





'''simple docstring'''



from __future__ import annotations




def       _lowerCAmelCase  ( lowerCamelCase_	:     str						,				lowerCamelCase_	:     dict[str, str] ):
			__lowercase  ,						__lowercase  ,						__lowercase   =							'''''', '''''', ''''''
			__lowercase   =							[]

			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       _lowerCAmelCase  ( lowerCamelCase_	:     str						,				lowerCamelCase_	:     dict[str, str] ):
			__lowercase  ,						__lowercase   =							'''''', ''''''
			__lowercase   =							[]

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

			for digit in this_part:
						tmp += digit
						if len(lowerCamelCase_ ) == len(lowerCamelCase_ ):
									result.append(lowerCamelCase_ )
									__lowercase   =							''''''

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




def       _lowerCAmelCase  ( lowerCamelCase_	:     str						,				lowerCamelCase_	:     str ):
			# Validate message and alphabet, set to upper and remove spaces
			__lowercase   =							alphabet.replace(''' '''						,				'''''' ).upper()
			__lowercase   =							message.replace(''' '''						,				'''''' ).upper()

			# Check length and characters
			if len(lowerCamelCase_ ) != 2_7:
						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
			__lowercase   =							(
			    '''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''',
			)
			__lowercase   =							{}
			__lowercase   =							{}
			for letter, number in zip(lowerCamelCase_						,				lowerCamelCase_ ):
						__lowercase   =							number
						__lowercase   =							letter

			return message, alphabet, character_to_number, number_to_character




def       _lowerCAmelCase  ( lowerCamelCase_	:     str						,				lowerCamelCase_	:     str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ."						,				lowerCamelCase_	:     int = 5 ):
			__lowercase  ,						__lowercase  ,						__lowercase  ,						__lowercase   =							__prepare(
			    lowerCamelCase_						,				lowerCamelCase_ )
			__lowercase  ,						__lowercase   =							'''''', ''''''

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

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

			return encrypted




def       _lowerCAmelCase  ( lowerCamelCase_	:     str						,				lowerCamelCase_	:     str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ."						,				lowerCamelCase_	:     int = 5 ):
			__lowercase  ,						__lowercase  ,						__lowercase  ,						__lowercase   =							__prepare(
			    lowerCamelCase_						,				lowerCamelCase_ )
			__lowercase   =							[]
			__lowercase   =							''''''

			for i in range(0						,				len(lowerCamelCase_ ) + 1						,				lowerCamelCase_ ):
						__lowercase  ,						__lowercase  ,						__lowercase   =							__decrypt_part(message[i : i + period]						,				lowerCamelCase_ )

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

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

			return decrypted


if __name__ == "__main__":
	_SCREAMING_SNAKE_CASE    =			'''DEFEND THE EAST WALL OF THE CASTLE.'''
	_SCREAMING_SNAKE_CASE    =			encrypt_message(msg, '''EPSDUCVWYM.ZLKXNBTFGORIJHAQ''')
	_SCREAMING_SNAKE_CASE    =			decrypt_message(encrypted, '''EPSDUCVWYM.ZLKXNBTFGORIJHAQ''')
	print(f'''Encrypted: {encrypted}\nDecrypted: {decrypted}''')












'''simple docstring'''



import importlib
import inspect
import json
import os
from collections import OrderedDict

# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...image_processing_utils import ImageProcessingMixin
from ...tokenization_utils import TOKENIZER_CONFIG_FILE
from ...utils import FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)
from .feature_extraction_auto import AutoFeatureExtractor
from .image_processing_auto import AutoImageProcessor
from .tokenization_auto import AutoTokenizer


