





'''simple docstring'''


import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest



class 							__UpperCAmelCase     (				_a  ):







		'''simple docstring'''







		__lowerCAmelCase        =			(DDIMParallelScheduler,)
		__lowerCAmelCase        =			(("""eta""", 0.0), ("""num_inference_steps""", 50))







		def 				A   (self  :					Optional[Any]					,					**_lowerCAmelCase  :					Any					):
			A							     =					{
			    """num_train_timesteps""": 1000,
			    """beta_start""": 0.0_001,
			    """beta_end""": 0.02,
			    """beta_schedule""": """linear""",
			    """clip_sample""": True,
			}

			config.update(**__lowerCamelCase					)
			return config







		def 				A   (self  :					Optional[Any]					,					**_lowerCAmelCase  :					List[Any]					):
			A							     =					self.scheduler_classes[0]
			A							     =					self.get_scheduler_config(**__lowerCamelCase					)
			A							     =					scheduler_class(**__lowerCamelCase					)

			A							     =					10, 0.0

			A							     =					self.dummy_model()
			A							     =					self.dummy_sample_deter

			scheduler.set_timesteps(__lowerCamelCase					)

			for t in scheduler.timesteps:
				A							     =					model(__lowerCamelCase					,					__lowerCamelCase					)
				A							     =					scheduler.step(__lowerCamelCase					,					__lowerCamelCase					,					__lowerCamelCase					,					__lowerCamelCase					).prev_sample

			return sample







		def 				A   (self  :					Optional[int]					):
			for timesteps in [100, 500, 1000]:
				self.check_over_configs(num_train_timesteps=__lowerCamelCase					)







		def 				A   (self  :					Any					):
			for steps_offset in [0, 1]:
				self.check_over_configs(steps_offset=__lowerCamelCase					)

			A							     =					self.scheduler_classes[0]
			A							     =					self.get_scheduler_config(steps_offset=1					)
			A							     =					scheduler_class(**__lowerCamelCase					)
			scheduler.set_timesteps(5					)
			assert torch.equal(scheduler.timesteps					,					torch.LongTensor([801, 601, 401, 201, 1]					)					)







		def 				A   (self  :					List[str]					):
			for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]					,					[0.002, 0.02, 0.2, 2]					):
				self.check_over_configs(beta_start=__lowerCamelCase					,					beta_end=__lowerCamelCase					)







		def 				A   (self  :					Tuple					):
			for schedule in ["linear", "squaredcos_cap_v2"]:
				self.check_over_configs(beta_schedule=__lowerCamelCase					)







		def 				A   (self  :					Optional[int]					):
			for prediction_type in ["epsilon", "v_prediction"]:
				self.check_over_configs(prediction_type=__lowerCamelCase					)







		def 				A   (self  :					List[Any]					):
			for clip_sample in [True, False]:
				self.check_over_configs(clip_sample=__lowerCamelCase					)







		def 				A   (self  :					List[str]					):
			for timestep_spacing in ["trailing", "leading"]:
				self.check_over_configs(timestep_spacing=__lowerCamelCase					)







		def 				A   (self  :					Optional[Any]					):
			for rescale_betas_zero_snr in [True, False]:
				self.check_over_configs(rescale_betas_zero_snr=__lowerCamelCase					)







		def 				A   (self  :					str					):
			self.check_over_configs(thresholding=__lowerCamelCase					)
			for threshold in [0.5, 1.0, 2.0]:
				for prediction_type in ["epsilon", "v_prediction"]:
					self.check_over_configs(
					    thresholding=__lowerCamelCase					,					prediction_type=__lowerCamelCase					,					sample_max_value=__lowerCamelCase					,					)







		def 				A   (self  :					Tuple					):
			for t in [1, 10, 49]:
				self.check_over_forward(time_step=__lowerCamelCase					)







		def 				A   (self  :					List[str]					):
			for t, num_inference_steps in zip([1, 10, 50]					,					[10, 50, 500]					):
				self.check_over_forward(time_step=__lowerCamelCase					,					num_inference_steps=__lowerCamelCase					)







		def 				A   (self  :					Dict					):
			for t, eta in zip([1, 10, 49]					,					[0.0, 0.5, 1.0]					):
				self.check_over_forward(time_step=__lowerCamelCase					,					eta=__lowerCamelCase					)







		def 				A   (self  :					Optional[Any]					):
			A							     =					self.scheduler_classes[0]
			A							     =					self.get_scheduler_config()
			A							     =					scheduler_class(**__lowerCamelCase					)

			assert torch.sum(torch.abs(scheduler._get_variance(0					,					0					) - 0.0					)					) < 1e-5
			assert torch.sum(torch.abs(scheduler._get_variance(420					,					400					) - 0.14_771					)					) < 1e-5
			assert torch.sum(torch.abs(scheduler._get_variance(980					,					960					) - 0.32_460					)					) < 1e-5
			assert torch.sum(torch.abs(scheduler._get_variance(0					,					0					) - 0.0					)					) < 1e-5
			assert torch.sum(torch.abs(scheduler._get_variance(487					,					486					) - 0.00_979					)					) < 1e-5
			assert torch.sum(torch.abs(scheduler._get_variance(999					,					998					) - 0.02					)					) < 1e-5







		def 				A   (self  :					Dict					):
			A							     =					self.scheduler_classes[0]
			A							     =					self.get_scheduler_config()
			A							     =					scheduler_class(**__lowerCamelCase					)

