




from math import ceil







def 			lowerCamelCase_		(       UpperCamelCase__				:      int = 1001      )     ->       int:





				"""simple docstring"""


				__lowerCamelCase		       =    1

				for i in range(1 ,     int(ceil(n / 2.0      )      )      ):
								__lowerCamelCase		       =    2 * i + 1
								__lowerCamelCase		       =    2 * i
								__lowerCamelCase		       =    total + 4 * odd**2 - 6 * even

				return total


if __name__ == "__main__":
					import sys

					if len(sys.argv) == 1:
										print(solution())
					else:
										try:
															__A        =    int(sys.argv[1])
															print(solution(n))
										except ValueError:
															print("Invalid entry - please enter a number")




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   UpperCAmelCase		:
   A__					:		str						     =     dataclasses.field(
       metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models."} )
   A__					:		Optional[str]						     =     dataclasses.field(
       default=A_       ,metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co."}       ,)






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






@dataclasses.dataclass
class   UpperCAmelCase		:
   A__					:		str						     =     dataclasses.field(
       metadata={"help": "The output directory where the model predictions and checkpoints will be written."} )
   A__					:		Optional[str]						     =     dataclasses.field(
       default="accuracy"       ,metadata={"help": "The evaluation metric used for the task."} )
   A__					:		Optional[str]						     =     dataclasses.field(
       default="no"       ,metadata={
           "help": "The evaluation strategy to adopt during training. Possible values are: [\"no\", \"step\", \"epoch]"
       }       ,)
   A__					:		Optional[int]						     =     dataclasses.field(
       default=10       ,metadata={"help": "Number of evaluation calls with no improvement after which training will be stopped."}       ,)
   A__					:		Optional[float]						     =     dataclasses.field(
       default=0.0       ,metadata={
           "help": "How much the specified evaluation metric must improve to satisfy early stopping conditions."
       }       ,)
   A__					:		Optional[bool]						     =     dataclasses.field(
       default=A_       ,metadata={"help": "Whether to filter the pseudo-labeled data based on the confidence score."}       ,)
   A__					:		Optional[bool]						     =     dataclasses.field(
       default=A_       ,metadata={"help": "Whether to filter the pseudo-labeled data based on the validation performance."}       ,)
   A__					:		Optional[bool]						     =     dataclasses.field(
       default=A_       ,metadata={"help": "Whether to fine-tune on labeled data after pseudo training."}       ,)
   A__					:		Optional[float]						     =     dataclasses.field(
       default=0.0       ,metadata={"help": "Confidence threshold for pseudo-labeled data filtering."}       ,)
   A__					:		Optional[int]						     =     dataclasses.field(
       default=1_00       ,metadata={"help": "Number of evaluation calls with no improvement after which training will be stopped."}       ,)
   A__					:		Optional[int]						     =     dataclasses.field(
       default=A_       ,metadata={"help": "Random seed for initialization."}       ,)



def 			UpperCamelCase					(    __lowerCamelCase     : int				,					__lowerCamelCase     : Tuple				,					__lowerCamelCase     : Union[str, Any]				,					__lowerCamelCase     : Tuple				,					__lowerCamelCase     : Dict				,					__lowerCamelCase     : Optional[int]    ):
       snake_case       :   Tuple							=      datasets.concatenate_datasets([infer_input, infer_output]				,					axis=1    )

       if args.do_filter_by_confidence:
              snake_case       :   Optional[int]							=      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
              snake_case       :   int							=      int(eval_result * len(__lowerCamelCase    )    )
              print(__lowerCamelCase    )
              snake_case       :   List[str]							=      dataset.sort("probability"				,					reverse=__lowerCamelCase    )
              snake_case       :   Tuple							=      dataset.select(range(__lowerCamelCase    )    )

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

       snake_case       :   int							=      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 			UpperCamelCase					(    __lowerCamelCase     : Optional[int]				,					__lowerCamelCase     : str				,					__lowerCamelCase     : Optional[int]				,					__lowerCamelCase     : Tuple				,					**__lowerCamelCase     : List[Any]    ):
       snake_case       :   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()

       snake_case       :   Dict							=      STModelArguments(model_name_or_path=__lowerCamelCase    )
       snake_case       :   Tuple							=      STDataArguments(train_file=__lowerCamelCase				,					infer_file=__lowerCamelCase    )
       snake_case       :   str							=      STTrainingArguments(output_dir=__lowerCamelCase    )
       snake_case       :   int							=      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
       snake_case       :   List[str]							=      {}
       snake_case       :   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
       snake_case       :   str							=      args.train_file
       snake_case       :   Tuple							=      args.infer_file

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

       for key in data_files:
              snake_case       :   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:
                     snake_case       :   Union[str, 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..."    )
       snake_case       :   List[Any]							=      f"""{args.output_dir}/self-train_iter-{{}}""".format
       snake_case       :   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=__lowerCamelCase    )
                     os.makedirs(__lowerCamelCase				,					exist_ok=__lowerCamelCase    )
       accelerator.wait_for_everyone()

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

              # Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
              # iteration > 0
              snake_case       :   Dict							=      os.path.join(__lowerCamelCase				,					"stage-1"    )
              snake_case       :   Optional[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}    )

              snake_case       :   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: 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
                     snake_case       :   str							=      os.path.join(__lowerCamelCase				,					"best-checkpoint"    )
                     snake_case       :   Dict							=      os.path.join(__lowerCamelCase				,					"stage-2"    )
                     # Update arguments_dict
                     snake_case       :   List[str]							=      model_path
                     snake_case       :   Optional[Any]							=      data_files["train"]
                     snake_case       :   Optional[Any]							=      current_output_dir

                     snake_case       :   Union[str, 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: 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    )

              snake_case       :   int							=      iteration
              snake_case       :   Tuple							=      data_dir_format(iteration + 1    )

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

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

              snake_case       :   str							=      os.path.join(__lowerCamelCase				,					f"""train_pseudo.{args.data_file_extension}"""    )

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

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

                            if early_stopping_patience_counter >= args.early_stopping_patience:
                                   snake_case       :   Tuple							=      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"    )				,					)





