






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



class 		a_      (	__lowerCAmelCase ):







      '''simple docstring'''

      def __init__(self,     lowerCamelCase_,     lowerCamelCase_=None,     lowerCamelCase_=None					):

       '''simple docstring'''



       lowerCamelCase__     :			Any     				=						file_names
       lowerCamelCase__     :			Union[str, Any]     				=						image_transform
       lowerCamelCase__     :			Optional[Any]     				=						label_to_id




      def __len__(self					):

       '''simple docstring'''



       return len(self.file_names					)





      def __getitem__(self,     lowerCamelCase_					):

       '''simple docstring'''



       lowerCamelCase__     :			int     				=						self.file_names[idx]
       lowerCamelCase__     :			Optional[int]     				=						PIL.Image.open(_lowerCamelCase					)
       lowerCamelCase__     :			Union[str, Any]     				=						raw_image.convert('RGB'					)
       if self.image_transform is not None:
        lowerCamelCase__     :			Optional[Any]     				=						self.image_transform(_lowerCamelCase					)
       lowerCamelCase__     :			Dict     				=						extract_label(_lowerCamelCase					)
       if self.label_to_id is not None:
        lowerCamelCase__     :			List[Any]     				=						self.label_to_id[label]
       return {"image": image, "label": label}






def        lowerCamelCase_  (						_lowerCamelCase			,   _lowerCamelCase				):
 lowerCamelCase__     :			Dict     				=						Accelerator(cpu=args.cpu			,   mixed_precision=args.mixed_precision				)

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

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

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

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

 # For evaluation, we use a deterministic Resize
 lowerCamelCase__     :			Optional[int]     				=						Compose([Resize(__lowerCAmelCase				), ToTensor()]				)
 lowerCamelCase__     :			List[Any]     				=						PetsDataset([file_names[i] for i in eval_split]			,   image_transform=__lowerCAmelCase			,   label_to_id=__lowerCAmelCase				)

 # Instantiate dataloaders.
 lowerCamelCase__     :			Optional[Any]     				=						DataLoader(__lowerCAmelCase			,   shuffle=__lowerCAmelCase			,   batch_size=__lowerCAmelCase			,   num_workers=4				)
 lowerCamelCase__     :			Optional[Any]     				=						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__     :			List[Any]     				=						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__     :			str     				=						model.to(accelerator.device				)

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

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

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

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

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

  lowerCamelCase__     :			Optional[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        lowerCamelCase_  (						):
 lowerCamelCase__     :			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.'				)
 lowerCamelCase__     :			str     				=						parser.parse_args()
 lowerCamelCase__     :			Union[str, Any]     				=						{'lr': 3e-2, 'num_epochs': 3, 'seed': 42, 'batch_size': 64, 'image_size': 224}
 training_function(__lowerCAmelCase			,   __lowerCAmelCase				)


if __name__ == "__main__":
      main()


"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging


a							=logging.get_logger(__name__)

a							={
    'microsoft/mpnet-base': 'https://huggingface.co/microsoft/mpnet-base/resolve/main/config.json',
}






class    __UpperCAmelCase						(	__lowerCAmelCase	):
				A__      :   Optional[int]  							=       '''mpnet'''

				def __init__(      self	, _lowerCamelCase=30527	, _lowerCamelCase=768	, _lowerCamelCase=12	, _lowerCamelCase=12	, _lowerCamelCase=3072	, _lowerCamelCase="gelu"	, _lowerCamelCase=0.1	, _lowerCamelCase=0.1	, _lowerCamelCase=512	, _lowerCamelCase=0.0_2	, _lowerCamelCase=1E-12	, _lowerCamelCase=32	, _lowerCamelCase=1	, _lowerCamelCase=0	, _lowerCamelCase=2	, **_lowerCamelCase	, ):
										super().__init__(pad_token_id=_lowerCamelCase	, bos_token_id=_lowerCamelCase	, eos_token_id=_lowerCamelCase	, **_lowerCamelCase      )

										lowerCamelCase__					=vocab_size
										lowerCamelCase__					=hidden_size
										lowerCamelCase__					=num_hidden_layers
										lowerCamelCase__					=num_attention_heads
										lowerCamelCase__					=hidden_act
										lowerCamelCase__					=intermediate_size
										lowerCamelCase__					=hidden_dropout_prob
										lowerCamelCase__					=attention_probs_dropout_prob
										lowerCamelCase__					=max_position_embeddings
										lowerCamelCase__					=initializer_range
										lowerCamelCase__					=layer_norm_eps
										lowerCamelCase__					=relative_attention_num_buckets


