


"""simple docstring"""



import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional

import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset

import transformers
from transformers import (
    AutoConfig,
    AutoFeatureExtractor,
    AutoModelForAudioClassification,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version


__snake_case             =   logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("""4.31.0""")

require_version("""datasets>=1.14.0""", """To fix: pip install -r examples/pytorch/audio-classification/requirements.txt""")







def   __lowerCAmelCase						(						lowercase  :  np.ndarray						,							lowercase  :  float						,							lowercase  :  int = 1_6000			)     ->						Union[str, Any]:


    """simple docstring"""





    snake_case   :				Tuple				 =   int(round(sample_rate * max_length			)			)
    if len(lowercase			) <= sample_length:
        return wav
    snake_case   :				Tuple				 =   randint(0						,							len(lowercase			) - sample_length - 1			)
    return wav[random_offset : random_offset + sample_length]







@dataclass
class _lowerCAmelCase    :
   __UpperCAmelCase			:	Optional[str]							  =  field(default=snake_case_   ,							metadata={'''help''': '''Name of a dataset from the datasets package'''}     )
   __UpperCAmelCase			:	Optional[str]							  =  field(
       default=snake_case_   ,							metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}     )
   __UpperCAmelCase			:	Optional[str]							  =  field(
       default=snake_case_   ,							metadata={'''help''': '''A file containing the training audio paths and labels.'''}     )
   __UpperCAmelCase			:	Optional[str]							  =  field(
       default=snake_case_   ,							metadata={'''help''': '''A file containing the validation audio paths and labels.'''}     )
   __UpperCAmelCase			:	str							  =  field(
       default='''train'''   ,							metadata={
           '''help''': '''The name of the training data set split to use (via the datasets library). Defaults to \'train\''''
       }   ,							)
   __UpperCAmelCase			:	str							  =  field(
       default='''validation'''   ,							metadata={
           '''help''': (
               '''The name of the training data set split to use (via the datasets library). Defaults to \'validation\''''
           )
       }   ,							)
   __UpperCAmelCase			:	str							  =  field(
       default='''audio'''   ,							metadata={'''help''': '''The name of the dataset column containing the audio data. Defaults to \'audio\''''}   ,							)
   __UpperCAmelCase			:	str							  =  field(
       default='''label'''   ,							metadata={'''help''': '''The name of the dataset column containing the labels. Defaults to \'label\''''}     )
   __UpperCAmelCase			:	Optional[int]							  =  field(
       default=snake_case_   ,							metadata={
           '''help''': (
               '''For debugging purposes or quicker training, truncate the number of training examples to this '''
               '''value if set.'''
           )
       }   ,							)
   __UpperCAmelCase			:	Optional[int]							  =  field(
       default=snake_case_   ,							metadata={
           '''help''': (
               '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
               '''value if set.'''
           )
       }   ,							)
   __UpperCAmelCase			:	float							  =  field(
       default=20   ,							metadata={'''help''': '''Audio clips will be randomly cut to this length during training if the value is set.'''}   ,							)







@dataclass
class _lowerCAmelCase    :
   __UpperCAmelCase			:	str							  =  field(
       default='''facebook/wav2vec2-base'''   ,							metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}   ,							)
   __UpperCAmelCase			:	Optional[str]							  =  field(
       default=snake_case_   ,							metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}     )
   __UpperCAmelCase			:	Optional[str]							  =  field(
       default=snake_case_   ,							metadata={'''help''': '''Where do you want to store the pretrained models downloaded from the Hub'''}     )
   __UpperCAmelCase			:	str							  =  field(
       default='''main'''   ,							metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}   ,							)
   __UpperCAmelCase			:	Optional[str]							  =  field(
       default=snake_case_   ,							metadata={'''help''': '''Name or path of preprocessor config.'''}     )
   __UpperCAmelCase			:	bool							  =  field(
       default=snake_case_   ,							metadata={'''help''': '''Whether to freeze the feature encoder layers of the model.'''}     )
   __UpperCAmelCase			:	bool							  =  field(
       default=snake_case_   ,							metadata={'''help''': '''Whether to generate an attention mask in the feature extractor.'''}     )
   __UpperCAmelCase			:	bool							  =  field(
       default=snake_case_   ,							metadata={
           '''help''': (
               '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
               '''with private models).'''
           )
       }   ,							)
   __UpperCAmelCase			:	Optional[bool]							  =  field(
       default=snake_case_   ,							metadata={'''help''': '''Whether to freeze the feature extractor layers of the model.'''}     )
   __UpperCAmelCase			:	bool							  =  field(
       default=snake_case_   ,							metadata={'''help''': '''Will enable to load a pretrained model whose head dimensions are different.'''}   ,							)







   def 			lowerCamelCase		(     self						)     ->		Dict:

