'''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_	:		Optional[int]			=     logging.getLogger(__name__)

lowerCAmelCase_	:		Tuple			=     """pytorch_model.bin"""




@dataclasses.dataclass
class 		SCREAMING_SNAKE_CASE     :







				'''simple docstring'''
				UpperCAmelCase__							=							dataclasses.field(
				    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models.'''}	)
				UpperCAmelCase__							=							dataclasses.field(
				    default=SCREAMING_SNAKE_CASE   , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co.'''}   , )





@dataclasses.dataclass
class 		SCREAMING_SNAKE_CASE     :







				'''simple docstring'''
				UpperCAmelCase__							=							dataclasses.field(metadata={'''help''': '''A csv or a json file containing the training data.'''}	)
				UpperCAmelCase__							=							dataclasses.field(metadata={'''help''': '''A csv or a json file containing the data to predict on.'''}	)
				UpperCAmelCase__							=							dataclasses.field(
				    default=SCREAMING_SNAKE_CASE   , metadata={'''help''': '''A csv or a json file containing the validation data.'''}	)
				UpperCAmelCase__							=							dataclasses.field(
				    default=SCREAMING_SNAKE_CASE   , metadata={'''help''': '''The name of the task to train on.'''}   , )
				UpperCAmelCase__							=							dataclasses.field(
				    default=SCREAMING_SNAKE_CASE   , metadata={'''help''': '''The list of labels for the task.'''}	)





@dataclasses.dataclass
class 		SCREAMING_SNAKE_CASE     :







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





def 						__A							(     UpperCAmelCase     ,UpperCAmelCase     ,UpperCAmelCase     ,UpperCAmelCase     ,UpperCAmelCase     ,UpperCAmelCase					)     ->			List[Any]:




						'''simple docstring'''
						_UpperCamelCase			:			List[Any]   			=						datasets.concatenate_datasets([infer_input, infer_output]     ,axis=1					)

						if args.do_filter_by_confidence:
												_UpperCamelCase			:			str   			=						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
												_UpperCamelCase			:			Any   			=						int(eval_result * len(UpperCAmelCase					)					)
												print(UpperCAmelCase					)
												_UpperCamelCase			:			Dict   			=						dataset.sort("probability"     ,reverse=UpperCAmelCase					)
												_UpperCamelCase			:			str   			=						dataset.select(range(UpperCAmelCase					)					)

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

						_UpperCamelCase			:			Optional[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 						__A							(     UpperCAmelCase     ,UpperCAmelCase     ,UpperCAmelCase     ,UpperCAmelCase     ,**UpperCAmelCase					)     ->			Optional[int]:




						'''simple docstring'''
						_UpperCamelCase			:			Optional[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()

						_UpperCamelCase			:			Any   			=						STModelArguments(model_name_or_path=UpperCAmelCase					)
						_UpperCamelCase			:			Optional[Any]   			=						STDataArguments(train_file=UpperCAmelCase     ,infer_file=UpperCAmelCase					)
						_UpperCamelCase			:			Any   			=						STTrainingArguments(output_dir=UpperCAmelCase					)
						_UpperCamelCase			:			str   			=						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
						_UpperCamelCase			:			List[Any]   			=						{}
						_UpperCamelCase			:			List[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
						_UpperCamelCase			:			List[str]   			=						args.train_file
						_UpperCamelCase			:			Union[str, Any]   			=						args.infer_file

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

						for key in data_files:
												_UpperCamelCase			:			Optional[int]   			=						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:
																		_UpperCamelCase			:			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..."					)
						_UpperCamelCase			:			Optional[Any]   			=						f'''{args.output_dir}/self-train_iter-{{}}'''.format
						_UpperCamelCase			:			str   			=						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()

						_UpperCamelCase			:			Union[str, Any]   			=						None
						_UpperCamelCase			:			Union[str, Any]   			=						None
						_UpperCamelCase			:			Tuple   			=						0
						_UpperCamelCase			:			Optional[Any]   			=						False
						# Show the progress bar
						_UpperCamelCase			:			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					)					):
												_UpperCamelCase			:			Optional[Any]   			=						data_dir_format(UpperCAmelCase					)
												assert os.path.exists(UpperCAmelCase					)

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

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

																		_UpperCamelCase			:			Optional[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					)

												_UpperCamelCase			:			Tuple   			=						iteration
												_UpperCamelCase			:			Any   			=						data_dir_format(iteration + 1					)

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

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

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

												if args.evaluation_strategy != IntervalStrategy.NO.value:
																		_UpperCamelCase			:			str   			=						eval_result

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

																								if early_stopping_patience_counter >= args.early_stopping_patience:
																														_UpperCamelCase			:			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'''





import re
from pathlib import Path
from unittest import TestCase

import pytest




@pytest.mark.integration
class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''





				def  snake_case__				(						self					:						Dict       ,		lowercase__					:						str					)							->Tuple:


										'''simple docstring'''




										with open(lowercase__       ,		encoding="utf-8"					) as input_file:
																_UpperCamelCase			:			Optional[int]   			=						re.compile(r"(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)"					)
																_UpperCamelCase			:			Dict   			=						input_file.read()
																_UpperCamelCase			:			Dict   			=						regexp.search(lowercase__					)

										return match





				def  snake_case__				(						self					:						str       ,		lowercase__					:						str					)							->Tuple:


										'''simple docstring'''




										with open(lowercase__       ,		encoding="utf-8"					) as input_file:
																_UpperCamelCase			:			str   			=						re.compile(r"#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()"       ,		re.DOTALL					)
																_UpperCamelCase			:			Optional[Any]   			=						input_file.read()
																# use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
																_UpperCamelCase			:			List[str]   			=						regexp.finditer(lowercase__					)

										_UpperCamelCase			:			Any   			=						[match for match in matches if match is not None and match.group(1					) is not None]
										return matches[0] if matches else None





				def  snake_case__				(						self					:						Optional[int]					)							->int:


										'''simple docstring'''




										_UpperCamelCase			:			Any   			=						Path("./datasets"					)
										_UpperCamelCase			:			List[Any]   			=						list(dataset_paths.absolute().glob("**/*.py"					)					)

										for dataset in dataset_files:
																if self._no_encoding_on_file_open(str(lowercase__					)					):
																						raise AssertionError(f'''open(...) must use utf-8 encoding in {dataset}'''					)







				def  snake_case__				(						self					:						Optional[Any]					)							->Union[str, Any]:


										'''simple docstring'''




										_UpperCamelCase			:			Any   			=						Path("./datasets"					)
										_UpperCamelCase			:			Any   			=						list(dataset_paths.absolute().glob("**/*.py"					)					)

										for dataset in dataset_files:
																if self._no_print_statements(str(lowercase__					)					):
																						raise AssertionError(f'''print statement found in {dataset}. Use datasets.logger/logging instead.'''					)


