
'''simple docstring'''



from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
						import PIL

if is_torch_available():
						import torch


_lowercase    =		logging.get_logger(__name__)




class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						_lowercase		:				int									=       ['''pixel_values''']




						def __init__( self   ,       _lowercase = True   ,       _lowercase = None   ,       _lowercase = PILImageResampling.BILINEAR   ,       _lowercase = True   ,       _lowercase = 1 / 255   ,       _lowercase = True   ,       _lowercase = None   ,       _lowercase = True   ,       **_lowercase   ,       ):

												"""simple docstring"""



												super().__init__(**_lowercase				)
												_lowerCAmelCase  						=			size if size is not None else {"""shortest_edge""": 224}
												_lowerCAmelCase  						=			get_size_dict(_lowercase   ,       default_to_square=_lowercase				)
												_lowerCAmelCase  						=			crop_size if crop_size is not None else {"""height""": 256, """width""": 256}
												_lowerCAmelCase  						=			get_size_dict(_lowercase   ,       param_name="""crop_size"""				)

												_lowerCAmelCase  						=			do_resize
												_lowerCAmelCase  						=			size
												_lowerCAmelCase  						=			resample
												_lowerCAmelCase  						=			do_rescale
												_lowerCAmelCase  						=			rescale_factor
												_lowerCAmelCase  						=			do_center_crop
												_lowerCAmelCase  						=			crop_size
												_lowerCAmelCase  						=			do_flip_channel_order




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase   ,       _lowercase = PIL.Image.BILINEAR   ,       _lowercase = None   ,       **_lowercase   ,       ):

												"""simple docstring"""



												_lowerCAmelCase  						=			get_size_dict(_lowercase   ,       default_to_square=_lowercase				)
												if "shortest_edge" not in size:
																		raise ValueError(F'The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}'				)
												_lowerCAmelCase  						=			get_resize_output_image_size(_lowercase   ,       size=size["""shortest_edge"""]   ,       default_to_square=_lowercase				)
												return resize(_lowercase   ,       size=_lowercase   ,       resample=_lowercase   ,       data_format=_lowercase   ,       **_lowercase				)




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase   ,       _lowercase = None   ,       **_lowercase   ,       ):

												"""simple docstring"""



												_lowerCAmelCase  						=			get_size_dict(_lowercase				)
												if "height" not in size or "width" not in size:
																		raise ValueError(F'The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'				)
												return center_crop(_lowercase   ,       size=(size["""height"""], size["""width"""])   ,       data_format=_lowercase   ,       **_lowercase				)




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase   ,       _lowercase = None   ,       **_lowercase   ,       ):

												"""simple docstring"""



												return rescale(_lowercase   ,       scale=_lowercase   ,       data_format=_lowercase   ,       **_lowercase				)




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase = None				):

												"""simple docstring"""



												return flip_channel_order(_lowercase   ,       data_format=_lowercase				)




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase = None   ,       _lowercase = None   ,       _lowercase = None   ,       _lowercase = None   ,       _lowercase = None   ,       _lowercase = None   ,       _lowercase = None   ,       _lowercase = None   ,       _lowercase = None   ,       _lowercase = ChannelDimension.FIRST   ,       **_lowercase   ,       ):

												"""simple docstring"""



												_lowerCAmelCase  						=			do_resize if do_resize is not None else self.do_resize
												_lowerCAmelCase  						=			resample if resample is not None else self.resample
												_lowerCAmelCase  						=			do_rescale if do_rescale is not None else self.do_rescale
												_lowerCAmelCase  						=			rescale_factor if rescale_factor is not None else self.rescale_factor
												_lowerCAmelCase  						=			do_center_crop if do_center_crop is not None else self.do_center_crop
												_lowerCAmelCase  						=			(
												    do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
												)

												_lowerCAmelCase  						=			size if size is not None else self.size
												_lowerCAmelCase  						=			get_size_dict(_lowercase   ,       default_to_square=_lowercase				)
												_lowerCAmelCase  						=			crop_size if crop_size is not None else self.crop_size
												_lowerCAmelCase  						=			get_size_dict(_lowercase   ,       param_name="""crop_size"""				)

