



"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForMaskedImageModeling,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
)
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
_lowerCAmelCase       :List[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')

_lowerCAmelCase       :int	=   list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
_lowerCAmelCase       :List[str]	=   tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class 				_UpperCAmelCase				:





     '''simple docstring'''



     a__   							=field(
         default='''cifar10'''						,metadata={'''help''': '''Name of a dataset from the datasets package'''} )
     a__   							=field(
         default=__A						,metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''} )
     a__   							=field(
         default=__A						,metadata={'''help''': '''The column name of the images in the files. If not set, will try to use \'image\' or \'img\'.'''}						,)
     a__   							=field(default=__A						,metadata={'''help''': '''A folder containing the training data.'''} )
     a__   							=field(default=__A						,metadata={'''help''': '''A folder containing the validation data.'''} )
     a__   							=field(
         default=0.15						,metadata={'''help''': '''Percent to split off of train for validation.'''} )
     a__   							=field(default=3_2						,metadata={'''help''': '''The size of the square patches to use for masking.'''} )
     a__   							=field(
         default=0.6						,metadata={'''help''': '''Percentage of patches to mask.'''}						,)
     a__   							=field(
         default=__A						,metadata={
             '''help''': (
                 '''For debugging purposes or quicker training, truncate the number of training examples to this '''
                 '''value if set.'''
             )
         }						,)
     a__   							=field(
         default=__A						,metadata={
             '''help''': (
                 '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
                 '''value if set.'''
             )
         }						,)





     def 					__lowerCAmelCase      (   self						)				->   List[str]:
         _UpperCAmelCase				:    Union[str, Any]											=       {}
         if self.train_dir is not None:
             _UpperCAmelCase				:    int											=       self.train_dir
         if self.validation_dir is not None:
             _UpperCAmelCase				:    Optional[int]											=       self.validation_dir
         _UpperCAmelCase				:    str											=       data_files if data_files else None



@dataclass
class 				_UpperCAmelCase				:





     '''simple docstring'''



     a__   							=field(
         default=__A						,metadata={
             '''help''': (
                 '''The model checkpoint for weights initialization. Can be a local path to a pytorch_model.bin or a '''
                 '''checkpoint identifier on the hub. '''
                 '''Don\'t set if you want to train a model from scratch.'''
             )
         }						,)
     a__   							=field(
         default=__A						,metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(__A )}						,)
     a__   							=field(
         default=__A						,metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
     a__   							=field(
         default=__A						,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'''
             )
         }						,)
     a__   							=field(
         default=__A						,metadata={'''help''': '''Where do you want to store (cache) the pretrained models/datasets downloaded from the hub'''}						,)
     a__   							=field(
         default='''main'''						,metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}						,)
     a__   							=field(default=__A						,metadata={'''help''': '''Name or path of preprocessor config.'''} )
     a__   							=field(
         default=__A						,metadata={
             '''help''': (
                 '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
                 '''with private models).'''
             )
         }						,)
     a__   							=field(
         default=__A						,metadata={
             '''help''': (
                 '''The size (resolution) of each image. If not specified, will use `image_size` of the configuration.'''
             )
         }						,)
     a__   							=field(
         default=__A						,metadata={
             '''help''': (
                 '''The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration.'''
             )
         }						,)
     a__   							=field(
         default=__A						,metadata={'''help''': '''Stride to use for the encoder.'''}						,)



class 				_UpperCAmelCase				:





     '''simple docstring'''



     def __init__(   self			,      A=1_9_2			,      A=3_2			,      A=4			,      A=0.6						)				->   Optional[Any]:
         _UpperCAmelCase				:    Optional[int]											=       input_size
         _UpperCAmelCase				:    str											=       mask_patch_size
         _UpperCAmelCase				:    Optional[int]											=       model_patch_size
         _UpperCAmelCase				:    Dict											=       mask_ratio

         if self.input_size % self.mask_patch_size != 0:
             raise ValueError('''Input size must be divisible by mask patch size'''						)
         if self.mask_patch_size % self.model_patch_size != 0:
             raise ValueError('''Mask patch size must be divisible by model patch size'''						)

         _UpperCAmelCase				:    Dict											=       self.input_size // self.mask_patch_size
         _UpperCAmelCase				:    Any											=       self.mask_patch_size // self.model_patch_size

