
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_utils import Literal


__lowerCAmelCase			:     int           =   datasets.utils.logging.get_logger(__name__)

__lowerCAmelCase			:     str           =   ['names', 'prefix']
__lowerCAmelCase			:     List[Any]           =   ['warn_bad_lines', 'error_bad_lines', 'mangle_dupe_cols']
__lowerCAmelCase			:     Dict           =   ['encoding_errors', 'on_bad_lines']
__lowerCAmelCase			:     int           =   ['date_format']




@dataclass
class        UpperCAmelCase_			( datasets.BuilderConfig     ):


  '''simple docstring'''







  a__           =		","
  a__           =		None
  a__           =		"infer"
  a__           =		None
  a__           =		None
  a__           =		None
  a__           =		None
  a__           =		None
  a__           =		True
  a__           =		None
  a__           =		None
  a__           =		None
  a__           =		None
  a__           =		False
  a__           =		None
  a__           =		None
  a__           =		None
  a__           =		True
  a__           =		True
  a__           =		False
  a__           =		True
  a__           =		None
  a__           =		"."
  a__           =		None
  a__           =		'"'
  a__           =		0
  a__           =		None
  a__           =		None
  a__           =		None
  a__           =		None
  a__           =		True
  a__           =		True
  a__           =		0
  a__           =		True
  a__           =		False
  a__           =		None
  a__           =		1_00_00
  a__           =		None
  a__           =		"strict"
  a__           =		"error"
  a__           =		None
  def 							_lowercase							( self			: Tuple    )			->	Optional[Any]:







    """simple docstring"""






    if self.delimiter is not None:
      __magic_name__				     =       self.delimiter
    if self.column_names is not None:
      __magic_name__				     =       self.column_names





  @property
  def 							_lowercase							( self			: Any    )			->	str:







    """simple docstring"""






    __magic_name__				     =       {
        """sep""": self.sep,
        """header""": self.header,
        """names""": self.names,
        """index_col""": self.index_col,
        """usecols""": self.usecols,
        """prefix""": self.prefix,
        """mangle_dupe_cols""": self.mangle_dupe_cols,
        """engine""": self.engine,
        """converters""": self.converters,
        """true_values""": self.true_values,
        """false_values""": self.false_values,
        """skipinitialspace""": self.skipinitialspace,
        """skiprows""": self.skiprows,
        """nrows""": self.nrows,
        """na_values""": self.na_values,
        """keep_default_na""": self.keep_default_na,
        """na_filter""": self.na_filter,
        """verbose""": self.verbose,
        """skip_blank_lines""": self.skip_blank_lines,
        """thousands""": self.thousands,
        """decimal""": self.decimal,
        """lineterminator""": self.lineterminator,
        """quotechar""": self.quotechar,
        """quoting""": self.quoting,
        """escapechar""": self.escapechar,
        """comment""": self.comment,
        """encoding""": self.encoding,
        """dialect""": self.dialect,
        """error_bad_lines""": self.error_bad_lines,
        """warn_bad_lines""": self.warn_bad_lines,
        """skipfooter""": self.skipfooter,
        """doublequote""": self.doublequote,
        """memory_map""": self.memory_map,
        """float_precision""": self.float_precision,
        """chunksize""": self.chunksize,
        """encoding_errors""": self.encoding_errors,
        """on_bad_lines""": self.on_bad_lines,
        """date_format""": self.date_format,
    }

    # some kwargs must not be passed if they don't have a default value
    # some others are deprecated and we can also not pass them if they are the default value
    for pd_read_csv_parameter in _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS + _PANDAS_READ_CSV_DEPRECATED_PARAMETERS:
      if pd_read_csv_kwargs[pd_read_csv_parameter] == getattr(CsvConfig()			,							UpperCamelCase__    ):
        del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 2.0 new arguments
    if not (datasets.config.PANDAS_VERSION.major >= 2):
      for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_2_0_0_PARAMETERS:
        del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 1.3 new arguments
    if not (datasets.config.PANDAS_VERSION.major >= 1 and datasets.config.PANDAS_VERSION.minor >= 3):
      for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_1_3_0_PARAMETERS:
        del pd_read_csv_kwargs[pd_read_csv_parameter]

    return pd_read_csv_kwargs







class        UpperCAmelCase_			( datasets.ArrowBasedBuilder     ):


  '''simple docstring'''







  a__           =		CsvConfig
  def 							_lowercase							( self			: Union[str, Any]    )			->	str:







    """simple docstring"""






    return datasets.DatasetInfo(features=self.config.features    )
  def 							_lowercase							( self			: Any			,							UpperCamelCase__			: List[Any]    )			->	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}'''    )
    __magic_name__				     =       dl_manager.download_and_extract(self.config.data_files    )
    if isinstance(UpperCamelCase__			,							(str, list, tuple)    ):
      __magic_name__				     =       data_files
      if isinstance(UpperCamelCase__			,							UpperCamelCase__    ):
        __magic_name__				     =       [files]
      __magic_name__				     =       [dl_manager.iter_files(UpperCamelCase__    ) for file in files]
      return [datasets.SplitGenerator(name=datasets.Split.TRAIN			,							gen_kwargs={"""files""": files}    )]
    __magic_name__				     =       []
    for split_name, files in data_files.items():
      if isinstance(UpperCamelCase__			,							UpperCamelCase__    ):
        __magic_name__				     =       [files]
      __magic_name__				     =       [dl_manager.iter_files(UpperCamelCase__    ) for file in files]
      splits.append(datasets.SplitGenerator(name=UpperCamelCase__			,							gen_kwargs={"""files""": files}    )    )
    return splits
  def 							_lowercase							( self			: Dict			,							UpperCamelCase__			: pa.Table    )			->	pa.Table:







    """simple docstring"""






    if self.config.features is not None:
      __magic_name__				     =       self.config.features.arrow_schema
      if all(not require_storage_cast(UpperCamelCase__    ) for feature in self.config.features.values()    ):
        # cheaper cast
        __magic_name__				     =       pa.Table.from_arrays([pa_table[field.name] for field in schema]			,							schema=UpperCamelCase__    )
      else:
        # more expensive cast; allows str <-> int/float or str to Audio for example
        __magic_name__				     =       table_cast(UpperCamelCase__			,							UpperCamelCase__    )
    return pa_table





  def 							_lowercase							( self			: Any			,							UpperCamelCase__			: List[Any]    )			->	Any:







    """simple docstring"""






    __magic_name__				     =       self.config.features.arrow_schema if self.config.features else None
    # dtype allows reading an int column as str
    __magic_name__				     =       (
        {
            name: dtype.to_pandas_dtype() if not require_storage_cast(UpperCamelCase__    ) else object
            for name, dtype, feature in zip(schema.names			,							schema.types			,							self.config.features.values()    )
        }
        if schema is not None
        else None
    )
    for file_idx, file in enumerate(itertools.chain.from_iterable(UpperCamelCase__    )    ):
      __magic_name__				     =       pd.read_csv(UpperCamelCase__			,							iterator=UpperCamelCase__			,							dtype=UpperCamelCase__			,							**self.config.pd_read_csv_kwargs    )
      try:
        for batch_idx, df in enumerate(UpperCamelCase__    ):
          __magic_name__				     =       pa.Table.from_pandas(UpperCamelCase__    )
          # 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__    )
      except ValueError as e:
        logger.error(F'''Failed to read file \'{file}\' with error {type(UpperCamelCase__    )}: {e}'''    )
        raise








'''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						(     __lowerCamelCase :    Tuple    )						->Tuple:
    _SCREAMING_SNAKE_CASE									=			fname.split(os.path.sep    )[-1]
    return re.search(R"""^(.*)_\d+\.jpg$"""					,       __lowerCamelCase    ).groups()[0]


class 					a_     (				snake_case_       ):

    '''simple docstring'''


    def __init__(		self ,		A ,		A=None ,		A=None       )		->   int:
        _SCREAMING_SNAKE_CASE									=			file_names
        _SCREAMING_SNAKE_CASE									=			image_transform
        _SCREAMING_SNAKE_CASE									=			label_to_id


    def __len__(		self       )		->   Optional[Any]:
        return len(self.file_names       )





    def __getitem__(		self ,		A       )		->   Union[str, Any]:
        _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						(     __lowerCamelCase :    Any					,       __lowerCamelCase :    Tuple    )						->str:
    # 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(__lowerCamelCase					,       (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(__lowerCamelCase    )[-1].split("""."""    )[0]
        accelerator.init_trackers(__lowerCamelCase					,       __lowerCamelCase    )

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

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

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

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

    # Instantiate dataloaders.
    _SCREAMING_SNAKE_CASE									=			DataLoader(__lowerCamelCase					,       shuffle=__lowerCamelCase					,       batch_size=__lowerCamelCase					,       num_workers=4    )
    _SCREAMING_SNAKE_CASE									=			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)
    _SCREAMING_SNAKE_CASE									=			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).
    _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=__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.
    _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			accelerator.prepare(
        __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase    )
    # 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(__lowerCamelCase    )[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(__lowerCamelCase    )
            resume_step -= starting_epoch * len(__lowerCamelCase    )

    # Now we train the model
    for epoch in range(__lowerCamelCase					,       __lowerCamelCase    ):
        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(__lowerCamelCase					,       __lowerCamelCase    )
            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(__lowerCamelCase    )
            _SCREAMING_SNAKE_CASE									=			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    ):
                _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					,       __lowerCamelCase    )
                    accelerator.save_state(__lowerCamelCase    )
        model.eval()
        _SCREAMING_SNAKE_CASE									=			0
        _SCREAMING_SNAKE_CASE									=			0
        for step, batch in enumerate(__lowerCamelCase    ):
            # 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(__lowerCamelCase    )
            _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}: {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":
            _SCREAMING_SNAKE_CASE									=			F'epoch_{epoch}'
            if args.output_dir is not None:
                _SCREAMING_SNAKE_CASE									=			os.path.join(args.output_dir					,       __lowerCamelCase    )
            accelerator.save_state(__lowerCamelCase    )

    if args.with_tracking:
        accelerator.end_training()





def lowerCamelCase						(     )						->int:
    _SCREAMING_SNAKE_CASE									=			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"""					,       )
    _SCREAMING_SNAKE_CASE									=			parser.parse_args()
    _SCREAMING_SNAKE_CASE									=			{"""lr""": 3e-2, """num_epochs""": 3, """seed""": 42, """batch_size""": 64, """image_size""": 224}
    training_function(__lowerCamelCase					,       __lowerCamelCase    )


if __name__ == "__main__":
      main()



