




'''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   (       snake_case__		):
			__UpperCamelCase :     Dict										=  fname.split(os.path.sep		)[-1]
			return re.search(r"^(.*)_\d+\.jpg$"   , _UpperCamelCase		).groups()[0]



class    A						(					_lowerCamelCase						):




				'''simple docstring'''





				def __init__(self					,    _UpperCAmelCase					,    _UpperCAmelCase=None					,    _UpperCAmelCase=None     )      ->			Dict:
							__UpperCamelCase :     int										=  file_names
							__UpperCamelCase :     Union[str, Any]										=  image_transform
							__UpperCamelCase :     Union[str, Any]										=  label_to_id
				def __len__(self     )      ->			str:
							return len(self.file_names     )





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







def      __lowerCAmelCase   (       snake_case__   , snake_case__		):
			# Initialize accelerator
			__UpperCamelCase :     int										=  Accelerator(cpu=args.cpu   , mixed_precision=args.mixed_precision		)

			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			__UpperCamelCase :     Optional[Any]										=  config['lr']
			__UpperCamelCase :     Union[str, Any]										=  int(config["num_epochs"]		)
			__UpperCamelCase :     List[str]										=  int(config["seed"]		)
			__UpperCamelCase :     str										=  int(config["batch_size"]		)
			__UpperCamelCase :     List[str]										=  config['image_size']
			if not isinstance(_UpperCamelCase   , (list, tuple)		):
						__UpperCamelCase :     List[Any]										=  (image_size, image_size)

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

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

			# For training we use a simple RandomResizedCrop
			__UpperCamelCase :     List[str]										=  Compose([RandomResizedCrop(_UpperCamelCase   , scale=(0.5, 1.0)		), ToTensor()]		)
			__UpperCamelCase :     int										=  PetsDataset(
			    [file_names[i] for i in train_split]   , image_transform=_UpperCamelCase   , label_to_id=_UpperCamelCase		)

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

			# Instantiate dataloaders.
			__UpperCamelCase :     Tuple										=  DataLoader(_UpperCamelCase   , shuffle=_UpperCamelCase   , batch_size=_UpperCamelCase   , num_workers=4		)
			__UpperCamelCase :     Union[str, Any]										=  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 :     Any										=  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 :     Optional[int]										=  model.to(accelerator.device		)

			# Freezing the base model
			for param in model.parameters():
						__UpperCamelCase :     List[Any]										=  False
			for param in model.get_classifier().parameters():
						__UpperCamelCase :     Tuple										=  True

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

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

			# Instantiate learning rate scheduler
			__UpperCamelCase :     Any										=  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 :     Union[str, Any]										=  accelerator.prepare(
			    _UpperCamelCase   , _UpperCamelCase   , _UpperCamelCase   , _UpperCamelCase   , _UpperCamelCase		)

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

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

						__UpperCamelCase :     List[str]										=  accurate.item() / num_elems
						# Use accelerator.print to print only on the main process.
						accelerator.print(F"epoch {epoch}: {100 * eval_metric:.2f}"		)

def      __lowerCAmelCase   (       ):
			__UpperCamelCase :     Any										=  argparse.ArgumentParser(description="Simple example of training script."		)
			parser.add_argument("--data_dir"   , required=_UpperCamelCase   , help="The data folder on disk."		)
			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(
			    "--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("--cpu"   , action="store_true"   , help="If passed, will train on the CPU."		)
			__UpperCamelCase :     int										=  parser.parse_args()
			__UpperCamelCase :     List[Any]										=  {'lr': 3E-2, 'num_epochs': 3, 'seed': 42, 'batch_size': 64, 'image_size': 224}
			training_function(_UpperCamelCase   , _UpperCamelCase		)


if __name__ == "__main__":
				main()





"""simple docstring"""







import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import DeiTImageProcessor




class 					A__			(      unittest.TestCase):

		def __init__( self			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE=7			,						_SCREAMING_SNAKE_CASE=3			,						_SCREAMING_SNAKE_CASE=18			,						_SCREAMING_SNAKE_CASE=30			,						_SCREAMING_SNAKE_CASE=4_00			,						_SCREAMING_SNAKE_CASE=True			,						_SCREAMING_SNAKE_CASE=None			,						_SCREAMING_SNAKE_CASE=True			,						_SCREAMING_SNAKE_CASE=None			,						_SCREAMING_SNAKE_CASE=True			,						_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]			,						_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]			,						):
			__lowerCAmelCase     :		List[str]		     = size if size is not None else {'height': 20, 'width': 20}
			__lowerCAmelCase     :		List[str]		     = crop_size if crop_size is not None else {'height': 18, 'width': 18}

			__lowerCAmelCase     :		Any		     = parent
			__lowerCAmelCase     :		Optional[Any]		     = batch_size
			__lowerCAmelCase     :		Any		     = num_channels
			__lowerCAmelCase     :		Dict		     = image_size
			__lowerCAmelCase     :		Tuple		     = min_resolution
			__lowerCAmelCase     :		Union[str, Any]		     = max_resolution
			__lowerCAmelCase     :		Optional[int]		     = do_resize
			__lowerCAmelCase     :		Any		     = size
			__lowerCAmelCase     :		Union[str, Any]		     = do_center_crop
			__lowerCAmelCase     :		List[Any]		     = crop_size
			__lowerCAmelCase     :		Optional[int]		     = do_normalize
			__lowerCAmelCase     :		int		     = image_mean
			__lowerCAmelCase     :		Tuple		     = image_std







		def 				__lowerCamelCase				( self					):
			return {
			    "do_resize": self.do_resize,
			    "size": self.size,
			    "do_center_crop": self.do_center_crop,
			    "crop_size": self.crop_size,
			    "do_normalize": self.do_normalize,
			    "image_mean": self.image_mean,
			    "image_std": self.image_std,
			}




