






"""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_					(	__A	:		List[str]      ):
					lowerCamelCase__					=fname.split(os.path.sep      )[-1]
					return re.search(r'^(.*)_\d+\.jpg$'      ,						__A      ).groups()[0]




class 							__lowercase(							_UpperCamelCase	):






						def __init__(       self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE=None     ,    _SCREAMING_SNAKE_CASE=None)      ->	int:




											"""simple docstring"""





											lowerCamelCase__					=file_names
											lowerCamelCase__					=image_transform
											lowerCamelCase__					=label_to_id






						def __len__(       self)      ->	Tuple:




											"""simple docstring"""





											return len(self.file_names)






						def __getitem__(       self     ,    _SCREAMING_SNAKE_CASE)      ->	Tuple:




											"""simple docstring"""





											lowerCamelCase__					=self.file_names[idx]
											lowerCamelCase__					=PIL.Image.open(_SCREAMING_SNAKE_CASE)
											lowerCamelCase__					=raw_image.convert('RGB')
											if self.image_transform is not None:
																lowerCamelCase__					=self.image_transform(_SCREAMING_SNAKE_CASE)
											lowerCamelCase__					=extract_label(_SCREAMING_SNAKE_CASE)
											if self.label_to_id is not None:
																lowerCamelCase__					=self.label_to_id[label]
											return {"image": image, "label": label}




def     lowerCamelCase_					(	__A	:		Any      ,						__A	:		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(__A      ,						(list, tuple)      ):
										lowerCamelCase__					=(image_size, image_size)

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

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

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

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

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

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

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

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

					# 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(
					    __A      ,						__A      ,						__A      ,						__A      ,						__A      )

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

										model.eval()
										lowerCamelCase__					=0
										lowerCamelCase__					=0
										for _, batch in enumerate(__A      ):
															# 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(__A      )
															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}: {100 * eval_metric:.2f}"      )




def     lowerCamelCase_					(	):
					lowerCamelCase__					=argparse.ArgumentParser(description='Simple example of training script.'      )
					parser.add_argument('--data_dir'      ,						required=__A      ,						help='The data folder on disk.'      )
					parser.add_argument(
					    '--mixed_precision'      ,						type=__A      ,						default=__A      ,						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=__A      ,						default=__A      ,						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': 224}
					training_function(__A      ,						__A      )


if __name__ == "__main__":
						main()







"""simple docstring"""



import inspect

from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
						import jax




@require_flax
class 							__lowercase:






						def 		lowerCAmelCase (       self)      ->	str:




											"""simple docstring"""





											lowerCamelCase__					,  lowerCamelCase__					=self.prepare_init_args_and_inputs_for_common()

											lowerCamelCase__					=self.model_class(**_SCREAMING_SNAKE_CASE)
											lowerCamelCase__					=model.init(inputs_dict['prng_key']     ,    inputs_dict['sample'])
											jax.lax.stop_gradient(_SCREAMING_SNAKE_CASE)

											lowerCamelCase__					=model.apply(_SCREAMING_SNAKE_CASE     ,    inputs_dict['sample'])

											if isinstance(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE):
																lowerCamelCase__					=output.sample

											self.assertIsNotNone(_SCREAMING_SNAKE_CASE)
											lowerCamelCase__					=inputs_dict['sample'].shape
											self.assertEqual(output.shape     ,    _SCREAMING_SNAKE_CASE     ,    'Input and output shapes do not match')






						def 		lowerCAmelCase (       self)      ->	Optional[Any]:




											"""simple docstring"""





											lowerCamelCase__					,  lowerCamelCase__					=self.prepare_init_args_and_inputs_for_common()

											lowerCamelCase__					=16
											lowerCamelCase__					=(16, 32)

											lowerCamelCase__					=self.model_class(**_SCREAMING_SNAKE_CASE)
											lowerCamelCase__					=model.init(inputs_dict['prng_key']     ,    inputs_dict['sample'])
											jax.lax.stop_gradient(_SCREAMING_SNAKE_CASE)

											lowerCamelCase__					=model.apply(_SCREAMING_SNAKE_CASE     ,    inputs_dict['sample'])

											if isinstance(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE):
																lowerCamelCase__					=output.sample

											self.assertIsNotNone(_SCREAMING_SNAKE_CASE)
											lowerCamelCase__					=inputs_dict['sample'].shape
											self.assertEqual(output.shape     ,    _SCREAMING_SNAKE_CASE     ,    'Input and output shapes do not match')






						def 		lowerCAmelCase (       self)      ->	Optional[Any]:




											"""simple docstring"""





											lowerCamelCase__					='kwargs' in inspect.signature(self.model_class.__init__).parameters
											lowerCamelCase__					=len(self.model_class._deprecated_kwargs) > 0

											if has_kwarg_in_model_class and not has_deprecated_kwarg:
																raise ValueError(
																    F"{self.model_class} has `**kwargs` in its __init__ method but has not defined any deprecated kwargs"
																    ' under the `_deprecated_kwargs` class attribute. Make sure to either remove `**kwargs` if there are'
																    ' no deprecated arguments or add the deprecated argument with `_deprecated_kwargs ='
																    ' [<deprecated_argument>]`')

											if not has_kwarg_in_model_class and has_deprecated_kwarg:
																raise ValueError(
																    F"{self.model_class} doesn't have `**kwargs` in its __init__ method but has defined deprecated kwargs"
																    ' under the `_deprecated_kwargs` class attribute. Make sure to either add the `**kwargs` argument to'
																    F" {self.model_class}.__init__ if there are deprecated arguments or remove the deprecated argument"
																    ' from `_deprecated_kwargs = [<deprecated_argument>]`')