


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							:   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")

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





def 	_lowerCAmelCase					( UpperCamelCase__:  str    )			-> Optional[int]:





	"""simple docstring"""







	with open(_lowercase				,       """rb"""    ) as f:
		A             =					Image.open(_lowercase    )
		return im.convert("""RGB"""    )






@dataclass
class    _UpperCamelCase     :

				"""simple docstring"""







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

				def   _UpperCAmelCase       (    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    _UpperCamelCase     :

				"""simple docstring"""







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





def 	_lowerCAmelCase					( UpperCamelCase__:  Optional[int]    )			-> Optional[Any]:





	"""simple docstring"""







	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 	_lowerCAmelCase					( )			-> Dict:





	"""simple docstring"""







	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             =					parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]    )    )
	else:
		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"""				,       _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()

	A             =					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.
	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=_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.
	A             =					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:
		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             =					{}, {}
	for i, label in enumerate(_lowercase    ):
		A             =					str(_lowercase    )
		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__:  List[Any]    ):
		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(_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				,       )
	A             =					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				,       )
	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(_lowercase    ),
	        RandomHorizontalFlip(),
	        ToTensor(),
	        normalize,
	    ]    )
	A             =					Compose(
	    [
	        Resize(_lowercase    ),
	        CenterCrop(_lowercase    ),
	        ToTensor(),
	        normalize,
	    ]    )

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

	def val_transforms(UpperCamelCase__:  List[Any]    ):
		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(_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:
			A             =					(
			    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
	A             =					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:
		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=_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:
		A             =					trainer.evaluate()
		trainer.log_metrics("""eval"""				,       _lowercase    )
		trainer.save_metrics("""eval"""				,       _lowercase    )

	# 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(**_lowercase    )
	else:
		trainer.create_model_card(**_lowercase    )


if __name__ == "__main__":
				main()










import enum
import shutil
import sys


__UpperCamelCase,	__UpperCamelCase:	Optional[int] = shutil.get_terminal_size()

__UpperCamelCase:	Optional[Any] = {"""UP""": """A""", """DOWN""": """B""", """RIGHT""": """C""", """LEFT""": """D"""}


class       __lowerCAmelCase					(       enum.Enum      ):


	'''simple docstring'''





	_A													=				0
	_A													=				1


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : List[Any]   ,    _lowercase     : Union[str, Any]=""			)   ->							int:




		'''simple docstring'''
		sys.stdout.write(str(_lowercase			) + end			)
		sys.stdout.flush()


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : Optional[Any]   ,    _lowercase     : Union[str, Any]   ,    _lowercase     : Optional[int]=""			)   ->							str:




		'''simple docstring'''
		forceWrite(f"""\u001b[{color}m{content}\u001b[0m"""   ,    _lowercase			)


def       SCREAMING_SNAKE_CASE__  (       )   ->							int:




		'''simple docstring'''
		forceWrite('\r'			)


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : int   ,    _lowercase     : str			)   ->							List[Any]:




		'''simple docstring'''
		forceWrite(f"""\033[{num_lines}{CURSOR_TO_CHAR[direction.upper()]}"""			)


def       SCREAMING_SNAKE_CASE__  (       )   ->							Dict:




		'''simple docstring'''
		forceWrite(' ' * TERMINAL_WIDTH			)
		reset_cursor()





def       SCREAMING_SNAKE_CASE__  (       )   ->							List[str]:




		'''simple docstring'''
		reset_cursor()
		forceWrite('-' * TERMINAL_WIDTH			)


