"""simple docstring"""



import math

import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute



def 					_lowerCamelCase(					a = 3							):
       if isinstance(a ,  a							):
              raise TypeError("number of qubits must be a integer."							)
       if number_of_qubits <= 0:
              raise ValueError("number of qubits must be > 0."							)
       if math.floor(a							) != number_of_qubits:
              raise ValueError("number of qubits must be exact integer."							)
       if number_of_qubits > 1_0:
              raise ValueError("number of qubits too large to simulate(>10)."							)

       __a     				=  QuantumRegister(a ,  "qr"							)
       __a     				=  ClassicalRegister(a ,  "cr"							)

       __a     				=  QuantumCircuit(a ,  a							)

       __a     				=  number_of_qubits

       for i in range(a							):
              quantum_circuit.h(number_of_qubits - i - 1							)
              counter -= 1
              for j in range(a							):
                     quantum_circuit.cp(np.pi / 2 ** (counter - j) ,  a ,  a							)

       for k in range(number_of_qubits // 2							):
              quantum_circuit.swap(a ,  number_of_qubits - k - 1							)

       # measure all the qubits
       quantum_circuit.measure(a ,  a							)
       # simulate with 10000 shots
       __a     				=  Aer.get_backend("qasm_simulator"							)
       __a     				=  execute(a ,  a ,  shots=1_0_0_0_0							)

       return job.result().get_counts(a							)


if __name__ == "__main__":
   print(
       F'''Total count for quantum fourier transform state is: \
    {quantum_fourier_transform(3)}'''
   )

"""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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
   import PIL


SCREAMING_SNAKE_CASE__:Tuple			=	logging.get_logger(__name__)






class 				snake_case__     (   snake_case_   ):
 _snake_case   :			Optional[Any]           =		["""pixel_values"""]


 def __init__(				self  ,						lowerCamelCase = True  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = PILImageResampling.BILINEAR  ,						lowerCamelCase = True  ,						lowerCamelCase = 1 / 255  ,						lowerCamelCase = True  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						**lowerCamelCase  ,						):
        super().__init__(**lowerCamelCase		)
        __a     				=  size if size is not None else {"shortest_edge": 384}
        __a     				=  get_size_dict(lowerCamelCase  ,						default_to_square=lowerCamelCase		)

        __a     				=  do_resize
        __a     				=  size
        # Default value set here for backwards compatibility where the value in config is None
        __a     				=  crop_pct if crop_pct is not None else 224 / 256
        __a     				=  resample
        __a     				=  do_rescale
        __a     				=  rescale_factor
        __a     				=  do_normalize
        __a     				=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        __a     				=  image_std if image_std is not None else IMAGENET_STANDARD_STD


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase = PILImageResampling.BICUBIC  ,						lowerCamelCase = None  ,						**lowerCamelCase  ,						):
        __a     				=  get_size_dict(lowerCamelCase  ,						default_to_square=lowerCamelCase		)
        if "shortest_edge" not in size:
               raise ValueError(F"Size dictionary must contain 'shortest_edge' key. Got {size.keys()}"		)
        __a     				=  size["shortest_edge"]

        if shortest_edge < 384:
               # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
               __a     				=  int(shortest_edge / crop_pct		)
               __a     				=  get_resize_output_image_size(lowerCamelCase  ,						size=lowerCamelCase  ,						default_to_square=lowerCamelCase		)
               __a     				=  resize(image=lowerCamelCase  ,						size=lowerCamelCase  ,						resample=lowerCamelCase  ,						data_format=lowerCamelCase  ,						**lowerCamelCase		)
               # then crop to (shortest_edge, shortest_edge)
               return center_crop(image=lowerCamelCase  ,						size=(shortest_edge, shortest_edge)  ,						data_format=lowerCamelCase  ,						**lowerCamelCase		)
        else:
               # warping (no cropping) when evaluated at 384 or larger
               return resize(
                   lowerCamelCase  ,						size=(shortest_edge, shortest_edge)  ,						resample=lowerCamelCase  ,						data_format=lowerCamelCase  ,						**lowerCamelCase		)


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase = None  ,						**lowerCamelCase  ,						):
        return rescale(lowerCamelCase  ,						scale=lowerCamelCase  ,						data_format=lowerCamelCase  ,						**lowerCamelCase		)


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase = None  ,						**lowerCamelCase  ,						):
        return normalize(lowerCamelCase  ,						mean=lowerCamelCase  ,						std=lowerCamelCase  ,						data_format=lowerCamelCase  ,						**lowerCamelCase		)






 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = ChannelDimension.FIRST  ,						**lowerCamelCase  ,						):
        __a     				=  do_resize if do_resize is not None else self.do_resize
        __a     				=  crop_pct if crop_pct is not None else self.crop_pct
        __a     				=  resample if resample is not None else self.resample
        __a     				=  do_rescale if do_rescale is not None else self.do_rescale
        __a     				=  rescale_factor if rescale_factor is not None else self.rescale_factor
        __a     				=  do_normalize if do_normalize is not None else self.do_normalize
        __a     				=  image_mean if image_mean is not None else self.image_mean
        __a     				=  image_std if image_std is not None else self.image_std

        __a     				=  size if size is not None else self.size
        __a     				=  get_size_dict(lowerCamelCase  ,						default_to_square=lowerCamelCase		)

        __a     				=  make_list_of_images(lowerCamelCase		)

        if not valid_images(lowerCamelCase		):
               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 or resample is None:
               raise ValueError("Size and resample must be specified if do_resize is True."		)

        if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
               raise ValueError("crop_pct must be specified if size < 384."		)

        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.
        __a     				=  [to_numpy_array(lowerCamelCase		) for image in images]

        if do_resize:
               __a     				=  [self.resize(image=lowerCamelCase  ,						size=lowerCamelCase  ,						crop_pct=lowerCamelCase  ,						resample=lowerCamelCase		) for image in images]

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

        if do_normalize:
               __a     				=  [self.normalize(image=lowerCamelCase  ,						mean=lowerCamelCase  ,						std=lowerCamelCase		) for image in images]

        __a     				=  [to_channel_dimension_format(lowerCamelCase  ,						lowerCamelCase		) for image in images]

        __a     				=  {"pixel_values": images}
        return BatchFeature(data=lowerCamelCase  ,						tensor_type=lowerCamelCase		)
