						{"configuration_reformer": ["REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "ReformerConfig"]}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_snake_case	=						["ReformerTokenizer"]

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_snake_case	=						["ReformerTokenizerFast"]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_snake_case	=						[
	    "REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "ReformerAttention",
	    "ReformerForMaskedLM",
	    "ReformerForQuestionAnswering",
	    "ReformerForSequenceClassification",
	    "ReformerLayer",
	    "ReformerModel",
	    "ReformerModelWithLMHead",
	    "ReformerPreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_reformer import ReformerTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_reformer_fast import ReformerTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_reformer import (
		    REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    ReformerAttention,
		    ReformerForMaskedLM,
		    ReformerForQuestionAnswering,
		    ReformerForSequenceClassification,
		    ReformerLayer,
		    ReformerModel,
		    ReformerModelWithLMHead,
		    ReformerPreTrainedModel,
		)

else:
	import sys

	_snake_case	=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





import string






def        A       (      _lowerCamelCase						):





							'''simple docstring'''




							for key in range(len(string.ascii_uppercase						)						):
														_lowerCAmelCase  :							str				     =					""
														for symbol in message:
																					if symbol in string.ascii_uppercase:
																												_lowerCAmelCase  :							List[str]				     =					string.ascii_uppercase.find(_lowerCamelCase						)
																												_lowerCAmelCase  :							Dict				     =					num - key
																												if num < 0:
																																			_lowerCAmelCase  :							Dict				     =					num + len(string.ascii_uppercase						)
																												_lowerCAmelCase  :							Optional[Any]				     =					translated + string.ascii_uppercase[num]
																					else:
																												_lowerCAmelCase  :							int				     =					translated + symbol
														print(F"Decryption using Key #{key}: {translated}"						)






def        A       (      ):





							'''simple docstring'''




							_lowerCAmelCase  :							Tuple				     =					input("Encrypted message: "						)
							_lowerCAmelCase  :							Dict				     =					message.upper()
							decrypt(_lowerCamelCase						)


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						main()
