





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	:					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""")

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



def 		A_    (					_lowerCAmelCase					)						->			int:
 with open(_lowerCAmelCase							,       "rb"					) as f:
  UpperCamelCase     : Tuple	     =       Image.open(_lowerCAmelCase					)
  return im.convert("RGB"					)



@dataclass
class  A__       :
      _UpperCAmelCase     :Optional[str]						   =       field(
          default=__snake_case	,       metadata={
              'help': 'Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub).'
          }	,       )
      _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 folder containing the training data.'}      )
      _UpperCAmelCase     :Optional[str]						   =       field(default=__snake_case	,       metadata={'help': 'A folder containing the validation data.'}      )
      _UpperCAmelCase     :Optional[float]						   =       field(
          default=0.15	,       metadata={'help': 'Percent to split off of train for validation.'}      )
      _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.'
              )
          }	,       )





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       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  A__       :
      _UpperCAmelCase     :str						   =       field(
          default='google/vit-base-patch16-224-in21k'	,       metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}	,       )
      _UpperCAmelCase     :Optional[str]						   =       field(
          default=__snake_case	,       metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(__snake_case      )}	,       )
      _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 s3'}      )
      _UpperCAmelCase     :str						   =       field(
          default='main'	,       metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}	,       )
      _UpperCAmelCase     :str						   =       field(default=__snake_case	,       metadata={'help': 'Name or path of preprocessor config.'}      )
      _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     :bool						   =       field(
          default=__snake_case	,       metadata={'help': 'Will enable to load a pretrained model whose head dimensions are different.'}	,       )



def 		A_    (					_lowerCAmelCase					)						->			Tuple:
 UpperCamelCase     : Union[str, Any]	     =       torch.stack([example["pixel_values"] for example in examples]					)
 UpperCamelCase     : Optional[Any]	     =       torch.tensor([example["labels"] for example in examples]					)
 return {"pixel_values": pixel_values, "labels": labels}



def 		A_    (					)						->			Optional[int]:
 # See all possible arguments in src/transformers/training_args.py
 # or by passing the --help flag to this script.
 # We now keep distinct sets of args, for a cleaner separation of concerns.

 UpperCamelCase     : int	     =       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     : Optional[int]	     =       parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
 else:
  UpperCamelCase     ,   UpperCamelCase     ,   UpperCamelCase     : Optional[int]	     =       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"							,       _lowerCAmelCase							,       _lowerCAmelCase					)

 # 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     : Any	     =       training_args.get_process_log_level()
 logger.setLevel(_lowerCAmelCase					)
 transformers.utils.logging.set_verbosity(_lowerCAmelCase					)
 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     : Optional[Any]	     =       None
 if os.path.isdir(training_args.output_dir					) and training_args.do_train and not training_args.overwrite_output_dir:
  UpperCamelCase     : Dict	     =       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     : Tuple	     =       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     : Any	     =       {}
  if data_args.train_dir is not None:
   UpperCamelCase     : Union[str, Any]	     =       os.path.join(data_args.train_dir							,       "**"					)
  if data_args.validation_dir is not None:
   UpperCamelCase     : Dict	     =       os.path.join(data_args.validation_dir							,       "**"					)
  UpperCamelCase     : Union[str, Any]	     =       load_dataset(
      "imagefolder"							,       data_files=_lowerCAmelCase							,       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     : int	     =       None if "validation" in dataset.keys() else data_args.train_val_split
 if isinstance(data_args.train_val_split							,       _lowerCAmelCase					) and data_args.train_val_split > 0.0:
  UpperCamelCase     : Tuple	     =       dataset["train"].train_test_split(data_args.train_val_split					)
  UpperCamelCase     : str	     =       split["train"]
  UpperCamelCase     : str	     =       split["test"]

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

 # Load the accuracy metric from the datasets package
 UpperCamelCase     : str	     =       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(_lowerCAmelCase					):
  return metric.compute(predictions=np.argmax(p.predictions							,       axis=1					)							,       references=p.label_ids					)

 UpperCamelCase     : Tuple	     =       AutoConfig.from_pretrained(
     model_args.config_name or model_args.model_name_or_path							,       num_labels=len(_lowerCAmelCase					)							,       labelaid=_lowerCAmelCase							,       idalabel=_lowerCAmelCase							,       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     : Optional[int]	     =       AutoModelForImageClassification.from_pretrained(
     model_args.model_name_or_path							,       from_tf=bool(".ckpt" in model_args.model_name_or_path					)							,       config=_lowerCAmelCase							,       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     : Optional[int]	     =       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     : Optional[Any]	     =       image_processor.size["shortest_edge"]
 else:
  UpperCamelCase     : Optional[Any]	     =       (image_processor.size["height"], image_processor.size["width"])
 UpperCamelCase     : Tuple	     =       Normalize(mean=image_processor.image_mean							,       std=image_processor.image_std					)
 UpperCamelCase     : int	     =       Compose(
     [
         RandomResizedCrop(_lowerCAmelCase					),
         RandomHorizontalFlip(),
         ToTensor(),
         normalize,
     ]					)
 UpperCamelCase     : Tuple	     =       Compose(
     [
         Resize(_lowerCAmelCase					),
         CenterCrop(_lowerCAmelCase					),
         ToTensor(),
         normalize,
     ]					)

 def train_transforms(_lowerCAmelCase					):
  UpperCamelCase     : Union[str, Any]	     =       [
      _train_transforms(pil_img.convert("RGB"					)					) for pil_img in example_batch["image"]
  ]
  return example_batch

 def val_transforms(_lowerCAmelCase					):
  UpperCamelCase     : List[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:
   UpperCamelCase     : List[Any]	     =       (
       dataset["train"].shuffle(seed=training_args.seed					).select(range(data_args.max_train_samples					)					)
   )
  # Set the training transforms
  dataset["train"].set_transform(_lowerCAmelCase					)

