import argparse
import os
import re

import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor

from accelerate import Accelerator






def 	a_		(	__lowercase       :     Optional[Any]			)			->  Any:
			_snake_case          =      fname.split(os.path.sep			)[-1]
			return re.search(r'^(.*)_\d+\.jpg$'	, __lowercase			).groups()[0]




class        SCREAMING_SNAKE_CASE__					(     UpperCAmelCase ):





	'''simple docstring'''



	def __init__(  self     :   Optional[int]      ,  lowercase     :   List[Any]      ,  lowercase     :   int=None      ,  lowercase     :   int=None     ):


				'''simple docstring'''


				_snake_case          =      file_names
				_snake_case          =      image_transform
				_snake_case          =      label_to_id



	def __len__(  self     :   List[str]     ):


				'''simple docstring'''


				return len(self.file_names     )





	def __getitem__(  self     :   Dict      ,  lowercase     :   str     ):


				'''simple docstring'''


				_snake_case          =      self.file_names[idx]
				_snake_case          =      PIL.Image.open(lowercase     )
				_snake_case          =      raw_image.convert('RGB'     )
				if self.image_transform is not None:
							_snake_case          =      self.image_transform(lowercase     )
				_snake_case          =      extract_label(lowercase     )
				if self.label_to_id is not None:
							_snake_case          =      self.label_to_id[label]
				return {"image": image, "label": label}






def 	a_		(	__lowercase       :     Union[str, Any]	, __lowercase       :     str			)			->  List[Any]:
			# Initialize accelerator
			if args.with_tracking:
						_snake_case          =      Accelerator(
						    cpu=args.cpu	, mixed_precision=args.mixed_precision	, log_with='all'	, project_dir=args.project_dir			)
			else:
						_snake_case          =      Accelerator(cpu=args.cpu	, mixed_precision=args.mixed_precision			)

			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			_snake_case          =      config['lr']
			_snake_case          =      int(config['num_epochs']			)
			_snake_case          =      int(config['seed']			)
			_snake_case          =      int(config['batch_size']			)
			_snake_case          =      config['image_size']
			if not isinstance(__lowercase	, (list, tuple)			):
						_snake_case          =      (image_size, image_size)

			# Parse out whether we are saving every epoch or after a certain number of batches
			if hasattr(args.checkpointing_steps	, 'isdigit'			):
						if args.checkpointing_steps == "epoch":
									_snake_case          =      args.checkpointing_steps
						elif args.checkpointing_steps.isdigit():
									_snake_case          =      int(args.checkpointing_steps			)
						else:
									raise ValueError(
									    f'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''			)
			else:
						_snake_case          =      None

			# We need to initialize the trackers we use, and also store our configuration
			if args.with_tracking:
						_snake_case          =      os.path.split(__lowercase			)[-1].split('.'			)[0]
						accelerator.init_trackers(__lowercase	, __lowercase			)

			# Grab all the image filenames
			_snake_case          =      [os.path.join(args.data_dir	, __lowercase			) for fname in os.listdir(args.data_dir			) if fname.endswith('.jpg'			)]

			# Build the label correspondences
			_snake_case          =      [extract_label(__lowercase			) for fname in file_names]
			_snake_case          =      list(set(__lowercase			)			)
			id_to_label.sort()
			_snake_case          =      {lbl: i for i, lbl in enumerate(__lowercase			)}

			# Set the seed before splitting the data.
			np.random.seed(__lowercase			)
			torch.manual_seed(__lowercase			)
			torch.cuda.manual_seed_all(__lowercase			)

			# Split our filenames between train and validation
			_snake_case          =      np.random.permutation(len(__lowercase			)			)
			_snake_case          =      int(0.8 * len(__lowercase			)			)
			_snake_case          =      random_perm[:cut]
			_snake_case          =      random_perm[cut:]

