

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      UpperCAmelCase__							(     _A :					List[Any]				):





  '''simple docstring'''







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







class 		__magic_name__     (				_UpperCAmelCase				):

   '''simple docstring'''




   def __init__(							self,       lowercase_,       lowercase_=None,       lowercase_=None			)				->       Tuple:






     """simple docstring"""






     a__    =file_names
     a__    =image_transform
     a__    =label_to_id



   def __len__(							self			)				->       Any:






     """simple docstring"""






     return len(self.file_names			)



   def __getitem__(							self,       lowercase_			)				->       Any:






     """simple docstring"""






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





def      UpperCAmelCase__							(     _A :					List[Any]      , _A :					Optional[int]				):





  '''simple docstring'''







  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(_lowerCAmelCase      , (list, tuple)				):
    a__    =(image_size, image_size)

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

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

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

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

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

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

  # Instantiate dataloaders.
  a__    =DataLoader(_lowerCAmelCase      , shuffle=_lowerCAmelCase      , batch_size=_lowerCAmelCase      , num_workers=4				)
  a__    =DataLoader(_lowerCAmelCase      , shuffle=_lowerCAmelCase      , batch_size=_lowerCAmelCase      , 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=_lowerCAmelCase      , num_classes=len(_lowerCAmelCase				)				)

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

  # 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__    =accelerator.prepare(
      _lowerCAmelCase      , _lowerCAmelCase      , _lowerCAmelCase      , _lowerCAmelCase      , _lowerCAmelCase				)

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

    model.eval()
    a__    =0
    a__    =0
    for _, batch in enumerate(_lowerCAmelCase				):
      # 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(_lowerCAmelCase				)
      a__    =outputs.argmax(dim=-1				)
      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}: {1_00 * eval_metric:.2f}"""				)




def      UpperCAmelCase__							(     ):





  '''simple docstring'''







  a__    =argparse.ArgumentParser(description='''Simple example of training script.'''				)
  parser.add_argument('''--data_dir'''      , required=_lowerCAmelCase      , help='''The data folder on disk.'''				)
  parser.add_argument(
      '''--mixed_precision'''      , type=_lowerCAmelCase      , default=_lowerCAmelCase      , 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=_lowerCAmelCase      , default=_lowerCAmelCase      , 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": 2_24}
  training_function(_lowerCAmelCase      , _lowerCAmelCase				)


if __name__ == "__main__":
       main()

'''simple docstring'''

import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize




class 		SCREAMING_SNAKE_CASE_					:



			"""simple docstring"""






			def __init__(							self				,			snake_case_				,			snake_case_=sys.maxsize					):
									snake_case :       Any				=    "bilinear"
									snake_case :       Dict				=    max_size
									snake_case :       Dict				=    short_edge_length






			def __call__(							self				,			snake_case_					):
									snake_case :       Optional[Any]				=    []
									for img in imgs:
															snake_case				,					snake_case :       Dict				=    img.shape[:2]
															# later: provide list and randomly choose index for resize
															snake_case :       Dict				=    np.random.randint(self.short_edge_length[0]				,			self.short_edge_length[1] + 1					)
															if size == 0:
																					return img
															snake_case :       Any				=    size * 1.0 / min(snake_case_				,			snake_case_					)
															if h < w:
																					snake_case				,					snake_case :       Optional[int]				=    size, scale * w
															else:
																					snake_case				,					snake_case :       Tuple				=    scale * h, size
															if max(snake_case_				,			snake_case_					) > self.max_size:
																					snake_case :       Optional[int]				=    self.max_size * 1.0 / max(snake_case_				,			snake_case_					)
																					snake_case :       Dict				=    newh * scale
																					snake_case :       List[Any]				=    neww * scale
															snake_case :       int				=    int(neww + 0.5					)
															snake_case :       List[Any]				=    int(newh + 0.5					)

															if img.dtype == np.uinta:
																					snake_case :       List[Any]				=    Image.fromarray(snake_case_					)
																					snake_case :       str				=    pil_image.resize((neww, newh)				,			PILImageResampling.BILINEAR					)
																					snake_case :       List[str]				=    np.asarray(snake_case_					)
															else:
																					snake_case :       List[Any]				=    img.permute(2				,			0				,			1					).unsqueeze(0					)  # 3, 0, 1)  # hw(c) -> nchw
																					snake_case :       Tuple				=    nn.functional.interpolate(
																					    snake_case_				,			(newh, neww)				,			mode=self.interp_method				,			align_corners=snake_case_					).squeeze(0					)
															img_augs.append(snake_case_					)

									return img_augs







class 		SCREAMING_SNAKE_CASE_					:



