


'''simple docstring'''






from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class 	a__       (							a__						):





							'''simple docstring'''






							lowercase__    :     Any					=   ["image_processor", "tokenizer"]
							lowercase__    :     Union[str, Any]					=   "BlipImageProcessor"
							lowercase__    :     str					=   ("BertTokenizer", "BertTokenizerFast")







							def __init__(	self      ,					lowerCamelCase_      ,					lowerCamelCase_     ) ->							List[str]:
														lowerCAmelCase__      =			False
														super().__init__(lowerCamelCase_      ,					lowerCamelCase_     )
														lowerCAmelCase__      =			self.image_processor







							def __call__(	self      ,					lowerCamelCase_ = None      ,					lowerCamelCase_ = None      ,					lowerCamelCase_ = True      ,					lowerCamelCase_ = False      ,					lowerCamelCase_ = None      ,					lowerCamelCase_ = None      ,					lowerCamelCase_ = 0      ,					lowerCamelCase_ = None      ,					lowerCamelCase_ = None      ,					lowerCamelCase_ = False      ,					lowerCamelCase_ = False      ,					lowerCamelCase_ = False      ,					lowerCamelCase_ = False      ,					lowerCamelCase_ = False      ,					lowerCamelCase_ = True      ,					lowerCamelCase_ = None      ,					**lowerCamelCase_      ,					) ->							BatchEncoding:
														if images is None and text is None:
																					raise ValueError('''You have to specify either images or text.'''     )

														# Get only text
														if images is None:
																					lowerCAmelCase__      =			self.tokenizer
																					lowerCAmelCase__      =			self.tokenizer(
																					    text=lowerCamelCase_      ,					add_special_tokens=lowerCamelCase_      ,					padding=lowerCamelCase_      ,					truncation=lowerCamelCase_      ,					max_length=lowerCamelCase_      ,					stride=lowerCamelCase_      ,					pad_to_multiple_of=lowerCamelCase_      ,					return_attention_mask=lowerCamelCase_      ,					return_overflowing_tokens=lowerCamelCase_      ,					return_special_tokens_mask=lowerCamelCase_      ,					return_offsets_mapping=lowerCamelCase_      ,					return_token_type_ids=lowerCamelCase_      ,					return_length=lowerCamelCase_      ,					verbose=lowerCamelCase_      ,					return_tensors=lowerCamelCase_      ,					**lowerCamelCase_      ,					)
																					return text_encoding

														# add pixel_values
														lowerCAmelCase__      =			self.image_processor(lowerCamelCase_      ,					return_tensors=lowerCamelCase_     )

														if text is not None:
																					lowerCAmelCase__      =			self.tokenizer(
																					    text=lowerCamelCase_      ,					add_special_tokens=lowerCamelCase_      ,					padding=lowerCamelCase_      ,					truncation=lowerCamelCase_      ,					max_length=lowerCamelCase_      ,					stride=lowerCamelCase_      ,					pad_to_multiple_of=lowerCamelCase_      ,					return_attention_mask=lowerCamelCase_      ,					return_overflowing_tokens=lowerCamelCase_      ,					return_special_tokens_mask=lowerCamelCase_      ,					return_offsets_mapping=lowerCamelCase_      ,					return_token_type_ids=lowerCamelCase_      ,					return_length=lowerCamelCase_      ,					verbose=lowerCamelCase_      ,					return_tensors=lowerCamelCase_      ,					**lowerCamelCase_      ,					)
														else:
																					lowerCAmelCase__      =			None

														if text_encoding is not None:
																					encoding_image_processor.update(lowerCamelCase_     )

														return encoding_image_processor







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					*lowerCamelCase_      ,					**lowerCamelCase_     ) ->							str:
														return self.tokenizer.batch_decode(*lowerCamelCase_      ,					**lowerCamelCase_     )







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					*lowerCamelCase_      ,					**lowerCamelCase_     ) ->							str:
														return self.tokenizer.decode(*lowerCamelCase_      ,					**lowerCamelCase_     )







							@property
							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Optional[Any]:
														lowerCAmelCase__      =			self.tokenizer.model_input_names
														lowerCAmelCase__      =			self.image_processor.model_input_names
														return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )



'''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()