"""simple docstring"""







import string

import numpy
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       int    ,							UpperCAmelCase_					:       int						)							->    int:

	'''simple docstring'''





	return b if a == 0 else greatest_common_divisor(b % a    ,							UpperCAmelCase_						)

class 	UpperCamelCase    :
				UpperCAmelCase       :   Any					=	string.ascii_uppercase + string.digits
				# This cipher takes alphanumerics into account
				# i.e. a total of 36 characters

				# take x and return x % len(key_string)
				UpperCAmelCase       :   List[Any]					=	numpy.vectorize(lambda lowercase		: x % 36		)

				UpperCAmelCase       :   Dict					=	numpy.vectorize(lowercase		)



				def __init__(self       :   str			,						_A       :   numpy.ndarray)					->			None:
					__snake_case    :							str   	=				self.modulus(_A)  # mod36 calc's on the encrypt key
					self.check_determinant()  # validate the determinant of the encryption key
					__snake_case    :							Optional[Any]   	=				encrypt_key.shape[0]



				def       _lowercase  (self       :   Any			,						_A       :   str)					->			int:
					return self.key_string.index(_A)



				def       _lowercase  (self       :   Union[str, Any]			,						_A       :   int)					->			str:
					return self.key_string[round(_A)]



				def       _lowercase  (self       :   Optional[int])					->			None:
					__snake_case    :							Any   	=				round(numpy.linalg.det(self.encrypt_key))

					if det < 0:
						__snake_case    :							Any   	=				det % len(self.key_string)

					__snake_case    :							Tuple   	=				len(self.key_string)
					if greatest_common_divisor(_A			,						len(self.key_string)) != 1:
						__snake_case    :							List[str]   	=				(
						    f"determinant modular {req_l} of encryption key({det}) "
						    f"is not co prime w.r.t {req_l}.\nTry another key."
						)
						raise ValueError(_A)



				def       _lowercase  (self       :   Dict			,						_A       :   str)					->			str:
					__snake_case    :							str   	=				[char for char in text.upper() if char in self.key_string]

					__snake_case    :							int   	=				chars[-1]
					while len(_A) % self.break_key != 0:
						chars.append(_A)

					return "".join(_A)



				def       _lowercase  (self       :   Union[str, Any]			,						_A       :   str)					->			str:
					__snake_case    :							Any   	=				self.process_text(text.upper())
					__snake_case    :							Dict   	=				''

					for i in range(0			,						len(_A) - self.break_key + 1			,						self.break_key):
						__snake_case    :							Dict   	=				text[i : i + self.break_key]
						__snake_case    :							List[str]   	=				[self.replace_letters(_A) for char in batch]
						__snake_case    :							str   	=				numpy.array([vec]).T
						__snake_case    :							List[Any]   	=				self.modulus(self.encrypt_key.dot(_A)).T.tolist()[
						    0
						]
						__snake_case    :							str   	=				''.join(
						    self.replace_digits(_A) for num in batch_encrypted)
						encrypted += encrypted_batch

					return encrypted



				def       _lowercase  (self       :   Optional[int])					->			numpy.ndarray:
					__snake_case    :							List[Any]   	=				round(numpy.linalg.det(self.encrypt_key))

					if det < 0:
						__snake_case    :							int   	=				det % len(self.key_string)
					__snake_case    :							Optional[Any]   	=				None
					for i in range(len(self.key_string)):
						if (det * i) % len(self.key_string) == 1:
							__snake_case    :							Dict   	=				i
							break

