'''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       lowerCAmelCase					(		UpperCamelCase__				:   Optional[Any]       ):


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






class 						A   (   UpperCAmelCase   ):





							def __init__(      self				:							Optional[Any]     ,		__a				:							Optional[Any]     ,		__a				:							List[Any]=None     ,		__a				:							int=None	)							->	List[Any]:
								__UpperCAmelCase							=			file_names
								__UpperCAmelCase							=			image_transform
								__UpperCAmelCase							=			label_to_id





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






							def __getitem__(      self				:							List[Any]     ,		__a				:							int	)							->	List[str]:
								__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       lowerCAmelCase					(		UpperCamelCase__				:   List[str]    ,					UpperCamelCase__				:   List[str]       ):


	"""simple docstring"""
	# Initialize accelerator
	if args.with_tracking:
		__UpperCAmelCase							=			Accelerator(
		    cpu=args.cpu    ,					mixed_precision=args.mixed_precision    ,					log_with='''all'''    ,					project_dir=args.project_dir       )
	else:
		__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)

	# 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":
			__UpperCAmelCase							=			args.checkpointing_steps
		elif args.checkpointing_steps.isdigit():
			__UpperCAmelCase							=			int(args.checkpointing_steps       )
		else:
			raise ValueError(
			    f"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""       )
	else:
		__UpperCAmelCase							=			None

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

	# 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 / 2_5       )

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

		if "epoch" in training_difference:
			__UpperCAmelCase							=			int(training_difference.replace('''epoch_'''    ,					''''''       )       ) + 1
			__UpperCAmelCase							=			None
		else:
			__UpperCAmelCase							=			int(training_difference.replace('''step_'''    ,					''''''       )       )
			__UpperCAmelCase							=			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:
			__UpperCAmelCase							=			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
			__UpperCAmelCase							=			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
			__UpperCAmelCase							=			train_dataloader
		for batch in active_dataloader:
			# 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''']       )
			# 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__       ):
				__UpperCAmelCase							=			f"""step_{overall_step}"""
				if overall_step % checkpointing_steps == 0:
					if args.output_dir is not None:
						__UpperCAmelCase							=			os.path.join(args.output_dir    ,					UpperCamelCase__       )
					accelerator.save_state(UpperCamelCase__       )
		model.eval()
		__UpperCAmelCase							=			0
		__UpperCAmelCase							=			0
		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
			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}: {1_0_0 * eval_metric:.2f}"""       )
		if args.with_tracking:
			accelerator.log(
			    {
			        '''accuracy''': 1_0_0 * eval_metric,
			        '''train_loss''': total_loss.item() / len(UpperCamelCase__       ),
			        '''epoch''': epoch,
			    }    ,					step=UpperCamelCase__    ,					)
		if checkpointing_steps == "epoch":
			__UpperCAmelCase							=			f"""epoch_{epoch}"""
			if args.output_dir is not None:
				__UpperCAmelCase							=			os.path.join(args.output_dir    ,					UpperCamelCase__       )
			accelerator.save_state(UpperCamelCase__       )

	if args.with_tracking:
		accelerator.end_training()





def       lowerCAmelCase					(		):


	"""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('''--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'''    ,					)
	__UpperCAmelCase							=			parser.parse_args()
	__UpperCAmelCase							=			{'''lr''': 3E-2, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 6_4, '''image_size''': 2_2_4}
	training_function(UpperCamelCase__    ,					UpperCamelCase__       )


if __name__ == "__main__":
					main()

'''simple docstring'''


import requests
from bsa import BeautifulSoup





def       lowerCAmelCase					(		UpperCamelCase__				:   str = "AAPL"       ):


	"""simple docstring"""
	__UpperCAmelCase							=			f"""https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"""
	__UpperCAmelCase							=			BeautifulSoup(requests.get(UpperCamelCase__       ).text    ,					'''html.parser'''       )
	__UpperCAmelCase							=			'''My(6px) Pos(r) smartphone_Mt(6px)'''
	return soup.find('''div'''    ,					class_=class_       ).find('''span'''       ).text


if __name__ == "__main__":
					for symbol in "AAPL AMZN IBM GOOG MSFT ORCL".split():
										print(F"""Current {symbol:<4} stock price is {stock_price(symbol):>8}""")
