


'''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      UpperCAmelCase      (		lowerCamelCase_  :str ):







		'''simple docstring'''
		snake_case_		:							Any			       =						fname.split(os.path.sep )[-1]
		return re.search(R"""^(.*)_\d+\.jpg$"""				,      lowerCamelCase_ ).groups()[0]
class 						__UpperCamelCase	(						lowercase__							):





							def __init__(  self		:str  ,_UpperCamelCase		:List[Any]  ,_UpperCamelCase		:Union[str, Any]=None  ,_UpperCamelCase		:Optional[Any]=None			):
									snake_case_		:							List[str]			       =						file_names
									snake_case_		:							Any			       =						image_transform
									snake_case_		:							List[str]			       =						label_to_id





							def __len__(  self		:Union[str, Any]			):
									return len(self.file_names			)





							def __getitem__(  self		:Dict  ,_UpperCamelCase		:str			):
									snake_case_		:							List[str]			       =						self.file_names[idx]
									snake_case_		:							Any			       =						PIL.Image.open(_UpperCamelCase			)
									snake_case_		:							Optional[int]			       =						raw_image.convert("""RGB"""			)
									if self.image_transform is not None:
											snake_case_		:							Optional[Any]			       =						self.image_transform(_UpperCamelCase			)
									snake_case_		:							str			       =						extract_label(_UpperCamelCase			)
									if self.label_to_id is not None:
											snake_case_		:							Optional[int]			       =						self.label_to_id[label]
									return {"image": image, "label": label}





def      UpperCAmelCase      (		lowerCamelCase_  :List[str]				,      lowerCamelCase_  :Dict ):







		'''simple docstring'''
		# Initialize accelerator
		if args.with_tracking:
				snake_case_		:							Union[str, Any]			       =						Accelerator(
				    cpu=args.cpu				,      mixed_precision=args.mixed_precision				,      log_with="""all"""				,      project_dir=args.project_dir )
		else:
				snake_case_		:							Union[str, Any]			       =						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_		:							Optional[Any]			       =						config["""lr"""]
		snake_case_		:							Dict			       =						int(config["""num_epochs"""] )
		snake_case_		:							Dict			       =						int(config["""seed"""] )
		snake_case_		:							int			       =						int(config["""batch_size"""] )
		snake_case_		:							Any			       =						config["""image_size"""]
		if not isinstance(lowerCamelCase_				,      (list, tuple) ):
				snake_case_		:							Optional[Any]			       =						(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_		:							Optional[int]			       =						args.checkpointing_steps
				elif args.checkpointing_steps.isdigit():
						snake_case_		:							str			       =						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_		:							Any			       =						None

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

		# Grab all the image filenames
		snake_case_		:							List[Any]			       =						[os.path.join(args.data_dir				,      lowerCamelCase_ ) for fname in os.listdir(args.data_dir ) if fname.endswith(""".jpg""" )]

		# Build the label correspondences
		snake_case_		:							List[str]			       =						[extract_label(lowerCamelCase_ ) for fname in file_names]
		snake_case_		:							Optional[Any]			       =						list(set(lowerCamelCase_ ) )
		id_to_label.sort()
		snake_case_		:							str			       =						{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
		snake_case_		:							str			       =						np.random.permutation(len(lowerCamelCase_ ) )
		snake_case_		:							Union[str, Any]			       =						int(0.8 * len(lowerCamelCase_ ) )
		snake_case_		:							int			       =						random_perm[:cut]
		snake_case_		:							Union[str, Any]			       =						random_perm[cut:]

		# For training we use a simple RandomResizedCrop
		snake_case_		:							Any			       =						Compose([RandomResizedCrop(lowerCamelCase_				,      scale=(0.5, 1.0) ), ToTensor()] )
		snake_case_		:							List[str]			       =						PetsDataset(
		    [file_names[i] for i in train_split]				,      image_transform=lowerCamelCase_				,      label_to_id=lowerCamelCase_ )

		# For evaluation, we use a deterministic Resize
		snake_case_		:							List[Any]			       =						Compose([Resize(lowerCamelCase_ ), ToTensor()] )
		snake_case_		:							Optional[int]			       =						PetsDataset([file_names[i] for i in eval_split]				,      image_transform=lowerCamelCase_				,      label_to_id=lowerCamelCase_ )

		# Instantiate dataloaders.
		snake_case_		:							List[Any]			       =						DataLoader(lowerCamelCase_				,      shuffle=lowerCamelCase_				,      batch_size=lowerCamelCase_				,      num_workers=4 )
		snake_case_		:							Dict			       =						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)
		snake_case_		:							int			       =						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).
		snake_case_		:							Tuple			       =						model.to(accelerator.device )

		# Freezing the base model
		for param in model.parameters():
				snake_case_		:							Optional[int]			       =						False
		for param in model.get_classifier().parameters():
				snake_case_		:							Any			       =						True

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

		# Instantiate optimizer
		snake_case_		:							List[Any]			       =						torch.optim.Adam(params=model.parameters()				,      lr=lr / 25 )