_SCREAMING_SNAKE_CASE    =			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE    =			OrderedDict(
    [
        ('''align''', '''AlignProcessor'''),
        ('''altclip''', '''AltCLIPProcessor'''),
        ('''bark''', '''BarkProcessor'''),
        ('''blip''', '''BlipProcessor'''),
        ('''blip-2''', '''Blip2Processor'''),
        ('''bridgetower''', '''BridgeTowerProcessor'''),
        ('''chinese_clip''', '''ChineseCLIPProcessor'''),
        ('''clap''', '''ClapProcessor'''),
        ('''clip''', '''CLIPProcessor'''),
        ('''clipseg''', '''CLIPSegProcessor'''),
        ('''flava''', '''FlavaProcessor'''),
        ('''git''', '''GitProcessor'''),
        ('''groupvit''', '''CLIPProcessor'''),
        ('''hubert''', '''Wav2Vec2Processor'''),
        ('''instructblip''', '''InstructBlipProcessor'''),
        ('''layoutlmv2''', '''LayoutLMv2Processor'''),
        ('''layoutlmv3''', '''LayoutLMv3Processor'''),
        ('''markuplm''', '''MarkupLMProcessor'''),
        ('''mctct''', '''MCTCTProcessor'''),
        ('''mgp-str''', '''MgpstrProcessor'''),
        ('''oneformer''', '''OneFormerProcessor'''),
        ('''owlvit''', '''OwlViTProcessor'''),
        ('''pix2struct''', '''Pix2StructProcessor'''),
        ('''sam''', '''SamProcessor'''),
        ('''sew''', '''Wav2Vec2Processor'''),
        ('''sew-d''', '''Wav2Vec2Processor'''),
        ('''speech_to_text''', '''Speech2TextProcessor'''),
        ('''speech_to_text_2''', '''Speech2Text2Processor'''),
        ('''speecht5''', '''SpeechT5Processor'''),
        ('''trocr''', '''TrOCRProcessor'''),
        ('''tvlt''', '''TvltProcessor'''),
        ('''unispeech''', '''Wav2Vec2Processor'''),
        ('''unispeech-sat''', '''Wav2Vec2Processor'''),
        ('''vilt''', '''ViltProcessor'''),
        ('''vision-text-dual-encoder''', '''VisionTextDualEncoderProcessor'''),
        ('''wav2vec2''', '''Wav2Vec2Processor'''),
        ('''wav2vec2-conformer''', '''Wav2Vec2Processor'''),
        ('''wavlm''', '''Wav2Vec2Processor'''),
        ('''whisper''', '''WhisperProcessor'''),
        ('''xclip''', '''XCLIPProcessor'''),
    ]
)

_SCREAMING_SNAKE_CASE    =			_LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)




def       _lowerCAmelCase  ( lowerCamelCase_	:     str ):
			for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
						if class_name in processors:
									__lowercase   =							model_type_to_module_name(lowerCamelCase_ )

									__lowercase   =							importlib.import_module(f".{module_name}"						,				'''transformers.models''' )
									try:
												return getattr(lowerCamelCase_						,				lowerCamelCase_ )
									except AttributeError:
												continue

			for processor in PROCESSOR_MAPPING._extra_content.values():
						if getattr(lowerCamelCase_						,				'''__name__'''						,				lowerCamelCase_ ) == class_name:
									return processor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
			__lowercase   =							importlib.import_module('''transformers''' )
			if hasattr(lowerCamelCase_						,				lowerCamelCase_ ):
						return getattr(lowerCamelCase_						,				lowerCamelCase_ )

			return None






class 							__lowercase				:







	'''simple docstring'''

	def __init__(self       )							->							List[Any]:







				'''simple docstring'''





				raise EnvironmentError(
				    '''AutoProcessor is designed to be instantiated '''
				    '''using the `AutoProcessor.from_pretrained(pretrained_model_name_or_path)` method.'''       )


	@classmethod
	@replace_list_option_in_docstrings(_lowerCamelCase       )
	def   _UpperCAmelCase   (cls   ,_lowerCamelCase   ,**_lowerCamelCase       )							->							Optional[Any]:







				'''simple docstring'''





				__lowercase   =							kwargs.pop('''config'''   ,_lowerCamelCase       )
				__lowercase   =							kwargs.pop('''trust_remote_code'''   ,_lowerCamelCase       )
				__lowercase   =							True

				__lowercase   =							None
				__lowercase   =							None

				# First, let's see if we have a preprocessor config.
				# Filter the kwargs for `get_file_from_repo`.
				__lowercase   =							{
				    key: kwargs[key] for key in inspect.signature(_lowerCamelCase       ).parameters.keys() if key in kwargs
				}
				# Let's start by checking whether the processor class is saved in an image processor
				__lowercase   =							get_file_from_repo(
				    _lowerCamelCase   ,_lowerCamelCase   ,**_lowerCamelCase       )
				if preprocessor_config_file is not None:
							__lowercase  ,						__lowercase   =							ImageProcessingMixin.get_image_processor_dict(_lowerCamelCase   ,**_lowerCamelCase       )
							__lowercase   =							config_dict.get('''processor_class'''   ,_lowerCamelCase       )
							if "AutoProcessor" in config_dict.get('''auto_map'''   ,{}       ):
										__lowercase   =							config_dict['''auto_map''']['''AutoProcessor''']

