


"""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        lowercase							(		lowerCAmelCase__ :     Optional[Any]			) ->      Any:
		__a           =			fname.split(os.path.sep			)[-1]
		return re.search(r'''^(.*)_\d+\.jpg$'''    ,					lowerCAmelCase__			).groups()[0]
class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		def __init__(  self							, _a							, _a=None							, _a=None      ):
				__a           =			file_names
				__a           =			image_transform
				__a           =			label_to_id

		def __len__(  self      ):
				return len(self.file_names      )





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







def        lowercase							(		lowerCAmelCase__ :     Dict    ,					lowerCAmelCase__ :     List[str]			) ->      Tuple:
		# Initialize accelerator
		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        lowercase							(		) ->      int:
		__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"""




lowercase_			     =  "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"

def        lowercase							(		lowerCAmelCase__ :     bytes			) ->      bytes:
		# Make sure the supplied data is a bytes-like object
		if not isinstance(lowerCAmelCase__    ,					lowerCAmelCase__			):
				__a           =			f'''a bytes-like object is required, not \'{data.__class__.__name__}\''''
				raise TypeError(lowerCAmelCase__			)

		__a           =			''''''.join(bin(lowerCAmelCase__			)[2:].zfill(8			) for byte in data			)

		__a           =			len(lowerCAmelCase__			) % 6 != 0

		if padding_needed:
				# The padding that will be added later
				__a           =			b'''=''' * ((6 - len(lowerCAmelCase__			) % 6) // 2)

				# Append binary_stream with arbitrary binary digits (0's by default) to make its
				# length a multiple of 6.
				binary_stream += "0" * (6 - len(lowerCAmelCase__			) % 6)
		else:
				__a           =			b''''''

		# Encode every 6 binary digits to their corresponding Base64 character
		return (
		    "".join(
		        B64_CHARSET[int(binary_stream[index : index + 6]    ,					2			)]
		        for index in range(0    ,					len(lowerCAmelCase__			)    ,					6			)			).encode()
		    + padding
		)

def        lowercase							(		lowerCAmelCase__ :     str			) ->      bytes:
		# Make sure encoded_data is either a string or a bytes-like object
		if not isinstance(lowerCAmelCase__    ,					lowerCAmelCase__			) and not isinstance(lowerCAmelCase__    ,					lowerCAmelCase__			):
				__a           =			(
				    '''argument should be a bytes-like object or ASCII string, '''
				    f'''not \'{encoded_data.__class__.__name__}\''''
				)
				raise TypeError(lowerCAmelCase__			)

		# In case encoded_data is a bytes-like object, make sure it contains only
		# ASCII characters so we convert it to a string object
		if isinstance(lowerCAmelCase__    ,					lowerCAmelCase__			):
				try:
						__a           =			encoded_data.decode('''utf-8'''			)
				except UnicodeDecodeError:
						raise ValueError('''base64 encoded data should only contain ASCII characters'''			)

		__a           =			encoded_data.count('''='''			)

		# Check if the encoded string contains non base64 characters
		if padding:
				assert all(
				    char in B64_CHARSET for char in encoded_data[:-padding]			), "Invalid base64 character(s) found."
		else:
				assert all(
				    char in B64_CHARSET for char in encoded_data			), "Invalid base64 character(s) found."

		# Check the padding
		assert len(lowerCAmelCase__			) % 4 == 0 and padding < 3, "Incorrect padding"

		if padding:
				# Remove padding if there is one
				__a           =			encoded_data[:-padding]

				__a           =			''''''.join(
				    bin(B64_CHARSET.index(lowerCAmelCase__			)			)[2:].zfill(6			) for char in encoded_data			)[: -padding * 2]
		else:
				__a           =			''''''.join(
				    bin(B64_CHARSET.index(lowerCAmelCase__			)			)[2:].zfill(6			) for char in encoded_data			)

		__a           =			[
		    int(binary_stream[index : index + 8]    ,					2			)
		    for index in range(0    ,					len(lowerCAmelCase__			)    ,					8			)
		]

		return bytes(lowerCAmelCase__			)


if __name__ == "__main__":
				import doctest

				doctest.testmod()