					__snake_case    :							List[str]   	=				(
					    det_inv
					    * numpy.linalg.det(self.encrypt_key)
					    * numpy.linalg.inv(self.encrypt_key)
					)

					return self.to_int(self.modulus(_A))






				def       _lowercase  (self       :   int			,						_A       :   str)					->			str:
					__snake_case    :							int   	=				self.make_decrypt_key()
					__snake_case    :							List[str]   	=				self.process_text(text.upper())
					__snake_case    :							str   	=				''

					for i in range(0			,						len(_A) - self.break_key + 1			,						self.break_key):
						__snake_case    :							Optional[Any]   	=				text[i : i + self.break_key]
						__snake_case    :							Union[str, Any]   	=				[self.replace_letters(_A) for char in batch]
						__snake_case    :							Tuple   	=				numpy.array([vec]).T
						__snake_case    :							List[str]   	=				self.modulus(decrypt_key.dot(_A)).T.tolist()[0]
						__snake_case    :							str   	=				''.join(
						    self.replace_digits(_A) for num in batch_decrypted)
						decrypted += decrypted_batch

					return decrypted







def 						__UpperCAmelCase       (					)							->    None:

	'''simple docstring'''





	__snake_case    :							List[str]   	=				int(input('Enter the order of the encryption key: '						)						)
	__snake_case    :							str   	=				[]

	print('Enter each row of the encryption key with space separated integers'						)
	for _ in range(UpperCAmelCase_						):
		__snake_case    :							Union[str, Any]   	=				[int(UpperCAmelCase_						) for x in input().split()]
		hill_matrix.append(UpperCAmelCase_						)

	__snake_case    :							Dict   	=				HillCipher(numpy.array(UpperCAmelCase_						)						)

	print('Would you like to encrypt or decrypt some text? (1 or 2)'						)
	__snake_case    :							Optional[Any]   	=				input('\n1. Encrypt\n2. Decrypt\n'						)
	if option == "1":
		__snake_case    :							int   	=				input('What text would you like to encrypt?: '						)
		print('Your encrypted text is:'						)
		print(hc.encrypt(UpperCAmelCase_						)						)
	elif option == "2":
		__snake_case    :							Tuple   	=				input('What text would you like to decrypt?: '						)
		print('Your decrypted text is:'						)
		print(hc.decrypt(UpperCAmelCase_						)						)


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	main()






"""simple docstring"""







from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


if TYPE_CHECKING:
	from ...processing_utils import ProcessorMixin
	from ...utils import TensorType


_a  :  Optional[int]=				logging.get_logger(__name__)

_a  :  Tuple=				{
    "microsoft/layoutlmv3-base": "https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json",
}

class 	UpperCamelCase    (					lowercase		):
				UpperCAmelCase       :   Dict					=	"""layoutlmv3"""



				def __init__(self       :   List[str]			,						_A       :   Optional[int]=5_02_65			,						_A       :   List[str]=7_68			,						_A       :   List[Any]=12			,						_A       :   List[str]=12			,						_A       :   Optional[int]=30_72			,						_A       :   str="gelu"			,						_A       :   int=0.1			,						_A       :   Tuple=0.1			,						_A       :   List[Any]=5_12			,						_A       :   List[str]=2			,						_A       :   List[Any]=0.02			,						_A       :   Tuple=1E-5			,						_A       :   Dict=1			,						_A       :   str=0			,						_A       :   str=2			,						_A       :   List[str]=10_24			,						_A       :   Optional[Any]=1_28			,						_A       :   Union[str, Any]=1_28			,						_A       :   Union[str, Any]=True			,						_A       :   Union[str, Any]=32			,						_A       :   Any=1_28			,						_A       :   Optional[Any]=64			,						_A       :   List[Any]=2_56			,						_A       :   str=True			,						_A       :   List[Any]=True			,						_A       :   Tuple=True			,						_A       :   Tuple=2_24			,						_A       :   Tuple=3			,						_A       :   Optional[Any]=16			,						_A       :   Tuple=None			,						**_A       :   Optional[int]			,						)					->			Optional[int]:
					super().__init__(
					    vocab_size=_A			,						hidden_size=_A			,						num_hidden_layers=_A			,						num_attention_heads=_A			,						intermediate_size=_A			,						hidden_act=_A			,						hidden_dropout_prob=_A			,						attention_probs_dropout_prob=_A			,						max_position_embeddings=_A			,						type_vocab_size=_A			,						initializer_range=_A			,						layer_norm_eps=_A			,						pad_token_id=_A			,						bos_token_id=_A			,						eos_token_id=_A			,						**_A			,						)
					__snake_case    :							List[Any]   	=				max_ad_position_embeddings
					__snake_case    :							List[str]   	=				coordinate_size
					__snake_case    :							int   	=				shape_size
					__snake_case    :							List[str]   	=				has_relative_attention_bias
					__snake_case    :							Union[str, Any]   	=				rel_pos_bins
					__snake_case    :							Tuple   	=				max_rel_pos
					__snake_case    :							Optional[Any]   	=				has_spatial_attention_bias
					__snake_case    :							Union[str, Any]   	=				rel_ad_pos_bins
					__snake_case    :							Any   	=				max_rel_ad_pos
					__snake_case    :							Tuple   	=				text_embed
					__snake_case    :							str   	=				visual_embed
					__snake_case    :							List[str]   	=				input_size
					__snake_case    :							List[Any]   	=				num_channels
					__snake_case    :							Union[str, Any]   	=				patch_size
					__snake_case    :							str   	=				classifier_dropout







