






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     a_   (__A      )				->						Dict:



					"""simple docstring"""
					__a					:						Union[str, Any]      =					fname.split(os.path.sep      )[-1]
					return re.search(r"^(.*)_\d+\.jpg$"				,       __A      ).groups()[0]




class 							snake_case_       (    __UpperCamelCase      ):

						"""simple docstring"""



						def __init__(self:					str ,					__UpperCAmelCase:					int ,					__UpperCAmelCase:					List[Any]=None ,					__UpperCAmelCase:					List[str]=None   )    ->     Any:




											'''simple docstring'''



											__a					:						Optional[Any]      =					file_names
											__a					:						Tuple      =					image_transform
											__a					:						List[str]      =					label_to_id



						def __len__(self:					Optional[Any]   )    ->     List[str]:




											'''simple docstring'''



											return len(self.file_names   )



						def __getitem__(self:					Optional[Any] ,					__UpperCAmelCase:					List[Any]   )    ->     Any:




											'''simple docstring'''



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







def     a_   (__A				,       __A      )				->						Optional[Any]:



					"""simple docstring"""
					# Initialize accelerator
					if args.with_tracking:
										__a					:						int      =					Accelerator(
										    cpu=args.cpu				,       mixed_precision=args.mixed_precision				,       log_with="all"				,       project_dir=args.project_dir      )
					else:
										__a					:						List[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					:						Union[str, Any]      =					config["lr"]
					__a					:						List[str]      =					int(config["num_epochs"]      )
					__a					:						Any      =					int(config["seed"]      )
					__a					:						Union[str, Any]      =					int(config["batch_size"]      )
					__a					:						Tuple      =					config["image_size"]
					if not isinstance(__A				,       (list, tuple)      ):
										__a					:						Tuple      =					(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[str]      =					args.checkpointing_steps
										elif args.checkpointing_steps.isdigit():
															__a					:						Optional[int]      =					int(args.checkpointing_steps      )
										else:
															raise ValueError(
															    f'Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'      )
					else:
										__a					:						List[str]      =					None

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

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

					# Build the label correspondences
					__a					:						Dict      =					[extract_label(__A      ) for fname in file_names]
					__a					:						Optional[int]      =					list(set(__A      )      )
					id_to_label.sort()
					__a					:						Tuple      =					{lbl: i for i, lbl in enumerate(__A      )}

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

					# Split our filenames between train and validation
					__a					:						str      =					np.random.permutation(len(__A      )      )
					__a					:						Any      =					int(0.8 * len(__A      )      )
					__a					:						Optional[int]      =					random_perm[:cut]
					__a					:						str      =					random_perm[cut:]

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

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

					# Instantiate dataloaders.
					__a					:						Any      =					DataLoader(__A				,       shuffle=__A				,       batch_size=__A				,       num_workers=4      )
					__a					:						Dict      =					DataLoader(__A				,       shuffle=__A				,       batch_size=__A				,       num_workers=4      )

					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					__a					:						Optional[int]      =					create_model("resnet50d"				,       pretrained=__A				,       num_classes=len(__A      )      )

					# 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					:						str      =					model.to(accelerator.device      )

					# Freezing the base model
					for param in model.parameters():
										__a					:						Tuple      =					False
					for param in model.get_classifier().parameters():
										__a					:						int      =					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					:						Dict      =					torch.optim.Adam(params=model.parameters()				,       lr=lr / 25      )

					# Instantiate learning rate scheduler
					__a					:						Optional[int]      =					OneCycleLR(optimizer=__A				,       max_lr=__A				,       epochs=__A				,       steps_per_epoch=len(__A      )      )

					# 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(
					    __A				,       __A				,       __A				,       __A				,       __A      )
					# We need to keep track of how many total steps we have iterated over
					__a					:						Optional[int]      =					0
					# We also need to keep track of the starting epoch so files are named properly
					__a					:						int      =					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					:						Any      =					os.path.basename(args.resume_from_checkpoint      )
										else:
															# Get the most recent checkpoint
															__a					:						Optional[int]      =					[f.name for f in os.scandir(os.getcwd()      ) if f.is_dir()]
															dirs.sort(key=os.path.getctime      )
															__a					:						Optional[Any]      =					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(__A      )[0]

										if "epoch" in training_difference:
															__a					:						List[str]      =					int(training_difference.replace("epoch_"				,       ""      )      ) + 1
															__a					:						Dict      =					None
										else:
															__a					:						List[str]      =					int(training_difference.replace("step_"				,       ""      )      )
															__a					:						Any      =					resume_step // len(__A      )
															resume_step -= starting_epoch * len(__A      )

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

					if args.with_tracking:
										accelerator.end_training()







def     a_   ()				->						List[Any]:



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


if __name__ == "__main__":
			main()













UpperCAmelCase__      			=						'''Input must be a string of 8 numbers plus letter'''
UpperCAmelCase__      			=						'''TRWAGMYFPDXBNJZSQVHLCKE'''







def     a_   (__A      )				->						bool:



					"""simple docstring"""

					if not isinstance(__A				,       __A      ):
										__a					:						Any      =					f'Expected string as input, found {type(__A      ).__name__}'
										raise TypeError(__A      )

					__a					:						int      =					spanish_id.replace("-"				,       ""      ).upper()
					if len(__A      ) != 9:
										raise ValueError(__A      )

					try:
										__a					:						Tuple      =					int(spanish_id_clean[0:8]      )
										__a					:						Optional[int]      =					spanish_id_clean[8]
					except ValueError as ex:
										raise ValueError(__A      ) from ex

					if letter.isdigit():
										raise ValueError(__A      )

					return letter == LOOKUP_LETTERS[number % 23]


if __name__ == "__main__":
			import doctest

			doctest.testmod()