			# For training we use a simple RandomResizedCrop
			_snake_case          =      Compose([RandomResizedCrop(__lowercase	, scale=(0.5, 1.0)			), ToTensor()]			)
			_snake_case          =      PetsDataset(
			    [file_names[i] for i in train_split]	, image_transform=__lowercase	, label_to_id=__lowercase			)

			# For evaluation, we use a deterministic Resize
			_snake_case          =      Compose([Resize(__lowercase			), ToTensor()]			)
			_snake_case          =      PetsDataset([file_names[i] for i in eval_split]	, image_transform=__lowercase	, label_to_id=__lowercase			)

			# Instantiate dataloaders.
			_snake_case          =      DataLoader(__lowercase	, shuffle=__lowercase	, batch_size=__lowercase	, num_workers=4			)
			_snake_case          =      DataLoader(__lowercase	, shuffle=__lowercase	, batch_size=__lowercase	, num_workers=4			)

			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			_snake_case          =      create_model('resnet50d'	, pretrained=__lowercase	, num_classes=len(__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).
			_snake_case          =      model.to(accelerator.device			)

			# Freezing the base model
			for param in model.parameters():
						_snake_case          =      False
			for param in model.get_classifier().parameters():
						_snake_case          =      True

			# We normalize the batches of images to be a bit faster.
			_snake_case          =      torch.tensor(model.default_cfg['mean']			)[None, :, None, None].to(accelerator.device			)
			_snake_case          =      torch.tensor(model.default_cfg['std']			)[None, :, None, None].to(accelerator.device			)

			# Instantiate optimizer
			_snake_case          =      torch.optim.Adam(params=model.parameters()	, lr=lr / 25			)

			# Instantiate learning rate scheduler
			_snake_case          =      OneCycleLR(optimizer=__lowercase	, max_lr=__lowercase	, epochs=__lowercase	, steps_per_epoch=len(__lowercase			)			)

			# 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.
			_snake_case     , _snake_case     , _snake_case     , _snake_case     , _snake_case          =      accelerator.prepare(
			    __lowercase	, __lowercase	, __lowercase	, __lowercase	, __lowercase			)
			# We need to keep track of how many total steps we have iterated over
			_snake_case          =      0
			# We also need to keep track of the starting epoch so files are named properly
			_snake_case          =      0

			# Potentially load in the weights and states from a previous save
			if args.resume_from_checkpoint:
						if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
									accelerator.print(f'''Resumed from checkpoint: {args.resume_from_checkpoint}'''			)
									accelerator.load_state(args.resume_from_checkpoint			)
									_snake_case          =      os.path.basename(args.resume_from_checkpoint			)
						else:
									# Get the most recent checkpoint
									_snake_case          =      [f.name for f in os.scandir(os.getcwd()			) if f.is_dir()]
									dirs.sort(key=os.path.getctime			)
									_snake_case          =      dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
						# Extract `epoch_{i}` or `step_{i}`
						_snake_case          =      os.path.splitext(__lowercase			)[0]

						if "epoch" in training_difference:
									_snake_case          =      int(training_difference.replace('epoch_'	, ''			)			) + 1
									_snake_case          =      None
						else:
									_snake_case          =      int(training_difference.replace('step_'	, ''			)			)
									_snake_case          =      resume_step // len(__lowercase			)
									resume_step -= starting_epoch * len(__lowercase			)

