



"""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__     (				lowerCAmelCase__				):
      UpperCAmelCase_      			=							fname.split(os.path.sep				)[-1]
      return re.search(r"^(.*)_\d+\.jpg$"     ,      lowerCAmelCase__				).groups()[0]
class 				lowercase__       (   SCREAMING_SNAKE_CASE				):







       '''simple docstring'''


       def __init__(							self      :  Optional[int]		,    _UpperCAmelCase      :  Dict		,    _UpperCAmelCase      :  Dict=None		,    _UpperCAmelCase      :  Dict=None   )			->						Dict:


             '''simple docstring'''




             UpperCAmelCase_      			=							file_names
             UpperCAmelCase_      			=							image_transform
             UpperCAmelCase_      			=							label_to_id



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


             '''simple docstring'''




             return len(self.file_names   )







       def __getitem__(							self      :  List[Any]		,    _UpperCAmelCase      :  List[Any]   )			->						Tuple:


             '''simple docstring'''




             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__     (				lowerCAmelCase__     ,      lowerCAmelCase__				):
      # Initialize accelerator
      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(lowerCAmelCase__     ,      (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(lowerCAmelCase__				)[-1].split("."				)[0]
            accelerator.init_trackers(lowerCAmelCase__     ,      lowerCAmelCase__				)

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

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

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

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

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

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

      # Instantiate dataloaders.
      UpperCAmelCase_      			=							DataLoader(lowerCAmelCase__     ,      shuffle=lowerCAmelCase__     ,      batch_size=lowerCAmelCase__     ,      num_workers=4				)
      UpperCAmelCase_      			=							DataLoader(lowerCAmelCase__     ,      shuffle=lowerCAmelCase__     ,      batch_size=lowerCAmelCase__     ,      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=lowerCAmelCase__     ,      num_classes=len(lowerCAmelCase__				)				)

      # 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=lowerCAmelCase__     ,      max_lr=lowerCAmelCase__     ,      epochs=lowerCAmelCase__     ,      steps_per_epoch=len(lowerCAmelCase__				)				)

      # 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(
          lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__				)
      # 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(lowerCAmelCase__				)[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(lowerCAmelCase__				)
                  resume_step -= starting_epoch * len(lowerCAmelCase__				)

    # Now we train the model
      for epoch in range(lowerCAmelCase__     ,      lowerCAmelCase__				):
            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(lowerCAmelCase__     ,      lowerCAmelCase__				)
                  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(lowerCAmelCase__				)
                  UpperCAmelCase_      			=							torch.nn.functional.cross_entropy(lowerCAmelCase__     ,      batch["label"]				)
                  # We keep track of the loss at each epoch
                  if args.with_tracking:
                        total_loss += loss.detach().float()
                  accelerator.backward(lowerCAmelCase__				)
                  optimizer.step()
                  lr_scheduler.step()
                  optimizer.zero_grad()
                  overall_step += 1
                  if isinstance(lowerCAmelCase__     ,      lowerCAmelCase__				):
                        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     ,      lowerCAmelCase__				)
                              accelerator.save_state(lowerCAmelCase__				)
            model.eval()
            UpperCAmelCase_      			=							0
            UpperCAmelCase_      			=							0
            for step, batch in enumerate(lowerCAmelCase__				):
                  # 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(lowerCAmelCase__				)
                  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(lowerCAmelCase__				),
                          "epoch": epoch,
                      }     ,      step=lowerCAmelCase__     ,      )
            if checkpointing_steps == "epoch":
                  UpperCAmelCase_      			=							f"""epoch_{epoch}"""
                  if args.output_dir is not None:
                        UpperCAmelCase_      			=							os.path.join(args.output_dir     ,      lowerCAmelCase__				)
                  accelerator.save_state(lowerCAmelCase__				)

      if args.with_tracking:
            accelerator.end_training()
def        a__     (				):
      UpperCAmelCase_      			=							argparse.ArgumentParser(description="Simple example of training script."				)
      parser.add_argument("--data_dir"     ,      required=lowerCAmelCase__     ,      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=lowerCAmelCase__     ,      default=lowerCAmelCase__     ,      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=lowerCAmelCase__     ,      default=lowerCAmelCase__     ,      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=lowerCAmelCase__     ,      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=lowerCAmelCase__     ,      default=lowerCAmelCase__     ,      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=lowerCAmelCase__     ,      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(lowerCAmelCase__     ,      lowerCAmelCase__				)


if __name__ == "__main__":
  main()









