





'''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 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase       			=fname.split(os.path.sep   )[-1]
  return re.search(r'''^(.*)_\d+\.jpg$'''  ,      __SCREAMING_SNAKE_CASE   ).groups()[0]







class 		UpperCAmelCase   (				lowercase_):


  """simple docstring"""







  def __init__(				self    :					Union[str, Any]							,	UpperCamelCase__    :					Optional[int]							,	UpperCamelCase__    :					Tuple=None							,	UpperCamelCase__    :					Union[str, Any]=None  )     ->						Union[str, Any]:
    _UpperCamelCase       			=file_names
    _UpperCamelCase       			=image_transform
    _UpperCamelCase       			=label_to_id


  def __len__(				self    :					Any  )     ->						Dict:
    return len(self.file_names  )




  def __getitem__(				self    :					str							,	UpperCamelCase__    :					str  )     ->						Any:
    _UpperCamelCase       			=self.file_names[idx]
    _UpperCamelCase       			=PIL.Image.open(UpperCamelCase__  )
    _UpperCamelCase       			=raw_image.convert('''RGB'''  )
    if self.image_transform is not None:
      _UpperCamelCase       			=self.image_transform(UpperCamelCase__  )
    _UpperCamelCase       			=extract_label(UpperCamelCase__  )
    if self.label_to_id is not None:
      _UpperCamelCase       			=self.label_to_id[label]
    return {"image": image, "label": label}




def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  if args.with_tracking:
    _UpperCamelCase       			=Accelerator(
        cpu=args.cpu  ,      mixed_precision=args.mixed_precision  ,      log_with='''all'''  ,      project_dir=args.project_dir   )
  else:
    _UpperCamelCase       			=Accelerator(cpu=args.cpu  ,      mixed_precision=args.mixed_precision   )

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

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

  # Grab all the image filenames
  _UpperCamelCase       			=[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
  _UpperCamelCase       			=[extract_label(__SCREAMING_SNAKE_CASE   ) for fname in file_names]
  _UpperCamelCase       			=list(set(__SCREAMING_SNAKE_CASE   )   )
  id_to_label.sort()
  _UpperCamelCase       			={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
  _UpperCamelCase       			=np.random.permutation(len(__SCREAMING_SNAKE_CASE   )   )
  _UpperCamelCase       			=int(0.8 * len(__SCREAMING_SNAKE_CASE   )   )
  _UpperCamelCase       			=random_perm[:cut]
  _UpperCamelCase       			=random_perm[cut:]

  # For training we use a simple RandomResizedCrop
  _UpperCamelCase       			=Compose([RandomResizedCrop(__SCREAMING_SNAKE_CASE  ,      scale=(0.5, 1.0)   ), ToTensor()]   )
  _UpperCamelCase       			=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
  _UpperCamelCase       			=Compose([Resize(__SCREAMING_SNAKE_CASE   ), ToTensor()]   )
  _UpperCamelCase       			=PetsDataset([file_names[i] for i in eval_split]  ,      image_transform=__SCREAMING_SNAKE_CASE  ,      label_to_id=__SCREAMING_SNAKE_CASE   )

  # Instantiate dataloaders.
  _UpperCamelCase       			=DataLoader(__SCREAMING_SNAKE_CASE  ,      shuffle=__SCREAMING_SNAKE_CASE  ,      batch_size=__SCREAMING_SNAKE_CASE  ,      num_workers=4   )
  _UpperCamelCase       			=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)
  _UpperCamelCase       			=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).
  _UpperCamelCase       			=model.to(accelerator.device   )

  # Freezing the base model
  for param in model.parameters():
    _UpperCamelCase       			=False
  for param in model.get_classifier().parameters():
    _UpperCamelCase       			=True

