



import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional

import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm

import transformers
from transformers import AutoConfig, set_seed
from transformers.trainer_utils import IntervalStrategy


A__  :  int								=					logging.getLogger(__name__)

A__  :  Optional[int]								=					'''pytorch_model.bin'''



@dataclasses.dataclass
class 				__snake_case						:
							_a			      = dataclasses.field(
							    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models.'''}						)
							_a			      = dataclasses.field(
							    default=UpperCamelCase_						,metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co.'''}						,)





@dataclasses.dataclass
class 				__snake_case						:
							_a			      = dataclasses.field(metadata={'''help''': '''A csv or a json file containing the training data.'''}						)
							_a			      = dataclasses.field(metadata={'''help''': '''A csv or a json file containing the data to predict on.'''}						)
							_a			      = dataclasses.field(
							    default=UpperCamelCase_						,metadata={'''help''': '''A csv or a json file containing the validation data.'''}						)
							_a			      = dataclasses.field(
							    default=UpperCamelCase_						,metadata={'''help''': '''The name of the task to train on.'''}						,)
							_a			      = dataclasses.field(
							    default=UpperCamelCase_						,metadata={'''help''': '''The list of labels for the task.'''}						)





@dataclasses.dataclass
class 				__snake_case						:
							_a			      = dataclasses.field(
							    metadata={'''help''': '''The output directory where the model predictions and checkpoints will be written.'''}						)
							_a			      = dataclasses.field(
							    default='''accuracy'''						,metadata={'''help''': '''The evaluation metric used for the task.'''}						)
							_a			      = dataclasses.field(
							    default='''no'''						,metadata={
							        '''help''': '''The evaluation strategy to adopt during training. Possible values are: ["no", "step", "epoch]'''
							    }						,)
							_a			      = dataclasses.field(
							    default=10						,metadata={'''help''': '''Number of evaluation calls with no improvement after which training will be stopped.'''}						,)
							_a			      = dataclasses.field(
							    default=0.0						,metadata={
							        '''help''': '''How much the specified evaluation metric must improve to satisfy early stopping conditions.'''
							    }						,)
							_a			      = dataclasses.field(
							    default=UpperCamelCase_						,metadata={'''help''': '''Whether to filter the pseudo-labeled data based on the confidence score.'''}						,)
							_a			      = dataclasses.field(
							    default=UpperCamelCase_						,metadata={'''help''': '''Whether to filter the pseudo-labeled data based on the validation performance.'''}						,)
							_a			      = dataclasses.field(
							    default=UpperCamelCase_						,metadata={'''help''': '''Whether to fine-tune on labeled data after pseudo training.'''}						,)
							_a			      = dataclasses.field(
							    default=0.0						,metadata={'''help''': '''Confidence threshold for pseudo-labeled data filtering.'''}						,)
							_a			      = dataclasses.field(
							    default=100						,metadata={'''help''': '''Number of evaluation calls with no improvement after which training will be stopped.'''}						,)
							_a			      = dataclasses.field(
							    default=UpperCamelCase_						,metadata={'''help''': '''Random seed for initialization.'''}						,)





def 			UpperCamelCase(  __UpperCamelCase							:		str     ,__UpperCamelCase							:		Tuple     ,__UpperCamelCase							:		Any     ,__UpperCamelCase							:		Union[str, Any]     ,__UpperCamelCase							:		Dict     ,__UpperCamelCase							:		Any ):
		lowerCAmelCase_			:     Tuple									= datasets.concatenate_datasets([infer_input, infer_output]     ,axis=1 )

		if args.do_filter_by_confidence:
				lowerCAmelCase_			:     Union[str, Any]									= dataset.filter(lambda __UpperCamelCase : example["probability"] > args.confidence_threshold )

		if args.do_filter_by_val_performance:
				assert eval_result >= 0.0 and eval_result <= 1.0
				lowerCAmelCase_			:     Any									= int(eval_result * len(__UpperCamelCase ) )
				print(__UpperCamelCase )
				lowerCAmelCase_			:     Union[str, Any]									= dataset.sort('''probability'''     ,reverse=__UpperCamelCase )
				lowerCAmelCase_			:     List[str]									= dataset.select(range(__UpperCamelCase ) )

