
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 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):
 lowercase					  =   fname.split(os.path.sep							)[-1]
 return re.search(r'^(.*)_\d+\.jpg$'			,							__SCREAMING_SNAKE_CASE							).groups()[0]







class 	A_       (      __lowerCamelCase       ):




 '''simple docstring'''


 def __init__(	self		,   snake_case		,   snake_case=None		,   snake_case=None	):
  lowercase					  =   file_names
  lowercase					  =   image_transform
  lowercase					  =   label_to_id





 def __len__(	self	):
  return len(self.file_names	)





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






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
 # Initialize accelerator
 lowercase					  =   Accelerator(cpu=args.cpu			,							mixed_precision=args.mixed_precision							)

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

 # Grab all the image filenames
 lowercase					  =   [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
 lowercase					  =   [extract_label(__SCREAMING_SNAKE_CASE							) for fname in file_names]
 lowercase					  =   list(set(__SCREAMING_SNAKE_CASE							)							)
 id_to_label.sort()
 lowercase					  =   {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
 lowercase					  =   np.random.permutation(len(__SCREAMING_SNAKE_CASE							)							)
 lowercase					  =   int(0.8 * len(__SCREAMING_SNAKE_CASE							)							)
 lowercase					  =   random_perm[:cut]
 lowercase					  =   random_perm[cut:]

 # For training we use a simple RandomResizedCrop
 lowercase					  =   Compose([RandomResizedCrop(__SCREAMING_SNAKE_CASE			,							scale=(0.5, 1.0)							), ToTensor()]							)
 lowercase					  =   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
 lowercase					  =   Compose([Resize(__SCREAMING_SNAKE_CASE							), ToTensor()]							)
 lowercase					  =   PetsDataset([file_names[i] for i in eval_split]			,							image_transform=__SCREAMING_SNAKE_CASE			,							label_to_id=__SCREAMING_SNAKE_CASE							)

 # Instantiate dataloaders.
 lowercase					  =   DataLoader(__SCREAMING_SNAKE_CASE			,							shuffle=__SCREAMING_SNAKE_CASE			,							batch_size=__SCREAMING_SNAKE_CASE			,							num_workers=4							)
 lowercase					  =   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)
 lowercase					  =   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).
 lowercase					  =   model.to(accelerator.device							)

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

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

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

 # Instantiate learning rate scheduler
 lowercase					  =   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.
 lowercase  , lowercase  , lowercase  , lowercase  , lowercase					  =   accelerator.prepare(
     __SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							)

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

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

  lowercase					  =   accurate.item() / num_elems
  # Use accelerator.print to print only on the main process.
  accelerator.print(F'''epoch {epoch}: {100 * eval_metric:.2f}'''							)






def 							UpperCAmelCase_		(							):
 lowercase					  =   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(
     '--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(
     '--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('--cpu'			,							action='store_true'			,							help='If passed, will train on the CPU.'							)
 lowercase					  =   parser.parse_args()
 lowercase					  =   {'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()



import unittest
from timeit import timeit






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
	lowercase					  =   first_num if first_num >= second_num else second_num
	lowercase					  =   max_num
	while (common_mult % first_num > 0) or (common_mult % second_num > 0):
		common_mult += max_num
	return common_mult






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
	return b if a == 0 else greatest_common_divisor(b % a			,							__SCREAMING_SNAKE_CASE							)






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
	return first_num // greatest_common_divisor(__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							) * second_num






def 							UpperCAmelCase_		(							):
	lowercase					  =   (
	    'from __main__ import least_common_multiple_slow, least_common_multiple_fast'
	)
	print(
	    'least_common_multiple_slow():'			,							timeit('least_common_multiple_slow(1000, 999)'			,							setup=__SCREAMING_SNAKE_CASE							)			,							)
	print(
	    'least_common_multiple_fast():'			,							timeit('least_common_multiple_fast(1000, 999)'			,							setup=__SCREAMING_SNAKE_CASE							)			,							)







class 	A_       (      unittest.TestCase       ):




	'''simple docstring'''


	_UpperCamelCase					:					Any										=	(
	    (10, 20),
	    (13, 15),
	    (4, 31),
	    (10, 42),
	    (43, 34),
	    (5, 12),
	    (12, 25),
	    (10, 25),
	    (6, 9),
	)
	_UpperCamelCase					:					int										=	(20, 195, 124, 210, 1462, 60, 300, 50, 18)





	def      SCREAMING_SNAKE_CASE__						(	self	):
		for i, (first_num, second_num) in enumerate(self.test_inputs	):
			lowercase					  =   least_common_multiple_slow(snake_case		,   snake_case	)
			lowercase					  =   least_common_multiple_fast(snake_case		,   snake_case	)
			with self.subTest(i=snake_case	):
				self.assertEqual(snake_case		,   self.expected_results[i]	)
				self.assertEqual(snake_case		,   self.expected_results[i]	)


if __name__ == "__main__":
	benchmark()
	unittest.main()