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

  # Instantiate optimizer
  _UpperCamelCase       			=torch.optim.Adam(params=model.parameters()  ,      lr=lr / 25   )

  # Instantiate learning rate scheduler
  _UpperCamelCase       			=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.
  _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase       			=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
  _UpperCamelCase       			=0
  # We also need to keep track of the starting epoch so files are named properly
  _UpperCamelCase       			=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   )
      _UpperCamelCase       			=os.path.basename(args.resume_from_checkpoint   )
    else:
      # Get the most recent checkpoint
      _UpperCamelCase       			=[f.name for f in os.scandir(os.getcwd()   ) if f.is_dir()]
      dirs.sort(key=os.path.getctime   )
      _UpperCamelCase       			=dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
    # Extract `epoch_{i}` or `step_{i}`
    _UpperCamelCase       			=os.path.splitext(__SCREAMING_SNAKE_CASE   )[0]

    if "epoch" in training_difference:
      _UpperCamelCase       			=int(training_difference.replace('''epoch_'''  ,      ''''''   )   ) + 1
      _UpperCamelCase       			=None
    else:
      _UpperCamelCase       			=int(training_difference.replace('''step_'''  ,      ''''''   )   )
      _UpperCamelCase       			=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:
      _UpperCamelCase       			=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
      _UpperCamelCase       			=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
      _UpperCamelCase       			=train_dataloader
    for batch in active_dataloader:
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      _UpperCamelCase       			={k: v.to(accelerator.device   ) for k, v in batch.items()}
      _UpperCamelCase       			=(batch['''image'''] - mean) / std
      _UpperCamelCase       			=model(__SCREAMING_SNAKE_CASE   )
      _UpperCamelCase       			=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   ):
        _UpperCamelCase       			=f'''step_{overall_step}'''
        if overall_step % checkpointing_steps == 0:
          if args.output_dir is not None:
            _UpperCamelCase       			=os.path.join(args.output_dir  ,      __SCREAMING_SNAKE_CASE   )
          accelerator.save_state(__SCREAMING_SNAKE_CASE   )
    model.eval()
    _UpperCamelCase       			=0
    _UpperCamelCase       			=0
    for step, batch in enumerate(__SCREAMING_SNAKE_CASE   ):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      _UpperCamelCase       			={k: v.to(accelerator.device   ) for k, v in batch.items()}
      _UpperCamelCase       			=(batch['''image'''] - mean) / std
      with torch.no_grad():
        _UpperCamelCase       			=model(__SCREAMING_SNAKE_CASE   )
      _UpperCamelCase       			=outputs.argmax(dim=-1   )
      _UpperCamelCase			,     _UpperCamelCase       			=accelerator.gather_for_metrics((predictions, batch['''label'''])   )
      _UpperCamelCase       			=predictions == references
      num_elems += accurate_preds.shape[0]
      accurate += accurate_preds.long().sum()

    _UpperCamelCase       			=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(__SCREAMING_SNAKE_CASE   ),
              '''epoch''': epoch,
          }  ,      step=__SCREAMING_SNAKE_CASE  ,      )
    if checkpointing_steps == "epoch":
      _UpperCamelCase       			=f'''epoch_{epoch}'''
      if args.output_dir is not None:
        _UpperCamelCase       			=os.path.join(args.output_dir  ,      __SCREAMING_SNAKE_CASE   )
      accelerator.save_state(__SCREAMING_SNAKE_CASE   )

  if args.with_tracking:
    accelerator.end_training()


def 					_a   ():


  """simple docstring"""

  _UpperCamelCase       			=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'''  ,      )
  _UpperCamelCase       			=parser.parse_args()
  _UpperCamelCase       			={'''lr''': 3E-2, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 64, '''image_size''': 224}
  training_function(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
      main()













'''simple docstring'''






# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCamelCase						: Optional[int]		=   {
    'configuration_xmod': [
        'XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'XmodConfig',
        'XmodOnnxConfig',
    ],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowerCamelCase						: int		=   [
          'XMOD_PRETRAINED_MODEL_ARCHIVE_LIST',
          'XmodForCausalLM',
          'XmodForMaskedLM',
          'XmodForMultipleChoice',
          'XmodForQuestionAnswering',
          'XmodForSequenceClassification',
          'XmodForTokenClassification',
          'XmodModel',
          'XmodPreTrainedModel',
      ]

if TYPE_CHECKING:
      from .configuration_xmod import XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP, XmodConfig, XmodOnnxConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_xmod import (
                XMOD_PRETRAINED_MODEL_ARCHIVE_LIST,
                XmodForCausalLM,
                XmodForMaskedLM,
                XmodForMultipleChoice,
                XmodForQuestionAnswering,
                XmodForSequenceClassification,
                XmodForTokenClassification,
                XmodModel,
                XmodPreTrainedModel,
            )

else:
      import sys

      __lowerCamelCase						: Dict		=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






