


"""simple docstring"""




import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    ViTImageProcessor,
    ViTMAEConfig,
    ViTMAEForPreTraining,
)
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-pretraining/requirements.txt")
@dataclass
class 					__lowerCAmelCase					:


		'''simple docstring'''



		__UpperCAmelCase		:			Optional[str]										=				field(
		    default='cifar10'     ,			metadata={'help': 'Name of a dataset from the datasets package'}       )
		__UpperCAmelCase		:			Optional[str]										=				field(
		    default=__SCREAMING_SNAKE_CASE     ,			metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}       )
		__UpperCAmelCase		:			Optional[str]										=				field(
		    default=__SCREAMING_SNAKE_CASE     ,			metadata={'help': 'The column name of the images in the files.'}       )
		__UpperCAmelCase		:			Optional[str]										=				field(default=__SCREAMING_SNAKE_CASE     ,			metadata={'help': 'A folder containing the training data.'}       )
		__UpperCAmelCase		:			Optional[str]										=				field(default=__SCREAMING_SNAKE_CASE     ,			metadata={'help': 'A folder containing the validation data.'}       )
		__UpperCAmelCase		:			Optional[float]										=				field(
		    default=0.15     ,			metadata={'help': 'Percent to split off of train for validation.'}       )
		__UpperCAmelCase		:			Optional[int]										=				field(
		    default=__SCREAMING_SNAKE_CASE     ,			metadata={
		        'help': (
		            'For debugging purposes or quicker training, truncate the number of training examples to this '
		            'value if set.'
		        )
		    }     ,			)
		__UpperCAmelCase		:			Optional[int]										=				field(
		    default=__SCREAMING_SNAKE_CASE     ,			metadata={
		        'help': (
		            'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
		            'value if set.'
		        )
		    }     ,			)

		def 			__UpperCAmelCase       (  self      ):
				__a           =			{}
				if self.train_dir is not None:
						__a           =			self.train_dir
				if self.validation_dir is not None:
						__a           =			self.validation_dir
				__a           =			data_files if data_files else None







@dataclass
class 					__lowerCAmelCase					:


		'''simple docstring'''



		__UpperCAmelCase		:			str										=				field(
		    default=__SCREAMING_SNAKE_CASE     ,			metadata={
		        'help': (
		            'The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'
		        )
		    }     ,			)
		__UpperCAmelCase		:			Optional[str]										=				field(
		    default=__SCREAMING_SNAKE_CASE     ,			metadata={'help': 'Pretrained config name or path if not the same as model_name_or_path'}       )
		__UpperCAmelCase		:			Optional[str]										=				field(
		    default=__SCREAMING_SNAKE_CASE     ,			metadata={
		        'help': (
		            'Override some existing default config settings when a model is trained from scratch. Example: '
		            'n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'
		        )
		    }     ,			)
		__UpperCAmelCase		:			Optional[str]										=				field(
		    default=__SCREAMING_SNAKE_CASE     ,			metadata={'help': 'Where do you want to store the pretrained models downloaded from s3'}       )
		__UpperCAmelCase		:			str										=				field(
		    default='main'     ,			metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}     ,			)
		__UpperCAmelCase		:			str										=				field(default=__SCREAMING_SNAKE_CASE     ,			metadata={'help': 'Name or path of preprocessor config.'}       )
		__UpperCAmelCase		:			bool										=				field(
		    default=__SCREAMING_SNAKE_CASE     ,			metadata={
		        'help': (
		            'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
		            'with private models).'
		        )
		    }     ,			)
		__UpperCAmelCase		:			float										=				field(
		    default=0.75     ,			metadata={'help': 'The ratio of the number of masked tokens in the input sequence.'}       )
		__UpperCAmelCase		:			bool										=				field(
		    default=__SCREAMING_SNAKE_CASE     ,			metadata={'help': 'Whether or not to train with normalized pixel values as target.'}       )







@dataclass
class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		__UpperCAmelCase		:			float										=				field(
		    default=1e-3     ,			metadata={'help': 'Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'}       )







def        lowercase							(		lowerCAmelCase__ :     int			) ->      Tuple:
		__a           =			torch.stack([example['''pixel_values'''] for example in examples]			)
		return {"pixel_values": pixel_values}

def        lowercase							(		) ->      Optional[Any]:
		# 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.

