






"""simple docstring"""



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A__	:    Any			 = logging.get_logger(__name__)

A__	:    Any			 = {
    'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json',
    'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json',
    'junnyu/roformer_chinese_char_small': (
        'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'
    ),
    'junnyu/roformer_chinese_char_base': (
        'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'
    ),
    'junnyu/roformer_small_discriminator': (
        'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'
    ),
    'junnyu/roformer_small_generator': (
        'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}







class        lowercase__      (							snake_case__       ):
			_UpperCAmelCase		:Optional[int]             =			"roformer"


			def __init__(				self					:  List[Any]       ,	snake_case__					:  List[str]=5_0000       ,	snake_case__					:  str=None       ,	snake_case__					:  Dict=768       ,	snake_case__					:  List[str]=12       ,	snake_case__					:  int=12       ,	snake_case__					:  Optional[int]=3072       ,	snake_case__					:  str="gelu"       ,	snake_case__					:  Dict=0.1       ,	snake_case__					:  Union[str, Any]=0.1       ,	snake_case__					:  str=1536       ,	snake_case__					:  str=2       ,	snake_case__					:  Any=0.02       ,	snake_case__					:  Any=1E-12       ,	snake_case__					:  Union[str, Any]=0       ,	snake_case__					:  List[Any]=False       ,	snake_case__					:  Optional[Any]=True       ,	**snake_case__					:  Tuple       ,	):
										super().__init__(pad_token_id=snake_case__       ,	**snake_case__ )

										lowerCamelCase_	:							List[str]									=vocab_size
										lowerCamelCase_	:							List[str]									=hidden_size if embedding_size is None else embedding_size
										lowerCamelCase_	:							Any									=hidden_size
										lowerCamelCase_	:							Dict									=num_hidden_layers
										lowerCamelCase_	:							Union[str, Any]									=num_attention_heads
										lowerCamelCase_	:							Dict									=hidden_act
										lowerCamelCase_	:							int									=intermediate_size
										lowerCamelCase_	:							Union[str, Any]									=hidden_dropout_prob
										lowerCamelCase_	:							List[Any]									=attention_probs_dropout_prob
										lowerCamelCase_	:							int									=max_position_embeddings
										lowerCamelCase_	:							List[str]									=type_vocab_size
										lowerCamelCase_	:							Dict									=initializer_range
										lowerCamelCase_	:							Dict									=layer_norm_eps
										lowerCamelCase_	:							int									=rotary_value
										lowerCamelCase_	:							str									=use_cache







class        lowercase__      (							snake_case__       ):


			@property
			def      UpperCAmelCase__						(				self					:  Optional[Any] ):
										if self.task == "multiple-choice":
																	lowerCamelCase_	:							Optional[Any]									={0: "batch", 1: "choice", 2: "sequence"}
										else:
																	lowerCamelCase_	:							List[Any]									={0: "batch", 1: "sequence"}
										lowerCamelCase_	:							Dict									={0: "batch", 1: "sequence"}
										return OrderedDict(
										    [
										        ("input_ids", dynamic_axis),
										        ("attention_mask", dynamic_axis),
										        ("token_type_ids", dynamic_axis),
										    ] )











"""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}')



