






import numpy as np
import qiskit

def   __lowercase       (			snake_case = 8,	snake_case = None			):







				"""simple docstring"""






				__magic_name__		:List[Any]   =   np.random.default_rng(seed=snake_case			)

				# Roughly 25% of the qubits will contribute to the key.
				# So we take more than we need.
				__magic_name__		:Any   =   6 * key_len
				# Measurement basis for Alice's qubits.
				__magic_name__		:List[str]   =   rng.integers(2,	size=snake_case			)
				# The set of states Alice will prepare.
				__magic_name__		:List[Any]   =   rng.integers(2,	size=snake_case			)
				# Measurement basis for Bob's qubits.
				__magic_name__		:str   =   rng.integers(2,	size=snake_case			)

				# Quantum Circuit to simulate BB84
				__magic_name__		:Optional[int]   =   qiskit.QuantumCircuit(snake_case,	name='''BB84'''			)

				# Alice prepares her qubits according to rules above.
				for index, _ in enumerate(snake_case			):
								if alice_state[index] == 1:
												bbaa_circ.x(snake_case			)
								if alice_basis[index] == 1:
												bbaa_circ.h(snake_case			)
				bbaa_circ.barrier()

				# Bob measures the received qubits according to rules above.
				for index, _ in enumerate(snake_case			):
								if bob_basis[index] == 1:
												bbaa_circ.h(snake_case			)

				bbaa_circ.barrier()
				bbaa_circ.measure_all()

				# Simulate the quantum circuit.
				__magic_name__		:Optional[Any]   =   qiskit.Aer.get_backend('''aer_simulator'''			)
				# We only need to run one shot because the key is unique.
				# Multiple shots will produce the same key.
				__magic_name__		:Dict   =   qiskit.execute(snake_case,	snake_case,	shots=1,	seed_simulator=snake_case			)
				# Returns the result of measurement.
				__magic_name__		:str   =   job.result().get_counts(snake_case			).most_frequent()

				# Extracting the generated key from the simulation results.
				# Only keep measurement results where Alice and Bob chose the same basis.
				__magic_name__		:Optional[int]   =   ''''''.join(
				    [
				        result_bit
				        for alice_basis_bit, bob_basis_bit, result_bit in zip(
				            snake_case,	snake_case,	snake_case			)
				        if alice_basis_bit == bob_basis_bit
				    ]			)

				# Get final key. Pad with 0 if too short, otherwise truncate.
				__magic_name__		:Union[str, Any]   =   gen_key[:key_len] if len(snake_case			) >= key_len else gen_key.ljust(snake_case,	'''0'''			)
				return key


if __name__ == "__main__":
					print(f"The generated key is : {bbaa(8, seed=0)}")
					from doctest import testmod

					testmod()














import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration

def   __lowercase       (			snake_case			):







				"""simple docstring"""






				__magic_name__		:Optional[Any]   =   [
				    '''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(snake_case,	snake_case			)

def   __lowercase       (			snake_case			):







				"""simple docstring"""






				__magic_name__  ,					__magic_name__		:Tuple   =   emb.weight.shape
				__magic_name__		:int   =   nn.Linear(snake_case,	snake_case,	bias=snake_case			)
				__magic_name__		:str   =   emb.weight.data
				return lin_layer

def   __lowercase       (			snake_case			):







				"""simple docstring"""






				__magic_name__		:int   =   torch.load(snake_case,	map_location='''cpu'''			)
				__magic_name__		:Optional[Any]   =   mam_aaa['''args'''] or mam_aaa['''cfg''']['''model''']
				__magic_name__		:List[Any]   =   mam_aaa['''model''']
				remove_ignore_keys_(snake_case			)
				__magic_name__		:Tuple   =   state_dict['''encoder.embed_tokens.weight'''].shape[0]

				__magic_name__		:List[str]   =   MaMaaaConfig(
				    vocab_size=snake_case,	max_position_embeddings=1_0_2_4,	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''',	)

				__magic_name__		:int   =   state_dict['''decoder.embed_tokens.weight''']
				__magic_name__		:List[str]   =   MaMaaaForConditionalGeneration(snake_case			)
				model.model.load_state_dict(snake_case,	strict=snake_case			)
				__magic_name__		:List[str]   =   make_linear_from_emb(model.model.shared			)

				return model


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__   :      Optional[Any]				=		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.""")
					SCREAMING_SNAKE_CASE__   :      int				=		parser.parse_args()
					SCREAMING_SNAKE_CASE__   :      Any				=		convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
					model.save_pretrained(args.pytorch_dump_folder_path)






