





'''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						(     __lowerCamelCase :    Tuple    )						->Tuple:
    _SCREAMING_SNAKE_CASE									=			fname.split(os.path.sep    )[-1]
    return re.search(R"""^(.*)_\d+\.jpg$"""					,       __lowerCamelCase    ).groups()[0]


class 					a_     (				snake_case_       ):

    '''simple docstring'''


    def __init__(		self ,		A ,		A=None ,		A=None       )		->   int:
        _SCREAMING_SNAKE_CASE									=			file_names
        _SCREAMING_SNAKE_CASE									=			image_transform
        _SCREAMING_SNAKE_CASE									=			label_to_id


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





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





def lowerCamelCase						(     __lowerCamelCase :    Any					,       __lowerCamelCase :    Tuple    )						->str:
    # Initialize accelerator
    if args.with_tracking:
        _SCREAMING_SNAKE_CASE									=			Accelerator(
            cpu=args.cpu					,       mixed_precision=args.mixed_precision					,       log_with="""all"""					,       project_dir=args.project_dir    )
    else:
        _SCREAMING_SNAKE_CASE									=			Accelerator(cpu=args.cpu					,       mixed_precision=args.mixed_precision    )

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

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

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

    # Build the label correspondences
    _SCREAMING_SNAKE_CASE									=			[extract_label(__lowerCamelCase    ) for fname in file_names]
    _SCREAMING_SNAKE_CASE									=			list(set(__lowerCamelCase    )    )
    id_to_label.sort()
    _SCREAMING_SNAKE_CASE									=			{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
    _SCREAMING_SNAKE_CASE									=			np.random.permutation(len(__lowerCamelCase    )    )
    _SCREAMING_SNAKE_CASE									=			int(0.8 * len(__lowerCamelCase    )    )
    _SCREAMING_SNAKE_CASE									=			random_perm[:cut]
    _SCREAMING_SNAKE_CASE									=			random_perm[cut:]

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

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

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

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

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

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

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

        if "epoch" in training_difference:
            _SCREAMING_SNAKE_CASE									=			int(training_difference.replace("""epoch_"""					,       """"""    )    ) + 1
            _SCREAMING_SNAKE_CASE									=			None
        else:
            _SCREAMING_SNAKE_CASE									=			int(training_difference.replace("""step_"""					,       """"""    )    )
            _SCREAMING_SNAKE_CASE									=			resume_step // len(__lowerCamelCase    )
            resume_step -= starting_epoch * len(__lowerCamelCase    )

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

        _SCREAMING_SNAKE_CASE									=			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(__lowerCamelCase    ),
                    """epoch""": epoch,
                }					,       step=__lowerCamelCase					,       )
        if checkpointing_steps == "epoch":
            _SCREAMING_SNAKE_CASE									=			F'epoch_{epoch}'
            if args.output_dir is not None:
                _SCREAMING_SNAKE_CASE									=			os.path.join(args.output_dir					,       __lowerCamelCase    )
            accelerator.save_state(__lowerCamelCase    )

    if args.with_tracking:
        accelerator.end_training()





def lowerCamelCase						(     )						->int:
    _SCREAMING_SNAKE_CASE									=			argparse.ArgumentParser(description="""Simple example of training script."""    )
    parser.add_argument("""--data_dir"""					,       required=__lowerCamelCase					,       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=__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("""--cpu"""					,       action="""store_true"""					,       help="""If passed, will train on the CPU."""    )
    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(
        """--output_dir"""					,       type=__lowerCamelCase					,       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=__lowerCamelCase					,       default=__lowerCamelCase					,       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=__lowerCamelCase					,       default="""logs"""					,       help="""Location on where to store experiment tracking logs` and relevent project information"""					,       )
    _SCREAMING_SNAKE_CASE									=			parser.parse_args()
    _SCREAMING_SNAKE_CASE									=			{"""lr""": 3e-2, """num_epochs""": 3, """seed""": 42, """batch_size""": 64, """image_size""": 224}
    training_function(__lowerCamelCase					,       __lowerCamelCase    )


