


'''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       a_				(	UpperCamelCase_ ):
							A_			    =		fname.split(os.path.sep )[-1]
							return re.search(R"^(.*)_\d+\.jpg$"						,  UpperCamelCase_ ).groups()[0]






class 					__lowerCAmelCase   (   lowercase						):

						"""simple docstring"""






						def __init__(  self							:      str ,  lowerCAmelCase							:      Optional[Any] ,  lowerCAmelCase							:      Optional[Any]=None ,  lowerCAmelCase							:      Any=None	):
													A_			    =		file_names
													A_			    =		image_transform
													A_			    =		label_to_id






						def __len__(  self							:      List[str]	):
													return len(self.file_names	)






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







def       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							# 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(UpperCamelCase_						,  (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(UpperCamelCase_ )[-1].split("." )[0]
														accelerator.init_trackers(UpperCamelCase_						,  UpperCamelCase_ )

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

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

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

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

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

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

							# Instantiate dataloaders.
							A_			    =		DataLoader(UpperCamelCase_						,  shuffle=UpperCamelCase_						,  batch_size=UpperCamelCase_						,  num_workers=4 )
							A_			    =		DataLoader(UpperCamelCase_						,  shuffle=UpperCamelCase_						,  batch_size=UpperCamelCase_						,  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=UpperCamelCase_						,  num_classes=len(UpperCamelCase_ ) )

							# 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 / 2_5 )

							# Instantiate learning rate scheduler
							A_			    =		OneCycleLR(optimizer=UpperCamelCase_						,  max_lr=UpperCamelCase_						,  epochs=UpperCamelCase_						,  steps_per_epoch=len(UpperCamelCase_ ) )

							# 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(
							    UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ )
							# 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(UpperCamelCase_ )[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(UpperCamelCase_ )
																					resume_step -= starting_epoch * len(UpperCamelCase_ )

    # Now we train the model
							for epoch in range(UpperCamelCase_						,  UpperCamelCase_ ):
														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(UpperCamelCase_						,  UpperCamelCase_ )
																					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(UpperCamelCase_ )
																					A_			    =		torch.nn.functional.cross_entropy(UpperCamelCase_						,  batch["label"] )
																					# We keep track of the loss at each epoch
																					if args.with_tracking:
																												total_loss += loss.detach().float()
																					accelerator.backward(UpperCamelCase_ )
																					optimizer.step()
																					lr_scheduler.step()
																					optimizer.zero_grad()
																					overall_step += 1
																					if isinstance(UpperCamelCase_						,  UpperCamelCase_ ):
																												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						,  UpperCamelCase_ )
																																			accelerator.save_state(UpperCamelCase_ )
														model.eval()
														A_			    =		0
														A_			    =		0
														for step, batch in enumerate(UpperCamelCase_ ):
																					# 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(UpperCamelCase_ )
																					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}: {1_0_0 * eval_metric:.2f}" )
														if args.with_tracking:
																					accelerator.log(
																					    {
																					        "accuracy": 1_0_0 * eval_metric,
																					        "train_loss": total_loss.item() / len(UpperCamelCase_ ),
																					        "epoch": epoch,
																					    }						,  step=UpperCamelCase_						,  )
														if checkpointing_steps == "epoch":
																					A_			    =		f"epoch_{epoch}"
																					if args.output_dir is not None:
																												A_			    =		os.path.join(args.output_dir						,  UpperCamelCase_ )
																					accelerator.save_state(UpperCamelCase_ )

							if args.with_tracking:
														accelerator.end_training()







def       a_				(	):
							A_			    =		argparse.ArgumentParser(description="Simple example of training script." )
							parser.add_argument("--data_dir"						,  required=UpperCamelCase_						,  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=UpperCamelCase_						,  default=UpperCamelCase_						,  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=UpperCamelCase_						,  default=UpperCamelCase_						,  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=UpperCamelCase_						,  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=UpperCamelCase_						,  default=UpperCamelCase_						,  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=UpperCamelCase_						,  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": 4_2, "batch_size": 6_4, "image_size": 2_2_4}
							training_function(UpperCamelCase_						,  UpperCamelCase_ )


if __name__ == "__main__":
							main()









'''simple docstring'''
from __future__ import annotations

from typing import Any







def       a_				(	UpperCamelCase_ ):
							create_state_space_tree(UpperCamelCase_						,  []						,  0 )







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):

							if index == len(UpperCamelCase_ ):
														print(UpperCamelCase_ )
														return

							create_state_space_tree(UpperCamelCase_						,  UpperCamelCase_						,  index + 1 )
							current_subsequence.append(sequence[index] )
							create_state_space_tree(UpperCamelCase_						,  UpperCamelCase_						,  index + 1 )
							current_subsequence.pop()


if __name__ == "__main__":
							__SCREAMING_SNAKE_CASE					:				list[Any]									=		[3, 1, 2, 4]
							generate_all_subsequences(seq)

							seq.clear()
							seq.extend(['''A''', '''B''', '''C'''])
							generate_all_subsequences(seq)





