code: 
  
  
  
  
  
  
  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	(	__lowerCamelCase					)   -> Union[str, Any]:
  
       '''simple docstring'''
       UpperCAmelCase__			:     Any										= fname.split(os.path.sep					)[-1]
       return re.search(r"""^(.*)_\d+\.jpg$"""   ,       __lowerCamelCase					).groups()[0]
  
  
  
  
  
  
  
  class 							UpperCAmelCase_     (		lowercase__		):
  
  
  
  
       def __init__(							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase=None  ,	_lowerCAmelCase=None		):
            UpperCAmelCase__			:     Union[str, Any]										= file_names
            UpperCAmelCase__			:     Dict										= image_transform
            UpperCAmelCase__			:     int										= label_to_id
  
  
  
  
       def __len__(							self		):
            return len(self.file_names		)
  
  
  
  
       def __getitem__(							self  ,	_lowerCAmelCase		):
            UpperCAmelCase__			:     List[str]										= self.file_names[idx]
            UpperCAmelCase__			:     str										= PIL.Image.open(_A		)
            UpperCAmelCase__			:     Tuple										= raw_image.convert("""RGB"""		)
            if self.image_transform is not None:
                 UpperCAmelCase__			:     Union[str, Any]										= self.image_transform(_A		)
            UpperCAmelCase__			:     Dict										= extract_label(_A		)
            if self.label_to_id is not None:
                 UpperCAmelCase__			:     Optional[Any]										= self.label_to_id[label]
            return {"image": image, "label": label}
  
  
  
  
  
  def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase					)   -> Optional[int]:
  
       '''simple docstring'''
       UpperCAmelCase__			:     int										= Accelerator(cpu=args.cpu   ,       mixed_precision=args.mixed_precision					)
  
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       UpperCAmelCase__			:     Any										= config['lr']
       UpperCAmelCase__			:     int										= int(config["""num_epochs"""]					)
       UpperCAmelCase__			:     Optional[int]										= int(config["""seed"""]					)
       UpperCAmelCase__			:     int										= int(config["""batch_size"""]					)
       UpperCAmelCase__			:     Dict										= config['image_size']
       if not isinstance(__lowerCamelCase   ,       (list, tuple)					):
            UpperCAmelCase__			:     str										= (image_size, image_size)
  
       # Grab all the image filenames
       UpperCAmelCase__			:     Any										= [os.path.join(args.data_dir   ,       __lowerCamelCase					) for fname in os.listdir(args.data_dir					) if fname.endswith(""".jpg"""					)]
  
       # Build the label correspondences
       UpperCAmelCase__			:     Dict										= [extract_label(__lowerCamelCase					) for fname in file_names]
       UpperCAmelCase__			:     str										= list(set(__lowerCamelCase					)					)
       id_to_label.sort()
       UpperCAmelCase__			:     Any										= {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
       UpperCAmelCase__			:     Dict										= np.random.permutation(len(__lowerCamelCase					)					)
       UpperCAmelCase__			:     Optional[Any]										= int(0.8 * len(__lowerCamelCase					)					)
       UpperCAmelCase__			:     Optional[int]										= random_perm[:cut]
       UpperCAmelCase__			:     Any										= random_perm[cut:]
  
       # For training we use a simple RandomResizedCrop
       UpperCAmelCase__			:     Optional[Any]										= Compose([RandomResizedCrop(__lowerCamelCase   ,       scale=(0.5, 1.0)					), ToTensor()]					)
       UpperCAmelCase__			:     Optional[Any]										= PetsDataset(
           [file_names[i] for i in train_split]   ,       image_transform=__lowerCamelCase   ,       label_to_id=__lowerCamelCase					)
  
       # For evaluation, we use a deterministic Resize
       UpperCAmelCase__			:     int										= Compose([Resize(__lowerCamelCase					), ToTensor()]					)
       UpperCAmelCase__			:     Tuple										= PetsDataset([file_names[i] for i in eval_split]   ,       image_transform=__lowerCamelCase   ,       label_to_id=__lowerCamelCase					)
  
