






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  (       snake_case__     :   Tuple  ):




     '''simple docstring'''






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




class 			snake_case__   (							lowercase_):

       '''simple docstring'''



       def __init__(							self			,						a__			,						a__=None			,						a__=None    ) ->      List[str]:






            '''simple docstring'''





            __snake_case			:Tuple          =						file_names
            __snake_case			:Optional[int]          =						image_transform
            __snake_case			:Optional[Any]          =						label_to_id



       def __len__(							self    ) ->      Tuple:






            '''simple docstring'''





            return len(self.file_names    )



       def __getitem__(							self			,						a__    ) ->      Tuple:






            '''simple docstring'''





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






def   UpperCamelCase  (       snake_case__     :   Optional[Any]	,snake_case__     :   Optional[Any]  ):




     '''simple docstring'''






     if args.with_tracking:
          __snake_case			:Union[str, Any]          =						Accelerator(
              cpu=args.cpu	,mixed_precision=args.mixed_precision	,log_with="""all"""	,project_dir=args.project_dir  )
     else:
          __snake_case			:Dict          =						Accelerator(cpu=args.cpu	,mixed_precision=args.mixed_precision  )

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

     # Parse out whether we are saving every epoch or after a certain number of batches
     if hasattr(args.checkpointing_steps	,"""isdigit"""  ):
          if args.checkpointing_steps == "epoch":
               __snake_case			:Tuple          =						args.checkpointing_steps
          elif args.checkpointing_steps.isdigit():
               __snake_case			:str          =						int(args.checkpointing_steps  )
          else:
               raise ValueError(
                   f'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''  )
     else:
          __snake_case			:List[str]          =						None

     # We need to initialize the trackers we use, and also store our configuration
     if args.with_tracking:
          __snake_case			:Union[str, Any]          =						os.path.split(snake_case__  )[-1].split("""."""  )[0]
          accelerator.init_trackers(snake_case__	,snake_case__  )

     # Grab all the image filenames
     __snake_case			:Optional[Any]          =						[os.path.join(args.data_dir	,snake_case__  ) for fname in os.listdir(args.data_dir  ) if fname.endswith(""".jpg"""  )]

     # Build the label correspondences
     __snake_case			:int          =						[extract_label(snake_case__  ) for fname in file_names]
     __snake_case			:int          =						list(set(snake_case__  )  )
     id_to_label.sort()
     __snake_case			:Any          =						{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
     __snake_case			:Union[str, Any]          =						np.random.permutation(len(snake_case__  )  )
     __snake_case			:Tuple          =						int(0.8 * len(snake_case__  )  )
     __snake_case			:int          =						random_perm[:cut]
     __snake_case			:Optional[int]          =						random_perm[cut:]

     # For training we use a simple RandomResizedCrop
     __snake_case			:List[str]          =						Compose([RandomResizedCrop(snake_case__	,scale=(0.5, 1.0)  ), ToTensor()]  )
     __snake_case			:int          =						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
     __snake_case			:Dict          =						Compose([Resize(snake_case__  ), ToTensor()]  )
     __snake_case			:Tuple          =						PetsDataset([file_names[i] for i in eval_split]	,image_transform=snake_case__	,label_to_id=snake_case__  )

     # Instantiate dataloaders.
     __snake_case			:List[Any]          =						DataLoader(snake_case__	,shuffle=snake_case__	,batch_size=snake_case__	,num_workers=4  )
     __snake_case			:int          =						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)
     __snake_case			:str          =						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).
     __snake_case			:List[Any]          =						model.to(accelerator.device  )

     # Freezing the base model
     for param in model.parameters():
          __snake_case			:List[str]          =						False
     for param in model.get_classifier().parameters():
          __snake_case			:Tuple          =						True

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

     # Instantiate optimizer
     __snake_case			:Optional[int]          =						torch.optim.Adam(params=model.parameters()	,lr=lr / 25  )

