

"""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      __SCREAMING_SNAKE_CASE		(		lowercase__   ):
					"""simple docstring"""
					A    =      fname.split(os.path.sep   )[-1]
					return re.search(r"^(.*)_\d+\.jpg$"    ,							lowercase__   ).groups()[0]





class     __UpperCamelCase				(     _A		):


		def __init__(self				:	str  ,  __SCREAMING_SNAKE_CASE				:	List[Any]  ,  __SCREAMING_SNAKE_CASE				:	Tuple=None  ,  __SCREAMING_SNAKE_CASE				:	Tuple=None):
							A    =      file_names
							A    =      image_transform
							A    =      label_to_id


		def __len__(self				:	Union[str, Any]):
							return len(self.file_names)


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

def      __SCREAMING_SNAKE_CASE		(		lowercase__    ,							lowercase__   ):
					"""simple docstring"""
					# Initialize accelerator
					A    =      Accelerator(cpu=args.cpu    ,							mixed_precision=args.mixed_precision   )

					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					A    =      config["lr"]
					A    =      int(config["num_epochs"]   )
					A    =      int(config["seed"]   )
					A    =      int(config["batch_size"]   )
					A    =      config["image_size"]
					if not isinstance(lowercase__    ,							(list, tuple)   ):
										A    =      (image_size, image_size)

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

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

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

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

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

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

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

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

					# Instantiate learning rate scheduler
					A    =      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.
					A    ,					A    ,					A    ,					A    ,					A    =      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`.
															A    =      {k: v.to(accelerator.device   ) for k, v in batch.items()}
															A    =      (batch["image"] - mean) / std
															A    =      model(lowercase__   )
															A    =      torch.nn.functional.cross_entropy(lowercase__    ,							batch["label"]   )
															accelerator.backward(lowercase__   )
															optimizer.step()
															lr_scheduler.step()
															optimizer.zero_grad()

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

										A    =      accurate.item() / num_elems
										# Use accelerator.print to print only on the main process.
										accelerator.print(F"""epoch {epoch}: {100 * eval_metric:.2f}"""   )
def      __SCREAMING_SNAKE_CASE		(		):
					"""simple docstring"""
					A    =      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."   )
					A    =      parser.parse_args()
					A    =      {"lr": 3e-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
					training_function(lowercase__    ,							lowercase__   )


if __name__ == "__main__":
	main()








"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__A  :					int												=	{
    'configuration_nystromformer': ['NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'NystromformerConfig'],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A  :					Any												=	[
	    'NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'NystromformerForMaskedLM',
	    'NystromformerForMultipleChoice',
	    'NystromformerForQuestionAnswering',
	    'NystromformerForSequenceClassification',
	    'NystromformerForTokenClassification',
	    'NystromformerLayer',
	    'NystromformerModel',
	    'NystromformerPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_nystromformer import (
		    NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    NystromformerForMaskedLM,
		    NystromformerForMultipleChoice,
		    NystromformerForQuestionAnswering,
		    NystromformerForSequenceClassification,
		    NystromformerForTokenClassification,
		    NystromformerLayer,
		    NystromformerModel,
		    NystromformerPreTrainedModel,
		)


else:
	import sys

	__A  :					Optional[Any]												=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





