





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







@dataclasses.dataclass
class      UpperCamelCase__					:




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







@dataclasses.dataclass
class      UpperCamelCase__					:




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







def 		__A		(  lowerCamelCase_			,			lowerCamelCase_			,			lowerCamelCase_			,			lowerCamelCase_			,			lowerCamelCase_			,			lowerCamelCase_	):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE :				Dict		=			datasets.concatenate_datasets([infer_input, infer_output]			,			axis=1	)

		if args.do_filter_by_confidence:
				SCREAMING_SNAKE_CASE :				List[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
				SCREAMING_SNAKE_CASE :				int		=			int(eval_result * len(lowerCamelCase_	)	)
				print(lowerCamelCase_	)
				SCREAMING_SNAKE_CASE :				Any		=			dataset.sort("""probability"""			,			reverse=lowerCamelCase_	)
				SCREAMING_SNAKE_CASE :				Any		=			dataset.select(range(lowerCamelCase_	)	)

		SCREAMING_SNAKE_CASE :				str		=			dataset.remove_columns(["""label""", """probability"""]	)
		SCREAMING_SNAKE_CASE :				Any		=			dataset.rename_column("""prediction"""			,			"""label"""	)
		SCREAMING_SNAKE_CASE :				List[Any]		=			dataset.map(lambda lowerCamelCase_	: {"label": idalabel[example["label"]]}	)
		SCREAMING_SNAKE_CASE :				int		=			dataset.shuffle(seed=args.seed	)

		SCREAMING_SNAKE_CASE :				Dict		=			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_	):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE :				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()

		SCREAMING_SNAKE_CASE :				Tuple		=			STModelArguments(model_name_or_path=lowerCamelCase_	)
		SCREAMING_SNAKE_CASE :				List[Any]		=			STDataArguments(train_file=lowerCamelCase_			,			infer_file=lowerCamelCase_	)
		SCREAMING_SNAKE_CASE :				int		=			STTrainingArguments(output_dir=lowerCamelCase_	)
		SCREAMING_SNAKE_CASE :				Optional[Any]		=			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
		SCREAMING_SNAKE_CASE :				Tuple		=			{}
		SCREAMING_SNAKE_CASE :				Optional[Any]		=			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
		SCREAMING_SNAKE_CASE :				int		=			args.train_file
		SCREAMING_SNAKE_CASE :				int		=			args.infer_file

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

		for key in data_files:
				SCREAMING_SNAKE_CASE :				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:
						SCREAMING_SNAKE_CASE :				List[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..."""	)
		SCREAMING_SNAKE_CASE :				Tuple		=			f'''{args.output_dir}/self-train_iter-{{}}'''.format
		SCREAMING_SNAKE_CASE :				Tuple		=			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()

		SCREAMING_SNAKE_CASE :				Optional[Any]		=			None
		SCREAMING_SNAKE_CASE :				List[str]		=			None
		SCREAMING_SNAKE_CASE :				Any		=			0
		SCREAMING_SNAKE_CASE :				Tuple		=			False
		# Show the progress bar
		SCREAMING_SNAKE_CASE :				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	)	):
				SCREAMING_SNAKE_CASE :				Optional[Any]		=			data_dir_format(lowerCamelCase_	)
				assert os.path.exists(lowerCamelCase_	)

				# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
				# iteration > 0
				SCREAMING_SNAKE_CASE :				Dict		=			os.path.join(lowerCamelCase_			,			"""stage-1"""	)
				SCREAMING_SNAKE_CASE :				Optional[int]		=			{
				    """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}	)

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

						SCREAMING_SNAKE_CASE :				Tuple		=			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_	)

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

				SCREAMING_SNAKE_CASE :				Optional[Any]		=			AutoConfig.from_pretrained(os.path.join(lowerCamelCase_			,			"""best-checkpoint"""	)	)
				SCREAMING_SNAKE_CASE :				List[str]		=			config.idalabel
				SCREAMING_SNAKE_CASE :				List[Any]		=			os.path.join(lowerCamelCase_			,			"""eval_results_best-checkpoint.json"""	)
				SCREAMING_SNAKE_CASE :				Dict		=			os.path.join(lowerCamelCase_			,			"""test_results_best-checkpoint.json"""	)
				assert os.path.exists(lowerCamelCase_	)

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

				SCREAMING_SNAKE_CASE :				Union[str, Any]		=			os.path.join(lowerCamelCase_			,			f'''train_pseudo.{args.data_file_extension}'''	)

				if args.evaluation_strategy != IntervalStrategy.NO.value:
						SCREAMING_SNAKE_CASE :				Union[str, Any]		=			eval_result

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

								if early_stopping_patience_counter >= args.early_stopping_patience:
										SCREAMING_SNAKE_CASE :				Union[str, 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"""			,			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"""	)			,			)










'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCAmelCase            =       logging.get_logger(__name__)

__UpperCAmelCase            =       {
    """naver-clova-ix/donut-base""": """https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json""",
    # See all Donut models at https://huggingface.co/models?filter=donut-swin
}




class      UpperCamelCase__					(					lowercase_			):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	'''donut-swin'''

					SCREAMING_SNAKE_CASE__				=	{
					    '''num_attention_heads''': '''num_heads''',
					    '''num_hidden_layers''': '''num_layers''',
					}


					def __init__(						self				:			str      ,						lowerCamelCase_				:			Union[str, Any]=2_24      ,						lowerCamelCase_				:			Union[str, Any]=4      ,						lowerCamelCase_				:			Optional[Any]=3      ,						lowerCamelCase_				:			Dict=96      ,						lowerCamelCase_				:			int=[2, 2, 6, 2]      ,						lowerCamelCase_				:			Optional[Any]=[3, 6, 12, 24]      ,						lowerCamelCase_				:			List[str]=7      ,						lowerCamelCase_				:			List[str]=4.0      ,						lowerCamelCase_				:			Optional[Any]=True      ,						lowerCamelCase_				:			List[str]=0.0      ,						lowerCamelCase_				:			Dict=0.0      ,						lowerCamelCase_				:			Dict=0.1      ,						lowerCamelCase_				:			Union[str, Any]="gelu"      ,						lowerCamelCase_				:			List[str]=False      ,						lowerCamelCase_				:			Optional[int]=0.02      ,						lowerCamelCase_				:			Any=1e-5      ,						**lowerCamelCase_				:			Union[str, Any]      ,						):

							'''simple docstring'''



							super().__init__(**lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				int		=			image_size
							SCREAMING_SNAKE_CASE :				Dict		=			patch_size
							SCREAMING_SNAKE_CASE :				str		=			num_channels
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			embed_dim
							SCREAMING_SNAKE_CASE :				Optional[int]		=			depths
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			len(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Optional[int]		=			num_heads
							SCREAMING_SNAKE_CASE :				Optional[int]		=			window_size
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			mlp_ratio
							SCREAMING_SNAKE_CASE :				Dict		=			qkv_bias
							SCREAMING_SNAKE_CASE :				Optional[int]		=			hidden_dropout_prob
							SCREAMING_SNAKE_CASE :				Tuple		=			attention_probs_dropout_prob
							SCREAMING_SNAKE_CASE :				Dict		=			drop_path_rate
							SCREAMING_SNAKE_CASE :				List[str]		=			hidden_act
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			use_absolute_embeddings
							SCREAMING_SNAKE_CASE :				Tuple		=			layer_norm_eps
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			initializer_range
							# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
							# this indicates the channel dimension after the last stage of the model
							SCREAMING_SNAKE_CASE :				str		=			int(embed_dim * 2 ** (len(lowerCamelCase_						) - 1)						)



