




import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel







def 						lowerCamelCase(   a__	,a__	,a__):
				_SCREAMING_SNAKE_CASE			  =('''dense.weight''', '''attention.self.query''', '''attention.self.key''', '''attention.self.value''')

				_SCREAMING_SNAKE_CASE			  =(
				    ('''layer.''', '''layer_'''),
				    ('''word_embeddings.weight''', '''word_embeddings'''),
				    ('''position_embeddings.weight''', '''position_embeddings'''),
				    ('''token_type_embeddings.weight''', '''token_type_embeddings'''),
				    ('''.''', '''/'''),
				    ('''LayerNorm/weight''', '''LayerNorm/gamma'''),
				    ('''LayerNorm/bias''', '''LayerNorm/beta'''),
				    ('''weight''', '''kernel'''),
				)

				if not os.path.isdir(UpperCamelCase__):
								os.makedirs(UpperCamelCase__)

				_SCREAMING_SNAKE_CASE			  =model.state_dict()

				def to_tf_var_name(a__):
								for patt, repl in iter(UpperCamelCase__):
												_SCREAMING_SNAKE_CASE			  =name.replace(UpperCamelCase__	,UpperCamelCase__)
								return f"bert/{name}"

				def create_tf_var(a__	,a__	,a__):
								_SCREAMING_SNAKE_CASE			  =tf.dtypes.as_dtype(tensor.dtype)
								_SCREAMING_SNAKE_CASE			  =tf.get_variable(dtype=UpperCamelCase__	,shape=tensor.shape	,name=UpperCamelCase__	,initializer=tf.zeros_initializer())
								session.run(tf.variables_initializer([tf_var]))
								session.run(UpperCamelCase__)
								return tf_var

				tf.reset_default_graph()
				with tf.Session() as session:
								for var_name in state_dict:
												_SCREAMING_SNAKE_CASE			  =to_tf_var_name(UpperCamelCase__)
												_SCREAMING_SNAKE_CASE			  =state_dict[var_name].numpy()
												if any(x in var_name for x in tensors_to_transpose):
																_SCREAMING_SNAKE_CASE			  =torch_tensor.T
												_SCREAMING_SNAKE_CASE			  =create_tf_var(tensor=UpperCamelCase__	,name=UpperCamelCase__	,session=UpperCamelCase__)
												tf.keras.backend.set_value(UpperCamelCase__	,UpperCamelCase__)
												_SCREAMING_SNAKE_CASE			  =session.run(UpperCamelCase__)
												print(f"Successfully created {tf_name}: {np.allclose(UpperCamelCase__	,UpperCamelCase__)}")

								_SCREAMING_SNAKE_CASE			  =tf.train.Saver(tf.trainable_variables())
								saver.save(UpperCamelCase__	,os.path.join(UpperCamelCase__	,model_name.replace('''-'''	,'''_''') + '''.ckpt'''))







def 						lowerCamelCase(   a__=None):
				_SCREAMING_SNAKE_CASE			  =argparse.ArgumentParser()
				parser.add_argument('''--model_name'''	,type=UpperCamelCase__	,required=UpperCamelCase__	,help='''model name e.g. bert-base-uncased''')
				parser.add_argument(
				    '''--cache_dir'''	,type=UpperCamelCase__	,default=UpperCamelCase__	,required=UpperCamelCase__	,help='''Directory containing pytorch model''')
				parser.add_argument('''--pytorch_model_path'''	,type=UpperCamelCase__	,required=UpperCamelCase__	,help='''/path/to/<pytorch-model-name>.bin''')
				parser.add_argument('''--tf_cache_dir'''	,type=UpperCamelCase__	,required=UpperCamelCase__	,help='''Directory in which to save tensorflow model''')
				_SCREAMING_SNAKE_CASE			  =parser.parse_args(UpperCamelCase__)

				_SCREAMING_SNAKE_CASE			  =BertModel.from_pretrained(
				    pretrained_model_name_or_path=args.model_name	,state_dict=torch.load(args.pytorch_model_path)	,cache_dir=args.cache_dir	,)

				convert_pytorch_checkpoint_to_tf(model=UpperCamelCase__	,ckpt_dir=args.tf_cache_dir	,model_name=args.model_name)


if __name__ == "__main__":
							main()



import string

import numpy







def 						lowerCamelCase(   a__	,a__):
				return b if a == 0 else greatest_common_divisor(b % a	,a__)

class 						A__							:
						UpperCAmelCase             =						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             =						numpy.vectorize(lambda UpperCamelCase__  : x % 36  )

