

'''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						    =      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(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
__lowerCamelCase						    =      tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     Optional[Any]:
       with open(UpperCAmelCase__,    """rb"""			) as f:
              A_		   = Image.open(UpperCAmelCase__			)
              return im.convert("""RGB"""			)


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



    def    snake_case_     (   self    )  -> List[str]:





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

def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     int:
       A_		   = torch.stack([example["""pixel_values"""] for example in examples]			)
       A_		   = torch.tensor([example["""labels"""] for example in examples]			)
       return {"pixel_values": pixel_values, "labels": labels}
def  UpperCAmelCase__      ( )     ->     List[Any]:
       # 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_		   = 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_		   = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
       else:
              A_   ,     A_   ,     A_		   = 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""",    UpperCAmelCase__,    UpperCAmelCase__			)

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

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

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

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

       def train_transforms(UpperCAmelCase__			):
              A_		   = [
                  _train_transforms(pil_img.convert("""RGB"""			)			) for pil_img in example_batch["""image"""]
              ]
              return example_batch

       def val_transforms(UpperCAmelCase__			):
              A_		   = [_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_		   = (
                         dataset["""train"""].shuffle(seed=training_args.seed			).select(range(data_args.max_train_samples			)			)
                     )
              # Set the training transforms
              dataset["train"].set_transform(UpperCAmelCase__			)

       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_		   = (
                         dataset["""validation"""].shuffle(seed=training_args.seed			).select(range(data_args.max_eval_samples			)			)
                     )
              # Set the validation transforms
              dataset["validation"].set_transform(UpperCAmelCase__			)

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

       # Training
       if training_args.do_train:
              A_		   = None
              if training_args.resume_from_checkpoint is not None:
                     A_		   = training_args.resume_from_checkpoint
              elif last_checkpoint is not None:
                     A_		   = last_checkpoint
              A_		   = trainer.train(resume_from_checkpoint=UpperCAmelCase__			)
              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_		   = trainer.evaluate()
              trainer.log_metrics("""eval""",    UpperCAmelCase__			)
              trainer.save_metrics("""eval""",    UpperCAmelCase__			)

       # Write model card and (optionally) push to hub
       A_		   = {
           """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(**UpperCAmelCase__			)
       else:
              trainer.create_model_card(**UpperCAmelCase__			)


if __name__ == "__main__":
      main()



'''simple docstring'''







from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch

from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch


@require_torch
@require_tf
class    A__ ( _snake_case     ):



    def    snake_case_     (   self    )  -> Dict:





           '''simple docstring'''
           A_		   = SMALL_MODEL_IDENTIFIER
           A_		   = """pt"""
           A_		   = """tf"""



    def    snake_case_     (   self						,			UpperCamelCase__    )  -> Tuple:





           '''simple docstring'''
           A_		   = AutoModel.from_pretrained(self.test_model    )
           model_pt.save_pretrained(UpperCamelCase__    )



    def    snake_case_     (   self						,			UpperCamelCase__    )  -> List[str]:





           '''simple docstring'''
           A_		   = TFAutoModel.from_pretrained(self.test_model						,			from_pt=UpperCamelCase__    )
           model_tf.save_pretrained(UpperCamelCase__    )



    def    snake_case_     (   self    )  -> Dict:





           '''simple docstring'''
           A_		   = """mock_framework"""

           # Framework provided - return whatever the user provides
           A_		   = FeaturesManager.determine_framework(self.test_model						,			UpperCamelCase__    )
           self.assertEqual(UpperCamelCase__						,			UpperCamelCase__    )

           # Local checkpoint and framework provided - return provided framework
           # PyTorch checkpoint
           with TemporaryDirectory() as local_pt_ckpt:
                  self._setup_pt_ckpt(UpperCamelCase__    )
                  A_		   = FeaturesManager.determine_framework(UpperCamelCase__						,			UpperCamelCase__    )
                  self.assertEqual(UpperCamelCase__						,			UpperCamelCase__    )

           # TensorFlow checkpoint
           with TemporaryDirectory() as local_tf_ckpt:
                  self._setup_tf_ckpt(UpperCamelCase__    )
                  A_		   = FeaturesManager.determine_framework(UpperCamelCase__						,			UpperCamelCase__    )
                  self.assertEqual(UpperCamelCase__						,			UpperCamelCase__    )



    def    snake_case_     (   self    )  -> int:





           '''simple docstring'''
           # PyTorch checkpoint
           with TemporaryDirectory() as local_pt_ckpt:
                  self._setup_pt_ckpt(UpperCamelCase__    )
                  A_		   = FeaturesManager.determine_framework(UpperCamelCase__    )
                  self.assertEqual(UpperCamelCase__						,			self.framework_pt    )

           # TensorFlow checkpoint
           with TemporaryDirectory() as local_tf_ckpt:
                  self._setup_tf_ckpt(UpperCamelCase__    )
                  A_		   = FeaturesManager.determine_framework(UpperCamelCase__    )
                  self.assertEqual(UpperCamelCase__						,			self.framework_tf    )

           # Invalid local checkpoint
           with TemporaryDirectory() as local_invalid_ckpt:
                  with self.assertRaises(UpperCamelCase__    ):
                         A_		   = FeaturesManager.determine_framework(UpperCamelCase__    )







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





           '''simple docstring'''
           A_		   = MagicMock(return_value=UpperCamelCase__    )
           with patch("""transformers.onnx.features.is_tf_available"""						,			UpperCamelCase__    ):
                  A_		   = FeaturesManager.determine_framework(self.test_model    )
                  self.assertEqual(UpperCamelCase__						,			self.framework_pt    )

           # PyTorch not in environment -> use TensorFlow
           A_		   = MagicMock(return_value=UpperCamelCase__    )
           with patch("""transformers.onnx.features.is_torch_available"""						,			UpperCamelCase__    ):
                  A_		   = FeaturesManager.determine_framework(self.test_model    )
                  self.assertEqual(UpperCamelCase__						,			self.framework_tf    )

           # Both in environment -> use PyTorch
           A_		   = MagicMock(return_value=UpperCamelCase__    )
           A_		   = MagicMock(return_value=UpperCamelCase__    )
           with patch("""transformers.onnx.features.is_tf_available"""						,			UpperCamelCase__    ), patch(
               """transformers.onnx.features.is_torch_available"""						,			UpperCamelCase__    ):
                  A_		   = FeaturesManager.determine_framework(self.test_model    )
                  self.assertEqual(UpperCamelCase__						,			self.framework_pt    )

           # Both not in environment -> raise error
           A_		   = MagicMock(return_value=UpperCamelCase__    )
           A_		   = MagicMock(return_value=UpperCamelCase__    )
           with patch("""transformers.onnx.features.is_tf_available"""						,			UpperCamelCase__    ), patch(
               """transformers.onnx.features.is_torch_available"""						,			UpperCamelCase__    ):
                  with self.assertRaises(UpperCamelCase__    ):
                         A_		   = FeaturesManager.determine_framework(self.test_model    )
