
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_			(				snake_case				: Union[str, Any]			)   ->     Optional[Any]:



						'''simple docstring'''

						__UpperCamelCase  	=      fname.split(os.path.sep			)[-1]
						return re.search(r'''^(.*)_\d+\.jpg$'''			,   snake_case			).groups()[0]






class 							SCREAMING_SNAKE_CASE__     (				SCREAMING_SNAKE_CASE_     ):

							"""simple docstring"""






							def __init__(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_=None  ,						SCREAMING_SNAKE_CASE_=None      )-> str:





													'''simple docstring'''






													__UpperCamelCase  	=      file_names
													__UpperCamelCase  	=      image_transform
													__UpperCamelCase  	=      label_to_id



							def __len__(	self      )-> Any:





													'''simple docstring'''






													return len(self.file_names      )



							def __getitem__(	self  ,						SCREAMING_SNAKE_CASE_      )-> Any:





													'''simple docstring'''






													__UpperCamelCase  	=      self.file_names[idx]
													__UpperCamelCase  	=      PIL.Image.open(SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      raw_image.convert('''RGB'''      )
													if self.image_transform is not None:
																			__UpperCamelCase  	=      self.image_transform(SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      extract_label(SCREAMING_SNAKE_CASE_      )
													if self.label_to_id is not None:
																			__UpperCamelCase  	=      self.label_to_id[label]
													return {"image": image, "label": label}





def 						A_			(				snake_case				: List[str]			,   snake_case				: Dict			)   ->     int:



						'''simple docstring'''

						__UpperCamelCase  	=      Accelerator(cpu=args.cpu			,   mixed_precision=args.mixed_precision			)

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

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

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

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

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

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

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

						# Instantiate dataloaders.
						__UpperCamelCase  	=      DataLoader(snake_case			,   shuffle=snake_case			,   batch_size=snake_case			,   num_workers=4			)
						__UpperCamelCase  	=      DataLoader(snake_case			,   shuffle=snake_case			,   batch_size=snake_case			,   num_workers=4			)

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

						# 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  	=      model.to(accelerator.device			)

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

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

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

						# Instantiate learning rate scheduler
						__UpperCamelCase  	=      OneCycleLR(optimizer=snake_case			,   max_lr=snake_case			,   epochs=snake_case			,   steps_per_epoch=len(snake_case			)			)

						# 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  	=      accelerator.prepare(
						    snake_case			,   snake_case			,   snake_case			,   snake_case			,   snake_case			)

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

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

												__UpperCamelCase  	=      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_			(				)   ->     Any:



						'''simple docstring'''

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


if __name__ == "__main__":
				main()








from collections import OrderedDict
from dataclasses import fields
from typing import Any, Tuple

import numpy as np

from .import_utils import is_torch_available






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




      """simple docstring"""

      if is_torch_available():
            import torch

            if isinstance(SCREAMING_SNAKE_CASE   ,   torch.Tensor   ):
                  return True

      return isinstance(SCREAMING_SNAKE_CASE   ,   np.ndarray   )







class lowerCAmelCase_		(					lowerCamelCase__			):

 '''simple docstring'''

 def 		UpperCamelCase__	(      self   ):
       snake_case_			= fields(self   )

       # Safety and consistency checks
       if not len(_UpperCAmelCase   ):
             raise ValueError(F'''{self.__class__.__name__} has no fields.'''   )

       snake_case_			= getattr(self						, class_fields[0].name   )
       snake_case_			= all(getattr(self						, field.name   ) is None for field in class_fields[1:]   )

       if other_fields_are_none and isinstance(_UpperCAmelCase						, _UpperCAmelCase   ):
             for key, value in first_field.items():
                   snake_case_			= value
       else:
             for field in class_fields:
                   snake_case_			= getattr(self						, field.name   )
                   if v is not None:
                         snake_case_			= v



 def __delitem__(      self						, *_UpperCAmelCase						, **_UpperCAmelCase   ):
       raise Exception(F'''You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.'''   )



 def 		UpperCamelCase__	(      self						, *_UpperCAmelCase						, **_UpperCAmelCase   ):
       raise Exception(F'''You cannot use ``setdefault`` on a {self.__class__.__name__} instance.'''   )



 def 		UpperCamelCase__	(      self						, *_UpperCAmelCase						, **_UpperCAmelCase   ):
       raise Exception(F'''You cannot use ``pop`` on a {self.__class__.__name__} instance.'''   )



 def 		UpperCamelCase__	(      self						, *_UpperCAmelCase						, **_UpperCAmelCase   ):
       raise Exception(F'''You cannot use ``update`` on a {self.__class__.__name__} instance.'''   )



 def __getitem__(      self						, _UpperCAmelCase   ):
       if isinstance(_UpperCAmelCase						, _UpperCAmelCase   ):
             snake_case_			= dict(self.items()   )
             return inner_dict[k]
       else:
             return self.to_tuple()[k]



 def __setattr__(      self						, _UpperCAmelCase						, _UpperCAmelCase   ):
       if name in self.keys() and value is not None:
             # Don't call self.__setitem__ to avoid recursion errors
             super().__setitem__(_UpperCAmelCase						, _UpperCAmelCase   )
       super().__setattr__(_UpperCAmelCase						, _UpperCAmelCase   )



 def __setitem__(      self						, _UpperCAmelCase						, _UpperCAmelCase   ):
       # Will raise a KeyException if needed
       super().__setitem__(_UpperCAmelCase						, _UpperCAmelCase   )
       # Don't call self.__setattr__ to avoid recursion errors
       super().__setattr__(_UpperCAmelCase						, _UpperCAmelCase   )





 def 		UpperCamelCase__	(      self   ):
       return tuple(self[k] for k in self.keys()   )