         _UpperCAmelCase				:    Any											=       self.rand_size**2
         _UpperCAmelCase				:    Union[str, Any]											=       int(np.ceil(self.token_count * self.mask_ratio						)						)





     def __call__(   self						)				->   Union[str, Any]:
         _UpperCAmelCase				:    int											=       np.random.permutation(self.token_count						)[: self.mask_count]
         _UpperCAmelCase				:    int											=       np.zeros(self.token_count			,      dtype=lowerCAmelCase_						)
         _UpperCAmelCase				:    Dict											=       1

         _UpperCAmelCase				:    Dict											=       mask.reshape((self.rand_size, self.rand_size)						)
         _UpperCAmelCase				:    Dict											=       mask.repeat(self.scale			,      axis=0						).repeat(self.scale			,      axis=1						)

         return torch.tensor(mask.flatten()						)





def 					lowerCamelCase_		(UpperCamelCase__						:						int				):
    _UpperCAmelCase				:    Optional[int]											=       torch.stack([example['''pixel_values'''] for example in examples]				)
    _UpperCAmelCase				:    List[Any]											=       torch.stack([example['''mask'''] for example in examples]				)
    return {"pixel_values": pixel_values, "bool_masked_pos": mask}





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.

    _UpperCAmelCase				:    List[Any]											=       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.
        _UpperCAmelCase				:    str											=       parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
    else:
        _UpperCAmelCase				:    Tuple											=       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_mim'''	,			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()

    _UpperCAmelCase				:    Optional[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.
    _UpperCAmelCase				:    Optional[Any]											=       None
    if os.path.isdir(training_args.output_dir				) and training_args.do_train and not training_args.overwrite_output_dir:
        _UpperCAmelCase				:    Tuple											=       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.
    _UpperCAmelCase				:    List[str]											=       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.
    _UpperCAmelCase				:    Union[str, Any]											=       None if "validation" in ds.keys() else data_args.train_val_split
    if isinstance(data_args.train_val_split	,			A__				) and data_args.train_val_split > 0.0:
        _UpperCAmelCase				:    Optional[Any]											=       ds["train"].train_test_split(data_args.train_val_split				)
        _UpperCAmelCase				:    List[str]											=       split["train"]
        _UpperCAmelCase				:    Union[str, Any]											=       split["test"]

    # Create config
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.
    _UpperCAmelCase				:    List[str]											=       {
        "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_or_path:
        _UpperCAmelCase				:    Any											=       AutoConfig.from_pretrained(model_args.config_name_or_path	,			**A__				)
    elif model_args.model_name_or_path:
        _UpperCAmelCase				:    Any											=       AutoConfig.from_pretrained(model_args.model_name_or_path	,			**A__				)
    else:
        _UpperCAmelCase				:    Optional[int]											=       CONFIG_MAPPING[model_args.model_type]()
        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}'				)

    # make sure the decoder_type is "simmim" (only relevant for BEiT)
    if hasattr(A__	,			'''decoder_type'''				):
        _UpperCAmelCase				:    Tuple											=       "simmim"

    # adapt config
    _UpperCAmelCase				:    Optional[int]											=       model_args.image_size if model_args.image_size is not None else config.image_size
    _UpperCAmelCase				:    int											=       model_args.patch_size if model_args.patch_size is not None else config.patch_size
    _UpperCAmelCase				:    Optional[Any]											=       (
        model_args.encoder_stride if model_args.encoder_stride is not None else config.encoder_stride
    )

    config.update(
        {
            '''image_size''': model_args.image_size,
            '''patch_size''': model_args.patch_size,
            '''encoder_stride''': model_args.encoder_stride,
        }				)

    # create image processor
    if model_args.image_processor_name:
        _UpperCAmelCase				:    Tuple											=       AutoImageProcessor.from_pretrained(model_args.image_processor_name	,			**A__				)
    elif model_args.model_name_or_path:
        _UpperCAmelCase				:    List[str]											=       AutoImageProcessor.from_pretrained(model_args.model_name_or_path	,			**A__				)
    else:
        _UpperCAmelCase				:    Dict											=       {
            conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
        }
        _UpperCAmelCase				:    Tuple											=       IMAGE_PROCESSOR_TYPES[model_args.model_type]()

