





'''simple docstring'''






from functools import partial
from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload

import torch
import torch.nn as nn
import torch.types





def      lowercase  (  lowerCAmelCase :						torch.Tensor   , lowerCAmelCase :						torch.Tensor   , lowerCAmelCase :						bool):



				"""simple docstring"""


				if not inplace:
								_A :				Union[str, Any]				=  ma + ma
				else:
								ma += ma

				return ma





def      lowercase  (  lowerCAmelCase :						torch.Tensor   , lowerCAmelCase :						List[int]):



				"""simple docstring"""


				_A :				Tuple				=  -1 * len(lowerCAmelCase)
				_A :				Optional[Any]				=  list(range(len(tensor.shape[:zero_index])))
				return tensor.permute(first_inds + [zero_index + i for i in inds])





def      lowercase  (  lowerCAmelCase :						torch.Tensor   , lowerCAmelCase :						int):



				"""simple docstring"""


				return t.reshape(t.shape[:-no_dims] + (-1,))





def      lowercase  (  lowerCAmelCase :						torch.Tensor   , lowerCAmelCase :						torch.Tensor   , lowerCAmelCase :						int   , lowerCAmelCase :						float = 1e-4):



				"""simple docstring"""


				_A :				Dict				=  mask.expand(*value.shape)
				return torch.sum(mask * value   , dim=lowerCAmelCase) / (eps + torch.sum(lowerCAmelCase   , dim=lowerCAmelCase))





def      lowercase  (  lowerCAmelCase :						torch.Tensor   , lowerCAmelCase :						torch.types.Number = 2.3_1_2_5   , lowerCAmelCase :						torch.types.Number = 2_1.6_8_7_5   , lowerCAmelCase :						int = 64):



				"""simple docstring"""


				_A :				int				=  torch.linspace(lowerCAmelCase   , lowerCAmelCase   , no_bins - 1   , device=pts.device)
				_A :				List[str]				=  torch.sqrt(torch.sum((pts.unsqueeze(-2) - pts.unsqueeze(-3)) ** 2   , dim=-1))
				return torch.bucketize(lowerCAmelCase   , lowerCAmelCase)





def      lowercase  (  lowerCAmelCase :						Callable[[list], Any]   , lowerCAmelCase :						List[dict]):



				"""simple docstring"""


				_A :				int				=  dicts[0]
				_A :				Tuple				=  {}
				for k, v in first.items():
								_A :				Tuple				=  [d[k] for d in dicts]
								if isinstance(lowerCAmelCase   , lowerCAmelCase):
												_A :				Any				=  dict_multimap(lowerCAmelCase   , lowerCAmelCase)
								else:
												_A :				Any				=  fn(lowerCAmelCase)

				return new_dict





def      lowercase  (  lowerCAmelCase :						torch.Tensor   , lowerCAmelCase :						torch.Tensor):



				"""simple docstring"""


				_A :				Union[str, Any]				=  v_bins.view(((1,) * len(x.shape)) + (len(lowerCAmelCase),))
				_A :				Tuple				=  x[..., None] - reshaped_bins
				_A :				Optional[int]				=  torch.argmin(torch.abs(lowerCAmelCase)   , dim=-1)
				return nn.functional.one_hot(lowerCAmelCase   , num_classes=len(lowerCAmelCase)).float()





def      lowercase  (  lowerCAmelCase :						torch.Tensor   , lowerCAmelCase :						torch.Tensor   , lowerCAmelCase :						int = 0   , lowerCAmelCase :						int = 0):



				"""simple docstring"""


				_A :				List[Union[slice, torch.Tensor]]				=  []
				for i, s in enumerate(data.shape[:no_batch_dims]):
								_A :				List[str]				=  torch.arange(lowerCAmelCase)
								_A :				str				=  r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape) - i - 1))))
								ranges.append(lowerCAmelCase)