"""simple docstring"""





import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa
import pyarrow.json as paj

import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline


lowerCamelCase       	=						datasets.utils.logging.get_logger(__name__)
@dataclass
class 				lowercase__       (   datasets.BuilderConfig				):







       '''simple docstring'''


       UpperCamelCase						=					None
       UpperCamelCase						=					"utf-8"
       UpperCamelCase						=					None
       UpperCamelCase						=					None
       UpperCamelCase						=					True  # deprecated
       UpperCamelCase						=					None  # deprecated
       UpperCamelCase						=					10 << 20  # 10MB
       UpperCamelCase						=					None



class 				lowercase__       (   datasets.ArrowBasedBuilder				):







       '''simple docstring'''


       UpperCamelCase						=					JsonConfig



       def 					lowercase__    (							self      :  List[Any]   )			->						Dict:


             '''simple docstring'''




             if self.config.block_size is not None:
                   logger.warning("The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead"   )
                   UpperCAmelCase_      			=							self.config.block_size
             if self.config.use_threads is not True:
                   logger.warning(
                       "The JSON loader parameter `use_threads` is deprecated and doesn't have any effect anymore."   )
             if self.config.newlines_in_values is not None:
                   raise ValueError("The JSON loader parameter `newlines_in_values` is no longer supported"   )
             return datasets.DatasetInfo(features=self.config.features   )



       def 					lowercase__    (							self      :  Tuple		,    _UpperCAmelCase      :  Optional[int]   )			->						Optional[int]:


             '''simple docstring'''




             if not self.config.data_files:
                   raise ValueError(F"""At least one data file must be specified, but got data_files={self.config.data_files}"""   )
             UpperCAmelCase_      			=							dl_manager.download_and_extract(self.config.data_files   )
             if isinstance(_UpperCAmelCase		,    (str, list, tuple)   ):
                   UpperCAmelCase_      			=							data_files
                   if isinstance(_UpperCAmelCase		,    _UpperCAmelCase   ):
                         UpperCAmelCase_      			=							[files]
                   UpperCAmelCase_      			=							[dl_manager.iter_files(_UpperCAmelCase   ) for file in files]
                   return [datasets.SplitGenerator(name=datasets.Split.TRAIN		,    gen_kwargs={"files": files}   )]
             UpperCAmelCase_      			=							[]
             for split_name, files in data_files.items():
                   if isinstance(_UpperCAmelCase		,    _UpperCAmelCase   ):
                         UpperCAmelCase_      			=							[files]
                   UpperCAmelCase_      			=							[dl_manager.iter_files(_UpperCAmelCase   ) for file in files]
                   splits.append(datasets.SplitGenerator(name=_UpperCAmelCase		,    gen_kwargs={"files": files}   )   )
             return splits



       def 					lowercase__    (							self      :  str		,    _UpperCAmelCase      :  pa.Table   )			->						pa.Table:


             '''simple docstring'''




             if self.config.features is not None:
                   # adding missing columns
                   for column_name in set(self.config.features   ) - set(pa_table.column_names   ):
                         UpperCAmelCase_      			=							self.config.features.arrow_schema.field(_UpperCAmelCase   ).type
                         UpperCAmelCase_      			=							pa_table.append_column(_UpperCAmelCase		,    pa.array([None] * len(_UpperCAmelCase   )		,    type=_UpperCAmelCase   )   )
                   # more expensive cast to support nested structures with keys in a different order
                   # allows str <-> int/float or str to Audio for example
                   UpperCAmelCase_      			=							table_cast(_UpperCAmelCase		,    self.config.features.arrow_schema   )
             return pa_table







       def 					lowercase__    (							self      :  Optional[int]		,    _UpperCAmelCase      :  List[Any]   )			->						str:


             '''simple docstring'''




             for file_idx, file in enumerate(itertools.chain.from_iterable(_UpperCAmelCase   )   ):
                   # If the file is one json object and if we need to look at the list of items in one specific field
                   if self.config.field is not None:
                         with open(_UpperCAmelCase		,    encoding=self.config.encoding		,    errors=self.config.encoding_errors   ) as f:
                               UpperCAmelCase_      			=							json.load(_UpperCAmelCase   )

                         # We keep only the field we are interested in
                         UpperCAmelCase_      			=							dataset[self.config.field]

