




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




class 		__A      (			UpperCamelCase__			):

						def __init__(		self   :Union[str, Any]    , __snake_case   :Any    , __snake_case   :Tuple=None    , __snake_case   :Union[str, Any]=None ):
										'''simple docstring'''

										__magic_name__	:	Union[str, Any]								=file_names
										__magic_name__	:	str								=image_transform
										__magic_name__	:	Optional[int]								=label_to_id

						def __len__(		self   :Union[str, Any] ):
										'''simple docstring'''

										return len(self.file_names )


						def __getitem__(		self   :List[str]    , __snake_case   :Tuple ):
										'''simple docstring'''

										__magic_name__	:	int								=self.file_names[idx]
										__magic_name__	:	Optional[Any]								=PIL.Image.open(__snake_case )
										__magic_name__	:	Tuple								=raw_image.convert("""RGB""" )
										if self.image_transform is not None:
														__magic_name__	:	Any								=self.image_transform(__snake_case )
										__magic_name__	:	Optional[int]								=extract_label(__snake_case )
										if self.label_to_id is not None:
														__magic_name__	:	Union[str, Any]								=self.label_to_id[label]
										return {"image": image, "label": label}




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase   ):
				# Initialize accelerator
				if args.with_tracking:
								__magic_name__	:	Any								=Accelerator(
								    cpu=args.cpu	,     mixed_precision=args.mixed_precision	,     log_with="""all"""	,     project_dir=args.project_dir   )
				else:
								__magic_name__	:	Optional[int]								=Accelerator(cpu=args.cpu	,     mixed_precision=args.mixed_precision   )

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

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

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

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

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

				# For evaluation, we use a deterministic Resize
				__magic_name__	:	Union[str, Any]								=Compose([Resize(lowerCamelCase   ), ToTensor()]   )
				__magic_name__	:	Optional[Any]								=PetsDataset([file_names[i] for i in eval_split]	,     image_transform=lowerCamelCase	,     label_to_id=lowerCamelCase   )

				# Instantiate dataloaders.
				__magic_name__	:	Optional[int]								=DataLoader(lowerCamelCase	,     shuffle=lowerCamelCase	,     batch_size=lowerCamelCase	,     num_workers=4   )
				__magic_name__	:	str								=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)
				__magic_name__	:	str								=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).
				__magic_name__	:	Optional[int]								=model.to(accelerator.device   )

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

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

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

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

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

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

								__magic_name__	:	Optional[Any]								=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(lowerCamelCase   ),
												        """epoch""": epoch,
												    }	,     step=lowerCamelCase	,     )
								if checkpointing_steps == "epoch":
												__magic_name__	:	str								=F"epoch_{epoch}"
												if args.output_dir is not None:
																__magic_name__	:	Dict								=os.path.join(args.output_dir	,     lowerCamelCase   )
												accelerator.save_state(lowerCamelCase   )

				if args.with_tracking:
								accelerator.end_training()




def 		lowerCAmelCase_   (	):
				__magic_name__	:	Dict								=argparse.ArgumentParser(description="""Simple example of training script."""   )
				parser.add_argument("""--data_dir"""	,     required=lowerCamelCase	,     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=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("""--cpu"""	,     action="""store_true"""	,     help="""If passed, will train on the CPU."""   )
				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(
				    """--output_dir"""	,     type=lowerCamelCase	,     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=lowerCamelCase	,     default=lowerCamelCase	,     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=lowerCamelCase	,     default="""logs"""	,     help="""Location on where to store experiment tracking logs` and relevent project information"""	,     )
				__magic_name__	:	Tuple								=parser.parse_args()
				__magic_name__	:	Any								={"""lr""": 3E-2, """num_epochs""": 3, """seed""": 42, """batch_size""": 64, """image_size""": 224}
				training_function(lowerCamelCase	,     lowerCamelCase   )


if __name__ == "__main__":
							main()










from json import JSONDecodeError  # Workaround for requests.exceptions.JSONDecodeError

import requests




def 		lowerCAmelCase_   (	lowerCamelCase = "isbn/0140328726"   ):
				__magic_name__	:	Optional[Any]								=olid.strip().strip("""/"""   )  # Remove leading/trailing whitespace & slashes
				if new_olid.count("""/"""   ) != 1:
								__magic_name__	:	Union[str, Any]								=F"{olid} is not a valid Open Library olid"
								raise ValueError(lowerCamelCase   )
				return requests.get(F"https://openlibrary.org/{new_olid}.json"   ).json()




def 		lowerCAmelCase_   (	lowerCamelCase   ):
				__magic_name__	:	Dict								={
				    """title""": """Title""",
				    """publish_date""": """Publish date""",
				    """authors""": """Authors""",
				    """number_of_pages""": """Number of pages:""",
				    """first_sentence""": """First sentence""",
				    """isbn_10""": """ISBN (10)""",
				    """isbn_13""": """ISBN (13)""",
				}
				__magic_name__	:	List[Any]								={better_key: ol_book_data[key] for key, better_key in desired_keys.items()}
				__magic_name__	:	Dict								=[
				    get_openlibrary_data(author["""key"""]   )["""name"""] for author in data["""Authors"""]
				]
				__magic_name__	:	List[str]								=data["""First sentence"""]["""value"""]
				for key, value in data.items():
								if isinstance(lowerCamelCase	,     lowerCamelCase   ):
												__magic_name__	:	Tuple								=""", """.join(lowerCamelCase   )
				return data


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							while True:
														UpperCAmelCase_		:				List[Any]									=     input("\nEnter the ISBN code to search (or 'quit' to stop): ").strip()
														if isbn.lower() in ("", "q", "quit", "exit", "stop"):
																					break

														if len(isbn) not in (10, 13) or not isbn.isdigit():
																					print(F"""Sorry, {isbn} is not a valid ISBN.  Please, input a valid ISBN.""")
																					continue

														print(F"""\nSearching Open Library for ISBN: {isbn}...\n""")

														try:
																					UpperCAmelCase_		:				str									=     summarize_book(get_openlibrary_data(F"""isbn/{isbn}"""))
																					print("\n".join(F"""{key}: {value}""" for key, value in book_summary.items()))
														except JSONDecodeError:  # Workaround for requests.exceptions.RequestException:
																					print(F"""Sorry, there are no results for ISBN: {isbn}.""")




