



'''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_							: Optional[int]						 =	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_							: Tuple						 =	list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
A_							: Optional[Any]						 =	tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)




def     snake_case_					(     lowerCAmelCase_		)->		str:





       '''simple docstring'''


       with open(lowerCAmelCase_       ,   """rb"""		) as f:
              _UpperCAmelCase  :						int			     = Image.open(lowerCAmelCase_		)
              return im.convert("""RGB"""		)



@dataclass
class 							lowercase       :


 """simple docstring"""


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







 def _snake_case (							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 							lowercase       :


 """simple docstring"""


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







def     snake_case_					(     lowerCAmelCase_		)->		List[Any]:





       '''simple docstring'''


       _UpperCAmelCase  :						Any			     = torch.stack([example["""pixel_values"""] for example in examples]		)
       _UpperCAmelCase  :						Optional[int]			     = torch.tensor([example["""labels"""] for example in examples]		)
       return {"pixel_values": pixel_values, "labels": labels}




def     snake_case_					(     )->		Tuple:





       '''simple docstring'''


       _UpperCAmelCase  :						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.
              _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase  :						Dict			     = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]		)		)
       else:
              _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase  :						Union[str, 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_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  :						Dict			     = 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[int]			     = None
       if os.path.isdir(training_args.output_dir		) and training_args.do_train and not training_args.overwrite_output_dir:
              _UpperCAmelCase  :						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:
              _UpperCAmelCase  :						int			     = 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  :						Optional[int]			     = {}
              if data_args.train_dir is not None:
                     _UpperCAmelCase  :						Optional[int]			     = os.path.join(data_args.train_dir       ,   """**"""		)
              if data_args.validation_dir is not None:
                     _UpperCAmelCase  :						Optional[Any]			     = os.path.join(data_args.validation_dir       ,   """**"""		)
              _UpperCAmelCase  :						Tuple			     = 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  :						Tuple			     = 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  :						Union[str, Any]			     = dataset["""train"""].train_test_split(data_args.train_val_split		)
              _UpperCAmelCase  :						str			     = split["""train"""]
              _UpperCAmelCase  :						Tuple			     = split["""test"""]

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

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

       _UpperCAmelCase  :						Dict			     = 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  :						str			     = 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  :						Tuple			     = 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  :						List[Any]			     = image_processor.size["""shortest_edge"""]
       else:
              _UpperCAmelCase  :						Union[str, Any]			     = (image_processor.size["""height"""], image_processor.size["""width"""])
       _UpperCAmelCase  :						str			     = Normalize(mean=image_processor.image_mean       ,   std=image_processor.image_std		)
       _UpperCAmelCase  :						Dict			     = Compose(
           [
               RandomResizedCrop(lowerCAmelCase_		),
               RandomHorizontalFlip(),
               ToTensor(),
               normalize,
           ]		)
       _UpperCAmelCase  :						Tuple			     = Compose(
           [
               Resize(lowerCAmelCase_		),
               CenterCrop(lowerCAmelCase_		),
               ToTensor(),
               normalize,
           ]		)

       def train_transforms(lowerCAmelCase_		):
              _UpperCAmelCase  :						str			     = [
                  _train_transforms(pil_img.convert("""RGB"""		)		) for pil_img in example_batch["""image"""]
              ]
              return example_batch

       def val_transforms(lowerCAmelCase_		):
              _UpperCAmelCase  :						Optional[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  :						int			     = (
                         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  :						Dict			     = (
                         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  :						Optional[int]			     = 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  :						Any			     = None
              if training_args.resume_from_checkpoint is not None:
                     _UpperCAmelCase  :						Dict			     = training_args.resume_from_checkpoint
              elif last_checkpoint is not None:
                     _UpperCAmelCase  :						Dict			     = last_checkpoint
              _UpperCAmelCase  :						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  :						int			     = trainer.evaluate()
              trainer.log_metrics("""eval"""       ,   lowerCAmelCase_		)
              trainer.save_metrics("""eval"""       ,   lowerCAmelCase_		)

       # Write model card and (optionally) push to hub
       _UpperCAmelCase  :						str			     = {
           """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()








'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A_							: Optional[int]						 =	{"""configuration_ibert""": ["""IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """IBertConfig""", """IBertOnnxConfig"""]}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A_							: str						 =	[
       """IBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """IBertForMaskedLM""",
       """IBertForMultipleChoice""",
       """IBertForQuestionAnswering""",
       """IBertForSequenceClassification""",
       """IBertForTokenClassification""",
       """IBertModel""",
       """IBertPreTrainedModel""",
   ]

if TYPE_CHECKING:
   from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_ibert import (
          IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          IBertForMaskedLM,
          IBertForMultipleChoice,
          IBertForQuestionAnswering,
          IBertForSequenceClassification,
          IBertForTokenClassification,
          IBertModel,
          IBertPreTrainedModel,
      )

else:
   import sys

   A_							: str						 =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



