






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()














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"""		)






