

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    :       Any        =							logging.getLogger(__name__)

_lowerCamelCase    :       Tuple        =							"pytorch_model.bin"





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






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






@dataclasses.dataclass
class  __UpperCAmelCase					:
			UpperCamelCase       					=					dataclasses.field(
			    metadata={"""help""": """The output directory where the model predictions and checkpoints will be written."""}						)
			UpperCamelCase       					=					dataclasses.field(
			    default="""accuracy"""      ,  metadata={"""help""": """The evaluation metric used for the task."""}						)
			UpperCamelCase       					=					dataclasses.field(
			    default="""no"""      ,  metadata={
			        """help""": """The evaluation strategy to adopt during training. Possible values are: [\"no\", \"step\", \"epoch]"""
			    }      ,  )
			UpperCamelCase       					=					dataclasses.field(
			    default=1_0      ,  metadata={"""help""": """Number of evaluation calls with no improvement after which training will be stopped."""}      ,  )
			UpperCamelCase       					=					dataclasses.field(
			    default=0.0      ,  metadata={
			        """help""": """How much the specified evaluation metric must improve to satisfy early stopping conditions."""
			    }      ,  )
			UpperCamelCase       					=					dataclasses.field(
			    default=lowerCamelCase__      ,  metadata={"""help""": """Whether to filter the pseudo-labeled data based on the confidence score."""}      ,  )
			UpperCamelCase       					=					dataclasses.field(
			    default=lowerCamelCase__      ,  metadata={"""help""": """Whether to filter the pseudo-labeled data based on the validation performance."""}      ,  )
			UpperCamelCase       					=					dataclasses.field(
			    default=lowerCamelCase__      ,  metadata={"""help""": """Whether to fine-tune on labeled data after pseudo training."""}      ,  )
			UpperCamelCase       					=					dataclasses.field(
			    default=0.0      ,  metadata={"""help""": """Confidence threshold for pseudo-labeled data filtering."""}      ,  )
			UpperCamelCase       					=					dataclasses.field(
			    default=1_0_0      ,  metadata={"""help""": """Number of evaluation calls with no improvement after which training will be stopped."""}      ,  )
			UpperCamelCase       					=					dataclasses.field(
			    default=lowerCamelCase__      ,  metadata={"""help""": """Random seed for initialization."""}      ,  )






def       a__					(		UpperCAmelCase							:      List[str]    ,			UpperCAmelCase							:      Union[str, Any]    ,			UpperCAmelCase							:      List[str]    ,			UpperCAmelCase							:      List[str]    ,			UpperCAmelCase							:      List[str]    ,			UpperCAmelCase							:      Tuple      )  ->						Dict:
		UpperCAmelCase	:							Tuple						=			datasets.concatenate_datasets([infer_input, infer_output]    ,			axis=1      )

		if args.do_filter_by_confidence:
				UpperCAmelCase	:							int						=			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
				UpperCAmelCase	:							List[str]						=			int(eval_result * len(UpperCAmelCase      )      )
				print(UpperCAmelCase      )
				UpperCAmelCase	:							Optional[int]						=			dataset.sort('''probability'''    ,			reverse=UpperCAmelCase      )
				UpperCAmelCase	:							str						=			dataset.select(range(UpperCAmelCase      )      )

		UpperCAmelCase	:							Optional[int]						=			dataset.remove_columns(['''label''', '''probability''']      )
		UpperCAmelCase	:							Union[str, Any]						=			dataset.rename_column('''prediction'''    ,			'''label'''      )
		UpperCAmelCase	:							Dict						=			dataset.map(lambda UpperCAmelCase      : {"label": idalabel[example["label"]]}      )
		UpperCAmelCase	:							Any						=			dataset.shuffle(seed=args.seed      )

		UpperCAmelCase	:							Optional[Any]						=			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							:      Union[str, Any]    ,			UpperCAmelCase							:      Optional[int]    ,			UpperCAmelCase							:      List[str]    ,			UpperCAmelCase							:      Optional[Any]    ,			**UpperCAmelCase							:      List[str]      )  ->						str:
		UpperCAmelCase	:							Union[str, 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	:							Optional[Any]						=			STModelArguments(model_name_or_path=UpperCAmelCase      )
		UpperCAmelCase	:							str						=			STDataArguments(train_file=UpperCAmelCase    ,			infer_file=UpperCAmelCase      )
		UpperCAmelCase	:							Optional[int]						=			STTrainingArguments(output_dir=UpperCAmelCase      )
		UpperCAmelCase	:							str						=			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
		UpperCAmelCase	:							Optional[Any]						=			{}
		UpperCAmelCase	:							Tuple						=			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	:							Optional[int]						=			args.train_file
		UpperCAmelCase	:							Optional[int]						=			args.infer_file