												_lowerCAmelCase  						=			make_list_of_images(_lowercase				)

												if not valid_images(_lowercase				):
																		raise ValueError(
																		    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
																		    """torch.Tensor, tf.Tensor or jax.ndarray."""				)

												if do_resize and size is None:
																		raise ValueError("""Size must be specified if do_resize is True."""				)

												if do_rescale and rescale_factor is None:
																		raise ValueError("""Rescale factor must be specified if do_rescale is True."""				)

												if do_center_crop and crop_size is None:
																		raise ValueError("""Crop size must be specified if do_center_crop is True."""				)

												# All transformations expect numpy arrays.
												_lowerCAmelCase  						=			[to_numpy_array(_lowercase				) for image in images]

												if do_resize:
																		_lowerCAmelCase  						=			[self.resize(image=_lowercase   ,       size=_lowercase   ,       resample=_lowercase				) for image in images]

												if do_center_crop:
																		_lowerCAmelCase  						=			[self.center_crop(image=_lowercase   ,       size=_lowercase				) for image in images]

												if do_rescale:
																		_lowerCAmelCase  						=			[self.rescale(image=_lowercase   ,       scale=_lowercase				) for image in images]

												# the pretrained checkpoints assume images are BGR, not RGB
												if do_flip_channel_order:
																		_lowerCAmelCase  						=			[self.flip_channel_order(image=_lowercase				) for image in images]

												_lowerCAmelCase  						=			[to_channel_dimension_format(_lowercase   ,       _lowercase				) for image in images]

												_lowerCAmelCase  						=			{"""pixel_values""": images}
												return BatchFeature(data=_lowercase   ,       tensor_type=_lowercase				)




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase = None				):

												"""simple docstring"""



												_lowerCAmelCase  						=			outputs.logits

												# Resize logits and compute semantic segmentation maps
												if target_sizes is not None:
																		if len(_lowercase				) != len(_lowercase				):
																								raise ValueError(
																								    """Make sure that you pass in as many target sizes as the batch dimension of the logits"""				)

																		if is_torch_tensor(_lowercase				):
																								_lowerCAmelCase  						=			target_sizes.numpy()

																		_lowerCAmelCase  						=			[]

																		for idx in range(len(_lowercase				)				):
																								_lowerCAmelCase  						=			torch.nn.functional.interpolate(
																								    logits[idx].unsqueeze(dim=0				)   ,       size=target_sizes[idx]   ,       mode="""bilinear"""   ,       align_corners=_lowercase				)
																								_lowerCAmelCase  						=			resized_logits[0].argmax(dim=0				)
																								semantic_segmentation.append(_lowercase				)
												else:
																		_lowerCAmelCase  						=			logits.argmax(dim=1				)
																		_lowerCAmelCase  						=			[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]				)]

												return semantic_segmentation


'''simple docstring'''



import numpy as np
import qiskit


def A				(__lowerCamelCase				:int = 8			,     __lowerCamelCase				:int | None = None		):
						_lowerCAmelCase  						=			np.random.default_rng(seed=__lowerCamelCase		)

						# 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=__lowerCamelCase		)
						# The set of states Alice will prepare.
						_lowerCAmelCase  						=			rng.integers(2			,     size=__lowerCamelCase		)
						# Measurement basis for Bob's qubits.
						_lowerCAmelCase  						=			rng.integers(2			,     size=__lowerCamelCase		)

						# Quantum Circuit to simulate BB84
						_lowerCAmelCase  						=			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.
						_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(__lowerCamelCase			,     __lowerCamelCase			,     shots=1			,     seed_simulator=__lowerCamelCase		)
						# Returns the result of measurement.
						_lowerCAmelCase  						=			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.
						_lowerCAmelCase  						=			"""""".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.
						_lowerCAmelCase  						=			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()
