



'''simple docstring'''



from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
							import datasets
class 	lowerCamelCase_							(					snake_case_			):







						@slow
						@require_torch
						def 					__lowercase    (  self   :      Any				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			EncoderDecoderModel.from_encoder_decoder_pretrained('''prajjwal1/bert-tiny'''				,    '''prajjwal1/bert-tiny'''				)
													SCREAMING_SNAKE_CASE					:      Tuple     					=			BertTokenizer.from_pretrained('''bert-base-uncased'''				)

													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			bertabert.config.encoder.vocab_size
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			tokenizer.sep_token_id
													SCREAMING_SNAKE_CASE					:      Any     					=			tokenizer.cls_token_id
													SCREAMING_SNAKE_CASE					:      Tuple     					=			1_28

													SCREAMING_SNAKE_CASE					:      Any     					=			datasets.load_dataset('''cnn_dailymail'''				,    '''3.0.0'''				,    split='''train[:1%]'''				)
													SCREAMING_SNAKE_CASE					:      Tuple     					=			datasets.load_dataset('''cnn_dailymail'''				,    '''3.0.0'''				,    split='''validation[:1%]'''				)

													SCREAMING_SNAKE_CASE					:      Any     					=			train_dataset.select(range(32				)				)
													SCREAMING_SNAKE_CASE					:      str     					=			val_dataset.select(range(16				)				)

													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			4

													def _map_to_encoder_decoder_inputs(lowerCAmelCase__   :      Dict				):
																				# Tokenizer will automatically set [BOS] <text> [EOS]
																				SCREAMING_SNAKE_CASE					:      int     					=			tokenizer(batch['''article''']				,    padding='''max_length'''				,    truncation=UpperCamelCase_				,    max_length=5_12				)
																				SCREAMING_SNAKE_CASE					:      List[str]     					=			tokenizer(batch['''highlights''']				,    padding='''max_length'''				,    truncation=UpperCamelCase_				,    max_length=1_28				)
																				SCREAMING_SNAKE_CASE					:      Dict     					=			inputs.input_ids
																				SCREAMING_SNAKE_CASE					:      str     					=			inputs.attention_mask

																				SCREAMING_SNAKE_CASE					:      List[Any]     					=			outputs.input_ids
																				SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			outputs.input_ids.copy()
																				SCREAMING_SNAKE_CASE					:      Optional[int]     					=			[
																				    [-1_00 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['labels']
																				]
																				SCREAMING_SNAKE_CASE					:      Any     					=			outputs.attention_mask

																				assert all(len(UpperCamelCase_				) == 5_12 for x in inputs.input_ids				)
																				assert all(len(UpperCamelCase_				) == 1_28 for x in outputs.input_ids				)

																				return batch

													def _compute_metrics(lowerCAmelCase__   :      Union[str, Any]				):
																				SCREAMING_SNAKE_CASE					:      int     					=			pred.label_ids
																				SCREAMING_SNAKE_CASE					:      Tuple     					=			pred.predictions

																				# all unnecessary tokens are removed
																				SCREAMING_SNAKE_CASE					:      Optional[int]     					=			tokenizer.batch_decode(UpperCamelCase_				,    skip_special_tokens=UpperCamelCase_				)
																				SCREAMING_SNAKE_CASE					:      List[Any]     					=			tokenizer.batch_decode(UpperCamelCase_				,    skip_special_tokens=UpperCamelCase_				)

																				SCREAMING_SNAKE_CASE					:      Tuple     					=			sum([int(pred_str[i] == label_str[i]				) for i in range(len(UpperCamelCase_				)				)]				) / len(UpperCamelCase_				)

																				return {"accuracy": accuracy}

													# map train dataset
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			train_dataset.map(
													    _map_to_encoder_decoder_inputs				,    batched=UpperCamelCase_				,    batch_size=UpperCamelCase_				,    remove_columns=['''article''', '''highlights''']				,    )
													train_dataset.set_format(
													    type='''torch'''				,    columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels''']				,    )

													# same for validation dataset
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			val_dataset.map(
													    _map_to_encoder_decoder_inputs				,    batched=UpperCamelCase_				,    batch_size=UpperCamelCase_				,    remove_columns=['''article''', '''highlights''']				,    )
													val_dataset.set_format(
													    type='''torch'''				,    columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels''']				,    )

													SCREAMING_SNAKE_CASE					:      Dict     					=			self.get_auto_remove_tmp_dir()

													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			SeqaSeqTrainingArguments(
													    output_dir=UpperCamelCase_				,    per_device_train_batch_size=UpperCamelCase_				,    per_device_eval_batch_size=UpperCamelCase_				,    predict_with_generate=UpperCamelCase_				,    evaluation_strategy='''steps'''				,    do_train=UpperCamelCase_				,    do_eval=UpperCamelCase_				,    warmup_steps=0				,    eval_steps=2				,    logging_steps=2				,    )

													# instantiate trainer
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			SeqaSeqTrainer(
													    model=UpperCamelCase_				,    args=UpperCamelCase_				,    compute_metrics=_compute_metrics				,    train_dataset=UpperCamelCase_				,    eval_dataset=UpperCamelCase_				,    tokenizer=UpperCamelCase_				,    )

													# start training
													trainer.train()









'''simple docstring'''



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


lowerCAmelCase_     :       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__":
							lowerCAmelCase_     :       int								=						'hopper-medium-v2'
							lowerCAmelCase_     :       str								=						gym.make(env_name)

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

							env.seed(0)
							lowerCAmelCase_     :       Any								=						env.reset()
							lowerCAmelCase_     :       Any								=						0
							lowerCAmelCase_     :       Tuple								=						0
							lowerCAmelCase_     :       str								=						1000
							lowerCAmelCase_     :       int								=						[obs.copy()]
							try:
														for t in tqdm.tqdm(range(T)):
																					# call the policy
																					lowerCAmelCase_     :       int								=						pipeline(obs, planning_horizon=32)

																					# execute action in environment
																					lowerCAmelCase_			,							lowerCAmelCase_			,							lowerCAmelCase_			,							lowerCAmelCase_     :       Union[str, Any]								=						env.step(denorm_actions)
																					lowerCAmelCase_     :       Optional[int]								=						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())

																					lowerCAmelCase_     :       Union[str, Any]								=						next_observation
							except KeyboardInterrupt:
														pass

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




