




from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin



@dataclass
class       a_       (     SCREAMING_SNAKE_CASE__     ):
	A    							= 42
	A    							= 42
	A    							= None




class       a_       (     SCREAMING_SNAKE_CASE__  ,    SCREAMING_SNAKE_CASE__     ):
	A    							= 2





	@register_to_config
	def __init__(							self      ,     SCREAMING_SNAKE_CASE = 0.0_2      ,     SCREAMING_SNAKE_CASE = 100      ,     SCREAMING_SNAKE_CASE = 1.0_0_7      ,     SCREAMING_SNAKE_CASE = 80      ,     SCREAMING_SNAKE_CASE = 0.0_5      ,     SCREAMING_SNAKE_CASE = 50      ,     )  ->					str:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						sigma_max

				# setable values
				SCREAMING_SNAKE_CASE_								=						None
				SCREAMING_SNAKE_CASE_								=						None
				SCREAMING_SNAKE_CASE_								=						None  # sigma(t_i)





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE = None      )  ->					torch.FloatTensor:




				"""simple docstring"""



				return sample





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE = None      )  ->					List[str]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						num_inference_steps
				SCREAMING_SNAKE_CASE_								=						np.arange(0      ,     self.num_inference_steps      )[::-1].copy()
				SCREAMING_SNAKE_CASE_								=						torch.from_numpy(SCREAMING_SNAKE_CASE      ).to(SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						[
				    (
				        self.config.sigma_max**2
				        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
				    )
				    for i in self.timesteps
				]
				SCREAMING_SNAKE_CASE_								=						torch.tensor(SCREAMING_SNAKE_CASE      ,     dtype=torch.floataa      ,     device=SCREAMING_SNAKE_CASE      )





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE = None      )  ->					Tuple[torch.FloatTensor, float]:




				"""simple docstring"""



				if self.config.s_min <= sigma <= self.config.s_max:
							SCREAMING_SNAKE_CASE_								=						min(self.config.s_churn / self.num_inference_steps      ,     2**0.5 - 1      )
				else:
							SCREAMING_SNAKE_CASE_								=						0

				# sample eps ~ N(0, S_noise^2 * I)
				SCREAMING_SNAKE_CASE_								=						self.config.s_noise * randn_tensor(sample.shape      ,     generator=SCREAMING_SNAKE_CASE      ).to(sample.device      )
				SCREAMING_SNAKE_CASE_								=						sigma + gamma * sigma
				SCREAMING_SNAKE_CASE_								=						sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

				return sample_hat, sigma_hat





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE = True      ,     )  ->					Union[KarrasVeOutput, Tuple]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						sample_hat + sigma_hat * model_output
				SCREAMING_SNAKE_CASE_								=						(sample_hat - pred_original_sample) / sigma_hat
				SCREAMING_SNAKE_CASE_								=						sample_hat + (sigma_prev - sigma_hat) * derivative

				if not return_dict:
							return (sample_prev, derivative)

				return KarrasVeOutput(
				    prev_sample=SCREAMING_SNAKE_CASE      ,     derivative=SCREAMING_SNAKE_CASE      ,     pred_original_sample=SCREAMING_SNAKE_CASE      )





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE = True      ,     )  ->					Union[KarrasVeOutput, Tuple]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						sample_prev + sigma_prev * model_output
				SCREAMING_SNAKE_CASE_								=						(sample_prev - pred_original_sample) / sigma_prev
				SCREAMING_SNAKE_CASE_								=						sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

				if not return_dict:
							return (sample_prev, derivative)

				return KarrasVeOutput(
				    prev_sample=SCREAMING_SNAKE_CASE      ,     derivative=SCREAMING_SNAKE_CASE      ,     pred_original_sample=SCREAMING_SNAKE_CASE      )





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )  ->					str:




				"""simple docstring"""



				raise NotImplementedError()






import qiskit

def lowercase    (  SCREAMING_SNAKE_CASE = 2 )			->							qiskit.result.counts.Counts:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						qubits

			# Using Aer's simulator
			SCREAMING_SNAKE_CASE_								=						qiskit.Aer.get_backend('aer_simulator' )

			# Creating a Quantum Circuit acting on the q register
			SCREAMING_SNAKE_CASE_								=						qiskit.QuantumCircuit(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )

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

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

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

			# 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
			SCREAMING_SNAKE_CASE_								=						qiskit.execute(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    shots=10_00 )

			return job.result().get_counts(SCREAMING_SNAKE_CASE )


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