





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_			(      SCREAMING_SNAKE_CASE       :							Dict   ):


							'''simple docstring'''







							__lowerCamelCase      :      Optional[int]         =fname.split(os.path.sep   )[-1]
							return re.search(r'''^(.*)_\d+\.jpg$'''    ,						SCREAMING_SNAKE_CASE   ).groups()[0]
class 		SCREAMING_SNAKE_CASE_							(    snake_case__   ):
				"""simple docstring"""




				def __init__(     self  :Union[str, Any]						,    __lowercase  :int						,    __lowercase  :List[str]=None						,    __lowercase  :Any=None			):
											__lowerCamelCase      :      Optional[int]         =file_names
											__lowerCamelCase      :      Optional[Any]         =image_transform
											__lowerCamelCase      :      Tuple         =label_to_id




				def __len__(     self  :Optional[Any]			):
											return len(self.file_names			)




				def __getitem__(     self  :str						,    __lowercase  :List[str]			):
											__lowerCamelCase      :      Optional[Any]         =self.file_names[idx]
											__lowerCamelCase      :      Dict         =PIL.Image.open(__lowercase			)
											__lowerCamelCase      :      List[Any]         =raw_image.convert('''RGB'''			)
											if self.image_transform is not None:
																		__lowerCamelCase      :      int         =self.image_transform(__lowercase			)
											__lowerCamelCase      :      Optional[Any]         =extract_label(__lowercase			)
											if self.label_to_id is not None:
																		__lowerCamelCase      :      Union[str, Any]         =self.label_to_id[label]
											return {"image": image, "label": label}



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Optional[Any]    ,						SCREAMING_SNAKE_CASE       :							int   ):


							'''simple docstring'''







							if args.with_tracking:
														__lowerCamelCase      :      List[Any]         =Accelerator(
														    cpu=args.cpu    ,						mixed_precision=args.mixed_precision    ,						log_with='''all'''    ,						project_dir=args.project_dir   )
							else:
														__lowerCamelCase      :      Tuple         =Accelerator(cpu=args.cpu    ,						mixed_precision=args.mixed_precision   )

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

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

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

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

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

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

							# For training we use a simple RandomResizedCrop
							__lowerCamelCase      :      Optional[Any]         =Compose([RandomResizedCrop(SCREAMING_SNAKE_CASE    ,						scale=(0.5, 1.0)   ), ToTensor()]   )
							__lowerCamelCase      :      Union[str, Any]         =PetsDataset(
							    [file_names[i] for i in train_split]    ,						image_transform=SCREAMING_SNAKE_CASE    ,						label_to_id=SCREAMING_SNAKE_CASE   )

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

							# Instantiate dataloaders.
							__lowerCamelCase      :      int         =DataLoader(SCREAMING_SNAKE_CASE    ,						shuffle=SCREAMING_SNAKE_CASE    ,						batch_size=SCREAMING_SNAKE_CASE    ,						num_workers=4   )
							__lowerCamelCase      :      Any         =DataLoader(SCREAMING_SNAKE_CASE    ,						shuffle=SCREAMING_SNAKE_CASE    ,						batch_size=SCREAMING_SNAKE_CASE    ,						num_workers=4   )

							# Instantiate the model (we build the model here so that the seed also control new weights initialization)
							__lowerCamelCase      :      Dict         =create_model('''resnet50d'''    ,						pretrained=SCREAMING_SNAKE_CASE    ,						num_classes=len(SCREAMING_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      :      List[Any]         =model.to(accelerator.device   )

							# Freezing the base model
							for param in model.parameters():
														__lowerCamelCase      :      Union[str, Any]         =False
							for param in model.get_classifier().parameters():
														__lowerCamelCase      :      List[str]         =True

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

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

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

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

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

														__lowerCamelCase      :      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(SCREAMING_SNAKE_CASE   ),
																					        '''epoch''': epoch,
																					    }    ,						step=SCREAMING_SNAKE_CASE    ,						)
														if checkpointing_steps == "epoch":
																					__lowerCamelCase      :      Optional[Any]         =F'epoch_{epoch}'
																					if args.output_dir is not None:
																												__lowerCamelCase      :      List[Any]         =os.path.join(args.output_dir    ,						SCREAMING_SNAKE_CASE   )
																					accelerator.save_state(SCREAMING_SNAKE_CASE   )

							if args.with_tracking:
														accelerator.end_training()



def 				lowerCAmelCase_			(      ):


							'''simple docstring'''







							__lowerCamelCase      :      Dict         =argparse.ArgumentParser(description='''Simple example of training script.'''   )
							parser.add_argument('''--data_dir'''    ,						required=SCREAMING_SNAKE_CASE    ,						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=SCREAMING_SNAKE_CASE    ,						default=SCREAMING_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('''--cpu'''    ,						action='''store_true'''    ,						help='''If passed, will train on the CPU.'''   )
							parser.add_argument(
							    '''--checkpointing_steps'''    ,						type=SCREAMING_SNAKE_CASE    ,						default=SCREAMING_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(
							    '''--output_dir'''    ,						type=SCREAMING_SNAKE_CASE    ,						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=SCREAMING_SNAKE_CASE    ,						default=SCREAMING_SNAKE_CASE    ,						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=SCREAMING_SNAKE_CASE    ,						default='''logs'''    ,						help='''Location on where to store experiment tracking logs` and relevent project information'''    ,						)
							__lowerCamelCase      :      int         =parser.parse_args()
							__lowerCamelCase      :      List[str]         ={'''lr''': 3E-2, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 64, '''image_size''': 224}
							training_function(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
							main()











