
"""simple docstring"""







from queue import Queue
from typing import TYPE_CHECKING, Optional


if TYPE_CHECKING:
		from ..models.auto import AutoTokenizer







class 							__magic_name__     :





					def 							lowerCAmelCase      (				self   :							Any							,						snake_case_   :							List[Any]    ):
												raise NotImplementedError()





					def 							lowerCAmelCase      (				self   :							str    ):
												raise NotImplementedError()







class 							__magic_name__     (		__A   ):





					def __init__(				self   :							int							,						snake_case_   :							Optional[Any]							,						snake_case_   :							Union[str, Any] = False							,						**snake_case_   :							List[Any]    ):
												__snake_case     			=       tokenizer
												__snake_case     			=       skip_prompt
												__snake_case     			=       decode_kwargs

												# variables used in the streaming process
												__snake_case     			=       []
												__snake_case     			=       0
												__snake_case     			=       True





					def 							lowerCAmelCase      (				self   :							Tuple							,						snake_case_   :							Any    ):





												if len(value.shape    ) > 1 and value.shape[0] > 1:
																			raise ValueError("TextStreamer only supports batch size 1"    )
												elif len(value.shape    ) > 1:
																			__snake_case     			=       value[0]

												if self.skip_prompt and self.next_tokens_are_prompt:
																			__snake_case     			=       False
																			return

												# Add the new token to the cache and decodes the entire thing.
												self.token_cache.extend(value.tolist()    )
												__snake_case     			=       self.tokenizer.decode(self.token_cache							,						**self.decode_kwargs    )

												# After the symbol for a new line, we flush the cache.
												if text.endswith("\n"    ):
																			__snake_case     			=       text[self.print_len :]
																			__snake_case     			=       []
																			__snake_case     			=       0
												# If the last token is a CJK character, we print the characters.
												elif len(snake_case_    ) > 0 and self._is_chinese_char(ord(text[-1]    )    ):
																			__snake_case     			=       text[self.print_len :]
																			self.print_len += len(snake_case_    )
												# Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
												# which may change with the subsequent token -- there are probably smarter ways to do this!)
												else:
																			__snake_case     			=       text[self.print_len : text.rfind(" "    ) + 1]
																			self.print_len += len(snake_case_    )

												self.on_finalized_text(snake_case_    )





					def 							lowerCAmelCase      (				self   :							List[str]    ):





												if len(self.token_cache    ) > 0:
																			__snake_case     			=       self.tokenizer.decode(self.token_cache							,						**self.decode_kwargs    )
																			__snake_case     			=       text[self.print_len :]
																			__snake_case     			=       []
																			__snake_case     			=       0
												else:
																			__snake_case     			=       ''

												__snake_case     			=       True
												self.on_finalized_text(snake_case_							,						stream_end=snake_case_    )





					def 							lowerCAmelCase      (				self   :							List[str]							,						snake_case_   :							Dict							,						snake_case_   :							Union[str, Any] = False    ):
												print(snake_case_							,						flush=snake_case_							,						end="" if not stream_end else None    )





					def 							lowerCAmelCase      (				self   :							int							,						snake_case_   :							List[str]    ):





												if (
												    (cp >= 0x4E_00 and cp <= 0x9F_FF)
												    or (cp >= 0x34_00 and cp <= 0x4D_BF)  #
												    or (cp >= 0x2_00_00 and cp <= 0x2_A6_DF)  #
												    or (cp >= 0x2_A7_00 and cp <= 0x2_B7_3F)  #
												    or (cp >= 0x2_B7_40 and cp <= 0x2_B8_1F)  #
												    or (cp >= 0x2_B8_20 and cp <= 0x2_CE_AF)  #
												    or (cp >= 0xF9_00 and cp <= 0xFA_FF)
												    or (cp >= 0x2_F8_00 and cp <= 0x2_FA_1F)  #
												):  #
																			return True

												return False







class 							__magic_name__     (		__A   ):





					def __init__(				self   :							Tuple							,						snake_case_   :							str							,						snake_case_   :							int = False							,						snake_case_   :							Optional[Any] = None							,						**snake_case_   :							Union[str, Any]    ):
												super().__init__(snake_case_							,						snake_case_							,						**snake_case_    )
												__snake_case     			=       Queue()
												__snake_case     			=       None
												__snake_case     			=       timeout





					def 							lowerCAmelCase      (				self   :							List[str]							,						snake_case_   :							List[Any]							,						snake_case_   :							Dict = False    ):
												self.text_queue.put(snake_case_							,						timeout=self.timeout    )
												if stream_end:
																			self.text_queue.put(self.stop_signal							,						timeout=self.timeout    )





					def __iter__(				self   :							Union[str, Any]    ):
												return self





					def 							lowerCAmelCase      (				self   :							Optional[int]    ):
												__snake_case     			=       self.text_queue.get(timeout=self.timeout    )
												if value == self.stop_signal:
																			raise StopIteration()
												else:
																			return value


"""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 							__magic_name__     (		lowercase__   ):





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





					def 							lowerCAmelCase      (				self   :							Any							,						snake_case_   :							str							,						snake_case_   :							Any    ):
												return (x_in - self.means[key]) / self.stds[key]





					def 							lowerCAmelCase      (				self   :							Any							,						snake_case_   :							Optional[Any]							,						snake_case_   :							Optional[Any]    ):
												return x_in * self.stds[key] + self.means[key]





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





					def 							lowerCAmelCase      (				self   :							List[str]							,						snake_case_   :							Tuple							,						snake_case_   :							Union[str, Any]							,						snake_case_   :							Union[str, Any]    ):
												for key, val in cond.items():
																			__snake_case     			=       val.clone()
												return x_in





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

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

																																	__snake_case     			=       self.scheduler._get_variance(snake_case_    )
																																	__snake_case     			=       torch.exp(0.5 * posterior_variance    )
																																	__snake_case     			=       model_std * grad

																										__snake_case     			=       0
																										__snake_case     			=       x.detach()
																										__snake_case     			=       x + scale * grad
																										__snake_case     			=       self.reset_xa(snake_case_							,						snake_case_							,						self.action_dim    )

																			__snake_case     			=       self.unet(x.permute(0							,						2							,						1    )							,						snake_case_    ).sample.permute(0							,						2							,						1    )

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

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





					def __call__(				self   :							Union[str, Any]							,						snake_case_   :							List[Any]							,						snake_case_   :							Any=64							,						snake_case_   :							str=32							,						snake_case_   :							List[str]=2							,						snake_case_   :							Tuple=0.1    ):
												# normalize the observations and create  batch dimension
												__snake_case     			=       self.normalize(snake_case_							,						"observations"    )
												__snake_case     			=       obs[None].repeat(snake_case_							,						axis=0    )

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

												# run the diffusion process
												__snake_case			,   __snake_case     			=       self.run_diffusion(snake_case_							,						snake_case_							,						snake_case_							,						snake_case_    )

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

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

												__snake_case     			=       denorm_actions[selected_index, 0]
												return denorm_actions
