
'''simple docstring'''





def      A (       A_							:					str       ):
						snake_case       :     List[str] =    ''''''
						for ch in key:
												if ch == " " or ch not in key_no_dups and ch.isalpha():
																		key_no_dups += ch
						return key_no_dups

def      A (       A_							:					str       ):
						snake_case       :     List[Any] =    [chr(i + 65       ) for i in range(26       )]
						# Remove duplicate characters from key
						snake_case       :     Any =    remove_duplicates(key.upper()       )
						snake_case       :     Any =    len(A_       )
						# First fill cipher with key characters
						snake_case       :     List[Any] =    {alphabet[i]: char for i, char in enumerate(A_       )}
						# Then map remaining characters in alphabet to
						# the alphabet from the beginning
						for i in range(len(A_       )    ,							26       ):
												snake_case       :     Optional[Any] =    alphabet[i - offset]
												# Ensure we are not mapping letters to letters previously mapped
												while char in key:
																		offset -= 1
																		snake_case       :     Union[str, Any] =    alphabet[i - offset]
												snake_case       :     List[Any] =    char
						return cipher_alphabet

def      A (       A_							:					str    ,							A_							:					dict[str, str]       ):
						return "".join(cipher_map.get(A_    ,							A_       ) for ch in message.upper()       )

def      A (       A_							:					str    ,							A_							:					dict[str, str]       ):
						snake_case       :     Union[str, Any] =    {v: k for k, v in cipher_map.items()}
						return "".join(rev_cipher_map.get(A_    ,							A_       ) for ch in message.upper()       )

def      A (       ):
						snake_case       :     Optional[int] =    input('''Enter message to encode or decode: '''       ).strip()
						snake_case       :     int =    input('''Enter keyword: '''       ).strip()
						snake_case       :     Any =    input('''Encipher or decipher? E/D:'''       ).strip()[0].lower()
						try:
												snake_case       :     Optional[Any] =    {'''e''': encipher, '''d''': decipher}[option]
						except KeyError:
												raise KeyError('''invalid input option'''       )
						snake_case       :     str =    create_cipher_map(A_       )
						print(func(A_    ,							A_       )       )


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,
)


UpperCAmelCase    					=   {
    "configuration_convbert": ["CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ConvBertConfig", "ConvBertOnnxConfig"],
    "tokenization_convbert": ["ConvBertTokenizer"],
}

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

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCAmelCase    					=   [
						    "CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "ConvBertForMaskedLM",
						    "ConvBertForMultipleChoice",
						    "ConvBertForQuestionAnswering",
						    "ConvBertForSequenceClassification",
						    "ConvBertForTokenClassification",
						    "ConvBertLayer",
						    "ConvBertModel",
						    "ConvBertPreTrainedModel",
						    "load_tf_weights_in_convbert",
						]


try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCAmelCase    					=   [
						    "TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "TFConvBertForMaskedLM",
						    "TFConvBertForMultipleChoice",
						    "TFConvBertForQuestionAnswering",
						    "TFConvBertForSequenceClassification",
						    "TFConvBertForTokenClassification",
						    "TFConvBertLayer",
						    "TFConvBertModel",
						    "TFConvBertPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
						from .tokenization_convbert import ConvBertTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_convbert_fast import ConvBertTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_convbert import (
												    CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ConvBertForMaskedLM,
												    ConvBertForMultipleChoice,
												    ConvBertForQuestionAnswering,
												    ConvBertForSequenceClassification,
												    ConvBertForTokenClassification,
												    ConvBertLayer,
												    ConvBertModel,
												    ConvBertPreTrainedModel,
												    load_tf_weights_in_convbert,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_convbert import (
												    TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFConvBertForMaskedLM,
												    TFConvBertForMultipleChoice,
												    TFConvBertForQuestionAnswering,
												    TFConvBertForSequenceClassification,
												    TFConvBertForTokenClassification,
												    TFConvBertLayer,
												    TFConvBertModel,
												    TFConvBertPreTrainedModel,
												)


else:
						import sys

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

