






"""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      snake_case    (A_ :str ):







  '''simple docstring'''

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






class 		snake_case    (    UpperCAmelCase			):





    def __init__(				self      :   List[str]							,						A      :   Union[str, Any]							,						A      :   Optional[int]=None							,						A      :   Dict=None     ):


      '''simple docstring'''




      a	:     Union[str, Any]							=  file_names
      a	:     List[Any]							=  image_transform
      a	:     Union[str, Any]							=  label_to_id





    def __len__(				self      :   Tuple     ):


      '''simple docstring'''




      return len(self.file_names     )





    def __getitem__(				self      :   List[Any]							,						A      :   Optional[Any]     ):


      '''simple docstring'''




      a	:     Dict							=  self.file_names[idx]
      a	:     Tuple							=  PIL.Image.open(A     )
      a	:     List[Any]							=  raw_image.convert('RGB'     )
      if self.image_transform is not None:
        a	:     Optional[Any]							=  self.image_transform(A     )
      a	:     Optional[int]							=  extract_label(A     )
      if self.label_to_id is not None:
        a	:     int							=  self.label_to_id[label]
      return {"image": image, "label": label}




def      snake_case    (A_ :Optional[Any]      ,     A_ :int ):







  '''simple docstring'''

  a	:     Dict							=  Accelerator(cpu=args.cpu      ,     mixed_precision=args.mixed_precision )

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

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

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

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

  # Split our filenames between train and validation
  a	:     Dict							=  np.random.permutation(len(A_ ) )
  a	:     List[str]							=  int(0.8 * len(A_ ) )
  a	:     int							=  random_perm[:cut]
  a	:     List[Any]							=  random_perm[cut:]

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

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

  # Instantiate dataloaders.
  a	:     Tuple							=  DataLoader(A_      ,     shuffle=A_      ,     batch_size=A_      ,     num_workers=4 )
  a	:     Union[str, Any]							=  DataLoader(A_      ,     shuffle=A_      ,     batch_size=A_      ,     num_workers=4 )

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  a	:     Union[str, Any]							=  create_model('resnet50d'      ,     pretrained=A_      ,     num_classes=len(A_ ) )

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

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

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

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

  # Instantiate learning rate scheduler
  a	:     List[Any]							=  OneCycleLR(optimizer=A_      ,     max_lr=A_      ,     epochs=A_      ,     steps_per_epoch=len(A_ ) )

  # 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	:     Optional[Any]							=  accelerator.prepare(
      A_      ,     A_      ,     A_      ,     A_      ,     A_ )

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

    model.eval()
    a	:     int							=  0
    a	:     Dict							=  0
    for _, batch in enumerate(A_ ):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      a	:     Optional[Any]							=  {k: v.to(accelerator.device ) for k, v in batch.items()}
      a	:     Dict							=  (batch['image'] - mean) / std
      with torch.no_grad():
        a	:     Tuple							=  model(A_ )
      a	:     Dict							=  outputs.argmax(dim=-1 )
      a,	a	:     Tuple							=  accelerator.gather_for_metrics((predictions, batch['label']) )
      a	:     str							=  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}: {1_0_0 * eval_metric:.2f}''' )




def      snake_case    ():







  '''simple docstring'''

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


if __name__ == "__main__":
   main()












"""simple docstring"""


import json

import requests

from .fetch_github_info import AUTHENTICATED_USER_ENDPOINT, fetch_github_info




def      snake_case    (A_ :Union[str, Any] ):







  '''simple docstring'''

  class 		snake_case    :





      def __init__(				self      :   Tuple							,						A      :   Dict     ):


        '''simple docstring'''




        assert isinstance(A							,						(bytes, str)     )
        a	:     Union[str, Any]							=  content





      def 							lowerCamelCase__ (				self      :   str     ):


        '''simple docstring'''




        return json.loads(self.content     )

  def mock_response(*A_ :Union[str, Any]      ,     **A_ :Tuple ):
    assert args[0] == AUTHENTICATED_USER_ENDPOINT
    assert "Authorization" in kwargs["headers"]
    assert kwargs["headers"]["Authorization"].startswith('token ' )
    assert "Accept" in kwargs["headers"]
    return FakeResponse(B'{"login":"test","id":1}' )

  monkeypatch.setattr(A_      ,     'get'      ,     A_ )
  a	:     List[str]							=  fetch_github_info('token' )
  assert result["login"] == "test"
  assert result["id"] == 1




