






'''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


_A	:			Optional[Any]         =logging.getLogger(__name__)

_A	:			Dict         ='''pytorch_model.bin'''

@dataclasses.dataclass
class _lowercase	:
							a				      =       dataclasses.field(
							    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models."""}		)
							a				      =       dataclasses.field(
							    default=_lowercase				,      metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co."""}				,      )



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



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





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase      )      ->       Dict:
							lowerCamelCase__					:	str         =       datasets.concatenate_datasets([infer_input, infer_output]				,			axis=1      )

							if args.do_filter_by_confidence:
														lowerCamelCase__					:	Optional[int]         =       dataset.filter(lambda UpperCamelCase      : example["probability"] > args.confidence_threshold      )

							if args.do_filter_by_val_performance:
														assert eval_result >= 0.0 and eval_result <= 1.0
														lowerCamelCase__					:	Any         =       int(eval_result * len(UpperCamelCase      )      )
														print(UpperCamelCase      )
														lowerCamelCase__					:	Any         =       dataset.sort("""probability"""				,			reverse=UpperCamelCase      )
														lowerCamelCase__					:	int         =       dataset.select(range(UpperCamelCase      )      )

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

							lowerCamelCase__					:	List[Any]         =       os.path.join(UpperCamelCase				,			f'''train_pseudo.{args.data_file_extension}'''      )
							if args.data_file_extension == "csv":
														dataset.to_csv(UpperCamelCase				,			index=UpperCamelCase      )
							else:
														dataset.to_json(UpperCamelCase      )





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			**UpperCamelCase      )      ->       str:
							lowerCamelCase__					:	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()

							lowerCamelCase__					:	List[str]         =       STModelArguments(model_name_or_path=UpperCamelCase      )
							lowerCamelCase__					:	Optional[Any]         =       STDataArguments(train_file=UpperCamelCase				,			infer_file=UpperCamelCase      )
							lowerCamelCase__					:	Dict         =       STTrainingArguments(output_dir=UpperCamelCase      )
							lowerCamelCase__					:	Optional[int]         =       argparse.Namespace()

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

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

    # Sanity checks
							lowerCamelCase__					:	List[Any]         =       {}
							lowerCamelCase__					:	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
							lowerCamelCase__					:	str         =       args.train_file
							lowerCamelCase__					:	Tuple         =       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__					:	Dict         =       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__					:	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..."""      )
							lowerCamelCase__					:	int         =       f'''{args.output_dir}/self-train_iter-{{}}'''.format
							lowerCamelCase__					:	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=UpperCamelCase      )
																					os.makedirs(UpperCamelCase				,			exist_ok=UpperCamelCase      )
							accelerator.wait_for_everyone()

							lowerCamelCase__					:	Dict         =       None
							lowerCamelCase__					:	Any         =       None
							lowerCamelCase__					:	Optional[int]         =       0
							lowerCamelCase__					:	Dict         =       False
							# Show the progress bar
							lowerCamelCase__					:	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      )      ):
														lowerCamelCase__					:	int         =       data_dir_format(UpperCamelCase      )
														assert os.path.exists(UpperCamelCase      )

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

														lowerCamelCase__					:	int         =       os.path.join(UpperCamelCase				,			"""best-checkpoint"""				,			UpperCamelCase      )
														if os.path.exists(UpperCamelCase      ):
																					logger.info(
																					    """Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1."""				,			UpperCamelCase				,			UpperCamelCase				,			)
														else:
																					logger.info("""***** Running self-training: iteration: %d, stage: 1 *****"""				,			UpperCamelCase      )
																					finetune(**UpperCamelCase      )
																					accelerator.wait_for_everyone()
																					assert os.path.exists(UpperCamelCase      )
																					logger.info("""Self-training job completed: iteration: %d, stage: 1."""				,			UpperCamelCase      )

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

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

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

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

														with open(UpperCamelCase				,			"""r"""      ) as f:
																					lowerCamelCase__					:	Union[str, Any]         =       float(json.load(UpperCamelCase      )[args.eval_metric]      )
														lowerCamelCase__					:	Any         =       os.path.join(UpperCamelCase				,			"""infer_output_best-checkpoint.csv"""      )
														assert os.path.exists(UpperCamelCase      )
														# Loading the dataset from local csv or json files.
														lowerCamelCase__					:	str         =       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(UpperCamelCase				,			exist_ok=UpperCamelCase      )
																					shutil.copy(UpperCamelCase				,			os.path.join(UpperCamelCase				,			f'''eval_results_iter-{iteration}.json'''      )      )
																					if os.path.exists(UpperCamelCase      ):
																												shutil.copy(UpperCamelCase				,			os.path.join(UpperCamelCase				,			f'''test_results_iter-{iteration}.json'''      )      )
																					create_pseudo_labeled_data(UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase      )
														accelerator.wait_for_everyone()

														lowerCamelCase__					:	Optional[Any]         =       os.path.join(UpperCamelCase				,			f'''train_pseudo.{args.data_file_extension}'''      )

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

																					if best_iteration is None:
																												lowerCamelCase__					:	Tuple         =       new_iteration
																												lowerCamelCase__					:	Any         =       new_eval_result
																					else:
																												if new_eval_result - best_eval_result > args.early_stopping_threshold:
																																			lowerCamelCase__					:	Dict         =       new_iteration
																																			lowerCamelCase__					:	int         =       new_eval_result
																																			lowerCamelCase__					:	Any         =       0
																												else:
																																			if new_eval_result == best_eval_result:
																																										lowerCamelCase__					:	Any         =       new_iteration
																																										lowerCamelCase__					:	int         =       new_eval_result
																																			early_stopping_patience_counter += 1

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









'''simple docstring'''







class _lowercase	:




							def __init__(							self:				Tuple			,							UpperCamelCase__:				list[int]     ):
														lowerCamelCase__					:	Union[str, Any]         =       len(UpperCamelCase__     )
														lowerCamelCase__					:	Union[str, Any]         =       [0] * len_array

														if len_array > 0:
																					lowerCamelCase__					:	Union[str, Any]         =       array[0]

														for i in range(1			,							UpperCamelCase__     ):
																					lowerCamelCase__					:	Tuple         =       self.prefix_sum[i - 1] + array[i]




							def 		lowerCamelCase_				(							self:				Tuple			,							UpperCamelCase__:				int			,							UpperCamelCase__:				int     ):
														if start == 0:
																					return self.prefix_sum[end]

														return self.prefix_sum[end] - self.prefix_sum[start - 1]




							def 		lowerCamelCase_				(							self:				Optional[int]			,							UpperCamelCase__:				int     ):
														lowerCamelCase__					:	Dict         =       {0}
														for sum_item in self.prefix_sum:
																					if sum_item - target_sum in sums:
																												return True

																					sums.add(UpperCamelCase__     )

														return False


if __name__ == "__main__":
			import doctest

			doctest.testmod()

