




from copy import deepcopy
from typing import Optional, Union

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available


if is_torch_available():
		import torch

if is_tf_available():
		import tensorflow as tf
class 						UpperCAmelCase  (							UpperCAmelCase__     ):







						'''simple docstring'''





						lowerCAmelCase_							=  ['''image_processor''']
						lowerCAmelCase_							=  '''SamImageProcessor'''






						def __init__(		self				:	Union[str, Any]			,      __lowercase				:	Dict		):






							"""simple docstring"""






							super().__init__(__lowercase		)
							snake_case_						       =	self.image_processor
							snake_case_						       =	-10
							snake_case_						       =	self.image_processor.size["longest_edge"]






						def __call__(		self				:	str			,      __lowercase				:	Tuple=None			,      __lowercase				:	List[Any]=None			,      __lowercase				:	List[Any]=None			,      __lowercase				:	int=None			,      __lowercase				:	Optional[Union[str, TensorType]] = None			,      **__lowercase				:	Union[str, Any]			,      ):






							"""simple docstring"""






							snake_case_						       =	self.image_processor(
							    __lowercase			,      return_tensors=__lowercase			,      **__lowercase			,      )

							# pop arguments that are not used in the foward but used nevertheless
							snake_case_						       =	encoding_image_processor["original_sizes"]

							if hasattr(__lowercase			,      "numpy"		):  # Checks if Torch or TF tensor
								snake_case_						       =	original_sizes.numpy()

							snake_case_       , snake_case_       , snake_case_						       =	self._check_and_preprocess_points(
							    input_points=__lowercase			,      input_labels=__lowercase			,      input_boxes=__lowercase			,      )

							snake_case_						       =	self._normalize_and_convert(
							    __lowercase			,      __lowercase			,      input_points=__lowercase			,      input_labels=__lowercase			,      input_boxes=__lowercase			,      return_tensors=__lowercase			,      )

							return encoding_image_processor






						def 			snake_case__		(		self				:	List[str]			,      __lowercase				:	Tuple			,      __lowercase				:	Optional[Any]			,      __lowercase				:	Tuple=None			,      __lowercase				:	Optional[Any]=None			,      __lowercase				:	List[str]=None			,      __lowercase				:	Tuple="pt"			,      ):






							"""simple docstring"""






							if input_points is not None:
								if len(__lowercase		) != len(__lowercase		):
									snake_case_						       =	[
									    self._normalize_coordinates(self.target_size			,      __lowercase			,      original_sizes[0]		) for point in input_points
									]
								else:
									snake_case_						       =	[
									    self._normalize_coordinates(self.target_size			,      __lowercase			,      __lowercase		)
									    for point, original_size in zip(__lowercase			,      __lowercase		)
									]
								# check that all arrays have the same shape
								if not all(point.shape == input_points[0].shape for point in input_points		):
									if input_labels is not None:
										snake_case_       , snake_case_						       =	self._pad_points_and_labels(__lowercase			,      __lowercase		)

								snake_case_						       =	np.array(__lowercase		)

							if input_labels is not None:
								snake_case_						       =	np.array(__lowercase		)

							if input_boxes is not None:
								if len(__lowercase		) != len(__lowercase		):
									snake_case_						       =	[
									    self._normalize_coordinates(self.target_size			,      __lowercase			,      original_sizes[0]			,      is_bounding_box=__lowercase		)
									    for box in input_boxes
									]
								else:
									snake_case_						       =	[
									    self._normalize_coordinates(self.target_size			,      __lowercase			,      __lowercase			,      is_bounding_box=__lowercase		)
									    for box, original_size in zip(__lowercase			,      __lowercase		)
									]
								snake_case_						       =	np.array(__lowercase		)

							if input_boxes is not None:
								if return_tensors == "pt":
									snake_case_						       =	torch.from_numpy(__lowercase		)
									# boxes batch size of 1 by default
									snake_case_						       =	input_boxes.unsqueeze(1		) if len(input_boxes.shape		) != 3 else input_boxes
								elif return_tensors == "tf":
									snake_case_						       =	tf.convert_to_tensor(__lowercase		)
									# boxes batch size of 1 by default
									snake_case_						       =	tf.expand_dims(__lowercase			,      1		) if len(input_boxes.shape		) != 3 else input_boxes
								encoding_image_processor.update({"input_boxes": input_boxes}		)
							if input_points is not None:
								if return_tensors == "pt":
									snake_case_						       =	torch.from_numpy(__lowercase		)
									# point batch size of 1 by default
									snake_case_						       =	input_points.unsqueeze(1		) if len(input_points.shape		) != 4 else input_points
								elif return_tensors == "tf":
									snake_case_						       =	tf.convert_to_tensor(__lowercase		)
									# point batch size of 1 by default
									snake_case_						       =	tf.expand_dims(__lowercase			,      1		) if len(input_points.shape		) != 4 else input_points
								encoding_image_processor.update({"input_points": input_points}		)
							if input_labels is not None:
								if return_tensors == "pt":
									snake_case_						       =	torch.from_numpy(__lowercase		)
									# point batch size of 1 by default
									snake_case_						       =	input_labels.unsqueeze(1		) if len(input_labels.shape		) != 3 else input_labels
								elif return_tensors == "tf":
									snake_case_						       =	tf.convert_to_tensor(__lowercase		)
									# point batch size of 1 by default
									snake_case_						       =	tf.expand_dims(__lowercase			,      1		) if len(input_labels.shape		) != 3 else input_labels
								encoding_image_processor.update({"input_labels": input_labels}		)

							return encoding_image_processor






						def 			snake_case__		(		self				:	Dict			,      __lowercase				:	Tuple			,      __lowercase				:	Dict		):






