




"""simple docstring"""






import qiskit





def 							UpperCAmelCase		(		snake_case			:					str = 2    ):
     _lowerCAmelCase:Tuple									=  qubits

     # Using Aer's simulator
     _lowerCAmelCase:List[Any]									=  qiskit.Aer.get_backend('''aer_simulator'''    )

     # Creating a Quantum Circuit acting on the q register
     _lowerCAmelCase:Tuple									=  qiskit.QuantumCircuit(snake_case      ,       snake_case    )

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

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

     # Mapping the quantum measurement to the classical bits
     circuit.measure(list(range(snake_case    )    )      ,       list(range(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
     _lowerCAmelCase:int									=  qiskit.execute(snake_case      ,       snake_case      ,       shots=1000    )

     return job.result().get_counts(snake_case    )


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












"""simple docstring"""






# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate




@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class   a__			(       UpperCamelCase_  ):
						snake_case__             = 42
						snake_case__             = None





def 							UpperCAmelCase		(		snake_case			:					int      ,       snake_case			:					int=0.9_99      ,       snake_case			:					Union[str, Any]="cosine"      ,       ):
					if alpha_transform_type == "cosine":

										def alpha_bar_fn(snake_case			:					Dict    ):
															return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2    ) ** 2

					elif alpha_transform_type == "exp":

										def alpha_bar_fn(snake_case			:					Optional[Any]    ):
															return math.exp(t * -12.0    )

					else:
										raise ValueError(F'Unsupported alpha_tranform_type: {alpha_transform_type}'    )

					_lowerCAmelCase:List[str]									=  []
					for i in range(snake_case    ):
										_lowerCAmelCase:Union[str, Any]									=  i / num_diffusion_timesteps
										_lowerCAmelCase:Dict									=  (i + 1) / num_diffusion_timesteps
										betas.append(min(1 - alpha_bar_fn(snake_case    ) / alpha_bar_fn(snake_case    )      ,       snake_case    )    )
					return torch.tensor(snake_case      ,       dtype=torch.floataa    )





class   a__			(       UpperCamelCase_					,	UpperCamelCase_  ):
						snake_case__             = 1





						@register_to_config
						def __init__(							self      :	Optional[Any]     ,a__      :	int = 1000     ,a__      :	float = 0.0001     ,a__      :	float = 0.02     ,a__      :	str = "linear"     ,a__      :	Optional[Union[np.ndarray, List[float]]] = None     ,a__      :	bool = True     ,a__      :	bool = True     ,a__      :	int = 0     ,a__      :	str = "epsilon"     ,a__      :	float = 1.0     ,**a__      :	Any     ,)						->       str:




											"""simple docstring"""







											if kwargs.get('''set_alpha_to_one'''     ,a__) is not None:
																_lowerCAmelCase:Tuple									=  (
																    '''The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead.'''
																)
																deprecate('''set_alpha_to_one'''     ,'''1.0.0'''     ,a__     ,standard_warn=a__)
																_lowerCAmelCase:Optional[Any]									=  kwargs['''set_alpha_to_one''']
											if trained_betas is not None:
																_lowerCAmelCase:Any									=  torch.tensor(a__     ,dtype=torch.floataa)
											elif beta_schedule == "linear":
																_lowerCAmelCase:str									=  torch.linspace(a__     ,a__     ,a__     ,dtype=torch.floataa)
											elif beta_schedule == "scaled_linear":
																# this schedule is very specific to the latent diffusion model.
																_lowerCAmelCase:int									=  (
																    torch.linspace(beta_start**0.5     ,beta_end**0.5     ,a__     ,dtype=torch.floataa) ** 2
																)
											elif beta_schedule == "squaredcos_cap_v2":
																# Glide cosine schedule
																_lowerCAmelCase:Optional[int]									=  betas_for_alpha_bar(a__)
											else:
																raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}')

											_lowerCAmelCase:Any									=  1.0 - self.betas
											_lowerCAmelCase:Optional[Any]									=  torch.cumprod(self.alphas     ,dim=0)

