

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_  (				UpperCAmelCase_				:  Tuple						)    ->					Union[str, Any]:




	'''simple docstring'''




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







class 						lowercase     (					_lowercase				):


		"""simple docstring"""




		def __init__(					self						,							__snake_case						,							__snake_case=None						,							__snake_case=None):
			_UpperCamelCase :		Union[str, Any]								=    file_names
			_UpperCamelCase :		Optional[Any]								=    image_transform
			_UpperCamelCase :		Union[str, Any]								=    label_to_id


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






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






def        lowerCamelCase_  (				UpperCAmelCase_				:  Dict				,							UpperCAmelCase_				:  str						)    ->					Tuple:




	'''simple docstring'''




	_UpperCamelCase :		Optional[Any]								=    Accelerator(cpu=args.cpu				,							mixed_precision=args.mixed_precision						)

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

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

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

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

	# Split our filenames between train and validation
	_UpperCamelCase :		Union[str, Any]								=    np.random.permutation(len(UpperCAmelCase_						)						)
	_UpperCamelCase :		Any								=    int(0.8 * len(UpperCAmelCase_						)						)
	_UpperCamelCase :		Optional[int]								=    random_perm[:cut]
	_UpperCamelCase :		Dict								=    random_perm[cut:]

	# For training we use a simple RandomResizedCrop
	_UpperCamelCase :		Tuple								=    Compose([RandomResizedCrop(UpperCAmelCase_				,							scale=(0.5, 1.0)						), ToTensor()]						)
	_UpperCamelCase :		str								=    PetsDataset(
	    [file_names[i] for i in train_split]				,							image_transform=UpperCAmelCase_				,							label_to_id=UpperCAmelCase_						)

	# For evaluation, we use a deterministic Resize
	_UpperCamelCase :		Dict								=    Compose([Resize(UpperCAmelCase_						), ToTensor()]						)
	_UpperCamelCase :		str								=    PetsDataset([file_names[i] for i in eval_split]				,							image_transform=UpperCAmelCase_				,							label_to_id=UpperCAmelCase_						)

	# Instantiate dataloaders.
	_UpperCamelCase :		str								=    DataLoader(UpperCAmelCase_				,							shuffle=UpperCAmelCase_				,							batch_size=UpperCAmelCase_				,							num_workers=4						)
	_UpperCamelCase :		Optional[Any]								=    DataLoader(UpperCAmelCase_				,							shuffle=UpperCAmelCase_				,							batch_size=UpperCAmelCase_				,							num_workers=4						)

	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	_UpperCamelCase :		int								=    create_model('resnet50d'				,							pretrained=UpperCAmelCase_				,							num_classes=len(UpperCAmelCase_						)						)

	# 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).
	_UpperCamelCase :		Any								=    model.to(accelerator.device						)

	# Freezing the base model
	for param in model.parameters():
		_UpperCamelCase :		Any								=    False
	for param in model.get_classifier().parameters():
		_UpperCamelCase :		List[Any]								=    True

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

	# Instantiate optimizer
	_UpperCamelCase :		Any								=    torch.optim.Adam(params=model.parameters()				,							lr=lr / 2_5						)

	# Instantiate learning rate scheduler
	_UpperCamelCase :		List[str]								=    OneCycleLR(optimizer=UpperCAmelCase_				,							max_lr=UpperCAmelCase_				,							epochs=UpperCAmelCase_				,							steps_per_epoch=len(UpperCAmelCase_						)						)

	# 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.
	_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase :		Any								=    accelerator.prepare(
	    UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_						)

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

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

		_UpperCamelCase :		Tuple								=    accurate.item() / num_elems
		# Use accelerator.print to print only on the main process.
		accelerator.print(F'''epoch {epoch}: {1_0_0 * eval_metric:.2f}'''						)






def        lowerCamelCase_  (				)    ->					Union[str, Any]:




	'''simple docstring'''




	_UpperCamelCase :		Optional[Any]								=    argparse.ArgumentParser(description='Simple example of training script.'						)
	parser.add_argument('--data_dir'				,							required=UpperCAmelCase_				,							help='The data folder on disk.'						)
	parser.add_argument(
	    '--mixed_precision'				,							type=UpperCAmelCase_				,							default=UpperCAmelCase_				,							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=UpperCAmelCase_				,							default=UpperCAmelCase_				,							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.'						)
	_UpperCamelCase :		int								=    parser.parse_args()
	_UpperCamelCase :		int								=    {'lr': 3e-2, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 6_4, 'image_size': 2_2_4}
	training_function(UpperCAmelCase_				,							UpperCAmelCase_						)


if __name__ == "__main__":
							main()









import json
import os
from collections.abc import Mapping
from typing import Dict, List, Optional, Union

import numpy as np
import torch

from .imports import is_safetensors_available


def 						_UpperCAmelCase							( snake_case      ,		snake_case      ,		snake_case      ,		snake_case=None     ):





					"""simple docstring"""

					_lowerCAmelCase         =       None
					# Check the string instead of the dtype to be compatible with versions of PyTorch that don't have bfloat16.
					if str(weight.dtype     ) == "torch.bfloat16":
										# Need to reinterpret the underlined data as int16 since NumPy does not handle bfloat16s.
										_lowerCAmelCase         =       weight.view(torch.intaa     )
										_lowerCAmelCase         =       """bfloat16"""
					_lowerCAmelCase         =       weight.cpu().numpy()
					_lowerCAmelCase         =       os.path.join(snake_case      ,		F'{weight_name}.dat'     )
					if index is not None:
										if dtype is None:
															_lowerCAmelCase         =       str(array.dtype     )
										_lowerCAmelCase         =       {"""dtype""": dtype, """shape""": list(array.shape     )}
					if array.ndim == 0:
										_lowerCAmelCase         =       array[None]
					_lowerCAmelCase         =       np.memmap(snake_case      ,		dtype=array.dtype      ,		mode="""w+"""      ,		shape=array.shape     )
					_lowerCAmelCase         =       array[:]
					file_array.flush()
					return index


def 						_UpperCAmelCase							( snake_case      ,		snake_case     ):





