





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
_A					:				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-pretraining/requirements.txt""")



@dataclass
class 					__snake_case	:


    '''simple docstring'''







    lowerCamelCase__    :		Optional[str]				=	field(
        default="""cifar10"""			,     metadata={"""help""": """Name of a dataset from the datasets package"""}    )
    lowerCamelCase__    :		Optional[str]				=	field(
        default=__SCREAMING_SNAKE_CASE			,     metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}    )
    lowerCamelCase__    :		Optional[str]				=	field(
        default=__SCREAMING_SNAKE_CASE			,     metadata={"""help""": """The column name of the images in the files."""}    )
    lowerCamelCase__    :		Optional[str]				=	field(default=__SCREAMING_SNAKE_CASE			,     metadata={"""help""": """A folder containing the training data."""}    )
    lowerCamelCase__    :		Optional[str]				=	field(default=__SCREAMING_SNAKE_CASE			,     metadata={"""help""": """A folder containing the validation data."""}    )
    lowerCamelCase__    :		Optional[float]				=	field(
        default=0.15			,     metadata={"""help""": """Percent to split off of train for validation."""}    )
    lowerCamelCase__    :		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."""
            )
        }			,     )
    lowerCamelCase__    :		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  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			{}
        if self.train_dir is not None:
            SCREAMING_SNAKE_CASE__											=			self.train_dir
        if self.validation_dir is not None:
            SCREAMING_SNAKE_CASE__											=			self.validation_dir
        SCREAMING_SNAKE_CASE__											=			data_files if data_files else None





@dataclass
class 					__snake_case	:


    '''simple docstring'''







    lowerCamelCase__    :		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."""
            )
        }			,     )
    lowerCamelCase__    :		Optional[str]				=	field(
        default=__SCREAMING_SNAKE_CASE			,     metadata={"""help""": """Pretrained config name or path if not the same as model_name_or_path"""}    )
    lowerCamelCase__    :		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"""
            )
        }			,     )
    lowerCamelCase__    :		Optional[str]				=	field(
        default=__SCREAMING_SNAKE_CASE			,     metadata={"""help""": """Where do you want to store the pretrained models downloaded from s3"""}    )
    lowerCamelCase__    :		str				=	field(
        default="""main"""			,     metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}			,     )
    lowerCamelCase__    :		str				=	field(default=__SCREAMING_SNAKE_CASE			,     metadata={"""help""": """Name or path of preprocessor config."""}    )
    lowerCamelCase__    :		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)."""
            )
        }			,     )
    lowerCamelCase__    :		float				=	field(
        default=0.75			,     metadata={"""help""": """The ratio of the number of masked tokens in the input sequence."""}    )
    lowerCamelCase__    :		bool				=	field(
        default=__SCREAMING_SNAKE_CASE			,     metadata={"""help""": """Whether or not to train with normalized pixel values as target."""}    )





@dataclass
class 					__snake_case	(  __SCREAMING_SNAKE_CASE    ):


    '''simple docstring'''







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





def 							__snake_case							(    lowerCAmelCase_	)				->						str:
    SCREAMING_SNAKE_CASE__											=			torch.stack([example['''pixel_values'''] for example in examples]	)
    return {"pixel_values": pixel_values}


def 							__snake_case							(    )				->						Dict:
    # 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__											=			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.
        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]	)	)
    else:
        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_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_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()

    SCREAMING_SNAKE_CASE__											=			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__											=			None
    if os.path.isdir(training_args.output_dir	) and training_args.do_train and not training_args.overwrite_output_dir:
        SCREAMING_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.'''	)

    # Initialize our dataset.
    SCREAMING_SNAKE_CASE__											=			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.
    SCREAMING_SNAKE_CASE__											=			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:
        SCREAMING_SNAKE_CASE__											=			ds['''train'''].train_test_split(data_args.train_val_split	)
        SCREAMING_SNAKE_CASE__											=			split['''train''']
        SCREAMING_SNAKE_CASE__											=			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.
    SCREAMING_SNAKE_CASE__											=			{
        '''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:
        SCREAMING_SNAKE_CASE__											=			ViTMAEConfig.from_pretrained(model_args.config_name ,							**lowerCAmelCase_	)
    elif model_args.model_name_or_path:
        SCREAMING_SNAKE_CASE__											=			ViTMAEConfig.from_pretrained(model_args.model_name_or_path ,							**lowerCAmelCase_	)
    else:
        SCREAMING_SNAKE_CASE__											=			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:
        SCREAMING_SNAKE_CASE__											=			ViTImageProcessor.from_pretrained(model_args.image_processor_name ,							**lowerCAmelCase_	)
    elif model_args.model_name_or_path:
        SCREAMING_SNAKE_CASE__											=			ViTImageProcessor.from_pretrained(model_args.model_name_or_path ,							**lowerCAmelCase_	)
    else:
        SCREAMING_SNAKE_CASE__											=			ViTImageProcessor()

