






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 				lowerCAmelCase_	(		__SCREAMING_SNAKE_CASE			):
				_UpperCamelCase					:       int							       = ["""image_processor""", """tokenizer"""]
				_UpperCamelCase					:       Any							       = """LayoutLMv2ImageProcessor"""
				_UpperCamelCase					:       Union[str, Any]							       = ("""LayoutXLMTokenizer""", """LayoutXLMTokenizerFast""")


				def __init__(					self ,						_lowerCAmelCase=None ,						_lowerCAmelCase=None ,						**_lowerCAmelCase      ):







											if "feature_extractor" in kwargs:
																		warnings.warn(
																		    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
																		    ' instead.' ,						_A ,						)
																		_lowercase							:   Optional[Any] 							=   kwargs.pop('feature_extractor'      )

											_lowercase							:   Dict 							=   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__(_A ,						_A      )


				def __call__(					self ,						_lowerCAmelCase ,						_lowerCAmelCase = None ,						_lowerCAmelCase = None ,						_lowerCAmelCase = None ,						_lowerCAmelCase = None ,						_lowerCAmelCase = True ,						_lowerCAmelCase = False ,						_lowerCAmelCase = None ,						_lowerCAmelCase = None ,						_lowerCAmelCase = 0 ,						_lowerCAmelCase = None ,						_lowerCAmelCase = None ,						_lowerCAmelCase = None ,						_lowerCAmelCase = False ,						_lowerCAmelCase = False ,						_lowerCAmelCase = False ,						_lowerCAmelCase = False ,						_lowerCAmelCase = True ,						_lowerCAmelCase = None ,						**_lowerCAmelCase ,						):







											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
											_lowercase							:   List[str] 							=   self.image_processor(images=_A ,						return_tensors=_A      )

											# second, apply the tokenizer
											if text is not None and self.image_processor.apply_ocr and text_pair is None:
																		if isinstance(_A ,						_A      ):
																									_lowercase							:   Any 							=   [text]  # add batch dimension (as the image processor always adds a batch dimension)
																		_lowercase							:   Optional[Any] 							=   features["""words"""]

											_lowercase							:   Optional[int] 							=   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=_A ,						add_special_tokens=_A ,						padding=_A ,						truncation=_A ,						max_length=_A ,						stride=_A ,						pad_to_multiple_of=_A ,						return_token_type_ids=_A ,						return_attention_mask=_A ,						return_overflowing_tokens=_A ,						return_special_tokens_mask=_A ,						return_offsets_mapping=_A ,						return_length=_A ,						verbose=_A ,						return_tensors=_A ,						**_A ,						)

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

											return encoded_inputs


				def 						__a					(					self ,						_lowerCAmelCase ,						_lowerCAmelCase      ):
											_lowercase							:   Dict 							=   []
											for sample_idx in overflow_to_sample_mapping:
																		images_with_overflow.append(images[sample_idx]      )

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

											return images_with_overflow


				def 						__a					(					self ,						*_lowerCAmelCase ,						**_lowerCAmelCase      ):
											return self.tokenizer.batch_decode(*_A ,						**_A      )


				def 						__a					(					self ,						*_lowerCAmelCase ,						**_lowerCAmelCase      ):
											return self.tokenizer.decode(*_A ,						**_A      )


				@property
				def 						__a					(					self      ):
											return ["input_ids", "bbox", "attention_mask", "image"]


				@property
				def 						__a					(					self      ):
											warnings.warn(
											    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.' ,						_A ,						)
											return self.image_processor_class


				@property
				def 						__a					(					self      ):
											warnings.warn(
											    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.' ,						_A ,						)
											return self.image_processor











"""simple docstring"""


def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		List[Any]				   =			""""""
						for i in table:
												res += inp[i - 1]
						return res

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						return data[1:] + data[0]

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		Union[str, Any]				   =			""""""
						for i in range(len(SCREAMING_SNAKE_CASE     )     ):
												if a[i] == b[i]:
																		res += "0"
												else:
																		res += "1"
						return res

