






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


__A      =				logging.getLogger(__name__)

__A      =				'pytorch_model.bin'





@dataclasses.dataclass
class 						SCREAMING_SNAKE_CASE				:





							"""simple docstring"""

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





@dataclasses.dataclass
class 						SCREAMING_SNAKE_CASE				:





							"""simple docstring"""

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





@dataclasses.dataclass
class 						SCREAMING_SNAKE_CASE				:





							"""simple docstring"""

							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=__UpperCAmelCase  ,	metadata={"help": "Whether to filter the pseudo-labeled data based on the confidence score."}  ,	)
							A_						  =					dataclasses.field(
							    default=__UpperCAmelCase  ,	metadata={"help": "Whether to filter the pseudo-labeled data based on the validation performance."}  ,	)
							A_						  =					dataclasses.field(
							    default=__UpperCAmelCase  ,	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=__UpperCAmelCase  ,	metadata={"help": "Random seed for initialization."}  ,	)



def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= datasets.concatenate_datasets([infer_input, infer_output]					,			axis=1 )

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

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

					_A								= 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					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase					,			**_lowercase ):

					'''simple docstring'''







					_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=_lowerCAmelCase )
					_A								= STDataArguments(train_file=_lowerCAmelCase					,			infer_file=_lowerCAmelCase )
					_A								= STTrainingArguments(output_dir=_lowerCAmelCase )
					_A								= 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
					_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=_lowerCAmelCase )
															os.makedirs(_lowerCAmelCase					,			exist_ok=_lowerCAmelCase )
					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(_lowerCAmelCase )
										assert os.path.exists(_lowerCAmelCase )

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

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

															_A								= 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 )

										_A								= iteration
										_A								= data_dir_format(iteration + 1 )

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

										with open(_lowerCAmelCase					,			'''r''' ) as f:
															_A								= float(json.load(_lowerCAmelCase )[args.eval_metric] )
										_A								= os.path.join(_lowerCAmelCase					,			'''infer_output_best-checkpoint.csv''' )
										assert os.path.exists(_lowerCAmelCase )
										# 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(_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()

										_A								= os.path.join(_lowerCAmelCase					,			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'''					,			_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''' )					,			)











import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


__A      =				logging.get_logger(__name__)





class 						SCREAMING_SNAKE_CASE				:





							"""simple docstring"""

							def __init__(  self:   int		,     __A:   Optional[int]		,     __A:   Optional[Any]   )    ->				str:
												_A								= question_encoder
												_A								= generator
												_A								= self.question_encoder

							def       __A (  self:   Optional[int]		,     __A:   Union[str, Any]   )    ->				Dict:
												if os.path.isfile(__A   ):
																	raise ValueError(f"""Provided path ({save_directory}) should be a directory, not a file"""   )
												os.makedirs(__A		,     exist_ok=__A   )
												_A								= os.path.join(__A		,     '''question_encoder_tokenizer'''   )
												_A								= os.path.join(__A		,     '''generator_tokenizer'''   )
												self.question_encoder.save_pretrained(__A   )
												self.generator.save_pretrained(__A   )

							@classmethod
							def       __A (  cls:   Optional[Any]		,     __A:   List[str]		,     **__A:   int   )    ->				Any:
												# dynamically import AutoTokenizer
												from ..auto.tokenization_auto import AutoTokenizer

												_A								= kwargs.pop('''config'''		,     __A   )

												if config is None:
																	_A								= RagConfig.from_pretrained(__A   )

												_A								= AutoTokenizer.from_pretrained(
												    __A		,     config=config.question_encoder		,     subfolder='''question_encoder_tokenizer'''   )
												_A								= AutoTokenizer.from_pretrained(
												    __A		,     config=config.generator		,     subfolder='''generator_tokenizer'''   )
												return cls(question_encoder=__A		,     generator=__A   )

							def __call__(  self:   int		,     *__A:   Optional[int]		,     **__A:   List[str]   )    ->				int:
												return self.current_tokenizer(*__A		,     **__A   )

							def       __A (  self:   Dict		,     *__A:   List[str]		,     **__A:   List[str]   )    ->				Dict:
												return self.generator.batch_decode(*__A		,     **__A   )

							def       __A (  self:   Union[str, Any]		,     *__A:   Tuple		,     **__A:   List[str]   )    ->				Tuple:
												return self.generator.decode(*__A		,     **__A   )

							def       __A (  self:   Dict   )    ->				List[str]:
												_A								= self.question_encoder

							def       __A (  self:   Union[str, Any]   )    ->				int:
												_A								= self.generator



							def       __A (  self:   Dict		,     __A:   List[str]		,     __A:   Optional[List[str]] = None		,     __A:   Optional[int] = None		,     __A:   Optional[int] = None		,     __A:   str = "longest"		,     __A:   str = None		,     __A:   bool = True		,     **__A:   Tuple		,     )    ->				BatchEncoding:
												warnings.warn(
												    '''`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '''
												    '''regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '''
												    '''context manager to prepare your targets. See the documentation of your specific tokenizer for more '''
												    '''details'''		,     __A		,     )
												if max_length is None:
																	_A								= self.current_tokenizer.model_max_length
												_A								= self(
												    __A		,     add_special_tokens=__A		,     return_tensors=__A		,     max_length=__A		,     padding=__A		,     truncation=__A		,     **__A		,     )
												if tgt_texts is None:
																	return model_inputs
												# Process tgt_texts
												if max_target_length is None:
																	_A								= self.current_tokenizer.model_max_length
												_A								= self(
												    text_target=__A		,     add_special_tokens=__A		,     return_tensors=__A		,     padding=__A		,     max_length=__A		,     truncation=__A		,     **__A		,     )
												_A								= labels['''input_ids''']
												return model_inputs



