






from collections.abc import Generator
from math import sin





def 					snake_case	(  lowerCamelCase       ):




	'''simple docstring'''







	if len(lowerCamelCase       ) != 32:
		raise ValueError("""Input must be of length 32"""       )

	__lowercase   							=	b""""""
	for i in [3, 2, 1, 0]:
		little_endian += string_aa[8 * i : 8 * i + 8]
	return little_endian





def 					snake_case	(  lowerCamelCase       ):




	'''simple docstring'''







	if i < 0:
		raise ValueError("""Input must be non-negative"""       )

	__lowercase   							=	format(lowerCamelCase					,	"""08x"""       )[-8:]
	__lowercase   							=	b""""""
	for i in [3, 2, 1, 0]:
		little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode("""utf-8"""       )
	return little_endian_hex





def 					snake_case	(  lowerCamelCase       ):




	'''simple docstring'''







	__lowercase   							=	b""""""
	for char in message:
		bit_string += format(lowerCamelCase					,	"""08b"""       ).encode("""utf-8"""       )
	__lowercase   							=	format(len(lowerCamelCase       )					,	"""064b"""       ).encode("""utf-8"""       )

	# Pad bit_string to a multiple of 512 chars
	bit_string += b"1"
	while len(lowerCamelCase       ) % 512 != 448:
		bit_string += b"0"
	bit_string += to_little_endian(start_len[32:]       ) + to_little_endian(start_len[:32]       )

	return bit_string





def 					snake_case	(  lowerCamelCase       ):




	'''simple docstring'''







	if len(lowerCamelCase       ) % 512 != 0:
		raise ValueError("""Input must have length that's a multiple of 512"""       )

	for pos in range(0					,	len(lowerCamelCase       )					,	512       ):
		__lowercase   							=	bit_string[pos : pos + 512]
		__lowercase   							=	[]
		for i in range(0					,	512					,	32       ):
			block_words.append(int(to_little_endian(block[i : i + 32]       )					,	2       )       )
		yield block_words





def 					snake_case	(  lowerCamelCase       ):




	'''simple docstring'''







	if i < 0:
		raise ValueError("""Input must be non-negative"""       )

	__lowercase   							=	format(lowerCamelCase					,	"""032b"""       )
	__lowercase   							=	""""""
	for c in i_str:
		new_str += "1" if c == "0" else "0"
	return int(lowerCamelCase					,	2       )





def 					snake_case	(  lowerCamelCase					,	lowerCamelCase       ):




	'''simple docstring'''







	return (a + b) % 2**32





def 					snake_case	(  lowerCamelCase					,	lowerCamelCase       ):




	'''simple docstring'''







	if i < 0:
		raise ValueError("""Input must be non-negative"""       )
	if shift < 0:
		raise ValueError("""Shift must be non-negative"""       )
	return ((i << shift) ^ (i >> (32 - shift))) % 2**32





def 					snake_case	(  lowerCamelCase       ):




	'''simple docstring'''







	__lowercase   							=	preprocess(lowerCamelCase       )

	__lowercase   							=	[int(2**32 * abs(sin(i + 1       )       )       ) for i in range(64       )]

	# Starting states
	__lowercase   							=	0x67452301
	__lowercase   							=	0xefcdab89
	__lowercase   							=	0x98badcfe
	__lowercase   							=	0x10325476

	__lowercase   							=	[
	    7,
	    12,
	    17,
	    22,
	    7,
	    12,
	    17,
	    22,
	    7,
	    12,
	    17,
	    22,
	    7,
	    12,
	    17,
	    22,
	    5,
	    9,
	    14,
	    20,
	    5,
	    9,
	    14,
	    20,
	    5,
	    9,
	    14,
	    20,
	    5,
	    9,
	    14,
	    20,
	    4,
	    11,
	    16,
	    23,
	    4,
	    11,
	    16,
	    23,
	    4,
	    11,
	    16,
	    23,
	    4,
	    11,
	    16,
	    23,
	    6,
	    10,
	    15,
	    21,
	    6,
	    10,
	    15,
	    21,
	    6,
	    10,
	    15,
	    21,
	    6,
	    10,
	    15,
	    21,
	]

	# Process bit string in chunks, each with 16 32-char words
	for block_words in get_block_words(lowerCamelCase       ):
		__lowercase   							=	aa
		__lowercase   							=	ba
		__lowercase   							=	ca
		__lowercase   							=	da