       '''simple docstring'''




       if not self.freeze_feature_extractor and self.freeze_feature_encoder:
           warnings.warn(
               "The argument `--freeze_feature_extractor` is deprecated and "
               "will be removed in a future version. Use `--freeze_feature_encoder`"
               "instead. Setting `freeze_feature_encoder==True`."						,     UpperCamelCase__						,     )
       if self.freeze_feature_extractor and not self.freeze_feature_encoder:
           raise ValueError(
               "The argument `--freeze_feature_extractor` is deprecated and "
               "should not be used in combination with `--freeze_feature_encoder`."
               "Only make use of `--freeze_feature_encoder`."						)







def   __lowerCAmelCase						(						)     ->						Union[str, Any]:


    """simple docstring"""





    snake_case   :				Dict				 =   HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)			)
    if len(sys.argv			) == 2 and sys.argv[1].endswith(".json"			):
        # If we pass only one argument to the script and it's the path to a json file,
        # let's parse it to get our arguments.
        snake_case ,snake_case ,snake_case   :				Union[str, Any]				 =   parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
    else:
        snake_case ,snake_case ,snake_case   :				Optional[Any]				 =   parser.parse_args_into_dataclasses()

    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
    # information sent is the one passed as arguments along with your Python/PyTorch versions.
    send_example_telemetry("run_audio_classification"						,							lowercase						,							lowercase			)

    # Setup logging
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"						,							datefmt="%m/%d/%Y %H:%M:%S"						,							handlers=[logging.StreamHandler(sys.stdout			)]						,							)

    if training_args.should_log:
        # The default of training_args.log_level is passive, so we set log level at info here to have that default.
        transformers.utils.logging.set_verbosity_info()

    snake_case   :				Optional[int]				 =   training_args.get_process_log_level()
    logger.setLevel(lowercase			)
    transformers.utils.logging.set_verbosity(lowercase			)
    transformers.utils.logging.enable_default_handler()
    transformers.utils.logging.enable_explicit_format()

    # Log on each process the small summary:
    logger.warning(
        F'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu} '
        + F'distributed training: {bool(training_args.local_rank != -1			)}, 16-bits training: {training_args.fpaa}'			)
    logger.info(F'Training/evaluation parameters {training_args}'			)

    # Set seed before initializing model.
    set_seed(training_args.seed			)

    # Detecting last checkpoint.
    snake_case   :				List[Any]				 =   None
    if os.path.isdir(training_args.output_dir			) and training_args.do_train and not training_args.overwrite_output_dir:
        snake_case   :				Any				 =   get_last_checkpoint(training_args.output_dir			)
        if last_checkpoint is None and len(os.listdir(training_args.output_dir			)			) > 0:
            raise ValueError(
                F'Output directory ({training_args.output_dir}) already exists and is not empty. '
                "Use --overwrite_output_dir to train from scratch."			)
        elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
            logger.info(
                F'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
                "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."			)

    # Initialize our dataset and prepare it for the audio classification task.
    snake_case   :				str				 =   DatasetDict()
    snake_case   :				Tuple				 =   load_dataset(
        data_args.dataset_name						,							data_args.dataset_config_name						,							split=data_args.train_split_name						,							use_auth_token=True if model_args.use_auth_token else None						,							)
    snake_case   :				Union[str, Any]				 =   load_dataset(
        data_args.dataset_name						,							data_args.dataset_config_name						,							split=data_args.eval_split_name						,							use_auth_token=True if model_args.use_auth_token else None						,							)

    if data_args.audio_column_name not in raw_datasets["train"].column_names:
        raise ValueError(
            F'--audio_column_name {data_args.audio_column_name} not found in dataset \'{data_args.dataset_name}\'. '
            "Make sure to set `--audio_column_name` to the correct audio column - one of "
            F'{", ".join(raw_datasets["train"].column_names			)}.'			)

    if data_args.label_column_name not in raw_datasets["train"].column_names:
        raise ValueError(
            F'--label_column_name {data_args.label_column_name} not found in dataset \'{data_args.dataset_name}\'. '
            "Make sure to set `--label_column_name` to the correct text column - one of "
            F'{", ".join(raw_datasets["train"].column_names			)}.'			)

