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






class     _SCREAMING_SNAKE_CASE       ( UpperCAmelCase							):



			'''simple docstring'''



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


						'''simple docstring'''

						__lowerCamelCase								=				file_names
						__lowerCamelCase								=				image_transform
						__lowerCamelCase								=				label_to_id







			def __len__(  self  ):


						'''simple docstring'''

						return len(self.file_names  )







			def __getitem__(  self   ,							_SCREAMING_SNAKE_CASE  ):


						'''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 			A__						(  lowercase_    :  str			,   lowercase_    :  Optional[int]	)     ->       Dict:
			# 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(lowercase_			,   (list, tuple)	):
						__lowerCamelCase								=				(image_size, image_size)

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

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

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

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

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

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

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

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

			# 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(
			    lowercase_			,   lowercase_			,   lowercase_			,   lowercase_			,   lowercase_	)

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

						model.eval()
						__lowerCamelCase								=				0
						__lowerCamelCase								=				0
						for _, batch in enumerate(lowercase_	):
									# 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(lowercase_	)
									__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 			A__						(  )     ->       str:
			__lowerCamelCase								=				argparse.ArgumentParser(description='Simple example of training script.'	)
			parser.add_argument('--data_dir'			,   required=lowercase_			,   help='The data folder on disk.'	)
			parser.add_argument(
			    '--mixed_precision'			,   type=lowercase_			,   default=lowercase_			,   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=lowercase_			,   default=lowercase_			,   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(lowercase_			,   lowercase_	)


if __name__ == "__main__":
					main()







"""simple docstring"""







import sys

from transformers import AutoTokenizer


_SCREAMING_SNAKE_CASE  :		List[str]        =      sys.argv[1]
_SCREAMING_SNAKE_CASE  :		List[str]        =      sys.argv[2]
_SCREAMING_SNAKE_CASE  :		Tuple        =      int(sys.argv[3])

_SCREAMING_SNAKE_CASE  :		List[str]        =      0

_SCREAMING_SNAKE_CASE  :		Optional[int]        =      AutoTokenizer.from_pretrained(model_name_or_path)
max_len -= tokenizer.num_special_tokens_to_add()

with open(dataset, '''rt''') as f_p:
     for line in f_p:
          _SCREAMING_SNAKE_CASE  :		List[Any]        =      line.rstrip()

          if not line:
               print(line)
               _SCREAMING_SNAKE_CASE  :		Union[str, Any]        =      0
               continue

          _SCREAMING_SNAKE_CASE  :		Dict        =      line.split()[0]

          _SCREAMING_SNAKE_CASE  :		Tuple        =      len(tokenizer.tokenize(token))

          # Token contains strange control characters like \x96 or \x95
          # Just filter out the complete line
          if current_subwords_len == 0:
               continue

          if (subword_len_counter + current_subwords_len) > max_len:
               print('''''')
               print(line)
               _SCREAMING_SNAKE_CASE  :		List[Any]        =      current_subwords_len
               continue

          subword_len_counter += current_subwords_len

          print(line)






