

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


_UpperCAmelCase            =		logging.getLogger(__name__)

_UpperCAmelCase            =		"""pytorch_model.bin"""





@dataclasses.dataclass
class UpperCAmelCase						:


				'''simple docstring'''






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





@dataclasses.dataclass
class UpperCAmelCase						:


				'''simple docstring'''






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





@dataclasses.dataclass
class UpperCAmelCase						:


				'''simple docstring'''






				lowerCamelCase_    = dataclasses.field(
				    metadata={'''help''': '''The output directory where the model predictions and checkpoints will be written.'''}  )
				lowerCamelCase_    = dataclasses.field(
				    default='''accuracy''' ,	metadata={'''help''': '''The evaluation metric used for the task.'''}  )
				lowerCamelCase_    = dataclasses.field(
				    default='''no''' ,	metadata={
				        '''help''': '''The evaluation strategy to adopt during training. Possible values are: ["no", "step", "epoch]'''
				    } ,	)
				lowerCamelCase_    = dataclasses.field(
				    default=1_0 ,	metadata={'''help''': '''Number of evaluation calls with no improvement after which training will be stopped.'''} ,	)
				lowerCamelCase_    = dataclasses.field(
				    default=0.0 ,	metadata={
				        '''help''': '''How much the specified evaluation metric must improve to satisfy early stopping conditions.'''
				    } ,	)
				lowerCamelCase_    = dataclasses.field(
				    default=__A ,	metadata={'''help''': '''Whether to filter the pseudo-labeled data based on the confidence score.'''} ,	)
				lowerCamelCase_    = dataclasses.field(
				    default=__A ,	metadata={'''help''': '''Whether to filter the pseudo-labeled data based on the validation performance.'''} ,	)
				lowerCamelCase_    = dataclasses.field(
				    default=__A ,	metadata={'''help''': '''Whether to fine-tune on labeled data after pseudo training.'''} ,	)
				lowerCamelCase_    = dataclasses.field(
				    default=0.0 ,	metadata={'''help''': '''Confidence threshold for pseudo-labeled data filtering.'''} ,	)
				lowerCamelCase_    = dataclasses.field(
				    default=1_0_0 ,	metadata={'''help''': '''Number of evaluation calls with no improvement after which training will be stopped.'''} ,	)
				lowerCamelCase_    = dataclasses.field(
				    default=__A ,	metadata={'''help''': '''Random seed for initialization.'''} ,	)




def     UpperCamelCase						(	__lowercase						:			Optional[int]						,__lowercase						:			Dict						,__lowercase						:			List[Any]						,__lowercase						:			Tuple						,__lowercase						:			Tuple						,__lowercase						:			str     ):
		'''simple docstring'''




		A_		:    Optional[Any]         =				datasets.concatenate_datasets([infer_input, infer_output]						,axis=1     )

		if args.do_filter_by_confidence:
				A_		:    Dict         =				dataset.filter(lambda __lowercase     : example["probability"] > args.confidence_threshold     )

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

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

		A_		:    Dict         =				os.path.join(__lowercase						,f'''train_pseudo.{args.data_file_extension}'''     )
		if args.data_file_extension == "csv":
				dataset.to_csv(__lowercase						,index=__lowercase     )
		else:
				dataset.to_json(__lowercase     )




def     UpperCamelCase						(	__lowercase						:			Optional[int]						,__lowercase						:			List[str]						,__lowercase						:			List[str]						,__lowercase						:			str						,**__lowercase						:			int     ):
		'''simple docstring'''




		A_		:    Tuple         =				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()

		A_		:    Dict         =				STModelArguments(model_name_or_path=__lowercase     )
		A_		:    Tuple         =				STDataArguments(train_file=__lowercase						,infer_file=__lowercase     )
		A_		:    Dict         =				STTrainingArguments(output_dir=__lowercase     )
		A_		:    Optional[int]         =				argparse.Namespace()

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

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

    # Sanity checks
		A_		:    Union[str, Any]         =				{}
		A_		:    Tuple         =				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
		A_		:    Union[str, Any]         =				args.train_file
		A_		:    List[str]         =				args.infer_file

		if args.evaluation_strategy != IntervalStrategy.NO.value:
				assert args.eval_file is not None
				A_		:    Any         =				args.eval_file

		for key in data_files:
				A_		:    Optional[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:
						A_		:    int         =				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...'     )
		A_		:    Optional[int]         =				f'''{args.output_dir}/self-train_iter-{{}}'''.format
		A_		:    List[str]         =				data_dir_format(0     )

