'''simple docstring'''





from typing import Any, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler

from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
    Adafactor,
    AdamW,
    get_constant_schedule,
    get_constant_schedule_with_warmup,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available


if is_fairscale_available():
 from fairscale.optim import OSS


_lowerCAmelCase  :Optional[Any]  				=    logging.get_logger(__name__)

_lowerCAmelCase  :Union[str, Any]  				=    {
    """linear""": get_linear_schedule_with_warmup,
    """cosine""": get_cosine_schedule_with_warmup,
    """cosine_w_restarts""": get_cosine_with_hard_restarts_schedule_with_warmup,
    """polynomial""": get_polynomial_decay_schedule_with_warmup,
    """constant""": get_constant_schedule,
    """constant_w_warmup""": get_constant_schedule_with_warmup,
}






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    def __init__(       self    ,							lowercase__=None    ,							lowercase__=None    ,							*lowercase__    ,							**lowercase__ )							-> Tuple:
         super().__init__(*lowercase__    ,							**lowercase__ )

         if config is None:
              assert isinstance(self.model    ,							lowercase__ ), (
                  "If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
                  F""" {self.model.__class__}"""
              )
              SCREAMING_SNAKE_CASE					:			str    	=       self.model.config
         else:
              SCREAMING_SNAKE_CASE					:			Optional[int]    	=       config

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       data_args
         SCREAMING_SNAKE_CASE					:			Any    	=       self.config.tgt_vocab_size if isinstance(self.config    ,							lowercase__ ) else self.config.vocab_size

         if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
              assert self.config.pad_token_id is not None, (
                  "Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
                  " calculation or doing label smoothing."
              )

         if self.config.pad_token_id is None and self.config.eos_token_id is not None:
              logger.warning(
                  F"""The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for"""
                  ' padding..' )

         if self.args.label_smoothing == 0:
              SCREAMING_SNAKE_CASE					:			Dict    	=       torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id )
         else:
              # dynamically import label_smoothed_nll_loss
              from utils import label_smoothed_nll_loss

              SCREAMING_SNAKE_CASE					:			Dict    	=       label_smoothed_nll_loss






    def       _UpperCamelCase    (       self    ,							lowercase__ )							-> Optional[int]:
         if self.optimizer is None:
              SCREAMING_SNAKE_CASE					:			List[Any]    	=       ['bias', 'LayerNorm.weight']
              SCREAMING_SNAKE_CASE					:			int    	=       [
                  {
                      'params': [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay )],
                      'weight_decay': self.args.weight_decay,
                  },
                  {
                      'params': [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay )],
                      'weight_decay': 0.0,
                  },
              ]
              SCREAMING_SNAKE_CASE					:			List[Any]    	=       Adafactor if self.args.adafactor else AdamW
              if self.args.adafactor:
                   SCREAMING_SNAKE_CASE					:			Any    	=       Adafactor
                   SCREAMING_SNAKE_CASE					:			int    	=       {'scale_parameter': False, 'relative_step': False}
              else:
                   SCREAMING_SNAKE_CASE					:			Any    	=       AdamW
                   SCREAMING_SNAKE_CASE					:			Any    	=       {
                       'betas': (self.args.adam_betaa, self.args.adam_betaa),
                       'eps': self.args.adam_epsilon,
                   }
              SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.args.learning_rate
              if self.sharded_ddp:
                   SCREAMING_SNAKE_CASE					:			Any    	=       OSS(
                       params=lowercase__    ,							optim=lowercase__    ,							**lowercase__    ,							)
              else:
                   SCREAMING_SNAKE_CASE					:			List[str]    	=       optimizer_cls(lowercase__    ,							**lowercase__ )

         if self.lr_scheduler is None:
              SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self._get_lr_scheduler(lowercase__ )
         else:  # ignoring --lr_scheduler
              logger.warning('scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.' )