 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     : Optional[Any]	     =       (
       dataset["validation"].shuffle(seed=training_args.seed					).select(range(data_args.max_eval_samples					)					)
   )
  # Set the validation transforms
  dataset["validation"].set_transform(_lowerCAmelCase					)

 # Initalize our trainer
 UpperCamelCase     : List[str]	     =       Trainer(
     model=_lowerCAmelCase							,       args=_lowerCAmelCase							,       train_dataset=dataset["train"] if training_args.do_train else None							,       eval_dataset=dataset["validation"] if training_args.do_eval else None							,       compute_metrics=_lowerCAmelCase							,       tokenizer=_lowerCAmelCase							,       data_collator=_lowerCAmelCase							,       )

 # Training
 if training_args.do_train:
  UpperCamelCase     : List[str]	     =       None
  if training_args.resume_from_checkpoint is not None:
   UpperCamelCase     : List[str]	     =       training_args.resume_from_checkpoint
  elif last_checkpoint is not None:
   UpperCamelCase     : Any	     =       last_checkpoint
  UpperCamelCase     : List[Any]	     =       trainer.train(resume_from_checkpoint=_lowerCAmelCase					)
  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     : List[str]	     =       trainer.evaluate()
  trainer.log_metrics("eval"							,       _lowerCAmelCase					)
  trainer.save_metrics("eval"							,       _lowerCAmelCase					)

 # Write model card and (optionally) push to hub
 UpperCamelCase     : Union[str, Any]	     =       {
     "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(**_lowerCAmelCase					)
 else:
  trainer.create_model_card(**_lowerCAmelCase					)


if __name__ == "__main__":
    main()







__lowerCamelCase	:					Tuple			  =					"""ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"""



def 		A_    (					_lowerCAmelCase					)						->			bytes:
 # Make sure the supplied data is a bytes-like object
 if not isinstance(_lowerCAmelCase							,       _lowerCAmelCase					):
  UpperCamelCase     : str	     =       F"""a bytes-like object is required, not '{data.__class__.__name__}'"""
  raise TypeError(_lowerCAmelCase					)

 UpperCamelCase     : Optional[int]	     =       "".join(bin(_lowerCAmelCase					)[2:].zfill(8					) for byte in data					)

 UpperCamelCase     : str	     =       len(_lowerCAmelCase					) % 6 != 0

 if padding_needed:
  # The padding that will be added later
  UpperCamelCase     : Optional[Any]	     =       b"=" * ((6 - len(_lowerCAmelCase					) % 6) // 2)

  # Append binary_stream with arbitrary binary digits (0's by default) to make its
  # length a multiple of 6.
  binary_stream += "0" * (6 - len(_lowerCAmelCase					) % 6)
 else:
  UpperCamelCase     : List[Any]	     =       b""

 # Encode every 6 binary digits to their corresponding Base64 character
 return (
     "".join(
         B64_CHARSET[int(binary_stream[index : index + 6]							,       2					)]
         for index in range(0							,       len(_lowerCAmelCase					)							,       6					)					).encode()
     + padding
 )



def 		A_    (					_lowerCAmelCase					)						->			bytes:
 # Make sure encoded_data is either a string or a bytes-like object
 if not isinstance(_lowerCAmelCase							,       _lowerCAmelCase					) and not isinstance(_lowerCAmelCase							,       _lowerCAmelCase					):
  UpperCamelCase     : List[Any]	     =       (
      "argument should be a bytes-like object or ASCII string, "
      F"""not '{encoded_data.__class__.__name__}'"""
  )
  raise TypeError(_lowerCAmelCase					)

 # In case encoded_data is a bytes-like object, make sure it contains only
 # ASCII characters so we convert it to a string object
 if isinstance(_lowerCAmelCase							,       _lowerCAmelCase					):
  try:
   UpperCamelCase     : Any	     =       encoded_data.decode("utf-8"					)
  except UnicodeDecodeError:
   raise ValueError("base64 encoded data should only contain ASCII characters"					)

 UpperCamelCase     : Union[str, Any]	     =       encoded_data.count("="					)

 # Check if the encoded string contains non base64 characters
 if padding:
  assert all(
      char in B64_CHARSET for char in encoded_data[:-padding]					), "Invalid base64 character(s) found."
 else:
  assert all(
      char in B64_CHARSET for char in encoded_data					), "Invalid base64 character(s) found."

 # Check the padding
 assert len(_lowerCAmelCase					) % 4 == 0 and padding < 3, "Incorrect padding"

 if padding:
  # Remove padding if there is one
  UpperCamelCase     : List[str]	     =       encoded_data[:-padding]

  UpperCamelCase     : List[Any]	     =       "".join(
      bin(B64_CHARSET.index(_lowerCAmelCase					)					)[2:].zfill(6					) for char in encoded_data					)[: -padding * 2]
 else:
  UpperCamelCase     : List[Any]	     =       "".join(
      bin(B64_CHARSET.index(_lowerCAmelCase					)					)[2:].zfill(6					) for char in encoded_data					)

 UpperCamelCase     : Any	     =       [
     int(binary_stream[index : index + 8]							,       2					)
     for index in range(0							,       len(_lowerCAmelCase					)							,       8					)
 ]

 return bytes(_lowerCAmelCase					)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
