


'''simple docstring'''






import numpy as np
import qiskit






def 	_A				(lowerCAmelCase__  :int = 8				,						lowerCAmelCase__  :int | None = None		)   ->  str:




     '''simple docstring'''



     _a			      =							np.random.default_rng(seed=lowerCAmelCase__		)

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

     # Quantum Circuit to simulate BB84
     _a			      =							qiskit.QuantumCircuit(lowerCAmelCase__				,						name='BB84'		)

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

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

     bbaa_circ.barrier()
     bbaa_circ.measure_all()

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

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

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


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

 testmod()





'''simple docstring'''






# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

import torch

from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
 from PIL import Image




class  a     (     _SCREAMING_SNAKE_CASE       ):
 _lowerCAmelCase 					=	"""dandelin/vilt-b32-finetuned-vqa"""
 _lowerCAmelCase 					=	(
     """This is a tool that answers a question about an image. It takes an input named `image` which should be the """
     """image containing the information, as well as a `question` which should be the question in English. It """
     """returns a text that is the answer to the question."""
 )
 _lowerCAmelCase 					=	"""image_qa"""
 _lowerCAmelCase 					=	AutoProcessor
 _lowerCAmelCase 					=	AutoModelForVisualQuestionAnswering

 _lowerCAmelCase 					=	["""image""", """text"""]
 _lowerCAmelCase 					=	["""text"""]
 def __init__( self      ,				*__magic_name__      ,				**__magic_name__						)					->							Tuple:
      requires_backends(self      ,				['vision']						)
      super().__init__(*__magic_name__      ,				**__magic_name__						)
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__						)					->							Tuple:
      return self.pre_processor(__magic_name__      ,				__magic_name__      ,				return_tensors='pt'						)
 def 							__UpperCAmelCase		( self      ,				__magic_name__						)					->							Any:
      with torch.no_grad():
           return self.model(**__magic_name__						).logits




 def 							__UpperCAmelCase		( self      ,				__magic_name__						)					->							Optional[int]:
      _a			      =							outputs.argmax(-1						).item()
      return self.model.config.idalabel[idx]

