



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


def        lowerCAmelCase_     (			_lowerCamelCase:     str      ):
		with open(_lowerCamelCase ,   """rb"""      ) as f:
				__SCREAMING_SNAKE_CASE :	Dict					 =	Image.open(_lowerCamelCase      )
				return im.convert("""RGB"""      )






@dataclass
class 		_UpperCamelCase						:


			'''simple docstring'''

			_A   :	Optional[str]    					=	field(
			    default=lowerCamelCase__					,      metadata={
			        '''help''': '''Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub).'''
			    }					,      )
			_A   :	Optional[str]    					=	field(
			    default=lowerCamelCase__					,      metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}					)
			_A   :	Optional[str]    					=	field(default=lowerCamelCase__					,      metadata={'''help''': '''A folder containing the training data.'''}					)
			_A   :	Optional[str]    					=	field(default=lowerCamelCase__					,      metadata={'''help''': '''A folder containing the validation data.'''}					)
			_A   :	Optional[float]    					=	field(
			    default=0.1_5					,      metadata={'''help''': '''Percent to split off of train for validation.'''}					)
			_A   :	Optional[int]    					=	field(
			    default=lowerCamelCase__					,      metadata={
			        '''help''': (
			            '''For debugging purposes or quicker training, truncate the number of training examples to this '''
			            '''value if set.'''
			        )
			    }					,      )
			_A   :	Optional[int]    					=	field(
			    default=lowerCamelCase__					,      metadata={
			        '''help''': (
			            '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
			            '''value if set.'''
			        )
			    }					,      )

			def UpperCamelCase__      (							self  :				int		):





					"""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 		_UpperCamelCase						:


			'''simple docstring'''

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


def        lowerCAmelCase_     (			_lowerCamelCase:     Optional[Any]      ):
		__SCREAMING_SNAKE_CASE :	Optional[int]					 =	torch.stack([example["""pixel_values"""] for example in examples]      )
		__SCREAMING_SNAKE_CASE :	List[Any]					 =	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.

		__SCREAMING_SNAKE_CASE :	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.
				__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]      )      )
		else:
				__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE :	Dict					 =	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""" ,   _lowerCamelCase ,   _lowerCamelCase      )

		# 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()

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

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

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

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

		def train_transforms(_lowerCamelCase:     Optional[Any]      ):
				__SCREAMING_SNAKE_CASE :	Optional[int]					 =	[
				    _train_transforms(pil_img.convert("""RGB"""      )      ) for pil_img in example_batch["""image"""]
				]
				return example_batch

		def val_transforms(_lowerCamelCase:     Optional[int]      ):
				__SCREAMING_SNAKE_CASE :	Tuple					 =	[_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:
						__SCREAMING_SNAKE_CASE :	Dict					 =	(
						    dataset["""train"""].shuffle(seed=training_args.seed      ).select(range(data_args.max_train_samples      )      )
						)
				# Set the training transforms
				dataset["train"].set_transform(_lowerCamelCase      )

		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:
						__SCREAMING_SNAKE_CASE :	int					 =	(
						    dataset["""validation"""].shuffle(seed=training_args.seed      ).select(range(data_args.max_eval_samples      )      )
						)
				# Set the validation transforms
				dataset["validation"].set_transform(_lowerCamelCase      )

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

		# Training
		if training_args.do_train:
				__SCREAMING_SNAKE_CASE :	Optional[int]					 =	None
				if training_args.resume_from_checkpoint is not None:
						__SCREAMING_SNAKE_CASE :	Dict					 =	training_args.resume_from_checkpoint
				elif last_checkpoint is not None:
						__SCREAMING_SNAKE_CASE :	str					 =	last_checkpoint
				__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	trainer.train(resume_from_checkpoint=_lowerCamelCase      )
				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:
				__SCREAMING_SNAKE_CASE :	List[str]					 =	trainer.evaluate()
				trainer.log_metrics("""eval""" ,   _lowerCamelCase      )
				trainer.save_metrics("""eval""" ,   _lowerCamelCase      )

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


if __name__ == "__main__":
						main()




'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase__ :			Optional[int]												=				{'''configuration_wavlm''': ['''WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''WavLMConfig''']}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase__ :			Tuple												=				[
						    '''WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''WavLMForAudioFrameClassification''',
						    '''WavLMForCTC''',
						    '''WavLMForSequenceClassification''',
						    '''WavLMForXVector''',
						    '''WavLMModel''',
						    '''WavLMPreTrainedModel''',
						]

if TYPE_CHECKING:
						from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_wavlm import (
												    WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
												    WavLMForAudioFrameClassification,
												    WavLMForCTC,
												    WavLMForSequenceClassification,
												    WavLMForXVector,
												    WavLMModel,
												    WavLMPreTrainedModel,
												)

else:
						import sys

						UpperCamelCase__ :			List[str]												=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)