
'''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  lowerCAmelCase_						(					SCREAMING_SNAKE_CASE_    )  ->  List[str]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE  			=					fname.split(os.path.sep    )[-1]
   return re.search(r"""^(.*)_\d+\.jpg$"""     ,   __UpperCAmelCase    ).groups()[0]

class  _a					(_lowercase):





       """simple docstring"""




       def __init__(				self							,				A__							,				A__=None							,				A__=None   )     ->      Tuple:
          _SCREAMING_SNAKE_CASE  			=					file_names
          _SCREAMING_SNAKE_CASE  			=					image_transform
          _SCREAMING_SNAKE_CASE  			=					label_to_id






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






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






def  lowerCAmelCase_						(					SCREAMING_SNAKE_CASE_     ,   SCREAMING_SNAKE_CASE_    )  ->  Any:




   """simple docstring"""
   # Initialize accelerator
   if args.with_tracking:
      _SCREAMING_SNAKE_CASE  			=					Accelerator(
          cpu=args.cpu     ,   mixed_precision=args.mixed_precision     ,   log_with="""all"""     ,   project_dir=args.project_dir    )
   else:
      _SCREAMING_SNAKE_CASE  			=					Accelerator(cpu=args.cpu     ,   mixed_precision=args.mixed_precision    )

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

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

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

   # Build the label correspondences
   _SCREAMING_SNAKE_CASE  			=					[extract_label(__UpperCAmelCase    ) for fname in file_names]
   _SCREAMING_SNAKE_CASE  			=					list(set(__UpperCAmelCase    )    )
   id_to_label.sort()
   _SCREAMING_SNAKE_CASE  			=					{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
   _SCREAMING_SNAKE_CASE  			=					np.random.permutation(len(__UpperCAmelCase    )    )
   _SCREAMING_SNAKE_CASE  			=					int(0.8 * len(__UpperCAmelCase    )    )
   _SCREAMING_SNAKE_CASE  			=					random_perm[:cut]
   _SCREAMING_SNAKE_CASE  			=					random_perm[cut:]

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

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

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

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

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

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

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

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

      _SCREAMING_SNAKE_CASE  			=					accurate.item() / num_elems
      # Use accelerator.print to print only on the main process.
      accelerator.print(F"epoch {epoch}: {1_00 * eval_metric:.2f}"    )
      if args.with_tracking:
         accelerator.log(
             {
                 """accuracy""": 1_00 * eval_metric,
                 """train_loss""": total_loss.item() / len(__UpperCAmelCase    ),
                 """epoch""": epoch,
             }     ,   step=__UpperCAmelCase     ,   )
      if checkpointing_steps == "epoch":
         _SCREAMING_SNAKE_CASE  			=					F"epoch_{epoch}"
         if args.output_dir is not None:
            _SCREAMING_SNAKE_CASE  			=					os.path.join(args.output_dir     ,   __UpperCAmelCase    )
         accelerator.save_state(__UpperCAmelCase    )

   if args.with_tracking:
      accelerator.end_training()
def  lowerCAmelCase_						(					)  ->  List[Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE  			=					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"""     ,   )
   _SCREAMING_SNAKE_CASE  			=					parser.parse_args()
   _SCREAMING_SNAKE_CASE  			=					{"""lr""": 3e-2, """num_epochs""": 3, """seed""": 42, """batch_size""": 64, """image_size""": 2_24}
   training_function(__UpperCAmelCase     ,   __UpperCAmelCase    )


if __name__ == "__main__":
     main()











from typing import Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
     from PIL import Image

     from ..image_utils import load_image

if is_torch_available():
     from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING

a_      :      str    =     logging.get_logger(__name__)
@add_end_docstrings(_lowercase				)
class    __UpperCamelCase			(       _lowercase				):

 """simple docstring"""



 def __init__(	self		,				*SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      ) ->			int:
  super().__init__(*SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      )
  self.check_model_type(SCREAMING_SNAKE_CASE      )

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE=None		,				SCREAMING_SNAKE_CASE=None		,				SCREAMING_SNAKE_CASE=None		,				**SCREAMING_SNAKE_CASE      ) ->			Union[str, Any]:
  a__							,			a__												= {}, {}
  if padding is not None:
   a__												= padding
  if truncation is not None:
   a__												= truncation
  if top_k is not None:
   a__												= top_k
  return preprocess_params, {}, postprocess_params

 def __call__(	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE = None		,				**SCREAMING_SNAKE_CASE      ) ->			Optional[int]:
  if isinstance(SCREAMING_SNAKE_CASE		,				(Image.Image, str)      ) and isinstance(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ):
   a__												= {'''image''': image, '''question''': question}
  else:
   a__												= image
  a__												= super().__call__(SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      )
  return results

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=False		,				SCREAMING_SNAKE_CASE=False      ) ->			Dict:
  a__												= load_image(inputs['''image''']      )
  a__												= self.tokenizer(
      inputs['''question''']		,				return_tensors=self.framework		,				padding=SCREAMING_SNAKE_CASE		,				truncation=SCREAMING_SNAKE_CASE      )
  a__												= self.image_processor(images=SCREAMING_SNAKE_CASE		,				return_tensors=self.framework      )
  model_inputs.update(SCREAMING_SNAKE_CASE      )
  return model_inputs

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE      ) ->			Union[str, Any]:
  a__												= self.model(**SCREAMING_SNAKE_CASE      )
  return model_outputs



 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=5      ) ->			Optional[Any]:
  if top_k > self.model.config.num_labels:
   a__												= self.model.config.num_labels

  if self.framework == "pt":
   a__												= model_outputs.logits.sigmoid()[0]
   a__							,			a__												= probs.topk(SCREAMING_SNAKE_CASE      )
  else:
   raise ValueError(f"Unsupported framework: {self.framework}"      )

  a__												= scores.tolist()
  a__												= ids.tolist()
  return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )]





