


"""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


__magic_name__						: List[str]    =				logging.getLogger(__name__)

__magic_name__						: Optional[int]    =				'pytorch_model.bin'




@dataclasses.dataclass
class 			__snake_case   :
							__a					 =       dataclasses.field(
							    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models.'''}  )
							__a					 =       dataclasses.field(
							    default=lowerCamelCase			,		metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co.'''}			,		)




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




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





def       a_ (	lowercase__ :int,			lowercase__ :Optional[int],			lowercase__ :str,			lowercase__ :Optional[int],			lowercase__ :Optional[Any],			lowercase__ :List[str]     ):
			__lowerCamelCase          =      datasets.concatenate_datasets([infer_input, infer_output],			axis=1     )

			if args.do_filter_by_confidence:
						__lowerCamelCase          =      dataset.filter(lambda lowercase__     : example["probability"] > args.confidence_threshold     )

			if args.do_filter_by_val_performance:
						assert eval_result >= 0.0 and eval_result <= 1.0
						__lowerCamelCase          =      int(eval_result * len(lowercase__     )     )
						print(lowercase__     )
						__lowerCamelCase          =      dataset.sort("""probability""",			reverse=lowercase__     )
						__lowerCamelCase          =      dataset.select(range(lowercase__     )     )

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

			__lowerCamelCase          =      os.path.join(lowercase__,			f'train_pseudo.{args.data_file_extension}'     )
			if args.data_file_extension == "csv":
						dataset.to_csv(lowercase__,			index=lowercase__     )
			else:
						dataset.to_json(lowercase__     )





def       a_ (	lowercase__ :Tuple,			lowercase__ :Any,			lowercase__ :Any,			lowercase__ :List[str],			**lowercase__ :List[str]     ):
			__lowerCamelCase          =      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()

			__lowerCamelCase          =      STModelArguments(model_name_or_path=lowercase__     )
			__lowerCamelCase          =      STDataArguments(train_file=lowercase__,			infer_file=lowercase__     )
			__lowerCamelCase          =      STTrainingArguments(output_dir=lowercase__     )
			__lowerCamelCase          =      argparse.Namespace()

			for arg_class in (model_args, data_args, training_args):
						for key, value in vars(lowercase__     ).items():
									setattr(lowercase__,			lowercase__,			lowercase__     )

			for key, value in kwargs.items():
						if hasattr(lowercase__,			lowercase__     ):
									setattr(lowercase__,			lowercase__,			lowercase__     )

    # Sanity checks
			__lowerCamelCase          =      {}
			__lowerCamelCase          =      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
			__lowerCamelCase          =      args.train_file
			__lowerCamelCase          =      args.infer_file

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

			for key in data_files:
						__lowerCamelCase          =      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:
									__lowerCamelCase          =      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..."""     )
			__lowerCamelCase          =      f'{args.output_dir}/self-train_iter-{{}}'.format
			__lowerCamelCase          =      data_dir_format(0     )

			if accelerator.is_main_process:
						if args.output_dir is not None:
									os.makedirs(args.output_dir,			exist_ok=lowercase__     )
									os.makedirs(lowercase__,			exist_ok=lowercase__     )
			accelerator.wait_for_everyone()

			__lowerCamelCase          =      None
			__lowerCamelCase          =      None
			__lowerCamelCase          =      0
			__lowerCamelCase          =      False
			# Show the progress bar
			__lowerCamelCase          =      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     )     ):
						__lowerCamelCase          =      data_dir_format(lowercase__     )
						assert os.path.exists(lowercase__     )

