



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


class 	lowerCamelCase__ (		lowercase__   ):


						def __init__( self						:  Tuple      ,       _a						:  List[Any]      ,       _a						:  Tuple=None      ,       _a						:  Tuple=None			):
													a__:       Union[str, Any]											=file_names
													a__:       Dict											=image_transform
													a__:       int											=label_to_id


						def __len__( self						:  Tuple			):
													return len(self.file_names			)







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





def 						__lowerCamelCase       (      __magic_name__	:  List[Any]     , __magic_name__	:  List[Any]						):
							a__:       int											=Accelerator(cpu=args.cpu     , mixed_precision=args.mixed_precision						)

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

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

							# Build the label correspondences
							a__:       Dict											=[extract_label(__magic_name__						) for fname in file_names]
							a__:       str											=list(set(__magic_name__						)						)
							id_to_label.sort()
							a__:       Any											={lbl: i for i, lbl in enumerate(__magic_name__						)}

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

							# Split our filenames between train and validation
							a__:       Dict											=np.random.permutation(len(__magic_name__						)						)
							a__:       Optional[Any]											=int(0.8 * len(__magic_name__						)						)
							a__:       Optional[int]											=random_perm[:cut]
							a__:       Any											=random_perm[cut:]

							# For training we use a simple RandomResizedCrop
							a__:       Optional[Any]											=Compose([RandomResizedCrop(__magic_name__     , scale=(0.5, 1.0)						), ToTensor()]						)
							a__:       Optional[Any]											=PetsDataset(
							    [file_names[i] for i in train_split]     , image_transform=__magic_name__     , label_to_id=__magic_name__						)

							# For evaluation, we use a deterministic Resize
							a__:       int											=Compose([Resize(__magic_name__						), ToTensor()]						)
							a__:       Tuple											=PetsDataset([file_names[i] for i in eval_split]     , image_transform=__magic_name__     , label_to_id=__magic_name__						)

							# Instantiate dataloaders.
							a__:       Dict											=DataLoader(__magic_name__     , shuffle=__magic_name__     , batch_size=__magic_name__     , num_workers=4						)
							a__:       Optional[Any]											=DataLoader(__magic_name__     , shuffle=__magic_name__     , batch_size=__magic_name__     , num_workers=4						)

							# Instantiate the model (we build the model here so that the seed also control new weights initialization)
							a__:       Optional[Any]											=create_model("resnet50d"     , pretrained=__magic_name__     , num_classes=len(__magic_name__						)						)

							# 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__:       Optional[int]											=model.to(accelerator.device						)

							# Freezing the base model
							for param in model.parameters():
														a__:       str											=False
							for param in model.get_classifier().parameters():
														a__:       Optional[int]											=True

							# We normalize the batches of images to be a bit faster.
							a__:       Dict											=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__:       Tuple											=OneCycleLR(optimizer=__magic_name__     , max_lr=__magic_name__     , epochs=__magic_name__     , steps_per_epoch=len(__magic_name__						)						)

							# 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__:       Tuple											=accelerator.prepare(
							    __magic_name__     , __magic_name__     , __magic_name__     , __magic_name__     , __magic_name__						)

							# Now we train the model
							for epoch in range(__magic_name__						):
														model.train()
														for step, batch in enumerate(__magic_name__						):
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					a__:       Optional[int]											={k: v.to(accelerator.device						) for k, v in batch.items()}
																					a__:       Union[str, Any]											=(batch['image'] - mean) / std
																					a__:       Tuple											=model(__magic_name__						)
																					a__:       List[str]											=torch.nn.functional.cross_entropy(__magic_name__     , batch["label"]						)
																					accelerator.backward(__magic_name__						)
																					optimizer.step()
																					lr_scheduler.step()
																					optimizer.zero_grad()

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

														a__:       Tuple											=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       (      ):
							a__:       Tuple											=argparse.ArgumentParser(description="Simple example of training script."						)
							parser.add_argument("--data_dir"     , required=__magic_name__     , help="The data folder on disk."						)
							parser.add_argument(
							    "--mixed_precision"     , type=__magic_name__     , default=__magic_name__     , 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=__magic_name__     , default=__magic_name__     , 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[Any]											=parser.parse_args()
							a__:       Optional[int]											={'lr': 3e-2, 'num_epochs': 3, 'seed': 42, 'batch_size': 64, 'image_size': 224}
							training_function(__magic_name__     , __magic_name__						)


if __name__ == "__main__":
		main()










from typing import TYPE_CHECKING

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


__UpperCAmelCase						  =     {
    '''configuration_splinter''': ['''SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SplinterConfig'''],
    '''tokenization_splinter''': ['''SplinterTokenizer'''],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase						  =     ['''SplinterTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase						  =     [
		    '''SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''SplinterForQuestionAnswering''',
		    '''SplinterForPreTraining''',
		    '''SplinterLayer''',
		    '''SplinterModel''',
		    '''SplinterPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_splinter import SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP, SplinterConfig
		from .tokenization_splinter import SplinterTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_splinter_fast import SplinterTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_splinter import (
				    SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    SplinterForPreTraining,
				    SplinterForQuestionAnswering,
				    SplinterLayer,
				    SplinterModel,
				    SplinterPreTrainedModel,
				)


else:
		import sys

		__UpperCAmelCase						  =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