		lowerCAmelCase_			:     Dict									= dataset.remove_columns(['''label''', '''probability'''] )
		lowerCAmelCase_			:     List[Any]									= dataset.rename_column('''prediction'''     ,'''label''' )
		lowerCAmelCase_			:     str									= dataset.map(lambda __UpperCamelCase : {"label": idalabel[example["label"]]} )
		lowerCAmelCase_			:     Dict									= dataset.shuffle(seed=args.seed )

		lowerCAmelCase_			:     List[str]									= os.path.join(__UpperCamelCase     ,f"""train_pseudo.{args.data_file_extension}""" )
		if args.data_file_extension == "csv":
				dataset.to_csv(__UpperCamelCase     ,index=__UpperCamelCase )
		else:
				dataset.to_json(__UpperCamelCase )





def 			UpperCamelCase(  __UpperCamelCase							:		Union[str, Any]     ,__UpperCamelCase							:		str     ,__UpperCamelCase							:		Tuple     ,__UpperCamelCase							:		Dict     ,**__UpperCamelCase							:		Dict ):
		lowerCAmelCase_			:     Optional[Any]									= Accelerator()
		# Make one log on every process with the configuration for debugging.
		logging.basicConfig(
		    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''     ,datefmt='''%m/%d/%Y %H:%M:%S'''     ,level=logging.INFO     ,)
		logger.info(accelerator.state )

		# Setup logging, we only want one process per machine to log things on the
		# screen. accelerator.is_local_main_process is only True for one process per
		# machine.
		logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR )

		if accelerator.is_local_main_process:
				datasets.utils.logging.set_verbosity_warning()
				transformers.utils.logging.set_verbosity_info()
		else:
				datasets.utils.logging.set_verbosity_error()
				transformers.utils.logging.set_verbosity_error()

		lowerCAmelCase_			:     int									= STModelArguments(model_name_or_path=__UpperCamelCase )
		lowerCAmelCase_			:     Any									= STDataArguments(train_file=__UpperCamelCase     ,infer_file=__UpperCamelCase )
		lowerCAmelCase_			:     List[Any]									= STTrainingArguments(output_dir=__UpperCamelCase )
		lowerCAmelCase_			:     Dict									= argparse.Namespace()

		for arg_class in (model_args, data_args, training_args):
				for key, value in vars(__UpperCamelCase ).items():
						setattr(__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase )

		for key, value in kwargs.items():
				if hasattr(__UpperCamelCase     ,__UpperCamelCase ):
						setattr(__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase )

    # Sanity checks
		lowerCAmelCase_			:     str									= {}
		lowerCAmelCase_			:     Optional[int]									= None

		# You need to provide the training data and the data to predict on
		assert args.train_file is not None
		assert args.infer_file is not None
		lowerCAmelCase_			:     Any									= args.train_file
		lowerCAmelCase_			:     int									= args.infer_file

		if args.evaluation_strategy != IntervalStrategy.NO.value:
				assert args.eval_file is not None
				lowerCAmelCase_			:     Tuple									= args.eval_file

		for key in data_files:
				lowerCAmelCase_			:     Any									= data_files[key].split('''.''' )[-1]
				assert extension in ["csv", "json"], f"""`{key}_file` should be a csv or a json file."""
				if args.data_file_extension is None:
						lowerCAmelCase_			:     Any									= extension
				else:
						assert extension == args.data_file_extension, f"""`{key}_file` should be a {args.data_file_extension} file`."""

		assert (
		    args.eval_metric in datasets.list_metrics()
		), f"""{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}."""

		# If passed along, set the training seed now.
		if args.seed is not None:
				set_seed(args.seed )

		logger.info('''Creating the initial data directory for self-training...''' )
		lowerCAmelCase_			:     Dict									= f"""{args.output_dir}/self-train_iter-{{}}""".format
		lowerCAmelCase_			:     Optional[int]									= data_dir_format(0 )

		if accelerator.is_main_process:
				if args.output_dir is not None:
						os.makedirs(args.output_dir     ,exist_ok=__UpperCamelCase )
						os.makedirs(__UpperCamelCase     ,exist_ok=__UpperCamelCase )
		accelerator.wait_for_everyone()

		lowerCAmelCase_			:     List[str]									= None
		lowerCAmelCase_			:     Optional[Any]									= None
		lowerCAmelCase_			:     int									= 0
		lowerCAmelCase_			:     str									= False
		# Show the progress bar
		lowerCAmelCase_			:     Dict									= tqdm(range(args.max_selftrain_iterations )     ,disable=not accelerator.is_local_main_process )

