"""simple docstring"""



# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union


_UpperCamelCase								=		re.compile(R"^(?P<major>\d+)" R"\.(?P<minor>\d+)" R"\.(?P<patch>\d+)$")
@total_ordering
@dataclass
class 						__UpperCAmelCase    :







			'''simple docstring'''
			_UpperCamelCase  :						str
			_UpperCamelCase  :						Optional[str]					     =				None
			_UpperCamelCase  :						Optional[Union[str, int]]					     =				None
			_UpperCamelCase  :						Optional[Union[str, int]]					     =				None
			_UpperCamelCase  :						Optional[Union[str, int]]					     =				None





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__				,							A__				,							A__       :     Optional[int]								=			_str_to_version_tuple(self.version_str							)





			def __repr__(			self							):







										'''simple docstring'''


										return F'''{self.tuple[0]}.{self.tuple[1]}.{self.tuple[2]}'''





			@property
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										return self.major, self.minor, self.patch





			def 		lowerCamelCase					(			self   ,      snake_case_							):







										'''simple docstring'''


										if isinstance(snake_case_   ,      snake_case_							):
																	return Version(snake_case_							)
										elif isinstance(snake_case_   ,      snake_case_							):
																	return other
										raise TypeError(F'''{other} (type {type(snake_case_							)}) cannot be compared to version.'''							)





			def __eq__(			self   ,      snake_case_							):







										'''simple docstring'''


										try:
																	A__       :     Optional[int]								=			self._validate_operand(snake_case_							)
										except (TypeError, ValueError):
																	return False
										else:
																	return self.tuple == other.tuple





			def __lt__(			self   ,      snake_case_							):







										'''simple docstring'''


										A__       :     Dict								=			self._validate_operand(snake_case_							)
										return self.tuple < other.tuple





			def __hash__(			self							):







										'''simple docstring'''


										return hash(_version_tuple_to_str(self.tuple							)							)





			@classmethod
			def 		lowerCamelCase					(			cls   ,      snake_case_							):







										'''simple docstring'''


										A__       :     Optional[Any]								=			{f.name for f in dataclasses.fields(cls							)}
										return cls(**{k: v for k, v in dic.items() if k in field_names}							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										return self.version_str





def  _A(		lowerCAmelCase							):
							A__       :     Optional[int]								=			_VERSION_REG.match(lowerCAmelCase							)
							if not res:
														raise ValueError(F'''Invalid version \'{version_str}\'. Format should be x.y.z with {{x,y,z}} being digits.'''							)
							return tuple(int(lowerCAmelCase							) for v in [res.group("""major"""							), res.group("""minor"""							), res.group("""patch"""							)]							)



def  _A(		lowerCAmelCase							):
							return ".".join(str(lowerCAmelCase							) for v in version_tuple							)






"""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


_UpperCamelCase								=		logging.getLogger(__name__)

_UpperCamelCase								=		"pytorch_model.bin"
@dataclasses.dataclass
class 						__UpperCAmelCase    :







			'''simple docstring'''
			_UpperCamelCase  :						str					     =				dataclasses.field(
			    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models.'}     )
			_UpperCamelCase  :						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    :







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





@dataclasses.dataclass
class 						__UpperCAmelCase    :







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





def  _A(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     Optional[int]								=			datasets.concatenate_datasets([infer_input, infer_output]			,  axis=1							)

							if args.do_filter_by_confidence:
														A__       :     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
														A__       :     Dict								=			int(eval_result * len(lowerCAmelCase							)							)
														print(lowerCAmelCase							)
														A__       :     Dict								=			dataset.sort("""probability"""			,  reverse=lowerCAmelCase							)
														A__       :     Tuple								=			dataset.select(range(lowerCAmelCase							)							)

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

							A__       :     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							):
							A__       :     Dict								=			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()

							A__       :     Union[str, Any]								=			STModelArguments(model_name_or_path=lowerCAmelCase							)
							A__       :     Tuple								=			STDataArguments(train_file=lowerCAmelCase			,  infer_file=lowerCAmelCase							)
							A__       :     List[str]								=			STTrainingArguments(output_dir=lowerCAmelCase							)
							A__       :     Tuple								=			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
							A__       :     Optional[int]								=			{}
							A__       :     Tuple								=			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
							A__       :     Tuple								=			args.train_file
							A__       :     int								=			args.infer_file

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

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

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

														# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
														# iteration > 0
														A__       :     int								=			os.path.join(lowerCAmelCase			,  """stage-1"""							)
														A__       :     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}							)

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

																					A__       :     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							)

														A__       :     List[str]								=			iteration
														A__       :     List[str]								=			data_dir_format(iteration + 1							)

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

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

														A__       :     int								=			os.path.join(lowerCAmelCase			,  F'''train_pseudo.{args.data_file_extension}'''							)

														if args.evaluation_strategy != IntervalStrategy.NO.value:
																					A__       :     Optional[int]								=			eval_result

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

																												if early_stopping_patience_counter >= args.early_stopping_patience:
																																			A__       :     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"""							)			,  )





