




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


_UpperCAmelCase :			Optional[int]              =	logging.getLogger(__name__)

_UpperCAmelCase :			Optional[Any]              =	"pytorch_model.bin"


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






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






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






def 				A	( lowercase   ,	lowercase   ,	lowercase   ,	lowercase   ,	lowercase   ,	lowercase			)       ->  Optional[int]:
		'''simple docstring'''

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

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

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

		UpperCamelCase          =					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   ,	lowercase   ,	lowercase   ,	lowercase   ,	**lowercase			)       ->  Any:
		'''simple docstring'''

		UpperCamelCase          =					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          =					STModelArguments(model_name_or_path=lowercase			)
		UpperCamelCase          =					STDataArguments(train_file=lowercase   ,	infer_file=lowercase			)
		UpperCamelCase          =					STTrainingArguments(output_dir=lowercase			)
		UpperCamelCase          =					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
		UpperCamelCase          =					{}
		UpperCamelCase          =					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          =					args.train_file
		UpperCamelCase          =					args.infer_file

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

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

		UpperCamelCase          =					None
		UpperCamelCase          =					None
		UpperCamelCase          =					0
		UpperCamelCase          =					False
		# Show the progress bar
		UpperCamelCase          =					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          =					data_dir_format(lowercase			)
				assert os.path.exists(lowercase			)

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

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

						UpperCamelCase          =					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			)

				UpperCamelCase          =					iteration
				UpperCamelCase          =					data_dir_format(iteration + 1			)

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

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

				UpperCamelCase          =					os.path.join(lowercase   ,	f'''train_pseudo.{args.data_file_extension}'''			)

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

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

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


import argparse

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


_UpperCAmelCase :			Union[str, Any]              =	16
_UpperCAmelCase :			Dict              =	32


def 				A	( lowercase   ,	lowercase = 16			)       ->  str:
		'''simple docstring'''

		UpperCamelCase          =					AutoTokenizer.from_pretrained('bert-base-cased'			)
		UpperCamelCase          =					load_dataset('glue'   ,	'mrpc'			)

		def tokenize_function(lowercase			):
				# max_length=None => use the model max length (it's actually the default)
				UpperCamelCase          =					tokenizer(examples['sentence1']   ,	examples['sentence2']   ,	truncation=lowercase   ,	max_length=lowercase			)
				return outputs

		# Apply the method we just defined to all the examples in all the splits of the dataset
		# starting with the main process first:
		with accelerator.main_process_first():
				UpperCamelCase          =					datasets.map(
				    lowercase   ,	batched=lowercase   ,	remove_columns=['idx', 'sentence1', 'sentence2']   ,	)

		# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
		# transformers library
		UpperCamelCase          =					tokenized_datasets.rename_column('label'   ,	'labels'			)

		def collate_fn(lowercase			):
				# On TPU it's best to pad everything to the same length or training will be very slow.
				UpperCamelCase          =					128 if accelerator.distributed_type == DistributedType.TPU else None
				# When using mixed precision we want round multiples of 8/16
				if accelerator.mixed_precision == "fp8":
						UpperCamelCase          =					16
				elif accelerator.mixed_precision != "no":
						UpperCamelCase          =					8
				else:
						UpperCamelCase          =					None

				return tokenizer.pad(
				    lowercase   ,	padding='longest'   ,	max_length=lowercase   ,	pad_to_multiple_of=lowercase   ,	return_tensors='pt'   ,	)

		# Instantiate dataloaders.
		UpperCamelCase          =					DataLoader(
		    tokenized_datasets['train']   ,	shuffle=lowercase   ,	collate_fn=lowercase   ,	batch_size=lowercase   ,	drop_last=lowercase			)
		UpperCamelCase          =					DataLoader(
		    tokenized_datasets['validation']   ,	shuffle=lowercase   ,	collate_fn=lowercase   ,	batch_size=lowercase   ,	drop_last=(accelerator.mixed_precision == 'fp8')   ,	)

		return train_dataloader, eval_dataloader


def 				A	( lowercase   ,	lowercase			)       ->  Optional[Any]:
		'''simple docstring'''

		UpperCamelCase          =					Accelerator(cpu=args.cpu   ,	mixed_precision=args.mixed_precision			)
		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		UpperCamelCase          =					config['lr']
		UpperCamelCase          =					int(config['num_epochs']			)
		UpperCamelCase          =					int(config['seed']			)
		UpperCamelCase          =					int(config['batch_size']			)

		UpperCamelCase          =					evaluate.load('glue'   ,	'mrpc'			)

		# If the batch size is too big we use gradient accumulation
		UpperCamelCase          =					1
		if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
				UpperCamelCase          =					batch_size // MAX_GPU_BATCH_SIZE
				UpperCamelCase          =					MAX_GPU_BATCH_SIZE

		set_seed(lowercase			)
		UpperCamelCase     ,  UpperCamelCase          =					get_dataloaders(lowercase   ,	lowercase			)
		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		UpperCamelCase          =					AutoModelForSequenceClassification.from_pretrained('bert-base-cased'   ,	return_dict=lowercase			)

		# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
		# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
		# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
		UpperCamelCase          =					model.to(accelerator.device			)
		# Instantiate optimizer
		UpperCamelCase          =					AdamW(params=model.parameters()   ,	lr=lowercase			)

		# Instantiate scheduler
		UpperCamelCase          =					get_linear_schedule_with_warmup(
		    optimizer=lowercase   ,	num_warmup_steps=100   ,	num_training_steps=(len(lowercase			) * num_epochs) // gradient_accumulation_steps   ,	)

		# Prepare everything
		# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
		# prepare method.

		UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase          =					accelerator.prepare(
		    lowercase   ,	lowercase   ,	lowercase   ,	lowercase   ,	lowercase			)

		# Now we train the model
		for epoch in range(lowercase			):
				model.train()
				for step, batch in enumerate(lowercase			):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						batch.to(accelerator.device			)
						UpperCamelCase          =					model(**lowercase			)
						UpperCamelCase          =					outputs.loss
						UpperCamelCase          =					loss / gradient_accumulation_steps
						accelerator.backward(lowercase			)
						if step % gradient_accumulation_steps == 0:
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

				model.eval()
				for step, batch in enumerate(lowercase			):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						batch.to(accelerator.device			)
						with torch.no_grad():
								UpperCamelCase          =					model(**lowercase			)
						UpperCamelCase          =					outputs.logits.argmax(dim=-1			)
						UpperCamelCase     ,  UpperCamelCase          =					accelerator.gather_for_metrics((predictions, batch['labels'])			)
						metric.add_batch(
						    predictions=lowercase   ,	references=lowercase   ,	)

				UpperCamelCase          =					metric.compute()
				# Use accelerator.print to print only on the main process.
				accelerator.print(f'''epoch {epoch}:'''   ,	lowercase			)


def 				A	( )       ->  Union[str, Any]:
		'''simple docstring'''

		UpperCamelCase          =					argparse.ArgumentParser(description='Simple example of training script.'			)
		parser.add_argument(
		    '--mixed_precision'   ,	type=lowercase   ,	default=lowercase   ,	choices=['no', 'fp16', 'bf16', 'fp8']   ,	help='Whether to use mixed precision. Choose'
		    'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
		    'and an Nvidia Ampere GPU.'   ,	)
		parser.add_argument('--cpu'   ,	action='store_true'   ,	help='If passed, will train on the CPU.'			)
		UpperCamelCase          =					parser.parse_args()
		UpperCamelCase          =					{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
		training_function(lowercase   ,	lowercase			)


if __name__ == "__main__":
					main()
