from typing import Literal

UpperCamelCase    				=		'''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''

def      lowercase_							(     _lowerCamelCase				:			int						,      _lowerCamelCase				:			Union[str, Any]						,      _lowerCamelCase				:			Dict):
    lowercase__					:  int          =							LETTERS if mode == "decrypt" else key
    lowercase__					:  Tuple          =							key if mode == "decrypt" else LETTERS
    lowercase__					:  int          =							""
    # loop through each symbol in the message
    for symbol in message:
        if symbol.upper() in chars_a:
            # encrypt/decrypt the symbol
            lowercase__					:  Optional[Any]          =							chars_a.find(symbol.upper())
            if symbol.isupper():
                translated += chars_b[sym_index].upper()
            else:
                translated += chars_b[sym_index].lower()
        else:
            # symbol is not in LETTERS, just add it
            translated += symbol
    return translated

def      lowercase_							(     _lowerCamelCase				:			int						,      _lowerCamelCase				:			Union[str, Any]):
    return translate_message(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      "encrypt")

def      lowercase_							(     _lowerCamelCase				:			Union[str, Any]						,      _lowerCamelCase				:			Union[str, Any]):
    return translate_message(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      "decrypt")

def      lowercase_							(     ):
    lowercase__					:  str          =							"Hello World"
    lowercase__					:  Dict          =							"QWERTYUIOPASDFGHJKLZXCVBNM"
    lowercase__					:  Optional[int]          =							"decrypt"  # set to 'encrypt' or 'decrypt'

    if mode == "encrypt":
        lowercase__					:  Any          =							encrypt_message(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE)
    elif mode == "decrypt":
        lowercase__					:  Tuple          =							decrypt_message(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE)
    print(f'''Using the key {key}, the {mode}ed message is: {translated}''')


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      main()










"""simple docstring"""





from typing import TYPE_CHECKING

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


lowerCAmelCase__        =      {
    '''configuration_deberta_v2''': ['''DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DebertaV2Config''', '''DebertaV2OnnxConfig'''],
    '''tokenization_deberta_v2''': ['''DebertaV2Tokenizer'''],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__        =      ['''DebertaV2TokenizerFast''']

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__        =      [
						    '''TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TFDebertaV2ForMaskedLM''',
						    '''TFDebertaV2ForQuestionAnswering''',
						    '''TFDebertaV2ForSequenceClassification''',
						    '''TFDebertaV2ForTokenClassification''',
						    '''TFDebertaV2Model''',
						    '''TFDebertaV2PreTrainedModel''',
						]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__        =      [
						    '''DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''DebertaV2ForMaskedLM''',
						    '''DebertaV2ForMultipleChoice''',
						    '''DebertaV2ForQuestionAnswering''',
						    '''DebertaV2ForSequenceClassification''',
						    '''DebertaV2ForTokenClassification''',
						    '''DebertaV2Model''',
						    '''DebertaV2PreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_deberta_va import (
						    DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    DebertaVaConfig,
						    DebertaVaOnnxConfig,
						)
						from .tokenization_deberta_va import DebertaVaTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_deberta_va import (
												    TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFDebertaVaForMaskedLM,
												    TFDebertaVaForQuestionAnswering,
												    TFDebertaVaForSequenceClassification,
												    TFDebertaVaForTokenClassification,
												    TFDebertaVaModel,
												    TFDebertaVaPreTrainedModel,
												)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_deberta_va import (
												    DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
												    DebertaVaForMaskedLM,
												    DebertaVaForMultipleChoice,
												    DebertaVaForQuestionAnswering,
												    DebertaVaForSequenceClassification,
												    DebertaVaForTokenClassification,
												    DebertaVaModel,
												    DebertaVaPreTrainedModel,
												)

else:
						import sys

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






