
"""simple docstring"""







from string import ascii_uppercase

lowerCamelCase__           =	{char: i for i, char in enumerate(ascii_uppercase)}
lowerCamelCase__           =	dict(enumerate(ascii_uppercase))
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase	):
	__lowerCAmelCase    :		Tuple		    =   len(_UpperCamelCase	)
	__lowerCAmelCase    :		Any		    =   0
	while True:
		if x == i:
			__lowerCAmelCase    :		Union[str, Any]		    =   0
		if len(_UpperCamelCase	) == len(_UpperCamelCase	):
			break
		key += key[i]
		i += 1
	return key
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase	):
	__lowerCAmelCase    :		str		    =   ''
	__lowerCAmelCase    :		Any		    =   0
	for letter in message:
		if letter == " ":
			cipher_text += " "
		else:
			__lowerCAmelCase    :		Union[str, Any]		    =   (dicta[letter] - dicta[key_new[i]]) % 26
			i += 1
			cipher_text += dicta[x]
	return cipher_text
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase	):
	__lowerCAmelCase    :		Union[str, Any]		    =   ''
	__lowerCAmelCase    :		Optional[Any]		    =   0
	for letter in cipher_text:
		if letter == " ":
			or_txt += " "
		else:
			__lowerCAmelCase    :		Any		    =   (dicta[letter] + dicta[key_new[i]] + 26) % 26
			i += 1
			or_txt += dicta[x]
	return or_txt
def 					__lowerCAmelCase       ():
	__lowerCAmelCase    :		str		    =   'THE GERMAN ATTACK'
	__lowerCAmelCase    :		Tuple		    =   'SECRET'
	__lowerCAmelCase    :		List[str]		    =   generate_key(_UpperCamelCase ,						_UpperCamelCase	)
	__lowerCAmelCase    :		Any		    =   cipher_text(_UpperCamelCase ,						_UpperCamelCase	)
	print(F"Encrypted Text = {s}"	)
	print(F"Original Text = {original_text(_UpperCamelCase ,						_UpperCamelCase	)}"	)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()

"""simple docstring"""







from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class 				A__ (			_lowerCamelCase):
		A_		: Any            =			['image_processor', 'tokenizer']
		A_		: Optional[Any]            =			'BlipImageProcessor'
		A_		: int            =			'AutoTokenizer'

		def __init__(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		Union[str, Any]		    =   False
			super().__init__(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		List[Any]		    =   self.image_processor

		def __call__(   self ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = True ,	_SCREAMING_SNAKE_CASE = False ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = 0 ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = False ,	_SCREAMING_SNAKE_CASE = False ,	_SCREAMING_SNAKE_CASE = False ,	_SCREAMING_SNAKE_CASE = False ,	_SCREAMING_SNAKE_CASE = False ,	_SCREAMING_SNAKE_CASE = True ,	_SCREAMING_SNAKE_CASE = None ,	**_SCREAMING_SNAKE_CASE ,	):
			if images is None and text is None:
				raise ValueError('You have to specify either images or text.'				)

			# Get only text
			if images is None:
				__lowerCAmelCase    :		Optional[int]		    =   self.tokenizer
				__lowerCAmelCase    :		List[str]		    =   self.tokenizer(
				    text=_SCREAMING_SNAKE_CASE ,	add_special_tokens=_SCREAMING_SNAKE_CASE ,	padding=_SCREAMING_SNAKE_CASE ,	truncation=_SCREAMING_SNAKE_CASE ,	max_length=_SCREAMING_SNAKE_CASE ,	stride=_SCREAMING_SNAKE_CASE ,	pad_to_multiple_of=_SCREAMING_SNAKE_CASE ,	return_attention_mask=_SCREAMING_SNAKE_CASE ,	return_overflowing_tokens=_SCREAMING_SNAKE_CASE ,	return_special_tokens_mask=_SCREAMING_SNAKE_CASE ,	return_offsets_mapping=_SCREAMING_SNAKE_CASE ,	return_token_type_ids=_SCREAMING_SNAKE_CASE ,	return_length=_SCREAMING_SNAKE_CASE ,	verbose=_SCREAMING_SNAKE_CASE ,	return_tensors=_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE ,	)
				return text_encoding

			# add pixel_values
			__lowerCAmelCase    :		List[Any]		    =   self.image_processor(_SCREAMING_SNAKE_CASE ,	return_tensors=_SCREAMING_SNAKE_CASE				)

			if text is not None:
				__lowerCAmelCase    :		Optional[int]		    =   self.tokenizer(
				    text=_SCREAMING_SNAKE_CASE ,	add_special_tokens=_SCREAMING_SNAKE_CASE ,	padding=_SCREAMING_SNAKE_CASE ,	truncation=_SCREAMING_SNAKE_CASE ,	max_length=_SCREAMING_SNAKE_CASE ,	stride=_SCREAMING_SNAKE_CASE ,	pad_to_multiple_of=_SCREAMING_SNAKE_CASE ,	return_attention_mask=_SCREAMING_SNAKE_CASE ,	return_overflowing_tokens=_SCREAMING_SNAKE_CASE ,	return_special_tokens_mask=_SCREAMING_SNAKE_CASE ,	return_offsets_mapping=_SCREAMING_SNAKE_CASE ,	return_token_type_ids=_SCREAMING_SNAKE_CASE ,	return_length=_SCREAMING_SNAKE_CASE ,	verbose=_SCREAMING_SNAKE_CASE ,	return_tensors=_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE ,	)
			else:
				__lowerCAmelCase    :		int		    =   None

			if text_encoding is not None:
				encoding_image_processor.update(_SCREAMING_SNAKE_CASE				)

			return encoding_image_processor

		def 							__lowerCamelCase							(   self ,	*_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				):
			return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self ,	*_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				):
			return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				)







		@property
		# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		int		    =   self.tokenizer.model_input_names
			__lowerCAmelCase    :		Union[str, Any]		    =   self.image_processor.model_input_names
			return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)