    # create model
    if model_args.model_name_or_path:
        SCREAMING_SNAKE_CASE__											=			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'''	)
        SCREAMING_SNAKE_CASE__											=			ViTMAEForPreTraining(lowerCAmelCase_	)

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

    if data_args.image_column_name is not None:
        SCREAMING_SNAKE_CASE__											=			data_args.image_column_name
    elif "image" in column_names:
        SCREAMING_SNAKE_CASE__											=			'''image'''
    elif "img" in column_names:
        SCREAMING_SNAKE_CASE__											=			'''img'''
    else:
        SCREAMING_SNAKE_CASE__											=			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:
        SCREAMING_SNAKE_CASE__											=			image_processor.size['''shortest_edge''']
    else:
        SCREAMING_SNAKE_CASE__											=			(image_processor.size['''height'''], image_processor.size['''width'''])
    SCREAMING_SNAKE_CASE__											=			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_	):
        SCREAMING_SNAKE_CASE__											=			[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:
            SCREAMING_SNAKE_CASE__											=			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:
            SCREAMING_SNAKE_CASE__											=			(
                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
    SCREAMING_SNAKE_CASE__											=			(
        training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
    )
    if training_args.base_learning_rate is not None:
        SCREAMING_SNAKE_CASE__											=			training_args.base_learning_rate * total_train_batch_size / 2_5_6

    # Initialize our trainer
    SCREAMING_SNAKE_CASE__											=			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:
        SCREAMING_SNAKE_CASE__											=			None
        if training_args.resume_from_checkpoint is not None:
            SCREAMING_SNAKE_CASE__											=			training_args.resume_from_checkpoint
        elif last_checkpoint is not None:
            SCREAMING_SNAKE_CASE__											=			last_checkpoint
        SCREAMING_SNAKE_CASE__											=			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__											=			trainer.evaluate()
        trainer.log_metrics('''eval''' ,							lowerCAmelCase_	)
        trainer.save_metrics('''eval''' ,							lowerCAmelCase_	)

    # Write model card and (optionally) push to hub
    SCREAMING_SNAKE_CASE__											=			{
        '''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 							__snake_case							(    lowerCAmelCase_	)				->						Any:
    # For xla_spawn (TPUs)
    main()


if __name__ == "__main__":
  main()




from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__a	 =      logging.get_logger(__name__)

__a	 =      {
    """google/mobilenet_v1_1.0_224""": """https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json""",
    """google/mobilenet_v1_0.75_192""": """https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json""",
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}






class 	__lowercase					(	__snake_case	):
      UpperCamelCase										=							'''mobilenet_v1'''





      def __init__(		self    :       Any       ,							__lowerCamelCase    :       Union[str, Any]=3       ,							__lowerCamelCase    :       Dict=2_2_4       ,							__lowerCamelCase    :       Union[str, Any]=1.0       ,							__lowerCamelCase    :       Optional[int]=8       ,							__lowerCamelCase    :       Optional[int]="relu6"       ,							__lowerCamelCase    :       Optional[int]=True       ,							__lowerCamelCase    :       List[str]=0.999       ,							__lowerCamelCase    :       Optional[Any]=0.02       ,							__lowerCamelCase    :       str=0.001       ,							**__lowerCamelCase    :       Optional[Any]       ,							)     -> Optional[int]:






         """simple docstring"""







         super().__init__(**__lowerCamelCase							)

         if depth_multiplier <= 0:
            raise ValueError("""depth_multiplier must be greater than zero."""							)

         UpperCAmelCase      					=  num_channels
         UpperCAmelCase      					=  image_size
         UpperCAmelCase      					=  depth_multiplier
         UpperCAmelCase      					=  min_depth
         UpperCAmelCase      					=  hidden_act
         UpperCAmelCase      					=  tf_padding
         UpperCAmelCase      					=  classifier_dropout_prob
         UpperCAmelCase      					=  initializer_range
         UpperCAmelCase      					=  layer_norm_eps






class 	__lowercase					(	__snake_case	):
      UpperCamelCase										=							version.parse('''1.11'''	)





      @property
      def 							_lowercase					(		self    :       Optional[int]							)     -> Mapping[str, Mapping[int, str]]:






         """simple docstring"""







         return OrderedDict([("""pixel_values""", {0: """batch"""})]							)





      @property
      def 							_lowercase					(		self    :       Union[str, Any]							)     -> Mapping[str, Mapping[int, str]]:






         """simple docstring"""







         if self.task == "image-classification":
            return OrderedDict([("""logits""", {0: """batch"""})]							)
         else:
            return OrderedDict([("""last_hidden_state""", {0: """batch"""}), ("""pooler_output""", {0: """batch"""})]							)





      @property
      def 							_lowercase					(		self    :       List[Any]							)     -> float:






         """simple docstring"""







         return 1e-4



