from __future__ import annotations

from collections.abc import Iterable
from dataclasses import dataclass
from typing import Any




class      lowercase_						:
	UpperCAmelCase_						:      Optional[Any]     =       ("_front", "_back", "_len")




	@dataclass
	class      lowercase_						:
		UpperCAmelCase_						:      Any     =       None
		UpperCAmelCase_						:      Deque._Node | None     =       None
		UpperCAmelCase_						:      Deque._Node | None     =       None




	class      lowercase_						:
		UpperCAmelCase_						:      Dict     =       ("_cur",)


		def __init__(							self	,    __SCREAMING_SNAKE_CASE       )		->None:
									lowerCAmelCase				=	cur


		def __iter__(							self       )		->Deque._Iterator:
									return self




		def     SCREAMING_SNAKE_CASE_       (							self       )		->Any:
									if self._cur is None:
																# finished iterating
																raise StopIteration
									lowerCAmelCase				=	self._cur.val
									lowerCAmelCase				=	self._cur.next_node

									return val


	def __init__(							self	,    __SCREAMING_SNAKE_CASE = None       )		->None:
								lowerCAmelCase				=	None
								lowerCAmelCase				=	None
								lowerCAmelCase				=	0

								if iterable is not None:
															# append every value to the deque
															for val in iterable:
																						self.append(SCREAMING_SNAKE_CASE_       )


	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE       )		->None:
								lowerCAmelCase				=	self._Node(SCREAMING_SNAKE_CASE_	,    SCREAMING_SNAKE_CASE_	,    SCREAMING_SNAKE_CASE_       )
								if self.is_empty():
															# front = back
															lowerCAmelCase				=	node
															lowerCAmelCase				=	1
								else:
															# connect nodes
															lowerCAmelCase				=	node
															lowerCAmelCase				=	self._back
															lowerCAmelCase				=	node  # assign new back to the new node

															self._len += 1

															# make sure there were no errors
															assert not self.is_empty(), "Error on appending value."


	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE       )		->None:
								lowerCAmelCase				=	self._Node(SCREAMING_SNAKE_CASE_	,    SCREAMING_SNAKE_CASE_	,    SCREAMING_SNAKE_CASE_       )
								if self.is_empty():
															# front = back
															lowerCAmelCase				=	node
															lowerCAmelCase				=	1
								else:
															# connect nodes
															lowerCAmelCase				=	self._front
															lowerCAmelCase				=	node
															lowerCAmelCase				=	node  # assign new front to the new node

															self._len += 1

															# make sure there were no errors
															assert not self.is_empty(), "Error on appending value."


	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE       )		->None:
								for val in iterable:
															self.append(SCREAMING_SNAKE_CASE_       )


	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE       )		->None:
								for val in iterable:
															self.appendleft(SCREAMING_SNAKE_CASE_       )


	def     SCREAMING_SNAKE_CASE_       (							self       )		->Any:
								assert not self.is_empty(), "Deque is empty."

								lowerCAmelCase				=	self._back
								lowerCAmelCase				=	self._back.prev_node  # set new back
								# drop the last node - python will deallocate memory automatically
								lowerCAmelCase				=	None

								self._len -= 1

								return topop.val


	def     SCREAMING_SNAKE_CASE_       (							self       )		->Any:
								assert not self.is_empty(), "Deque is empty."

								lowerCAmelCase				=	self._front
								lowerCAmelCase				=	self._front.next_node  # set new front and drop the first node
								lowerCAmelCase				=	None

								self._len -= 1

								return topop.val


	def     SCREAMING_SNAKE_CASE_       (							self       )		->bool:
								return self._front is None


	def __len__(							self       )		->int:
								return self._len


	def __eq__(							self	,    __SCREAMING_SNAKE_CASE       )		->bool:

								if not isinstance(SCREAMING_SNAKE_CASE_	,    SCREAMING_SNAKE_CASE_       ):
															return NotImplemented