    def       _UpperCamelCase    (       self    ,							lowercase__ )							-> int:
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       arg_to_scheduler[self.args.lr_scheduler]
         if self.args.lr_scheduler == "constant":
              SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       schedule_func(self.optimizer )
         elif self.args.lr_scheduler == "constant_w_warmup":
              SCREAMING_SNAKE_CASE					:			Tuple    	=       schedule_func(self.optimizer    ,							num_warmup_steps=self.args.warmup_steps )
         else:
              SCREAMING_SNAKE_CASE					:			int    	=       schedule_func(
                  self.optimizer    ,							num_warmup_steps=self.args.warmup_steps    ,							num_training_steps=lowercase__ )
         return scheduler






    def       _UpperCamelCase    (       self )							-> Optional[torch.utils.data.Sampler]:
         if isinstance(self.train_dataset    ,							torch.utils.data.IterableDataset ):
              return None
         elif is_torch_tpu_available():
              return get_tpu_sampler(self.train_dataset )
         else:
              if self.args.sortish_sampler:
                   self.train_dataset.make_sortish_sampler(
                       self.args.per_device_train_batch_size    ,							distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED)    ,							)

              return (
                  RandomSampler(self.train_dataset )
                  if self.args.local_rank == -1
                  else DistributedSampler(self.train_dataset )
              )






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__ )							-> Any:
         if self.args.label_smoothing == 0:
              if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
                   # force training to ignore pad token
                   SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       model(**lowercase__    ,							use_cache=lowercase__ )[0]
                   SCREAMING_SNAKE_CASE					:			int    	=       self.loss_fn(logits.view(-1    ,							logits.shape[-1] )    ,							labels.view(-1 ) )
              else:
                   # compute usual loss via models
                   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			int    	=       model(**lowercase__    ,							labels=lowercase__    ,							use_cache=lowercase__ )[:2]
         else:
              # compute label smoothed loss
              SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       model(**lowercase__    ,							use_cache=lowercase__ )[0]
              SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       torch.nn.functional.log_softmax(lowercase__    ,							dim=-1 )
              SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.loss_fn(lowercase__    ,							lowercase__    ,							self.args.label_smoothing    ,							ignore_index=self.config.pad_token_id )
         return loss, logits






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__ )							-> List[str]:
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       inputs.pop('labels' )
         SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			str    	=       self._compute_loss(lowercase__    ,							lowercase__    ,							lowercase__ )
         return loss






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ = None    ,							)							-> Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
         SCREAMING_SNAKE_CASE					:			str    	=       self._prepare_inputs(lowercase__ )

         SCREAMING_SNAKE_CASE					:			str    	=       {
             'max_length': self.data_args.val_max_target_length
             if self.data_args is not None
             else self.config.max_length,
             'num_beams': self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
         }

         if self.args.predict_with_generate and not self.args.prediction_loss_only:
              SCREAMING_SNAKE_CASE					:			Tuple    	=       self.model.generate(
                  inputs['input_ids']    ,							attention_mask=inputs['attention_mask']    ,							**lowercase__    ,							)
              # in case the batch is shorter than max length, the output should be padded
              if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
                   SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self._pad_tensors_to_max_len(lowercase__    ,							gen_kwargs['max_length'] )

         SCREAMING_SNAKE_CASE					:			Dict    	=       inputs.pop('labels' )
         with torch.no_grad():
              # compute loss on predict data
              SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			Any    	=       self._compute_loss(lowercase__    ,							lowercase__    ,							lowercase__ )

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       loss.mean().detach()
         if self.args.prediction_loss_only:
              return (loss, None, None)

         SCREAMING_SNAKE_CASE					:			int    	=       generated_tokens if self.args.predict_with_generate else logits

         if labels.shape[-1] < gen_kwargs["max_length"]:
              SCREAMING_SNAKE_CASE					:			Any    	=       self._pad_tensors_to_max_len(lowercase__    ,							gen_kwargs['max_length'] )

         return (loss, logits, labels)






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__ )							-> Tuple:
         # If PAD token is not defined at least EOS token has to be defined
         SCREAMING_SNAKE_CASE					:			List[str]    	=       self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id

         if pad_token_id is None:
              raise ValueError(
                  'Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be'
                  F""" padded to `max_length`={max_length}""" )

