"""simple docstring"""







import qiskit
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       int = 2						)							->    qiskit.result.counts.Counts:

	'''simple docstring'''





	__snake_case    :							Any   	=				qubits

	# Using Aer's simulator
	__snake_case    :							Any   	=				qiskit.Aer.get_backend('aer_simulator'						)

	# Creating a Quantum Circuit acting on the q register
	__snake_case    :							Optional[int]   	=				qiskit.QuantumCircuit(UpperCAmelCase_    ,							UpperCAmelCase_						)

	# Adding a H gate on qubit 0 (now q0 in superposition)
	circuit.h(0						)

	for i in range(1    ,							UpperCAmelCase_						):
		# Adding CX (CNOT) gate
		circuit.cx(i - 1    ,							UpperCAmelCase_						)

	# Mapping the quantum measurement to the classical bits
	circuit.measure(list(range(UpperCAmelCase_						)						)    ,							list(range(UpperCAmelCase_						)						)						)

	# Now measuring any one qubit would affect other qubits to collapse
	# their super position and have same state as the measured one.

	# Executing the circuit on the simulator
	__snake_case    :							Dict   	=				qiskit.execute(UpperCAmelCase_    ,							UpperCAmelCase_    ,							shots=10_00						)

	return job.result().get_counts(UpperCAmelCase_						)


if __name__ == "__main__":
	print(f'''Total count for various states are: {quantum_entanglement(3)}''')






"""simple docstring"""







import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize

class 	UpperCamelCase    :



				def __init__(self       :   Tuple			,						_A       :   List[Any]			,						_A       :   int=sys.maxsize)					->			int:
					__snake_case    :							Dict   	=				'bilinear'
					__snake_case    :							int   	=				max_size
					__snake_case    :							Any   	=				short_edge_length






				def __call__(self       :   Optional[Any]			,						_A       :   Any)					->			Dict:
					__snake_case    :							Any   	=				[]
					for img in imgs:
						__snake_case	,       __snake_case    :							int   	=				img.shape[:2]
						# later: provide list and randomly choose index for resize
						__snake_case    :							Optional[Any]   	=				np.random.randint(self.short_edge_length[0]			,						self.short_edge_length[1] + 1)
						if size == 0:
							return img
						__snake_case    :							int   	=				size * 1.0 / min(_A			,						_A)
						if h < w:
							__snake_case	,       __snake_case    :							str   	=				size, scale * w
						else:
							__snake_case	,       __snake_case    :							List[str]   	=				scale * h, size
						if max(_A			,						_A) > self.max_size:
							__snake_case    :							str   	=				self.max_size * 1.0 / max(_A			,						_A)
							__snake_case    :							Any   	=				newh * scale
							__snake_case    :							Any   	=				neww * scale
						__snake_case    :							Optional[int]   	=				int(neww + 0.5)
						__snake_case    :							str   	=				int(newh + 0.5)

						if img.dtype == np.uinta:
							__snake_case    :							str   	=				Image.fromarray(_A)
							__snake_case    :							str   	=				pil_image.resize((neww, newh)			,						PILImageResampling.BILINEAR)
							__snake_case    :							Tuple   	=				np.asarray(_A)
						else:
							__snake_case    :							str   	=				img.permute(2			,						0			,						1).unsqueeze(0)  # 3, 0, 1)  # hw(c) -> nchw
							__snake_case    :							Dict   	=				nn.functional.interpolate(
							    _A			,						(newh, neww)			,						mode=self.interp_method			,						align_corners=_A).squeeze(0)
						img_augs.append(_A)

					return img_augs







class 	UpperCamelCase    :