		# Hash current chunk
		for i in range(64       ):
			if i <= 15:
				# f = (b & c) | (not_32(b) & d)     # Alternate definition for f
				__lowercase   							=	d ^ (b & (c ^ d))
				__lowercase   							=	i
			elif i <= 31:
				# f = (d & b) | (not_32(d) & c)     # Alternate definition for f
				__lowercase   							=	c ^ (d & (b ^ c))
				__lowercase   							=	(5 * i + 1) % 16
			elif i <= 47:
				__lowercase   							=	b ^ c ^ d
				__lowercase   							=	(3 * i + 5) % 16
			else:
				__lowercase   							=	c ^ (b | not_aa(lowerCamelCase       ))
				__lowercase   							=	(7 * i) % 16
			__lowercase   							=	(f + a + added_consts[i] + block_words[g]) % 2**32
			__lowercase   							=	d
			__lowercase   							=	c
			__lowercase   							=	b
			__lowercase   							=	sum_aa(lowerCamelCase					,	left_rotate_aa(lowerCamelCase					,	shift_amounts[i]       )       )

		# Add hashed chunk to running total
		__lowercase   							=	sum_aa(lowerCamelCase					,	lowerCamelCase       )
		__lowercase   							=	sum_aa(lowerCamelCase					,	lowerCamelCase       )
		__lowercase   							=	sum_aa(lowerCamelCase					,	lowerCamelCase       )
		__lowercase   							=	sum_aa(lowerCamelCase					,	lowerCamelCase       )

	__lowercase   							=	reformat_hex(lowerCamelCase       ) + reformat_hex(lowerCamelCase       ) + reformat_hex(lowerCamelCase       ) + reformat_hex(lowerCamelCase       )
	return digest


if __name__ == "__main__":
						import doctest

						doctest.testmod()






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      __lowercase(						UpperCAmelCase__			):


      """simple docstring"""



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





class  lowerCamelCase__	(    UpperCAmelCase_):
     """simple docstring"""



     def __init__(self					,      __a					,      __a=None					,      __a=None     ):






           '''simple docstring'''



           lowerCamelCase             =		file_names
           lowerCamelCase             =		image_transform
           lowerCamelCase             =		label_to_id







     def __len__(self     ):






           '''simple docstring'''



           return len(self.file_names     )







     def __getitem__(self					,      __a     ):






           '''simple docstring'''



           lowerCamelCase             =		self.file_names[idx]
           lowerCamelCase             =		PIL.Image.open(__a     )
           lowerCamelCase             =		raw_image.convert("RGB"     )
           if self.image_transform is not None:
                 lowerCamelCase             =		self.image_transform(__a     )
           lowerCamelCase             =		extract_label(__a     )
           if self.label_to_id is not None:
                 lowerCamelCase             =		self.label_to_id[label]
           return {"image": image, "label": label}



def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__			):


      """simple docstring"""



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

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

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

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

      # Build the label correspondences
      lowerCamelCase             =		[extract_label(UpperCAmelCase__			) for fname in file_names]
      lowerCamelCase             =		list(set(UpperCAmelCase__			)			)
      id_to_label.sort()
      lowerCamelCase             =		{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
      lowerCamelCase             =		np.random.permutation(len(UpperCAmelCase__			)			)
      lowerCamelCase             =		int(0.8 * len(UpperCAmelCase__			)			)
      lowerCamelCase             =		random_perm[:cut]
      lowerCamelCase             =		random_perm[cut:]

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

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

      # Instantiate dataloaders.
      lowerCamelCase             =		DataLoader(UpperCAmelCase__ , shuffle=UpperCAmelCase__ , batch_size=UpperCAmelCase__ , num_workers=4			)
      lowerCamelCase             =		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)
      lowerCamelCase             =		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).
      lowerCamelCase             =		model.to(accelerator.device			)

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

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

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

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

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

            lowerCamelCase             =		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":
                  lowerCamelCase             =		F"""epoch_{epoch}"""
                  if args.output_dir is not None:
                        lowerCamelCase             =		os.path.join(args.output_dir , UpperCAmelCase__			)
                  accelerator.save_state(UpperCAmelCase__			)

      if args.with_tracking:
            accelerator.end_training()

def      __lowercase(						):


      """simple docstring"""



      lowerCamelCase             =		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" , )
      lowerCamelCase             =		parser.parse_args()
      lowerCamelCase             =		{"lr": 3E-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
      training_function(UpperCAmelCase__ , UpperCAmelCase__			)


if __name__ == "__main__":
     main()