






"""simple docstring"""



import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union

import tqdm
from filelock import FileLock

from transformers import (
    BartTokenizer,
    BartTokenizerFast,
    DataProcessor,
    PreTrainedTokenizer,
    RobertaTokenizer,
    RobertaTokenizerFast,
    XLMRobertaTokenizer,
    is_tf_available,
    is_torch_available,
)


A__	:    Union[str, Any]			 = logging.getLogger(__name__)







@dataclass(frozen=snake_case__       )
class        lowercase__      :
			_UpperCAmelCase		:str
			_UpperCAmelCase		:str
			_UpperCAmelCase		:Optional[str]             =			None
			_UpperCAmelCase		:Optional[str]             =			None
			_UpperCAmelCase		:Optional[str]             =			None







@dataclass(frozen=snake_case__       )
class        lowercase__      :
			_UpperCAmelCase		:List[int]
			_UpperCAmelCase		:Optional[List[int]]             =			None
			_UpperCAmelCase		:Optional[List[int]]             =			None
			_UpperCAmelCase		:Optional[Union[int, float]]             =			None
			_UpperCAmelCase		:Optional[int]             =			None


if is_torch_available():
						import torch
						from torch.utils.data import Dataset







						class        lowercase__      (							snake_case__       ):
									_UpperCAmelCase		:List[InputFeatures]


									def __init__(				self					:  Any       ,	snake_case__					:  str       ,	snake_case__					:  PreTrainedTokenizer       ,	snake_case__					:  str       ,	snake_case__					:  Optional[int] = None       ,	snake_case__					:  Optional[Any]=False       ,	snake_case__					:  bool = False       ,	):
																lowerCamelCase_	:							List[str]									=hans_processors[task]()

																lowerCamelCase_	:							List[Any]									=os.path.join(
																    snake_case__       ,	"cached_{}_{}_{}_{}".format(
																        "dev" if evaluate else "train"       ,	tokenizer.__class__.__name__       ,	str(snake_case__ )       ,	snake_case__       ,	)       ,	)
																lowerCamelCase_	:							Tuple									=processor.get_labels()
																if tokenizer.__class__ in (
																    RobertaTokenizer,
																    RobertaTokenizerFast,
																    XLMRobertaTokenizer,
																    BartTokenizer,
																    BartTokenizerFast,
																):
																							# HACK(label indices are swapped in RoBERTa pretrained model)
																							lowerCamelCase_							,						lowerCamelCase_	:							List[str]									=label_list[2], label_list[1]
																lowerCamelCase_	:							int									=label_list

																# Make sure only the first process in distributed training processes the dataset,
																# and the others will use the cache.
																lowerCamelCase_	:							Optional[Any]									=cached_features_file + ".lock"
																with FileLock(snake_case__ ):
																							if os.path.exists(snake_case__ ) and not overwrite_cache:
																														logger.info(F"""Loading features from cached file {cached_features_file}""" )
																														lowerCamelCase_	:							int									=torch.load(snake_case__ )
																							else:
																														logger.info(F"""Creating features from dataset file at {data_dir}""" )

																														lowerCamelCase_	:							List[Any]									=(
																														    processor.get_dev_examples(snake_case__ ) if evaluate else processor.get_train_examples(snake_case__ )
																														)

																														logger.info("Training examples: %s"       ,	len(snake_case__ ) )
																														lowerCamelCase_	:							Union[str, Any]									=hans_convert_examples_to_features(snake_case__       ,	snake_case__       ,	snake_case__       ,	snake_case__ )
																														logger.info("Saving features into cached file %s"       ,	snake_case__ )
																														torch.save(self.features       ,	snake_case__ )


									def __len__(				self					:  str ):
																return len(self.features )


									def __getitem__(				self					:  Dict       ,	snake_case__					:  str ):
																return self.features[i]


									def      UpperCAmelCase__						(				self					:  Union[str, Any] ):
																return self.label_list


if is_tf_available():
						import tensorflow as tf







						class        lowercase__      :
									_UpperCAmelCase		:List[InputFeatures]


									def __init__(				self					:  Tuple       ,	snake_case__					:  str       ,	snake_case__					:  PreTrainedTokenizer       ,	snake_case__					:  str       ,	snake_case__					:  Optional[int] = 128       ,	snake_case__					:  List[str]=False       ,	snake_case__					:  bool = False       ,	):
																lowerCamelCase_	:							Any									=hans_processors[task]()
																lowerCamelCase_	:							str									=processor.get_labels()
																if tokenizer.__class__ in (
																    RobertaTokenizer,
																    RobertaTokenizerFast,
																    XLMRobertaTokenizer,
																    BartTokenizer,
																    BartTokenizerFast,
																):
																							# HACK(label indices are swapped in RoBERTa pretrained model)
																							lowerCamelCase_							,						lowerCamelCase_	:							Any									=label_list[2], label_list[1]
																lowerCamelCase_	:							int									=label_list

																lowerCamelCase_	:							Optional[int]									=processor.get_dev_examples(snake_case__ ) if evaluate else processor.get_train_examples(snake_case__ )
																lowerCamelCase_	:							Tuple									=hans_convert_examples_to_features(snake_case__       ,	snake_case__       ,	snake_case__       ,	snake_case__ )

																def gen():
																							for ex_index, ex in tqdm.tqdm(enumerate(self.features )       ,	desc="convert examples to features" ):
																														if ex_index % 1_0000 == 0:
																																					logger.info("Writing example %d of %d" % (ex_index, len(snake_case__ )) )