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 MobileViTImageProcessor


class 				a (						unittest.TestCase      ):


    def __init__(    self		:     Dict      , __lowerCAmelCase		:     Tuple      , __lowerCAmelCase		:     Optional[Any]=7      , __lowerCAmelCase		:     Optional[Any]=3      , __lowerCAmelCase		:     Optional[Any]=18      , __lowerCAmelCase		:     str=30      , __lowerCAmelCase		:     List[str]=400      , __lowerCAmelCase		:     Union[str, Any]=True      , __lowerCAmelCase		:     str=None      , __lowerCAmelCase		:     Optional[int]=True      , __lowerCAmelCase		:     int=None      , __lowerCAmelCase		:     List[str]=True      , ):
      _UpperCAmelCase         =					size if size is not None else {"""shortest_edge""": 20}
      _UpperCAmelCase         =					crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
      _UpperCAmelCase         =					parent
      _UpperCAmelCase         =					batch_size
      _UpperCAmelCase         =					num_channels
      _UpperCAmelCase         =					image_size
      _UpperCAmelCase         =					min_resolution
      _UpperCAmelCase         =					max_resolution
      _UpperCAmelCase         =					do_resize
      _UpperCAmelCase         =					size
      _UpperCAmelCase         =					do_center_crop
      _UpperCAmelCase         =					crop_size
      _UpperCAmelCase         =					do_flip_channel_order


    def    lowerCAmelCase_     (    self		:     List[str]    ):
      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_center_crop": self.do_center_crop,
          "crop_size": self.crop_size,
          "do_flip_channel_order": self.do_flip_channel_order,
      }



@require_torch
@require_vision
class 				a (						lowerCAmelCase_		,							unittest.TestCase      ):
    _snake_case   :     Optional[int]   							=							MobileViTImageProcessor if is_vision_available() else None


    def    lowerCAmelCase_     (    self		:     Optional[Any]    ):
      _UpperCAmelCase         =					MobileViTImageProcessingTester(self    )


    @property
    def    lowerCAmelCase_     (    self		:     Tuple    ):
      return self.image_processor_tester.prepare_image_processor_dict()


    def    lowerCAmelCase_     (    self		:     Dict    ):
      _UpperCAmelCase         =					self.image_processing_class(**self.image_processor_dict    )
      self.assertTrue(hasattr(__lowerCAmelCase      , """do_resize"""    )    )
      self.assertTrue(hasattr(__lowerCAmelCase      , """size"""    )    )
      self.assertTrue(hasattr(__lowerCAmelCase      , """do_center_crop"""    )    )
      self.assertTrue(hasattr(__lowerCAmelCase      , """center_crop"""    )    )
      self.assertTrue(hasattr(__lowerCAmelCase      , """do_flip_channel_order"""    )    )


    def    lowerCAmelCase_     (    self		:     Union[str, Any]    ):
      _UpperCAmelCase         =					self.image_processing_class.from_dict(self.image_processor_dict    )
      self.assertEqual(image_processor.size      , {"""shortest_edge""": 20}    )
      self.assertEqual(image_processor.crop_size      , {"""height""": 18, """width""": 18}    )

      _UpperCAmelCase         =					self.image_processing_class.from_dict(self.image_processor_dict      , size=42      , crop_size=84    )
      self.assertEqual(image_processor.size      , {"""shortest_edge""": 42}    )
      self.assertEqual(image_processor.crop_size      , {"""height""": 84, """width""": 84}    )


    def    lowerCAmelCase_     (    self		:     List[str]    ):
      pass


    def    lowerCAmelCase_     (    self		:     Dict    ):
      # Initialize image_processing
      _UpperCAmelCase         =					self.image_processing_class(**self.image_processor_dict    )
      # create random PIL images
      _UpperCAmelCase         =					prepare_image_inputs(self.image_processor_tester      , equal_resolution=__lowerCAmelCase    )
      for image in image_inputs:
        self.assertIsInstance(__lowerCAmelCase      , Image.Image    )

      # Test not batched input
      _UpperCAmelCase         =					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
      _UpperCAmelCase         =					image_processing(__lowerCAmelCase      , 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		:     str    ):
      # Initialize image_processing
      _UpperCAmelCase         =					self.image_processing_class(**self.image_processor_dict    )
      # create random numpy tensors
      _UpperCAmelCase         =					prepare_image_inputs(self.image_processor_tester      , equal_resolution=__lowerCAmelCase      , numpify=__lowerCAmelCase    )
      for image in image_inputs:
        self.assertIsInstance(__lowerCAmelCase      , np.ndarray    )

      # Test not batched input
      _UpperCAmelCase         =					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
      _UpperCAmelCase         =					image_processing(__lowerCAmelCase      , 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		:     Optional[int]    ):
      # Initialize image_processing
      _UpperCAmelCase         =					self.image_processing_class(**self.image_processor_dict    )
      # create random PyTorch tensors
      _UpperCAmelCase         =					prepare_image_inputs(self.image_processor_tester      , equal_resolution=__lowerCAmelCase      , torchify=__lowerCAmelCase    )
      for image in image_inputs:
        self.assertIsInstance(__lowerCAmelCase      , torch.Tensor    )

      # Test not batched input
      _UpperCAmelCase         =					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
      _UpperCAmelCase         =					image_processing(__lowerCAmelCase      , 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"""],
          )      , )
