


import numpy as np
import qiskit

def    lowercase_	(	_A    :					int = 8					,      _A    :					int | None = None		):

						"""simple docstring"""




						lowerCamelCase__	:       str					=						np.random.default_rng(seed=_A		)

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

						# Quantum Circuit to simulate BB84
						lowerCamelCase__	:       List[str]					=						qiskit.QuantumCircuit(_A					,      name="BB84"		)

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

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

						bbaa_circ.barrier()
						bbaa_circ.measure_all()

						# Simulate the quantum circuit.
						lowerCamelCase__	:       List[str]					=						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.
						lowerCamelCase__	:       List[Any]					=						qiskit.execute(_A					,      _A					,      shots=1					,      seed_simulator=_A		)
						# Returns the result of measurement.
						lowerCamelCase__	:       List[Any]					=						job.result().get_counts(_A		).most_frequent()

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

						# Get final key. Pad with 0 if too short, otherwise truncate.
						lowerCamelCase__	:       Any					=						gen_key[:key_len] if len(_A		) >= key_len else gen_key.ljust(_A					,      "0"		)
						return key


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

				testmod()






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A   :					int	=      logging.get_logger(__name__)

A   :					Optional[int]	=      {
    "facebook/xmod-base": "https://huggingface.co/facebook/xmod-base/resolve/main/config.json",
    "facebook/xmod-large-prenorm": "https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json",
    "facebook/xmod-base-13-125k": "https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json",
    "facebook/xmod-base-30-125k": "https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json",
    "facebook/xmod-base-30-195k": "https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json",
    "facebook/xmod-base-60-125k": "https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json",
    "facebook/xmod-base-60-265k": "https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json",
    "facebook/xmod-base-75-125k": "https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json",
    "facebook/xmod-base-75-269k": "https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json",
}







class     _lowercase				(							lowercase__):


       """simple docstring"""




       A__      		=    "xmod"





       def __init__(     self							:    int      ,   __lowerCamelCase							:    Any=30522      ,   __lowerCamelCase							:    Any=768      ,   __lowerCamelCase							:    str=12      ,   __lowerCamelCase							:    Any=12      ,   __lowerCamelCase							:    List[str]=3072      ,   __lowerCamelCase							:    List[Any]="gelu"      ,   __lowerCamelCase							:    Union[str, Any]=0.1      ,   __lowerCamelCase							:    int=0.1      ,   __lowerCamelCase							:    Tuple=512      ,   __lowerCamelCase							:    str=2      ,   __lowerCamelCase							:    List[str]=0.0_2      ,   __lowerCamelCase							:    List[str]=1E-1_2      ,   __lowerCamelCase							:    str=1      ,   __lowerCamelCase							:    Optional[int]=0      ,   __lowerCamelCase							:    Optional[Any]=2      ,   __lowerCamelCase							:    str="absolute"      ,   __lowerCamelCase							:    List[str]=True      ,   __lowerCamelCase							:    Dict=None      ,   __lowerCamelCase							:    Optional[Any]=False      ,   __lowerCamelCase							:    Optional[Any]=2      ,   __lowerCamelCase							:    Tuple=False      ,   __lowerCamelCase							:    Tuple=True      ,   __lowerCamelCase							:    Union[str, Any]=True      ,   __lowerCamelCase							:    str=("en_XX",)      ,   __lowerCamelCase							:    Union[str, Any]=None      ,   **__lowerCamelCase							:    Optional[int]      ,   ):


             '''simple docstring'''



             super().__init__(pad_token_id=__lowerCamelCase      ,   bos_token_id=__lowerCamelCase      ,   eos_token_id=__lowerCamelCase      ,   **__lowerCamelCase )

             lowerCamelCase__	:       Union[str, Any]					=						vocab_size
             lowerCamelCase__	:       Union[str, Any]					=						hidden_size
             lowerCamelCase__	:       Optional[int]					=						num_hidden_layers
             lowerCamelCase__	:       List[Any]					=						num_attention_heads
             lowerCamelCase__	:       Union[str, Any]					=						hidden_act
             lowerCamelCase__	:       Optional[int]					=						intermediate_size
             lowerCamelCase__	:       Optional[int]					=						hidden_dropout_prob
             lowerCamelCase__	:       List[Any]					=						attention_probs_dropout_prob
             lowerCamelCase__	:       Any					=						max_position_embeddings
             lowerCamelCase__	:       List[Any]					=						type_vocab_size
             lowerCamelCase__	:       int					=						initializer_range
             lowerCamelCase__	:       Tuple					=						layer_norm_eps
             lowerCamelCase__	:       Union[str, Any]					=						position_embedding_type
             lowerCamelCase__	:       str					=						use_cache
             lowerCamelCase__	:       Union[str, Any]					=						classifier_dropout
             lowerCamelCase__	:       Any					=						pre_norm
             lowerCamelCase__	:       Tuple					=						adapter_reduction_factor
             lowerCamelCase__	:       Tuple					=						adapter_layer_norm
             lowerCamelCase__	:       List[Any]					=						adapter_reuse_layer_norm
             lowerCamelCase__	:       Dict					=						ln_before_adapter
             lowerCamelCase__	:       List[Any]					=						list(__lowerCamelCase )
             lowerCamelCase__	:       Optional[Any]					=						default_language







class     _lowercase				(							lowercase__):


       """simple docstring"""




       @property
       def 		lowerCAmelCase  (     self							:    Tuple ):


             '''simple docstring'''



             if self.task == "multiple-choice":
                   lowerCamelCase__	:       Dict					=						{0: "batch", 1: "choice", 2: "sequence"}
             else:
                   lowerCamelCase__	:       List[str]					=						{0: "batch", 1: "sequence"}
             return OrderedDict(
                 [
                     ("input_ids", dynamic_axis),
                     ("attention_mask", dynamic_axis),
                 ] )