		__a           =			HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments)			)
		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      ,     __a      ,     __a           =			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
		else:
				__a      ,     __a      ,     __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_mae'''    ,					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()

		__a           =			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.
		__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.'''			)

    # Initialize our dataset.
		__a           =			load_dataset(
		    data_args.dataset_name    ,					data_args.dataset_config_name    ,					data_files=data_args.data_files    ,					cache_dir=model_args.cache_dir    ,					use_auth_token=True if model_args.use_auth_token else None    ,					)

		# If we don't have a validation split, split off a percentage of train as validation.
		__a           =			None if '''validation''' in ds.keys() else data_args.train_val_split
		if isinstance(data_args.train_val_split    ,					lowerCAmelCase__			) and data_args.train_val_split > 0.0:
				__a           =			ds['''train'''].train_test_split(data_args.train_val_split			)
				__a           =			split['''train''']
				__a           =			split['''test''']

		# Load pretrained model and image processor
		#
		# Distributed training:
		# The .from_pretrained methods guarantee that only one local process can concurrently
		# download model & vocab.
		__a           =			{
		    '''cache_dir''': model_args.cache_dir,
		    '''revision''': model_args.model_revision,
		    '''use_auth_token''': True if model_args.use_auth_token else None,
		}
		if model_args.config_name:
				__a           =			ViTMAEConfig.from_pretrained(model_args.config_name    ,					**lowerCAmelCase__			)
		elif model_args.model_name_or_path:
				__a           =			ViTMAEConfig.from_pretrained(model_args.model_name_or_path    ,					**lowerCAmelCase__			)
		else:
				__a           =			ViTMAEConfig()
				logger.warning('''You are instantiating a new config instance from scratch.'''			)
				if model_args.config_overrides is not None:
						logger.info(f'''Overriding config: {model_args.config_overrides}'''			)
						config.update_from_string(model_args.config_overrides			)
						logger.info(f'''New config: {config}'''			)

    # adapt config
		config.update(
		    {
		        '''mask_ratio''': model_args.mask_ratio,
		        '''norm_pix_loss''': model_args.norm_pix_loss,
		    }			)

		# create image processor
		if model_args.image_processor_name:
				__a           =			ViTImageProcessor.from_pretrained(model_args.image_processor_name    ,					**lowerCAmelCase__			)
		elif model_args.model_name_or_path:
				__a           =			ViTImageProcessor.from_pretrained(model_args.model_name_or_path    ,					**lowerCAmelCase__			)
		else:
				__a           =			ViTImageProcessor()

		# create model
		if model_args.model_name_or_path:
				__a           =			ViTMAEForPreTraining.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    ,					)
		else:
				logger.info('''Training new model from scratch'''			)
				__a           =			ViTMAEForPreTraining(lowerCAmelCase__			)

		if training_args.do_train:
				__a           =			ds['''train'''].column_names
		else:
				__a           =			ds['''validation'''].column_names

		if data_args.image_column_name is not None:
				__a           =			data_args.image_column_name
		elif "image" in column_names:
				__a           =			'''image'''
		elif "img" in column_names:
				__a           =			'''img'''
		else:
				__a           =			column_names[0]

