





'''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
a__							:							List[str] =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''')

a__							:							Union[str, Any] =list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
a__							:							Optional[Any] =tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
def 				lowercase__   (   __lowercase						:  str     ) ->      int:



       """simple docstring"""






       with open(__lowercase				,  'rb'     ) as f:
              __UpperCamelCase       =  Image.open(__lowercase     )
              return im.convert('RGB'     )

@dataclass
class      snake_case     :




 """simple docstring"""



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






 def        _lowerCamelCase					(       self       :							Tuple	):
        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      snake_case     :




 """simple docstring"""



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






def 				lowercase__   (   __lowercase						:  Any     ) ->      str:



       """simple docstring"""






       __UpperCamelCase       =  torch.stack([example['pixel_values'] for example in examples]     )
       __UpperCamelCase       =  torch.tensor([example['labels'] for example in examples]     )
       return {"pixel_values": pixel_values, "labels": labels}
def 				lowercase__   (   ) ->      Optional[int]:



       """simple docstring"""






       __UpperCamelCase       =  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.
              __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       =  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]     )     )
       else:
              __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       =  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'				,  __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()

       __UpperCamelCase       =  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}'''     )

       # Detecting last checkpoint.
       __UpperCamelCase       =  None
       if os.path.isdir(training_args.output_dir     ) and training_args.do_train and not training_args.overwrite_output_dir:
              __UpperCamelCase       =  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:
              __UpperCamelCase       =  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:
              __UpperCamelCase       =  {}
              if data_args.train_dir is not None:
                     __UpperCamelCase       =  os.path.join(data_args.train_dir				,  '**'     )
              if data_args.validation_dir is not None:
                     __UpperCamelCase       =  os.path.join(data_args.validation_dir				,  '**'     )
              __UpperCamelCase       =  load_dataset(
                  'imagefolder'				,  data_files=__lowercase				,  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.
       __UpperCamelCase       =  None if 'validation' in dataset.keys() else data_args.train_val_split
       if isinstance(data_args.train_val_split				,  __lowercase     ) and data_args.train_val_split > 0.0:
              __UpperCamelCase       =  dataset['train'].train_test_split(data_args.train_val_split     )
              __UpperCamelCase       =  split['train']
              __UpperCamelCase       =  split['test']

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

       # Load the accuracy metric from the datasets package
       __UpperCamelCase       =  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(__lowercase						:  Optional[int]     ):
              return metric.compute(predictions=np.argmax(p.predictions				,  axis=1     )				,  references=p.label_ids     )

       __UpperCamelCase       =  AutoConfig.from_pretrained(
           model_args.config_name or model_args.model_name_or_path				,  num_labels=len(__lowercase     )				,  labelaid=__lowercase				,  idalabel=__lowercase				,  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				,  )
       __UpperCamelCase       =  AutoModelForImageClassification.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				,  )
       __UpperCamelCase       =  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:
              __UpperCamelCase       =  image_processor.size['shortest_edge']
       else:
              __UpperCamelCase       =  (image_processor.size['height'], image_processor.size['width'])
       __UpperCamelCase       =  Normalize(mean=image_processor.image_mean				,  std=image_processor.image_std     )
       __UpperCamelCase       =  Compose(
           [
               RandomResizedCrop(__lowercase     ),
               RandomHorizontalFlip(),
               ToTensor(),
               normalize,
           ]     )
       __UpperCamelCase       =  Compose(
           [
               Resize(__lowercase     ),
               CenterCrop(__lowercase     ),
               ToTensor(),
               normalize,
           ]     )

       def train_transforms(__lowercase						:  List[str]     ):
              __UpperCamelCase       =  [
                  _train_transforms(pil_img.convert('RGB'     )     ) for pil_img in example_batch['image']
              ]
              return example_batch

       def val_transforms(__lowercase						:  Union[str, Any]     ):
              __UpperCamelCase       =  [_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:
                     __UpperCamelCase       =  (
                         dataset['train'].shuffle(seed=training_args.seed     ).select(range(data_args.max_train_samples     )     )
                     )
              # Set the training transforms
              dataset["train"].set_transform(__lowercase     )

       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:
                     __UpperCamelCase       =  (
                         dataset['validation'].shuffle(seed=training_args.seed     ).select(range(data_args.max_eval_samples     )     )
                     )
              # Set the validation transforms
              dataset["validation"].set_transform(__lowercase     )

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

       # Training
       if training_args.do_train:
              __UpperCamelCase       =  None
              if training_args.resume_from_checkpoint is not None:
                     __UpperCamelCase       =  training_args.resume_from_checkpoint
              elif last_checkpoint is not None:
                     __UpperCamelCase       =  last_checkpoint
              __UpperCamelCase       =  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:
              __UpperCamelCase       =  trainer.evaluate()
              trainer.log_metrics('eval'				,  __lowercase     )
              trainer.save_metrics('eval'				,  __lowercase     )