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       pad_token_id * torch.ones(
             (tensor.shape[0], max_length)    ,							dtype=tensor.dtype    ,							device=tensor.device )
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       tensor
         return padded_tensor




'''simple docstring'''





import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
    CenterCrop,
    Compose,
    Normalize,
    RandomHorizontalFlip,
    RandomResizedCrop,
    Resize,
    ToTensor,
)

import transformers
from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForImageClassification,
    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
_lowerCAmelCase  :Union[str, Any]  				=    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>=1.8.0""", """To fix: pip install -r examples/pytorch/image-classification/requirements.txt""")

_lowerCAmelCase  :List[str]  				=    list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
_lowerCAmelCase  :List[Any]  				=    tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)


def     __lowerCAmelCase						(       a_		)      ->	List[str]:





     '''simple docstring'''





     with open(a_       ,      'rb'		) as f:
          SCREAMING_SNAKE_CASE					:			List[str]    	=       Image.open(a_		)
          return im.convert('RGB'		)






@dataclass
class 				UpperCAmelCase :




    '''simple docstring'''







    snake_case__							:   Optional[str]							=						field(
        default=_SCREAMING_SNAKE_CASE   ,    metadata={
            "help": "Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub)."
        }   ,    )
    snake_case__							:   Optional[str]							=						field(
        default=_SCREAMING_SNAKE_CASE   ,    metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} )
    snake_case__							:   Optional[str]							=						field(default=_SCREAMING_SNAKE_CASE   ,    metadata={"help": "A folder containing the training data."} )
    snake_case__							:   Optional[str]							=						field(default=_SCREAMING_SNAKE_CASE   ,    metadata={"help": "A folder containing the validation data."} )
    snake_case__							:   Optional[float]							=						field(
        default=0.15   ,    metadata={"help": "Percent to split off of train for validation."} )
    snake_case__							:   Optional[int]							=						field(
        default=_SCREAMING_SNAKE_CASE   ,    metadata={
            "help": (
                "For debugging purposes or quicker training, truncate the number of training examples to this "
                "value if set."
            )
        }   ,    )
    snake_case__							:   Optional[int]							=						field(
        default=_SCREAMING_SNAKE_CASE   ,    metadata={
            "help": (
                "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
                "value if set."
            )
        }   ,    )






    def       _UpperCamelCase    (       self )							-> Optional[Any]:
         if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None):
              raise ValueError(
                  'You must specify either a dataset name from the hub or a train and/or validation directory.' )






@dataclass
class 				UpperCAmelCase :




    '''simple docstring'''







    snake_case__							:   str							=						field(
        default="google/vit-base-patch16-224-in21k"   ,    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}   ,    )
    snake_case__							:   Optional[str]							=						field(
        default=_SCREAMING_SNAKE_CASE   ,    metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(_SCREAMING_SNAKE_CASE )}   ,    )
    snake_case__							:   Optional[str]							=						field(
        default=_SCREAMING_SNAKE_CASE   ,    metadata={"help": "Pretrained config name or path if not the same as model_name"} )
    snake_case__							:   Optional[str]							=						field(
        default=_SCREAMING_SNAKE_CASE   ,    metadata={"help": "Where do you want to store the pretrained models downloaded from s3"} )
    snake_case__							:   str							=						field(
        default="main"   ,    metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}   ,    )
    snake_case__							:   str							=						field(default=_SCREAMING_SNAKE_CASE   ,    metadata={"help": "Name or path of preprocessor config."} )
    snake_case__							:   bool							=						field(
        default=_SCREAMING_SNAKE_CASE   ,    metadata={
            "help": (
                "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
                "with private models)."
            )
        }   ,    )
    snake_case__							:   bool							=						field(
        default=_SCREAMING_SNAKE_CASE   ,    metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."}   ,    )






def     __lowerCAmelCase						(       a_		)      ->	Tuple:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			int    	=       torch.stack([example['pixel_values'] for example in examples]		)
     SCREAMING_SNAKE_CASE					:			List[str]    	=       torch.tensor([example['labels'] for example in examples]		)
     return {"pixel_values": pixel_values, "labels": labels}


def     __lowerCAmelCase						(       )      ->	Optional[Any]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			List[Any]    	=       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.
          SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			List[Any]    	=       parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]		)		)
     else:
          SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			List[str]    	=       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_image_classification'       ,      a_       ,      a_		)

