






'''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:      Union[str, 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")

A:      str  				=				list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
A:      Union[str, Any]  				=				tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)






def 	_UpperCAmelCase    (  a		:	str							)			->			Any:





						"""simple docstring"""
						with open(a						,			'rb'							) as f:
												lowercase_      :   List[str]        =			Image.open(a							)
												return im.convert('RGB'							)






@dataclass
class 							__magic_name__     :







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_					:      Optional[str]        =  field(
	    default=UpperCAmelCase_,						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=UpperCAmelCase_,						metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}				)
	SCREAMING_SNAKE_CASE_					:      Optional[str]        =  field(default=UpperCAmelCase_,						metadata={'help': 'A folder containing the training data.'}				)
	SCREAMING_SNAKE_CASE_					:      Optional[str]        =  field(default=UpperCAmelCase_,						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=UpperCAmelCase_,						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=UpperCAmelCase_,						metadata={
	        'help': (
	            'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
	            'value if set.'
	        )
	    },						)
	def        lowerCamelCase__		(							self       )						->  Any:
							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 							__magic_name__     :







	"""simple docstring"""



	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=UpperCAmelCase_,						metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(UpperCAmelCase_				)},						)
	SCREAMING_SNAKE_CASE_					:      Optional[str]        =  field(
	    default=UpperCAmelCase_,						metadata={'help': 'Pretrained config name or path if not the same as model_name'}				)
	SCREAMING_SNAKE_CASE_					:      Optional[str]        =  field(
	    default=UpperCAmelCase_,						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=UpperCAmelCase_,						metadata={'help': 'Name or path of preprocessor config.'}				)
	SCREAMING_SNAKE_CASE_					:      bool        =  field(
	    default=UpperCAmelCase_,						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=UpperCAmelCase_,						metadata={'help': 'Will enable to load a pretrained model whose head dimensions are different.'},						)






def 	_UpperCAmelCase    (  a		:	List[Any]							)			->			List[str]:





						"""simple docstring"""
						lowercase_      :   Optional[int]        =			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 	_UpperCAmelCase    (  )			->			str:





						"""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_      :   Optional[int]        =			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_      :   List[Any]        =			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]							)							)
						else:
												lowercase_  ,      lowercase_  ,      lowercase_      :   Optional[int]        =			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'						,			a						,			a							)

						# 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(a							)
						transformers.utils.logging.set_verbosity(a							)
						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_      :   Any        =			None
						if os.path.isdir(training_args.output_dir							) and training_args.do_train and not training_args.overwrite_output_dir:
												lowercase_      :   Union[str, 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:
												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_      :   Tuple        =			{}
												if data_args.train_dir is not None:
																		lowercase_      :   List[Any]        =			os.path.join(data_args.train_dir						,			'**'							)
												if data_args.validation_dir is not None:
																		lowercase_      :   Any        =			os.path.join(data_args.validation_dir						,			'**'							)
												lowercase_      :   Optional[int]        =			load_dataset(
												    'imagefolder'						,			data_files=a						,			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_      :   List[str]        =			None if 'validation' in dataset.keys() else data_args.train_val_split
						if isinstance(data_args.train_val_split						,			a							) and data_args.train_val_split > 0.0:
												lowercase_      :   Optional[Any]        =			dataset['train'].train_test_split(data_args.train_val_split							)
												lowercase_      :   int        =			split['train']
												lowercase_      :   Tuple        =			split['test']

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

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

						lowercase_      :   Union[str, Any]        =			AutoConfig.from_pretrained(
						    model_args.config_name or model_args.model_name_or_path						,			num_labels=len(a							)						,			labelaid=a						,			idalabel=a						,			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_      :   Dict        =			AutoModelForImageClassification.from_pretrained(
						    model_args.model_name_or_path						,			from_tf=bool('.ckpt' in model_args.model_name_or_path							)						,			config=a						,			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_      :   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_      :   Tuple        =			image_processor.size['shortest_edge']
						else:
												lowercase_      :   int        =			(image_processor.size['height'], image_processor.size['width'])
						lowercase_      :   Union[str, Any]        =			Normalize(mean=image_processor.image_mean						,			std=image_processor.image_std							)
						lowercase_      :   List[str]        =			Compose(
						    [
						        RandomResizedCrop(a							),
						        RandomHorizontalFlip(),
						        ToTensor(),
						        normalize,
						    ]							)
						lowercase_      :   Tuple        =			Compose(
						    [
						        Resize(a							),
						        CenterCrop(a							),
						        ToTensor(),
						        normalize,
						    ]							)

						def train_transforms(a		:	Any							):
												lowercase_      :   int        =			[
												    _train_transforms(pil_img.convert('RGB'							)							) for pil_img in example_batch['image']
												]
												return example_batch

						def val_transforms(a		:	Any							):
												lowercase_      :   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:
																		lowercase_      :   Optional[Any]        =			(
																		    dataset['train'].shuffle(seed=training_args.seed							).select(range(data_args.max_train_samples							)							)
																		)
												# Set the training transforms
												dataset["train"].set_transform(a							)

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

						# Initalize our trainer
						lowercase_      :   List[str]        =			Trainer(
						    model=a						,			args=a						,			train_dataset=dataset['train'] if training_args.do_train else None						,			eval_dataset=dataset['validation'] if training_args.do_eval else None						,			compute_metrics=a						,			tokenizer=a						,			data_collator=a						,			)

						# Training
						if training_args.do_train:
												lowercase_      :   Optional[Any]        =			None
												if training_args.resume_from_checkpoint is not None:
																		lowercase_      :   Tuple        =			training_args.resume_from_checkpoint
												elif last_checkpoint is not None:
																		lowercase_      :   Optional[Any]        =			last_checkpoint
												lowercase_      :   str        =			trainer.train(resume_from_checkpoint=a							)
												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_      :   str        =			trainer.evaluate()
												trainer.log_metrics('eval'						,			a							)
												trainer.save_metrics('eval'						,			a							)

						# Write model card and (optionally) push to hub
						lowercase_      :   Tuple        =			{
						    '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(**a							)
						else:
												trainer.create_model_card(**a							)


if __name__ == "__main__":
						main()








'''simple docstring'''

from ..utils import DummyObject, requires_backends






class 							__magic_name__     (	metaclass=UpperCAmelCase_				):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_					:      str        =  ['transformers', 'torch', 'note_seq']
	def __init__(							self  ,      *_lowercase  ,      **_lowercase       )						->  Dict:
							requires_backends(self  ,      ['transformers', 'torch', 'note_seq']       )
	@classmethod
	def        lowerCamelCase__		(							cls  ,      *_lowercase  ,      **_lowercase       )						->  List[str]:
							requires_backends(cls  ,      ['transformers', 'torch', 'note_seq']       )







	@classmethod
	def        lowerCamelCase__		(							cls  ,      *_lowercase  ,      **_lowercase       )						->  Dict:
							requires_backends(cls  ,      ['transformers', 'torch', 'note_seq']       )