class 	UpperCamelCase    (					lowercase		):
				UpperCAmelCase       :   Optional[int]					=	version.parse("""1.12"""		)



				@property
				def       _lowercase  (self       :   Optional[int])					->			Mapping[str, Mapping[int, str]]:
					# The order of inputs is different for question answering and sequence classification
					if self.task in ["question-answering", "sequence-classification"]:
						return OrderedDict(
						    [
						        ('input_ids', {0: 'batch', 1: 'sequence'}),
						        ('attention_mask', {0: 'batch', 1: 'sequence'}),
						        ('bbox', {0: 'batch', 1: 'sequence'}),
						        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
						    ])
					else:
						return OrderedDict(
						    [
						        ('input_ids', {0: 'batch', 1: 'sequence'}),
						        ('bbox', {0: 'batch', 1: 'sequence'}),
						        ('attention_mask', {0: 'batch', 1: 'sequence'}),
						        ('pixel_values', {0: 'batch', 1: 'num_channels'}),
						    ])



				@property
				def       _lowercase  (self       :   Tuple)					->			float:
					return 1E-5



				@property
				def       _lowercase  (self       :   Optional[int])					->			int:
					return 12






				def       _lowercase  (self       :   str			,						_A       :   "ProcessorMixin"			,						_A       :   int = -1			,						_A       :   int = -1			,						_A       :   bool = False			,						_A       :   Optional["TensorType"] = None			,						_A       :   int = 3			,						_A       :   int = 40			,						_A       :   int = 40			,						)					->			Mapping[str, Any]:
					setattr(processor.image_processor			,						'apply_ocr'			,						_A)

					# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
					__snake_case    :							Union[str, Any]   	=				compute_effective_axis_dimension(
					    _A			,						fixed_dimension=OnnxConfig.default_fixed_batch			,						num_token_to_add=0)
					# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
					__snake_case    :							Any   	=				processor.tokenizer.num_special_tokens_to_add(_A)
					__snake_case    :							Optional[int]   	=				compute_effective_axis_dimension(
					    _A			,						fixed_dimension=OnnxConfig.default_fixed_sequence			,						num_token_to_add=_A)
					# Generate dummy inputs according to compute batch and sequence
					__snake_case    :							Optional[int]   	=				[[' '.join([processor.tokenizer.unk_token]) * seq_length]] * batch_size

					# Generate dummy bounding boxes
					__snake_case    :							Dict   	=				[[[48, 84, 73, 1_28]]] * batch_size

					# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
					# batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
					__snake_case    :							Optional[Any]   	=				self._generate_dummy_images(_A			,						_A			,						_A			,						_A)

					__snake_case    :							Dict   	=				dict(
					    processor(
					        _A			,						text=_A			,						boxes=_A			,						return_tensors=_A			,						))

					return inputs