@require_torch
@require_vision
class 					A__			(      _lowerCamelCase			,	unittest.TestCase):
		A_		:			List[str]							  =      DeiTImageProcessor if is_vision_available() else None
		A_		:			int							  =      True

		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     :		Optional[int]		     = DeiTImageProcessingTester(self					)

		@property
		def 				__lowerCamelCase				( self					):
			return self.image_processor_tester.prepare_image_processor_dict()

		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     :		Any		     = self.image_processing_class(**self.image_processor_dict					)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,						'do_resize'					)					)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,						'size'					)					)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,						'do_center_crop'					)					)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,						'center_crop'					)					)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,						'do_normalize'					)					)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,						'image_mean'					)					)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,						'image_std'					)					)

		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     :		Tuple		     = self.image_processing_class.from_dict(self.image_processor_dict					)
			self.assertEqual(image_processor.size			,						{'height': 20, 'width': 20}					)
			self.assertEqual(image_processor.crop_size			,						{'height': 18, 'width': 18}					)

			__lowerCAmelCase     :		str		     = self.image_processing_class.from_dict(self.image_processor_dict			,						size=42			,						crop_size=84					)
			self.assertEqual(image_processor.size			,						{'height': 42, 'width': 42}					)
			self.assertEqual(image_processor.crop_size			,						{'height': 84, 'width': 84}					)

		def 				__lowerCamelCase				( self					):
			pass

		def 				__lowerCamelCase				( self					):
			# Initialize image_processing
			__lowerCAmelCase     :		Dict		     = self.image_processing_class(**self.image_processor_dict					)
			# create random PIL images
			__lowerCAmelCase     :		List[str]		     = prepare_image_inputs(self.image_processor_tester			,						equal_resolution=_SCREAMING_SNAKE_CASE					)
			for image in image_inputs:
				self.assertIsInstance(_SCREAMING_SNAKE_CASE			,						Image.Image					)

			# Test not batched input
			__lowerCAmelCase     :		Dict		     = image_processing(image_inputs[0]			,						return_tensors='pt'					).pixel_values
			self.assertEqual(
			    encoded_images.shape			,						(
			        1,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['height'],
			        self.image_processor_tester.crop_size['width'],
			    )			,						)

			# Test batched
			__lowerCAmelCase     :		int		     = image_processing(_SCREAMING_SNAKE_CASE			,						return_tensors='pt'					).pixel_values
			self.assertEqual(
			    encoded_images.shape			,						(
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['height'],
			        self.image_processor_tester.crop_size['width'],
			    )			,						)

		def 				__lowerCamelCase				( self					):
			# Initialize image_processing
			__lowerCAmelCase     :		Optional[int]		     = self.image_processing_class(**self.image_processor_dict					)
			# create random numpy tensors
			__lowerCAmelCase     :		List[str]		     = prepare_image_inputs(self.image_processor_tester			,						equal_resolution=_SCREAMING_SNAKE_CASE			,						numpify=_SCREAMING_SNAKE_CASE					)
			for image in image_inputs:
				self.assertIsInstance(_SCREAMING_SNAKE_CASE			,						np.ndarray					)

			# Test not batched input
			__lowerCAmelCase     :		str		     = image_processing(image_inputs[0]			,						return_tensors='pt'					).pixel_values
			self.assertEqual(
			    encoded_images.shape			,						(
			        1,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['height'],
			        self.image_processor_tester.crop_size['width'],
			    )			,						)

			# Test batched
			__lowerCAmelCase     :		Dict		     = image_processing(_SCREAMING_SNAKE_CASE			,						return_tensors='pt'					).pixel_values
			self.assertEqual(
			    encoded_images.shape			,						(
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['height'],
			        self.image_processor_tester.crop_size['width'],
			    )			,						)







		def 				__lowerCamelCase				( self					):
			# Initialize image_processing
			__lowerCAmelCase     :		Union[str, Any]		     = self.image_processing_class(**self.image_processor_dict					)
			# create random PyTorch tensors
			__lowerCAmelCase     :		List[str]		     = prepare_image_inputs(self.image_processor_tester			,						equal_resolution=_SCREAMING_SNAKE_CASE			,						torchify=_SCREAMING_SNAKE_CASE					)
			for image in image_inputs:
				self.assertIsInstance(_SCREAMING_SNAKE_CASE			,						torch.Tensor					)

			# Test not batched input
			__lowerCAmelCase     :		Union[str, Any]		     = image_processing(image_inputs[0]			,						return_tensors='pt'					).pixel_values
			self.assertEqual(
			    encoded_images.shape			,						(
			        1,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['height'],
			        self.image_processor_tester.crop_size['width'],
			    )			,						)

			# Test batched
			__lowerCAmelCase     :		Optional[int]		     = image_processing(_SCREAMING_SNAKE_CASE			,						return_tensors='pt'					).pixel_values
			self.assertEqual(
			    encoded_images.shape			,						(
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['height'],
			        self.image_processor_tester.crop_size['width'],
			    )			,						)