


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		)					->							Union[str, Any]:
     '''simple docstring'''





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






class      UpperCamelCase					(		_UpperCAmelCase     ):


   '''simple docstring'''







   def __init__(     self   ,							UpperCamelCase_   ,							UpperCamelCase_=None   ,							UpperCamelCase_=None		):
        lowercase_       :Union[str, Any]     =       file_names
        lowercase_       :Tuple     =       image_transform
        lowercase_       :Union[str, Any]     =       label_to_id
   def __len__(     self		):
        return len(self.file_names		)




   def __getitem__(     self   ,							UpperCamelCase_		):
        lowercase_       :Optional[Any]     =       self.file_names[idx]
        lowercase_       :Dict     =       PIL.Image.open(lowercase__		)
        lowercase_       :Union[str, Any]     =       raw_image.convert('''RGB'''		)
        if self.image_transform is not None:
             lowercase_       :Union[str, Any]     =       self.image_transform(lowercase__		)
        lowercase_       :Dict     =       extract_label(lowercase__		)
        if self.label_to_id is not None:
             lowercase_       :Union[str, Any]     =       self.label_to_id[label]
        return {"image": image, "label": label}




def     UpperCamelCase    (      _a       ,		_a		)					->							Tuple:
     '''simple docstring'''





     if args.with_tracking:
          lowercase_       :Union[str, Any]     =       Accelerator(
              cpu=args.cpu       ,		mixed_precision=args.mixed_precision       ,		log_with='''all'''       ,		project_dir=args.project_dir		)
     else:
          lowercase_       :List[Any]     =       Accelerator(cpu=args.cpu       ,		mixed_precision=args.mixed_precision		)

     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     lowercase_       :Tuple     =       config["""lr"""]
     lowercase_       :Any     =       int(config['''num_epochs''']		)
     lowercase_       :str     =       int(config['''seed''']		)
     lowercase_       :Any     =       int(config['''batch_size''']		)
     lowercase_       :Any     =       config["""image_size"""]
     if not isinstance(SCREAMING_SNAKE_CASE__       ,		(list, tuple)		):
          lowercase_       :Dict     =       (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":
               lowercase_       :List[str]     =       args.checkpointing_steps
          elif args.checkpointing_steps.isdigit():
               lowercase_       :Any     =       int(args.checkpointing_steps		)
          else:
               raise ValueError(
                   f"Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."		)
     else:
          lowercase_       :Dict     =       None

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

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

     # Build the label correspondences
     lowercase_       :int     =       [extract_label(SCREAMING_SNAKE_CASE__		) for fname in file_names]
     lowercase_       :List[str]     =       list(set(SCREAMING_SNAKE_CASE__		)		)
     id_to_label.sort()
     lowercase_       :str     =       {lbl: i for i, lbl in enumerate(SCREAMING_SNAKE_CASE__		)}

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

     # Split our filenames between train and validation
     lowercase_       :Union[str, Any]     =       np.random.permutation(len(SCREAMING_SNAKE_CASE__		)		)
     lowercase_       :Dict     =       int(0.8 * len(SCREAMING_SNAKE_CASE__		)		)
     lowercase_       :Union[str, Any]     =       random_perm[:cut]
     lowercase_       :Optional[int]     =       random_perm[cut:]

     # For training we use a simple RandomResizedCrop
     lowercase_       :Any     =       Compose([RandomResizedCrop(SCREAMING_SNAKE_CASE__       ,		scale=(0.5, 1.0)		), ToTensor()]		)
     lowercase_       :Tuple     =       PetsDataset(
         [file_names[i] for i in train_split]       ,		image_transform=SCREAMING_SNAKE_CASE__       ,		label_to_id=SCREAMING_SNAKE_CASE__		)

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

     # Instantiate dataloaders.
     lowercase_       :int     =       DataLoader(SCREAMING_SNAKE_CASE__       ,		shuffle=SCREAMING_SNAKE_CASE__       ,		batch_size=SCREAMING_SNAKE_CASE__       ,		num_workers=4		)
     lowercase_       :Optional[int]     =       DataLoader(SCREAMING_SNAKE_CASE__       ,		shuffle=SCREAMING_SNAKE_CASE__       ,		batch_size=SCREAMING_SNAKE_CASE__       ,		num_workers=4		)

