"""simple docstring"""
import warnings
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from .configuration_utils import ConfigMixin, register_to_config
from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate




class   __snake_case			(	__lowerCAmelCase							):
      a__								=						CONFIG_NAME



      @register_to_config
      def __init__(		self       ,     lowercase = True       ,     lowercase = 8       ,     lowercase = "lanczos"       ,     lowercase = True       ,     lowercase = False       ,     )      ->     Any:





        '''simple docstring'''





        super().__init__()



      @staticmethod
      def     lowerCamelCase_  (		lowercase)      ->     PIL.Image.Image:





        '''simple docstring'''





        if images.ndim == 3:
          a__:	List[Any]     				=      images[None, ...]
        a__:	List[Any]     				=      (images * 2_55).round().astype('uint8')
        if images.shape[-1] == 1:
          # special case for grayscale (single channel) images
          a__:	Tuple     				=      [Image.fromarray(image.squeeze()       ,     mode='L') for image in images]
        else:
          a__:	str     				=      [Image.fromarray(lowercase) for image in images]

        return pil_images



      @staticmethod
      def     lowerCamelCase_  (		lowercase)      ->     np.ndarray:





        '''simple docstring'''





        if not isinstance(lowercase       ,     lowercase):
          a__:	int     				=      [images]
        a__:	Optional[Any]     				=      [np.array(lowercase).astype(np.floataa) / 255.0 for image in images]
        a__:	int     				=      np.stack(lowercase       ,     axis=0)

        return images



      @staticmethod
      def     lowerCamelCase_  (		lowercase)      ->     torch.FloatTensor:





        '''simple docstring'''





        if images.ndim == 3:
          a__:	Any     				=      images[..., None]

        a__:	Any     				=      torch.from_numpy(images.transpose(0       ,     3       ,     1       ,     2))
        return images



      @staticmethod
      def     lowerCamelCase_  (		lowercase)      ->     np.ndarray:





        '''simple docstring'''





        a__:	Tuple     				=      images.cpu().permute(0       ,     2       ,     3       ,     1).float().numpy()
        return images



      @staticmethod
      def     lowerCamelCase_  (		lowercase)      ->     Any:





        '''simple docstring'''





        return 2.0 * images - 1.0



      @staticmethod
      def     lowerCamelCase_  (		lowercase)      ->     List[str]:





        '''simple docstring'''





        return (images / 2 + 0.5).clamp(0       ,     1)



      @staticmethod
      def     lowerCamelCase_  (		lowercase)      ->     PIL.Image.Image:





        '''simple docstring'''





        a__:	List[Any]     				=      image.convert('RGB')
        return image



      def     lowerCamelCase_  (		self       ,     lowercase       ,     lowercase = None       ,     lowercase = None       ,     )      ->     PIL.Image.Image:





        '''simple docstring'''





        if height is None:
          a__:	Union[str, Any]     				=      image.height
        if width is None:
          a__:	Optional[Any]     				=      image.width

        a__				,       a__:	int     				=      (
            x - x % self.config.vae_scale_factor for x in (width, height)
        )  # resize to integer multiple of vae_scale_factor
        a__:	Dict     				=      image.resize((width, height)       ,     resample=PIL_INTERPOLATION[self.config.resample])
        return image



      def     lowerCamelCase_  (		self       ,     lowercase       ,     lowercase = None       ,     lowercase = None       ,     )      ->     torch.Tensor:





        '''simple docstring'''





        a__:	Union[str, Any]     				=      (PIL.Image.Image, np.ndarray, torch.Tensor)
        if isinstance(lowercase       ,     lowercase):
          a__:	str     				=      [image]
        elif not (isinstance(lowercase       ,     lowercase) and all(isinstance(lowercase       ,     lowercase) for i in image)):
          raise ValueError(
              f'Input is in incorrect format: {[type(lowercase) for i in image]}. Currently, we only support {", ".join(lowercase)}')

        if isinstance(image[0]       ,     PIL.Image.Image):
          if self.config.do_convert_rgb:
            a__:	Any     				=      [self.convert_to_rgb(lowercase) for i in image]
          if self.config.do_resize:
            a__:	Any     				=      [self.resize(lowercase       ,     lowercase       ,     lowercase) for i in image]
          a__:	Any     				=      self.pil_to_numpy(lowercase)  # to np
          a__:	Tuple     				=      self.numpy_to_pt(lowercase)  # to pt

