






import builtins
import sys

from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP


SCREAMING_SNAKE_CASE__		:							int 				=				False
try:
      SCREAMING_SNAKE_CASE__		:							Optional[int] 				=				_is_package_available("""google.colab""")
except ModuleNotFoundError:
      pass







@input.register
class 				UpperCAmelCase_  :




     def __init__(				self	,						_lowerCAmelCase = None	,						_lowerCAmelCase = []		):
          UpperCAmelCase__      :  Dict				      =			0
          UpperCAmelCase__      :  Union[str, Any]				      =			choices
          UpperCAmelCase__      :  int				      =			prompt
          if sys.platform == "win32":
               UpperCAmelCase__      :  List[str]				      =			"""*"""
          else:
               UpperCAmelCase__      :  Union[str, Any]				      =			"""➔ """




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase = ""		):
          if sys.platform != "win32":
               writeColor(self.choices[index]	,						32	,						lowerCAmelCase__		)
          else:
               forceWrite(self.choices[index]	,						lowerCAmelCase__		)




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase		):
          if index == self.position:
               forceWrite(f" {self.arrow_char} "		)
               self.write_choice(lowerCAmelCase__		)
          else:
               forceWrite(f"    {self.choices[index]}"		)
          reset_cursor()




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase = 1		):
          UpperCAmelCase__      :  Optional[int]				      =			self.position
          if direction == Direction.DOWN:
               if self.position + 1 >= len(self.choices		):
                    return
               self.position += num_spaces
          else:
               if self.position - 1 < 0:
                    return
               self.position -= num_spaces
          clear_line()
          self.print_choice(lowerCAmelCase__		)
          move_cursor(lowerCAmelCase__	,						direction.name		)
          self.print_choice(self.position		)




     @input.mark(KEYMAP["""up"""]		)
     def   __UpperCAmelCase  (				self		):
          self.move_direction(Direction.UP		)




     @input.mark(KEYMAP["""down"""]		)
     def   __UpperCAmelCase  (				self		):
          self.move_direction(Direction.DOWN		)




     @input.mark(KEYMAP["""newline"""]		)
     def   __UpperCAmelCase  (				self		):
          move_cursor(len(self.choices		) - self.position	,						"""DOWN"""		)
          return self.position




     @input.mark(KEYMAP["""interrupt"""]		)
     def   __UpperCAmelCase  (				self		):
          move_cursor(len(self.choices		) - self.position	,						"""DOWN"""		)
          raise KeyboardInterrupt




     @input.mark_multiple(*[KEYMAP[str(lowerCAmelCase__		)] for number in range(10		)]		)
     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  Optional[Any]				      =			int(chr(self.current_selection		)		)
          UpperCAmelCase__      :  Optional[int]				      =			index - self.position
          if index == self.position:
               return
          if index < len(self.choices		):
               if self.position > index:
                    self.move_direction(Direction.UP	,						-movement		)
               elif self.position < index:
                    self.move_direction(Direction.DOWN	,						lowerCAmelCase__		)
               else:
                    return
          else:
               return




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase = 0		):
          if self.prompt:
               linebreak()
               forceWrite(self.prompt	,						"""\n"""		)
               if in_colab:
                    forceWrite("""Please input a choice index (starting from 0), and press enter"""	,						"""\n"""		)
               else:
                    forceWrite("""Please select a choice using the arrow or number keys, and selecting with enter"""	,						"""\n"""		)
          UpperCAmelCase__      :  Dict				      =			default_choice
          for i in range(len(self.choices		)		):
               self.print_choice(lowerCAmelCase__		)
               forceWrite("""\n"""		)
          move_cursor(len(self.choices		) - self.position	,						"""UP"""		)
          with cursor.hide():
               while True:
                    if in_colab:
                         try:
                              UpperCAmelCase__      :  List[str]				      =			int(builtins.input()		)
                         except ValueError:
                              UpperCAmelCase__      :  Union[str, Any]				      =			default_choice
                    else:
                         UpperCAmelCase__      :  Any				      =			self.handle_input()
                    if choice is not None:
                         reset_cursor()
                         for _ in range(len(self.choices		) + 1		):
                              move_cursor(1	,						"""UP"""		)
                              clear_line()
                         self.write_choice(lowerCAmelCase__	,						"""\n"""		)
                         return choice








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   UpperCamelCase_      (      __a		):







						'''simple docstring'''



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





						def        SCREAMING_SNAKE_CASE(			self							:List[str]		,    lowerCAmelCase__							:Union[str, Any]		,    lowerCAmelCase__							:str     )  ->Any:
										return (x_in - self.means[key]) / self.stds[key]





						def        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]		,    lowerCAmelCase__							:Tuple		,    lowerCAmelCase__							:List[Any]     )  ->Optional[Any]:
										return x_in * self.stds[key] + self.means[key]





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





						def        SCREAMING_SNAKE_CASE(			self							:Optional[int]		,    lowerCAmelCase__							:Union[str, Any]		,    lowerCAmelCase__							:Optional[Any]		,    lowerCAmelCase__							:List[str]     )  ->Any:
										for key, val in cond.items():
														lowercase         =      val.clone()
										return x_in





						def        SCREAMING_SNAKE_CASE(			self							:str		,    lowerCAmelCase__							:Union[str, Any]		,    lowerCAmelCase__							:str		,    lowerCAmelCase__							:Optional[int]		,    lowerCAmelCase__							:Dict     )  ->Tuple:
										lowercase         =      x.shape[0]
										lowercase         =      None
										for i in tqdm.tqdm(self.scheduler.timesteps     ):
														# create batch of timesteps to pass into model
														lowercase         =      torch.full((batch_size,)		,    lowerCAmelCase__		,    device=self.unet.device		,    dtype=torch.long     )
														for _ in range(lowerCAmelCase__     ):
																		with torch.enable_grad():
																						x.requires_grad_()

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

																						lowercase         =      self.scheduler._get_variance(lowerCAmelCase__     )
																						lowercase         =      torch.exp(0.5 * posterior_variance     )
																						lowercase         =      model_std * grad

																		lowercase         =      0
																		lowercase         =      x.detach()
																		lowercase         =      x + scale * grad
																		lowercase         =      self.reset_xa(lowerCAmelCase__		,    lowerCAmelCase__		,    self.action_dim     )

														lowercase         =      self.unet(x.permute(0		,    2		,    1     )		,    lowerCAmelCase__     ).sample.permute(0		,    2		,    1     )

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

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





						def __call__(			self							:Optional[Any]		,    lowerCAmelCase__							:Any		,    lowerCAmelCase__							:Optional[Any]=64		,    lowerCAmelCase__							:int=32		,    lowerCAmelCase__							:int=2		,    lowerCAmelCase__							:List[str]=0.1     )  ->Tuple:
										# normalize the observations and create  batch dimension
										lowercase         =      self.normalize(lowerCAmelCase__		,    "observations"     )
										lowercase         =      obs[None].repeat(lowerCAmelCase__		,    axis=0     )

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

										# run the diffusion process
										lowercase     ,	lowercase         =      self.run_diffusion(lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__     )

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

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

										lowercase         =      denorm_actions[selected_index, 0]
										return denorm_actions
