






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__						(    lowerCamelCase	:			Any    ):
   lowerCamelCase									=fname.split(os.path.sep    )[-1]
   return re.search(R"""^(.*)_\d+\.jpg$"""			,lowerCamelCase    ).groups()[0]


class 		lowerCAmelCase_				(  UpperCamelCase ):





     def __init__(	self							, _UpperCAmelCase							, _UpperCAmelCase=None							, _UpperCAmelCase=None  )		->				Tuple:
        lowerCamelCase									=file_names
        lowerCamelCase									=image_transform
        lowerCamelCase									=label_to_id





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





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






def  UpperCAmelCase__						(    lowerCamelCase	:			List[Any]			,lowerCamelCase	:			Optional[int]    ):
   # 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(lowerCamelCase			,(list, tuple)    ):
      lowerCamelCase									=(image_size, image_size)

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

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

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

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

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

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

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

   # 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 / 25    )

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

   # 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(
       lowerCamelCase			,lowerCamelCase			,lowerCamelCase			,lowerCamelCase			,lowerCamelCase    )

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

      model.eval()
      lowerCamelCase									=0
      lowerCamelCase									=0
      for _, batch in enumerate(lowerCamelCase    ):
         # 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(lowerCamelCase    )
         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_00 * eval_metric:.2f}'''    )






def  UpperCAmelCase__						(    ):
   lowerCamelCase									=argparse.ArgumentParser(description="""Simple example of training script."""    )
   parser.add_argument("""--data_dir"""			,required=lowerCamelCase			,help="""The data folder on disk."""    )
   parser.add_argument(
       """--mixed_precision"""			,type=lowerCamelCase			,default=lowerCamelCase			,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=lowerCamelCase			,default=lowerCamelCase			,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""": 42, """batch_size""": 64, """image_size""": 2_24}
   training_function(lowerCamelCase			,lowerCamelCase    )


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,
            )							, )