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

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

		UpperCAmelCase	:							Union[str, Any]						=			None
		UpperCAmelCase	:							Optional[int]						=			None
		UpperCAmelCase	:							Any						=			0
		UpperCAmelCase	:							Optional[Any]						=			False
		# Show the progress bar
		UpperCAmelCase	:							str						=			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	:							Optional[Any]						=			data_dir_format(UpperCAmelCase      )
				assert os.path.exists(UpperCAmelCase      )

				# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
				# iteration > 0
				UpperCAmelCase	:							List[str]						=			os.path.join(UpperCAmelCase    ,			'''stage-1'''      )
				UpperCAmelCase	:							Dict						=			{
				    '''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}      )

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

						UpperCAmelCase	:							List[str]						=			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      )

				UpperCAmelCase	:							Dict						=			iteration
				UpperCAmelCase	:							int						=			data_dir_format(iteration + 1      )

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

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

				UpperCAmelCase	:							List[str]						=			os.path.join(UpperCAmelCase    ,			f'''train_pseudo.{args.data_file_extension}'''      )

				if args.evaluation_strategy != IntervalStrategy.NO.value:
						UpperCAmelCase	:							int						=			eval_result

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

								if early_stopping_patience_counter >= args.early_stopping_patience:
										UpperCAmelCase	:							Tuple						=			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 Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
     from PIL import Image

     from ..image_utils import load_image

if is_torch_available():
     from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING

_lowerCamelCase    :       Dict        =							logging.get_logger(__name__)





@add_end_docstrings(lowerCamelCase__						)
class  __UpperCAmelCase					(	lowerCamelCase__						):



   def __init__(							self							:					Optional[Any],		*__A							:					Tuple,		**__A							:					Tuple						):
     super().__init__(*__A,		**__A						)
     self.check_model_type(__A						)



   def 	__magic_name__   (							self							:					Union[str, Any],		__A							:					int=None,		__A							:					Tuple=None,		__A							:					Any=None,		**__A							:					Optional[int]						):
     UpperCAmelCase			,						UpperCAmelCase	:							List[Any]						=			{}, {}
     if padding is not None:
       UpperCAmelCase	:							Optional[int]						=			padding
     if truncation is not None:
       UpperCAmelCase	:							Optional[int]						=			truncation
     if top_k is not None:
       UpperCAmelCase	:							Tuple						=			top_k
     return preprocess_params, {}, postprocess_params



   def __call__(							self							:					Union[str, Any],		__A							:					Union["Image.Image", str],		__A							:					str = None,		**__A							:					Optional[int]						):
     if isinstance(__A,		(Image.Image, str)						) and isinstance(__A,		__A						):
       UpperCAmelCase	:							int						=			{'''image''': image, '''question''': question}
     else:
       UpperCAmelCase	:							str						=			image
     UpperCAmelCase	:							Union[str, Any]						=			super().__call__(__A,		**__A						)
     return results



   def 	__magic_name__   (							self							:					List[str],		__A							:					Union[str, Any],		__A							:					Tuple=False,		__A							:					List[Any]=False						):
     UpperCAmelCase	:							int						=			load_image(inputs['''image''']						)
     UpperCAmelCase	:							List[str]						=			self.tokenizer(
         inputs['''question'''],		return_tensors=self.framework,		padding=__A,		truncation=__A						)
     UpperCAmelCase	:							Union[str, Any]						=			self.image_processor(images=__A,		return_tensors=self.framework						)
     model_inputs.update(__A						)
     return model_inputs



   def 	__magic_name__   (							self							:					Optional[Any],		__A							:					List[Any]						):
     UpperCAmelCase	:							Optional[int]						=			self.model(**__A						)
     return model_outputs





   def 	__magic_name__   (							self							:					Any,		__A							:					List[str],		__A							:					Union[str, Any]=5						):
     if top_k > self.model.config.num_labels:
       UpperCAmelCase	:							Any						=			self.model.config.num_labels

     if self.framework == "pt":
       UpperCAmelCase	:							Any						=			model_outputs.logits.sigmoid()[0]
       UpperCAmelCase			,						UpperCAmelCase	:							Union[str, Any]						=			probs.topk(__A						)
     else:
       raise ValueError(F'''Unsupported framework: {self.framework}'''						)

     UpperCAmelCase	:							str						=			scores.tolist()
     UpperCAmelCase	:							Tuple						=			ids.tolist()
     return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(__A,		__A						)]