								lowerCAmelCase				=	self._front
								lowerCAmelCase				=	other._front

								# if the length of the dequeues are not the same, they are not equal
								if len(self       ) != len(SCREAMING_SNAKE_CASE_       ):
															return False

								while me is not None and oth is not None:
															# compare every value
															if me.val != oth.val:
																						return False
															lowerCAmelCase				=	me.next_node
															lowerCAmelCase				=	oth.next_node

								return True


	def __iter__(							self       )		->Deque._Iterator:
								return Deque._Iterator(self._front       )




	def __repr__(							self       )		->str:
								lowerCAmelCase				=	[]
								lowerCAmelCase				=	self._front
								while aux is not None:
															# append the values in a list to display
															values_list.append(aux.val       )
															lowerCAmelCase				=	aux.next_node

								return F"[{', '.join(repr(SCREAMING_SNAKE_CASE_       ) for val in values_list       )}]"


if __name__ == "__main__":
	import doctest

	doctest.testmod()




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




class      lowercase_						(						UpperCamelCase_		):



	"""simple docstring"""





	def __init__(							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE=None	,    __SCREAMING_SNAKE_CASE=None       )		->Optional[Any]:
								lowerCAmelCase				=	file_names
								lowerCAmelCase				=	image_transform
								lowerCAmelCase				=	label_to_id


	def __len__(							self       )		->Optional[int]:
								return len(self.file_names       )




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

def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__							)  ->			Any:
							# Initialize accelerator
							lowerCAmelCase				=	Accelerator(cpu=args.cpu     ,						mixed_precision=args.mixed_precision							)

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

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

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

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

							# Split our filenames between train and validation
							lowerCAmelCase				=	np.random.permutation(len(snake_case__							)							)
							lowerCAmelCase				=	int(0.8 * len(snake_case__							)							)
							lowerCAmelCase				=	random_perm[:cut]
							lowerCAmelCase				=	random_perm[cut:]

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

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

							# Instantiate dataloaders.
							lowerCAmelCase				=	DataLoader(snake_case__     ,						shuffle=snake_case__     ,						batch_size=snake_case__     ,						num_workers=4							)
							lowerCAmelCase				=	DataLoader(snake_case__     ,						shuffle=snake_case__     ,						batch_size=snake_case__     ,						num_workers=4							)

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

							# 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).
							lowerCAmelCase				=	model.to(accelerator.device							)

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

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

							# Instantiate optimizer
							lowerCAmelCase				=	torch.optim.Adam(params=model.parameters()     ,						lr=lr / 2_5							)

							# Instantiate learning rate scheduler
							lowerCAmelCase				=	OneCycleLR(optimizer=snake_case__     ,						max_lr=snake_case__     ,						epochs=snake_case__     ,						steps_per_epoch=len(snake_case__							)							)

							# 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.
							lowerCAmelCase	,   lowerCAmelCase	,   lowerCAmelCase	,   lowerCAmelCase	,   lowerCAmelCase				=	accelerator.prepare(
							    snake_case__     ,						snake_case__     ,						snake_case__     ,						snake_case__     ,						snake_case__							)

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

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

														lowerCAmelCase				=	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}"							)

def      SCREAMING_SNAKE_CASE_  (		)  ->			List[str]:
							lowerCAmelCase				=	argparse.ArgumentParser(description='''Simple example of training script.'''							)
							parser.add_argument('''--data_dir'''     ,						required=snake_case__     ,						help='''The data folder on disk.'''							)
							parser.add_argument(
							    '''--mixed_precision'''     ,						type=snake_case__     ,						default=snake_case__     ,						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=snake_case__     ,						default=snake_case__     ,						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.'''							)
							lowerCAmelCase				=	parser.parse_args()
							lowerCAmelCase				=	{'''lr''': 3E-2, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 6_4, '''image_size''': 2_2_4}
							training_function(snake_case__     ,						snake_case__							)


if __name__ == "__main__":
	main()



