



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					(		__UpperCAmelCase       ):
 a__												= fname.split(os.path.sep       )[-1]
 return re.search(R'''^(.*)_\d+\.jpg$'''							,  __UpperCAmelCase       ).groups()[0]
class    __UpperCamelCase			(       _lowercase				):

 """simple docstring"""



 def __init__(	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=None		,				SCREAMING_SNAKE_CASE=None      ) ->			Tuple:
  a__												= file_names
  a__												= image_transform
  a__												= label_to_id

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



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






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase       ):
 # Initialize accelerator
 if args.with_tracking:
  a__												= Accelerator(
      cpu=args.cpu							,  mixed_precision=args.mixed_precision							,  log_with='''all'''							,  project_dir=args.project_dir       )
 else:
  a__												= Accelerator(cpu=args.cpu							,  mixed_precision=args.mixed_precision       )

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

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

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

 # Build the label correspondences
 a__												= [extract_label(__UpperCAmelCase       ) for fname in file_names]
 a__												= list(set(__UpperCAmelCase       )       )
 id_to_label.sort()
 a__												= {lbl: i for i, lbl in enumerate(__UpperCAmelCase       )}

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

 # Split our filenames between train and validation
 a__												= np.random.permutation(len(__UpperCAmelCase       )       )
 a__												= int(0.8 * len(__UpperCAmelCase       )       )
 a__												= random_perm[:cut]
 a__												= random_perm[cut:]

 # For training we use a simple RandomResizedCrop
 a__												= Compose([RandomResizedCrop(__UpperCAmelCase							,  scale=(0.5, 1.0)       ), ToTensor()]       )
 a__												= PetsDataset(
     [file_names[i] for i in train_split]							,  image_transform=__UpperCAmelCase							,  label_to_id=__UpperCAmelCase       )

 # For evaluation, we use a deterministic Resize
 a__												= Compose([Resize(__UpperCAmelCase       ), ToTensor()]       )
 a__												= PetsDataset([file_names[i] for i in eval_split]							,  image_transform=__UpperCAmelCase							,  label_to_id=__UpperCAmelCase       )

 # Instantiate dataloaders.
 a__												= DataLoader(__UpperCAmelCase							,  shuffle=__UpperCAmelCase							,  batch_size=__UpperCAmelCase							,  num_workers=4       )
 a__												= DataLoader(__UpperCAmelCase							,  shuffle=__UpperCAmelCase							,  batch_size=__UpperCAmelCase							,  num_workers=4       )

 # Instantiate the model (we build the model here so that the seed also control new weights initialization)
 a__												= create_model('''resnet50d'''							,  pretrained=__UpperCAmelCase							,  num_classes=len(__UpperCAmelCase       )       )

 # 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).
 a__												= model.to(accelerator.device       )

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

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

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

 # Instantiate learning rate scheduler
 a__												= OneCycleLR(optimizer=__UpperCAmelCase							,  max_lr=__UpperCAmelCase							,  epochs=__UpperCAmelCase							,  steps_per_epoch=len(__UpperCAmelCase       )       )

 # 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.
 a__							,			a__							,			a__							,			a__							,			a__												= accelerator.prepare(
     __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase       )
 # We need to keep track of how many total steps we have iterated over
 a__												= 0
 # We also need to keep track of the starting epoch so files are named properly
 a__												= 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       )
   a__												= os.path.basename(args.resume_from_checkpoint       )
  else:
   # Get the most recent checkpoint
   a__												= [f.name for f in os.scandir(os.getcwd()       ) if f.is_dir()]
   dirs.sort(key=os.path.getctime       )
   a__												= dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
  # Extract `epoch_{i}` or `step_{i}`
  a__												= os.path.splitext(__UpperCAmelCase       )[0]

  if "epoch" in training_difference:
   a__												= int(training_difference.replace('''epoch_'''							,  ''''''       )       ) + 1
   a__												= None
  else:
   a__												= int(training_difference.replace('''step_'''							,  ''''''       )       )
   a__												= resume_step // len(__UpperCAmelCase       )
   resume_step -= starting_epoch * len(__UpperCAmelCase       )

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

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

 if args.with_tracking:
  accelerator.end_training()






def     __a					(		):
 a__												= argparse.ArgumentParser(description='''Simple example of training script.'''       )
 parser.add_argument('''--data_dir'''							,  required=__UpperCAmelCase							,  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=__UpperCAmelCase							,  default=__UpperCAmelCase							,  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=__UpperCAmelCase							,  default=__UpperCAmelCase							,  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=__UpperCAmelCase							,  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=__UpperCAmelCase							,  default=__UpperCAmelCase							,  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=__UpperCAmelCase							,  default='''logs'''							,  help='''Location on where to store experiment tracking logs` and relevent project information'''							,  )
 a__												= parser.parse_args()
 a__												= {'''lr''': 3e-2, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 64, '''image_size''': 224}
 training_function(__UpperCAmelCase							,  __UpperCAmelCase       )


if __name__ == "__main__":
     main()










import torch
from torch import nn
class    __UpperCamelCase			(       nn.Module				):