       # Instantiate dataloaders.
       UpperCAmelCase__			:     Dict										= DataLoader(__lowerCamelCase   ,       shuffle=__lowerCamelCase   ,       batch_size=__lowerCamelCase   ,       num_workers=4					)
       UpperCAmelCase__			:     Optional[Any]										= 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)
       UpperCAmelCase__			:     Optional[Any]										= 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).
       UpperCAmelCase__			:     Optional[int]										= model.to(accelerator.device					)
  
       # Freezing the base model
       for param in model.parameters():
            UpperCAmelCase__			:     str										= False
       for param in model.get_classifier().parameters():
            UpperCAmelCase__			:     Optional[int]										= 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__			:     int										= torch.tensor(model.default_cfg["""std"""]					)[None, :, None, None].to(accelerator.device					)
  
       # Instantiate optimizer
       UpperCAmelCase__			:     Optional[Any]										= torch.optim.Adam(params=model.parameters()   ,       lr=lr / 25					)
  
       # Instantiate learning rate scheduler
       UpperCAmelCase__			:     Tuple										= 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.
       UpperCAmelCase__			:     Tuple										= 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`.
                 UpperCAmelCase__			:     Optional[int]										= {k: v.to(accelerator.device					) for k, v in batch.items()}
                 UpperCAmelCase__			:     Union[str, Any]										= (batch['image'] - mean) / std
                 UpperCAmelCase__			:     Tuple										= model(__lowerCamelCase					)
                 UpperCAmelCase__			:     List[str]										= torch.nn.functional.cross_entropy(__lowerCamelCase   ,       batch["""label"""]					)
                 accelerator.backward(__lowerCamelCase					)
                 optimizer.step()
                 lr_scheduler.step()
                 optimizer.zero_grad()
  
            model.eval()
            UpperCAmelCase__			:     Dict										= 0
            UpperCAmelCase__			:     Optional[Any]										= 0
            for _, batch in enumerate(__lowerCamelCase					):
                 # We could avoid this line since we set the accelerator with `device_placement=True`.
                 UpperCAmelCase__			:     Union[str, Any]										= {k: v.to(accelerator.device					) for k, v in batch.items()}
                 UpperCAmelCase__			:     Union[str, Any]										= (batch['image'] - mean) / std
                 with torch.no_grad():
                      UpperCAmelCase__			:     Optional[Any]										= model(__lowerCamelCase					)
                 UpperCAmelCase__			:     List[str]										= outputs.argmax(dim=-1					)
                 UpperCAmelCase__			:     Union[str, Any]										= accelerator.gather_for_metrics((predictions, batch["""label"""])					)
                 UpperCAmelCase__			:     Union[str, 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}: {100 * eval_metric:.2f}"					)
  
  
  
  
  
  def    _lowerCamelCase	(	)   -> Dict:
  
       '''simple docstring'''
       UpperCAmelCase__			:     Tuple										= 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."""					)
       UpperCAmelCase__			:     Optional[Any]										= parser.parse_args()
       UpperCAmelCase__			:     Optional[int]										= {'lr': 3E-2, 'num_epochs': 3, 'seed': 42, 'batch_size': 64, 'image_size': 224}
       training_function(__lowerCamelCase   ,       __lowerCamelCase					)
  
  
  if __name__ == "__main__":
        main()
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  import concurrent.futures
  import json
  import os
  import shutil
  import tempfile
  import unittest
  
  from transformers import AutoTokenizer, PreTrainedTokenizerFast
  from transformers.testing_utils import require_tokenizers
  
  from ..test_tokenization_common import TokenizerTesterMixin
  
  
  
  
  
  
  
  @require_tokenizers
  class 							UpperCAmelCase_     (		__lowerCamelCase       ,			unittest.TestCase		):
       __lowerCamelCase										=     PreTrainedTokenizerFast
       __lowerCamelCase										=     False
       __lowerCamelCase										=     True
       __lowerCamelCase										=     'tokenizer_file'
  
  
  
  
       def      __UpperCAmelCase     (							self		):
            UpperCAmelCase__			:     Union[str, Any]										= False  # because we don't have pretrained_vocab_files_map
            super().setUp()
            UpperCAmelCase__			:     Dict										= True
  
            UpperCAmelCase__			:     int										= ["""robot-test/dummy-tokenizer-fast""", """robot-test/dummy-tokenizer-wordlevel"""]
            UpperCAmelCase__			:     List[Any]										= """SaulLu/dummy-tokenizer-bytelevel-bpe"""
  