     # 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()

     SCREAMING_SNAKE_CASE					:			List[Any]    	=       training_args.get_process_log_level()
     logger.setLevel(a_		)
     transformers.utils.logging.set_verbosity(a_		)
     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}"""		)

     # Detecting last checkpoint.
     SCREAMING_SNAKE_CASE					:			List[str]    	=       None
     if os.path.isdir(training_args.output_dir		) and training_args.do_train and not training_args.overwrite_output_dir:
          SCREAMING_SNAKE_CASE					:			Union[str, 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 overcome.'		)
          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.'		)

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

     # Initialize our dataset and prepare it for the 'image-classification' task.
     if data_args.dataset_name is not None:
          SCREAMING_SNAKE_CASE					:			List[str]    	=       load_dataset(
              data_args.dataset_name       ,      data_args.dataset_config_name       ,      cache_dir=model_args.cache_dir       ,      task='image-classification'       ,      use_auth_token=True if model_args.use_auth_token else None       ,      )
     else:
          SCREAMING_SNAKE_CASE					:			Optional[int]    	=       {}
          if data_args.train_dir is not None:
               SCREAMING_SNAKE_CASE					:			Optional[int]    	=       os.path.join(data_args.train_dir       ,      '**'		)
          if data_args.validation_dir is not None:
               SCREAMING_SNAKE_CASE					:			int    	=       os.path.join(data_args.validation_dir       ,      '**'		)
          SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       load_dataset(
              'imagefolder'       ,      data_files=a_       ,      cache_dir=model_args.cache_dir       ,      task='image-classification'       ,      )

     # If we don't have a validation split, split off a percentage of train as validation.
     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       None if 'validation' in dataset.keys() else data_args.train_val_split
     if isinstance(data_args.train_val_split       ,      a_		) and data_args.train_val_split > 0.0:
          SCREAMING_SNAKE_CASE					:			Optional[int]    	=       dataset['train'].train_test_split(data_args.train_val_split		)
          SCREAMING_SNAKE_CASE					:			List[Any]    	=       split['train']
          SCREAMING_SNAKE_CASE					:			Tuple    	=       split['test']

     # Prepare label mappings.
     # We'll include these in the model's config to get human readable labels in the Inference API.
     SCREAMING_SNAKE_CASE					:			List[Any]    	=       dataset['train'].features['labels'].names
     SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			Dict    	=       {}, {}
     for i, label in enumerate(a_		):
          SCREAMING_SNAKE_CASE					:			List[str]    	=       str(a_		)
          SCREAMING_SNAKE_CASE					:			Dict    	=       label

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

     # Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
     # predictions and label_ids field) and has to return a dictionary string to float.
     def compute_metrics(a_		):
          return metric.compute(predictions=np.argmax(p.predictions       ,      axis=1		)       ,      references=p.label_ids		)

     SCREAMING_SNAKE_CASE					:			Tuple    	=       AutoConfig.from_pretrained(
         model_args.config_name or model_args.model_name_or_path       ,      num_labels=len(a_		)       ,      labelaid=a_       ,      idalabel=a_       ,      finetuning_task='image-classification'       ,      cache_dir=model_args.cache_dir       ,      revision=model_args.model_revision       ,      use_auth_token=True if model_args.use_auth_token else None       ,      )
     SCREAMING_SNAKE_CASE					:			str    	=       AutoModelForImageClassification.from_pretrained(
         model_args.model_name_or_path       ,      from_tf=bool('.ckpt' in model_args.model_name_or_path		)       ,      config=a_       ,      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       ,      )
     SCREAMING_SNAKE_CASE					:			List[str]    	=       AutoImageProcessor.from_pretrained(
         model_args.image_processor_name or model_args.model_name_or_path       ,      cache_dir=model_args.cache_dir       ,      revision=model_args.model_revision       ,      use_auth_token=True if model_args.use_auth_token else None       ,      )

