
'''simple docstring'''



import numpy as np
import qiskit
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_ = 8    ,      SCREAMING_SNAKE_CASE_ = None						)     ->     str:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				np.random.default_rng(seed=SCREAMING_SNAKE_CASE_						)

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

   # Quantum Circuit to simulate BB84
   _SCREAMING_SNAKE_CASE      =				qiskit.QuantumCircuit(SCREAMING_SNAKE_CASE_    ,      name="""BB84"""						)

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

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

   bbaa_circ.barrier()
   bbaa_circ.measure_all()

   # Simulate the quantum circuit.
   _SCREAMING_SNAKE_CASE      =				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.
   _SCREAMING_SNAKE_CASE      =				qiskit.execute(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      shots=1    ,      seed_simulator=SCREAMING_SNAKE_CASE_						)
   # Returns the result of measurement.
   _SCREAMING_SNAKE_CASE      =				job.result().get_counts(SCREAMING_SNAKE_CASE_						).most_frequent()

   # Extracting the generated key from the simulation results.
   # Only keep measurement results where Alice and Bob chose the same basis.
   _SCREAMING_SNAKE_CASE      =				"""""".join(
       [
           result_bit
           for alice_basis_bit, bob_basis_bit, result_bit in zip(
               SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
           if alice_basis_bit == bob_basis_bit
       ]						)

   # Get final key. Pad with 0 if too short, otherwise truncate.
   _SCREAMING_SNAKE_CASE      =				gen_key[:key_len] if len(SCREAMING_SNAKE_CASE_						) >= key_len else gen_key.ljust(SCREAMING_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    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     Any:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				[
       """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(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     str:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				emb.weight.shape
   _SCREAMING_SNAKE_CASE      =				nn.Linear(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      bias=SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE      =				emb.weight.data
   return lin_layer
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     List[Any]:




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

   _SCREAMING_SNAKE_CASE      =				MaMaaaConfig(
       vocab_size=SCREAMING_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"""    ,      )

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

   return model


if __name__ == "__main__":
     UpperCamelCase__      :		Dict       							=  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.")
     UpperCamelCase__      :		List[str]       							=  parser.parse_args()
     UpperCamelCase__      :		Any       							=  convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
     model.save_pretrained(args.pytorch_dump_folder_path)






