"""simple docstring"""







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

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


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

_a  :  List[str]=				{
    "Helsinki-NLP/opus-mt-en-de": "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json",
    # See all Marian models at https://huggingface.co/models?filter=marian
}

class 	UpperCamelCase    (					lowercase		):
				UpperCAmelCase       :   int					=	"""marian"""
				UpperCAmelCase       :   List[str]					=	["""past_key_values"""]
				UpperCAmelCase       :   Union[str, Any]					=	{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}



				def __init__(self       :   int			,						_A       :   Dict=5_81_01			,						_A       :   Dict=None			,						_A       :   Any=10_24			,						_A       :   int=12			,						_A       :   Optional[int]=40_96			,						_A       :   Optional[Any]=16			,						_A       :   str=12			,						_A       :   Optional[int]=40_96			,						_A       :   List[Any]=16			,						_A       :   Dict=0.0			,						_A       :   Dict=0.0			,						_A       :   Dict=True			,						_A       :   List[str]=True			,						_A       :   Dict="gelu"			,						_A       :   Optional[int]=10_24			,						_A       :   List[str]=0.1			,						_A       :   Optional[Any]=0.0			,						_A       :   Dict=0.0			,						_A       :   List[str]=0.02			,						_A       :   Dict=5_81_00			,						_A       :   str=False			,						_A       :   List[str]=5_81_00			,						_A       :   Optional[Any]=0			,						_A       :   Optional[int]=0			,						_A       :   Union[str, Any]=True			,						**_A       :   Optional[Any]			,						)					->			Tuple:
					__snake_case    :							Dict   	=				vocab_size
					__snake_case    :							str   	=				decoder_vocab_size or vocab_size
					__snake_case    :							Union[str, Any]   	=				max_position_embeddings
					__snake_case    :							str   	=				d_model
					__snake_case    :							Union[str, Any]   	=				encoder_ffn_dim
					__snake_case    :							int   	=				encoder_layers
					__snake_case    :							Tuple   	=				encoder_attention_heads
					__snake_case    :							Optional[int]   	=				decoder_ffn_dim
					__snake_case    :							str   	=				decoder_layers
					__snake_case    :							Any   	=				decoder_attention_heads
					__snake_case    :							Any   	=				dropout
					__snake_case    :							List[Any]   	=				attention_dropout
					__snake_case    :							Dict   	=				activation_dropout
					__snake_case    :							str   	=				activation_function
					__snake_case    :							Dict   	=				init_std
					__snake_case    :							List[Any]   	=				encoder_layerdrop
					__snake_case    :							str   	=				decoder_layerdrop
					__snake_case    :							Dict   	=				use_cache
					__snake_case    :							List[Any]   	=				encoder_layers
					__snake_case    :							Optional[Any]   	=				scale_embedding  # scale factor will be sqrt(d_model) if True
					__snake_case    :							List[Any]   	=				share_encoder_decoder_embeddings
					super().__init__(
					    pad_token_id=_A			,						eos_token_id=_A			,						is_encoder_decoder=_A			,						decoder_start_token_id=_A			,						forced_eos_token_id=_A			,						**_A			,						)







class 	UpperCamelCase    (					lowercase		):



				@property
				# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
				def       _lowercase  (self       :   int)					->			Mapping[str, Mapping[int, str]]:
					if self.task in ["default", "seq2seq-lm"]:
						__snake_case    :							Union[str, Any]   	=				OrderedDict(
						    [
						        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
						        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
						    ])

						if self.use_past:
							__snake_case    :							Optional[Any]   	=				{0: 'batch'}
							__snake_case    :							Any   	=				{0: 'batch', 1: 'past_decoder_sequence + sequence'}
						else:
							__snake_case    :							Tuple   	=				{0: 'batch', 1: 'decoder_sequence'}
							__snake_case    :							Union[str, Any]   	=				{0: 'batch', 1: 'decoder_sequence'}