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		Optional[int]				   =			int("""0b""" + data[0] + data[-1]							,     2     )
						UpperCamelCase				:		Any				   =			int("""0b""" + data[1:3]							,     2     )
						return bin(s[row][col]     )[2:]

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		Any				   =			message[:4]
						UpperCamelCase				:		int				   =			message[4:]
						UpperCamelCase				:		int				   =			apply_table(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )
						UpperCamelCase				:		str				   =			xor(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )
						UpperCamelCase				:		Union[str, Any]				   =			apply_sbox(SCREAMING_SNAKE_CASE							,     temp[:4]     )  # noqa: E741
						UpperCamelCase				:		str				   =			apply_sbox(SCREAMING_SNAKE_CASE							,     temp[4:]     )
						UpperCamelCase				:		Union[str, Any]				   =			"""0""" * (2 - len(SCREAMING_SNAKE_CASE     )) + l  # noqa: E741
						UpperCamelCase				:		Union[str, Any]				   =			"""0""" * (2 - len(SCREAMING_SNAKE_CASE     )) + r
						UpperCamelCase				:		int				   =			apply_table(l + r							,     SCREAMING_SNAKE_CASE     )
						UpperCamelCase				:		int				   =			xor(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )
						return temp + right


if __name__ == "__main__":
		__magic_name__       :					Union[str, Any]						=			input("""Enter 10 bit key: """)
		__magic_name__       :					Union[str, Any]						=			input("""Enter 8 bit message: """)

		__magic_name__       :					Dict						=			[6, 3, 7, 4, 8, 5, 1_0, 9]
		__magic_name__       :					str						=			[3, 5, 2, 7, 4, 1_0, 1, 9, 8, 6]
		__magic_name__       :					Optional[Any]						=			[2, 4, 3, 1]
		__magic_name__       :					Tuple						=			[2, 6, 3, 1, 4, 8, 5, 7]
		__magic_name__       :					str						=			[4, 1, 3, 5, 7, 2, 8, 6]
		__magic_name__       :					str						=			[4, 1, 2, 3, 2, 3, 4, 1]
		__magic_name__       :					Any						=			[[1, 0, 3, 2], [3, 2, 1, 0], [0, 2, 1, 3], [3, 1, 3, 2]]
		__magic_name__       :					Any						=			[[0, 1, 2, 3], [2, 0, 1, 3], [3, 0, 1, 0], [2, 1, 0, 3]]

		# key generation
		__magic_name__       :					Dict						=			apply_table(key, paa_table)
		__magic_name__       :					Union[str, Any]						=			temp[:5]
		__magic_name__       :					Optional[Any]						=			temp[5:]
		__magic_name__       :					str						=			left_shift(left)
		__magic_name__       :					List[str]						=			left_shift(right)
		__magic_name__       :					Tuple						=			apply_table(left + right, pa_table)
		__magic_name__       :					Dict						=			left_shift(left)
		__magic_name__       :					Union[str, Any]						=			left_shift(right)
		__magic_name__       :					List[Any]						=			left_shift(left)
		__magic_name__       :					str						=			left_shift(right)
		__magic_name__       :					List[str]						=			apply_table(left + right, pa_table)

		# encryption
		__magic_name__       :					List[str]						=			apply_table(message, IP)
		__magic_name__       :					str						=			function(expansion, sa, sa, keya, temp)
		__magic_name__       :					str						=			temp[4:] + temp[:4]
		__magic_name__       :					Optional[Any]						=			function(expansion, sa, sa, keya, temp)
		__magic_name__       :					Any						=			apply_table(temp, IP_inv)
		print("""Cipher text is:""", CT)

		# decryption
		__magic_name__       :					Tuple						=			apply_table(CT, IP)
		__magic_name__       :					List[str]						=			function(expansion, sa, sa, keya, temp)
		__magic_name__       :					Dict						=			temp[4:] + temp[:4]
		__magic_name__       :					int						=			function(expansion, sa, sa, keya, temp)
		__magic_name__       :					Any						=			apply_table(temp, IP_inv)
		print("""Plain text after decypting is:""", PT)



