





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      a    (   _lowercase :  Any							)  ->Any:
		UpperCamelCase__								=						fname.split(os.path.sep							)[-1]
		return re.search(r"""^(.*)_\d+\.jpg$"""			,    _lowercase							).groups()[0]




class 	_UpperCAmelCase						( _lowercase       ):




					def __init__(    self     ,   __lowerCAmelCase     ,   __lowerCAmelCase=None     ,   __lowerCAmelCase=None							)    ->	Tuple:
							UpperCamelCase__								=						file_names
							UpperCamelCase__								=						image_transform
							UpperCamelCase__								=						label_to_id




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




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



def      a    (   _lowercase :  List[Any]			,    _lowercase :  Optional[int]							)  ->List[Any]:
		# Initialize accelerator
		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(_lowercase			,    (list, tuple)							):
				UpperCamelCase__								=						(image_size, image_size)

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

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

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

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

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

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

		# Instantiate dataloaders.
		UpperCamelCase__								=						DataLoader(_lowercase			,    shuffle=_lowercase			,    batch_size=_lowercase			,    num_workers=4							)
		UpperCamelCase__								=						DataLoader(_lowercase			,    shuffle=_lowercase			,    batch_size=_lowercase			,    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=_lowercase			,    num_classes=len(_lowercase							)							)

		# 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=_lowercase			,    max_lr=_lowercase			,    epochs=_lowercase			,    steps_per_epoch=len(_lowercase							)							)

		# 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(
		    _lowercase			,    _lowercase			,    _lowercase			,    _lowercase			,    _lowercase							)

		# Now we train the model
		for epoch in range(_lowercase							):
				model.train()
				for step, batch in enumerate(_lowercase							):
						# 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(_lowercase							)
						UpperCamelCase__								=						torch.nn.functional.cross_entropy(_lowercase			,    batch["""label"""]							)
						accelerator.backward(_lowercase							)
						optimizer.step()
						lr_scheduler.step()
						optimizer.zero_grad()

				model.eval()
				UpperCamelCase__								=						0
				UpperCamelCase__								=						0
				for _, batch in enumerate(_lowercase							):
						# 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(_lowercase							)
						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}"""							)

def      a    (   )  ->str:
		UpperCamelCase__								=						argparse.ArgumentParser(description="""Simple example of training script."""							)
		parser.add_argument("""--data_dir"""			,    required=_lowercase			,    help="""The data folder on disk."""							)
		parser.add_argument(
		    """--mixed_precision"""			,    type=_lowercase			,    default=_lowercase			,    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=_lowercase			,    default=_lowercase			,    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__								=						parser.parse_args()
		UpperCamelCase__								=						{"""lr""": 3e-2, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 6_4, """image_size""": 2_2_4}
		training_function(_lowercase			,    _lowercase							)


if __name__ == "__main__":
				main()













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


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import SwinaSRImageProcessor
   from transformers.image_transforms import get_image_size


class 		lowerCAmelCase_				(  unittest.TestCase ):





     def __init__(	self							, _UpperCAmelCase							, _UpperCAmelCase=7							, _UpperCAmelCase=3							, _UpperCAmelCase=18							, _UpperCAmelCase=30							, _UpperCAmelCase=400							, _UpperCAmelCase=True							, _UpperCAmelCase=1 / 255							, _UpperCAmelCase=True							, _UpperCAmelCase=8							, )		->				Tuple:
        lowerCamelCase									=parent
        lowerCamelCase									=batch_size
        lowerCamelCase									=num_channels
        lowerCamelCase									=image_size
        lowerCamelCase									=min_resolution
        lowerCamelCase									=max_resolution
        lowerCamelCase									=do_rescale
        lowerCamelCase									=rescale_factor
        lowerCamelCase									=do_pad
        lowerCamelCase									=pad_size





     def 						lowerCamelCase							(	self  )		->				Optional[Any]:
        return {
            "do_rescale": self.do_rescale,
            "rescale_factor": self.rescale_factor,
            "do_pad": self.do_pad,
            "pad_size": self.pad_size,
        }





     def 						lowerCamelCase							(	self							, _UpperCAmelCase=False							, _UpperCAmelCase=False							, _UpperCAmelCase=False  )		->				int:
        assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

