
from __future__ import annotations

import time

import numpy as np

__a         =   [8, 5, 9, 7]
__a         =   [
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
__a         =   [
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]
class 					lowercase__:



	"""simple docstring"""




	def __init__(			self    :     int			,				SCREAMING_SNAKE_CASE_    :     list[int]			,				SCREAMING_SNAKE_CASE_    :     list[list[int]]			,				SCREAMING_SNAKE_CASE_    :     list[list[int]]			,				)			->	None:
					lowercase_	    =   claim_vector
					lowercase_	    =   allocated_resources_table
					lowercase_	    =   maximum_claim_table
	def 		_lowercase    (			self    :     Dict			)			->	list[int]:
					return [
					    sum(p_item[i] for p_item in self.__allocated_resources_table			)
					    for i in range(len(self.__allocated_resources_table[0]			)			)
					]
	def 		_lowercase    (			self    :     int			)			->	list[int]:
					return np.array(self.__claim_vector			) - np.array(
					    self.__processes_resource_summation()			)
	def 		_lowercase    (			self    :     Tuple			)			->	list[list[int]]:
					return [
					    list(np.array(self.__maximum_claim_table[i]			) - np.array(SCREAMING_SNAKE_CASE_			)			)
					    for i, allocated_resource in enumerate(self.__allocated_resources_table			)
					]
	def 		_lowercase    (			self    :     List[str]			)			->	dict[int, list[int]]:
					return {self.__need().index(SCREAMING_SNAKE_CASE_			): i for i in self.__need()}
	def 		_lowercase    (			self    :     Tuple			,				**SCREAMING_SNAKE_CASE_    :     List[Any]			)			->	None:
					lowercase_	    =   self.__need()
					lowercase_	    =   self.__allocated_resources_table
					lowercase_	    =   self.__available_resources()
					lowercase_	    =   self.__need_index_manager()
					for kw, val in kwargs.items():
									if kw and val is True:
													self.__pretty_data()
					print('''_''' * 5_0 + '''\n'''			)
					while need_list:
									lowercase_	    =   False
									for each_need in need_list:
													lowercase_	    =   True
													for index, need in enumerate(SCREAMING_SNAKE_CASE_			):
																	if need > available_resources[index]:
																					lowercase_	    =   False
																					break
													if execution:
																	lowercase_	    =   True
																	# get the original index of the process from ind_ctrl db
																	for original_need_index, need_clone in need_index_manager.items():
																					if each_need == need_clone:
																									lowercase_	    =   original_need_index
																	print(f'''Process {process_number + 1} is executing.'''			)
																	# remove the process run from stack
																	need_list.remove(SCREAMING_SNAKE_CASE_			)
																	# update available/freed resources stack
																	lowercase_	    =   np.array(SCREAMING_SNAKE_CASE_			) + np.array(
																	    alloc_resources_table[process_number]			)
																	print(
																	    '''Updated available resource stack for processes: '''
																	    + ''' '''.join([str(SCREAMING_SNAKE_CASE_			) for x in available_resources]			)			)
																	break
									if safe:
													print('''The process is in a safe state.\n'''			)
									else:
													print('''System in unsafe state. Aborting...\n'''			)
													break




	def 		_lowercase    (			self    :     Tuple			)			->	Optional[Any]:
					print(''' ''' * 9 + '''Allocated Resource Table'''			)
					for item in self.__allocated_resources_table:
									print(
									    f'''P{self.__allocated_resources_table.index(SCREAMING_SNAKE_CASE_			) + 1}'''
									    + ''' '''.join(f'''{it:>8}''' for it in item			)
									    + '''\n'''			)
					print(''' ''' * 9 + '''System Resource Table'''			)
					for item in self.__maximum_claim_table:
									print(
									    f'''P{self.__maximum_claim_table.index(SCREAMING_SNAKE_CASE_			) + 1}'''
									    + ''' '''.join(f'''{it:>8}''' for it in item			)
									    + '''\n'''			)
					print(
					    '''Current Usage by Active Processes: '''
					    + ''' '''.join(str(SCREAMING_SNAKE_CASE_			) for x in self.__claim_vector			)			)
					print(
					    '''Initial Available Resources:       '''
					    + ''' '''.join(str(SCREAMING_SNAKE_CASE_			) for x in self.__available_resources()			)			)
					time.sleep(1			)


if __name__ == "__main__":
						import doctest

						doctest.testmod()