    # Setting `return_attention_mask=True` is the way to get a correctly masked mean-pooling over
    # transformer outputs in the classifier, but it doesn't always lead to better accuracy
    snake_case   :				Any				 =   AutoFeatureExtractor.from_pretrained(
        model_args.feature_extractor_name or model_args.model_name_or_path						,							return_attention_mask=model_args.attention_mask						,							cache_dir=model_args.cache_dir						,							revision=model_args.model_revision						,							use_auth_token=True if model_args.use_auth_token else None						,							)

    # `datasets` takes care of automatically loading and resampling the audio,
    # so we just need to set the correct target sampling rate.
    snake_case   :				List[str]				 =   raw_datasets.cast_column(
        data_args.audio_column_name						,							datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate			)			)

    snake_case   :				Optional[int]				 =   feature_extractor.model_input_names[0]

    def train_transforms(lowercase  :  Union[str, Any]			):
        snake_case   :				Union[str, Any]				 =   []
        for audio in batch[data_args.audio_column_name]:
            snake_case   :				Optional[int]				 =   random_subsample(
                audio["array"]						,							max_length=data_args.max_length_seconds						,							sample_rate=feature_extractor.sampling_rate			)
            subsampled_wavs.append(lowercase			)
        snake_case   :				List[str]				 =   feature_extractor(lowercase						,							sampling_rate=feature_extractor.sampling_rate			)
        snake_case   :				List[str]				 =   {model_input_name: inputs.get(lowercase			)}
        snake_case   :				Optional[Any]				 =   list(batch[data_args.label_column_name]			)

        return output_batch

    def val_transforms(lowercase  :  Any			):
        snake_case   :				Tuple				 =   [audio["array"] for audio in batch[data_args.audio_column_name]]
        snake_case   :				Optional[int]				 =   feature_extractor(lowercase						,							sampling_rate=feature_extractor.sampling_rate			)
        snake_case   :				List[str]				 =   {model_input_name: inputs.get(lowercase			)}
        snake_case   :				Tuple				 =   list(batch[data_args.label_column_name]			)

        return output_batch

    # Prepare label mappings.
    # We'll include these in the model's config to get human readable labels in the Inference API.
    snake_case   :				Optional[int]				 =   raw_datasets["train"].features[data_args.label_column_name].names
    snake_case ,snake_case   :				Union[str, Any]				 =   {}, {}
    for i, label in enumerate(lowercase			):
        snake_case   :				Dict				 =   str(lowercase			)
        snake_case   :				Union[str, Any]				 =   label

    # Load the accuracy metric from the datasets package
    snake_case   :				List[Any]				 =   evaluate.load("accuracy"			)

    # Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with
    # `predictions` and `label_ids` fields) and has to return a dictionary string to float.
    def compute_metrics(lowercase  :  List[str]			):
        snake_case   :				str				 =   np.argmax(eval_pred.predictions						,							axis=1			)
        return metric.compute(predictions=lowercase						,							references=eval_pred.label_ids			)

    snake_case   :				int				 =   AutoConfig.from_pretrained(
        model_args.config_name or model_args.model_name_or_path						,							num_labels=len(lowercase			)						,							labelaid=lowercase						,							idalabel=lowercase						,							finetuning_task="audio-classification"						,							cache_dir=model_args.cache_dir						,							revision=model_args.model_revision						,							use_auth_token=True if model_args.use_auth_token else None						,							)
    snake_case   :				Any				 =   AutoModelForAudioClassification.from_pretrained(
        model_args.model_name_or_path						,							from_tf=bool(".ckpt" in model_args.model_name_or_path			)						,							config=lowercase						,							cache_dir=model_args.cache_dir						,							revision=model_args.model_revision						,							use_auth_token=True if model_args.use_auth_token else None						,							ignore_mismatched_sizes=model_args.ignore_mismatched_sizes						,							)