				_A :				List[Union[slice, torch.Tensor]]				=  [slice(lowerCAmelCase) for _ in range(len(data.shape) - no_batch_dims)]
				_A :				Tuple				=  inds
				ranges.extend(lowerCAmelCase)
				# Matt note: Editing this to get around the behaviour of using a list as an array index changing
				# in recent Numpy versions
				return data[tuple(lowerCAmelCase)]


__UpperCamelCase      :   int								=     TypeVar('''T''')





def      lowercase  (  lowerCAmelCase :						Callable[[T], Any]   , lowerCAmelCase :						Dict[Any, Union[dict, list, tuple, T]]   , lowerCAmelCase :						Type[T]):



				"""simple docstring"""


				_A :				Dict[Any, Union[dict, list, tuple, Any]]				=  {}
				for k, v in dic.items():
								if isinstance(lowerCAmelCase   , lowerCAmelCase):
												_A :				Optional[Any]				=  dict_map(lowerCAmelCase   , lowerCAmelCase   , lowerCAmelCase)
								else:
												_A :				Any				=  tree_map(lowerCAmelCase   , lowerCAmelCase   , lowerCAmelCase)

				return new_dict





@overload
def      lowercase  (  lowerCAmelCase :						Callable[[T], Any]   , lowerCAmelCase :						T   , lowerCAmelCase :						Type[T]):



				"""simple docstring"""


				...





@overload
def      lowercase  (  lowerCAmelCase :						Callable[[T], Any]   , lowerCAmelCase :						dict   , lowerCAmelCase :						Type[T]):



				"""simple docstring"""


				...





@overload
def      lowercase  (  lowerCAmelCase :						Callable[[T], Any]   , lowerCAmelCase :						list   , lowerCAmelCase :						Type[T]):



				"""simple docstring"""


				...





@overload
def      lowercase  (  lowerCAmelCase :						Callable[[T], Any]   , lowerCAmelCase :						tuple   , lowerCAmelCase :						Type[T]):



				"""simple docstring"""


				...





def      lowercase  (  lowerCAmelCase :						Tuple   , lowerCAmelCase :						List[Any]   , lowerCAmelCase :						Union[str, Any]):



				"""simple docstring"""


				if isinstance(lowerCAmelCase   , lowerCAmelCase):
								return dict_map(lowerCAmelCase   , lowerCAmelCase   , lowerCAmelCase)
				elif isinstance(lowerCAmelCase   , lowerCAmelCase):
								return [tree_map(lowerCAmelCase   , lowerCAmelCase   , lowerCAmelCase) for x in tree]
				elif isinstance(lowerCAmelCase   , lowerCAmelCase):
								return tuple(tree_map(lowerCAmelCase   , lowerCAmelCase   , lowerCAmelCase) for x in tree)
				elif isinstance(lowerCAmelCase   , lowerCAmelCase):
								return fn(lowerCAmelCase)
				else:
								print(type(lowerCAmelCase))
								raise ValueError('''Not supported''')




__UpperCamelCase      :   str								=     partial(tree_map, leaf_type=torch.Tensor)







'''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      lowercase  (  lowerCAmelCase :						Any):



				"""simple docstring"""


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



class  lowerCamelCase__				(						snake_case_						):


	"""simple docstring"""





	def __init__(					self   ,     UpperCAmelCase__   ,     UpperCAmelCase__=None   ,     UpperCAmelCase__=None			)       ->				int:
					_A :				Tuple				=  file_names
					_A :				Any				=  image_transform
					_A :				str				=  label_to_id




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




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





def      lowercase  (  lowerCAmelCase :						str   , lowerCAmelCase :						List[Any]):



				"""simple docstring"""


				_A :				Union[str, 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 :				int				=  config['''lr''']
				_A :				str				=  int(config['''num_epochs'''])
				_A :				Dict				=  int(config['''seed'''])
				_A :				Any				=  int(config['''batch_size'''])
				_A :				List[str]				=  config['''image_size''']
				if not isinstance(lowerCAmelCase   , (list, tuple)):
								_A :				List[str]				=  (image_size, image_size)

				# Grab all the image filenames
				_A :				Optional[Any]				=  [os.path.join(args.data_dir   , lowerCAmelCase) for fname in os.listdir(args.data_dir) if fname.endswith('''.jpg''')]