				def __init__(self       :   List[str]			,						_A       :   List[str])					->			Optional[int]:
					__snake_case    :							Union[str, Any]   	=				ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]			,						cfg.INPUT.MAX_SIZE_TEST)
					__snake_case    :							Union[str, Any]   	=				cfg.INPUT.FORMAT
					__snake_case    :							List[Any]   	=				cfg.SIZE_DIVISIBILITY
					__snake_case    :							Tuple   	=				cfg.PAD_VALUE
					__snake_case    :							int   	=				cfg.INPUT.MAX_SIZE_TEST
					__snake_case    :							List[Any]   	=				cfg.MODEL.DEVICE
					__snake_case    :							Tuple   	=				torch.tensor(cfg.MODEL.PIXEL_STD).to(self.device).view(len(cfg.MODEL.PIXEL_STD)			,						1			,						1)
					__snake_case    :							Any   	=				torch.tensor(cfg.MODEL.PIXEL_MEAN).to(self.device).view(len(cfg.MODEL.PIXEL_STD)			,						1			,						1)
					__snake_case    :							Union[str, Any]   	=				lambda _A: (x - self.pixel_mean) / self.pixel_std



				def       _lowercase  (self       :   Tuple			,						_A       :   str)					->			int:
					__snake_case    :							str   	=				tuple(max(_A) for s in zip(*[img.shape for img in images]))
					__snake_case    :							int   	=				[im.shape[-2:] for im in images]
					__snake_case    :							int   	=				[
					    nn.functional.pad(
					        _A			,						[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]			,						value=self.pad_value			,						)
					    for size, im in zip(_A			,						_A)
					]

					return torch.stack(_A), torch.tensor(_A)






				def __call__(self       :   Optional[Any]			,						_A       :   Dict			,						_A       :   Union[str, Any]=False)					->			Optional[int]:
					with torch.no_grad():
						if not isinstance(_A			,						_A):
							__snake_case    :							str   	=				[images]
						if single_image:
							assert len(_A) == 1
						for i in range(len(_A)):
							if isinstance(images[i]			,						torch.Tensor):
								images.insert(_A			,						images.pop(_A).to(self.device).float())
							elif not isinstance(images[i]			,						torch.Tensor):
								images.insert(
								    _A			,						torch.as_tensor(img_tensorize(images.pop(_A)			,						input_format=self.input_format))
								    .to(self.device)
								    .float()			,						)
            # resize smallest edge
						__snake_case    :							Any   	=				torch.tensor([im.shape[:2] for im in images])
						__snake_case    :							Tuple   	=				self.aug(_A)
						# transpose images and convert to torch tensors
						# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
						# now normalize before pad to avoid useless arithmetic
						__snake_case    :							List[str]   	=				[self.normalizer(_A) for x in images]
						# now pad them to do the following operations
						__snake_case	,       __snake_case    :							List[Any]   	=				self.pad(_A)
						# Normalize

						if self.size_divisibility > 0:
							raise NotImplementedError()
						# pad
						__snake_case    :							int   	=				torch.true_divide(_A			,						_A)
						if single_image:
							return images[0], sizes[0], scales_yx[0]
						else:
							return images, sizes, scales_yx







def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Any    ,							UpperCAmelCase_					:       Any						)							->    int:

	'''simple docstring'''





	boxes[:, 0::2] *= scale_yx[:, 1]
	boxes[:, 1::2] *= scale_yx[:, 0]
	return boxes

def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Tuple    ,							UpperCAmelCase_					:       Tuple[int, int]						)							->    Union[str, Any]:

	'''simple docstring'''





	assert torch.isfinite(UpperCAmelCase_						).all(), "Box tensor contains infinite or NaN!"
	__snake_case	,       __snake_case    :							Optional[int]   	=				box_size
	tensor[:, 0].clamp_(min=0    ,							max=UpperCAmelCase_						)
	tensor[:, 1].clamp_(min=0    ,							max=UpperCAmelCase_						)
	tensor[:, 2].clamp_(min=0    ,							max=UpperCAmelCase_						)
	tensor[:, 3].clamp_(min=0    ,							max=UpperCAmelCase_						)