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





class 						SCREAMING_SNAKE_CASE       (       SCREAMING_SNAKE_CASE_							):






			def __init__(			self   :      List[Any]	,    __SCREAMING_SNAKE_CASE   :      str	,    __SCREAMING_SNAKE_CASE   :      Optional[Any]=None	,    __SCREAMING_SNAKE_CASE   :      Any=None       )      ->      Optional[Any]:
							a_		:	List[str]									=   file_names
							a_		:	str									=   image_transform
							a_		:	Optional[Any]									=   label_to_id






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







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





def 		_UpperCAmelCase (				__A				: List[Any]    ,		__A				: Any   ):
				# Initialize accelerator
				if args.with_tracking:
								a_		:	Any									=   Accelerator(
								    cpu=args.cpu    ,		mixed_precision=args.mixed_precision    ,		log_with='''all'''    ,		project_dir=args.project_dir   )
				else:
								a_		:	Optional[Any]									=   Accelerator(cpu=args.cpu    ,		mixed_precision=args.mixed_precision   )

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

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

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

				# Build the label correspondences
				a_		:	Dict									=   [extract_label(__A   ) for fname in file_names]
				a_		:	Optional[Any]									=   list(set(__A   )   )
				id_to_label.sort()
				a_		:	Any									=   {lbl: i for i, lbl in enumerate(__A   )}

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

				# Split our filenames between train and validation
				a_		:	Optional[Any]									=   np.random.permutation(len(__A   )   )
				a_		:	Dict									=   int(0.8 * len(__A   )   )
				a_		:	Optional[Any]									=   random_perm[:cut]
				a_		:	Optional[int]									=   random_perm[cut:]

				# For training we use a simple RandomResizedCrop
				a_		:	Optional[int]									=   Compose([RandomResizedCrop(__A    ,		scale=(0.5, 1.0)   ), ToTensor()]   )
				a_		:	str									=   PetsDataset(
				    [file_names[i] for i in train_split]    ,		image_transform=__A    ,		label_to_id=__A   )

				# For evaluation, we use a deterministic Resize
				a_		:	Any									=   Compose([Resize(__A   ), ToTensor()]   )
				a_		:	Tuple									=   PetsDataset([file_names[i] for i in eval_split]    ,		image_transform=__A    ,		label_to_id=__A   )

				# Instantiate dataloaders.
				a_		:	Dict									=   DataLoader(__A    ,		shuffle=__A    ,		batch_size=__A    ,		num_workers=4   )
				a_		:	str									=   DataLoader(__A    ,		shuffle=__A    ,		batch_size=__A    ,		num_workers=4   )

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				a_		:	Union[str, Any]									=   create_model('''resnet50d'''    ,		pretrained=__A    ,		num_classes=len(__A   )   )

				# 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_		:	Tuple									=   model.to(accelerator.device   )

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

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

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

				# Instantiate learning rate scheduler
				a_		:	Tuple									=   OneCycleLR(optimizer=__A    ,		max_lr=__A    ,		epochs=__A    ,		steps_per_epoch=len(__A   )   )

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

								if "epoch" in training_difference:
												a_		:	Tuple									=   int(training_difference.replace('''epoch_'''    ,		''''''   )   ) + 1
												a_		:	str									=   None
								else:
												a_		:	List[Any]									=   int(training_difference.replace('''step_'''    ,		''''''   )   )
												a_		:	Union[str, Any]									=   resume_step // len(__A   )
												resume_step -= starting_epoch * len(__A   )

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

								a_		:	Any									=   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(__A   ),
												        '''epoch''': epoch,
												    }    ,		step=__A    ,		)
								if checkpointing_steps == "epoch":
												a_		:	Dict									=   f'epoch_{epoch}'
												if args.output_dir is not None:
																a_		:	int									=   os.path.join(args.output_dir    ,		__A   )
												accelerator.save_state(__A   )

				if args.with_tracking:
								accelerator.end_training()





def 		_UpperCAmelCase (				):
				a_		:	Optional[int]									=   argparse.ArgumentParser(description='''Simple example of training script.'''   )
				parser.add_argument('''--data_dir'''    ,		required=__A    ,		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=__A    ,		default=__A    ,		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=__A    ,		default=__A    ,		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=__A    ,		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=__A    ,		default=__A    ,		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=__A    ,		default='''logs'''    ,		help='''Location on where to store experiment tracking logs` and relevent project information'''    ,		)
				a_		:	Union[str, Any]									=   parser.parse_args()
				a_		:	List[Any]									=   {'''lr''': 3E-2, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 64, '''image_size''': 2_24}
				training_function(__A    ,		__A   )


if __name__ == "__main__":
		main()