						if self.use_past:
							self.fill_with_past_key_values_(_A			,						direction='inputs')
					elif self.task == "causal-lm":
						# TODO: figure this case out.
						__snake_case    :							Optional[int]   	=				OrderedDict(
						    [
						        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
						        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
						    ])
						if self.use_past:
							__snake_case	,       __snake_case    :							Dict   	=				self.num_layers
							for i in range(_A):
								__snake_case    :							Dict   	=				{0: 'batch', 2: 'past_sequence + sequence'}
								__snake_case    :							str   	=				{0: 'batch', 2: 'past_sequence + sequence'}
					else:
						__snake_case    :							List[Any]   	=				OrderedDict(
						    [
						        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
						        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
						        ('decoder_input_ids', {0: 'batch', 1: 'decoder_sequence'}),
						        ('decoder_attention_mask', {0: 'batch', 1: 'decoder_sequence'}),
						    ])

					return common_inputs



				@property
				# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
				def       _lowercase  (self       :   List[str])					->			Mapping[str, Mapping[int, str]]:
					if self.task in ["default", "seq2seq-lm"]:
						__snake_case    :							Dict   	=				super().outputs
					else:
						__snake_case    :							int   	=				super(_A			,						self).outputs
						if self.use_past:
							__snake_case	,       __snake_case    :							Any   	=				self.num_layers
							for i in range(_A):
								__snake_case    :							Any   	=				{0: 'batch', 2: 'past_sequence + sequence'}
								__snake_case    :							List[str]   	=				{0: 'batch', 2: 'past_sequence + sequence'}
					return common_outputs



				def       _lowercase  (self       :   Tuple			,						_A       :   PreTrainedTokenizer			,						_A       :   int = -1			,						_A       :   int = -1			,						_A       :   bool = False			,						_A       :   Optional[TensorType] = None			,						)					->			Mapping[str, Any]:
					__snake_case    :							List[Any]   	=				self._generate_dummy_inputs_for_encoder_and_decoder(
					    _A			,						_A			,						_A			,						_A			,						_A)

					# Generate decoder inputs
					__snake_case    :							Optional[int]   	=				seq_length if not self.use_past else 1
					__snake_case    :							int   	=				self._generate_dummy_inputs_for_encoder_and_decoder(
					    _A			,						_A			,						_A			,						_A			,						_A)
					__snake_case    :							Optional[Any]   	=				{f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
					__snake_case    :							str   	=				dict(**_A			,						**_A)

					if self.use_past:
						if not is_torch_available():
							raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.')
						else:
							import torch
						__snake_case	,       __snake_case    :							List[Any]   	=				common_inputs['input_ids'].shape
						__snake_case    :							Optional[Any]   	=				common_inputs['decoder_input_ids'].shape[1]
						__snake_case	,       __snake_case    :							List[Any]   	=				self.num_attention_heads
						__snake_case    :							List[Any]   	=				(
						    batch,
						    num_encoder_attention_heads,
						    encoder_seq_length,
						    self._config.hidden_size // num_encoder_attention_heads,
						)
						__snake_case    :							str   	=				decoder_seq_length + 3
						__snake_case    :							Optional[Any]   	=				(
						    batch,
						    num_decoder_attention_heads,
						    decoder_past_length,
						    self._config.hidden_size // num_decoder_attention_heads,
						)

						__snake_case    :							List[Any]   	=				torch.cat(
						    [common_inputs['decoder_attention_mask'], torch.ones(_A			,						_A)]			,						dim=1)

						__snake_case    :							Optional[int]   	=				[]
						# If the number of encoder and decoder layers are present in the model configuration, both are considered
						__snake_case	,       __snake_case    :							Tuple   	=				self.num_layers
						__snake_case    :							Optional[Any]   	=				min(_A			,						_A)
						__snake_case    :							str   	=				max(_A			,						_A) - min_num_layers
						__snake_case    :							Dict   	=				'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