       # Write model card and (optionally) push to hub
       __UpperCamelCase       =  {
           '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(**__lowercase     )
       else:
              trainer.create_model_card(**__lowercase     )


if __name__ == "__main__":
    main()







'''simple docstring'''
import os
from typing import BinaryIO, Optional, Union

import numpy as np
import pyarrow.parquet as pq

from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
def 				lowercase__   (   __lowercase						:  Features     ) ->      Optional[int]:



       """simple docstring"""






       __UpperCamelCase       =  np.inf

       def set_batch_size(__lowercase						:  FeatureType     ) -> None:
              nonlocal batch_size
              if isinstance(__lowercase				,  __lowercase     ):
                     __UpperCamelCase       =  min(__lowercase				,  config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS     )
              elif isinstance(__lowercase				,  __lowercase     ):
                     __UpperCamelCase       =  min(__lowercase				,  config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS     )
              elif isinstance(__lowercase				,  __lowercase     ) and feature.dtype == "binary":
                     __UpperCamelCase       =  min(__lowercase				,  config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS     )

       _visit(__lowercase				,  __lowercase     )

       return None if batch_size is np.inf else batch_size

class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 def __init__(       self       :							List[str]							,	__A       :							NestedDataStructureLike[PathLike]							,	__A       :							Optional[NamedSplit] = None							,	__A       :							Optional[Features] = None							,	__A       :							str = None							,	__A       :							bool = False							,	__A       :							bool = False							,	__A       :							Optional[int] = None							,	**__A       :							Dict							,	):
        super().__init__(
            __A							,	split=__A							,	features=__A							,	cache_dir=__A							,	keep_in_memory=__A							,	streaming=__A							,	num_proc=__A							,	**__A							,	)
        __UpperCamelCase       =  path_or_paths if isinstance(__A							,	__A	) else {self.split: path_or_paths}
        __UpperCamelCase       =  _PACKAGED_DATASETS_MODULES['parquet'][1]
        __UpperCamelCase       =  Parquet(
            cache_dir=__A							,	data_files=__A							,	features=__A							,	hash=__A							,	**__A							,	)






 def        _lowerCamelCase					(       self       :							Optional[int]	):
        # Build iterable dataset
        if self.streaming:
               __UpperCamelCase       =  self.builder.as_streaming_dataset(split=self.split	)
        # Build regular (map-style) dataset
        else:
               __UpperCamelCase       =  None
               __UpperCamelCase       =  None
               __UpperCamelCase       =  None
               __UpperCamelCase       =  None

               self.builder.download_and_prepare(
                   download_config=__A							,	download_mode=__A							,	verification_mode=__A							,	base_path=__A							,	num_proc=self.num_proc							,	)
               __UpperCamelCase       =  self.builder.as_dataset(
                   split=self.split							,	verification_mode=__A							,	in_memory=self.keep_in_memory	)
        return dataset






class      snake_case     :




 """simple docstring"""



 def __init__(       self       :							List[str]							,	__A       :							Dataset							,	__A       :							Union[PathLike, BinaryIO]							,	__A       :							Optional[int] = None							,	**__A       :							Dict							,	):
        __UpperCamelCase       =  dataset
        __UpperCamelCase       =  path_or_buf
        __UpperCamelCase       =  batch_size or get_writer_batch_size(dataset.features	)
        __UpperCamelCase       =  parquet_writer_kwargs






 def        _lowerCamelCase					(       self       :							Optional[int]	):
        __UpperCamelCase       =  self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE

        if isinstance(self.path_or_buf							,	(str, bytes, os.PathLike)	):
               with open(self.path_or_buf							,	'wb+'	) as buffer:
                      __UpperCamelCase       =  self._write(file_obj=__A							,	batch_size=__A							,	**self.parquet_writer_kwargs	)
        else:
               __UpperCamelCase       =  self._write(file_obj=self.path_or_buf							,	batch_size=__A							,	**self.parquet_writer_kwargs	)
        return written






 def        _lowerCamelCase					(       self       :							List[str]							,	__A       :							BinaryIO							,	__A       :							int							,	**__A       :							List[str]	):
        __UpperCamelCase       =  0
        __UpperCamelCase       =  parquet_writer_kwargs.pop('path_or_buf'							,	__A	)
        __UpperCamelCase       =  self.dataset.features.arrow_schema

        __UpperCamelCase       =  pq.ParquetWriter(__A							,	schema=__A							,	**__A	)

        for offset in logging.tqdm(
            range(0							,	len(self.dataset	)							,	__A	)							,	unit='ba'							,	disable=not logging.is_progress_bar_enabled()							,	desc='Creating parquet from Arrow format'							,	):
               __UpperCamelCase       =  query_table(
                   table=self.dataset._data							,	key=slice(__A							,	offset + batch_size	)							,	indices=self.dataset._indices if self.dataset._indices is not None else None							,	)
               writer.write_table(__A	)
               written += batch.nbytes
        writer.close()
        return written