     # Define torchvision transforms to be applied to each image.
     if "shortest_edge" in image_processor.size:
          SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       image_processor.size['shortest_edge']
     else:
          SCREAMING_SNAKE_CASE					:			Optional[int]    	=       (image_processor.size['height'], image_processor.size['width'])
     SCREAMING_SNAKE_CASE					:			Any    	=       Normalize(mean=image_processor.image_mean       ,      std=image_processor.image_std		)
     SCREAMING_SNAKE_CASE					:			List[Any]    	=       Compose(
         [
             RandomResizedCrop(a_		),
             RandomHorizontalFlip(),
             ToTensor(),
             normalize,
         ]		)
     SCREAMING_SNAKE_CASE					:			Dict    	=       Compose(
         [
             Resize(a_		),
             CenterCrop(a_		),
             ToTensor(),
             normalize,
         ]		)

     def train_transforms(a_		):
          SCREAMING_SNAKE_CASE					:			int    	=       [
              _train_transforms(pil_img.convert('RGB'		)		) for pil_img in example_batch['image']
          ]
          return example_batch

     def val_transforms(a_		):
          SCREAMING_SNAKE_CASE					:			Any    	=       [_val_transforms(pil_img.convert('RGB'		)		) for pil_img in example_batch['image']]
          return example_batch

     if training_args.do_train:
          if "train" not in dataset:
               raise ValueError('--do_train requires a train dataset'		)
          if data_args.max_train_samples is not None:
               SCREAMING_SNAKE_CASE					:			Optional[int]    	=       (
                   dataset['train'].shuffle(seed=training_args.seed		).select(range(data_args.max_train_samples		)		)
               )
          # Set the training transforms
          dataset["train"].set_transform(a_		)

     if training_args.do_eval:
          if "validation" not in dataset:
               raise ValueError('--do_eval requires a validation dataset'		)
          if data_args.max_eval_samples is not None:
               SCREAMING_SNAKE_CASE					:			int    	=       (
                   dataset['validation'].shuffle(seed=training_args.seed		).select(range(data_args.max_eval_samples		)		)
               )
          # Set the validation transforms
          dataset["validation"].set_transform(a_		)

     # Initalize our trainer
     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       Trainer(
         model=a_       ,      args=a_       ,      train_dataset=dataset['train'] if training_args.do_train else None       ,      eval_dataset=dataset['validation'] if training_args.do_eval else None       ,      compute_metrics=a_       ,      tokenizer=a_       ,      data_collator=a_       ,      )

     # Training
     if training_args.do_train:
          SCREAMING_SNAKE_CASE					:			int    	=       None
          if training_args.resume_from_checkpoint is not None:
               SCREAMING_SNAKE_CASE					:			Dict    	=       training_args.resume_from_checkpoint
          elif last_checkpoint is not None:
               SCREAMING_SNAKE_CASE					:			Tuple    	=       last_checkpoint
          SCREAMING_SNAKE_CASE					:			Optional[int]    	=       trainer.train(resume_from_checkpoint=a_		)
          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:
          SCREAMING_SNAKE_CASE					:			List[Any]    	=       trainer.evaluate()
          trainer.log_metrics('eval'       ,      a_		)
          trainer.save_metrics('eval'       ,      a_		)

     # Write model card and (optionally) push to hub
     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       {
         'finetuned_from': model_args.model_name_or_path,
         'tasks': 'image-classification',
         'dataset': data_args.dataset_name,
         'tags': ['image-classification', 'vision'],
     }
     if training_args.push_to_hub:
          trainer.push_to_hub(**a_		)
     else:
          trainer.create_model_card(**a_		)


if __name__ == "__main__":
 main()



