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

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCamelCase_   	=	logging.get_logger(__name__)

class       a		(   __UpperCAmelCase					):
							lowercase_		:						List[str]   							=  ['pixel_values']







							def __init__(						self  :  str      ,				snake_case__  :  bool = True      ,				snake_case__  :  Dict[str, int] = None      ,				snake_case__  :  PILImageResampling = PILImageResampling.BICUBIC      ,				snake_case__  :  bool = True      ,				snake_case__  :  Dict[str, int] = None      ,				snake_case__  :  bool = True      ,				snake_case__  :  Union[int, float] = 1 / 255      ,				snake_case__  :  bool = True      ,				snake_case__  :  Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN      ,				snake_case__  :  Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD      ,				**snake_case__  :  Optional[Any]      ,				):
								"""simple docstring"""







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

								__lowerCAmelCase         =							do_resize
								__lowerCAmelCase         =							size
								__lowerCAmelCase         =							resample
								__lowerCAmelCase         =							do_center_crop
								__lowerCAmelCase         =							crop_size
								__lowerCAmelCase         =							do_rescale
								__lowerCAmelCase         =							rescale_factor
								__lowerCAmelCase         =							do_normalize
								__lowerCAmelCase         =							image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
								__lowerCAmelCase         =							image_std if image_std is not None else IMAGENET_DEFAULT_STD







							def       UpperCAmelCase__					(						self  :  Tuple      ,				snake_case__  :  np.ndarray      ,				snake_case__  :  Dict[str, int]      ,				snake_case__  :  PILImageResampling = PILImageResampling.BICUBIC      ,				snake_case__  :  Optional[Union[str, ChannelDimension]] = None      ,				**snake_case__  :  Union[str, Any]      ,				):
								"""simple docstring"""







								__lowerCAmelCase         =							get_size_dict(snake_case__      ,				default_to_square=snake_case__ )
								# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
								if "shortest_edge" in size:
									__lowerCAmelCase         =							int((256 / 224) * size["shortest_edge"] )
									__lowerCAmelCase         =							get_resize_output_image_size(snake_case__      ,				size=snake_case__      ,				default_to_square=snake_case__ )
									__lowerCAmelCase         =							{"height": output_size[0], "width": output_size[1]}
								if "height" not in size_dict or "width" not in size_dict:
									raise ValueError(
									    F"Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}" )
								return resize(
								    snake_case__      ,				size=(size_dict["height"], size_dict["width"])      ,				resample=snake_case__      ,				data_format=snake_case__      ,				**snake_case__ )







							def       UpperCAmelCase__					(						self  :  List[str]      ,				snake_case__  :  np.ndarray      ,				snake_case__  :  Dict[str, int]      ,				snake_case__  :  Optional[Union[str, ChannelDimension]] = None      ,				**snake_case__  :  Optional[Any]      ,				):
								"""simple docstring"""







								__lowerCAmelCase         =							get_size_dict(snake_case__ )
								if "height" not in size or "width" not in size:
									raise ValueError(F"Size dict must have keys 'height' and 'width'. Got {size.keys()}" )
								return center_crop(snake_case__      ,				size=(size["height"], size["width"])      ,				data_format=snake_case__      ,				**snake_case__ )







							def       UpperCAmelCase__					(						self  :  Tuple      ,				snake_case__  :  np.ndarray      ,				snake_case__  :  Union[int, float]      ,				snake_case__  :  Optional[Union[str, ChannelDimension]] = None      ,				**snake_case__  :  Tuple      ,				):
								"""simple docstring"""







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







							def       UpperCAmelCase__					(						self  :  List[Any]      ,				snake_case__  :  np.ndarray      ,				snake_case__  :  Union[float, List[float]]      ,				snake_case__  :  Union[float, List[float]]      ,				snake_case__  :  Optional[Union[str, ChannelDimension]] = None      ,				**snake_case__  :  List[Any]      ,				):
								"""simple docstring"""







								return normalize(snake_case__      ,				mean=snake_case__      ,				std=snake_case__      ,				data_format=snake_case__      ,				**snake_case__ )







							def       UpperCAmelCase__					(						self  :  List[str]      ,				snake_case__  :  ImageInput      ,				snake_case__  :  Optional[bool] = None      ,				snake_case__  :  Optional[Dict[str, int]] = None      ,				snake_case__  :  PILImageResampling = None      ,				snake_case__  :  Optional[bool] = None      ,				snake_case__  :  Optional[Dict[str, int]] = None      ,				snake_case__  :  Optional[bool] = None      ,				snake_case__  :  Optional[float] = None      ,				snake_case__  :  Optional[bool] = None      ,				snake_case__  :  Optional[Union[float, Iterable[float]]] = None      ,				snake_case__  :  Optional[Union[float, Iterable[float]]] = None      ,				snake_case__  :  Optional[TensorType] = None      ,				snake_case__  :  ChannelDimension = ChannelDimension.FIRST      ,				**snake_case__  :  Optional[Any]      ,				):
								"""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_center_crop if do_center_crop is not None else self.do_center_crop
								__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_normalize if do_normalize is not None else self.do_normalize
								__lowerCAmelCase         =							image_mean if image_mean is not None else self.image_mean
								__lowerCAmelCase         =							image_std if image_std is not None else self.image_std

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

								__lowerCAmelCase         =							make_list_of_images(snake_case__ )

								if not valid_images(snake_case__ ):
									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_center_crop and crop_size is None:
									raise ValueError("Crop size must be specified if do_center_crop is True." )

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

								if do_normalize and (image_mean is None or image_std is None):
									raise ValueError("Image mean and std must be specified if do_normalize is True." )

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

								if do_resize:
									__lowerCAmelCase         =							[self.resize(snake_case__      ,				snake_case__      ,				snake_case__ ) for image in images]

								if do_center_crop:
									__lowerCAmelCase         =							[self.center_crop(snake_case__      ,				snake_case__ ) for image in images]

								if do_rescale:
									__lowerCAmelCase         =							[self.rescale(snake_case__      ,				snake_case__ ) for image in images]

								if do_normalize:
									__lowerCAmelCase         =							[self.normalize(snake_case__      ,				snake_case__      ,				snake_case__ ) for image in images]

								__lowerCAmelCase         =							[to_channel_dimension_format(snake_case__      ,				snake_case__ ) for image in images]

								__lowerCAmelCase         =							{"pixel_values": images}
								return BatchFeature(data=snake_case__      ,				tensor_type=snake_case__ )








import numpy as np
import qiskit






def 		_UpperCAmelCase   (	UpperCamelCase:     int = 8			,							UpperCamelCase:     int | None = None  ):






	"""simple docstring"""


	__lowerCAmelCase         =							np.random.default_rng(seed=UpperCamelCase  )

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

	# Quantum Circuit to simulate BB84
	__lowerCAmelCase         =							qiskit.QuantumCircuit(UpperCamelCase			,							name="BB84"  )

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

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

	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(UpperCamelCase			,							UpperCamelCase			,							shots=1			,							seed_simulator=UpperCamelCase  )
	# Returns the result of measurement.
	__lowerCAmelCase         =							job.result().get_counts(UpperCamelCase  ).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(
	            UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase  )
	        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(UpperCamelCase  ) >= key_len else gen_key.ljust(UpperCamelCase			,							"0"  )
	return key


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

					testmod()