											# At every step in inverted ddim, we are looking into the next alphas_cumprod
											# For the final step, there is no next alphas_cumprod, and the index is out of bounds
											# `set_alpha_to_zero` decides whether we set this parameter simply to zero
											# in this case, self.step() just output the predicted noise
											# or whether we use the final alpha of the "non-previous" one.
											_lowerCAmelCase:Optional[Any]									=  torch.tensor(0.0) if set_alpha_to_zero else self.alphas_cumprod[-1]

											# standard deviation of the initial noise distribution
											_lowerCAmelCase:Any									=  1.0

											# setable values
											_lowerCAmelCase:Dict									=  None
											_lowerCAmelCase:List[Any]									=  torch.from_numpy(np.arange(0     ,a__).copy().astype(np.intaa))





						def       __UpperCamelCase     (							self      :	Union[str, Any]     ,a__      :	torch.FloatTensor     ,a__      :	Optional[int] = None)						->       torch.FloatTensor:




											"""simple docstring"""







											return sample





						def       __UpperCamelCase     (							self      :	Optional[int]     ,a__      :	int     ,a__      :	Union[str, torch.device] = None)						->       List[Any]:




											"""simple docstring"""







											if num_inference_steps > self.config.num_train_timesteps:
																raise ValueError(
																    F'`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'
																    F' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'
																    F' maximal {self.config.num_train_timesteps} timesteps.')

											_lowerCAmelCase:Union[str, Any]									=  num_inference_steps
											_lowerCAmelCase:Union[str, Any]									=  self.config.num_train_timesteps // self.num_inference_steps
											# creates integer timesteps by multiplying by ratio
											# casting to int to avoid issues when num_inference_step is power of 3
											_lowerCAmelCase:str									=  (np.arange(0     ,a__) * step_ratio).round().copy().astype(np.intaa)
											_lowerCAmelCase:str									=  torch.from_numpy(a__).to(a__)
											self.timesteps += self.config.steps_offset





						def       __UpperCamelCase     (							self      :	int     ,a__      :	torch.FloatTensor     ,a__      :	int     ,a__      :	torch.FloatTensor     ,a__      :	float = 0.0     ,a__      :	bool = False     ,a__      :	Optional[torch.FloatTensor] = None     ,a__      :	bool = True     ,)						->       Union[DDIMSchedulerOutput, Tuple]:




											"""simple docstring"""







											_lowerCAmelCase:Union[str, Any]									=  timestep + self.config.num_train_timesteps // self.num_inference_steps

											# 2. compute alphas, betas
											# change original implementation to exactly match noise levels for analogous forward process
											_lowerCAmelCase:Optional[Any]									=  self.alphas_cumprod[timestep]
											_lowerCAmelCase:List[Any]									=  (
											    self.alphas_cumprod[prev_timestep]
											    if prev_timestep < self.config.num_train_timesteps
											    else self.final_alpha_cumprod
											)

											_lowerCAmelCase:str									=  1 - alpha_prod_t

											# 3. compute predicted original sample from predicted noise also called
											# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
											if self.config.prediction_type == "epsilon":
																_lowerCAmelCase:int									=  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
																_lowerCAmelCase:str									=  model_output
											elif self.config.prediction_type == "sample":
																_lowerCAmelCase:List[str]									=  model_output
																_lowerCAmelCase:Optional[int]									=  (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
											elif self.config.prediction_type == "v_prediction":
																_lowerCAmelCase:str									=  (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
																_lowerCAmelCase:Dict									=  (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
											else:
																raise ValueError(
																    F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'
																    ''' `v_prediction`''')

											# 4. Clip or threshold "predicted x_0"
											if self.config.clip_sample:
																_lowerCAmelCase:List[Any]									=  pred_original_sample.clamp(
																    -self.config.clip_sample_range     ,self.config.clip_sample_range)

											# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
											_lowerCAmelCase:str									=  (1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

											# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
											_lowerCAmelCase:Optional[Any]									=  alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

											if not return_dict:
																return (prev_sample, pred_original_sample)
											return DDIMSchedulerOutput(prev_sample=a__     ,pred_original_sample=a__)







						def __len__(							self      :	Dict)						->       Union[str, Any]:




											"""simple docstring"""







											return self.config.num_train_timesteps






