"""simple docstring"""







import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Tuple						)							->    Optional[int]:

 '''simple docstring'''





 __snake_case    :							str   	=				[
     "encoder.version",
     "decoder.version",
     "model.encoder.version",
     "model.decoder.version",
     "decoder.output_projection.weight",
     "_float_tensor",
     "encoder.embed_positions._float_tensor",
     "decoder.embed_positions._float_tensor",
 ]
 for k in ignore_keys:
  state_dict.pop(UpperCAmelCase_    ,							UpperCAmelCase_						)
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       str						)							->    int:

 '''simple docstring'''





 __snake_case    :							Optional[int]   	=				emb.weight.shape
 __snake_case    :							List[Any]   	=				nn.Linear(UpperCAmelCase_    ,							UpperCAmelCase_    ,							bias=UpperCAmelCase_						)
 __snake_case    :							Optional[int]   	=				emb.weight.data
 return lin_layer
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Dict						)							->    Dict:

 '''simple docstring'''





 __snake_case    :							str   	=				torch.load(UpperCAmelCase_    ,							map_location='cpu'						)
 __snake_case    :							int   	=				mam_aaa["args"] or mam_aaa["cfg"]["model"]
 __snake_case    :							Dict   	=				mam_aaa["model"]
 remove_ignore_keys_(UpperCAmelCase_						)
 __snake_case    :							Union[str, Any]   	=				state_dict["encoder.embed_tokens.weight"].shape[0]

 __snake_case    :							str   	=				MaMaaaConfig(
     vocab_size=UpperCAmelCase_    ,							max_position_embeddings=10_24    ,							encoder_layers=args.encoder_layers    ,							decoder_layers=args.decoder_layers    ,							encoder_attention_heads=args.encoder_attention_heads    ,							decoder_attention_heads=args.decoder_attention_heads    ,							encoder_ffn_dim=args.encoder_ffn_embed_dim    ,							decoder_ffn_dim=args.decoder_ffn_embed_dim    ,							d_model=args.encoder_embed_dim    ,							encoder_layerdrop=args.encoder_layerdrop    ,							decoder_layerdrop=args.decoder_layerdrop    ,							dropout=args.dropout    ,							attention_dropout=args.attention_dropout    ,							activation_dropout=args.activation_dropout    ,							activation_function='relu'    ,							)

 __snake_case    :							str   	=				state_dict["decoder.embed_tokens.weight"]
 __snake_case    :							List[Any]   	=				MaMaaaForConditionalGeneration(UpperCAmelCase_						)
 model.model.load_state_dict(UpperCAmelCase_    ,							strict=UpperCAmelCase_						)
 __snake_case    :							Dict   	=				make_linear_from_emb(model.model.shared						)

 return model


if __name__ == "__main__":
 _a  :  int=				argparse.ArgumentParser()
 # Required parameters
 parser.add_argument("fairseq_path", type=str, help="path to a model.pt on local filesystem.")
 parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
 _a  :  Any=				parser.parse_args()
 _a  :  List[str]=				convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
 model.save_pretrained(args.pytorch_dump_folder_path)






"""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()





