





from math import pi, sqrt
def 							_lowerCamelCase(  UpperCamelCase__    : float		)		->  str:
    if num <= 0:
        raise ValueError('''math domain error'''		)
    if num > 1_7_1.5:
        raise OverflowError('''math range error'''		)
    elif num - int(snake_case__		) not in (0, 0.5):
        raise NotImplementedError('''num must be an integer or a half-integer'''		)
    elif num == 0.5:
        return sqrt(snake_case__		)
    else:
        return 1.0 if num == 1 else (num - 1) * gamma(num - 1		)
def 							_lowerCamelCase(  )		->  str:
    assert gamma(0.5		) == sqrt(snake_case__		)
    assert gamma(1		) == 1.0
    assert gamma(2		) == 1.0


if __name__ == "__main__":
     from doctest import testmod

     testmod()
     snake_case_     =	1.0
     while num:
          snake_case_     =	float(input("""Gamma of: """))
          print(f'''gamma({num}) = {gamma(num)}''')
          print("""\nEnter 0 to exit...""")







'''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(  UpperCamelCase__    : Dict		)		->  Union[str, Any]:
    A       :       Optional[Any]							=						fname.split(os.path.sep		)[-1]
    return re.search(R'''^(.*)_\d+\.jpg$'''    ,       UpperCamelCase__		).groups()[0]


class   _lowercase						(							a				):


   def __init__( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=None					):
       A       :       str							=						file_names
       A       :       Optional[int]							=						image_transform
       A       :       str							=						label_to_id


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


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







def 							_lowerCamelCase(  UpperCamelCase__    : List[Any]    ,       UpperCamelCase__    : int		)		->  Any:
    # Initialize accelerator
    if args.with_tracking:
        A       :       Union[str, Any]							=						Accelerator(
            cpu=args.cpu    ,       mixed_precision=args.mixed_precision    ,       log_with='''all'''    ,       project_dir=args.project_dir		)
    else:
        A       :       Dict							=						Accelerator(cpu=args.cpu    ,       mixed_precision=args.mixed_precision		)

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

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

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

    # Build the label correspondences
    A       :       int							=						[extract_label(UpperCamelCase__		) for fname in file_names]
    A       :       str							=						list(set(UpperCamelCase__		)		)
    id_to_label.sort()
    A       :       Dict							=						{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       :       Dict							=						np.random.permutation(len(UpperCamelCase__		)		)
    A       :       str							=						int(0.8 * len(UpperCamelCase__		)		)
    A       :       Tuple							=						random_perm[:cut]
    A       :       List[Any]							=						random_perm[cut:]

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

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

    # Instantiate dataloaders.
    A       :       List[str]							=						DataLoader(UpperCamelCase__    ,       shuffle=UpperCamelCase__    ,       batch_size=UpperCamelCase__    ,       num_workers=4		)
    A       :       Tuple							=						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       :       Union[str, Any]							=						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       :       Optional[int]							=						model.to(accelerator.device		)

    # Freezing the base model
    for param in model.parameters():
        A       :       Union[str, Any]							=						False
    for param in model.get_classifier().parameters():
        A       :       Any							=						True

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

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

    # Instantiate learning rate scheduler
    A       :       int							=						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       :       Optional[int]							=						accelerator.prepare(
        UpperCamelCase__    ,       UpperCamelCase__    ,       UpperCamelCase__    ,       UpperCamelCase__    ,       UpperCamelCase__		)
    # 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       :       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		)
            A       :       Optional[int]							=						os.path.basename(args.resume_from_checkpoint		)
        else:
            # Get the most recent checkpoint
            A       :       Dict							=						[f.name for f in os.scandir(os.getcwd()		) if f.is_dir()]
            dirs.sort(key=os.path.getctime		)
            A       :       Optional[int]							=						dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
        # Extract `epoch_{i}` or `step_{i}`
        A       :       Optional[int]							=						os.path.splitext(UpperCamelCase__		)[0]

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

        A       :       str							=						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(UpperCamelCase__		),
                    '''epoch''': epoch,
                }    ,       step=UpperCamelCase__    ,       )
        if checkpointing_steps == "epoch":
            A       :       Dict							=						F'''epoch_{epoch}'''
            if args.output_dir is not None:
                A       :       Dict							=						os.path.join(args.output_dir    ,       UpperCamelCase__		)
            accelerator.save_state(UpperCamelCase__		)

    if args.with_tracking:
        accelerator.end_training()
def 							_lowerCamelCase(  )		->  int:
    A       :       List[str]							=						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       :       Tuple							=						parser.parse_args()
    A       :       List[str]							=						{'''lr''': 3e-2, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 64, '''image_size''': 224}
    training_function(UpperCamelCase__    ,       UpperCamelCase__		)


if __name__ == "__main__":
     main()
