



"""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							:   int				=				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							:   Union[str, Any]				=				list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
_lowerCAmelCase							:   Tuple				=				tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)






@dataclass
class       UpperCAmelCase_    :
      __SCREAMING_SNAKE_CASE		:  Optional[str]						=					field(
          default='cifar10'			,     metadata={'help': 'Name of a dataset from the datasets package'}		)
      __SCREAMING_SNAKE_CASE		:  Optional[str]						=					field(
          default=_UpperCamelCase			,     metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}		)
      __SCREAMING_SNAKE_CASE		:  Optional[str]						=					field(
          default=_UpperCamelCase			,     metadata={'help': 'The column name of the images in the files. If not set, will try to use \'image\' or \'img\'.'}			,     )
      __SCREAMING_SNAKE_CASE		:  Optional[str]						=					field(default=_UpperCamelCase			,     metadata={'help': 'A folder containing the training data.'}		)
      __SCREAMING_SNAKE_CASE		:  Optional[str]						=					field(default=_UpperCamelCase			,     metadata={'help': 'A folder containing the validation data.'}		)
      __SCREAMING_SNAKE_CASE		:  Optional[float]						=					field(
          default=0.15			,     metadata={'help': 'Percent to split off of train for validation.'}		)
      __SCREAMING_SNAKE_CASE		:  int						=					field(default=3_2			,     metadata={'help': 'The size of the square patches to use for masking.'}		)
      __SCREAMING_SNAKE_CASE		:  float						=					field(
          default=0.6			,     metadata={'help': 'Percentage of patches to mask.'}			,     )
      __SCREAMING_SNAKE_CASE		:  Optional[int]						=					field(
          default=_UpperCamelCase			,     metadata={
              'help': (
                  'For debugging purposes or quicker training, truncate the number of training examples to this '
                  'value if set.'
              )
          }			,     )
      __SCREAMING_SNAKE_CASE		:  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 		snake_case_   (	self	:      str		):
           _UpperCAmelCase     :      List[str]														=       {}
           if self.train_dir is not None:
                _UpperCAmelCase     :      Optional[int]														=       self.train_dir
           if self.validation_dir is not None:
                _UpperCAmelCase     :      Union[str, Any]														=       self.validation_dir
           _UpperCAmelCase     :      str														=       data_files if data_files else None







@dataclass
class       UpperCAmelCase_    :
      __SCREAMING_SNAKE_CASE		:  str						=					field(
          default=_UpperCamelCase			,     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.'
              )
          }			,     )
      __SCREAMING_SNAKE_CASE		:  Optional[str]						=					field(
          default=_UpperCamelCase			,     metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(_UpperCamelCase		)}			,     )
      __SCREAMING_SNAKE_CASE		:  Optional[str]						=					field(
          default=_UpperCamelCase			,     metadata={'help': 'Pretrained config name or path if not the same as model_name'}		)
      __SCREAMING_SNAKE_CASE		:  Optional[str]						=					field(
          default=_UpperCamelCase			,     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'
              )
          }			,     )
      __SCREAMING_SNAKE_CASE		:  Optional[str]						=					field(
          default=_UpperCamelCase			,     metadata={'help': 'Where do you want to store (cache) the pretrained models/datasets downloaded from the hub'}			,     )
      __SCREAMING_SNAKE_CASE		:  str						=					field(
          default='main'			,     metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}			,     )
      __SCREAMING_SNAKE_CASE		:  str						=					field(default=_UpperCamelCase			,     metadata={'help': 'Name or path of preprocessor config.'}		)
      __SCREAMING_SNAKE_CASE		:  bool						=					field(
          default=_UpperCamelCase			,     metadata={
              'help': (
                  'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
                  'with private models).'
              )
          }			,     )
      __SCREAMING_SNAKE_CASE		:  Optional[int]						=					field(
          default=_UpperCamelCase			,     metadata={
              'help': (
                  'The size (resolution) of each image. If not specified, will use `image_size` of the configuration.'
              )
          }			,     )
      __SCREAMING_SNAKE_CASE		:  Optional[int]						=					field(
          default=_UpperCamelCase			,     metadata={
              'help': (
                  'The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration.'
              )
          }			,     )
      __SCREAMING_SNAKE_CASE		:  Optional[int]						=					field(
          default=_UpperCamelCase			,     metadata={'help': 'Stride to use for the encoder.'}			,     )







class       UpperCAmelCase_    :



      def __init__(	self	:      Tuple	,      A	:      Dict=1_9_2	,      A	:      Tuple=3_2	,      A	:      str=4	,      A	:      Any=0.6		):
           _UpperCAmelCase     :      Union[str, Any]														=       input_size
           _UpperCAmelCase     :      str														=       mask_patch_size
           _UpperCAmelCase     :      Any														=       model_patch_size
           _UpperCAmelCase     :      List[Any]														=       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     :      int														=       self.input_size // self.mask_patch_size
           _UpperCAmelCase     :      int														=       self.mask_patch_size // self.model_patch_size

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



      def __call__(	self	:      List[Any]		):
           _UpperCAmelCase     :      Optional[Any]														=       np.random.permutation(self.token_count		)[: self.mask_count]
           _UpperCAmelCase     :      List[str]														=       np.zeros(self.token_count	,      dtype=A		)
           _UpperCAmelCase     :      Union[str, Any]														=       1

           _UpperCAmelCase     :      Union[str, Any]														=       mask.reshape((self.rand_size, self.rand_size)		)
           _UpperCAmelCase     :      Any														=       mask.repeat(self.scale	,      axis=0		).repeat(self.scale	,      axis=1		)

           return torch.tensor(mask.flatten()		)







def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		str							)    ->  int:





     '''simple docstring'''

