



"""simple docstring"""






import warnings
from typing import List, Optional, Union

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

class 			__SCREAMING_SNAKE_CASE      ( lowerCAmelCase_  ):


					'''simple docstring'''





					_a       =				['image_processor', 'tokenizer']
					_a       =				'LayoutLMv2ImageProcessor'
					_a       =				('LayoutLMv2Tokenizer', 'LayoutLMv2TokenizerFast')



					def __init__(			self				:    Optional[Any],    lowerCamelCase				:    Any=None,    lowerCamelCase				:    int=None,    **lowerCamelCase				:    Any	)->   Optional[int]:
								lowerCamelCase__						:						List[str]						      =None
								if "feature_extractor" in kwargs:
											warnings.warn(
											    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
											    ''' instead.''',    lowerCamelCase,    )
											lowerCamelCase__						:						Dict						      =kwargs.pop('''feature_extractor'''	)

								lowerCamelCase__						:						Tuple						      =image_processor if image_processor is not None else feature_extractor
								if image_processor is None:
											raise ValueError('''You need to specify an `image_processor`.'''	)
								if tokenizer is None:
											raise ValueError('''You need to specify a `tokenizer`.'''	)

								super().__init__(lowerCamelCase,    lowerCamelCase	)



					def __call__(			self				:    Union[str, Any],    lowerCamelCase				:    Tuple,    lowerCamelCase				:    Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,    lowerCamelCase				:    Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None,    lowerCamelCase				:    Union[List[List[int]], List[List[List[int]]]] = None,    lowerCamelCase				:    Optional[Union[List[int], List[List[int]]]] = None,    lowerCamelCase				:    bool = True,    lowerCamelCase				:    Union[bool, str, PaddingStrategy] = False,    lowerCamelCase				:    Union[bool, str, TruncationStrategy] = False,    lowerCamelCase				:    Optional[int] = None,    lowerCamelCase				:    int = 0,    lowerCamelCase				:    Optional[int] = None,    lowerCamelCase				:    Optional[bool] = None,    lowerCamelCase				:    Optional[bool] = None,    lowerCamelCase				:    bool = False,    lowerCamelCase				:    bool = False,    lowerCamelCase				:    bool = False,    lowerCamelCase				:    bool = False,    lowerCamelCase				:    bool = True,    lowerCamelCase				:    Optional[Union[str, TensorType]] = None,    **lowerCamelCase				:    Optional[Any],    )->   BatchEncoding:
								# verify input
								if self.image_processor.apply_ocr and (boxes is not None):
											raise ValueError(
											    '''You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.'''	)

								if self.image_processor.apply_ocr and (word_labels is not None):
											raise ValueError(
											    '''You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'''	)

								if return_overflowing_tokens is True and return_offsets_mapping is False:
											raise ValueError('''You cannot return overflowing tokens without returning the offsets mapping.'''	)

								# first, apply the image processor
								lowerCamelCase__						:						Dict						      =self.image_processor(images=lowerCamelCase,    return_tensors=lowerCamelCase	)

								# second, apply the tokenizer
								if text is not None and self.image_processor.apply_ocr and text_pair is None:
											if isinstance(lowerCamelCase,    lowerCamelCase	):
														lowerCamelCase__						:						List[str]						      =[text]  # add batch dimension (as the image processor always adds a batch dimension)
											lowerCamelCase__						:						int						      =features['''words''']

								lowerCamelCase__						:						Tuple						      =self.tokenizer(
								    text=text if text is not None else features['''words'''],    text_pair=text_pair if text_pair is not None else None,    boxes=boxes if boxes is not None else features['''boxes'''],    word_labels=lowerCamelCase,    add_special_tokens=lowerCamelCase,    padding=lowerCamelCase,    truncation=lowerCamelCase,    max_length=lowerCamelCase,    stride=lowerCamelCase,    pad_to_multiple_of=lowerCamelCase,    return_token_type_ids=lowerCamelCase,    return_attention_mask=lowerCamelCase,    return_overflowing_tokens=lowerCamelCase,    return_special_tokens_mask=lowerCamelCase,    return_offsets_mapping=lowerCamelCase,    return_length=lowerCamelCase,    verbose=lowerCamelCase,    return_tensors=lowerCamelCase,    **lowerCamelCase,    )

								# add pixel values
								lowerCamelCase__						:						List[Any]						      =features.pop('''pixel_values'''	)
								if return_overflowing_tokens is True:
											lowerCamelCase__						:						Union[str, Any]						      =self.get_overflowing_images(lowerCamelCase,    encoded_inputs['''overflow_to_sample_mapping''']	)
								lowerCamelCase__						:						Optional[int]						      =images

								return encoded_inputs



					def 						snake_case			(			self				:    List[str],    lowerCamelCase				:    Tuple,    lowerCamelCase				:    Dict	)->   Optional[int]:
								# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
								lowerCamelCase__						:						Optional[int]						      =[]
								for sample_idx in overflow_to_sample_mapping:
											images_with_overflow.append(images[sample_idx]	)

								if len(lowerCamelCase	) != len(lowerCamelCase	):
											raise ValueError(
											    '''Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'''
											    F''' {len(lowerCamelCase	)} and {len(lowerCamelCase	)}'''	)

								return images_with_overflow



					def 						snake_case			(			self				:    Tuple,    *lowerCamelCase				:    List[str],    **lowerCamelCase				:    str	)->   List[Any]:
								return self.tokenizer.batch_decode(*lowerCamelCase,    **lowerCamelCase	)



					def 						snake_case			(			self				:    Tuple,    *lowerCamelCase				:    Dict,    **lowerCamelCase				:    Tuple	)->   Any:
								return self.tokenizer.decode(*lowerCamelCase,    **lowerCamelCase	)



					@property
					def 						snake_case			(			self				:    List[str]	)->   Tuple:
								return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]



					@property
					def 						snake_case			(			self				:    List[Any]	)->   List[str]:
								warnings.warn(
								    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''',    lowerCamelCase,    )
								return self.image_processor_class




					@property
					def 						snake_case			(			self				:    Optional[Any]	)->   Optional[Any]:
								warnings.warn(
								    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''',    lowerCamelCase,    )
								return self.image_processor









"""simple docstring"""






def 	snake_case__  (						__lowerCamelCase							:       str				,						__lowerCamelCase							:       int = 13      ):
			"""simple docstring"""
			lowerCamelCase__						:						List[str]						      =''''''
			for c in s:
						if "A" <= c <= "Z":
									out += chr(ord('''A'''      ) + (ord(__lowerCamelCase      ) - ord('''A'''      ) + n) % 26      )
						elif "a" <= c <= "z":
									out += chr(ord('''a'''      ) + (ord(__lowerCamelCase      ) - ord('''a'''      ) + n) % 26      )
						else:
									out += c
			return out






def 	snake_case__  (						):
			"""simple docstring"""
			lowerCamelCase__						:						List[str]						      =input('''Enter message: '''      )

			lowerCamelCase__						:						str						      =dencrypt(__lowerCamelCase				,						13      )
			print('''Encryption:'''				,						__lowerCamelCase      )

			lowerCamelCase__						:						Any						      =dencrypt(__lowerCamelCase				,						13      )
			print('''Decryption: '''				,						__lowerCamelCase      )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		main()




