






"""simple docstring"""
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


lowerCAmelCase__    						=			logging.getLogger(__name__)

lowerCAmelCase__    						=			'pytorch_model.bin'


@dataclasses.dataclass
class _lowerCAmelCase						:
						SCREAMING_SNAKE_CASE_:					str 					=				dataclasses.field(
						    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models.'}       )
						SCREAMING_SNAKE_CASE_:					Optional[str] 					=				dataclasses.field(
						    default=__UpperCAmelCase   ,  metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co.'}   ,  )







@dataclasses.dataclass
class _lowerCAmelCase						:
						SCREAMING_SNAKE_CASE_:					str 					=				dataclasses.field(metadata={'help': 'A csv or a json file containing the training data.'}       )
						SCREAMING_SNAKE_CASE_:					str 					=				dataclasses.field(metadata={'help': 'A csv or a json file containing the data to predict on.'}       )
						SCREAMING_SNAKE_CASE_:					Optional[str] 					=				dataclasses.field(
						    default=__UpperCAmelCase   ,  metadata={'help': 'A csv or a json file containing the validation data.'}       )
						SCREAMING_SNAKE_CASE_:					Optional[str] 					=				dataclasses.field(
						    default=__UpperCAmelCase   ,  metadata={'help': 'The name of the task to train on.'}   ,  )
						SCREAMING_SNAKE_CASE_:					Optional[List[str]] 					=				dataclasses.field(
						    default=__UpperCAmelCase   ,  metadata={'help': 'The list of labels for the task.'}       )







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







def 				lowercase__		(					lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						lowerCamelCase       ):
				_SCREAMING_SNAKE_CASE   :		Tuple       		=    datasets.concatenate_datasets([infer_input, infer_output],						axis=1       )

				if args.do_filter_by_confidence:
								_SCREAMING_SNAKE_CASE   :		Tuple       		=    dataset.filter(lambda lowerCamelCase       : example["probability"] > args.confidence_threshold       )

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

				_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    dataset.remove_columns(['label', 'probability']       )
				_SCREAMING_SNAKE_CASE   :		Tuple       		=    dataset.rename_column('prediction',						'label'       )
				_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    dataset.map(lambda lowerCamelCase       : {"label": idalabel[example["label"]]}       )
				_SCREAMING_SNAKE_CASE   :		Tuple       		=    dataset.shuffle(seed=args.seed       )

				_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    os.path.join(lowerCamelCase,						f"""train_pseudo.{args.data_file_extension}"""       )
				if args.data_file_extension == "csv":
								dataset.to_csv(lowerCamelCase,						index=lowerCamelCase       )
				else:
								dataset.to_json(lowerCamelCase       )






def 				lowercase__		(					lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						**lowerCamelCase       ):
				_SCREAMING_SNAKE_CASE   :		List[str]       		=    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()

				_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    STModelArguments(model_name_or_path=lowerCamelCase       )
				_SCREAMING_SNAKE_CASE   :		List[Any]       		=    STDataArguments(train_file=lowerCamelCase,						infer_file=lowerCamelCase       )
				_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    STTrainingArguments(output_dir=lowerCamelCase       )
				_SCREAMING_SNAKE_CASE   :		List[str]       		=    argparse.Namespace()

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

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

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

				if args.evaluation_strategy != IntervalStrategy.NO.value:
								assert args.eval_file is not None
								_SCREAMING_SNAKE_CASE   :		int       		=    args.eval_file

				for key in data_files:
								_SCREAMING_SNAKE_CASE   :		Tuple       		=    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:
												_SCREAMING_SNAKE_CASE   :		Optional[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...'       )
				_SCREAMING_SNAKE_CASE   :		List[Any]       		=    f"""{args.output_dir}/self-train_iter-{{}}""".format
				_SCREAMING_SNAKE_CASE   :		List[Any]       		=    data_dir_format(0       )

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

				_SCREAMING_SNAKE_CASE   :		List[str]       		=    None
				_SCREAMING_SNAKE_CASE   :		Any       		=    None
				_SCREAMING_SNAKE_CASE   :		Tuple       		=    0
				_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    False
				# Show the progress bar
				_SCREAMING_SNAKE_CASE   :		Tuple       		=    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       )       ):
								_SCREAMING_SNAKE_CASE   :		Any       		=    data_dir_format(lowerCamelCase       )
								assert os.path.exists(lowerCamelCase       )

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

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

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

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

								_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    iteration
								_SCREAMING_SNAKE_CASE   :		Tuple       		=    data_dir_format(iteration + 1       )

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

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

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

								_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    os.path.join(lowerCamelCase,						f"""train_pseudo.{args.data_file_extension}"""       )

								if args.evaluation_strategy != IntervalStrategy.NO.value:
												_SCREAMING_SNAKE_CASE   :		List[Any]       		=    eval_result

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

																if early_stopping_patience_counter >= args.early_stopping_patience:
																				_SCREAMING_SNAKE_CASE   :		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',						lowerCamelCase       )
								logger.info('Best evaluation result: %s = %f',						args.eval_metric,						lowerCamelCase       )
								accelerator.wait_for_everyone()
								if accelerator.is_main_process:
												shutil.copy(
												    os.path.join(lowerCamelCase,						f"""eval_results_iter-{iteration}.json"""       ),						os.path.join(lowerCamelCase,						'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,						lowerCamelCase       )
								accelerator.wait_for_everyone()
								if accelerator.is_main_process:
												shutil.copy(
												    os.path.join(lowerCamelCase,						f"""eval_results_iter-{args.max_selftrain_iterations - 1}.json"""       ),						os.path.join(lowerCamelCase,						'eval_results_best-iteration.json'       ),						)









"""simple docstring"""
from datetime import datetime

import requests
from bsa import BeautifulSoup

if __name__ == "__main__":
			lowerCAmelCase__    						=			input('Enter image url: ').strip()
			print(F'Downloading image from {url} ...')
			lowerCAmelCase__    						=			BeautifulSoup(requests.get(url).content, 'html.parser')
			# The image URL is in the content field of the first meta tag with property og:image
			lowerCAmelCase__    						=			soup.find('meta', {'property': 'og:image'})['content']
			lowerCAmelCase__    						=			requests.get(image_url).content
			lowerCAmelCase__    						=			F'{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg'
			with open(file_name, 'wb') as fp:
						fp.write(image_data)
			print(F'Done. Image saved to disk as {file_name}.')

