





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
__UpperCamelCase:	Optional[Any] = 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""")

__UpperCamelCase:	Any = list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
__UpperCamelCase:	Optional[int] = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : str			)   ->							Optional[int]:




		'''simple docstring'''
		with open(_lowercase   ,    'rb'			) as f:
				lowercase__					:      Union[str, Any]      =		Image.open(_lowercase			)
				return im.convert('RGB'			)


@dataclass
class       __lowerCAmelCase					:


	'''simple docstring'''





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

	def    snake_case__(						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       __lowerCAmelCase					:


	'''simple docstring'''





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


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : Optional[int]			)   ->							Optional[Any]:




		'''simple docstring'''
		lowercase__					:      Any      =		torch.stack([example['pixel_values'] for example in examples]			)
		lowercase__					:      int      =		torch.tensor([example['labels'] for example in examples]			)
		return {"pixel_values": pixel_values, "labels": labels}


def       SCREAMING_SNAKE_CASE__  (       )   ->							Dict:




		'''simple docstring'''
		# 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.

		lowercase__					:      Dict      =		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.
				lowercase__   ,				lowercase__   ,				lowercase__					:      Any      =		parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
		else:
				lowercase__   ,				lowercase__   ,				lowercase__					:      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'   ,    _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()

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

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

		# Load the accuracy metric from the datasets package
		lowercase__					:      Dict      =		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     : List[Any]			):
				return metric.compute(predictions=np.argmax(p.predictions   ,    axis=1			)   ,    references=p.label_ids			)

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

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

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

		# Write model card and (optionally) push to hub
		lowercase__					:      Optional[int]      =		{
		    '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()









# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__UpperCamelCase:	List[Any] = {
    """configuration_efficientnet""": [
        """EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """EfficientNetConfig""",
        """EfficientNetOnnxConfig""",
    ]
}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__UpperCamelCase:	Dict = ["""EfficientNetImageProcessor"""]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__UpperCamelCase:	Dict = [
			    """EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """EfficientNetForImageClassification""",
			    """EfficientNetModel""",
			    """EfficientNetPreTrainedModel""",
			]

if TYPE_CHECKING:
			from .configuration_efficientnet import (
			    EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    EfficientNetConfig,
			    EfficientNetOnnxConfig,
			)

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .image_processing_efficientnet import EfficientNetImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_efficientnet import (
						    EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST,
						    EfficientNetForImageClassification,
						    EfficientNetModel,
						    EfficientNetPreTrainedModel,
						)

else:
			import sys

			__UpperCamelCase:	List[str] = _LazyModule(__name__, globals()["""__file__"""], _import_structure)