			"""simple docstring"""






			def __init__(							self				,			snake_case_					):
									snake_case :       Union[str, Any]				=    ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]				,			cfg.INPUT.MAX_SIZE_TEST					)
									snake_case :       Any				=    cfg.INPUT.FORMAT
									snake_case :       Optional[int]				=    cfg.SIZE_DIVISIBILITY
									snake_case :       Optional[int]				=    cfg.PAD_VALUE
									snake_case :       int				=    cfg.INPUT.MAX_SIZE_TEST
									snake_case :       Optional[int]				=    cfg.MODEL.DEVICE
									snake_case :       int				=    torch.tensor(cfg.MODEL.PIXEL_STD					).to(self.device					).view(len(cfg.MODEL.PIXEL_STD					)				,			1				,			1					)
									snake_case :       List[Any]				=    torch.tensor(cfg.MODEL.PIXEL_MEAN					).to(self.device					).view(len(cfg.MODEL.PIXEL_STD					)				,			1				,			1					)
									snake_case :       Dict				=    lambda snake_case_					: (x - self.pixel_mean) / self.pixel_std



			def   lowerCamelCase (							self				,			snake_case_					):
									snake_case :       List[Any]				=    tuple(max(snake_case_					) for s in zip(*[img.shape for img in images]					)					)
									snake_case :       int				=    [im.shape[-2:] for im in images]
									snake_case :       List[Any]				=    [
									    nn.functional.pad(
									        snake_case_				,			[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]				,			value=self.pad_value				,			)
									    for size, im in zip(snake_case_				,			snake_case_					)
									]

									return torch.stack(snake_case_					), torch.tensor(snake_case_					)






			def __call__(							self				,			snake_case_				,			snake_case_=False					):
									with torch.no_grad():
															if not isinstance(snake_case_				,			snake_case_					):
																					snake_case :       int				=    [images]
															if single_image:
																					assert len(snake_case_					) == 1
															for i in range(len(snake_case_					)					):
																					if isinstance(images[i]				,			torch.Tensor					):
																											images.insert(snake_case_				,			images.pop(snake_case_					).to(self.device					).float()					)
																					elif not isinstance(images[i]				,			torch.Tensor					):
																											images.insert(
																											    snake_case_				,			torch.as_tensor(img_tensorize(images.pop(snake_case_					)				,			input_format=self.input_format					)					)
																											    .to(self.device					)
																											    .float()				,			)
            # resize smallest edge
															snake_case :       List[Any]				=    torch.tensor([im.shape[:2] for im in images]					)
															snake_case :       Any				=    self.aug(snake_case_					)
															# transpose images and convert to torch tensors
															# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
															# now normalize before pad to avoid useless arithmetic
															snake_case :       str				=    [self.normalizer(snake_case_					) for x in images]
															# now pad them to do the following operations
															snake_case				,					snake_case :       int				=    self.pad(snake_case_					)
															# Normalize

															if self.size_divisibility > 0:
																					raise NotImplementedError()
															# pad
															snake_case :       Union[str, Any]				=    torch.true_divide(snake_case_				,			snake_case_					)
															if single_image:
																					return images[0], sizes[0], scales_yx[0]
															else:
																					return images, sizes, scales_yx







def        UpperCAmelCase__   (				_lowerCAmelCase    :			Any,      _lowerCAmelCase    :			Optional[int])    ->		int:



						"""simple docstring"""

						boxes[:, 0::2] *= scale_yx[:, 1]
						boxes[:, 1::2] *= scale_yx[:, 0]
						return boxes



def        UpperCAmelCase__   (				_lowerCAmelCase    :			Union[str, Any],      _lowerCAmelCase    :			Tuple[int, int])    ->		List[str]:



						"""simple docstring"""

						assert torch.isfinite(_lowerCAmelCase).all(), "Box tensor contains infinite or NaN!"
						snake_case				,					snake_case :       Union[str, Any]				=    box_size
						tensor[:, 0].clamp_(min=0,      max=_lowerCAmelCase)
						tensor[:, 1].clamp_(min=0,      max=_lowerCAmelCase)
						tensor[:, 2].clamp_(min=0,      max=_lowerCAmelCase)
						tensor[:, 3].clamp_(min=0,      max=_lowerCAmelCase)