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

						__lowerCamelCase          =      os.path.join(lowercase__,			"""best-checkpoint""",			lowercase__     )
						if os.path.exists(lowercase__     ):
									logger.info(
									    """Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1.""",			lowercase__,			lowercase__,			)
						else:
									logger.info("""***** Running self-training: iteration: %d, stage: 1 *****""",			lowercase__     )
									finetune(**lowercase__     )
									accelerator.wait_for_everyone()
									assert os.path.exists(lowercase__     )
									logger.info("""Self-training job completed: iteration: %d, stage: 1.""",			lowercase__     )

						if iteration > 0 and args.finetune_on_labeled_data:
									# Stage 2 (optional): fine-tuning on the original labeled data
									__lowerCamelCase          =      os.path.join(lowercase__,			"""best-checkpoint"""     )
									__lowerCamelCase          =      os.path.join(lowercase__,			"""stage-2"""     )
									# Update arguments_dict
									__lowerCamelCase          =      model_path
									__lowerCamelCase          =      data_files["""train"""]
									__lowerCamelCase          =      current_output_dir

									__lowerCamelCase          =      os.path.join(lowercase__,			"""best-checkpoint""",			lowercase__     )
									if os.path.exists(lowercase__     ):
												logger.info(
												    """Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2.""",			lowercase__,			lowercase__,			)
									else:
												logger.info("""***** Running self-training: iteration: %d, stage: 2 *****""",			lowercase__     )
												finetune(**lowercase__     )
												accelerator.wait_for_everyone()
												assert os.path.exists(lowercase__     )
												logger.info("""Self-training job completed: iteration: %d, stage: 2.""",			lowercase__     )

						__lowerCamelCase          =      iteration
						__lowerCamelCase          =      data_dir_format(iteration + 1     )

						__lowerCamelCase          =      AutoConfig.from_pretrained(os.path.join(lowercase__,			"""best-checkpoint"""     )     )
						__lowerCamelCase          =      config.idalabel
						__lowerCamelCase          =      os.path.join(lowercase__,			"""eval_results_best-checkpoint.json"""     )
						__lowerCamelCase          =      os.path.join(lowercase__,			"""test_results_best-checkpoint.json"""     )
						assert os.path.exists(lowercase__     )

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

						if accelerator.is_main_process:
									os.makedirs(lowercase__,			exist_ok=lowercase__     )
									shutil.copy(lowercase__,			os.path.join(lowercase__,			f'eval_results_iter-{iteration}.json'     )     )
									if os.path.exists(lowercase__     ):
												shutil.copy(lowercase__,			os.path.join(lowercase__,			f'test_results_iter-{iteration}.json'     )     )
									create_pseudo_labeled_data(lowercase__,			lowercase__,			lowercase__,			lowercase__,			lowercase__,			lowercase__     )
						accelerator.wait_for_everyone()

						__lowerCamelCase          =      os.path.join(lowercase__,			f'train_pseudo.{args.data_file_extension}'     )

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

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

												if early_stopping_patience_counter >= args.early_stopping_patience:
															__lowerCamelCase          =      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""",			lowercase__     )
						logger.info("""Best evaluation result: %s = %f""",			args.eval_metric,			lowercase__     )
						accelerator.wait_for_everyone()
						if accelerator.is_main_process:
									shutil.copy(
									    os.path.join(lowercase__,			f'eval_results_iter-{iteration}.json'     ),			os.path.join(lowercase__,			"""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,			lowercase__     )
						accelerator.wait_for_everyone()
						if accelerator.is_main_process:
									shutil.copy(
									    os.path.join(lowercase__,			f'eval_results_iter-{args.max_selftrain_iterations - 1}.json'     ),			os.path.join(lowercase__,			"""eval_results_best-iteration.json"""     ),			)








"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__magic_name__						: Any    =				{'configuration_glpn': ['GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GLPNConfig']}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__magic_name__						: Dict    =				['GLPNFeatureExtractor']
					__magic_name__						: Union[str, Any]    =				['GLPNImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__magic_name__						: int    =				[
					    'GLPN_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'GLPNForDepthEstimation',
					    'GLPNLayer',
					    'GLPNModel',
					    'GLPNPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_glpn import GLPNFeatureExtractor
										from .image_processing_glpn import GLPNImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_glpn import (
										    GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
										    GLPNForDepthEstimation,
										    GLPNLayer,
										    GLPNModel,
										    GLPNPreTrainedModel,
										)


else:
					import sys

					__magic_name__						: Optional[int]    =				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