		# transformations as done in original MAE paper
		# source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
		if "shortest_edge" in image_processor.size:
				__a           =			image_processor.size['''shortest_edge''']
		else:
				__a           =			(image_processor.size['''height'''], image_processor.size['''width'''])
		__a           =			Compose(
		    [
		        Lambda(lambda lowerCAmelCase__			: img.convert('''RGB'''			) if img.mode != "RGB" else img			),
		        RandomResizedCrop(lowerCAmelCase__    ,					scale=(0.2, 1.0)    ,					interpolation=InterpolationMode.BICUBIC			),
		        RandomHorizontalFlip(),
		        ToTensor(),
		        Normalize(mean=image_processor.image_mean    ,					std=image_processor.image_std			),
		    ]			)

		def preprocess_images(lowerCAmelCase__ :     Union[str, Any]			):
				__a           =			[transforms(lowerCAmelCase__			) for image in examples[image_column_name]]
				return examples

		if training_args.do_train:
				if "train" not in ds:
						raise ValueError('''--do_train requires a train dataset'''			)
				if data_args.max_train_samples is not None:
						__a           =			ds['''train'''].shuffle(seed=training_args.seed			).select(range(data_args.max_train_samples			)			)
				# Set the training transforms
				ds["train"].set_transform(lowerCAmelCase__			)

		if training_args.do_eval:
				if "validation" not in ds:
						raise ValueError('''--do_eval requires a validation dataset'''			)
				if data_args.max_eval_samples is not None:
						__a           =			(
						    ds['''validation'''].shuffle(seed=training_args.seed			).select(range(data_args.max_eval_samples			)			)
						)
				# Set the validation transforms
				ds["validation"].set_transform(lowerCAmelCase__			)

		# Compute absolute learning rate
		__a           =			(
		    training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
		)
		if training_args.base_learning_rate is not None:
				__a           =			training_args.base_learning_rate * total_train_batch_size / 256

		# Initialize our trainer
		__a           =			Trainer(
		    model=lowerCAmelCase__    ,					args=lowerCAmelCase__    ,					train_dataset=ds['''train'''] if training_args.do_train else None    ,					eval_dataset=ds['''validation'''] if training_args.do_eval else None    ,					tokenizer=lowerCAmelCase__    ,					data_collator=lowerCAmelCase__    ,					)

		# 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=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:
				__a           =			trainer.evaluate()
				trainer.log_metrics('''eval'''    ,					lowerCAmelCase__			)
				trainer.save_metrics('''eval'''    ,					lowerCAmelCase__			)

		# Write model card and (optionally) push to hub
		__a           =			{
		    '''tasks''': '''masked-auto-encoding''',
		    '''dataset''': data_args.dataset_name,
		    '''tags''': ['''masked-auto-encoding'''],
		}
		if training_args.push_to_hub:
				trainer.push_to_hub(**lowerCAmelCase__			)
		else:
				trainer.create_model_card(**lowerCAmelCase__			)

def        lowercase							(		lowerCAmelCase__ :     List[Any]			) ->      Tuple:
		# For xla_spawn (TPUs)
		main()


if __name__ == "__main__":
				main()







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


a_    					=       {
    'configuration_efficientformer': [
        'EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'EfficientFormerConfig',
    ]
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a_    					=       ['EfficientFormerImageProcessor']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a_    					=       [
       'EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'EfficientFormerForImageClassification',
       'EfficientFormerForImageClassificationWithTeacher',
       'EfficientFormerModel',
       'EfficientFormerPreTrainedModel',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a_    					=       [
       'TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFEfficientFormerForImageClassification',
       'TFEfficientFormerForImageClassificationWithTeacher',
       'TFEfficientFormerModel',
       'TFEfficientFormerPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_efficientformer import EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientFormerConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_efficientformer import EfficientFormerImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_efficientformer import (
          EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          EfficientFormerForImageClassification,
          EfficientFormerForImageClassificationWithTeacher,
          EfficientFormerModel,
          EfficientFormerPreTrainedModel,
      )
   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_efficientformer import (
          TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFEfficientFormerForImageClassification,
          TFEfficientFormerForImageClassificationWithTeacher,
          TFEfficientFormerModel,
          TFEfficientFormerPreTrainedModel,
      )

else:
   import sys

   a_    					=       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



