






"""simple docstring"""



from queue import Queue
from typing import TYPE_CHECKING, Optional


if TYPE_CHECKING:
	from ..models.auto import AutoTokenizer







class        lowercase__      :


			def      UpperCAmelCase__						(				self					:  Optional[int]       ,	snake_case__					:  Optional[Any] ):
										raise NotImplementedError()


			def      UpperCAmelCase__						(				self					:  Union[str, Any] ):
										raise NotImplementedError()







class        lowercase__      (							snake_case__       ):


			def __init__(				self					:  Optional[int]       ,	snake_case__					:  "AutoTokenizer"       ,	snake_case__					:  bool = False       ,	**snake_case__					:  int ):
										lowerCamelCase_	:							int									=tokenizer
										lowerCamelCase_	:							Optional[int]									=skip_prompt
										lowerCamelCase_	:							Optional[int]									=decode_kwargs

										# variables used in the streaming process
										lowerCamelCase_	:							Any									=[]
										lowerCamelCase_	:							int									=0
										lowerCamelCase_	:							Union[str, Any]									=True


			def      UpperCAmelCase__						(				self					:  Tuple       ,	snake_case__					:  str ):
										if len(value.shape ) > 1 and value.shape[0] > 1:
																	raise ValueError("TextStreamer only supports batch size 1" )
										elif len(value.shape ) > 1:
																	lowerCamelCase_	:							Tuple									=value[0]

										if self.skip_prompt and self.next_tokens_are_prompt:
																	lowerCamelCase_	:							List[str]									=False
																	return

										# Add the new token to the cache and decodes the entire thing.
										self.token_cache.extend(value.tolist() )
										lowerCamelCase_	:							List[str]									=self.tokenizer.decode(self.token_cache       ,	**self.decode_kwargs )

										# After the symbol for a new line, we flush the cache.
										if text.endswith("\n" ):
																	lowerCamelCase_	:							Any									=text[self.print_len :]
																	lowerCamelCase_	:							List[Any]									=[]
																	lowerCamelCase_	:							Tuple									=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] ) ):
																	lowerCamelCase_	:							List[Any]									=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:
																	lowerCamelCase_	:							Optional[int]									=text[self.print_len : text.rfind(" " ) + 1]
																	self.print_len += len(snake_case__ )

										self.on_finalized_text(snake_case__ )


			def      UpperCAmelCase__						(				self					:  Any ):
										# Flush the cache, if it exists
										if len(self.token_cache ) > 0:
																	lowerCamelCase_	:							Dict									=self.tokenizer.decode(self.token_cache       ,	**self.decode_kwargs )
																	lowerCamelCase_	:							List[Any]									=text[self.print_len :]
																	lowerCamelCase_	:							int									=[]
																	lowerCamelCase_	:							List[str]									=0
										else:
																	lowerCamelCase_	:							List[Any]									=""

										lowerCamelCase_	:							str									=True
										self.on_finalized_text(snake_case__       ,	stream_end=snake_case__ )


			def      UpperCAmelCase__						(				self					:  int       ,	snake_case__					:  str       ,	snake_case__					:  bool = False ):
										print(snake_case__       ,	flush=snake_case__       ,	end="" if not stream_end else None )


			def      UpperCAmelCase__						(				self					:  Tuple       ,	snake_case__					:  int ):
										# This defines a "chinese character" as anything in the CJK Unicode block:
										#   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
										#
										# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
										# despite its name. The modern Korean Hangul alphabet is a different block,
										# as is Japanese Hiragana and Katakana. Those alphabets are used to write
										# space-separated words, so they are not treated specially and handled
										# like the all of the other languages.
										if (
										    (cp >= 0x4_e00 and cp <= 0x9_fff)
										    or (cp >= 0x3_400 and cp <= 0x4_dbf)  #
										    or (cp >= 0x20_000 and cp <= 0x2a_6df)  #
										    or (cp >= 0x2a_700 and cp <= 0x2b_73f)  #
										    or (cp >= 0x2b_740 and cp <= 0x2b_81f)  #
										    or (cp >= 0x2b_820 and cp <= 0x2c_eaf)  #
										    or (cp >= 0xf_900 and cp <= 0xf_aff)
										    or (cp >= 0x2f_800 and cp <= 0x2f_a1f)  #
										):  #
																	return True

										return False







class        lowercase__      (							snake_case__       ):


			def __init__(				self					:  List[str]       ,	snake_case__					:  "AutoTokenizer"       ,	snake_case__					:  bool = False       ,	snake_case__					:  Optional[float] = None       ,	**snake_case__					:  Dict ):
										super().__init__(snake_case__       ,	snake_case__       ,	**snake_case__ )
										lowerCamelCase_	:							Optional[Any]									=Queue()
										lowerCamelCase_	:							Union[str, Any]									=None
										lowerCamelCase_	:							List[Any]									=timeout


			def      UpperCAmelCase__						(				self					:  Any       ,	snake_case__					:  str       ,	snake_case__					:  bool = 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					:  Tuple ):
										return self


			def      UpperCAmelCase__						(				self					:  Union[str, Any] ):
										lowerCamelCase_	:							Optional[int]									=self.text_queue.get(timeout=self.timeout )
										if value == self.stop_signal:
																	raise StopIteration()
										else:
																	return value











"""simple docstring"""



import darl  # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline


A__	:    int			 = {
    'n_samples': 64,
    'horizon': 32,
    'num_inference_steps': 20,
    'n_guide_steps': 2,  # can set to 0 for faster sampling, does not use value network
    'scale_grad_by_std': True,
    'scale': 0.1,
    'eta': 0.0,
    't_grad_cutoff': 2,
    'device': 'cpu',
}


if __name__ == "__main__":
						A__	:    str			 = 'hopper-medium-v2'
						A__	:    Dict			 = gym.make(env_name)

						A__	:    List[Any]			 = ValueGuidedRLPipeline.from_pretrained(
						    'bglick13/hopper-medium-v2-value-function-hor32',
						    env=env,
						)

						env.seed(0)
						A__	:    Dict			 = env.reset()
						A__	:    Optional[int]			 = 0
						A__	:    str			 = 0
						A__	:    List[Any]			 = 1_000
						A__	:    Tuple			 = [obs.copy()]
						try:
												for t in tqdm.tqdm(range(T)):
																		# call the policy
																		A__	:    Union[str, Any]			 = pipeline(obs, planning_horizon=32)

																		# execute action in environment
																		A__ ,						A__ ,						A__ ,						A__	:    Any			 = env.step(denorm_actions)
																		A__	:    List[Any]			 = env.get_normalized_score(total_reward)

																		# update return
																		total_reward += reward
																		total_score += score
																		print(
																		    f'Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:'
																		    f' {total_score}'
																		)

																		# save observations for rendering
																		rollout.append(next_observation.copy())

																		A__	:    Optional[Any]			 = next_observation
						except KeyboardInterrupt:
												pass

						print(f'Total reward: {total_reward}')