 """simple docstring"""



 def __init__(	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=1		,				SCREAMING_SNAKE_CASE=False      ) ->			Tuple:
  super().__init__()

  a__												= n_token
  a__												= d_embed
  a__												= d_proj

  a__												= cutoffs + [n_token]
  a__												= [0] + self.cutoffs
  a__												= div_val

  a__												= self.cutoffs[0]
  a__												= len(self.cutoffs      ) - 1
  a__												= self.shortlist_size + self.n_clusters

  if self.n_clusters > 0:
   a__												= nn.Parameter(torch.zeros(self.n_clusters		,				self.d_embed      )      )
   a__												= nn.Parameter(torch.zeros(self.n_clusters      )      )

  a__												= nn.ModuleList()
  a__												= nn.ParameterList()

  if div_val == 1:
   for i in range(len(self.cutoffs      )      ):
    if d_proj != d_embed:
     self.out_projs.append(nn.Parameter(torch.FloatTensor(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )      )      )
    else:
     self.out_projs.append(SCREAMING_SNAKE_CASE      )

   self.out_layers.append(nn.Linear(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )      )
  else:
   for i in range(len(self.cutoffs      )      ):
    a__							,			a__												= self.cutoff_ends[i], self.cutoff_ends[i + 1]
    a__												= d_embed // (div_val**i)

    self.out_projs.append(nn.Parameter(torch.FloatTensor(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )      )      )

    self.out_layers.append(nn.Linear(SCREAMING_SNAKE_CASE		,				r_idx - l_idx      )      )

  a__												= keep_order

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ) ->			str:
  if proj is None:
   a__												= nn.functional.linear(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				bias=SCREAMING_SNAKE_CASE      )
  else:
   # if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
   a__												= nn.functional.linear(SCREAMING_SNAKE_CASE		,				proj.t().contiguous()      )
   a__												= nn.functional.linear(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				bias=SCREAMING_SNAKE_CASE      )
   # else:
   #     logit = torch.einsum('bd,de,ev->bv', (hidden, proj, weight.t()))
   #     if bias is not None:
   #         logit = logit + bias

  return logit

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=None		,				SCREAMING_SNAKE_CASE=False      ) ->			Optional[int]:

  if labels is not None:
   # Shift so that tokens < n predict n
   a__												= hidden[..., :-1, :].contiguous()
   a__												= labels[..., 1:].contiguous()
   a__												= hidden.view(-1		,				hidden.size(-1      )      )
   a__												= labels.view(-1      )
   if hidden.size(0      ) != labels.size(0      ):
    raise RuntimeError('''Input and labels should have the same size in the batch dimension.'''      )
  else:
   a__												= hidden.view(-1		,				hidden.size(-1      )      )

  if self.n_clusters == 0:
   a__												= self._compute_logit(SCREAMING_SNAKE_CASE		,				self.out_layers[0].weight		,				self.out_layers[0].bias		,				self.out_projs[0]      )
   if labels is not None:
    a__												= labels != -1_0_0
    a__												= torch.zeros_like(SCREAMING_SNAKE_CASE		,				dtype=hidden.dtype		,				device=hidden.device      )
    a__												= (
        -nn.functional.log_softmax(SCREAMING_SNAKE_CASE		,				dim=-1      )[mask].gather(1		,				labels[mask].unsqueeze(1      )      ).squeeze(1      )
    )
   else:
    a__												= nn.functional.log_softmax(SCREAMING_SNAKE_CASE		,				dim=-1      )
  else:
   # construct weights and biases
   a__							,			a__												= [], []
   for i in range(len(self.cutoffs      )      ):
    if self.div_val == 1:
     a__							,			a__												= self.cutoff_ends[i], self.cutoff_ends[i + 1]
     a__												= self.out_layers[0].weight[l_idx:r_idx]
     a__												= self.out_layers[0].bias[l_idx:r_idx]
    else:
     a__												= self.out_layers[i].weight
     a__												= self.out_layers[i].bias

    if i == 0:
     a__												= torch.cat([weight_i, self.cluster_weight]		,				dim=0      )
     a__												= torch.cat([bias_i, self.cluster_bias]		,				dim=0      )