            # Inclusion of 2 tokenizers to test different types of models (Unigram and WordLevel for the moment)
            UpperCAmelCase__			:     int										= [(PreTrainedTokenizerFast, model_path, {}) for model_path in model_paths]
  
            UpperCAmelCase__			:     Union[str, Any]										= PreTrainedTokenizerFast.from_pretrained(model_paths[0]		)
            tokenizer.save_pretrained(self.tmpdirname		)
  
  
  
  
       def      __UpperCAmelCase     (							self		):
            # We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
            # model
            pass
  
  
  
  
       def      __UpperCAmelCase     (							self		):
            # We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
            # model
            pass
  
  
  
  
       def      __UpperCAmelCase     (							self		):
            # We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
            # model
            pass
  
  
  
  
       def      __UpperCAmelCase     (							self		):
            # PreTrainedTokenizerFast doesn't have tokenizer_file in its signature
            pass
  
  
  
  
       def      __UpperCAmelCase     (							self		):
            UpperCAmelCase__			:     Optional[int]										= self.tmpdirname
            # Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
            for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"		):
                      try:
                           UpperCAmelCase__			:     str										= tempfile.mkdtemp()
                           UpperCAmelCase__			:     Any										= self.rust_tokenizer_class.from_pretrained(_lowerCAmelCase  ,	**_lowerCAmelCase		)
  
                           tokenizer.save_pretrained(self.tmpdirname		)
                           super().test_training_new_tokenizer()
                      finally:
                           # Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
                           # is restored
                           shutil.rmtree(self.tmpdirname		)
                           UpperCAmelCase__			:     str										= tmpdirname_orig
  
  
  
  
       def      __UpperCAmelCase     (							self		):
            UpperCAmelCase__			:     Tuple										= self.tmpdirname
            # Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
            for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"		):
                      try:
                           UpperCAmelCase__			:     Optional[Any]										= tempfile.mkdtemp()
                           UpperCAmelCase__			:     Any										= self.rust_tokenizer_class.from_pretrained(_lowerCAmelCase  ,	**_lowerCAmelCase		)
  
                           tokenizer.save_pretrained(self.tmpdirname		)
                           super().test_training_new_tokenizer_with_special_tokens_change()
                      finally:
                           # Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
                           # is restored
                           shutil.rmtree(self.tmpdirname		)
                           UpperCAmelCase__			:     str										= tmpdirname_orig
  
  
  
  
       def      __UpperCAmelCase     (							self		):
            UpperCAmelCase__			:     Tuple										= self.rust_tokenizer_class.from_pretrained(self.bytelevel_bpe_model_name		)
  
            UpperCAmelCase__			:     Dict										= ("""a""" for _ in range(1000		))
            UpperCAmelCase__			:     int										= tokenizer.train_new_from_iterator(text_iterator=_lowerCAmelCase  ,	length=1000  ,	vocab_size=50		)
  
            UpperCAmelCase__			:     Optional[Any]										= new_tokenizer.encode("""a🤗"""		)
            self.assertEqual(_lowerCAmelCase  ,	[64, 172, 253, 97, 245]		)
  
  
  
  
  
  
  
  @require_tokenizers
  class 							UpperCAmelCase_     (		unittest.TestCase		):
  
  
  
  
       def      __UpperCAmelCase     (							self		):
            UpperCAmelCase__			:     List[str]										= AutoTokenizer.from_pretrained("""bert-base-cased"""		)
            UpperCAmelCase__			:     List[Any]										= json.loads(tokenizer._tokenizer.to_str()		)
            UpperCAmelCase__			:     Optional[Any]										= len(_lowerCAmelCase		)
  
            with tempfile.TemporaryDirectory() as tmp_dir:
                 # Hack to save this in the tokenizer_config.json
                 UpperCAmelCase__			:     Tuple										= ["""tokenizer.4.0.0.json"""]
                 tokenizer.save_pretrained(_lowerCAmelCase		)
                 json.dump(_lowerCAmelCase  ,	open(os.path.join(_lowerCAmelCase  ,	"""tokenizer.4.0.0.json"""		)  ,	"""w"""		)		)
  