							"""simple docstring"""






							snake_case_						       =	max([point.shape[0] for point in input_points]		)
							snake_case_						       =	[]
							for i, point in enumerate(__lowercase		):
								if point.shape[0] != expected_nb_points:
									snake_case_						       =	np.concatenate(
									    [point, np.zeros((expected_nb_points - point.shape[0], 2)		) + self.point_pad_value]			,      axis=0		)
									snake_case_						       =	np.append(input_labels[i]			,      [self.point_pad_value]		)
								processed_input_points.append(__lowercase		)
							snake_case_						       =	processed_input_points
							return input_points, input_labels






						def 			snake_case__		(		self				:	int			,      __lowercase				:	int			,      __lowercase				:	np.ndarray			,      __lowercase				:	Tuple			,      __lowercase				:	Tuple=False		):






							"""simple docstring"""






							snake_case_       , snake_case_						       =	original_size
							snake_case_       , snake_case_						       =	self.image_processor._get_preprocess_shape(__lowercase			,      longest_edge=__lowercase		)
							snake_case_						       =	deepcopy(__lowercase		).astype(__lowercase		)

							if is_bounding_box:
								snake_case_						       =	coords.reshape(-1			,      2			,      2		)

							snake_case_						       =	coords[..., 0] * (new_w / old_w)
							snake_case_						       =	coords[..., 1] * (new_h / old_h)

							if is_bounding_box:
								snake_case_						       =	coords.reshape(-1			,      4		)

							return coords






						def 			snake_case__		(		self				:	Tuple			,      __lowercase				:	List[Any]=None			,      __lowercase				:	Optional[int]=None			,      __lowercase				:	List[str]=None			,      ):






							"""simple docstring"""






							if input_points is not None:
								if hasattr(__lowercase			,      "numpy"		):  # Checks for TF or Torch tensor
									snake_case_						       =	input_points.numpy().tolist()

								if not isinstance(__lowercase			,      __lowercase		) or not isinstance(input_points[0]			,      __lowercase		):
									raise ValueError("Input points must be a list of list of floating points."		)
								snake_case_						       =	[np.array(__lowercase		) for input_point in input_points]
							else:
								snake_case_						       =	None

							if input_labels is not None:
								if hasattr(__lowercase			,      "numpy"		):
									snake_case_						       =	input_labels.numpy().tolist()

								if not isinstance(__lowercase			,      __lowercase		) or not isinstance(input_labels[0]			,      __lowercase		):
									raise ValueError("Input labels must be a list of list integers."		)
								snake_case_						       =	[np.array(__lowercase		) for label in input_labels]
							else:
								snake_case_						       =	None

							if input_boxes is not None:
								if hasattr(__lowercase			,      "numpy"		):
									snake_case_						       =	input_boxes.numpy().tolist()

								if (
								    not isinstance(__lowercase			,      __lowercase		)
								    or not isinstance(input_boxes[0]			,      __lowercase		)
								    or not isinstance(input_boxes[0][0]			,      __lowercase		)
								):
									raise ValueError("Input boxes must be a list of list of list of floating points."		)
								snake_case_						       =	[np.array(__lowercase		).astype(np.floataa		) for box in input_boxes]
							else:
								snake_case_						       =	None

							return input_points, input_labels, input_boxes






						@property
						def 			snake_case__		(		self				:	str		):






							"""simple docstring"""






							snake_case_						       =	self.image_processor.model_input_names
							return list(dict.fromkeys(__lowercase		)		)






						def 			snake_case__		(		self				:	Optional[Any]			,      *__lowercase				:	Optional[Any]			,      **__lowercase				:	List[Any]		):






							"""simple docstring"""






							return self.image_processor.post_process_masks(*__lowercase			,      **__lowercase		)






import numpy as np
import qiskit


def 		lowerCamelCase__       (						_A = 8	,      _A = None				):







	'''simple docstring'''





	snake_case_						       =	np.random.default_rng(seed=_A				)

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

	# Quantum Circuit to simulate BB84
	snake_case_						       =	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.
	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.
	snake_case_						       =	qiskit.execute(_A	,      _A	,      shots=1	,      seed_simulator=_A				)
	# Returns the result of measurement.
	snake_case_						       =	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.
	snake_case_						       =	"".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.
	snake_case_						       =	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()