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

				if "epoch" in training_difference:
						snake_case_		:							Union[str, Any]			       =						int(training_difference.replace("""epoch_"""				,      """""" ) ) + 1
						snake_case_		:							int			       =						None
				else:
						snake_case_		:							List[str]			       =						int(training_difference.replace("""step_"""				,      """""" ) )
						snake_case_		:							str			       =						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:
						snake_case_		:							Tuple			       =						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_		:							List[str]			       =						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
						snake_case_		:							Optional[Any]			       =						train_dataloader
				for batch in active_dataloader:
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						snake_case_		:							Dict			       =						{k: v.to(accelerator.device ) for k, v in batch.items()}
						snake_case_		:							Optional[Any]			       =						(batch["""image"""] - mean) / std
						snake_case_		:							Dict			       =						model(lowerCamelCase_ )
						snake_case_		:							Tuple			       =						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_ ):
								snake_case_		:							Optional[int]			       =						F'''step_{overall_step}'''
								if overall_step % checkpointing_steps == 0:
										if args.output_dir is not None:
												snake_case_		:							Optional[int]			       =						os.path.join(args.output_dir				,      lowerCamelCase_ )
										accelerator.save_state(lowerCamelCase_ )
				model.eval()
				snake_case_		:							int			       =						0
				snake_case_		:							str			       =						0
				for step, batch in enumerate(lowerCamelCase_ ):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						snake_case_		:							Any			       =						{k: v.to(accelerator.device ) for k, v in batch.items()}
						snake_case_		:							List[Any]			       =						(batch["""image"""] - mean) / std
						with torch.no_grad():
								snake_case_		:							Dict			       =						model(lowerCamelCase_ )
						snake_case_		:							Optional[int]			       =						outputs.argmax(dim=-1 )
						snake_case_		:							List[str]			       =						accelerator.gather_for_metrics((predictions, batch["""label"""]) )
						snake_case_		:							str			       =						predictions == references
						num_elems += accurate_preds.shape[0]
						accurate += accurate_preds.long().sum()

				snake_case_		:							int			       =						accurate.item() / num_elems
				# Use accelerator.print to print only on the main process.
				accelerator.print(F'''epoch {epoch}: {1_00 * eval_metric:.2f}''' )
				if args.with_tracking:
						accelerator.log(
						    {
						        """accuracy""": 1_00 * eval_metric,
						        """train_loss""": total_loss.item() / len(lowerCamelCase_ ),
						        """epoch""": epoch,
						    }				,      step=lowerCamelCase_				,      )
				if checkpointing_steps == "epoch":
						snake_case_		:							List[Any]			       =						F'''epoch_{epoch}'''
						if args.output_dir is not None:
								snake_case_		:							Union[str, Any]			       =						os.path.join(args.output_dir				,      lowerCamelCase_ )
						accelerator.save_state(lowerCamelCase_ )

		if args.with_tracking:
				accelerator.end_training()





def      UpperCAmelCase      (		):







		'''simple docstring'''
		snake_case_		:							List[Any]			       =						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"""				,      )
		snake_case_		:							Union[str, Any]			       =						parser.parse_args()
		snake_case_		:							int			       =						{"""lr""": 3E-2, """num_epochs""": 3, """seed""": 42, """batch_size""": 64, """image_size""": 2_24}
		training_function(lowerCamelCase_				,      lowerCamelCase_ )


if __name__ == "__main__":
				main()



'''simple docstring'''






import numpy as np
import qiskit





def      UpperCAmelCase      (		lowerCamelCase_  :int = 8				,      lowerCamelCase_  :int | None = None ):







		'''simple docstring'''
		snake_case_		:							Tuple			       =						np.random.default_rng(seed=lowerCamelCase_ )

		# Roughly 25% of the qubits will contribute to the key.
		# So we take more than we need.
		snake_case_		:							Union[str, Any]			       =						6 * key_len
		# Measurement basis for Alice's qubits.
		snake_case_		:							Tuple			       =						rng.integers(2				,      size=lowerCamelCase_ )
		# The set of states Alice will prepare.
		snake_case_		:							Tuple			       =						rng.integers(2				,      size=lowerCamelCase_ )
		# Measurement basis for Bob's qubits.
		snake_case_		:							List[Any]			       =						rng.integers(2				,      size=lowerCamelCase_ )

		# Quantum Circuit to simulate BB84
		snake_case_		:							Optional[int]			       =						qiskit.QuantumCircuit(lowerCamelCase_				,      name="""BB84""" )

		# Alice prepares her qubits according to rules above.
		for index, _ in enumerate(lowerCamelCase_ ):
				if alice_state[index] == 1:
						bbaa_circ.x(lowerCamelCase_ )
				if alice_basis[index] == 1:
						bbaa_circ.h(lowerCamelCase_ )
		bbaa_circ.barrier()

		# Bob measures the received qubits according to rules above.
		for index, _ in enumerate(lowerCamelCase_ ):
				if bob_basis[index] == 1:
						bbaa_circ.h(lowerCamelCase_ )

		bbaa_circ.barrier()
		bbaa_circ.measure_all()

		# Simulate the quantum circuit.
		snake_case_		:							Union[str, Any]			       =						qiskit.Aer.get_backend("""aer_simulator""" )
		# We only need to run one shot because the key is unique.
		# Multiple shots will produce the same key.
		snake_case_		:							Tuple			       =						qiskit.execute(lowerCamelCase_				,      lowerCamelCase_				,      shots=1				,      seed_simulator=lowerCamelCase_ )
		# Returns the result of measurement.
		snake_case_		:							Tuple			       =						job.result().get_counts(lowerCamelCase_ ).most_frequent()

		# Extracting the generated key from the simulation results.
		# Only keep measurement results where Alice and Bob chose the same basis.
		snake_case_		:							Optional[Any]			       =						"""""".join(
		    [
		        result_bit
		        for alice_basis_bit, bob_basis_bit, result_bit in zip(
		            lowerCamelCase_				,      lowerCamelCase_				,      lowerCamelCase_ )
		        if alice_basis_bit == bob_basis_bit
		    ] )

		# Get final key. Pad with 0 if too short, otherwise truncate.
		snake_case_		:							str			       =						gen_key[:key_len] if len(lowerCamelCase_ ) >= key_len else gen_key.ljust(lowerCamelCase_				,      """0""" )
		return key


if __name__ == "__main__":
							print(F'The generated key is : {bbaa(8, seed=0)}')
							from doctest import testmod

							testmod()