    # freeze the convolutional waveform encoder
    if model_args.freeze_feature_encoder:
        model.freeze_feature_encoder()

    if training_args.do_train:
        if data_args.max_train_samples is not None:
            snake_case   :				Optional[Any]				 =   (
                raw_datasets["train"].shuffle(seed=training_args.seed			).select(range(data_args.max_train_samples			)			)
            )
        # Set the training transforms
        raw_datasets["train"].set_transform(lowercase						,							output_all_columns=lowercase			)

    if training_args.do_eval:
        if data_args.max_eval_samples is not None:
            snake_case   :				List[Any]				 =   (
                raw_datasets["eval"].shuffle(seed=training_args.seed			).select(range(data_args.max_eval_samples			)			)
            )
        # Set the validation transforms
        raw_datasets["eval"].set_transform(lowercase						,							output_all_columns=lowercase			)

    # Initialize our trainer
    snake_case   :				str				 =   Trainer(
        model=lowercase						,							args=lowercase						,							train_dataset=raw_datasets["train"] if training_args.do_train else None						,							eval_dataset=raw_datasets["eval"] if training_args.do_eval else None						,							compute_metrics=lowercase						,							tokenizer=lowercase						,							)

    # Training
    if training_args.do_train:
        snake_case   :				Dict				 =   None
        if training_args.resume_from_checkpoint is not None:
            snake_case   :				Tuple				 =   training_args.resume_from_checkpoint
        elif last_checkpoint is not None:
            snake_case   :				Optional[Any]				 =   last_checkpoint
        snake_case   :				str				 =   trainer.train(resume_from_checkpoint=lowercase			)
        trainer.save_model()
        trainer.log_metrics("train"						,							train_result.metrics			)
        trainer.save_metrics("train"						,							train_result.metrics			)
        trainer.save_state()

    # Evaluation
    if training_args.do_eval:
        snake_case   :				List[Any]				 =   trainer.evaluate()
        trainer.log_metrics("eval"						,							lowercase			)
        trainer.save_metrics("eval"						,							lowercase			)

    # Write model card and (optionally) push to hub
    snake_case   :				Tuple				 =   {
        "finetuned_from": model_args.model_name_or_path,
        "tasks": "audio-classification",
        "dataset": data_args.dataset_name,
        "tags": ["audio-classification"],
    }
    if training_args.push_to_hub:
        trainer.push_to_hub(**lowercase			)
    else:
        trainer.create_model_card(**lowercase			)


if __name__ == "__main__":
  main()









"""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						(						lowercase  :  int			)     ->						Tuple:


    """simple docstring"""





    snake_case   :				Any				 =   fname.split(os.path.sep			)[-1]
    return re.search(R"^(.*)_\d+\.jpg$"						,							lowercase			).groups()[0]







class _lowerCAmelCase    (				snake_case_     ):







   def __init__(     self						,     UpperCamelCase__						,     UpperCamelCase__=None						,     UpperCamelCase__=None						)     ->		Union[str, Any]:

       '''simple docstring'''




       snake_case   :				Union[str, Any]				 =   file_names
       snake_case   :				Optional[Any]				 =   image_transform
       snake_case   :				Optional[int]				 =   label_to_id







   def __len__(     self						)     ->		Tuple:

       '''simple docstring'''




       return len(self.file_names						)







   def __getitem__(     self						,     UpperCamelCase__						)     ->		int:

       '''simple docstring'''




       snake_case   :				str				 =   self.file_names[idx]
       snake_case   :				Any				 =   PIL.Image.open(UpperCamelCase__						)
       snake_case   :				Optional[int]				 =   raw_image.convert("RGB"						)
       if self.image_transform is not None:
           snake_case   :				Optional[Any]				 =   self.image_transform(UpperCamelCase__						)
       snake_case   :				Optional[Any]				 =   extract_label(UpperCamelCase__						)
       if self.label_to_id is not None:
           snake_case   :				Optional[Any]				 =   self.label_to_id[label]
       return {"image": image, "label": label}







def   __lowerCAmelCase						(						lowercase  :  Any						,							lowercase  :  List[Any]			)     ->						List[str]:


    """simple docstring"""





    if args.with_tracking:
        snake_case   :				List[str]				 =   Accelerator(
            cpu=args.cpu						,							mixed_precision=args.mixed_precision						,							log_with="all"						,							project_dir=args.project_dir			)
    else:
        snake_case   :				Optional[int]				 =   Accelerator(cpu=args.cpu						,							mixed_precision=args.mixed_precision			)

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

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

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

    # Build the label correspondences
    snake_case   :				Union[str, Any]				 =   [extract_label(lowercase			) for fname in file_names]
    snake_case   :				Any				 =   list(set(lowercase			)			)
    id_to_label.sort()
    snake_case   :				int				 =   {lbl: i for i, lbl in enumerate(lowercase			)}

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

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

    # For training we use a simple RandomResizedCrop
    snake_case   :				List[Any]				 =   Compose([RandomResizedCrop(lowercase						,							scale=(0.5, 1.0)			), ToTensor()]			)
    snake_case   :				List[str]				 =   PetsDataset(
        [file_names[i] for i in train_split]						,							image_transform=lowercase						,							label_to_id=lowercase			)

    # For evaluation, we use a deterministic Resize
    snake_case   :				Optional[Any]				 =   Compose([Resize(lowercase			), ToTensor()]			)
    snake_case   :				Optional[Any]				 =   PetsDataset([file_names[i] for i in eval_split]						,							image_transform=lowercase						,							label_to_id=lowercase			)

    # Instantiate dataloaders.
    snake_case   :				Optional[Any]				 =   DataLoader(lowercase						,							shuffle=lowercase						,							batch_size=lowercase						,							num_workers=4			)
    snake_case   :				Tuple				 =   DataLoader(lowercase						,							shuffle=lowercase						,							batch_size=lowercase						,							num_workers=4			)

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    snake_case   :				Optional[int]				 =   create_model("resnet50d"						,							pretrained=lowercase						,							num_classes=len(lowercase			)			)

    # 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).
    snake_case   :				Any				 =   model.to(accelerator.device			)

    # Freezing the base model
    for param in model.parameters():
        snake_case   :				Dict				 =   False
    for param in model.get_classifier().parameters():
        snake_case   :				List[Any]				 =   True

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

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

    # Instantiate learning rate scheduler
    snake_case   :				Dict				 =   OneCycleLR(optimizer=lowercase						,							max_lr=lowercase						,							epochs=lowercase						,							steps_per_epoch=len(lowercase			)			)

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

        if "epoch" in training_difference:
            snake_case   :				Any				 =   int(training_difference.replace("epoch_"						,							""			)			) + 1
            snake_case   :				int				 =   None
        else:
            snake_case   :				Any				 =   int(training_difference.replace("step_"						,							""			)			)
            snake_case   :				Optional[int]				 =   resume_step // len(lowercase			)
            resume_step -= starting_epoch * len(lowercase			)

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

        snake_case   :				List[Any]				 =   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(lowercase			),
                    "epoch": epoch,
                }						,							step=lowercase						,							)
        if checkpointing_steps == "epoch":
            snake_case   :				Optional[Any]				 =   F'epoch_{epoch}'
            if args.output_dir is not None:
                snake_case   :				Union[str, Any]				 =   os.path.join(args.output_dir						,							lowercase			)
            accelerator.save_state(lowercase			)

    if args.with_tracking:
        accelerator.end_training()







def   __lowerCAmelCase						(						)     ->						str:


    """simple docstring"""





    snake_case   :				Optional[Any]				 =   argparse.ArgumentParser(description="Simple example of training script."			)
    parser.add_argument("--data_dir"						,							required=lowercase						,							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=lowercase						,							default=lowercase						,							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=lowercase						,							default=lowercase						,							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=lowercase						,							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=lowercase						,							default=lowercase						,							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=lowercase						,							default="logs"						,							help="Location on where to store experiment tracking logs` and relevent project information"						,							)
    snake_case   :				Optional[Any]				 =   parser.parse_args()
    snake_case   :				List[str]				 =   {"lr": 3e-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
    training_function(lowercase						,							lowercase			)


if __name__ == "__main__":
  main()