						UpperCAmelCase             =						numpy.vectorize(UpperCamelCase__  )




						def __init__(       self					:      int				,    _a					:      numpy.ndarray  )							->     None:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.modulus(_a  )  # mod36 calc's on the encrypt key
										self.check_determinant()  # validate the determinant of the encryption key
										_SCREAMING_SNAKE_CASE			  =encrypt_key.shape[0]




						def 							__UpperCamelCase				(       self					:      Dict				,    _a					:      str  )							->     int:


										"""simple docstring"""





										return self.key_string.index(_a  )




						def 							__UpperCamelCase				(       self					:      List[str]				,    _a					:      int  )							->     str:


										"""simple docstring"""





										return self.key_string[round(_a  )]




						def 							__UpperCamelCase				(       self					:      int  )							->     None:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =round(numpy.linalg.det(self.encrypt_key  )  )

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

										_SCREAMING_SNAKE_CASE			  =len(self.key_string  )
										if greatest_common_divisor(_a				,    len(self.key_string  )  ) != 1:
														_SCREAMING_SNAKE_CASE			  =(
														    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 							__UpperCamelCase				(       self					:      List[str]				,    _a					:      str  )							->     str:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =[char for char in text.upper() if char in self.key_string]

										_SCREAMING_SNAKE_CASE			  =chars[-1]
										while len(_a  ) % self.break_key != 0:
														chars.append(_a  )

										return "".join(_a  )




						def 							__UpperCamelCase				(       self					:      List[str]				,    _a					:      str  )							->     str:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.process_text(text.upper()  )
										_SCREAMING_SNAKE_CASE			  =''''''

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

										return encrypted




						def 							__UpperCamelCase				(       self					:      Union[str, Any]  )							->     numpy.ndarray:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =round(numpy.linalg.det(self.encrypt_key  )  )

										if det < 0:
														_SCREAMING_SNAKE_CASE			  =det % len(self.key_string  )
										_SCREAMING_SNAKE_CASE			  =None
										for i in range(len(self.key_string  )  ):
														if (det * i) % len(self.key_string  ) == 1:
																		_SCREAMING_SNAKE_CASE			  =i
																		break

										_SCREAMING_SNAKE_CASE			  =(
										    det_inv
										    * numpy.linalg.det(self.encrypt_key  )
										    * numpy.linalg.inv(self.encrypt_key  )
										)

										return self.to_int(self.modulus(_a  )  )




						def 							__UpperCamelCase				(       self					:      str				,    _a					:      str  )							->     str:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.make_decrypt_key()
										_SCREAMING_SNAKE_CASE			  =self.process_text(text.upper()  )
										_SCREAMING_SNAKE_CASE			  =''''''

										for i in range(0				,    len(_a  ) - self.break_key + 1				,    self.break_key  ):
														_SCREAMING_SNAKE_CASE			  =text[i : i + self.break_key]
														_SCREAMING_SNAKE_CASE			  =[self.replace_letters(_a  ) for char in batch]
														_SCREAMING_SNAKE_CASE			  =numpy.array([vec]  ).T
														_SCREAMING_SNAKE_CASE			  =self.modulus(decrypt_key.dot(_a  )  ).T.tolist()[0]
														_SCREAMING_SNAKE_CASE			  =''''''.join(
														    self.replace_digits(_a  ) for num in batch_decrypted  )
														decrypted += decrypted_batch

										return decrypted







def 						lowerCamelCase(   ):
				_SCREAMING_SNAKE_CASE			  =int(input('''Enter the order of the encryption key: '''))
				_SCREAMING_SNAKE_CASE			  =[]

				print('''Enter each row of the encryption key with space separated integers''')
				for _ in range(a__):
								_SCREAMING_SNAKE_CASE			  =[int(a__) for x in input().split()]
								hill_matrix.append(a__)

				_SCREAMING_SNAKE_CASE			  =HillCipher(numpy.array(a__))

				print('''Would you like to encrypt or decrypt some text? (1 or 2)''')
				_SCREAMING_SNAKE_CASE			  =input('''\n1. Encrypt\n2. Decrypt\n''')
				if option == "1":
								_SCREAMING_SNAKE_CASE			  =input('''What text would you like to encrypt?: ''')
								print('''Your encrypted text is:''')
								print(hc.encrypt(a__))
				elif option == "2":
								_SCREAMING_SNAKE_CASE			  =input('''What text would you like to decrypt?: ''')
								print('''Your decrypted text is:''')
								print(hc.decrypt(a__))


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							main()