
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
lowercase__       =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')

lowercase__       =list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
lowercase__       =tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)




def    __UpperCamelCase     (				lowerCAmelCase__							:				str  ):
 with open(lowerCAmelCase__  ,   '''rb'''  ) as f:
  __a :  Optional[int]						=  Image.open(lowerCAmelCase__  )
  return im.convert('''RGB'''  )



@dataclass
class 		UpperCamelCase__  :
   _SCREAMING_SNAKE_CASE      :							Optional[str]	       =      field(
       default=__lowercase    ,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=__lowercase    ,metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}    )
   _SCREAMING_SNAKE_CASE      :							Optional[str]	       =      field(default=__lowercase    ,metadata={"help": "A folder containing the training data."}    )
   _SCREAMING_SNAKE_CASE      :							Optional[str]	       =      field(default=__lowercase    ,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=__lowercase    ,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=__lowercase    ,metadata={
           "help": (
               "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
               "value if set."
           )
       }    ,)
   def       lowerCAmelCase			(self :					Optional[int]   ):
    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__  :
   _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=__lowercase    ,metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(__lowercase    )}    ,)
   _SCREAMING_SNAKE_CASE      :							Optional[str]	       =      field(
       default=__lowercase    ,metadata={"help": "Pretrained config name or path if not the same as model_name"}    )
   _SCREAMING_SNAKE_CASE      :							Optional[str]	       =      field(
       default=__lowercase    ,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=__lowercase    ,metadata={"help": "Name or path of preprocessor config."}    )
   _SCREAMING_SNAKE_CASE      :							bool	       =      field(
       default=__lowercase    ,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=__lowercase    ,metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."}    ,)





def    __UpperCamelCase     (				lowerCAmelCase__							:				Tuple  ):
 __a :  Tuple						=  torch.stack([example['''pixel_values'''] for example in examples]  )
 __a :  Union[str, Any]						=  torch.tensor([example['''labels'''] for example in examples]  )
 return {"pixel_values": pixel_values, "labels": labels}




def    __UpperCamelCase     (				):
 # 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.

 __a :  List[str]						=  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.
  __a				,       __a				,       __a :  Tuple						=  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]  )  )
 else:
  __a				,       __a				,       __a :  Tuple						=  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()

 __a :  str						=  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.
 __a :  Tuple						=  None
 if os.path.isdir(training_args.output_dir  ) and training_args.do_train and not training_args.overwrite_output_dir:
  __a :  Tuple						=  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:
  __a :  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:
  __a :  List[Any]						=  {}
  if data_args.train_dir is not None:
   __a :  str						=  os.path.join(data_args.train_dir  ,   '''**'''  )
  if data_args.validation_dir is not None:
   __a :  Dict						=  os.path.join(data_args.validation_dir  ,   '''**'''  )
  __a :  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.
 __a :  str						=  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:
  __a :  Dict						=  dataset['''train'''].train_test_split(data_args.train_val_split  )
  __a :  Optional[Any]						=  split['''train''']
  __a :  List[Any]						=  split['''test''']

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

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

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

 def train_transforms(lowerCAmelCase__							:				Dict  ):
  __a :  List[Any]						=  [
      _train_transforms(pil_img.convert('''RGB'''  )  ) for pil_img in example_batch['''image''']
  ]
  return example_batch

 def val_transforms(lowerCAmelCase__							:				List[str]  ):
  __a :  Dict						=  [_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:
   __a :  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:
   __a :  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
 __a :  Union[str, Any]						=  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:
  __a :  List[str]						=  None
  if training_args.resume_from_checkpoint is not None:
   __a :  List[str]						=  training_args.resume_from_checkpoint
  elif last_checkpoint is not None:
   __a :  List[Any]						=  last_checkpoint
  __a :  int						=  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:
  __a :  Optional[Any]						=  trainer.evaluate()
  trainer.log_metrics('''eval'''  ,   lowerCAmelCase__  )
  trainer.save_metrics('''eval'''  ,   lowerCAmelCase__  )

 # Write model card and (optionally) push to hub
 __a :  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()


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase__       ={
    'configuration_roformer': ['ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoFormerConfig', 'RoFormerOnnxConfig'],
    'tokenization_roformer': ['RoFormerTokenizer'],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__       =['RoFormerTokenizerFast']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__       =[
          'ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'RoFormerForCausalLM',
          'RoFormerForMaskedLM',
          'RoFormerForMultipleChoice',
          'RoFormerForQuestionAnswering',
          'RoFormerForSequenceClassification',
          'RoFormerForTokenClassification',
          'RoFormerLayer',
          'RoFormerModel',
          'RoFormerPreTrainedModel',
          'load_tf_weights_in_roformer',
      ]


try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__       =[
          'TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'TFRoFormerForCausalLM',
          'TFRoFormerForMaskedLM',
          'TFRoFormerForMultipleChoice',
          'TFRoFormerForQuestionAnswering',
          'TFRoFormerForSequenceClassification',
          'TFRoFormerForTokenClassification',
          'TFRoFormerLayer',
          'TFRoFormerModel',
          'TFRoFormerPreTrainedModel',
      ]


try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__       =[
          'FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'FlaxRoFormerForMaskedLM',
          'FlaxRoFormerForMultipleChoice',
          'FlaxRoFormerForQuestionAnswering',
          'FlaxRoFormerForSequenceClassification',
          'FlaxRoFormerForTokenClassification',
          'FlaxRoFormerModel',
          'FlaxRoFormerPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
      from .tokenization_roformer import RoFormerTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_roformer_fast import RoFormerTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_roformer import (
                ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                RoFormerForCausalLM,
                RoFormerForMaskedLM,
                RoFormerForMultipleChoice,
                RoFormerForQuestionAnswering,
                RoFormerForSequenceClassification,
                RoFormerForTokenClassification,
                RoFormerLayer,
                RoFormerModel,
                RoFormerPreTrainedModel,
                load_tf_weights_in_roformer,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_roformer import (
                TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRoFormerForCausalLM,
                TFRoFormerForMaskedLM,
                TFRoFormerForMultipleChoice,
                TFRoFormerForQuestionAnswering,
                TFRoFormerForSequenceClassification,
                TFRoFormerForTokenClassification,
                TFRoFormerLayer,
                TFRoFormerModel,
                TFRoFormerPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_roformer import (
                FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                FlaxRoFormerForMaskedLM,
                FlaxRoFormerForMultipleChoice,
                FlaxRoFormerForQuestionAnswering,
                FlaxRoFormerForSequenceClassification,
                FlaxRoFormerForTokenClassification,
                FlaxRoFormerModel,
                FlaxRoFormerPreTrainedModel,
            )


else:
      import sys

      lowercase__       =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