    # Now we train the model
			for epoch in range(__lowercase	, __lowercase			):
						model.train()
						if args.with_tracking:
									_snake_case          =      0
						if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
									# We need to skip steps until we reach the resumed step
									_snake_case          =      accelerator.skip_first_batches(__lowercase	, __lowercase			)
									overall_step += resume_step
						else:
									# After the first iteration though, we need to go back to the original dataloader
									_snake_case          =      train_dataloader
						for batch in active_dataloader:
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									_snake_case          =      {k: v.to(accelerator.device			) for k, v in batch.items()}
									_snake_case          =      (batch['image'] - mean) / std
									_snake_case          =      model(__lowercase			)
									_snake_case          =      torch.nn.functional.cross_entropy(__lowercase	, batch['label']			)
									# We keep track of the loss at each epoch
									if args.with_tracking:
												total_loss += loss.detach().float()
									accelerator.backward(__lowercase			)
									optimizer.step()
									lr_scheduler.step()
									optimizer.zero_grad()
									overall_step += 1
									if isinstance(__lowercase	, __lowercase			):
												_snake_case          =      f'''step_{overall_step}'''
												if overall_step % checkpointing_steps == 0:
															if args.output_dir is not None:
																		_snake_case          =      os.path.join(args.output_dir	, __lowercase			)
															accelerator.save_state(__lowercase			)
						model.eval()
						_snake_case          =      0
						_snake_case          =      0
						for step, batch in enumerate(__lowercase			):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									_snake_case          =      {k: v.to(accelerator.device			) for k, v in batch.items()}
									_snake_case          =      (batch['image'] - mean) / std
									with torch.no_grad():
												_snake_case          =      model(__lowercase			)
									_snake_case          =      outputs.argmax(dim=-1			)
									_snake_case     , _snake_case          =      accelerator.gather_for_metrics((predictions, batch['label'])			)
									_snake_case          =      predictions == references
									num_elems += accurate_preds.shape[0]
									accurate += accurate_preds.long().sum()

						_snake_case          =      accurate.item() / num_elems
						# Use accelerator.print to print only on the main process.
						accelerator.print(f'''epoch {epoch}: {100 * eval_metric:.2f}'''			)
						if args.with_tracking:
									accelerator.log(
									    {
									        'accuracy': 100 * eval_metric,
									        'train_loss': total_loss.item() / len(__lowercase			),
									        'epoch': epoch,
									    }	, step=__lowercase	, )
						if checkpointing_steps == "epoch":
									_snake_case          =      f'''epoch_{epoch}'''
									if args.output_dir is not None:
												_snake_case          =      os.path.join(args.output_dir	, __lowercase			)
									accelerator.save_state(__lowercase			)

			if args.with_tracking:
						accelerator.end_training()






def 	a_		(	)			->  Tuple:
			_snake_case          =      argparse.ArgumentParser(description='Simple example of training script.'			)
			parser.add_argument('--data_dir'	, required=__lowercase	, help='The data folder on disk.'			)
			parser.add_argument('--fp16'	, action='store_true'	, help='If passed, will use FP16 training.'			)
			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.'			)
			parser.add_argument(
			    '--checkpointing_steps'	, type=__lowercase	, default=__lowercase	, help='Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'	, )
			parser.add_argument(
			    '--output_dir'	, type=__lowercase	, default='.'	, help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'	, )
			parser.add_argument(
			    '--resume_from_checkpoint'	, type=__lowercase	, default=__lowercase	, help='If the training should continue from a checkpoint folder.'	, )
			parser.add_argument(
			    '--with_tracking'	, action='store_true'	, help='Whether to load in all available experiment trackers from the environment and use them for logging.'	, )
			parser.add_argument(
			    '--project_dir'	, type=__lowercase	, default='logs'	, help='Location on where to store experiment tracking logs` and relevent project information'	, )
			_snake_case          =      parser.parse_args()
			_snake_case          =      {'lr': 3E-2, 'num_epochs': 3, 'seed': 42, 'batch_size': 64, 'image_size': 224}
			training_function(__lowercase	, __lowercase			)


if __name__ == "__main__":
			main()

import argparse
import glob
import logging
import os
import time
from argparse import Namespace

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset

from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors


_lowerCamelCase	:    str          =      logging.getLogger(__name__)




class        SCREAMING_SNAKE_CASE__					(     UpperCAmelCase ):





	'''simple docstring'''



	_UpperCAmelCase	:		Any          = "sequence-classification"



	def __init__(  self     :   Optional[int]      ,  lowercase     :   Optional[Any]     ):


