



'''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	(					_snake_case	:    str							):





    '''simple docstring'''


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


class 					snake_case			(UpperCamelCase ):



       def __init__(					self  ,UpperCAmelCase_  ,UpperCAmelCase_=None  ,UpperCAmelCase_=None   )	->				List[Any]:
           lowercase__									=    file_names
           lowercase__									=    image_transform
           lowercase__									=    label_to_id



       def __len__(					self   )	->				int:
           return len(self.file_names   )







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





def 							lowerCamelCase	(					_snake_case	:    Optional[int]  ,_snake_case	:    List[Any]							):





    '''simple docstring'''


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

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

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

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

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

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

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

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

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

    # Instantiate dataloaders.
    lowercase__									=    DataLoader(_snake_case  ,shuffle=_snake_case  ,batch_size=_snake_case  ,num_workers=4							)
    lowercase__									=    DataLoader(_snake_case  ,shuffle=_snake_case  ,batch_size=_snake_case  ,num_workers=4							)

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

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

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

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

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

    # Instantiate learning rate scheduler
    lowercase__									=    OneCycleLR(optimizer=_snake_case  ,max_lr=_snake_case  ,epochs=_snake_case  ,steps_per_epoch=len(_snake_case							)							)

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

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

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

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

    if args.with_tracking:
        accelerator.end_training()




def 							lowerCamelCase	(					):





    '''simple docstring'''


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


if __name__ == "__main__":
 main()









'''simple docstring'''





from __future__ import annotations

import time

import numpy as np

SCREAMING_SNAKE_CASE__          =  [8, 5, 9, 7]
SCREAMING_SNAKE_CASE__          =  [
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
SCREAMING_SNAKE_CASE__          =  [
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]


class 					snake_case			:



       def __init__(					self  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_  ,)	->				None:
           lowercase__									=    claim_vector
           lowercase__									=    allocated_resources_table
           lowercase__									=    maximum_claim_table



       def 				_a   (					self   )	->				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 				_a   (					self   )	->				list[int]:
           return np.array(self.__claim_vector   ) - np.array(
               self.__processes_resource_summation()   )



       def 				_a   (					self   )	->				list[list[int]]:
           return [
               list(np.array(self.__maximum_claim_table[i]   ) - np.array(UpperCAmelCase_   )   )
               for i, allocated_resource in enumerate(self.__allocated_resources_table   )
           ]



       def 				_a   (					self   )	->				dict[int, list[int]]:
           return {self.__need().index(UpperCAmelCase_   ): i for i in self.__need()}



       def 				_a   (					self  ,**UpperCAmelCase_   )	->				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("_" * 50 + "\n"   )
           while need_list:
               lowercase__									=    False
               for each_need in need_list:
                   lowercase__									=    True
                   for index, need in enumerate(UpperCAmelCase_   ):
                       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(UpperCAmelCase_   )
                       # update available/freed resources stack
                       lowercase__									=    np.array(UpperCAmelCase_   ) + np.array(
                           alloc_resources_table[process_number]   )
                       print(
                           "Updated available resource stack for processes: "
                           + " ".join([str(UpperCAmelCase_   ) 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 				_a   (					self   )	->				List[Any]:
           print(" " * 9 + "Allocated Resource Table"   )
           for item in self.__allocated_resources_table:
               print(
                   F'''P{self.__allocated_resources_table.index(UpperCAmelCase_   ) + 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(UpperCAmelCase_   ) + 1}'''
                   + " ".join(F'''{it:>8}''' for it in item   )
                   + "\n"   )
           print(
               "Current Usage by Active Processes: "
               + " ".join(str(UpperCAmelCase_   ) for x in self.__claim_vector   )   )
           print(
               "Initial Available Resources:       "
               + " ".join(str(UpperCAmelCase_   ) for x in self.__available_resources()   )   )
           time.sleep(1   )


if __name__ == "__main__":
 import doctest

 doctest.testmod()