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

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

				# Split our filenames between train and validation
				_A :				Dict				=  np.random.permutation(len(lowerCAmelCase))
				_A :				List[str]				=  int(0.8 * len(lowerCAmelCase))
				_A :				Tuple				=  random_perm[:cut]
				_A :				Union[str, Any]				=  random_perm[cut:]

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

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

				# Instantiate dataloaders.
				_A :				str				=  DataLoader(lowerCAmelCase   , shuffle=lowerCAmelCase   , batch_size=lowerCAmelCase   , num_workers=4)
				_A :				Optional[int]				=  DataLoader(lowerCAmelCase   , shuffle=lowerCAmelCase   , batch_size=lowerCAmelCase   , num_workers=4)

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				_A :				Tuple				=  create_model('''resnet50d'''   , pretrained=lowerCAmelCase   , num_classes=len(lowerCAmelCase))

				# 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 :				List[Any]				=  torch.tensor(model.default_cfg['''mean'''])[None, :, None, None].to(accelerator.device)
				_A :				int				=  torch.tensor(model.default_cfg['''std'''])[None, :, None, None].to(accelerator.device)

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

				# Instantiate learning rate scheduler
				_A :				List[str]				=  OneCycleLR(optimizer=lowerCAmelCase   , max_lr=lowerCAmelCase   , epochs=lowerCAmelCase   , steps_per_epoch=len(lowerCAmelCase))

				# 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 :				List[str]				=  accelerator.prepare(
				    lowerCAmelCase   , lowerCAmelCase   , lowerCAmelCase   , lowerCAmelCase   , lowerCAmelCase)

				# Now we train the model
				for epoch in range(lowerCAmelCase):
								model.train()
								for step, batch in enumerate(lowerCAmelCase):
												# 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 :				Tuple				=  (batch['''image'''] - mean) / std
												_A :				Any				=  model(lowerCAmelCase)
												_A :				Optional[Any]				=  torch.nn.functional.cross_entropy(lowerCAmelCase   , batch['''label'''])
												accelerator.backward(lowerCAmelCase)
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

								model.eval()
								_A :				Optional[Any]				=  0
								_A :				Union[str, Any]				=  0
								for _, batch in enumerate(lowerCAmelCase):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												_A :				int				=  {k: v.to(accelerator.device) for k, v in batch.items()}
												_A :				List[Any]				=  (batch['''image'''] - mean) / std
												with torch.no_grad():
																_A :				Tuple				=  model(lowerCAmelCase)
												_A :				List[str]				=  outputs.argmax(dim=-1)
												_A	,       _A :				Union[str, Any]				=  accelerator.gather_for_metrics((predictions, batch['''label''']))
												_A :				Dict				=  predictions == references
												num_elems += accurate_preds.shape[0]
												accurate += accurate_preds.long().sum()

								_A :				int				=  accurate.item() / num_elems
								# Use accelerator.print to print only on the main process.
								accelerator.print(f"""epoch {epoch}: {100 * eval_metric:.2f}""")





def      lowercase  (  ):



				"""simple docstring"""


				_A :				Any				=  argparse.ArgumentParser(description='''Simple example of training script.''')
				parser.add_argument('''--data_dir'''   , required=lowerCAmelCase   , help='''The data folder on disk.''')
				parser.add_argument(
				    '''--mixed_precision'''   , type=lowerCAmelCase   , default=lowerCAmelCase   , 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(
				    '''--checkpointing_steps'''   , type=lowerCAmelCase   , default=lowerCAmelCase   , help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''   , )
				parser.add_argument('''--cpu'''   , action='''store_true'''   , help='''If passed, will train on the CPU.''')
				_A :				Optional[int]				=  parser.parse_args()
				_A :				Optional[Any]				=  {'''lr''': 3e-2, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 64, '''image_size''': 224}
				training_function(lowerCAmelCase   , lowerCAmelCase)


if __name__ == "__main__":
	main()