				'''simple docstring'''


				if type(lowercase     ) == dict:
							_snake_case          =      Namespace(**lowercase     )
				_snake_case          =      glue_output_modes[hparams.task]
				_snake_case          =      glue_tasks_num_labels[hparams.task]

				super().__init__(lowercase      ,  lowercase      ,  self.mode     )



	def 			A							(  self     :   List[str]      ,  **lowercase     :   Optional[Any]     ):


				'''simple docstring'''


				return self.model(**lowercase     )



	def 			A							(  self     :   Tuple      ,  lowercase     :   Optional[Any]      ,  lowercase     :   int     ):


				'''simple docstring'''


				_snake_case          =      {'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}

				if self.config.model_type not in ["distilbert", "bart"]:
							_snake_case          =      batch[2] if self.config.model_type in ['bert', 'xlnet', 'albert'] else None

				_snake_case          =      self(**lowercase     )
				_snake_case          =      outputs[0]

				_snake_case          =      self.trainer.lr_schedulers[0]['scheduler']
				_snake_case          =      {'loss': loss, 'rate': lr_scheduler.get_last_lr()[-1]}
				return {"loss": loss, "log": tensorboard_logs}



	def 			A							(  self     :   str     ):


				'''simple docstring'''


				_snake_case          =      self.hparams
				_snake_case          =      processors[args.task]()
				_snake_case          =      processor.get_labels()

				for mode in ["train", "dev"]:
							_snake_case          =      self._feature_file(lowercase     )
							if os.path.exists(lowercase     ) and not args.overwrite_cache:
										logger.info('Loading features from cached file %s'      ,  lowercase     )
							else:
										logger.info('Creating features from dataset file at %s'      ,  args.data_dir     )
										_snake_case          =      (
										    processor.get_dev_examples(args.data_dir     )
										    if mode == 'dev'
										    else processor.get_train_examples(args.data_dir     )
										)
										_snake_case          =      convert_examples_to_features(
										    lowercase      ,  self.tokenizer      ,  max_length=args.max_seq_length      ,  label_list=self.labels      ,  output_mode=args.glue_output_mode      ,  )
										logger.info('Saving features into cached file %s'      ,  lowercase     )
										torch.save(lowercase      ,  lowercase     )



	def 			A							(  self     :   List[str]      ,  lowercase     :   str      ,  lowercase     :   int      ,  lowercase     :   bool = False     ):


				'''simple docstring'''


				_snake_case          =      'dev' if mode == 'test' else mode

				_snake_case          =      self._feature_file(lowercase     )
				logger.info('Loading features from cached file %s'      ,  lowercase     )
				_snake_case          =      torch.load(lowercase     )
				_snake_case          =      torch.tensor([f.input_ids for f in features]      ,  dtype=torch.long     )
				_snake_case          =      torch.tensor([f.attention_mask for f in features]      ,  dtype=torch.long     )
				_snake_case          =      torch.tensor([f.token_type_ids for f in features]      ,  dtype=torch.long     )
				if self.hparams.glue_output_mode == "classification":
							_snake_case          =      torch.tensor([f.label for f in features]      ,  dtype=torch.long     )
				elif self.hparams.glue_output_mode == "regression":
							_snake_case          =      torch.tensor([f.label for f in features]      ,  dtype=torch.float     )

				return DataLoader(
				    TensorDataset(lowercase      ,  lowercase      ,  lowercase      ,  lowercase     )      ,  batch_size=lowercase      ,  shuffle=lowercase      ,  )



	def 			A							(  self     :   str      ,  lowercase     :   Dict      ,  lowercase     :   Optional[Any]     ):


				'''simple docstring'''


				_snake_case          =      {'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}

				if self.config.model_type not in ["distilbert", "bart"]:
							_snake_case          =      batch[2] if self.config.model_type in ['bert', 'xlnet', 'albert'] else None