    weights.append(SCREAMING_SNAKE_CASE      )
    biases.append(SCREAMING_SNAKE_CASE      )

   a__							,			a__							,			a__												= weights[0], biases[0], self.out_projs[0]

   a__												= self._compute_logit(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )
   a__												= nn.functional.log_softmax(SCREAMING_SNAKE_CASE		,				dim=1      )

   if labels is None:
    a__												= hidden.new_empty((head_logit.size(0      ), self.n_token)      )
   else:
    a__												= torch.zeros_like(SCREAMING_SNAKE_CASE		,				dtype=hidden.dtype		,				device=hidden.device      )

   a__												= 0
   a__												= [0] + self.cutoffs
   for i in range(len(SCREAMING_SNAKE_CASE      ) - 1      ):
    a__							,			a__												= cutoff_values[i], cutoff_values[i + 1]

    if labels is not None:
     a__												= (labels >= l_idx) & (labels < r_idx)
     a__												= mask_i.nonzero().squeeze()

     if indices_i.numel() == 0:
      continue

     a__												= labels.index_select(0		,				SCREAMING_SNAKE_CASE      ) - l_idx
     a__												= head_logprob.index_select(0		,				SCREAMING_SNAKE_CASE      )
     a__												= hidden.index_select(0		,				SCREAMING_SNAKE_CASE      )
    else:
     a__												= hidden

    if i == 0:
     if labels is not None:
      a__												= head_logprob_i.gather(1		,				target_i[:, None]      ).squeeze(1      )
     else:
      a__												= head_logprob[:, : self.cutoffs[0]]
    else:
     a__							,			a__							,			a__												= weights[i], biases[i], self.out_projs[i]

     a__												= self._compute_logit(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )
     a__												= nn.functional.log_softmax(SCREAMING_SNAKE_CASE		,				dim=1      )
     a__												= self.cutoffs[0] + i - 1  # No probability for the head cluster
     if labels is not None:
      a__												= head_logprob_i[:, cluster_prob_idx] + tail_logprob_i.gather(
          1		,				target_i[:, None]      ).squeeze(1      )
     else:
      a__												= head_logprob[:, cluster_prob_idx, None] + tail_logprob_i
      a__												= logprob_i

    if labels is not None:
     if (hasattr(self		,				'''keep_order'''      ) and self.keep_order) or keep_order:
      out.index_copy_(0		,				SCREAMING_SNAKE_CASE		,				-logprob_i      )
     else:
      out[offset : offset + logprob_i.size(0      )].copy_(-logprob_i      )
     offset += logprob_i.size(0      )

  return out



 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE      ) ->			List[str]:
  if self.n_clusters == 0:
   a__												= self._compute_logit(SCREAMING_SNAKE_CASE		,				self.out_layers[0].weight		,				self.out_layers[0].bias		,				self.out_projs[0]      )
   return nn.functional.log_softmax(SCREAMING_SNAKE_CASE		,				dim=-1      )
  else:
   # construct weights and biases
   a__							,			a__												= [], []
   for i in range(len(self.cutoffs      )      ):
    if self.div_val == 1:
     a__							,			a__												= self.cutoff_ends[i], self.cutoff_ends[i + 1]
     a__												= self.out_layers[0].weight[l_idx:r_idx]
     a__												= self.out_layers[0].bias[l_idx:r_idx]
    else:
     a__												= self.out_layers[i].weight
     a__												= self.out_layers[i].bias

    if i == 0:
     a__												= torch.cat([weight_i, self.cluster_weight]		,				dim=0      )
     a__												= torch.cat([bias_i, self.cluster_bias]		,				dim=0      )

    weights.append(SCREAMING_SNAKE_CASE      )
    biases.append(SCREAMING_SNAKE_CASE      )

   a__							,			a__							,			a__												= weights[0], biases[0], self.out_projs[0]
   a__												= self._compute_logit(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )

   a__												= hidden.new_empty((head_logit.size(0      ), self.n_token)      )
   a__												= nn.functional.log_softmax(SCREAMING_SNAKE_CASE		,				dim=1      )

   a__												= [0] + self.cutoffs
   for i in range(len(SCREAMING_SNAKE_CASE      ) - 1      ):
    a__							,			a__												= cutoff_values[i], cutoff_values[i + 1]

    if i == 0:
     a__												= head_logprob[:, : self.cutoffs[0]]
    else:
     a__							,			a__							,			a__												= weights[i], biases[i], self.out_projs[i]

     a__												= self._compute_logit(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )
     a__												= nn.functional.log_softmax(SCREAMING_SNAKE_CASE		,				dim=1      )

     a__												= head_logprob[:, -i] + tail_logprob_i
     a__												= logprob_i

   return out