					"""simple docstring"""

					_lowerCAmelCase         =       tuple(weight_info["""shape"""]     )
					if shape == ():
										# NumPy memory-mapped arrays can't have 0 dims so it was saved as 1d tensor
										_lowerCAmelCase         =       (1,)

					_lowerCAmelCase         =       weight_info["""dtype"""]
					if dtype == "bfloat16":
										# NumPy does not support bfloat16 so this was saved as a int16
										_lowerCAmelCase         =       """int16"""

					_lowerCAmelCase         =       np.memmap(snake_case      ,		dtype=snake_case      ,		shape=snake_case      ,		mode="""r"""     )

					if len(weight_info["""shape"""]     ) == 0:
										_lowerCAmelCase         =       weight[0]
					_lowerCAmelCase         =       torch.tensor(snake_case     )
					if weight_info["dtype"] == "bfloat16":
										_lowerCAmelCase         =       weight.view(torch.bfloataa     )

					return weight


def 						_UpperCAmelCase							( snake_case      ,		snake_case     ):





					"""simple docstring"""

					if index is None or len(snake_case     ) == 0:
										# Nothing to save
										return

					_lowerCAmelCase         =       os.path.join(snake_case      ,		"""index.json"""     )
					if os.path.isfile(snake_case     ):
										with open(snake_case      ,		"""r"""      ,		encoding="""utf-8"""     ) as f:
															_lowerCAmelCase         =       json.load(snake_case     )
					else:
										_lowerCAmelCase         =       {}
					current_index.update(snake_case     )

					with open(snake_case      ,		"""w"""      ,		encoding="""utf-8"""     ) as f:
										json.dump(snake_case      ,		snake_case      ,		indent=2     )


def 						_UpperCAmelCase							( snake_case      ,		snake_case     ):





					"""simple docstring"""

					os.makedirs(snake_case      ,		exist_ok=snake_case     )
					_lowerCAmelCase         =       {}
					for name, parameter in state_dict.items():
										_lowerCAmelCase         =       offload_weight(snake_case      ,		snake_case      ,		snake_case      ,		index=snake_case     )

					# Update index
					save_offload_index(snake_case      ,		snake_case     )





class    __lowerCAmelCase					(	lowerCamelCase__			):




				def __init__(				self		,							_snake_case		,							_snake_case						):



									"""simple docstring"""





									_lowerCAmelCase         =       dataset
									_lowerCAmelCase         =       prefix




				def __getitem__(				self		,							_snake_case						):



									"""simple docstring"""





									return self.dataset[F'{self.prefix}{key}']




				def __iter__(				self						):



									"""simple docstring"""





									return iter([key for key in self.dataset if key.startswith(self.prefix						)]						)




				def __len__(				self						):



									"""simple docstring"""





									return len(self.dataset						)





class    __lowerCAmelCase					(	lowerCamelCase__			):




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



									"""simple docstring"""





									if state_dict is None and save_folder is None:
														raise ValueError("""Need either a `state_dict` or a `save_folder` containing offloaded weights."""						)

									_lowerCAmelCase         =       {} if state_dict is None else state_dict
									_lowerCAmelCase         =       save_folder
									if index is None and save_folder is not None:
														with open(os.path.join(_snake_case		,							"""index.json"""						)						) as f:
																			_lowerCAmelCase         =       json.load(_snake_case						)
									_lowerCAmelCase         =       {} if index is None else index
									_lowerCAmelCase         =       list(self.state_dict.keys()						)
									self.all_keys.extend([key for key in self.index if key not in self.all_keys]						)
									_lowerCAmelCase         =       device




				def __getitem__(				self		,							_snake_case						):



									"""simple docstring"""





									if key in self.state_dict:
														return self.state_dict[key]
									_lowerCAmelCase         =       self.index[key]
									if weight_info.get("""safetensors_file"""						) is not None:
														if not is_safetensors_available():
																			raise ImportError("""These offloaded weights require the use of safetensors: `pip install safetensors`."""						)

														from safetensors import safe_open

														_lowerCAmelCase         =       """cpu""" if self.device is None else self.device
														with safe_open(weight_info["""safetensors_file"""]		,							framework="""pt"""		,							device=_snake_case						) as f:
																			_lowerCAmelCase         =       f.get_tensor(weight_info.get("""weight_name"""		,							_snake_case						)						)

														if "dtype" in weight_info:
																			return tensor.to(getattr(_snake_case		,							weight_info["""dtype"""]						)						)
														else:
																			return tensor

									_lowerCAmelCase         =       os.path.join(self.save_folder		,							F'{key}.dat'						)
									return load_offloaded_weight(_snake_case		,							_snake_case						)




				def __iter__(				self						):



									"""simple docstring"""





									return iter(self.all_keys						)




				def __len__(				self						):



									"""simple docstring"""





									return len(self.all_keys						)


def 						_UpperCAmelCase							( snake_case      ,		snake_case     ):





					"""simple docstring"""

					_lowerCAmelCase         =       {}
					for module_name in submodule_names:
										# We want to catch module_name parameter (module_name.xxx) or potentially module_name, but not any of the
										# submodules that could being like module_name (transformers.h.1 and transformers.h.10 for instance)
										result.update(
										    {
										        key: param
										        for key, param in state_dict.items()
										        if key == module_name or key.startswith(module_name + """."""     )
										    }     )
					return result



