





'''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__(		_UpperCamelCase  : Dict				)->   Tuple:






					"""simple docstring"""

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

class     A_ (  lowerCAmelCase_  ):

			'''simple docstring'''





			def __init__(					self			,     A_			,     A_=None			,     A_=None   ):
								_UpperCamelCase											=   file_names
								_UpperCamelCase											=   image_transform
								_UpperCamelCase											=   label_to_id




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




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




def  lowercase__(		_UpperCamelCase  : List[str] ,   _UpperCamelCase  : str				)->   Any:






					"""simple docstring"""

					_UpperCamelCase											=   Accelerator(cpu=args.cpu ,   mixed_precision=args.mixed_precision				)

					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					_UpperCamelCase											=   config["lr"]
					_UpperCamelCase											=   int(config["num_epochs"]				)
					_UpperCamelCase											=   int(config["seed"]				)
					_UpperCamelCase											=   int(config["batch_size"]				)
					_UpperCamelCase											=   config["image_size"]
					if not isinstance(_UpperCamelCase ,   (list, tuple)				):
										_UpperCamelCase											=   (image_size, image_size)

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

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

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

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

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

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

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

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

					# Instantiate learning rate scheduler
					_UpperCamelCase											=   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.
					_UpperCamelCase				,	_UpperCamelCase				,	_UpperCamelCase				,	_UpperCamelCase				,	_UpperCamelCase											=   accelerator.prepare(
					    _UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase				)

					# Now we train the model
					for epoch in range(_UpperCamelCase				):
										model.train()
										for step, batch in enumerate(_UpperCamelCase				):
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															_UpperCamelCase											=   {k: v.to(accelerator.device				) for k, v in batch.items()}
															_UpperCamelCase											=   (batch["image"] - mean) / std
															_UpperCamelCase											=   model(_UpperCamelCase				)
															_UpperCamelCase											=   torch.nn.functional.cross_entropy(_UpperCamelCase ,   batch["label"]				)
															accelerator.backward(_UpperCamelCase				)
															optimizer.step()
															lr_scheduler.step()
															optimizer.zero_grad()

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

										_UpperCamelCase											=   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__(		)->   Tuple:






					"""simple docstring"""

					_UpperCamelCase											=   argparse.ArgumentParser(description="Simple example of training script."				)
					parser.add_argument("--data_dir" ,   required=_UpperCamelCase ,   help="The data folder on disk."				)
					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(
					    "--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("--cpu" ,   action="store_true" ,   help="If passed, will train on the CPU."				)
					_UpperCamelCase											=   parser.parse_args()
					_UpperCamelCase											=   {"lr": 3E-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
					training_function(_UpperCamelCase ,   _UpperCamelCase				)


if __name__ == "__main__":
			main()










'''simple docstring'''



from __future__ import annotations



def  lowercase__(		_UpperCamelCase  : list[list[int]] ,   _UpperCamelCase  : int ,   _UpperCamelCase  : int ,   _UpperCamelCase  : int | float				)->   None:






					"""simple docstring"""

					_UpperCamelCase				,	_UpperCamelCase											=   m - 1, 0
					while i >= 0 and j < n:
										if key == mat[i][j]:
															print(f"Key {key} found at row- {i + 1} column- {j + 1}"				)
															return
										if key < mat[i][j]:
															i -= 1
										else:
															j += 1
					print(f"Key {key} not found"				)



def  lowercase__(		)->   None:






					"""simple docstring"""

					_UpperCamelCase											=   [[2, 5, 7], [4, 8, 13], [9, 11, 15], [12, 17, 20]]
					_UpperCamelCase											=   int(input("Enter the element to be searched:"				)				)
					print(_UpperCamelCase				)
					search_in_a_sorted_matrix(_UpperCamelCase ,   len(_UpperCamelCase				) ,   len(mat[0]				) ,   _UpperCamelCase				)


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			main()