    # create model
    if model_args.model_name_or_path:
        _UpperCAmelCase				:    Union[str, Any]											=       AutoModelForMaskedImageModeling.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	,			)
    else:
        logger.info('''Training new model from scratch'''				)
        _UpperCAmelCase				:    List[str]											=       AutoModelForMaskedImageModeling.from_config(A__				)

    if training_args.do_train:
        _UpperCAmelCase				:    List[str]											=       ds["train"].column_names
    else:
        _UpperCAmelCase				:    str											=       ds["validation"].column_names

    if data_args.image_column_name is not None:
        _UpperCAmelCase				:    Any											=       data_args.image_column_name
    elif "image" in column_names:
        _UpperCAmelCase				:    Tuple											=       "image"
    elif "img" in column_names:
        _UpperCAmelCase				:    List[str]											=       "img"
    else:
        _UpperCAmelCase				:    Optional[Any]											=       column_names[0]

    # transformations as done in original SimMIM paper
    # source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
    _UpperCAmelCase				:    Optional[int]											=       Compose(
        [
            Lambda(lambda UpperCamelCase__				: img.convert('''RGB'''				) if img.mode != "RGB" else img				),
            RandomResizedCrop(model_args.image_size	,			scale=(0.67, 1.0)	,			ratio=(3.0 / 4.0, 4.0 / 3.0)				),
            RandomHorizontalFlip(),
            ToTensor(),
            Normalize(mean=image_processor.image_mean	,			std=image_processor.image_std				),
        ]				)

    # create mask generator
    _UpperCAmelCase				:    Optional[Any]											=       MaskGenerator(
        input_size=model_args.image_size	,			mask_patch_size=data_args.mask_patch_size	,			model_patch_size=model_args.patch_size	,			mask_ratio=data_args.mask_ratio	,			)

    def preprocess_images(UpperCamelCase__						:						Optional[int]				):
        _UpperCAmelCase				:    Union[str, Any]											=       [transforms(A__				) for image in examples[image_column_name]]
        _UpperCAmelCase				:    Any											=       [mask_generator() for i in range(len(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:
            _UpperCAmelCase				:    Optional[Any]											=       ds["train"].shuffle(seed=training_args.seed				).select(range(data_args.max_train_samples				)				)
        # Set the training transforms
        ds["train"].set_transform(A__				)

    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:
            _UpperCAmelCase				:    List[str]											=       (
                ds["validation"].shuffle(seed=training_args.seed				).select(range(data_args.max_eval_samples				)				)
            )
        # Set the validation transforms
        ds["validation"].set_transform(A__				)

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

    # Training
    if training_args.do_train:
        _UpperCAmelCase				:    List[str]											=       None
        if training_args.resume_from_checkpoint is not None:
            _UpperCAmelCase				:    int											=       training_args.resume_from_checkpoint
        elif last_checkpoint is not None:
            _UpperCAmelCase				:    int											=       last_checkpoint
        _UpperCAmelCase				:    Any											=       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:
        _UpperCAmelCase				:    Optional[Any]											=       trainer.evaluate()
        trainer.log_metrics('''eval'''	,			A__				)
        trainer.save_metrics('''eval'''	,			A__				)

    # Write model card and (optionally) push to hub
    _UpperCAmelCase				:    int											=       {
        "finetuned_from": model_args.model_name_or_path,
        "tasks": "masked-image-modeling",
        "dataset": data_args.dataset_name,
        "tags": ["masked-image-modeling"],
    }
    if training_args.push_to_hub:
        trainer.push_to_hub(**A__				)
    else:
        trainer.create_model_card(**A__				)


if __name__ == "__main__":
  main()










"""simple docstring"""
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


if TYPE_CHECKING:
			from ... import PreTrainedTokenizerBase, TensorType

lowerCamelCase_							      = logging.get_logger(__name__)







class      UpperCamelCase_			(__A	):
	__magic_name__       =							'''vision-encoder-decoder'''
	__magic_name__       =							True





	def __init__( self   :					Union[str, Any]      ,      **lowerCAmelCase_   :					List[Any]		)		->     Union[str, Any]:
				super().__init__(**lowerCAmelCase_		)
				if "encoder" not in kwargs or "decoder" not in kwargs:
							raise ValueError(
							    f"""A configuraton of type {self.model_type} cannot be instantiated because """
							    f"""not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}"""		)

				UpperCAmelCase_	:					str     	=   kwargs.pop("encoder"		)
				UpperCAmelCase_	:					Dict     	=   encoder_config.pop("model_type"		)
				UpperCAmelCase_	:					List[Any]     	=   kwargs.pop("decoder"		)
				UpperCAmelCase_	:					Dict     	=   decoder_config.pop("model_type"		)

