





import numpy as np
import torch
import tqdm

from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler



class  __lowerCAmelCase	(      UpperCAmelCase__					):






			def __init__(self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							)					->       List[Any]:


							'''simple docstring'''



							super().__init__()
							snake_case_						:   Tuple           =  value_function
							snake_case_						:   Dict           =  unet
							snake_case_						:   Union[str, Any]           =  scheduler
							snake_case_						:   List[Any]           =  env
							snake_case_						:   int           =  env.get_dataset()
							snake_case_						:   Optional[int]           =  {}
							for key in self.data.keys():
											try:
															snake_case_						:   Dict           =  self.data[key].mean()
											except:  # noqa: E722
															pass
							snake_case_						:   int           =  {}
							for key in self.data.keys():
											try:
															snake_case_						:   Optional[Any]           =  self.data[key].std()
											except:  # noqa: E722
															pass
							snake_case_						:   Any           =  env.observation_space.shape[0]
							snake_case_						:   str           =  env.action_space.shape[0]






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__   )					->       Tuple:


							'''simple docstring'''



							return (x_in - self.means[key]) / self.stds[key]






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__   )					->       Union[str, Any]:


							'''simple docstring'''



							return x_in * self.stds[key] + self.means[key]






			def       lowerCamelCase  (self ,							__magic_name__   )					->       Optional[Any]:


							'''simple docstring'''



							if type(_SCREAMING_SNAKE_CASE   ) is dict:
											return {k: self.to_torch(_SCREAMING_SNAKE_CASE   ) for k, v in x_in.items()}
							elif torch.is_tensor(_SCREAMING_SNAKE_CASE   ):
											return x_in.to(self.unet.device   )
							return torch.tensor(_SCREAMING_SNAKE_CASE ,							device=self.unet.device   )






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__   )					->       str:


							'''simple docstring'''



							for key, val in cond.items():
											snake_case_						:   Optional[Any]           =  val.clone()
							return x_in






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__   )					->       int:


							'''simple docstring'''



							snake_case_						:   Dict           =  x.shape[0]
							snake_case_						:   Optional[int]           =  None
							for i in tqdm.tqdm(self.scheduler.timesteps   ):
											# create batch of timesteps to pass into model
											snake_case_						:   Tuple           =  torch.full((batch_size,) ,							_SCREAMING_SNAKE_CASE ,							device=self.unet.device ,							dtype=torch.long   )
											for _ in range(_SCREAMING_SNAKE_CASE   ):
															with torch.enable_grad():
																			x.requires_grad_()

																			# permute to match dimension for pre-trained models
																			snake_case_						:   Dict           =  self.value_function(x.permute(0 ,							2 ,							1   ) ,							_SCREAMING_SNAKE_CASE   ).sample
																			snake_case_						:   Optional[int]           =  torch.autograd.grad([y.sum()] ,							[x]   )[0]

																			snake_case_						:   List[Any]           =  self.scheduler._get_variance(_SCREAMING_SNAKE_CASE   )
																			snake_case_						:   List[str]           =  torch.exp(0.5 * posterior_variance   )
																			snake_case_						:   str           =  model_std * grad

															snake_case_						:   str           =  0
															snake_case_						:   Any           =  x.detach()
															snake_case_						:   int           =  x + scale * grad
															snake_case_						:   Any           =  self.reset_xa(_SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE ,							self.action_dim   )

											snake_case_						:   Optional[int]           =  self.unet(x.permute(0 ,							2 ,							1   ) ,							_SCREAMING_SNAKE_CASE   ).sample.permute(0 ,							2 ,							1   )

											# TODO: verify deprecation of this kwarg
											snake_case_						:   Any           =  self.scheduler.step(_SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE ,							predict_epsilon=_SCREAMING_SNAKE_CASE   )["""prev_sample"""]

											# apply conditions to the trajectory (set the initial state)
											snake_case_						:   Dict           =  self.reset_xa(_SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE ,							self.action_dim   )
											snake_case_						:   int           =  self.to_torch(_SCREAMING_SNAKE_CASE   )
							return x, y






			def __call__(self ,							__magic_name__ ,							__magic_name__=64 ,							__magic_name__=32 ,							__magic_name__=2 ,							__magic_name__=0.1   )					->       Tuple:


							'''simple docstring'''



							snake_case_						:   int           =  self.normalize(_SCREAMING_SNAKE_CASE ,							'''observations'''   )
							snake_case_						:   int           =  obs[None].repeat(_SCREAMING_SNAKE_CASE ,							axis=0   )

							snake_case_						:   Dict           =  {0: self.to_torch(_SCREAMING_SNAKE_CASE   )}
							snake_case_						:   Dict           =  (batch_size, planning_horizon, self.state_dim + self.action_dim)

							# generate initial noise and apply our conditions (to make the trajectories start at current state)
							snake_case_						:   str           =  randn_tensor(_SCREAMING_SNAKE_CASE ,							device=self.unet.device   )
							snake_case_						:   Any           =  self.reset_xa(_SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE ,							self.action_dim   )
							snake_case_						:   str           =  self.to_torch(_SCREAMING_SNAKE_CASE   )

							# run the diffusion process
							snake_case_						:   Any           =  self.run_diffusion(_SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE   )

							# sort output trajectories by value
							snake_case_						:   List[str]           =  y.argsort(0 ,							descending=_SCREAMING_SNAKE_CASE   ).squeeze()
							snake_case_						:   Any           =  x[sorted_idx]
							snake_case_						:   Dict           =  sorted_values[:, :, : self.action_dim]
							snake_case_						:   int           =  actions.detach().cpu().numpy()
							snake_case_						:   List[str]           =  self.de_normalize(_SCREAMING_SNAKE_CASE ,							key='''actions'''   )

							# select the action with the highest value
							if y is not None:
											snake_case_						:   Any           =  0
							else:
											# if we didn't run value guiding, select a random action
											snake_case_						:   Optional[int]           =  np.random.randint(0 ,							_SCREAMING_SNAKE_CASE   )

							snake_case_						:   Any           =  denorm_actions[selected_index, 0]
							return denorm_actions







import random

from .binary_exp_mod import bin_exp_mod






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase=1_000			)		->						str:






				"""simple docstring"""


				if n < 2:
								return False

				if n % 2 == 0:
								return n == 2

				# this means n is odd
				snake_case_						:   int           =  n - 1
				snake_case_						:   Union[str, Any]           =  0
				while d % 2 == 0:
								d /= 2
								exp += 1

				# n - 1=d*(2**exp)
				snake_case_						:   List[Any]           =  0
				while count < prec:
								snake_case_						:   Tuple           =  random.randint(2			,    n - 1			)
								snake_case_						:   List[Any]           =  bin_exp_mod(_UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)
								if b != 1:
												snake_case_						:   Any           =  True
												for _ in range(_UpperCamelCase			):
																if b == n - 1:
																				snake_case_						:   Tuple           =  False
																				break
																snake_case_						:   List[Any]           =  b * b
																b %= n
												if flag:
																return False
												count += 1
				return True


if __name__ == "__main__":
	lowerCAmelCase_    =    abs(int(input('''Enter bound : ''').strip()))
	print('''Here\'s the list of primes:''')
	print(''', '''.join(str(i) for i in range(n + 1) if is_prime_big(i)))