                 # This should pick the new tokenizer file as the version of Transformers is > 4.0.0
                 UpperCAmelCase__			:     Optional[int]										= AutoTokenizer.from_pretrained(_lowerCAmelCase		)
                 self.assertEqual(len(_lowerCAmelCase		)  ,	len(_lowerCAmelCase		) + 1		)
                 UpperCAmelCase__			:     Any										= json.loads(new_tokenizer._tokenizer.to_str()		)
                 self.assertIn("""huggingface"""  ,	json_tokenizer["""model"""]["""vocab"""]		)
  
                 # Will need to be adjusted if we reach v42 and this test is still here.
                 # Should pick the old tokenizer file as the version of Transformers is < 4.0.0
                 shutil.move(os.path.join(_lowerCAmelCase  ,	"""tokenizer.4.0.0.json"""		)  ,	os.path.join(_lowerCAmelCase  ,	"""tokenizer.42.0.0.json"""		)		)
                 UpperCAmelCase__			:     Union[str, Any]										= ["""tokenizer.42.0.0.json"""]
                 tokenizer.save_pretrained(_lowerCAmelCase		)
                 UpperCAmelCase__			:     Optional[Any]										= AutoTokenizer.from_pretrained(_lowerCAmelCase		)
                 self.assertEqual(len(_lowerCAmelCase		)  ,	len(_lowerCAmelCase		)		)
                 UpperCAmelCase__			:     List[Any]										= json.loads(new_tokenizer._tokenizer.to_str()		)
                 self.assertNotIn("""huggingface"""  ,	json_tokenizer["""model"""]["""vocab"""]		)
  
  
  
  
       def      __UpperCAmelCase     (							self		):
            # This repo has two tokenizer files, one for v4.0.0 and above with an added token, one for versions lower.
            UpperCAmelCase__			:     List[str]										= """hf-internal-testing/test-two-tokenizers"""
  
            # This should pick the new tokenizer file as the version of Transformers is > 4.0.0
            UpperCAmelCase__			:     int										= AutoTokenizer.from_pretrained(_lowerCAmelCase		)
            self.assertEqual(len(_lowerCAmelCase		)  ,	28997		)
            UpperCAmelCase__			:     List[Any]										= json.loads(tokenizer._tokenizer.to_str()		)
            self.assertIn("""huggingface"""  ,	json_tokenizer["""model"""]["""vocab"""]		)
  
            # Testing an older version by monkey-patching the version in the module it's used.
            import transformers as old_transformers
  
            UpperCAmelCase__			:     Any										= """3.0.0"""
            UpperCAmelCase__			:     Optional[int]										= old_transformers.models.auto.AutoTokenizer.from_pretrained(_lowerCAmelCase		)
            self.assertEqual(len(_lowerCAmelCase		)  ,	28996		)
            UpperCAmelCase__			:     Optional[int]										= json.loads(old_tokenizer._tokenizer.to_str()		)
            self.assertNotIn("""huggingface"""  ,	json_tokenizer["""model"""]["""vocab"""]		)
  
  
  
  
  
  
  
  @require_tokenizers
  class 							UpperCAmelCase_     (		unittest.TestCase		):
  
  
  
  
       def      __UpperCAmelCase     (							self		):
            # See https://github.com/huggingface/transformers/pull/12550
            # and https://github.com/huggingface/tokenizers/issues/537
            UpperCAmelCase__			:     int										= PreTrainedTokenizerFast.from_pretrained("""robot-test/dummy-tokenizer-wordlevel"""		)
            UpperCAmelCase__			:     List[str]										= """The Matrix is a 1999 science fiction action film."""
  
            with concurrent.futures.ThreadPoolExecutor() as executor:
                 UpperCAmelCase__			:     List[str]										= [executor.submit(self.fetch  ,	_lowerCAmelCase  ,	_lowerCAmelCase		) for i in range(10		)]
                 UpperCAmelCase__			:     Dict										= [future.result() for future in futures]
                 self.assertEqual(_lowerCAmelCase  ,	[[1, 10, 0, 8, 0, 18, 0, 0, 0, 2] for i in range(10		)]		)
  
  
  
  
       def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase		):
            return tokenizer.encode(_lowerCAmelCase  ,	truncation="""longest_first"""  ,	padding="""longest"""		)
  
  
  
  
  
  
  