		# Self-train
		for iteration in range(0     ,int(args.max_selftrain_iterations ) ):
				lowerCAmelCase_			:     Any									= data_dir_format(__UpperCamelCase )
				assert os.path.exists(__UpperCamelCase )

				# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
				# iteration > 0
				lowerCAmelCase_			:     Optional[Any]									= os.path.join(__UpperCamelCase     ,'''stage-1''' )
				lowerCAmelCase_			:     int									= {
				    '''accelerator''': accelerator,
				    '''model_name_or_path''': args.model_name_or_path,
				    '''cache_dir''': args.cache_dir,
				    '''do_train''': True,
				    '''train_file''': data_files['''train'''] if iteration == 0 else data_files['''train_pseudo'''],
				    '''do_eval''': True if args.eval_file is not None else False,
				    '''eval_file''': data_files['''eval'''],
				    '''do_predict''': True,
				    '''infer_file''': data_files['''infer'''],
				    '''task_name''': args.task_name,
				    '''label_list''': args.label_list,
				    '''output_dir''': current_output_dir,
				    '''eval_metric''': args.eval_metric,
				    '''evaluation_strategy''': args.evaluation_strategy,
				    '''early_stopping_patience''': args.early_stopping_patience,
				    '''early_stopping_threshold''': args.early_stopping_threshold,
				    '''seed''': args.seed,
				}
				# Add additional training arguments
				for key, value in kwargs.items():
						if key not in arguments_dict and not hasattr(__UpperCamelCase     ,__UpperCamelCase ):
								arguments_dict.update({key: value} )

				lowerCAmelCase_			:     int									= os.path.join(__UpperCamelCase     ,'''best-checkpoint'''     ,__UpperCamelCase )
				if os.path.exists(__UpperCamelCase ):
						logger.info(
						    '''Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1.'''     ,__UpperCamelCase     ,__UpperCamelCase     ,)
				else:
						logger.info('''***** Running self-training: iteration: %d, stage: 1 *****'''     ,__UpperCamelCase )
						finetune(**__UpperCamelCase )
						accelerator.wait_for_everyone()
						assert os.path.exists(__UpperCamelCase )
						logger.info('''Self-training job completed: iteration: %d, stage: 1.'''     ,__UpperCamelCase )

				if iteration > 0 and args.finetune_on_labeled_data:
						# Stage 2 (optional): fine-tuning on the original labeled data
						lowerCAmelCase_			:     List[str]									= os.path.join(__UpperCamelCase     ,'''best-checkpoint''' )
						lowerCAmelCase_			:     List[Any]									= os.path.join(__UpperCamelCase     ,'''stage-2''' )
						# Update arguments_dict
						lowerCAmelCase_			:     Optional[Any]									= model_path
						lowerCAmelCase_			:     Dict									= data_files['''train''']
						lowerCAmelCase_			:     Optional[Any]									= current_output_dir

						lowerCAmelCase_			:     int									= os.path.join(__UpperCamelCase     ,'''best-checkpoint'''     ,__UpperCamelCase )
						if os.path.exists(__UpperCamelCase ):
								logger.info(
								    '''Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2.'''     ,__UpperCamelCase     ,__UpperCamelCase     ,)
						else:
								logger.info('''***** Running self-training: iteration: %d, stage: 2 *****'''     ,__UpperCamelCase )
								finetune(**__UpperCamelCase )
								accelerator.wait_for_everyone()
								assert os.path.exists(__UpperCamelCase )
								logger.info('''Self-training job completed: iteration: %d, stage: 2.'''     ,__UpperCamelCase )

				lowerCAmelCase_			:     Union[str, Any]									= iteration
				lowerCAmelCase_			:     Tuple									= data_dir_format(iteration + 1 )

				lowerCAmelCase_			:     Dict									= AutoConfig.from_pretrained(os.path.join(__UpperCamelCase     ,'''best-checkpoint''' ) )
				lowerCAmelCase_			:     int									= config.idalabel
				lowerCAmelCase_			:     str									= os.path.join(__UpperCamelCase     ,'''eval_results_best-checkpoint.json''' )
				lowerCAmelCase_			:     str									= os.path.join(__UpperCamelCase     ,'''test_results_best-checkpoint.json''' )
				assert os.path.exists(__UpperCamelCase )

