



'''simple docstring'''





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 lowerCAmelCase						(__A):



					"""simple docstring"""



					_a											=						fname.split(os.path.sep)[-1]
					return re.search(r'''^(.*)_\d+\.jpg$'''						,			lowerCAmelCase__).groups()[0]






class __A    (						A		):



	'''simple docstring'''







	def __init__(self				,     A				,     A=None				,     A=None )					->  Optional[Any]:


						"""simple docstring"""



						_a											=						file_names
						_a											=						image_transform
						_a											=						label_to_id



	def __len__(self )					->  int:


						"""simple docstring"""



						return len(self.file_names )



	def __getitem__(self				,     A )					->  Tuple:


						"""simple docstring"""



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





def lowerCAmelCase						(__A						,			__A):



					"""simple docstring"""



					if args.with_tracking:
										_a											=						Accelerator(
										    cpu=args.cpu						,			mixed_precision=args.mixed_precision						,			log_with='''all'''						,			project_dir=args.project_dir)
					else:
										_a											=						Accelerator(cpu=args.cpu						,			mixed_precision=args.mixed_precision)

					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					_a											=						config['''lr''']
					_a											=						int(config['''num_epochs'''])
					_a											=						int(config['''seed'''])
					_a											=						int(config['''batch_size'''])
					_a											=						config['''image_size''']
					if not isinstance(lowerCAmelCase__						,			(list, tuple)):
										_a											=						(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":
															_a											=						args.checkpointing_steps
										elif args.checkpointing_steps.isdigit():
															_a											=						int(args.checkpointing_steps)
										else:
															raise ValueError(
															    F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.''')
					else:
										_a											=						None

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

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

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

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

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

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

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

					# Instantiate dataloaders.
					_a											=						DataLoader(lowerCAmelCase__						,			shuffle=lowerCAmelCase__						,			batch_size=lowerCAmelCase__						,			num_workers=4)
					_a											=						DataLoader(lowerCAmelCase__						,			shuffle=lowerCAmelCase__						,			batch_size=lowerCAmelCase__						,			num_workers=4)

					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					_a											=						create_model('''resnet50d'''						,			pretrained=lowerCAmelCase__						,			num_classes=len(lowerCAmelCase__))

					# 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).
					_a											=						model.to(accelerator.device)

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

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

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

					# Instantiate learning rate scheduler
					_a											=						OneCycleLR(optimizer=lowerCAmelCase__						,			max_lr=lowerCAmelCase__						,			epochs=lowerCAmelCase__						,			steps_per_epoch=len(lowerCAmelCase__))

					# 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.
					_a						,					_a						,					_a						,					_a						,					_a											=						accelerator.prepare(
					    lowerCAmelCase__						,			lowerCAmelCase__						,			lowerCAmelCase__						,			lowerCAmelCase__						,			lowerCAmelCase__)
					# We need to keep track of how many total steps we have iterated over
					_a											=						0
					# We also need to keep track of the starting epoch so files are named properly
					_a											=						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)
															_a											=						os.path.basename(args.resume_from_checkpoint)
										else:
															# Get the most recent checkpoint
															_a											=						[f.name for f in os.scandir(os.getcwd()) if f.is_dir()]
															dirs.sort(key=os.path.getctime)
															_a											=						dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
										# Extract `epoch_{i}` or `step_{i}`
										_a											=						os.path.splitext(lowerCAmelCase__)[0]

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

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

										_a											=						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(lowerCAmelCase__),
															        '''epoch''': epoch,
															    }						,			step=lowerCAmelCase__						,			)
										if checkpointing_steps == "epoch":
															_a											=						F'''epoch_{epoch}'''
															if args.output_dir is not None:
																				_a											=						os.path.join(args.output_dir						,			lowerCAmelCase__)
															accelerator.save_state(lowerCAmelCase__)

					if args.with_tracking:
										accelerator.end_training()




def lowerCAmelCase						():



					"""simple docstring"""



					_a											=						argparse.ArgumentParser(description='''Simple example of training script.''')
					parser.add_argument('''--data_dir'''						,			required=lowerCAmelCase__						,			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=lowerCAmelCase__						,			default=lowerCAmelCase__						,			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=lowerCAmelCase__						,			default=lowerCAmelCase__						,			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=lowerCAmelCase__						,			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=lowerCAmelCase__						,			default=lowerCAmelCase__						,			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=lowerCAmelCase__						,			default='''logs'''						,			help='''Location on where to store experiment tracking logs` and relevent project information'''						,			)
					_a											=						parser.parse_args()
					_a											=						{'''lr''': 3e-2, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 64, '''image_size''': 224}
					training_function(lowerCAmelCase__						,			lowerCAmelCase__)


if __name__ == "__main__":
		main()








'''simple docstring'''





# This is the module that test_patching.py uses to test patch_submodule()

import os  # noqa: this is just for tests
import os as renamed_os  # noqa: this is just for tests
from os import path  # noqa: this is just for tests
from os import path as renamed_path  # noqa: this is just for tests
from os.path import join  # noqa: this is just for tests
from os.path import join as renamed_join  # noqa: this is just for tests


lowercase_	 =						open  # noqa: we just need to have a builtin inside this module to test it properly



