




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


__SCREAMING_SNAKE_CASE 						=logging.getLogger(__name__)

__SCREAMING_SNAKE_CASE 						="""pytorch_model.bin"""






@dataclasses.dataclass
class  __magic_name__						:
				'''simple docstring'''







				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  __magic_name__						:
				'''simple docstring'''







				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  __magic_name__						:
				'''simple docstring'''







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

							if args.do_filter_by_confidence:
														SCREAMING_SNAKE_CASE_   	=    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_   	=    int(eval_result * len(_lowerCAmelCase     )     )
														print(_lowerCAmelCase     )
														SCREAMING_SNAKE_CASE_   	=    dataset.sort('''probability'''  ,    reverse=_lowerCAmelCase     )
														SCREAMING_SNAKE_CASE_   	=    dataset.select(range(_lowerCAmelCase     )     )

							SCREAMING_SNAKE_CASE_   	=    dataset.remove_columns(['''label''', '''probability''']     )
							SCREAMING_SNAKE_CASE_   	=    dataset.rename_column('''prediction'''  ,    '''label'''     )
							SCREAMING_SNAKE_CASE_   	=    dataset.map(lambda _lowerCAmelCase     : {"label": idalabel[example["label"]]}     )
							SCREAMING_SNAKE_CASE_   	=    dataset.shuffle(seed=args.seed     )

							SCREAMING_SNAKE_CASE_   	=    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     ):
							SCREAMING_SNAKE_CASE_   	=    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_   	=    STModelArguments(model_name_or_path=_lowerCAmelCase     )
							SCREAMING_SNAKE_CASE_   	=    STDataArguments(train_file=_lowerCAmelCase  ,    infer_file=_lowerCAmelCase     )
							SCREAMING_SNAKE_CASE_   	=    STTrainingArguments(output_dir=_lowerCAmelCase     )
							SCREAMING_SNAKE_CASE_   	=    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_   	=    {}
							SCREAMING_SNAKE_CASE_   	=    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_   	=    args.train_file
							SCREAMING_SNAKE_CASE_   	=    args.infer_file

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

							for key in data_files:
														SCREAMING_SNAKE_CASE_   	=    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_   	=    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_   	=    F"{args.output_dir}/self-train_iter-{{}}".format
							SCREAMING_SNAKE_CASE_   	=    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_   	=    None
							SCREAMING_SNAKE_CASE_   	=    None
							SCREAMING_SNAKE_CASE_   	=    0
							SCREAMING_SNAKE_CASE_   	=    False
							# Show the progress bar
							SCREAMING_SNAKE_CASE_   	=    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_   	=    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_   	=    os.path.join(_lowerCAmelCase  ,    '''stage-1'''     )
														SCREAMING_SNAKE_CASE_   	=    {
														    '''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_   	=    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_   	=    os.path.join(_lowerCAmelCase  ,    '''best-checkpoint'''     )
																					SCREAMING_SNAKE_CASE_   	=    os.path.join(_lowerCAmelCase  ,    '''stage-2'''     )
																					# Update arguments_dict
																					SCREAMING_SNAKE_CASE_   	=    model_path
																					SCREAMING_SNAKE_CASE_   	=    data_files['''train''']
																					SCREAMING_SNAKE_CASE_   	=    current_output_dir

																					SCREAMING_SNAKE_CASE_   	=    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_   	=    iteration
														SCREAMING_SNAKE_CASE_   	=    data_dir_format(iteration + 1     )

														SCREAMING_SNAKE_CASE_   	=    AutoConfig.from_pretrained(os.path.join(_lowerCAmelCase  ,    '''best-checkpoint'''     )     )
														SCREAMING_SNAKE_CASE_   	=    config.idalabel
														SCREAMING_SNAKE_CASE_   	=    os.path.join(_lowerCAmelCase  ,    '''eval_results_best-checkpoint.json'''     )
														SCREAMING_SNAKE_CASE_   	=    os.path.join(_lowerCAmelCase  ,    '''test_results_best-checkpoint.json'''     )
														assert os.path.exists(_lowerCAmelCase     )

														with open(_lowerCAmelCase  ,    '''r'''     ) as f:
																					SCREAMING_SNAKE_CASE_   	=    float(json.load(_lowerCAmelCase     )[args.eval_metric]     )
														SCREAMING_SNAKE_CASE_   	=    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_   	=    load_dataset(args.data_file_extension  ,    data_files={'''data''': data_files['''infer''']}     )['''data''']
														SCREAMING_SNAKE_CASE_   	=    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_   	=    os.path.join(_lowerCAmelCase  ,    F"train_pseudo.{args.data_file_extension}"     )

														if args.evaluation_strategy != IntervalStrategy.NO.value:
																					SCREAMING_SNAKE_CASE_   	=    eval_result

																					if best_iteration is None:
																												SCREAMING_SNAKE_CASE_   	=    new_iteration
																												SCREAMING_SNAKE_CASE_   	=    new_eval_result
																					else:
																												if new_eval_result - best_eval_result > args.early_stopping_threshold:
																																			SCREAMING_SNAKE_CASE_   	=    new_iteration
																																			SCREAMING_SNAKE_CASE_   	=    new_eval_result
																																			SCREAMING_SNAKE_CASE_   	=    0
																												else:
																																			if new_eval_result == best_eval_result:
																																										SCREAMING_SNAKE_CASE_   	=    new_iteration
																																										SCREAMING_SNAKE_CASE_   	=    new_eval_result
																																			early_stopping_patience_counter += 1

																												if early_stopping_patience_counter >= args.early_stopping_patience:
																																			SCREAMING_SNAKE_CASE_   	=    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'''     )  ,    )








from __future__ import annotations

from math import pi

# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
__SCREAMING_SNAKE_CASE 						=1.0_5_4_5_7_1_8_1_7e-3_4  # unit of ℏ : J * s

__SCREAMING_SNAKE_CASE 						=3e8  # unit of c : m * s^-1

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     ):

							if (force, area, distance).count(0     ) != 1:
														raise ValueError('''One and only one argument must be 0'''     )
							if force < 0:
														raise ValueError('''Magnitude of force can not be negative'''     )
							if distance < 0:
														raise ValueError('''Distance can not be negative'''     )
							if area < 0:
														raise ValueError('''Area can not be negative'''     )
							if force == 0:
														SCREAMING_SNAKE_CASE_   	=    (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
														    2_4_0 * (distance) ** 4
														)
														return {"force": force}
							elif area == 0:
														SCREAMING_SNAKE_CASE_   	=    (2_4_0 * force * (distance) ** 4) / (
														    REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
														)
														return {"area": area}
							elif distance == 0:
														SCREAMING_SNAKE_CASE_   	=    (
														    (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (2_4_0 * force)
														) ** (1 / 4)
														return {"distance": distance}
							raise ValueError('''One and only one argument must be 0'''     )


# Run doctest
if __name__ == "__main__":
			import doctest

			doctest.testmod()


