



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


_snake_case	=						logging.getLogger(__name__)

_snake_case	=						"pytorch_model.bin"



@dataclasses.dataclass
class 						UpperCAmelCase_					:
							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_					:
							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_					:
							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=10     ,      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=100     ,      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        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							int				     =					datasets.concatenate_datasets([infer_input, infer_output]       ,				axis=1						)

							if args.do_filter_by_confidence:
														_lowerCAmelCase  :							Union[str, Any]				     =					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
														_lowerCAmelCase  :							Optional[Any]				     =					int(eval_result * len(_lowerCamelCase						)						)
														print(_lowerCamelCase						)
														_lowerCAmelCase  :							str				     =					dataset.sort("probability"       ,				reverse=_lowerCamelCase						)
														_lowerCAmelCase  :							Tuple				     =					dataset.select(range(_lowerCamelCase						)						)

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

							_lowerCAmelCase  :							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        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				**_lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							int				     =					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  :							Optional[Any]				     =					STModelArguments(model_name_or_path=_lowerCamelCase						)
							_lowerCAmelCase  :							Any				     =					STDataArguments(train_file=_lowerCamelCase       ,				infer_file=_lowerCamelCase						)
							_lowerCAmelCase  :							Optional[int]				     =					STTrainingArguments(output_dir=_lowerCamelCase						)
							_lowerCAmelCase  :							List[Any]				     =					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
							_lowerCAmelCase  :							Dict				     =					{}
							_lowerCAmelCase  :							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  :							str				     =					args.train_file
							_lowerCAmelCase  :							List[str]				     =					args.infer_file

							if args.evaluation_strategy != IntervalStrategy.NO.value:
														assert args.eval_file is not None
														_lowerCAmelCase  :							Optional[int]				     =					args.eval_file

							for key in data_files:
														_lowerCAmelCase  :							List[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  :							List[str]				     =					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  :							Union[str, Any]				     =					F"{args.output_dir}/self-train_iter-{{}}".format
							_lowerCAmelCase  :							Dict				     =					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()

							_lowerCAmelCase  :							Optional[int]				     =					None
							_lowerCAmelCase  :							List[str]				     =					None
							_lowerCAmelCase  :							Union[str, Any]				     =					0
							_lowerCAmelCase  :							Any				     =					False
							# Show the progress bar
							_lowerCAmelCase  :							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						)						):
														_lowerCAmelCase  :							Union[str, Any]				     =					data_dir_format(_lowerCamelCase						)
														assert os.path.exists(_lowerCamelCase						)

														# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
														# iteration > 0
														_lowerCAmelCase  :							int				     =					os.path.join(_lowerCamelCase       ,				"stage-1"						)
														_lowerCAmelCase  :							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(_lowerCamelCase       ,				_lowerCamelCase						):
																												arguments_dict.update({key: value}						)

														_lowerCAmelCase  :							Optional[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
																					_lowerCAmelCase  :							Any				     =					os.path.join(_lowerCamelCase       ,				"best-checkpoint"						)
																					_lowerCAmelCase  :							int				     =					os.path.join(_lowerCamelCase       ,				"stage-2"						)
																					# Update arguments_dict
																					_lowerCAmelCase  :							str				     =					model_path
																					_lowerCAmelCase  :							Optional[Any]				     =					data_files["train"]
																					_lowerCAmelCase  :							Union[str, Any]				     =					current_output_dir

																					_lowerCAmelCase  :							Optional[int]				     =					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						)

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

														_lowerCAmelCase  :							List[Any]				     =					AutoConfig.from_pretrained(os.path.join(_lowerCamelCase       ,				"best-checkpoint"						)						)
														_lowerCAmelCase  :							Any				     =					config.idalabel
														_lowerCAmelCase  :							Optional[int]				     =					os.path.join(_lowerCamelCase       ,				"eval_results_best-checkpoint.json"						)
														_lowerCAmelCase  :							Any				     =					os.path.join(_lowerCamelCase       ,				"test_results_best-checkpoint.json"						)
														assert os.path.exists(_lowerCamelCase						)

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

														_lowerCAmelCase  :							List[Any]				     =					os.path.join(_lowerCamelCase       ,				F"train_pseudo.{args.data_file_extension}"						)

														if args.evaluation_strategy != IntervalStrategy.NO.value:
																					_lowerCAmelCase  :							Optional[Any]				     =					eval_result

																					if best_iteration is None:
																												_lowerCAmelCase  :							Tuple				     =					new_iteration
																												_lowerCAmelCase  :							Union[str, Any]				     =					new_eval_result
																					else:
																												if new_eval_result - best_eval_result > args.early_stopping_threshold:
																																			_lowerCAmelCase  :							Dict				     =					new_iteration
																																			_lowerCAmelCase  :							str				     =					new_eval_result
																																			_lowerCAmelCase  :							Union[str, Any]				     =					0
																												else:
																																			if new_eval_result == best_eval_result:
																																										_lowerCAmelCase  :							Any				     =					new_iteration
																																										_lowerCAmelCase  :							Union[str, Any]				     =					new_eval_result
																																			early_stopping_patience_counter += 1

																												if early_stopping_patience_counter >= args.early_stopping_patience:
																																			_lowerCAmelCase  :							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"       ,				_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"						)       ,				)





import requests
from bsa import BeautifulSoup






def        A       (      _lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							List[str]				     =					BeautifulSoup(requests.get(_lowerCamelCase       ,				params=_lowerCamelCase						).content       ,				"html.parser"						)
							_lowerCAmelCase  :							Any				     =					soup.find("div"       ,				attrs={"class": "gs_ri"}						)
							_lowerCAmelCase  :							str				     =					div.find("div"       ,				attrs={"class": "gs_fl"}						).find_all("a"						)
							return anchors[2].get_text()


if __name__ == "__main__":
						_snake_case	=						{
						    "title": (
						        "Precisely geometry controlled microsupercapacitors for ultrahigh areal "
						        "capacitance, volumetric capacitance, and energy density"
						    ),
						    "journal": "Chem. Mater.",
						    "volume": 30,
						    "pages": "3979-3990",
						    "year": 2018,
						    "hl": "en",
						}
						print(get_citation("https://scholar.google.com/scholar_lookup", params=params))
