
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


__snake_case								=  logging.getLogger(__name__)

__snake_case								=  """pytorch_model.bin"""




@dataclasses.dataclass
class 						lowercase__  :
	A__    :   str				       =dataclasses.field(
	    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models."""}  )
	A__    :   Optional[str]				       =dataclasses.field(
	    default=_UpperCAmelCase     ,       metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co."""}     ,       )





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





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






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		)		->		Any:
					'''simple docstring'''


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

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

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

					SCREAMING_SNAKE_CASE__													= 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 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	**UpperCamelCase_		)		->		Tuple:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= 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__													= STModelArguments(model_name_or_path=UpperCamelCase_		)
					SCREAMING_SNAKE_CASE__													= STDataArguments(train_file=UpperCamelCase_	,	infer_file=UpperCamelCase_		)
					SCREAMING_SNAKE_CASE__													= STTrainingArguments(output_dir=UpperCamelCase_		)
					SCREAMING_SNAKE_CASE__													= 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
					SCREAMING_SNAKE_CASE__													= {}
					SCREAMING_SNAKE_CASE__													= 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__													= args.train_file
					SCREAMING_SNAKE_CASE__													= args.infer_file

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

					for key in data_files:
										SCREAMING_SNAKE_CASE__													= 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__													= 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__													= F'{args.output_dir}/self-train_iter-{{}}'.format
					SCREAMING_SNAKE_CASE__													= 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()

					SCREAMING_SNAKE_CASE__													= None
					SCREAMING_SNAKE_CASE__													= None
					SCREAMING_SNAKE_CASE__													= 0
					SCREAMING_SNAKE_CASE__													= False
					# Show the progress bar
					SCREAMING_SNAKE_CASE__													= 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__													= data_dir_format(UpperCamelCase_		)
										assert os.path.exists(UpperCamelCase_		)

										# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
										# iteration > 0
										SCREAMING_SNAKE_CASE__													= os.path.join(UpperCamelCase_	,	'stage-1'		)
										SCREAMING_SNAKE_CASE__													= {
										    '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}		)

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

															SCREAMING_SNAKE_CASE__													= 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_		)

										SCREAMING_SNAKE_CASE__													= iteration
										SCREAMING_SNAKE_CASE__													= data_dir_format(iteration + 1		)

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

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

										SCREAMING_SNAKE_CASE__													= os.path.join(UpperCamelCase_	,	F'train_pseudo.{args.data_file_extension}'		)

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

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

																				if early_stopping_patience_counter >= args.early_stopping_patience:
																									SCREAMING_SNAKE_CASE__													= 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'		)	,	)







import os
import unittest

from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class 						lowercase__  (						_UpperCAmelCase     ,       unittest.TestCase  ):
	A__    :   Dict				       =LxmertTokenizer
	A__    :   List[Any]				       =LxmertTokenizerFast
	A__    :   Any				       =True
	A__    :   List[Any]				       =True
	def      A_				(			self							:   Optional[Any]							):
						super().setUp()

						SCREAMING_SNAKE_CASE__													= [
						    '[UNK]',
						    '[CLS]',
						    '[SEP]',
						    'want',
						    '##want',
						    '##ed',
						    'wa',
						    'un',
						    'runn',
						    '##ing',
						    ',',
						    'low',
						    'lowest',
						]
						SCREAMING_SNAKE_CASE__													= os.path.join(self.tmpdirname       ,   VOCAB_FILES_NAMES['vocab_file']							)
						with open(self.vocab_file       ,   'w'       ,   encoding='utf-8'							) as vocab_writer:
											vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]							)							)
	def      A_				(			self							:   int       ,   UpperCAmelCase_							:   int							):
						SCREAMING_SNAKE_CASE__													= 'UNwant\u00E9d,running'
						SCREAMING_SNAKE_CASE__													= 'unwanted, running'
						return input_text, output_text
	def      A_				(			self							:   Optional[Any]							):
						SCREAMING_SNAKE_CASE__													= self.tokenizer_class(self.vocab_file							)

						SCREAMING_SNAKE_CASE__													= tokenizer.tokenize('UNwant\u00E9d,running'							)
						self.assertListEqual(UpperCAmelCase_       ,   ['un', '##want', '##ed', ',', 'runn', '##ing']							)
						self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_							)       ,   [7, 4, 5, 10, 8, 9]							)





	def      A_				(			self							:   List[str]							):
						if not self.test_rust_tokenizer:
											return

						SCREAMING_SNAKE_CASE__													= self.get_tokenizer()
						SCREAMING_SNAKE_CASE__													= self.get_rust_tokenizer()

						SCREAMING_SNAKE_CASE__													= 'I was born in 92000, and this is falsé.'

						SCREAMING_SNAKE_CASE__													= tokenizer.tokenize(UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= rust_tokenizer.tokenize(UpperCAmelCase_							)
						self.assertListEqual(UpperCAmelCase_       ,   UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= tokenizer.encode(UpperCAmelCase_       ,   add_special_tokens=UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= rust_tokenizer.encode(UpperCAmelCase_       ,   add_special_tokens=UpperCAmelCase_							)
						self.assertListEqual(UpperCAmelCase_       ,   UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= self.get_rust_tokenizer()
						SCREAMING_SNAKE_CASE__													= tokenizer.encode(UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= rust_tokenizer.encode(UpperCAmelCase_							)
						self.assertListEqual(UpperCAmelCase_       ,   UpperCAmelCase_							)





