



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   __lowerCAmelCase  :
					"""simple docstring"""






					A__					:		str      							=     dataclasses.field(
					    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models."}   )
					A__					:		Optional[str]      							=     dataclasses.field(
					    default=UpperCAmelCase_				,  metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co."}				,  )







@dataclasses.dataclass
class   __lowerCAmelCase  :
					"""simple docstring"""






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







@dataclasses.dataclass
class   __lowerCAmelCase  :
					"""simple docstring"""






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







def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   Optional[Any]:
			A__	  =     datasets.concatenate_datasets([infer_input, infer_output]			,						axis=1       )

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

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

			A__	  =     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       )    ->   int:
			A__	  =     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__	  =     STModelArguments(model_name_or_path=__UpperCamelCase       )
			A__	  =     STDataArguments(train_file=__UpperCamelCase			,						infer_file=__UpperCamelCase       )
			A__	  =     STTrainingArguments(output_dir=__UpperCamelCase       )
			A__	  =     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
			A__	  =     {}
			A__	  =     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__	  =     args.train_file
			A__	  =     args.infer_file

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

			for key in data_files:
						A__	  =     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__	  =     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__	  =     f'''{args.output_dir}/self-train_iter-{{}}'''.format
			A__	  =     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()

			A__	  =     None
			A__	  =     None
			A__	  =     0
			A__	  =     False
			# Show the progress bar
			A__	  =     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__	  =     data_dir_format(__UpperCamelCase       )
						assert os.path.exists(__UpperCamelCase       )

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

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

									A__	  =     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       )

						A__	  =     iteration
						A__	  =     data_dir_format(iteration + 1       )

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

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

						A__	  =     os.path.join(__UpperCamelCase			,						f'''train_pseudo.{args.data_file_extension}'''       )

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

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

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









import warnings

from ...utils import logging
from .image_processing_clip import CLIPImageProcessor


SCREAMING_SNAKE_CASE__   			=   logging.get_logger(__name__)


class   __lowerCAmelCase  (		UpperCAmelCase_   ):
					"""simple docstring"""






					def __init__(  self       :							Dict					,       *_snake_case       :							int					,       **_snake_case       :							Optional[int]       ):






								"""simple docstring"""


								warnings.warn(
								    'The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
								    ' use CLIPImageProcessor instead.'					,       _snake_case					,       )
								super().__init__(*_snake_case					,       **_snake_case       )