     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     lowercase_       :Tuple     =       create_model('''resnet50d'''       ,		pretrained=SCREAMING_SNAKE_CASE__       ,		num_classes=len(SCREAMING_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).
     lowercase_       :Tuple     =       model.to(accelerator.device		)

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

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

     # Instantiate optimizer
     lowercase_       :str     =       torch.optim.Adam(params=model.parameters()       ,		lr=lr / 2_5		)

     # Instantiate learning rate scheduler
     lowercase_       :Dict     =       OneCycleLR(optimizer=SCREAMING_SNAKE_CASE__       ,		max_lr=SCREAMING_SNAKE_CASE__       ,		epochs=SCREAMING_SNAKE_CASE__       ,		steps_per_epoch=len(SCREAMING_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.
     lowercase_       :Any     =       accelerator.prepare(
         SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__		)
     # We need to keep track of how many total steps we have iterated over
     lowercase_       :Optional[Any]     =       0
     # We also need to keep track of the starting epoch so files are named properly
     lowercase_       :List[str]     =       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		)
               lowercase_       :Tuple     =       os.path.basename(args.resume_from_checkpoint		)
          else:
               # Get the most recent checkpoint
               lowercase_       :List[Any]     =       [f.name for f in os.scandir(os.getcwd()		) if f.is_dir()]
               dirs.sort(key=os.path.getctime		)
               lowercase_       :int     =       dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
          # Extract `epoch_{i}` or `step_{i}`
          lowercase_       :Union[str, Any]     =       os.path.splitext(SCREAMING_SNAKE_CASE__		)[0]

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

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

          lowercase_       :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}"		)
          if args.with_tracking:
               accelerator.log(
                   {
                       '''accuracy''': 1_0_0 * eval_metric,
                       '''train_loss''': total_loss.item() / len(SCREAMING_SNAKE_CASE__		),
                       '''epoch''': epoch,
                   }       ,		step=SCREAMING_SNAKE_CASE__       ,		)
          if checkpointing_steps == "epoch":
               lowercase_       :Dict     =       f"epoch_{epoch}"
               if args.output_dir is not None:
                    lowercase_       :Dict     =       os.path.join(args.output_dir       ,		SCREAMING_SNAKE_CASE__		)
               accelerator.save_state(SCREAMING_SNAKE_CASE__		)

     if args.with_tracking:
          accelerator.end_training()




def     UpperCamelCase    (      )					->							str:
     '''simple docstring'''





     lowercase_       :Tuple     =       argparse.ArgumentParser(description='''Simple example of training script.'''		)
     parser.add_argument('''--data_dir'''       ,		required=SCREAMING_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=SCREAMING_SNAKE_CASE__       ,		default=SCREAMING_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=SCREAMING_SNAKE_CASE__       ,		default=SCREAMING_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=SCREAMING_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=SCREAMING_SNAKE_CASE__       ,		default=SCREAMING_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=SCREAMING_SNAKE_CASE__       ,		default='''logs'''       ,		help='''Location on where to store experiment tracking logs` and relevent project information'''       ,		)
     lowercase_       :Tuple     =       parser.parse_args()
     lowercase_       :Dict     =       {"""lr""": 3E-2, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 6_4, """image_size""": 2_2_4}
     training_function(SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__		)


if __name__ == "__main__":
    main()





import unittest

import numpy as np

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
   from transformers.models.bert.modeling_flax_bert import (
       FlaxBertForMaskedLM,
       FlaxBertForMultipleChoice,
       FlaxBertForNextSentencePrediction,
       FlaxBertForPreTraining,
       FlaxBertForQuestionAnswering,
       FlaxBertForSequenceClassification,
       FlaxBertForTokenClassification,
       FlaxBertModel,
   )






class      UpperCamelCase					(		unittest.TestCase     ):


   '''simple docstring'''







