


'''simple docstring'''
from typing import Dict, List, Optional, Union

import numpy as np

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


__snake_case							   =  logging.get_logger(__name__)


if is_vision_available():
		import PIL
class      lowercase			(							A__      ):

		"""simple docstring"""

		_a									=       ['pixel_values']




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

							'''simple docstring'''


							super().__init__(**UpperCamelCase_							)
							UpperCamelCase__     :Tuple							      =				size if size is not None else {'''shortest_edge''': 224}
							UpperCamelCase__     :Optional[int]							      =				get_size_dict(UpperCamelCase_					,     default_to_square=UpperCamelCase_							)
							UpperCamelCase__     :str							      =				crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
							UpperCamelCase__     :Union[str, Any]							      =				get_size_dict(UpperCamelCase_					,     default_to_square=UpperCamelCase_					,     param_name='''crop_size'''							)

							UpperCamelCase__     :Any							      =				do_resize
							UpperCamelCase__     :Union[str, Any]							      =				size
							UpperCamelCase__     :Any							      =				resample
							UpperCamelCase__     :Optional[Any]							      =				do_center_crop
							UpperCamelCase__     :List[str]							      =				crop_size
							UpperCamelCase__     :Optional[int]							      =				do_rescale
							UpperCamelCase__     :Optional[Any]							      =				rescale_factor
							UpperCamelCase__     :Any							      =				do_normalize
							UpperCamelCase__     :int							      =				image_mean if image_mean is not None else OPENAI_CLIP_MEAN
							UpperCamelCase__     :List[str]							      =				image_std if image_std is not None else OPENAI_CLIP_STD
							UpperCamelCase__     :Union[str, Any]							      =				do_convert_rgb




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_ = PILImageResampling.BICUBIC					,     UpperCamelCase_ = None					,     **UpperCamelCase_					,     ):

							'''simple docstring'''


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




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_ = None					,     **UpperCamelCase_					,     ):

							'''simple docstring'''


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




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_ = None					,     **UpperCamelCase_					,     ):

							'''simple docstring'''


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




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_ = None					,     **UpperCamelCase_					,     ):

							'''simple docstring'''


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




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

							'''simple docstring'''


							UpperCamelCase__     :Optional[int]							      =				do_resize if do_resize is not None else self.do_resize
							UpperCamelCase__     :Optional[Any]							      =				size if size is not None else self.size
							UpperCamelCase__     :Optional[int]							      =				get_size_dict(UpperCamelCase_					,     param_name='''size'''					,     default_to_square=UpperCamelCase_							)
							UpperCamelCase__     :Dict							      =				resample if resample is not None else self.resample
							UpperCamelCase__     :int							      =				do_center_crop if do_center_crop is not None else self.do_center_crop
							UpperCamelCase__     :Any							      =				crop_size if crop_size is not None else self.crop_size
							UpperCamelCase__     :Any							      =				get_size_dict(UpperCamelCase_					,     param_name='''crop_size'''					,     default_to_square=UpperCamelCase_							)
							UpperCamelCase__     :List[str]							      =				do_rescale if do_rescale is not None else self.do_rescale
							UpperCamelCase__     :List[Any]							      =				rescale_factor if rescale_factor is not None else self.rescale_factor
							UpperCamelCase__     :Optional[int]							      =				do_normalize if do_normalize is not None else self.do_normalize
							UpperCamelCase__     :Tuple							      =				image_mean if image_mean is not None else self.image_mean
							UpperCamelCase__     :str							      =				image_std if image_std is not None else self.image_std
							UpperCamelCase__     :Optional[Any]							      =				do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

							UpperCamelCase__     :str							      =				make_list_of_images(UpperCamelCase_							)

							if not valid_images(UpperCamelCase_							):
												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.'''							)

							# PIL RGBA images are converted to RGB
							if do_convert_rgb:
												UpperCamelCase__     :Any							      =				[convert_to_rgb(UpperCamelCase_							) for image in images]

							# All transformations expect numpy arrays.
							UpperCamelCase__     :str							      =				[to_numpy_array(UpperCamelCase_							) for image in images]

							if do_resize:
												UpperCamelCase__     :Optional[Any]							      =				[self.resize(image=UpperCamelCase_					,     size=UpperCamelCase_					,     resample=UpperCamelCase_							) for image in images]

							if do_center_crop:
												UpperCamelCase__     :Dict							      =				[self.center_crop(image=UpperCamelCase_					,     size=UpperCamelCase_							) for image in images]

							if do_rescale:
												UpperCamelCase__     :Optional[Any]							      =				[self.rescale(image=UpperCamelCase_					,     scale=UpperCamelCase_							) for image in images]

							if do_normalize:
												UpperCamelCase__     :Tuple							      =				[self.normalize(image=UpperCamelCase_					,     mean=UpperCamelCase_					,     std=UpperCamelCase_							) for image in images]

							UpperCamelCase__     :List[str]							      =				[to_channel_dimension_format(UpperCamelCase_					,     UpperCamelCase_							) for image in images]

							UpperCamelCase__     :Optional[Any]							      =				{'''pixel_values''': images}
							return BatchFeature(data=UpperCamelCase_					,     tensor_type=UpperCamelCase_							)



'''simple docstring'''
import qiskit


def     a   (    __a				,      __a     )						->			qiskit.result.counts.Counts:



					'''simple docstring'''






					UpperCamelCase__     :int							      =				qiskit.Aer.get_backend('''aer_simulator'''     )

					# Create a Quantum Circuit acting on the q register
					UpperCamelCase__     :Any							      =				qiskit.QuantumCircuit(__a				,      __a     )

					# Map the quantum measurement to the classical bits
					circuit.measure([0]				,      [0]     )

					# Execute the circuit on the simulator
					UpperCamelCase__     :Optional[int]							      =				qiskit.execute(__a				,      __a				,      shots=1000     )

					# Return the histogram data of the results of the experiment.
					return job.result().get_counts(__a     )


if __name__ == "__main__":
		print(F"""Total count for various states are: {single_qubit_measure(1, 1)}""")