                         # We accept two format: a list of dicts or a dict of lists
                         if isinstance(_UpperCAmelCase		,    (list, tuple)   ):
                               UpperCAmelCase_      			=							set().union(*[row.keys() for row in dataset]   )
                               UpperCAmelCase_      			=							{col: [row.get(_UpperCAmelCase   ) for row in dataset] for col in keys}
                         else:
                               UpperCAmelCase_      			=							dataset
                         UpperCAmelCase_      			=							pa.Table.from_pydict(_UpperCAmelCase   )
                         yield file_idx, self._cast_table(_UpperCAmelCase   )

                   # If the file has one json object per line
                   else:
                         with open(_UpperCAmelCase		,    "rb"   ) as f:
                               UpperCAmelCase_      			=							0
                               # Use block_size equal to the chunk size divided by 32 to leverage multithreading
                               # Set a default minimum value of 16kB if the chunk size is really small
                               UpperCAmelCase_      			=							max(self.config.chunksize // 32		,    16 << 10   )
                               UpperCAmelCase_      			=							(
                                   self.config.encoding_errors if self.config.encoding_errors is not None else "strict"
                               )
                               while True:
                                     UpperCAmelCase_      			=							f.read(self.config.chunksize   )
                                     if not batch:
                                           break
                                     # Finish current line
                                     try:
                                           batch += f.readline()
                                     except (AttributeError, io.UnsupportedOperation):
                                           batch += readline(_UpperCAmelCase   )
                                     # PyArrow only accepts utf-8 encoded bytes
                                     if self.config.encoding != "utf-8":
                                           UpperCAmelCase_      			=							batch.decode(self.config.encoding		,    errors=_UpperCAmelCase   ).encode("utf-8"   )
                                     try:
                                           while True:
                                                 try:
                                                       UpperCAmelCase_      			=							paj.read_json(
                                                           io.BytesIO(_UpperCAmelCase   )		,    read_options=paj.ReadOptions(block_size=_UpperCAmelCase   )   )
                                                       break
                                                 except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
                                                       if (
                                                           isinstance(_UpperCAmelCase		,    pa.ArrowInvalid   )
                                                           and "straddling" not in str(_UpperCAmelCase   )
                                                           or block_size > len(_UpperCAmelCase   )
                                                       ):
                                                             raise
                                                       else:
                                                             # Increase the block size in case it was too small.
                                                             # The block size will be reset for the next file.
                                                             logger.debug(
                                                                 F"""Batch of {len(_UpperCAmelCase   )} bytes couldn't be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}."""   )
                                                             block_size *= 2
                                     except pa.ArrowInvalid as e:
                                           try:
                                                 with open(
                                                     _UpperCAmelCase		,    encoding=self.config.encoding		,    errors=self.config.encoding_errors   ) as f:
                                                       UpperCAmelCase_      			=							json.load(_UpperCAmelCase   )
                                           except json.JSONDecodeError:
                                                 logger.error(F"""Failed to read file '{file}' with error {type(_UpperCAmelCase   )}: {e}"""   )
                                                 raise e
                                           # If possible, parse the file as a list of json objects and exit the loop
                                           if isinstance(_UpperCAmelCase		,    _UpperCAmelCase   ):  # list is the only sequence type supported in JSON
                                                 try:
                                                       UpperCAmelCase_      			=							set().union(*[row.keys() for row in dataset]   )
                                                       UpperCAmelCase_      			=							{col: [row.get(_UpperCAmelCase   ) for row in dataset] for col in keys}
                                                       UpperCAmelCase_      			=							pa.Table.from_pydict(_UpperCAmelCase   )
                                                 except (pa.ArrowInvalid, AttributeError) as e:
                                                       logger.error(F"""Failed to read file '{file}' with error {type(_UpperCAmelCase   )}: {e}"""   )
                                                       raise ValueError(F"""Not able to read records in the JSON file at {file}."""   ) from None
                                                 yield file_idx, self._cast_table(_UpperCAmelCase   )
                                                 break
                                           else:
                                                 logger.error(F"""Failed to read file '{file}' with error {type(_UpperCAmelCase   )}: {e}"""   )
                                                 raise ValueError(
                                                     F"""Not able to read records in the JSON file at {file}. """
                                                     F"""You should probably indicate the field of the JSON file containing your records. """
                                                     F"""This JSON file contain the following fields: {str(list(dataset.keys()   )   )}. """
                                                     F"""Select the correct one and provide it as `field='XXX'` to the dataset loading method. """   ) from None
                        # Uncomment for debugging (will print the Arrow table size and elements)
                        # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                        # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                                     yield (file_idx, batch_idx), self._cast_table(_UpperCAmelCase   )
                                     batch_idx += 1




