





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





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





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





def 						lowerCamelCase__     (     UpperCamelCase__			:       Any       ,    UpperCamelCase__			:       Optional[int]       ,    UpperCamelCase__			:       Optional[Any]       ,    UpperCamelCase__			:       Dict       ,    UpperCamelCase__			:       List[Any]       ,    UpperCamelCase__			:       str						)			-> int:



					'''simple docstring'''




					_snake_case									=				datasets.concatenate_datasets([infer_input, infer_output]       ,    axis=1						)

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

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

					_snake_case									=				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 						lowerCamelCase__     (     UpperCamelCase__			:       Optional[int]       ,    UpperCamelCase__			:       Union[str, Any]       ,    UpperCamelCase__			:       Tuple       ,    UpperCamelCase__			:       int       ,    **UpperCamelCase__			:       List[str]						)			-> Optional[Any]:



					'''simple docstring'''




					_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()

					_snake_case									=				STModelArguments(model_name_or_path=UpperCamelCase__						)
					_snake_case									=				STDataArguments(train_file=UpperCamelCase__       ,    infer_file=UpperCamelCase__						)
					_snake_case									=				STTrainingArguments(output_dir=UpperCamelCase__						)
					_snake_case									=				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
					_snake_case									=				{}
					_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
					_snake_case									=				args.train_file
					_snake_case									=				args.infer_file

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

					for key in data_files:
										_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:
															_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...'						)
					_snake_case									=				F'''{args.output_dir}/self-train_iter-{{}}'''.format
					_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=UpperCamelCase__						)
															os.makedirs(UpperCamelCase__       ,    exist_ok=UpperCamelCase__						)
					accelerator.wait_for_everyone()

					_snake_case									=				None
					_snake_case									=				None
					_snake_case									=				0
					_snake_case									=				False
					# Show the progress bar
					_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						)						):
										_snake_case									=				data_dir_format(UpperCamelCase__						)
										assert os.path.exists(UpperCamelCase__						)

										# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
										# iteration > 0
										_snake_case									=				os.path.join(UpperCamelCase__       ,    'stage-1'						)
										_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(UpperCamelCase__       ,    UpperCamelCase__						):
																				arguments_dict.update({key: value}						)

										_snake_case									=				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
															_snake_case									=				os.path.join(UpperCamelCase__       ,    'best-checkpoint'						)
															_snake_case									=				os.path.join(UpperCamelCase__       ,    'stage-2'						)
															# Update arguments_dict
															_snake_case									=				model_path
															_snake_case									=				data_files['train']
															_snake_case									=				current_output_dir

															_snake_case									=				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__						)

										_snake_case									=				iteration
										_snake_case									=				data_dir_format(iteration + 1						)

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

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

										_snake_case									=				os.path.join(UpperCamelCase__       ,    F'''train_pseudo.{args.data_file_extension}'''						)

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

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

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










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_      							=     {
    """configuration_luke""": ["""LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LukeConfig"""],
    """tokenization_luke""": ["""LukeTokenizer"""],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase_      							=     [
					    """LUKE_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """LukeForEntityClassification""",
					    """LukeForEntityPairClassification""",
					    """LukeForEntitySpanClassification""",
					    """LukeForMultipleChoice""",
					    """LukeForQuestionAnswering""",
					    """LukeForSequenceClassification""",
					    """LukeForTokenClassification""",
					    """LukeForMaskedLM""",
					    """LukeModel""",
					    """LukePreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
					from .tokenization_luke import LukeTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_luke import (
										    LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
										    LukeForEntityClassification,
										    LukeForEntityPairClassification,
										    LukeForEntitySpanClassification,
										    LukeForMaskedLM,
										    LukeForMultipleChoice,
										    LukeForQuestionAnswering,
										    LukeForSequenceClassification,
										    LukeForTokenClassification,
										    LukeModel,
										    LukePreTrainedModel,
										)

else:
					import sys

					UpperCAmelCase_      							=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