				_snake_case          =      self(**lowercase     )
				_snake_case     , _snake_case          =      outputs[:2]
				_snake_case          =      logits.detach().cpu().numpy()
				_snake_case          =      inputs['labels'].detach().cpu().numpy()

				return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}



	def 			A							(  self     :   Optional[Any]      ,  lowercase     :   Optional[Any]     ):


				'''simple docstring'''


				_snake_case          =      torch.stack([x['val_loss'] for x in outputs]     ).mean().detach().cpu().item()
				_snake_case          =      np.concatenate([x['pred'] for x in outputs]      ,  axis=0     )

				if self.hparams.glue_output_mode == "classification":
							_snake_case          =      np.argmax(lowercase      ,  axis=1     )
				elif self.hparams.glue_output_mode == "regression":
							_snake_case          =      np.squeeze(lowercase     )

				_snake_case          =      np.concatenate([x['target'] for x in outputs]      ,  axis=0     )
				_snake_case          =      [[] for _ in range(out_label_ids.shape[0]     )]
				_snake_case          =      [[] for _ in range(out_label_ids.shape[0]     )]

				_snake_case          =      {**{'val_loss': val_loss_mean}, **compute_metrics(self.hparams.task      ,  lowercase      ,  lowercase     )}

				_snake_case          =      dict(results.items()     )
				_snake_case          =      results
				return ret, preds_list, out_label_list



	def 			A							(  self     :   List[str]      ,  lowercase     :   list     ):


				'''simple docstring'''


				_snake_case     , _snake_case     , _snake_case          =      self._eval_end(lowercase     )
				_snake_case          =      ret['log']
				return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}



	def 			A							(  self     :   Tuple      ,  lowercase     :   Tuple     ):


				'''simple docstring'''


				_snake_case     , _snake_case     , _snake_case          =      self._eval_end(lowercase     )
				_snake_case          =      ret['log']
				# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
				return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}





	@staticmethod
	def 			A							(  lowercase     :   Optional[int]      ,  lowercase     :   Optional[int]     ):


				'''simple docstring'''


				BaseTransformer.add_model_specific_args(lowercase      ,  lowercase     )
				parser.add_argument(
				    '--max_seq_length'      ,  default=128      ,  type=lowercase      ,  help=(
				        'The maximum total input sequence length after tokenization. Sequences longer '
				        'than this will be truncated, sequences shorter will be padded.'
				    )      ,  )

				parser.add_argument(
				    '--task'      ,  default=''      ,  type=lowercase      ,  required=lowercase      ,  help='The GLUE task to run'      ,  )
				parser.add_argument(
				    '--gpus'      ,  default=0      ,  type=lowercase      ,  help='The number of GPUs allocated for this, it is by default 0 meaning none'      ,  )

				parser.add_argument(
				    '--overwrite_cache'      ,  action='store_true'      ,  help='Overwrite the cached training and evaluation sets'     )

				return parser






def 	a_		(	)			->  List[str]:
			_snake_case          =      argparse.ArgumentParser()
			add_generic_args(__lowercase	, os.getcwd()			)
			_snake_case          =      GLUETransformer.add_model_specific_args(__lowercase	, os.getcwd()			)
			_snake_case          =      parser.parse_args()

			# If output_dir not provided, a folder will be generated in pwd
			if args.output_dir is None:
						_snake_case          =      os.path.join(
						    './results'	, f'''{args.task}_{time.strftime('%Y%m%d_%H%M%S'			)}'''	, )
						os.makedirs(args.output_dir			)

			_snake_case          =      GLUETransformer(__lowercase			)
			_snake_case          =      generic_train(__lowercase	, __lowercase			)

			# Optionally, predict on dev set and write to output_dir
			if args.do_predict:
						_snake_case          =      sorted(glob.glob(os.path.join(args.output_dir	, 'checkpoint-epoch=*.ckpt'			)	, recursive=__lowercase			)			)
						_snake_case          =      model.load_from_checkpoint(checkpoints[-1]			)
						return trainer.test(__lowercase			)


if __name__ == "__main__":
			main()