						for _ in range(_A):
							common_inputs["past_key_values"].append(
							    (
							        torch.zeros(_A),
							        torch.zeros(_A),
							        torch.zeros(_A),
							        torch.zeros(_A),
							    ))
						# TODO: test this.
						__snake_case    :							Dict   	=				encoder_shape if remaining_side_name == 'encoder' else decoder_shape
						for _ in range(_A			,						_A):
							common_inputs["past_key_values"].append((torch.zeros(_A), torch.zeros(_A)))
					return common_inputs



				def       _lowercase  (self       :   Any			,						_A       :   PreTrainedTokenizer			,						_A       :   int = -1			,						_A       :   int = -1			,						_A       :   bool = False			,						_A       :   Optional[TensorType] = None			,						)					->			Mapping[str, Any]:
					__snake_case    :							Union[str, Any]   	=				self._generate_dummy_inputs_for_encoder_and_decoder(
					    _A			,						_A			,						_A			,						_A			,						_A)

					if self.use_past:
						if not is_torch_available():
							raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.')
						else:
							import torch
						__snake_case	,       __snake_case    :							Dict   	=				common_inputs['input_ids'].shape
						# Not using the same length for past_key_values
						__snake_case    :							Dict   	=				seqlen + 2
						__snake_case	,       __snake_case    :							Tuple   	=				self.num_layers
						__snake_case	,       __snake_case    :							Dict   	=				self.num_attention_heads
						__snake_case    :							List[str]   	=				(
						    batch,
						    num_encoder_attention_heads,
						    past_key_values_length,
						    self._config.hidden_size // num_encoder_attention_heads,
						)

						__snake_case    :							List[str]   	=				common_inputs['attention_mask'].dtype
						__snake_case    :							Dict   	=				torch.cat(
						    [common_inputs['attention_mask'], torch.ones(_A			,						_A			,						dtype=_A)]			,						dim=1)
						__snake_case    :							str   	=				[
						    (torch.zeros(_A), torch.zeros(_A)) for _ in range(_A)
						]
					return common_inputs



				def       _lowercase  (self       :   Tuple			,						_A       :   PreTrainedTokenizer			,						_A       :   int = -1			,						_A       :   int = -1			,						_A       :   bool = False			,						_A       :   Optional[TensorType] = None			,						)					->			Mapping[str, Any]:
					# Copied from OnnxConfig.generate_dummy_inputs
					# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
					# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
					__snake_case    :							int   	=				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    :							int   	=				tokenizer.num_special_tokens_to_add(_A)
					__snake_case    :							Optional[Any]   	=				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([tokenizer.unk_token]) * seq_length] * batch_size
					__snake_case    :							Union[str, Any]   	=				dict(tokenizer(_A			,						return_tensors=_A))
					return common_inputs



				def       _lowercase  (self       :   Optional[Any]			,						_A       :   PreTrainedTokenizer			,						_A       :   int = -1			,						_A       :   int = -1			,						_A       :   bool = False			,						_A       :   Optional[TensorType] = None			,						)					->			Mapping[str, Any]:
					if self.task in ["default", "seq2seq-lm"]:
						__snake_case    :							Tuple   	=				self._generate_dummy_inputs_for_default_and_seqaseq_lm(
						    _A			,						batch_size=_A			,						seq_length=_A			,						is_pair=_A			,						framework=_A)

					else:
						__snake_case    :							str   	=				self._generate_dummy_inputs_for_causal_lm(
						    _A			,						batch_size=_A			,						seq_length=_A			,						is_pair=_A			,						framework=_A)

					return common_inputs



				def       _lowercase  (self       :   Optional[int]			,						_A       :   List[Any]			,						_A       :   int			,						_A       :   Optional[Any]			,						_A       :   Dict)					->			List[str]:
					if self.task in ["default", "seq2seq-lm"]:
						__snake_case    :							List[Any]   	=				super()._flatten_past_key_values_(_A			,						_A			,						_A			,						_A)
					else:
						__snake_case    :							int   	=				super(_A			,						self)._flatten_past_key_values_(
						    _A			,						_A			,						_A			,						_A)






				@property
				def       _lowercase  (self       :   Dict)					->			float:
					return 1E-4






"""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()