   def __init__(     self   ,							UpperCamelCase_   ,							UpperCamelCase_=13   ,							UpperCamelCase_=7   ,							UpperCamelCase_=True   ,							UpperCamelCase_=True   ,							UpperCamelCase_=True   ,							UpperCamelCase_=True   ,							UpperCamelCase_=99   ,							UpperCamelCase_=32   ,							UpperCamelCase_=5   ,							UpperCamelCase_=4   ,							UpperCamelCase_=37   ,							UpperCamelCase_="gelu"   ,							UpperCamelCase_=0.1   ,							UpperCamelCase_=0.1   ,							UpperCamelCase_=512   ,							UpperCamelCase_=16   ,							UpperCamelCase_=2   ,							UpperCamelCase_=0.02   ,							UpperCamelCase_=4   ,							):
        lowercase_       :Union[str, Any]     =       parent
        lowercase_       :int     =       batch_size
        lowercase_       :int     =       seq_length
        lowercase_       :str     =       is_training
        lowercase_       :Dict     =       use_attention_mask
        lowercase_       :List[Any]     =       use_token_type_ids
        lowercase_       :str     =       use_labels
        lowercase_       :str     =       vocab_size
        lowercase_       :Optional[int]     =       hidden_size
        lowercase_       :Dict     =       num_hidden_layers
        lowercase_       :List[str]     =       num_attention_heads
        lowercase_       :int     =       intermediate_size
        lowercase_       :Union[str, Any]     =       hidden_act
        lowercase_       :Optional[int]     =       hidden_dropout_prob
        lowercase_       :Tuple     =       attention_probs_dropout_prob
        lowercase_       :int     =       max_position_embeddings
        lowercase_       :List[Any]     =       type_vocab_size
        lowercase_       :Any     =       type_sequence_label_size
        lowercase_       :Tuple     =       initializer_range
        lowercase_       :Any     =       num_choices
   def 		UpperCamelCase  (     self		):
        lowercase_       :Optional[int]     =       ids_tensor([self.batch_size, self.seq_length]   ,							self.vocab_size		)

        lowercase_       :List[str]     =       None
        if self.use_attention_mask:
             lowercase_       :Any     =       random_attention_mask([self.batch_size, self.seq_length]		)

        lowercase_       :List[Any]     =       None
        if self.use_token_type_ids:
             lowercase_       :Union[str, Any]     =       ids_tensor([self.batch_size, self.seq_length]   ,							self.type_vocab_size		)

        lowercase_       :Union[str, Any]     =       BertConfig(
            vocab_size=self.vocab_size   ,							hidden_size=self.hidden_size   ,							num_hidden_layers=self.num_hidden_layers   ,							num_attention_heads=self.num_attention_heads   ,							intermediate_size=self.intermediate_size   ,							hidden_act=self.hidden_act   ,							hidden_dropout_prob=self.hidden_dropout_prob   ,							attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,							max_position_embeddings=self.max_position_embeddings   ,							type_vocab_size=self.type_vocab_size   ,							is_decoder=UpperCamelCase_   ,							initializer_range=self.initializer_range   ,							)

        return config, input_ids, token_type_ids, attention_mask
   def 		UpperCamelCase  (     self		):
        lowercase_       :Optional[Any]     =       self.prepare_config_and_inputs()
        lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_       :List[str]     =       config_and_inputs
        lowercase_       :Union[str, Any]     =       {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
        return config, inputs_dict




   def 		UpperCamelCase  (     self		):
        lowercase_       :Dict     =       self.prepare_config_and_inputs()
        lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_       :Dict     =       config_and_inputs

        lowercase_       :str     =       True
        lowercase_       :Dict     =       floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
        lowercase_       :Optional[Any]     =       ids_tensor([self.batch_size, self.seq_length]   ,							vocab_size=2		)

        return (
            config,
            input_ids,
            attention_mask,
            encoder_hidden_states,
            encoder_attention_mask,
        )






@require_flax
class      UpperCamelCase					(		lowercase__						,      unittest.TestCase     ):


   '''simple docstring'''







   lowercase					:	int									=True

   lowercase					:	Dict									=(
       (
           FlaxBertModel,
           FlaxBertForPreTraining,
           FlaxBertForMaskedLM,
           FlaxBertForMultipleChoice,
           FlaxBertForQuestionAnswering,
           FlaxBertForNextSentencePrediction,
           FlaxBertForSequenceClassification,
           FlaxBertForTokenClassification,
           FlaxBertForQuestionAnswering,
       )
       if is_flax_available()
       else ()
   )
   def 		UpperCamelCase  (     self		):
        lowercase_       :Union[str, Any]     =       FlaxBertModelTester(self		)




   @slow
   def 		UpperCamelCase  (     self		):
        # Only check this for base model, not necessary for all model classes.
        # This will also help speed-up tests.
        lowercase_       :Dict     =       FlaxBertModel.from_pretrained('''bert-base-cased'''		)
        lowercase_       :str     =       model(np.ones((1, 1)		)		)
        self.assertIsNotNone(UpperCamelCase_		)

