


'''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


lowerCamelCase__    	=     logging.getLogger(__name__)

lowerCamelCase__    	=     'pytorch_model.bin'






@dataclasses.dataclass
class 	lowerCAmelCase__			:
			lowerCAmelCase :	Tuple          =     dataclasses.field(
			    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models."}	)
			lowerCAmelCase :	Any          =     dataclasses.field(
			    default=lowerCamelCase__    ,       metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co."}    ,       )






@dataclasses.dataclass
class 	lowerCAmelCase__			:
			lowerCAmelCase :	int          =     dataclasses.field(metadata={"help": "A csv or a json file containing the training data."}	)
			lowerCAmelCase :	Tuple          =     dataclasses.field(metadata={"help": "A csv or a json file containing the data to predict on."}	)
			lowerCAmelCase :	List[str]          =     dataclasses.field(
			    default=lowerCamelCase__    ,       metadata={"help": "A csv or a json file containing the validation data."}	)
			lowerCAmelCase :	Optional[int]          =     dataclasses.field(
			    default=lowerCamelCase__    ,       metadata={"help": "The name of the task to train on."}    ,       )
			lowerCAmelCase :	Union[str, Any]          =     dataclasses.field(
			    default=lowerCamelCase__    ,       metadata={"help": "The list of labels for the task."}	)






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






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase   ):
		_UpperCAmelCase		:       Optional[int]								=      datasets.concatenate_datasets([infer_input, infer_output]      , axis=1   )

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

		_UpperCAmelCase		:       Tuple								=      dataset.remove_columns(["label", "probability"]   )
		_UpperCAmelCase		:       str								=      dataset.rename_column("prediction"      , "label"   )
		_UpperCAmelCase		:       List[str]								=      dataset.map(lambda __lowerCAmelCase   : {"label": idalabel[example["label"]]}   )
		_UpperCAmelCase		:       Dict								=      dataset.shuffle(seed=args.seed   )

		_UpperCAmelCase		:       Union[str, 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  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , **__lowerCAmelCase   ):
		_UpperCAmelCase		:       Any								=      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()

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

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

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

		_UpperCAmelCase		:       List[Any]								=      None
		_UpperCAmelCase		:       Union[str, Any]								=      None
		_UpperCAmelCase		:       Tuple								=      0
		_UpperCAmelCase		:       int								=      False
		# Show the progress bar
		_UpperCAmelCase		:       Dict								=      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   )   ):
				_UpperCAmelCase		:       int								=      data_dir_format(lowerCamelCase_   )
				assert os.path.exists(lowerCamelCase_   )

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

				_UpperCAmelCase		:       Dict								=      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
						_UpperCAmelCase		:       str								=      os.path.join(lowerCamelCase_      , "best-checkpoint"   )
						_UpperCAmelCase		:       List[str]								=      os.path.join(lowerCamelCase_      , "stage-2"   )
						# Update arguments_dict
						_UpperCAmelCase		:       Optional[Any]								=      model_path
						_UpperCAmelCase		:       List[str]								=      data_files['''train''']
						_UpperCAmelCase		:       Tuple								=      current_output_dir

						_UpperCAmelCase		:       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: 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_   )

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

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

				with open(lowerCamelCase_      , "r"   ) as f:
						_UpperCAmelCase		:       List[str]								=      float(json.load(lowerCamelCase_   )[args.eval_metric]   )
				_UpperCAmelCase		:       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.
				_UpperCAmelCase		:       Optional[int]								=      load_dataset(args.data_file_extension      , data_files={"data": data_files["infer"]}   )['''data''']
				_UpperCAmelCase		:       Tuple								=      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()

				_UpperCAmelCase		:       Union[str, Any]								=      os.path.join(lowerCamelCase_      , F"""train_pseudo.{args.data_file_extension}"""   )

				if args.evaluation_strategy != IntervalStrategy.NO.value:
						_UpperCAmelCase		:       Optional[Any]								=      eval_result

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

								if early_stopping_patience_counter >= args.early_stopping_patience:
										_UpperCAmelCase		:       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 typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
						import timm


if is_torch_available():
						from torch import Tensor






class 	lowerCAmelCase__			(			UpperCAmelCase__    ,       UpperCAmelCase__	):
			lowerCAmelCase :	int          =     "pixel_values"
			lowerCAmelCase :	Dict          =     False
			lowerCAmelCase :	Union[str, Any]          =     TimmBackboneConfig




			def __init__(		self  :     List[str]	,  lowerCamelCase__  :     Dict	,  **lowerCamelCase__  :     str )   ->Dict:



					'''simple docstring'''







					requires_backends(self	,  "timm" )
					super().__init__(lowerCamelCase__ )
					_UpperCAmelCase		:       Any								=      config

					if config.backbone is None:
							raise ValueError("backbone is not set in the config. Please set it to a timm model name." )

					if config.backbone not in timm.list_models():
							raise ValueError(F"""backbone {config.backbone} is not supported by timm.""" )

					if hasattr(lowerCamelCase__	,  "out_features" ) and config.out_features is not None:
							raise ValueError("out_features is not supported by TimmBackbone. Please use out_indices instead." )

