
import os
import string
import sys


_UpperCAmelCase					: List[Any]      =       1 << 8

_UpperCAmelCase					: Dict      =       {
    "tab": ord("\t"),
    "newline": ord("\r"),
    "esc": 27,
    "up": 65 + ARROW_KEY_FLAG,
    "down": 66 + ARROW_KEY_FLAG,
    "right": 67 + ARROW_KEY_FLAG,
    "left": 68 + ARROW_KEY_FLAG,
    "mod_int": 91,
    "undefined": sys.maxsize,
    "interrupt": 3,
    "insert": 50,
    "delete": 51,
    "pg_up": 53,
    "pg_down": 54,
}

_UpperCAmelCase					: Any      =       KEYMAP["up"]
_UpperCAmelCase					: Optional[int]      =       KEYMAP["left"]

if sys.platform == "win32":
					_UpperCAmelCase					: str      =       []
					_UpperCAmelCase					: Dict      =       {
					    b"\xe0H": KEYMAP["up"] - ARROW_KEY_FLAG,
					    b"\x00H": KEYMAP["up"] - ARROW_KEY_FLAG,
					    b"\xe0P": KEYMAP["down"] - ARROW_KEY_FLAG,
					    b"\x00P": KEYMAP["down"] - ARROW_KEY_FLAG,
					    b"\xe0M": KEYMAP["right"] - ARROW_KEY_FLAG,
					    b"\x00M": KEYMAP["right"] - ARROW_KEY_FLAG,
					    b"\xe0K": KEYMAP["left"] - ARROW_KEY_FLAG,
					    b"\x00K": KEYMAP["left"] - ARROW_KEY_FLAG,
					}

for i in range(10):
					_UpperCAmelCase					: Dict      =       ord(str(i))


def       A       (					)   ->  Tuple:
		'''simple docstring'''

		if os.name == "nt":
				import msvcrt

				UpperCamelCase		=     'mbcs'
				# Flush the keyboard buffer
				while msvcrt.kbhit():
						msvcrt.getch()
				if len(lowercase ) == 0:
						# Read the keystroke
						UpperCamelCase		=     msvcrt.getch()

						# If it is a prefix char, get second part
						if ch in (b"\x00", b"\xe0"):
								UpperCamelCase		=     ch + msvcrt.getch()
								# Translate actual Win chars to bullet char types
								try:
										UpperCamelCase		=     chr(WIN_KEYMAP[cha] )
										WIN_CH_BUFFER.append(chr(KEYMAP['mod_int'] ) )
										WIN_CH_BUFFER.append(lowercase )
										if ord(lowercase ) in (
										    KEYMAP["insert"] - 1 << 9,
										    KEYMAP["delete"] - 1 << 9,
										    KEYMAP["pg_up"] - 1 << 9,
										    KEYMAP["pg_down"] - 1 << 9,
										):
												WIN_CH_BUFFER.append(chr(126 ) )
										UpperCamelCase		=     chr(KEYMAP['esc'] )
								except KeyError:
										UpperCamelCase		=     cha[1]
						else:
								UpperCamelCase		=     ch.decode(lowercase )
				else:
						UpperCamelCase		=     WIN_CH_BUFFER.pop(0 )
		elif os.name == "posix":
				import termios
				import tty

				UpperCamelCase		=     sys.stdin.fileno()
				UpperCamelCase		=     termios.tcgetattr(lowercase )
				try:
						tty.setraw(lowercase )
						UpperCamelCase		=     sys.stdin.read(1 )
				finally:
						termios.tcsetattr(lowercase    ,       termios.TCSADRAIN    ,       lowercase )
		return ch


def       A       (					)   ->  List[Any]:
		'''simple docstring'''

		UpperCamelCase		=     get_raw_chars()
		if ord(lowercase ) in [KEYMAP["interrupt"], KEYMAP["newline"]]:
				return char

		elif ord(lowercase ) == KEYMAP["esc"]:
				UpperCamelCase		=     get_raw_chars()
				if ord(lowercase ) == KEYMAP["mod_int"]:
						UpperCamelCase		=     get_raw_chars()
						if ord(lowercase ) >= KEYMAP["arrow_begin"] - ARROW_KEY_FLAG and ord(lowercase ) <= KEYMAP["arrow_end"] - ARROW_KEY_FLAG:
								return chr(ord(lowercase ) + ARROW_KEY_FLAG )
						else:
								return KEYMAP["undefined"]
				else:
						return get_raw_chars()

		else:
				if char in string.printable:
						return char
				else:
						return KEYMAP["undefined"]


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 				lowercase					(		_SCREAMING_SNAKE_CASE   ):



		def __init__( self				,      A_				,      A_				,      A_				,      A_				,      )  ->					Optional[int]:
				"""simple docstring"""
				super().__init__()
				UpperCamelCase		=     value_function
				UpperCamelCase		=     unet
				UpperCamelCase		=     scheduler
				UpperCamelCase		=     env
				UpperCamelCase		=     env.get_dataset()
				UpperCamelCase		=     {}
				for key in self.data.keys():
						try:
								UpperCamelCase		=     self.data[key].mean()
						except:  # noqa: E722
								pass
				UpperCamelCase		=     {}
				for key in self.data.keys():
						try:
								UpperCamelCase		=     self.data[key].std()
						except:  # noqa: E722
								pass
				UpperCamelCase		=     env.observation_space.shape[0]
				UpperCamelCase		=     env.action_space.shape[0]



		def __UpperCamelCase ( self				,      A_				,      A_       )  ->					Optional[Any]:
				"""simple docstring"""
				return (x_in - self.means[key]) / self.stds[key]



		def __UpperCamelCase ( self				,      A_				,      A_       )  ->					Union[str, Any]:
				"""simple docstring"""
				return x_in * self.stds[key] + self.means[key]



		def __UpperCamelCase ( self				,      A_       )  ->					Any:
				"""simple docstring"""
				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]:
				"""simple docstring"""
				for key, val in cond.items():
						UpperCamelCase		=     val.clone()
				return x_in



		def __UpperCamelCase ( self				,      A_				,      A_				,      A_				,      A_       )  ->					Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase		=     x.shape[0]
				UpperCamelCase		=     None
				for i in tqdm.tqdm(self.scheduler.timesteps       ):
						# create batch of timesteps to pass into model
						UpperCamelCase		=     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
										UpperCamelCase		=     self.value_function(x.permute(0				,      2				,      1       )				,      A_       ).sample
										UpperCamelCase		=     torch.autograd.grad([y.sum()]				,      [x]       )[0]

										UpperCamelCase		=     self.scheduler._get_variance(A_       )
										UpperCamelCase		=     torch.exp(0.5 * posterior_variance       )
										UpperCamelCase		=     model_std * grad

								UpperCamelCase		=     0
								UpperCamelCase		=     x.detach()
								UpperCamelCase		=     x + scale * grad
								UpperCamelCase		=     self.reset_xa(A_				,      A_				,      self.action_dim       )

						UpperCamelCase		=     self.unet(x.permute(0				,      2				,      1       )				,      A_       ).sample.permute(0				,      2				,      1       )

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

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



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

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

				# run the diffusion process
				UpperCamelCase	,  UpperCamelCase		=     self.run_diffusion(A_				,      A_				,      A_				,      A_       )

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

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

				UpperCamelCase		=     denorm_actions[selected_index, 0]
				return denorm_actions