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

		A_		:    Dict         =				None
		A_		:    Dict         =				None
		A_		:    Union[str, Any]         =				0
		A_		:    List[str]         =				False
		# Show the progress bar
		A_		:    List[str]         =				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     )     ):
				A_		:    Union[str, Any]         =				data_dir_format(__lowercase     )
				assert os.path.exists(__lowercase     )

				# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
				# iteration > 0
				A_		:    Tuple         =				os.path.join(__lowercase						,'stage-1'     )
				A_		:    List[Any]         =				{
				    '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(__lowercase						,__lowercase     ):
								arguments_dict.update({key: value}     )

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

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

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

				A_		:    Optional[int]         =				iteration
				A_		:    Optional[Any]         =				data_dir_format(iteration + 1     )

				A_		:    Any         =				AutoConfig.from_pretrained(os.path.join(__lowercase						,'best-checkpoint'     )     )
				A_		:    str         =				config.idalabel
				A_		:    Optional[int]         =				os.path.join(__lowercase						,'eval_results_best-checkpoint.json'     )
				A_		:    Union[str, Any]         =				os.path.join(__lowercase						,'test_results_best-checkpoint.json'     )
				assert os.path.exists(__lowercase     )

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

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

				A_		:    List[str]         =				os.path.join(__lowercase						,f'''train_pseudo.{args.data_file_extension}'''     )

				if args.evaluation_strategy != IntervalStrategy.NO.value:
						A_		:    List[str]         =				eval_result

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

								if early_stopping_patience_counter >= args.early_stopping_patience:
										A_		:    Union[str, Any]         =				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'						,__lowercase     )
				logger.info('Best evaluation result: %s = %f'						,args.eval_metric						,__lowercase     )
				accelerator.wait_for_everyone()
				if accelerator.is_main_process:
						shutil.copy(
						    os.path.join(__lowercase						,f'''eval_results_iter-{iteration}.json'''     )						,os.path.join(__lowercase						,'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						,__lowercase     )
				accelerator.wait_for_everyone()
				if accelerator.is_main_process:
						shutil.copy(
						    os.path.join(__lowercase						,f'''eval_results_iter-{args.max_selftrain_iterations - 1}.json'''     )						,os.path.join(__lowercase						,'eval_results_best-iteration.json'     )						,)







from __future__ import annotations

import requests

_UpperCAmelCase            =		set(
    """approved_at_utc approved_by author_flair_background_color
author_flair_css_class author_flair_richtext author_flair_template_id author_fullname
author_premium can_mod_post category clicked content_categories created_utc downs
edited gilded gildings hidden hide_score is_created_from_ads_ui is_meta
is_original_content is_reddit_media_domain is_video link_flair_css_class
link_flair_richtext link_flair_text link_flair_text_color media_embed mod_reason_title
name permalink pwls quarantine saved score secure_media secure_media_embed selftext
subreddit subreddit_name_prefixed subreddit_type thumbnail title top_awarded_type
total_awards_received ups upvote_ratio url user_reports""".split()
)




def     UpperCamelCase						(	__lowercase						:			str						,__lowercase						:			int = 1						,__lowercase						:			str = "new"						,__lowercase						:			list | None = None     ):
		'''simple docstring'''




		A_		:    Tuple         =				wanted_data or []
		if invalid_search_terms := ", ".join(sorted(set(__lowercase     ) - valid_terms     )     ):
				A_		:    int         =				f'''Invalid search term: {invalid_search_terms}'''
				raise ValueError(__lowercase     )
		A_		:    Optional[int]         =				requests.get(
		    f'''https://reddit.com/r/{subreddit}/{age}.json?limit={limit}'''						,headers={'User-agent': 'A random string'}						,)
		if response.status_code == 4_29:
				raise requests.HTTPError

		A_		:    Optional[Any]         =				response.json()
		if not wanted_data:
				return {id_: data["data"]["children"][id_] for id_ in range(__lowercase     )}

		A_		:    Union[str, Any]         =				{}
		for id_ in range(__lowercase     ):
				A_		:    List[str]         =				{
				    item: data['data']['children'][id_]['data'][item] for item in wanted_data
				}
		return data_dict


if __name__ == "__main__":
	# If you get Error 429, that means you are rate limited.Try after some time
	print(get_subreddit_data("""learnpython""", wanted_data=["""title""", """url""", """selftext"""]))






