


'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCAmelCase					       =							logging.get_logger(__name__)

__UpperCAmelCase					       =							{
    '''naver-clova-ix/donut-base''': '''https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json''',
    # See all Donut models at https://huggingface.co/models?filter=donut-swin
}




class 	a__       (							a__						):





							'''simple docstring'''






							lowercase__    :     Union[str, Any]					=   "donut-swin"

							lowercase__    :     List[Any]					=   {
							    "num_attention_heads": "num_heads",
							    "num_hidden_layers": "num_layers",
							}







							def __init__(	self      ,					lowerCamelCase_=2_24      ,					lowerCamelCase_=4      ,					lowerCamelCase_=3      ,					lowerCamelCase_=96      ,					lowerCamelCase_=[2, 2, 6, 2]      ,					lowerCamelCase_=[3, 6, 12, 24]      ,					lowerCamelCase_=7      ,					lowerCamelCase_=4.0      ,					lowerCamelCase_=True      ,					lowerCamelCase_=0.0      ,					lowerCamelCase_=0.0      ,					lowerCamelCase_=0.1      ,					lowerCamelCase_="gelu"      ,					lowerCamelCase_=False      ,					lowerCamelCase_=0.02      ,					lowerCamelCase_=1e-5      ,					**lowerCamelCase_      ,					) ->							List[Any]:
														super().__init__(**lowerCamelCase_     )

														lowerCAmelCase__      =			image_size
														lowerCAmelCase__      =			patch_size
														lowerCAmelCase__      =			num_channels
														lowerCAmelCase__      =			embed_dim
														lowerCAmelCase__      =			depths
														lowerCAmelCase__      =			len(lowerCamelCase_     )
														lowerCAmelCase__      =			num_heads
														lowerCAmelCase__      =			window_size
														lowerCAmelCase__      =			mlp_ratio
														lowerCAmelCase__      =			qkv_bias
														lowerCAmelCase__      =			hidden_dropout_prob
														lowerCAmelCase__      =			attention_probs_dropout_prob
														lowerCAmelCase__      =			drop_path_rate
														lowerCAmelCase__      =			hidden_act
														lowerCAmelCase__      =			use_absolute_embeddings
														lowerCAmelCase__      =			layer_norm_eps
														lowerCAmelCase__      =			initializer_range
														# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
														# this indicates the channel dimension after the last stage of the model
														lowerCAmelCase__      =			int(embed_dim * 2 ** (len(lowerCamelCase_     ) - 1)     )



'''simple docstring'''






import numpy as np
import qiskit


def       _snake_case     (  A = 8				,					A = None )		->					str:
							lowerCAmelCase__      =			np.random.default_rng(seed=A )

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

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