




'''simple docstring'''







import numpy as np
import qiskit





def 						snake_case_							(_a	:							int = 8    ,   _a	:							int | None = None     ):
    UpperCAmelCase			       =		np.random.default_rng(seed=snake_case_     )

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

    # Quantum Circuit to simulate BB84
    UpperCAmelCase			       =		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.
    UpperCAmelCase			       =		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.
    UpperCAmelCase			       =		qiskit.execute(snake_case_    ,   snake_case_    ,   shots=1    ,   seed_simulator=snake_case_     )
    # Returns the result of measurement.
    UpperCAmelCase			       =		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.
    UpperCAmelCase			       =		''''''.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.
    UpperCAmelCase			       =		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()





'''simple docstring'''

import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration







def      UpperCamelCase_						(					snake_case_       :       Any      )					->     Optional[Any]:






 '''simple docstring'''

 __lowerCAmelCase											=						[
     """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      UpperCamelCase_						(					snake_case_       :       Optional[Any]      )					->     List[Any]:






 '''simple docstring'''

 __lowerCAmelCase							,       __lowerCAmelCase											=						emb.weight.shape
 __lowerCAmelCase											=						nn.Linear(snake_case_						,		snake_case_						,		bias=snake_case_      )
 __lowerCAmelCase											=						emb.weight.data
 return lin_layer







def      UpperCamelCase_						(					snake_case_       :       Any      )					->     Any:






 '''simple docstring'''

 __lowerCAmelCase											=						torch.load(snake_case_						,		map_location="""cpu"""      )
 __lowerCAmelCase											=						mam_aaa["""args"""] or mam_aaa["""cfg"""]["""model"""]
 __lowerCAmelCase											=						mam_aaa["""model"""]
 remove_ignore_keys_(snake_case_      )
 __lowerCAmelCase											=						state_dict["""encoder.embed_tokens.weight"""].shape[0]

 __lowerCAmelCase											=						MaMaaaConfig(
     vocab_size=snake_case_						,		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"""						,		)

 __lowerCAmelCase											=						state_dict["""decoder.embed_tokens.weight"""]
 __lowerCAmelCase											=						MaMaaaForConditionalGeneration(snake_case_      )
 model.model.load_state_dict(snake_case_						,		strict=snake_case_      )
 __lowerCAmelCase											=						make_linear_from_emb(model.model.shared      )

 return model


if __name__ == "__main__":
 _A			:      Union[str, 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.''')
 _A			:      str					      =				parser.parse_args()
 _A			:      Optional[int]					      =				convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
 model.save_pretrained(args.pytorch_dump_folder_path)






