




'''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
_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 						lowerCamelCase__   (				a							):
						with open(a    ,	'rb'							) as f:
												__snake_case						 =   Image.open(a							)
												return im.convert('RGB'							)
@dataclass
class 						a_				:
	lowercase_       :     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).'''
	    }     ,						)
	lowercase_       :     Optional[str]			  =      field(
	    default=UpperCAmelCase__     ,						metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}						)
	lowercase_       :     Optional[str]			  =      field(default=UpperCAmelCase__     ,						metadata={'''help''': '''A folder containing the training data.'''}						)
	lowercase_       :     Optional[str]			  =      field(default=UpperCAmelCase__     ,						metadata={'''help''': '''A folder containing the validation data.'''}						)
	lowercase_       :     Optional[float]			  =      field(
	    default=0.15     ,						metadata={'''help''': '''Percent to split off of train for validation.'''}						)
	lowercase_       :     Optional[int]			  =      field(
	    default=UpperCAmelCase__     ,						metadata={
	        '''help''': (
	            '''For debugging purposes or quicker training, truncate the number of training examples to this '''
	            '''value if set.'''
	        )
	    }     ,						)
	lowercase_       :     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   lowercase__   (       self     :  Union[str, 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 						a_				:
	lowercase_       :     str			  =      field(
	    default='''google/vit-base-patch16-224-in21k'''     ,						metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}     ,						)
	lowercase_       :     Optional[str]			  =      field(
	    default=UpperCAmelCase__     ,						metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(UpperCAmelCase__						)}     ,						)
	lowercase_       :     Optional[str]			  =      field(
	    default=UpperCAmelCase__     ,						metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}						)
	lowercase_       :     Optional[str]			  =      field(
	    default=UpperCAmelCase__     ,						metadata={'''help''': '''Where do you want to store the pretrained models downloaded from s3'''}						)
	lowercase_       :     str			  =      field(
	    default='''main'''     ,						metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}     ,						)
	lowercase_       :     str			  =      field(default=UpperCAmelCase__     ,						metadata={'''help''': '''Name or path of preprocessor config.'''}						)
	lowercase_       :     bool			  =      field(
	    default=UpperCAmelCase__     ,						metadata={
	        '''help''': (
	            '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
	            '''with private models).'''
	        )
	    }     ,						)
	lowercase_       :     bool			  =      field(
	    default=UpperCAmelCase__     ,						metadata={'''help''': '''Will enable to load a pretrained model whose head dimensions are different.'''}     ,						)





def 						lowerCamelCase__   (				a							):
						__snake_case						 =   torch.stack([example['pixel_values'] for example in examples]							)
						__snake_case						 =   torch.tensor([example['labels'] for example in examples]							)
						return {"pixel_values": pixel_values, "labels": labels}
def 						lowerCamelCase__   (				):
						# 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.

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

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

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

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

						__snake_case						 =   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    ,	)
						__snake_case						 =   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    ,	)
						__snake_case						 =   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:
												__snake_case						 =   image_processor.size['shortest_edge']
						else:
												__snake_case						 =   (image_processor.size['height'], image_processor.size['width'])
						__snake_case						 =   Normalize(mean=image_processor.image_mean    ,	std=image_processor.image_std							)
						__snake_case						 =   Compose(
						    [
						        RandomResizedCrop(a							),
						        RandomHorizontalFlip(),
						        ToTensor(),
						        normalize,
						    ]							)
						__snake_case						 =   Compose(
						    [
						        Resize(a							),
						        CenterCrop(a							),
						        ToTensor(),
						        normalize,
						    ]							)

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

						def val_transforms(a							):
												__snake_case						 =   [_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:
																		__snake_case						 =   (
																		    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:
																		__snake_case						 =   (
																		    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
						__snake_case						 =   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:
												__snake_case						 =   None
												if training_args.resume_from_checkpoint is not None:
																		__snake_case						 =   training_args.resume_from_checkpoint
												elif last_checkpoint is not None:
																		__snake_case						 =   last_checkpoint
												__snake_case						 =   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:
												__snake_case						 =   trainer.evaluate()
												trainer.log_metrics('eval'    ,	a							)
												trainer.save_metrics('eval'    ,	a							)

						# Write model card and (optionally) push to hub
						__snake_case						 =   {
						    '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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_lowercase  =			{
    """configuration_biogpt""": ["""BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BioGptConfig"""],
    """tokenization_biogpt""": ["""BioGptTokenizer"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase  =			[
	    """BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """BioGptForCausalLM""",
	    """BioGptForTokenClassification""",
	    """BioGptForSequenceClassification""",
	    """BioGptModel""",
	    """BioGptPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
	from .tokenization_biogpt import BioGptTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_biogpt import (
		    BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BioGptForCausalLM,
		    BioGptForSequenceClassification,
		    BioGptForTokenClassification,
		    BioGptModel,
		    BioGptPreTrainedModel,
		)


else:
	import sys

	_lowercase  =			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