					_UpperCAmelCase		:       Optional[Any]								=      getattr(lowerCamelCase__	,  "use_pretrained_backbone"	,  lowerCamelCase__ )
					if pretrained is None:
							raise ValueError("use_pretrained_backbone is not set in the config. Please set it to True or False." )

					# We just take the final layer by default. This matches the default for the transformers models.
					_UpperCAmelCase		:       int								=      config.out_indices if getattr(lowerCamelCase__	,  "out_indices"	,  lowerCamelCase__ ) is not None else (-1,)

					_UpperCAmelCase		:       List[Any]								=      timm.create_model(
					    config.backbone	,  pretrained=lowerCamelCase__	,  features_only=config.features_only	,  in_chans=config.num_channels	,  out_indices=lowerCamelCase__	,  **lowerCamelCase__	,  )
					# These are used to control the output of the model when called. If output_hidden_states is True, then
					# return_layers is modified to include all layers.
					_UpperCAmelCase		:       List[str]								=      self._backbone.return_layers
					_UpperCAmelCase		:       Optional[int]								=      {layer["module"]: str(lowerCamelCase__ ) for i, layer in enumerate(self._backbone.feature_info.info )}
					super()._init_backbone(lowerCamelCase__ )




			@classmethod
			def 							lowerCAmelCase__    (		cls  :     List[str]	,  lowerCamelCase__  :     str	,  *lowerCamelCase__  :     Tuple	,  **lowerCamelCase__  :     Optional[Any] )   ->Optional[Any]:



					'''simple docstring'''







					requires_backends(cls	,  ["vision", "timm"] )
					from ...models.timm_backbone import TimmBackboneConfig

					_UpperCAmelCase		:       Any								=      kwargs.pop("config"	,  TimmBackboneConfig() )

					_UpperCAmelCase		:       Dict								=      kwargs.pop("use_timm_backbone"	,  lowerCamelCase__ )
					if not use_timm:
							raise ValueError("use_timm_backbone must be True for timm backbones" )

					_UpperCAmelCase		:       str								=      kwargs.pop("num_channels"	,  config.num_channels )
					_UpperCAmelCase		:       Dict								=      kwargs.pop("features_only"	,  config.features_only )
					_UpperCAmelCase		:       str								=      kwargs.pop("use_pretrained_backbone"	,  config.use_pretrained_backbone )
					_UpperCAmelCase		:       Optional[Any]								=      kwargs.pop("out_indices"	,  config.out_indices )
					_UpperCAmelCase		:       Dict								=      TimmBackboneConfig(
					    backbone=lowerCamelCase__	,  num_channels=lowerCamelCase__	,  features_only=lowerCamelCase__	,  use_pretrained_backbone=lowerCamelCase__	,  out_indices=lowerCamelCase__	,  )
					return super()._from_config(lowerCamelCase__	,  **lowerCamelCase__ )




			def 							lowerCAmelCase__    (		self  :     Tuple	,  lowerCamelCase__  :     str )   ->Optional[int]:



					'''simple docstring'''







					pass






			def 							lowerCAmelCase__    (		self  :     Union[str, Any]	,  lowerCamelCase__  :     Tuple	,  lowerCamelCase__  :     Union[str, Any]=None	,  lowerCamelCase__  :     List[Any]=None	,  lowerCamelCase__  :     Union[str, Any]=None	,  **lowerCamelCase__  :     Dict )   ->Union[BackboneOutput, Tuple[Tensor, ...]]:



					'''simple docstring'''







					_UpperCAmelCase		:       Any								=      return_dict if return_dict is not None else self.config.use_return_dict
					_UpperCAmelCase		:       Optional[int]								=      (
					    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
					)
					_UpperCAmelCase		:       Dict								=      output_attentions if output_attentions is not None else self.config.output_attentions

					if output_attentions:
							raise ValueError("Cannot output attentions for timm backbones at the moment" )

					if output_hidden_states:
							# We modify the return layers to include all the stages of the backbone
							_UpperCAmelCase		:       Optional[int]								=      self._all_layers
							_UpperCAmelCase		:       List[str]								=      self._backbone(lowerCamelCase__	,  **lowerCamelCase__ )
							_UpperCAmelCase		:       List[Any]								=      self._return_layers
							_UpperCAmelCase		:       Tuple								=      tuple(hidden_states[i] for i in self.out_indices )
					else:
							_UpperCAmelCase		:       Any								=      self._backbone(lowerCamelCase__	,  **lowerCamelCase__ )
							_UpperCAmelCase		:       Tuple								=      None

					_UpperCAmelCase		:       Dict								=      tuple(lowerCamelCase__ )
					_UpperCAmelCase		:       Optional[Any]								=      tuple(lowerCamelCase__ ) if hidden_states is not None else None

					if not return_dict:
							_UpperCAmelCase		:       Dict								=      (feature_maps,)
							if output_hidden_states:
									_UpperCAmelCase		:       List[str]								=      output + (hidden_states,)
							return output

					return BackboneOutput(feature_maps=lowerCamelCase__	,  hidden_states=lowerCamelCase__	,  attentions=lowerCamelCase__ )