				UpperCAmelCase_	:					Union[str, Any]     	=   AutoConfig.for_model(lowerCAmelCase_      ,      **lowerCAmelCase_		)
				UpperCAmelCase_	:					List[str]     	=   AutoConfig.for_model(lowerCAmelCase_      ,      **lowerCAmelCase_		)
				UpperCAmelCase_	:					Tuple     	=   True





	@classmethod
	def 	_SCREAMING_SNAKE_CASE ( cls   :					List[str]      ,      lowerCAmelCase_   :					PretrainedConfig      ,      lowerCAmelCase_   :					PretrainedConfig      ,      **lowerCAmelCase_   :					Dict		)		->     PretrainedConfig:
				logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"		)
				UpperCAmelCase_	:					Tuple     	=   True
				UpperCAmelCase_	:					str     	=   True

				return cls(encoder=encoder_config.to_dict()      ,      decoder=decoder_config.to_dict()      ,      **lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]		)		->     List[Any]:
				UpperCAmelCase_	:					Union[str, Any]     	=   copy.deepcopy(self.__dict__		)
				UpperCAmelCase_	:					List[str]     	=   self.encoder.to_dict()
				UpperCAmelCase_	:					List[Any]     	=   self.decoder.to_dict()
				UpperCAmelCase_	:					Any     	=   self.__class__.model_type
				return output







class      UpperCamelCase_			(__A	):
	__magic_name__       =							version.parse('''1.11'''	)





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]		)		->     Mapping[str, Mapping[int, str]]:
				return OrderedDict(
				    [
				        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
				    ]		)





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict		)		->     float:
				return 1e-4





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					Any		)		->     Mapping[str, Mapping[int, str]]:
				return OrderedDict({"last_hidden_state": {0: "batch", 1: "encoder_sequence"}}		)







class      UpperCamelCase_			(__A	):





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict		)		->     Mapping[str, Mapping[int, str]]:
				UpperCAmelCase_	:					List[Any]     	=   OrderedDict()
				UpperCAmelCase_	:					int     	=   {0: "batch", 1: "past_decoder_sequence + sequence"}
				UpperCAmelCase_	:					Dict     	=   {0: "batch", 1: "past_decoder_sequence + sequence"}
				UpperCAmelCase_	:					Union[str, Any]     	=   {0: "batch", 1: "encoder_sequence"}

				return common_inputs





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]      ,      lowerCAmelCase_   :					"PreTrainedTokenizerBase"      ,      lowerCAmelCase_   :					int = -1      ,      lowerCAmelCase_   :					int = -1      ,      lowerCAmelCase_   :					bool = False      ,      lowerCAmelCase_   :					Optional["TensorType"] = None      ,      )		->     Mapping[str, Any]:
				import torch

				UpperCAmelCase_	:					Optional[int]     	=   OrderedDict()

				UpperCAmelCase_	:					Any     	=   super().generate_dummy_inputs(
				    lowerCAmelCase_      ,      batch_size=lowerCAmelCase_      ,      seq_length=lowerCAmelCase_      ,      is_pair=lowerCAmelCase_      ,      framework=lowerCAmelCase_		)

				UpperCAmelCase_	,      UpperCAmelCase_	:					Optional[Any]     	=   dummy_input["input_ids"].shape
				UpperCAmelCase_	:					List[str]     	=   (batch, encoder_sequence, self._config.encoder_hidden_size)
				UpperCAmelCase_	:					List[str]     	=   dummy_input.pop("input_ids"		)
				UpperCAmelCase_	:					List[str]     	=   dummy_input.pop("attention_mask"		)
				UpperCAmelCase_	:					List[Any]     	=   torch.zeros(lowerCAmelCase_		)

				return common_inputs







class      UpperCamelCase_			(__A	):





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					List[str]		)		->     None:
				pass





	def 	_SCREAMING_SNAKE_CASE ( self   :					Any      ,      lowerCAmelCase_   :					PretrainedConfig		)		->     OnnxConfig:
				return VisionEncoderDecoderEncoderOnnxConfig(lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]      ,      lowerCAmelCase_   :					PretrainedConfig      ,      lowerCAmelCase_   :					PretrainedConfig      ,      lowerCAmelCase_   :					str = "default"		)		->     OnnxConfig:
				UpperCAmelCase_	:					Optional[int]     	=   encoder_config.hidden_size
				return VisionEncoderDecoderDecoderOnnxConfig(lowerCAmelCase_      ,      lowerCAmelCase_		)