     # Instantiate learning rate scheduler
     __snake_case			:Optional[Any]          =						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.
     __snake_case       , __snake_case       , __snake_case       , __snake_case       , __snake_case			:Dict          =						accelerator.prepare(
         snake_case__	,snake_case__	,snake_case__	,snake_case__	,snake_case__  )
     # We need to keep track of how many total steps we have iterated over
     __snake_case			:List[str]          =						0
     # We also need to keep track of the starting epoch so files are named properly
     __snake_case			:Optional[Any]          =						0

     # Potentially load in the weights and states from a previous save
     if args.resume_from_checkpoint:
          if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
               accelerator.print(f'''Resumed from checkpoint: {args.resume_from_checkpoint}'''  )
               accelerator.load_state(args.resume_from_checkpoint  )
               __snake_case			:Any          =						os.path.basename(args.resume_from_checkpoint  )
          else:
               # Get the most recent checkpoint
               __snake_case			:Optional[Any]          =						[f.name for f in os.scandir(os.getcwd()  ) if f.is_dir()]
               dirs.sort(key=os.path.getctime  )
               __snake_case			:Optional[Any]          =						dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
          # Extract `epoch_{i}` or `step_{i}`
          __snake_case			:Any          =						os.path.splitext(snake_case__  )[0]

          if "epoch" in training_difference:
               __snake_case			:List[str]          =						int(training_difference.replace("""epoch_"""	,""""""  )  ) + 1
               __snake_case			:Tuple          =						None
          else:
               __snake_case			:Any          =						int(training_difference.replace("""step_"""	,""""""  )  )
               __snake_case			:str          =						resume_step // len(snake_case__  )
               resume_step -= starting_epoch * len(snake_case__  )

    # Now we train the model
     for epoch in range(snake_case__	,snake_case__  ):
          model.train()
          if args.with_tracking:
               __snake_case			:Optional[int]          =						0
          if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
               # We need to skip steps until we reach the resumed step
               __snake_case			:Tuple          =						accelerator.skip_first_batches(snake_case__	,snake_case__  )
               overall_step += resume_step
          else:
               # After the first iteration though, we need to go back to the original dataloader
               __snake_case			:Dict          =						train_dataloader
          for batch in active_dataloader:
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               __snake_case			:Tuple          =						{k: v.to(accelerator.device  ) for k, v in batch.items()}
               __snake_case			:Optional[Any]          =						(batch["""image"""] - mean) / std
               __snake_case			:Optional[int]          =						model(snake_case__  )
               __snake_case			:List[str]          =						torch.nn.functional.cross_entropy(snake_case__	,batch["""label"""]  )
               # We keep track of the loss at each epoch
               if args.with_tracking:
                    total_loss += loss.detach().float()
               accelerator.backward(snake_case__  )
               optimizer.step()
               lr_scheduler.step()
               optimizer.zero_grad()
               overall_step += 1
               if isinstance(snake_case__	,snake_case__  ):
                    __snake_case			:Union[str, Any]          =						f'''step_{overall_step}'''
                    if overall_step % checkpointing_steps == 0:
                         if args.output_dir is not None:
                              __snake_case			:str          =						os.path.join(args.output_dir	,snake_case__  )
                         accelerator.save_state(snake_case__  )
          model.eval()
          __snake_case			:int          =						0
          __snake_case			:Any          =						0
          for step, batch in enumerate(snake_case__  ):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               __snake_case			:str          =						{k: v.to(accelerator.device  ) for k, v in batch.items()}
               __snake_case			:List[str]          =						(batch["""image"""] - mean) / std
               with torch.no_grad():
                    __snake_case			:List[Any]          =						model(snake_case__  )
               __snake_case			:Optional[Any]          =						outputs.argmax(dim=-1  )
               __snake_case       , __snake_case			:List[Any]          =						accelerator.gather_for_metrics((predictions, batch["""label"""])  )
               __snake_case			:Union[str, Any]          =						predictions == references
               num_elems += accurate_preds.shape[0]
               accurate += accurate_preds.long().sum()