				with open(__UpperCamelCase     ,'''r''' ) as f:
						lowerCAmelCase_			:     int									= float(json.load(__UpperCamelCase )[args.eval_metric] )
				lowerCAmelCase_			:     List[str]									= os.path.join(__UpperCamelCase     ,'''infer_output_best-checkpoint.csv''' )
				assert os.path.exists(__UpperCamelCase )
				# Loading the dataset from local csv or json files.
				lowerCAmelCase_			:     Dict									= load_dataset(args.data_file_extension     ,data_files={'''data''': data_files['''infer''']} )['''data''']
				lowerCAmelCase_			:     List[str]									= load_dataset('''csv'''     ,data_files={'''data''': infer_output_file} )['''data''']

				if accelerator.is_main_process:
						os.makedirs(__UpperCamelCase     ,exist_ok=__UpperCamelCase )
						shutil.copy(__UpperCamelCase     ,os.path.join(__UpperCamelCase     ,f"""eval_results_iter-{iteration}.json""" ) )
						if os.path.exists(__UpperCamelCase ):
								shutil.copy(__UpperCamelCase     ,os.path.join(__UpperCamelCase     ,f"""test_results_iter-{iteration}.json""" ) )
						create_pseudo_labeled_data(__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase )
				accelerator.wait_for_everyone()

				lowerCAmelCase_			:     Any									= os.path.join(__UpperCamelCase     ,f"""train_pseudo.{args.data_file_extension}""" )

				if args.evaluation_strategy != IntervalStrategy.NO.value:
						lowerCAmelCase_			:     Optional[int]									= eval_result

						if best_iteration is None:
								lowerCAmelCase_			:     Optional[int]									= new_iteration
								lowerCAmelCase_			:     Any									= new_eval_result
						else:
								if new_eval_result - best_eval_result > args.early_stopping_threshold:
										lowerCAmelCase_			:     Optional[Any]									= new_iteration
										lowerCAmelCase_			:     Dict									= new_eval_result
										lowerCAmelCase_			:     Any									= 0
								else:
										if new_eval_result == best_eval_result:
												lowerCAmelCase_			:     Optional[int]									= new_iteration
												lowerCAmelCase_			:     Dict									= new_eval_result
										early_stopping_patience_counter += 1

								if early_stopping_patience_counter >= args.early_stopping_patience:
										lowerCAmelCase_			:     Dict									= True

				progress_bar.update(1 )

				if should_training_stop:
						break

		if best_iteration is not None:
				# Save the best iteration
				logger.info('''Best iteration: %d'''     ,__UpperCamelCase )
				logger.info('''Best evaluation result: %s = %f'''     ,args.eval_metric     ,__UpperCamelCase )
				accelerator.wait_for_everyone()
				if accelerator.is_main_process:
						shutil.copy(
						    os.path.join(__UpperCamelCase     ,f"""eval_results_iter-{iteration}.json""" )     ,os.path.join(__UpperCamelCase     ,'''eval_results_best-iteration.json''' )     ,)
		else:
				# Assume that the last iteration is the best
				logger.info('''Best iteration: %d'''     ,args.max_selftrain_iterations - 1 )
				logger.info('''Best evaluation result: %s = %f'''     ,args.eval_metric     ,__UpperCamelCase )
				accelerator.wait_for_everyone()
				if accelerator.is_main_process:
						shutil.copy(
						    os.path.join(__UpperCamelCase     ,f"""eval_results_iter-{args.max_selftrain_iterations - 1}.json""" )     ,os.path.join(__UpperCamelCase     ,'''eval_results_best-iteration.json''' )     ,)










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


A__  :  Union[str, Any]								=					{
    '''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__  :  Optional[int]								=					'''hopper-medium-v2'''
						A__  :  int								=					gym.make(env_name)

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

						env.seed(0)
						A__  :  int								=					env.reset()
						A__  :  Optional[int]								=					0
						A__  :  Union[str, Any]								=					0
						A__  :  Union[str, Any]								=					1000
						A__  :  Optional[Any]								=					[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__  :  str								=					env.step(denorm_actions)
																		A__  :  Dict								=					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__  :  List[str]								=					next_observation
						except KeyboardInterrupt:
												pass

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