			A							     =					10, 0.0
			scheduler.set_timesteps(__lowerCamelCase					)

			A							     =					self.dummy_model()
			A							     =					self.dummy_sample_deter
			A							     =					self.dummy_sample_deter + 0.1
			A							     =					self.dummy_sample_deter - 0.1

			A							     =					samplea.shape[0]
			A							     =					torch.stack([samplea, samplea, samplea]					,					dim=0					)
			A							     =					torch.arange(__lowerCamelCase					)[0:3, None].repeat(1					,					__lowerCamelCase					)

			A							     =					model(samples.flatten(0					,					1					)					,					timesteps.flatten(0					,					1					)					)
			A							     =					scheduler.batch_step_no_noise(__lowerCamelCase					,					timesteps.flatten(0					,					1					)					,					samples.flatten(0					,					1					)					,					__lowerCamelCase					)

			A							     =					torch.sum(torch.abs(__lowerCamelCase					)					)
			A							     =					torch.mean(torch.abs(__lowerCamelCase					)					)

			assert abs(result_sum.item() - 1_147.7_904					) < 1e-2
			assert abs(result_mean.item() - 0.4_982					) < 1e-3







		def 				A   (self  :					List[str]					):
			A							     =					self.full_loop()

			A							     =					torch.sum(torch.abs(__lowerCamelCase					)					)
			A							     =					torch.mean(torch.abs(__lowerCamelCase					)					)

			assert abs(result_sum.item() - 172.0_067					) < 1e-2
			assert abs(result_mean.item() - 0.223_967					) < 1e-3







		def 				A   (self  :					Optional[Any]					):
			A							     =					self.full_loop(prediction_type="""v_prediction"""					)

			A							     =					torch.sum(torch.abs(__lowerCamelCase					)					)
			A							     =					torch.mean(torch.abs(__lowerCamelCase					)					)

			assert abs(result_sum.item() - 52.5_302					) < 1e-2
			assert abs(result_mean.item() - 0.0_684					) < 1e-3







		def 				A   (self  :					List[str]					):
			# We specify different beta, so that the first alpha is 0.99
			A							     =					self.full_loop(set_alpha_to_one=__lowerCamelCase					,					beta_start=0.01					)
			A							     =					torch.sum(torch.abs(__lowerCamelCase					)					)
			A							     =					torch.mean(torch.abs(__lowerCamelCase					)					)

			assert abs(result_sum.item() - 149.8_295					) < 1e-2
			assert abs(result_mean.item() - 0.1_951					) < 1e-3







		def 				A   (self  :					List[Any]					):
			# We specify different beta, so that the first alpha is 0.99
			A							     =					self.full_loop(set_alpha_to_one=__lowerCamelCase					,					beta_start=0.01					)
			A							     =					torch.sum(torch.abs(__lowerCamelCase					)					)
			A							     =					torch.mean(torch.abs(__lowerCamelCase					)					)

			assert abs(result_sum.item() - 149.0_784					) < 1e-2
			assert abs(result_mean.item() - 0.1_941					) < 1e-3









import math

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

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  int = 3					)		->			qiskit.result.counts.Counts:






	"""simple docstring"""



	if isinstance(__magic_name__	,			__magic_name__					):
		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(__magic_name__					) != number_of_qubits:
		raise ValueError("""number of qubits must be exact integer."""					)
	if number_of_qubits > 10:
		raise ValueError("""number of qubits too large to simulate(>10)."""					)

	UpperCamelCase     :int          =	QuantumRegister(__magic_name__	,			"""qr"""					)
	UpperCamelCase     :str          =	ClassicalRegister(__magic_name__	,			"""cr"""					)

	UpperCamelCase     :str          =	QuantumCircuit(__magic_name__	,			__magic_name__					)

	UpperCamelCase     :List[Any]          =	number_of_qubits

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

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

	# measure all the qubits
	quantum_circuit.measure(__magic_name__	,			__magic_name__					)
	# simulate with 10000 shots
	UpperCamelCase     :str          =	Aer.get_backend("""qasm_simulator"""					)
	UpperCamelCase     :Dict          =	execute(__magic_name__	,			__magic_name__	,			shots=1_0000					)

	return job.result().get_counts(__magic_name__					)


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