"""simple docstring"""





import builtins
import sys

from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP


_UpperCamelCase							=      False
try:
							_UpperCamelCase							=      _is_package_available('google.colab')
except ModuleNotFoundError:
							pass
@input.register
class 		SCREAMING_SNAKE_CASE_							:
				"""simple docstring"""




				def __init__(     self  :Union[str, Any]						,    __lowercase  :str = None						,    __lowercase  :list = []			):
											__lowerCamelCase      :      Any         =0
											__lowerCamelCase      :      List[str]         =choices
											__lowerCamelCase      :      int         =prompt
											if sys.platform == "win32":
																		__lowerCamelCase      :      Dict         ='''*'''
											else:
																		__lowerCamelCase      :      Union[str, Any]         ='''➔ '''




				def 					__lowercase  (     self  :Tuple						,    __lowercase  :Any						,    __lowercase  :str = ""			):
											if sys.platform != "win32":
																		writeColor(self.choices[index]						,    32						,    __lowercase			)
											else:
																		forceWrite(self.choices[index]						,    __lowercase			)




				def 					__lowercase  (     self  :Tuple						,    __lowercase  :int			):
											if index == self.position:
																		forceWrite(f' {self.arrow_char} '			)
																		self.write_choice(__lowercase			)
											else:
																		forceWrite(f'    {self.choices[index]}'			)
											reset_cursor()




				def 					__lowercase  (     self  :Tuple						,    __lowercase  :Direction						,    __lowercase  :int = 1			):
											__lowerCamelCase      :      List[str]         =self.position
											if direction == Direction.DOWN:
																		if self.position + 1 >= len(self.choices			):
																									return
																		self.position += num_spaces
											else:
																		if self.position - 1 < 0:
																									return
																		self.position -= num_spaces
											clear_line()
											self.print_choice(__lowercase			)
											move_cursor(__lowercase						,    direction.name			)
											self.print_choice(self.position			)




				@input.mark(KEYMAP['''up''']			)
				def 					__lowercase  (     self  :Union[str, Any]			):
											self.move_direction(Direction.UP			)




				@input.mark(KEYMAP['''down''']			)
				def 					__lowercase  (     self  :Union[str, Any]			):
											self.move_direction(Direction.DOWN			)




				@input.mark(KEYMAP['''newline''']			)
				def 					__lowercase  (     self  :Any			):
											move_cursor(len(self.choices			) - self.position						,    '''DOWN'''			)
											return self.position




				@input.mark(KEYMAP['''interrupt''']			)
				def 					__lowercase  (     self  :Any			):
											move_cursor(len(self.choices			) - self.position						,    '''DOWN'''			)
											raise KeyboardInterrupt




				@input.mark_multiple(*[KEYMAP[str(__lowercase			)] for number in range(10			)]			)
				def 					__lowercase  (     self  :Any			):
											__lowerCamelCase      :      Tuple         =int(chr(self.current_selection			)			)
											__lowerCamelCase      :      Dict         =index - self.position
											if index == self.position:
																		return
											if index < len(self.choices			):
																		if self.position > index:
																									self.move_direction(Direction.UP						,    -movement			)
																		elif self.position < index:
																									self.move_direction(Direction.DOWN						,    __lowercase			)
																		else:
																									return
											else:
																		return




				def 					__lowercase  (     self  :Optional[int]						,    __lowercase  :int = 0			):
											if self.prompt:
																		linebreak()
																		forceWrite(self.prompt						,    '''\n'''			)
																		if in_colab:
																									forceWrite('''Please input a choice index (starting from 0), and press enter'''						,    '''\n'''			)
																		else:
																									forceWrite('''Please select a choice using the arrow or number keys, and selecting with enter'''						,    '''\n'''			)
											__lowerCamelCase      :      Union[str, Any]         =default_choice
											for i in range(len(self.choices			)			):
																		self.print_choice(__lowercase			)
																		forceWrite('''\n'''			)
											move_cursor(len(self.choices			) - self.position						,    '''UP'''			)
											with cursor.hide():
																		while True:
																									if in_colab:
																																try:
																																							__lowerCamelCase      :      Union[str, Any]         =int(builtins.input()			)
																																except ValueError:
																																							__lowerCamelCase      :      Optional[Any]         =default_choice
																									else:
																																__lowerCamelCase      :      Dict         =self.handle_input()
																									if choice is not None:
																																reset_cursor()
																																for _ in range(len(self.choices			) + 1			):
																																							move_cursor(1						,    '''UP'''			)
																																							clear_line()
																																self.write_choice(__lowercase						,    '''\n'''			)
																																return choice