        # If not found, let's check whether the processor class is saved in a feature extractor config
				if preprocessor_config_file is not None and processor_class is None:
							__lowercase  ,						__lowercase   =							FeatureExtractionMixin.get_feature_extractor_dict(_lowerCamelCase   ,**_lowerCamelCase       )
							__lowercase   =							config_dict.get('''processor_class'''   ,_lowerCamelCase       )
							if "AutoProcessor" in config_dict.get('''auto_map'''   ,{}       ):
										__lowercase   =							config_dict['''auto_map''']['''AutoProcessor''']

				if processor_class is None:
							# Next, let's check whether the processor class is saved in a tokenizer
							__lowercase   =							get_file_from_repo(
							    _lowerCamelCase   ,_lowerCamelCase   ,**_lowerCamelCase       )
							if tokenizer_config_file is not None:
										with open(_lowerCamelCase   ,encoding='''utf-8'''       ) as reader:
													__lowercase   =							json.load(_lowerCamelCase       )

										__lowercase   =							config_dict.get('''processor_class'''   ,_lowerCamelCase       )
										if "AutoProcessor" in config_dict.get('''auto_map'''   ,{}       ):
													__lowercase   =							config_dict['''auto_map''']['''AutoProcessor''']

				if processor_class is None:
							# Otherwise, load config, if it can be loaded.
							if not isinstance(_lowerCamelCase   ,_lowerCamelCase       ):
										__lowercase   =							AutoConfig.from_pretrained(
										    _lowerCamelCase   ,trust_remote_code=_lowerCamelCase   ,**_lowerCamelCase       )

							# And check if the config contains the processor class.
							__lowercase   =							getattr(_lowerCamelCase   ,'''processor_class'''   ,_lowerCamelCase       )
							if hasattr(_lowerCamelCase   ,'''auto_map'''       ) and "AutoProcessor" in config.auto_map:
										__lowercase   =							config.auto_map['''AutoProcessor''']

				if processor_class is not None:
							__lowercase   =							processor_class_from_name(_lowerCamelCase       )

				__lowercase   =							processor_auto_map is not None
				__lowercase   =							processor_class is not None or type(_lowerCamelCase       ) in PROCESSOR_MAPPING
				__lowercase   =							resolve_trust_remote_code(
				    _lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase       )

				if has_remote_code and trust_remote_code:
							__lowercase   =							get_class_from_dynamic_module(
							    _lowerCamelCase   ,_lowerCamelCase   ,**_lowerCamelCase       )
							__lowercase   =							kwargs.pop('''code_revision'''   ,_lowerCamelCase       )
							if os.path.isdir(_lowerCamelCase       ):
										processor_class.register_for_auto_class()
							return processor_class.from_pretrained(
							    _lowerCamelCase   ,trust_remote_code=_lowerCamelCase   ,**_lowerCamelCase       )
				elif processor_class is not None:
							return processor_class.from_pretrained(
							    _lowerCamelCase   ,trust_remote_code=_lowerCamelCase   ,**_lowerCamelCase       )
				# Last try: we use the PROCESSOR_MAPPING.
				elif type(_lowerCamelCase       ) in PROCESSOR_MAPPING:
							return PROCESSOR_MAPPING[type(_lowerCamelCase       )].from_pretrained(_lowerCamelCase   ,**_lowerCamelCase       )

				# At this stage, there doesn't seem to be a `Processor` class available for this model, so let's try a
				# tokenizer.
				try:
							return AutoTokenizer.from_pretrained(
							    _lowerCamelCase   ,trust_remote_code=_lowerCamelCase   ,**_lowerCamelCase       )
				except Exception:
							try:
										return AutoImageProcessor.from_pretrained(
										    _lowerCamelCase   ,trust_remote_code=_lowerCamelCase   ,**_lowerCamelCase       )
							except Exception:
										pass

							try:
										return AutoFeatureExtractor.from_pretrained(
										    _lowerCamelCase   ,trust_remote_code=_lowerCamelCase   ,**_lowerCamelCase       )
							except Exception:
										pass

				raise ValueError(
				    f"Unrecognized processing class in {pretrained_model_name_or_path}. Can't instantiate a processor, a "
				    '''tokenizer, an image processor or a feature extractor for this model. Make sure the repository contains'''
				    '''the files of at least one of those processing classes.'''       )


	@staticmethod
	def   _UpperCAmelCase   (_lowerCamelCase   ,_lowerCamelCase       )							->							int:







				'''simple docstring'''





				PROCESSOR_MAPPING.register(_lowerCamelCase   ,_lowerCamelCase       )