        elif isinstance(image[0]       ,     np.ndarray):
          a__:	Any     				=      np.concatenate(lowercase       ,     axis=0) if image[0].ndim == 4 else np.stack(lowercase       ,     axis=0)
          a__:	List[Any]     				=      self.numpy_to_pt(lowercase)
          a__				,       a__				,       a__				,       a__:	Dict     				=      image.shape
          if self.config.do_resize and (
              height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
          ):
            raise ValueError(
                f'Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}'
                f'currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor')

        elif isinstance(image[0]       ,     torch.Tensor):
          a__:	int     				=      torch.cat(lowercase       ,     axis=0) if image[0].ndim == 4 else torch.stack(lowercase       ,     axis=0)
          a__				,       a__				,       a__				,       a__:	Tuple     				=      image.shape

          # don't need any preprocess if the image is latents
          if channel == 4:
            return image

          if self.config.do_resize and (
              height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
          ):
            raise ValueError(
                f'Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}'
                f'currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor')

        # expected range [0,1], normalize to [-1,1]
        a__:	Optional[Any]     				=      self.config.do_normalize
        if image.min() < 0:
          warnings.warn(
              'Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] '
              f'when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]'       ,     lowercase       ,     )
          a__:	Union[str, Any]     				=      False

        if do_normalize:
          a__:	Optional[Any]     				=      self.normalize(lowercase)

        return image



      def     lowerCamelCase_  (		self       ,     lowercase       ,     lowercase = "pil"       ,     lowercase = None       ,     )      ->     Dict:





        '''simple docstring'''





        if not isinstance(lowercase       ,     torch.Tensor):
          raise ValueError(
              f'Input for postprocessing is in incorrect format: {type(lowercase)}. We only support pytorch tensor')
        if output_type not in ["latent", "pt", "np", "pil"]:
          a__:	str     				=      (
              f'the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: '
              '`pil`, `np`, `pt`, `latent`'
          )
          deprecate('Unsupported output_type'       ,     '1.0.0'       ,     lowercase       ,     standard_warn=lowercase)
          a__:	int     				=      'np'

        if output_type == "latent":
          return image

        if do_denormalize is None:
          a__:	Dict     				=      [self.config.do_normalize] * image.shape[0]

        a__:	int     				=      torch.stack(
            [self.denormalize(image[i]) if do_denormalize[i] else image[i] for i in range(image.shape[0])])

        if output_type == "pt":
          return image

        a__:	Optional[Any]     				=      self.pt_to_numpy(lowercase)

        if output_type == "np":
          return image

        if output_type == "pil":
          return self.numpy_to_pil(lowercase)







class   __snake_case			(	__lowerCAmelCase							):
      a__								=						CONFIG_NAME



      @register_to_config
      def __init__(		self       ,     lowercase = True       ,     lowercase = 8       ,     lowercase = "lanczos"       ,     lowercase = True       ,     )      ->     Union[str, Any]:





        '''simple docstring'''





        super().__init__()



      @staticmethod
      def     lowerCamelCase_  (		lowercase)      ->     Union[str, Any]:





        '''simple docstring'''





        if images.ndim == 3:
          a__:	Tuple     				=      images[None, ...]
        a__:	Optional[Any]     				=      (images * 2_55).round().astype('uint8')
        if images.shape[-1] == 1:
          # special case for grayscale (single channel) images
          a__:	int     				=      [Image.fromarray(image.squeeze()       ,     mode='L') for image in images]
        else:
          a__:	Optional[Any]     				=      [Image.fromarray(image[:, :, :3]) for image in images]

        return pil_images



      @staticmethod
      def     lowerCamelCase_  (		lowercase)      ->     List[str]:





        '''simple docstring'''





        return image[:, :, 1] * 2**8 + image[:, :, 2]



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     Dict:





        '''simple docstring'''





        if images.ndim == 3:
          a__:	Optional[Any]     				=      images[None, ...]
        a__:	List[Any]     				=      images[:, :, :, 3:]
        if images.shape[-1] == 6:
          a__:	List[str]     				=      (images_depth * 2_55).round().astype('uint8')
          a__:	Any     				=      [
              Image.fromarray(self.rgblike_to_depthmap(lowercase)       ,     mode='I;16') for image_depth in images_depth
          ]
        elif images.shape[-1] == 4:
          a__:	Dict     				=      (images_depth * 65535.0).astype(np.uintaa)
          a__:	Optional[Any]     				=      [Image.fromarray(lowercase       ,     mode='I;16') for image_depth in images_depth]
        else:
          raise Exception('Not supported')

        return pil_images



      def     lowerCamelCase_  (		self       ,     lowercase       ,     lowercase = "pil"       ,     lowercase = None       ,     )      ->     List[str]:





        '''simple docstring'''





        if not isinstance(lowercase       ,     torch.Tensor):
          raise ValueError(
              f'Input for postprocessing is in incorrect format: {type(lowercase)}. We only support pytorch tensor')
        if output_type not in ["latent", "pt", "np", "pil"]:
          a__:	Optional[Any]     				=      (
              f'the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: '
              '`pil`, `np`, `pt`, `latent`'
          )
          deprecate('Unsupported output_type'       ,     '1.0.0'       ,     lowercase       ,     standard_warn=lowercase)
          a__:	List[str]     				=      'np'

        if do_denormalize is None:
          a__:	int     				=      [self.config.do_normalize] * image.shape[0]

        a__:	Optional[Any]     				=      torch.stack(
            [self.denormalize(image[i]) if do_denormalize[i] else image[i] for i in range(image.shape[0])])

        a__:	Optional[Any]     				=      self.pt_to_numpy(lowercase)

        if output_type == "np":
          if image.shape[-1] == 6:
            a__:	Any     				=      np.stack([self.rgblike_to_depthmap(im[:, :, 3:]) for im in image]       ,     axis=0)
          else:
            a__:	str     				=      image[:, :, :, 3:]
          return image[:, :, :, :3], image_depth

        if output_type == "pil":
          return self.numpy_to_pil(lowercase), self.numpy_to_depth(lowercase)
        else:
          raise Exception(f'This type {output_type} is not supported')









'''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      lowercase  (  lowerCAmelCase :						Any):



				"""simple docstring"""


				_A :				Union[str, Any]				=  fname.split(os.path.sep)[-1]
				return re.search(R'''^(.*)_\d+\.jpg$'''   , lowerCAmelCase).groups()[0]



class  lowerCamelCase__				(						snake_case_						):


	"""simple docstring"""





	def __init__(					self   ,     UpperCAmelCase__   ,     UpperCAmelCase__=None   ,     UpperCAmelCase__=None			)       ->				int:
					_A :				Tuple				=  file_names
					_A :				Any				=  image_transform
					_A :				str				=  label_to_id




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




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





def      lowercase  (  lowerCAmelCase :						str   , lowerCAmelCase :						List[Any]):



				"""simple docstring"""


				_A :				Union[str, Any]				=  Accelerator(cpu=args.cpu   , mixed_precision=args.mixed_precision)

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

				# Grab all the image filenames
				_A :				Optional[Any]				=  [os.path.join(args.data_dir   , lowerCAmelCase) for fname in os.listdir(args.data_dir) if fname.endswith('''.jpg''')]

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

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

				# For evaluation, we use a deterministic Resize
				_A :				List[Any]				=  Compose([Resize(lowerCAmelCase), ToTensor()])
				_A :				str				=  PetsDataset([file_names[i] for i in eval_split]   , image_transform=lowerCAmelCase   , label_to_id=lowerCAmelCase)

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

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

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

				# Instantiate optimizer
				_A :				Optional[Any]				=  torch.optim.Adam(params=model.parameters()   , lr=lr / 25)

				# Instantiate learning rate scheduler
				_A :				List[str]				=  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.
				_A	,       _A	,       _A	,       _A	,       _A :				List[str]				=  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`.
												_A :				Optional[Any]				=  {k: v.to(accelerator.device) for k, v in batch.items()}
												_A :				Tuple				=  (batch['''image'''] - mean) / std
												_A :				Any				=  model(lowerCAmelCase)
												_A :				Optional[Any]				=  torch.nn.functional.cross_entropy(lowerCAmelCase   , batch['''label'''])
												accelerator.backward(lowerCAmelCase)
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

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

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





def      lowercase  (  ):



				"""simple docstring"""


				_A :				Any				=  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.''')
				_A :				Optional[int]				=  parser.parse_args()
				_A :				Optional[Any]				=  {'''lr''': 3e-2, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 64, '''image_size''': 224}
				training_function(lowerCAmelCase   , lowerCAmelCase)


if __name__ == "__main__":
	main()
