'''simple docstring'''

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   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""






		def __init__(   self		,						__A		,						__A		,						__A		,						__A		,						) ->     Union[str, Any]:
								super().__init__()
								_lowerCAmelCase										=value_function
								_lowerCAmelCase										=unet
								_lowerCAmelCase										=scheduler
								_lowerCAmelCase										=env
								_lowerCAmelCase										=env.get_dataset()
								_lowerCAmelCase										={}
								for key in self.data.keys():
														try:
																				_lowerCAmelCase										=self.data[key].mean()
														except:  # noqa: E722
																				pass
								_lowerCAmelCase										={}
								for key in self.data.keys():
														try:
																				_lowerCAmelCase										=self.data[key].std()
														except:  # noqa: E722
																				pass
								_lowerCAmelCase										=env.observation_space.shape[0]
								_lowerCAmelCase										=env.action_space.shape[0]






		def 		UpperCamelCase__       (   self		,						__A		,						__A				) ->     str:
								return (x_in - self.means[key]) / self.stds[key]






		def 		UpperCamelCase__       (   self		,						__A		,						__A				) ->     List[Any]:
								return x_in * self.stds[key] + self.means[key]






		def 		UpperCamelCase__       (   self		,						__A				) ->     List[Any]:
								if type(__A				) is dict:
														return {k: self.to_torch(__A				) for k, v in x_in.items()}
								elif torch.is_tensor(__A				):
														return x_in.to(self.unet.device				)
								return torch.tensor(__A		,						device=self.unet.device				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A				) ->     Optional[int]:
								for key, val in cond.items():
														_lowerCAmelCase										=val.clone()
								return x_in






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A		,						__A				) ->     Tuple:
								_lowerCAmelCase										=x.shape[0]
								_lowerCAmelCase										=None
								for i in tqdm.tqdm(self.scheduler.timesteps				):
														# create batch of timesteps to pass into model
														_lowerCAmelCase										=torch.full((batch_size,)		,						__A		,						device=self.unet.device		,						dtype=torch.long				)
														for _ in range(__A				):
																				with torch.enable_grad():
																										x.requires_grad_()

																										# permute to match dimension for pre-trained models
																										_lowerCAmelCase										=self.value_function(x.permute(0		,						2		,						1				)		,						__A				).sample
																										_lowerCAmelCase										=torch.autograd.grad([y.sum()]		,						[x]				)[0]

																										_lowerCAmelCase										=self.scheduler._get_variance(__A				)
																										_lowerCAmelCase										=torch.exp(0.5 * posterior_variance				)
																										_lowerCAmelCase										=model_std * grad

																				_lowerCAmelCase										=0
																				_lowerCAmelCase										=x.detach()
																				_lowerCAmelCase										=x + scale * grad
																				_lowerCAmelCase										=self.reset_xa(__A		,						__A		,						self.action_dim				)

														_lowerCAmelCase										=self.unet(x.permute(0		,						2		,						1				)		,						__A				).sample.permute(0		,						2		,						1				)

														# TODO: verify deprecation of this kwarg
														_lowerCAmelCase										=self.scheduler.step(__A		,						__A		,						__A		,						predict_epsilon=__A				)['prev_sample']

														# apply conditions to the trajectory (set the initial state)
														_lowerCAmelCase										=self.reset_xa(__A		,						__A		,						self.action_dim				)
														_lowerCAmelCase										=self.to_torch(__A				)
								return x, y






		def __call__(   self		,						__A		,						__A=64		,						__A=32		,						__A=2		,						__A=0.1				) ->     str:
								# normalize the observations and create  batch dimension
								_lowerCAmelCase										=self.normalize(__A		,						'observations'				)
								_lowerCAmelCase										=obs[None].repeat(__A		,						axis=0				)

								_lowerCAmelCase										={0: self.to_torch(__A				)}
								_lowerCAmelCase										=(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)
								_lowerCAmelCase										=randn_tensor(__A		,						device=self.unet.device				)
								_lowerCAmelCase										=self.reset_xa(__A		,						__A		,						self.action_dim				)
								_lowerCAmelCase										=self.to_torch(__A				)

								# run the diffusion process
								_lowerCAmelCase						,				_lowerCAmelCase										=self.run_diffusion(__A		,						__A		,						__A		,						__A				)

								# sort output trajectories by value
								_lowerCAmelCase										=y.argsort(0		,						descending=__A				).squeeze()
								_lowerCAmelCase										=x[sorted_idx]
								_lowerCAmelCase										=sorted_values[:, :, : self.action_dim]
								_lowerCAmelCase										=actions.detach().cpu().numpy()
								_lowerCAmelCase										=self.de_normalize(__A		,						key='actions'				)

								# select the action with the highest value
								if y is not None:
														_lowerCAmelCase										=0
								else:
														# if we didn't run value guiding, select a random action
														_lowerCAmelCase										=np.random.randint(0		,						__A				)

								_lowerCAmelCase										=denorm_actions[selected_index, 0]
								return denorm_actions






'''simple docstring'''

lowercase_     		= {
    '''A''': '''.-''', '''B''': '''-...''', '''C''': '''-.-.''', '''D''': '''-..''', '''E''': '''.''', '''F''': '''..-.''', '''G''': '''--.''',
    '''H''': '''....''', '''I''': '''..''', '''J''': '''.---''', '''K''': '''-.-''', '''L''': '''.-..''', '''M''': '''--''', '''N''': '''-.''',
    '''O''': '''---''', '''P''': '''.--.''', '''Q''': '''--.-''', '''R''': '''.-.''', '''S''': '''...''', '''T''': '''-''', '''U''': '''..-''',
    '''V''': '''...-''', '''W''': '''.--''', '''X''': '''-..-''', '''Y''': '''-.--''', '''Z''': '''--..''', '''1''': '''.----''',
    '''2''': '''..---''', '''3''': '''...--''', '''4''': '''....-''', '''5''': '''.....''', '''6''': '''-....''', '''7''': '''--...''',
    '''8''': '''---..''', '''9''': '''----.''', '''0''': '''-----''', '''&''': '''.-...''', '''@''': '''.--.-.''',
    ''':''': '''---...''', ''',''': '''--..--''', '''.''': '''.-.-.-''', '''\'''': '''.----.''', '''"''': '''.-..-.''',
    '''?''': '''..--..''', '''/''': '''-..-.''', '''=''': '''-...-''', '''+''': '''.-.-.''', '''-''': '''-....-''',
    '''(''': '''-.--.''', ''')''': '''-.--.-''', '''!''': '''-.-.--''', ''' ''': '''/'''
}  # Exclamation mark is not in ITU-R recommendation
# fmt: on
lowercase_     		= {value: key for key, value in MORSE_CODE_DICT.items()}



def 							UpperCamelCase__			(			a__      ):


						'''simple docstring'''






						return " ".join(MORSE_CODE_DICT[char] for char in message.upper()      )



def 							UpperCamelCase__			(			a__      ):


						'''simple docstring'''






						return "".join(REVERSE_DICT[char] for char in message.split()      )



def 							UpperCamelCase__			(			):


						'''simple docstring'''






						_lowerCAmelCase										='Morse code here!'
						print(a__      )
						_lowerCAmelCase										=encrypt(a__      )
						print(a__      )
						_lowerCAmelCase										=decrypt(a__      )
						print(a__      )


if __name__ == "__main__":
							main()