        if equal_resolution:
           lowerCamelCase									=[]
           for i in range(self.batch_size  ):
              image_inputs.append(
                  np.random.randint(
                      255							, size=(self.num_channels, self.max_resolution, self.max_resolution)							, dtype=np.uinta  )  )
        else:
           lowerCamelCase									=[]
           for i in range(self.batch_size  ):
              lowerCamelCase				,       lowerCamelCase									=np.random.choice(np.arange(self.min_resolution							, self.max_resolution  )							, 2  )
              image_inputs.append(np.random.randint(255							, size=(self.num_channels, width, height)							, dtype=np.uinta  )  )

        if not numpify and not torchify:
           # PIL expects the channel dimension as last dimension
           lowerCamelCase									=[Image.fromarray(np.moveaxis(_UpperCAmelCase							, 0							, -1  )  ) for x in image_inputs]

        if torchify:
           lowerCamelCase									=[torch.from_numpy(_UpperCAmelCase  ) for x in image_inputs]

        return image_inputs




@require_torch
@require_vision
class 		lowerCAmelCase_				(  UpperCamelCase				,     unittest.TestCase ):
     __lowerCamelCase       :    Optional[Any] 	=     SwinaSRImageProcessor if is_vision_available() else None





     def 						lowerCamelCase							(	self  )		->				Dict:
        lowerCamelCase									=SwinaSRImageProcessingTester(self  )





     @property
     def 						lowerCamelCase							(	self  )		->				Any:
        return self.image_processor_tester.prepare_image_processor_dict()





     def 						lowerCamelCase							(	self  )		->				Any:
        lowerCamelCase									=self.image_processing_class(**self.image_processor_dict  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """do_rescale"""  )  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """rescale_factor"""  )  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """do_pad"""  )  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """pad_size"""  )  )





     def 						lowerCamelCase							(	self  )		->				Any:
        pass





     def 						lowerCamelCase							(	self							, _UpperCAmelCase  )		->				int:
        lowerCamelCase				,       lowerCamelCase									=get_image_size(_UpperCAmelCase  )
        lowerCamelCase									=self.image_processor_tester.pad_size

        lowerCamelCase									=(old_height // size + 1) * size - old_height
        lowerCamelCase									=(old_width // size + 1) * size - old_width
        return old_height + pad_height, old_width + pad_width





     def 						lowerCamelCase							(	self  )		->				Optional[Any]:
        # Initialize image_processor
        lowerCamelCase									=self.image_processing_class(**self.image_processor_dict  )
        # create random PIL images
        lowerCamelCase									=self.image_processor_tester.prepare_inputs(equal_resolution=_UpperCAmelCase  )
        for image in image_inputs:
           self.assertIsInstance(_UpperCAmelCase							, Image.Image  )

        # Test not batched input
        lowerCamelCase									=image_processor(image_inputs[0]							, return_tensors="""pt"""  ).pixel_values
        lowerCamelCase				,       lowerCamelCase									=self.calculate_expected_size(np.array(image_inputs[0]  )  )
        self.assertEqual(
            encoded_images.shape							, (
                1,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )							, )





     def 						lowerCamelCase							(	self  )		->				Any:
        # Initialize image_processor
        lowerCamelCase									=self.image_processing_class(**self.image_processor_dict  )
        # create random numpy tensors
        lowerCamelCase									=self.image_processor_tester.prepare_inputs(equal_resolution=_UpperCAmelCase							, numpify=_UpperCAmelCase  )
        for image in image_inputs:
           self.assertIsInstance(_UpperCAmelCase							, np.ndarray  )

        # Test not batched input
        lowerCamelCase									=image_processor(image_inputs[0]							, return_tensors="""pt"""  ).pixel_values
        lowerCamelCase				,       lowerCamelCase									=self.calculate_expected_size(image_inputs[0]  )
        self.assertEqual(
            encoded_images.shape							, (
                1,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )							, )





     def 						lowerCamelCase							(	self  )		->				Dict:
        # Initialize image_processor
        lowerCamelCase									=self.image_processing_class(**self.image_processor_dict  )
        # create random PyTorch tensors
        lowerCamelCase									=self.image_processor_tester.prepare_inputs(equal_resolution=_UpperCAmelCase							, torchify=_UpperCAmelCase  )
        for image in image_inputs:
           self.assertIsInstance(_UpperCAmelCase							, torch.Tensor  )

        # Test not batched input
        lowerCamelCase									=image_processor(image_inputs[0]							, return_tensors="""pt"""  ).pixel_values
        lowerCamelCase				,       lowerCamelCase									=self.calculate_expected_size(image_inputs[0]  )
        self.assertEqual(
            encoded_images.shape							, (
                1,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )							, )