






def 	lowerCAmelCase_		(lowercase__						:       str       )		->    str:


				'''simple docstring'''




				lowerCAmelCase__      			=						''''''
				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 	lowerCAmelCase_		(lowercase__						:       str       )		->    dict[str, str]:


				'''simple docstring'''




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





def 	lowerCAmelCase_		(lowercase__						:       str     ,					lowercase__						:       dict[str, str]       )		->    str:


				'''simple docstring'''




				return "".join(cipher_map.get(lowercase__     ,					lowercase__       ) for ch in message.upper()       )





def 	lowerCAmelCase_		(lowercase__						:       str     ,					lowercase__						:       dict[str, str]       )		->    str:


				'''simple docstring'''




				lowerCAmelCase__      			=						{v: k for k, v in cipher_map.items()}
				return "".join(rev_cipher_map.get(lowercase__     ,					lowercase__       ) for ch in message.upper()       )





def 	lowerCAmelCase_		()		->    None:


				'''simple docstring'''




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


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			main()













from typing import TYPE_CHECKING

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


_UpperCAmelCase			:		str   				=    {
    "configuration_blenderbot_small": [
        "BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlenderbotSmallConfig",
        "BlenderbotSmallOnnxConfig",
    ],
    "tokenization_blenderbot_small": ["BlenderbotSmallTokenizer"],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		List[str]   				=    ["BlenderbotSmallTokenizerFast"]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		List[str]   				=    [
			    "BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "BlenderbotSmallForCausalLM",
			    "BlenderbotSmallForConditionalGeneration",
			    "BlenderbotSmallModel",
			    "BlenderbotSmallPreTrainedModel",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		Dict   				=    [
			    "TFBlenderbotSmallForConditionalGeneration",
			    "TFBlenderbotSmallModel",
			    "TFBlenderbotSmallPreTrainedModel",
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		List[str]   				=    [
			    "FlaxBlenderbotSmallForConditionalGeneration",
			    "FlaxBlenderbotSmallModel",
			    "FlaxBlenderbotSmallPreTrainedModel",
			]

if TYPE_CHECKING:
			from .configuration_blenderbot_small import (
			    BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    BlenderbotSmallConfig,
			    BlenderbotSmallOnnxConfig,
			)
			from .tokenization_blenderbot_small import BlenderbotSmallTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_blenderbot_small_fast import BlenderbotSmallTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_blenderbot_small import (
						    BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST,
						    BlenderbotSmallForCausalLM,
						    BlenderbotSmallForConditionalGeneration,
						    BlenderbotSmallModel,
						    BlenderbotSmallPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_blenderbot_small import (
						    TFBlenderbotSmallForConditionalGeneration,
						    TFBlenderbotSmallModel,
						    TFBlenderbotSmallPreTrainedModel,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_blenderbot_small import (
						    FlaxBlenderbotSmallForConditionalGeneration,
						    FlaxBlenderbotSmallModel,
						    FlaxBlenderbotSmallPreTrainedModel,
						)

else:
			import sys

			_UpperCAmelCase			:		int   				=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