          __snake_case			:Optional[Any]          =						accurate.item() / num_elems
          # Use accelerator.print to print only on the main process.
          accelerator.print(f'''epoch {epoch}: {100 * eval_metric:.2f}'''  )
          if args.with_tracking:
               accelerator.log(
                   {
                       """accuracy""": 100 * eval_metric,
                       """train_loss""": total_loss.item() / len(snake_case__  ),
                       """epoch""": epoch,
                   }	,step=snake_case__	,)
          if checkpointing_steps == "epoch":
               __snake_case			:Optional[Any]          =						f'''epoch_{epoch}'''
               if args.output_dir is not None:
                    __snake_case			:Union[str, Any]          =						os.path.join(args.output_dir	,snake_case__  )
               accelerator.save_state(snake_case__  )

     if args.with_tracking:
          accelerator.end_training()






def   UpperCamelCase  (       ):




     '''simple docstring'''






     __snake_case			:Dict          =						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("""--fp16"""	,action="""store_true"""	,help="""If passed, will use FP16 training."""  )
     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("""--cpu"""	,action="""store_true"""	,help="""If passed, will train on the CPU."""  )
     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(
         """--output_dir"""	,type=snake_case__	,default="""."""	,help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""	,)
     parser.add_argument(
         """--resume_from_checkpoint"""	,type=snake_case__	,default=snake_case__	,help="""If the training should continue from a checkpoint folder."""	,)
     parser.add_argument(
         """--with_tracking"""	,action="""store_true"""	,help="""Whether to load in all available experiment trackers from the environment and use them for logging."""	,)
     parser.add_argument(
         """--project_dir"""	,type=snake_case__	,default="""logs"""	,help="""Location on where to store experiment tracking logs` and relevent project information"""	,)
     __snake_case			:Union[str, Any]          =						parser.parse_args()
     __snake_case			:List[Any]          =						{"""lr""": 3e-2, """num_epochs""": 3, """seed""": 42, """batch_size""": 64, """image_size""": 224}
     training_function(snake_case__	,snake_case__  )


if __name__ == "__main__":
     main()










import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.bert.configuration_bert import BertConfig
from transformers.models.roberta.configuration_roberta import RobertaConfig
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir


sys.path.append(str(Path(__file__).parent.parent.parent.parent / """utils"""))

from test_module.custom_configuration import CustomConfig  # noqa E402


lowerCamelCase__							=     get_tests_dir("""fixtures/dummy-config.json""")




class 			snake_case__   (							unittest.TestCase):

       '''simple docstring'''



       def 					__lowercase						(							self    ) ->      Union[str, Any]:






            '''simple docstring'''





            __snake_case			:Optional[int]          =						0



       def 					__lowercase						(							self    ) ->      int:






            '''simple docstring'''





            self.assertIsNotNone(transformers.models.auto.__spec__    )
            self.assertIsNotNone(importlib.util.find_spec("""transformers.models.auto"""    )    )



       def 					__lowercase						(							self    ) ->      List[str]:






            '''simple docstring'''





            __snake_case			:Optional[Any]          =						AutoConfig.from_pretrained("""bert-base-uncased"""    )
            self.assertIsInstance(a__			,						a__    )



       def 					__lowercase						(							self    ) ->      Optional[int]:






            '''simple docstring'''





            __snake_case			:List[str]          =						AutoConfig.from_pretrained(a__    )
            self.assertIsInstance(a__			,						a__    )



       def 					__lowercase						(							self    ) ->      List[Any]:






            '''simple docstring'''





            __snake_case			:Tuple          =						AutoConfig.from_pretrained(a__    )
            self.assertIsInstance(a__			,						a__    )



       def 					__lowercase						(							self    ) ->      str:






            '''simple docstring'''





            __snake_case			:Optional[int]          =						AutoConfig.for_model("""roberta"""    )
            self.assertIsInstance(a__			,						a__    )



       def 					__lowercase						(							self    ) ->      List[Any]:






            '''simple docstring'''





            with tempfile.TemporaryDirectory() as tmp_dir:
                 # This model name contains bert and roberta, but roberta ends up being picked.
                 __snake_case			:List[Any]          =						os.path.join(a__			,						"""fake-roberta"""    )
                 os.makedirs(a__			,						exist_ok=a__    )
                 with open(os.path.join(a__			,						"""config.json"""    )			,						"""w"""    ) as f:
                      f.write(json.dumps({}    )    )
                 __snake_case			:Optional[Any]          =						AutoConfig.from_pretrained(a__    )
                 self.assertEqual(type(a__    )			,						a__    )



       def 					__lowercase						(							self    ) ->      Dict:






            '''simple docstring'''





            try:
                 AutoConfig.register("""custom"""			,						a__    )
                 # Wrong model type will raise an error
                 with self.assertRaises(a__    ):
                      AutoConfig.register("""model"""			,						a__    )
                 # Trying to register something existing in the Transformers library will raise an error
                 with self.assertRaises(a__    ):
                      AutoConfig.register("""bert"""			,						a__    )

                 # Now that the config is registered, it can be used as any other config with the auto-API
                 __snake_case			:Optional[int]          =						CustomConfig()
                 with tempfile.TemporaryDirectory() as tmp_dir:
                      config.save_pretrained(a__    )
                      __snake_case			:Tuple          =						AutoConfig.from_pretrained(a__    )
                      self.assertIsInstance(a__			,						a__    )

            finally:
                 if "custom" in CONFIG_MAPPING._extra_content:
                      del CONFIG_MAPPING._extra_content["custom"]



       def 					__lowercase						(							self    ) ->      int:






            '''simple docstring'''





            with self.assertRaisesRegex(
                a__			,						"""bert-base is not a local folder and is not a valid model identifier"""    ):
                 __snake_case			:str          =						AutoConfig.from_pretrained("""bert-base"""    )



       def 					__lowercase						(							self    ) ->      str:






            '''simple docstring'''





            with self.assertRaisesRegex(
                a__			,						R"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"""    ):
                 __snake_case			:Dict          =						AutoConfig.from_pretrained(a__			,						revision="""aaaaaa"""    )



       def 					__lowercase						(							self    ) ->      Optional[Any]:






            '''simple docstring'''





            with self.assertRaisesRegex(
                a__			,						"""hf-internal-testing/no-config-test-repo does not appear to have a file named config.json."""			,						):
                 __snake_case			:Optional[int]          =						AutoConfig.from_pretrained("""hf-internal-testing/no-config-test-repo"""    )



       def 					__lowercase						(							self    ) ->      int:






            '''simple docstring'''





            with self.assertRaises(a__    ):
                 __snake_case			:Dict          =						AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model"""    )
            # If remote code is disabled, we can't load this config.
            with self.assertRaises(a__    ):
                 __snake_case			:int          =						AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model"""			,						trust_remote_code=a__    )

            __snake_case			:Tuple          =						AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model"""			,						trust_remote_code=a__    )
            self.assertEqual(config.__class__.__name__			,						"""NewModelConfig"""    )

            # Test config can be reloaded.
            with tempfile.TemporaryDirectory() as tmp_dir:
                 config.save_pretrained(a__    )
                 __snake_case			:Union[str, Any]          =						AutoConfig.from_pretrained(a__			,						trust_remote_code=a__    )
            self.assertEqual(reloaded_config.__class__.__name__			,						"""NewModelConfig"""    )



       def 					__lowercase						(							self    ) ->      Dict:






            '''simple docstring'''





            class 			snake_case__   (							lowercase_):

                   '''simple docstring'''
                   lowerCamelCase							:				Optional[Any] 				= "new-model"

            try:
                 AutoConfig.register("""new-model"""			,						a__    )
                 # If remote code is not set, the default is to use local
                 __snake_case			:List[Any]          =						AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model"""    )
                 self.assertEqual(config.__class__.__name__			,						"""NewModelConfigLocal"""    )

                 # If remote code is disabled, we load the local one.
                 __snake_case			:Tuple          =						AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model"""			,						trust_remote_code=a__    )
                 self.assertEqual(config.__class__.__name__			,						"""NewModelConfigLocal"""    )

                 # If remote is enabled, we load from the Hub
                 __snake_case			:List[Any]          =						AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model"""			,						trust_remote_code=a__    )
                 self.assertEqual(config.__class__.__name__			,						"""NewModelConfig"""    )

            finally:
                 if "new-model" in CONFIG_MAPPING._extra_content:
                      del CONFIG_MAPPING._extra_content["new-model"]


