






from __future__ import annotations






def 				A	(			a_				,a_   )	->		str:
					__UpperCamelCase						,							__UpperCamelCase						,							__UpperCamelCase :   Optional[Any]									='', '', ''
					__UpperCamelCase :   str									=[]

					for character in message_part:
										tmp.append(character_to_number[character]   )

					for each in tmp:
										one += each[0]
										two += each[1]
										three += each[2]

					return one + two + three






def 				A	(			a_				,a_   )	->		tuple[str, str, str]:
					__UpperCamelCase						,							__UpperCamelCase :   Union[str, Any]									='', ''
					__UpperCamelCase :   List[str]									=[]

					for character in message_part:
										this_part += character_to_number[character]

					for digit in this_part:
										tmp += digit
										if len(a_   ) == len(a_   ):
															result.append(a_   )
															__UpperCamelCase :   int									=''

					return result[0], result[1], result[2]






def 				A	(			a_				,a_   )	->		tuple[str, str, dict[str, str], dict[str, str]]:
					# Validate message and alphabet, set to upper and remove spaces
					__UpperCamelCase :   Tuple									=alphabet.replace(' '				,''   ).upper()
					__UpperCamelCase :   int									=message.replace(' '				,''   ).upper()

					# Check length and characters
					if len(a_   ) != 27:
										raise KeyError('Length of alphabet has to be 27.'   )
					for each in message:
										if each not in alphabet:
															raise ValueError('Each message character has to be included in alphabet!'   )

    # Generate dictionares
					__UpperCamelCase :   List[str]									=(
					    '111',
					    '112',
					    '113',
					    '121',
					    '122',
					    '123',
					    '131',
					    '132',
					    '133',
					    '211',
					    '212',
					    '213',
					    '221',
					    '222',
					    '223',
					    '231',
					    '232',
					    '233',
					    '311',
					    '312',
					    '313',
					    '321',
					    '322',
					    '323',
					    '331',
					    '332',
					    '333',
					)
					__UpperCamelCase :   Optional[int]									={}
					__UpperCamelCase :   str									={}
					for letter, number in zip(a_				,a_   ):
										__UpperCamelCase :   List[str]									=number
										__UpperCamelCase :   Dict									=letter

					return message, alphabet, character_to_number, number_to_character






def 				A	(			a_				,a_ = "ABCDEFGHIJKLMNOPQRSTUVWXYZ."				,a_ = 5   )	->		str:
					__UpperCamelCase						,							__UpperCamelCase						,							__UpperCamelCase						,							__UpperCamelCase :   Dict									=__prepare(
					    a_				,a_   )
					__UpperCamelCase						,							__UpperCamelCase :   Optional[Any]									='', ''

					for i in range(0				,len(a_   ) + 1				,a_   ):
										encrypted_numeric += __encrypt_part(
										    message[i : i + period]				,a_   )

					for i in range(0				,len(a_   )				,3   ):
										encrypted += number_to_character[encrypted_numeric[i : i + 3]]

					return encrypted






def 				A	(			a_				,a_ = "ABCDEFGHIJKLMNOPQRSTUVWXYZ."				,a_ = 5   )	->		str:
					__UpperCamelCase						,							__UpperCamelCase						,							__UpperCamelCase						,							__UpperCamelCase :   List[str]									=__prepare(
					    a_				,a_   )
					__UpperCamelCase :   Dict									=[]
					__UpperCamelCase :   str									=''

					for i in range(0				,len(a_   ) + 1				,a_   ):
										__UpperCamelCase						,							__UpperCamelCase						,							__UpperCamelCase :   List[str]									=__decrypt_part(message[i : i + period]				,a_   )

										for j in range(0				,len(a_   )   ):
															decrypted_numeric.append(a[j] + b[j] + c[j]   )

					for each in decrypted_numeric:
										decrypted += number_to_character[each]

					return decrypted


if __name__ == "__main__":
							A_			:Optional[int]	    =		'''DEFEND THE EAST WALL OF THE CASTLE.'''
							A_			:str	    =		encrypt_message(msg, '''EPSDUCVWYM.ZLKXNBTFGORIJHAQ''')
							A_			:List[Any]	    =		decrypt_message(encrypted, '''EPSDUCVWYM.ZLKXNBTFGORIJHAQ''')
							print(f"Encrypted: {encrypted}\nDecrypted: {decrypted}")









import os

from packaging import version

from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
    CONFIG_NAME,
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    DIFFUSERS_DYNAMIC_MODULE_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    TEXT_ENCODER_ATTN_MODULE,
    WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
    HF_HUB_OFFLINE,
    _add_variant,
    _get_model_file,
    extract_commit_hash,
    http_user_agent,
)
from .import_utils import (
    BACKENDS_MAPPING,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    is_accelerate_available,
    is_accelerate_version,
    is_bsa_available,
    is_flax_available,
    is_ftfy_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_omegaconf_available,
    is_onnx_available,
    is_safetensors_available,
    is_scipy_available,
    is_tensorboard_available,
    is_tf_available,
    is_torch_available,
    is_torch_version,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    is_wandb_available,
    is_xformers_available,
    requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor


if is_torch_available():
							from .testing_utils import (
							    floats_tensor,
							    load_hf_numpy,
							    load_image,
							    load_numpy,
							    load_pt,
							    nightly,
							    parse_flag_from_env,
							    print_tensor_test,
							    require_torch_a,
							    require_torch_gpu,
							    skip_mps,
							    slow,
							    torch_all_close,
							    torch_device,
							)
							from .torch_utils import maybe_allow_in_graph

from .testing_utils import export_to_gif, export_to_video


A_			:List[str]	    =		get_logger(__name__)






def 				A	(			a_   )	->		Tuple:
					if version.parse(a_   ) < version.parse(a_   ):
										if "dev" in min_version:
															__UpperCamelCase :   List[str]									=(
															    'This example requires a source install from HuggingFace diffusers (see '
															    '`https://huggingface.co/docs/diffusers/installation#install-from-source`),'
															)
										else:
															__UpperCamelCase :   Tuple									=F'This example requires a minimum version of {min_version},'
										error_message += F' but the version found is {__version__}.\n'
										raise ImportError(a_   )