																														yield (
																														    {
																														        "example_id": 0,
																														        "input_ids": ex.input_ids,
																														        "attention_mask": ex.attention_mask,
																														        "token_type_ids": ex.token_type_ids,
																														    },
																														    ex.label,
																														)

																lowerCamelCase_	:							Dict									=tf.data.Dataset.from_generator(
																    snake_case__       ,	(
																        {
																            "example_id": tf.intaa,
																            "input_ids": tf.intaa,
																            "attention_mask": tf.intaa,
																            "token_type_ids": tf.intaa,
																        },
																        tf.intaa,
																    )       ,	(
																        {
																            "example_id": tf.TensorShape([] ),
																            "input_ids": tf.TensorShape([None, None] ),
																            "attention_mask": tf.TensorShape([None, None] ),
																            "token_type_ids": tf.TensorShape([None, None] ),
																        },
																        tf.TensorShape([] ),
																    )       ,	)


									def      UpperCAmelCase__						(				self					:  Dict ):
																return self.dataset


									def __len__(				self					:  int ):
																return len(self.features )


									def __getitem__(				self					:  List[str]       ,	snake_case__					:  Tuple ):
																return self.features[i]


									def      UpperCAmelCase__						(				self					:  Tuple ):
																return self.label_list







class        lowercase__      (							snake_case__       ):


			def      UpperCAmelCase__						(				self					:  Optional[Any]       ,	snake_case__					:  List[str] ):
										return self._create_examples(self._read_tsv(os.path.join(snake_case__       ,	"heuristics_train_set.txt" ) )       ,	"train" )


			def      UpperCAmelCase__						(				self					:  List[Any]       ,	snake_case__					:  int ):
										return self._create_examples(self._read_tsv(os.path.join(snake_case__       ,	"heuristics_evaluation_set.txt" ) )       ,	"dev" )


			def      UpperCAmelCase__						(				self					:  int ):
										return ["contradiction", "entailment", "neutral"]


			def      UpperCAmelCase__						(				self					:  int       ,	snake_case__					:  Any       ,	snake_case__					:  Dict ):
										lowerCamelCase_	:							List[Any]									=[]
										for i, line in enumerate(snake_case__ ):
																	if i == 0:
																								continue
																	lowerCamelCase_	:							Any									="%s-%s" % (set_type, line[0])
																	lowerCamelCase_	:							int									=line[5]
																	lowerCamelCase_	:							Optional[Any]									=line[6]
																	lowerCamelCase_	:							Dict									=line[7][2:] if line[7].startswith("ex" ) else line[7]
																	lowerCamelCase_	:							Optional[int]									=line[0]
																	examples.append(InputExample(guid=snake_case__       ,	text_a=snake_case__       ,	text_b=snake_case__       ,	label=snake_case__       ,	pairID=snake_case__ ) )
										return examples


def 			_snake_case    (					lowerCamelCase__     :   List[InputExample]    ,						lowerCamelCase__     :   List[str]    ,						lowerCamelCase__     :   int    ,						lowerCamelCase__     :   PreTrainedTokenizer    ,						)    ->     List[Any]:
							lowerCamelCase_	:							Any									={label: i for i, label in enumerate(lowerCamelCase__  )}

							lowerCamelCase_	:							Dict									=[]
							for ex_index, example in tqdm.tqdm(enumerate(lowerCamelCase__  )    ,						desc="convert examples to features"  ):
														if ex_index % 10_000 == 0:
																					logger.info("Writing example %d" % (ex_index)  )

														lowerCamelCase_	:							Optional[int]									=tokenizer(
														    example.text_a    ,						example.text_b    ,						add_special_tokens=lowerCamelCase__    ,						max_length=lowerCamelCase__    ,						padding="max_length"    ,						truncation=lowerCamelCase__    ,						return_overflowing_tokens=lowerCamelCase__    ,						)

														lowerCamelCase_	:							int									=label_map[example.label] if example.label in label_map else 0

														lowerCamelCase_	:							List[Any]									=int(example.pairID  )

														features.append(InputFeatures(**lowerCamelCase__    ,						label=lowerCamelCase__    ,						pairID=lowerCamelCase__  )  )

							for i, example in enumerate(examples[:5]  ):
														logger.info("*** Example ***"  )
														logger.info(F"""guid: {example}"""  )
														logger.info(F"""features: {features[i]}"""  )

							return features


A__	:    str			 = {
    'hans': 3,
}

A__	:    Tuple			 = {
    'hans': HansProcessor,
}











"""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}')