     _UpperCAmelCase     :      Dict														=       torch.stack([example["pixel_values"] for example in examples]							)
     _UpperCAmelCase     :      int														=       torch.stack([example["mask"] for example in examples]							)
     return {"pixel_values": pixel_values, "bool_masked_pos": mask}

def 		__snake_case		( )    ->  Union[str, Any]:





     '''simple docstring'''

     _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							,			_UpperCAmelCase							,			_UpperCAmelCase     :      Dict														=       parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]							)							)
     else:
          _UpperCAmelCase							,			_UpperCAmelCase							,			_UpperCAmelCase     :      Optional[int]														=       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"		,    SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)

     # 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     :      List[str]														=       training_args.get_process_log_level()
     logger.setLevel(SCREAMING_SNAKE_CASE__							)
     transformers.utils.logging.set_verbosity(SCREAMING_SNAKE_CASE__							)
     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[int]														=       None
     if os.path.isdir(training_args.output_dir							) and training_args.do_train and not training_args.overwrite_output_dir:
          _UpperCAmelCase     :      List[Any]														=       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     :      Optional[Any]														=       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     :      int														=       None if "validation" in ds.keys() else data_args.train_val_split
     if isinstance(data_args.train_val_split		,    SCREAMING_SNAKE_CASE__							) and data_args.train_val_split > 0.0:
          _UpperCAmelCase     :      int														=       ds["train"].train_test_split(data_args.train_val_split							)
          _UpperCAmelCase     :      List[Any]														=       split["train"]
          _UpperCAmelCase     :      Any														=       split["test"]

     # Create config
     # Distributed training:
     # The .from_pretrained methods guarantee that only one local process can concurrently
     # download model & vocab.
     _UpperCAmelCase     :      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     :      Union[str, Any]														=       AutoConfig.from_pretrained(model_args.config_name_or_path		,    **SCREAMING_SNAKE_CASE__							)
     elif model_args.model_name_or_path:
          _UpperCAmelCase     :      Any														=       AutoConfig.from_pretrained(model_args.model_name_or_path		,    **SCREAMING_SNAKE_CASE__							)
     else:
          _UpperCAmelCase     :      Union[str, Any]														=       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(SCREAMING_SNAKE_CASE__		,    "decoder_type"							):
          _UpperCAmelCase     :      Tuple														=       "simmim"

     # adapt config
     _UpperCAmelCase     :      Any														=       model_args.image_size if model_args.image_size is not None else config.image_size
     _UpperCAmelCase     :      str														=       model_args.patch_size if model_args.patch_size is not None else config.patch_size
     _UpperCAmelCase     :      Dict														=       (
         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     :      int														=       AutoImageProcessor.from_pretrained(model_args.image_processor_name		,    **SCREAMING_SNAKE_CASE__							)
     elif model_args.model_name_or_path:
          _UpperCAmelCase     :      Union[str, Any]														=       AutoImageProcessor.from_pretrained(model_args.model_name_or_path		,    **SCREAMING_SNAKE_CASE__							)
     else:
          _UpperCAmelCase     :      Optional[int]														=       {
              conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
          }
          _UpperCAmelCase     :      List[str]														=       IMAGE_PROCESSOR_TYPES[model_args.model_type]()

     # create model
     if model_args.model_name_or_path:
          _UpperCAmelCase     :      Tuple														=       AutoModelForMaskedImageModeling.from_pretrained(
              model_args.model_name_or_path		,    from_tf=bool(".ckpt" in model_args.model_name_or_path							)		,    config=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		,    )
     else:
          logger.info("Training new model from scratch"							)
          _UpperCAmelCase     :      int														=       AutoModelForMaskedImageModeling.from_config(SCREAMING_SNAKE_CASE__							)

     if training_args.do_train:
          _UpperCAmelCase     :      Tuple														=       ds["train"].column_names
     else:
          _UpperCAmelCase     :      Union[str, Any]														=       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     :      List[Any]														=       "image"
     elif "img" in column_names:
          _UpperCAmelCase     :      List[Any]														=       "img"
     else:
          _UpperCAmelCase     :      List[Any]														=       column_names[0]

     # transformations as done in original SimMIM paper
     # source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
     _UpperCAmelCase     :      Dict														=       Compose(
         [
             Lambda(lambda SCREAMING_SNAKE_CASE__							: 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     :      str														=       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(SCREAMING_SNAKE_CASE__      :		List[str]							):
          _UpperCAmelCase     :      List[Any]														=       [transforms(SCREAMING_SNAKE_CASE__							) for image in examples[image_column_name]]
          _UpperCAmelCase     :      Dict														=       [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(SCREAMING_SNAKE_CASE__							)

     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[Any]														=       (
                   ds["validation"].shuffle(seed=training_args.seed							).select(range(data_args.max_eval_samples							)							)
               )
          # Set the validation transforms
          ds["validation"].set_transform(SCREAMING_SNAKE_CASE__							)

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

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

     # Write model card and (optionally) push to hub
     _UpperCAmelCase     :      Tuple														=       {
         "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(**SCREAMING_SNAKE_CASE__							)
     else:
          trainer.create_model_card(**SCREAMING_SNAKE_CASE__							)


if __name__ == "__main__":
 main()





"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCAmelCase							:   List[Any]				=				{
    "configuration_lilt": ["LILT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LiltConfig"],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase							:   int				=				[
     "LILT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "LiltForQuestionAnswering",
     "LiltForSequenceClassification",
     "LiltForTokenClassification",
     "LiltModel",
     "LiltPreTrainedModel",
 ]

if TYPE_CHECKING:
 from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_lilt import (
      LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
      LiltForQuestionAnswering,
      LiltForSequenceClassification,
      LiltForTokenClassification,
      LiltModel,
      LiltPreTrainedModel,
  )

else:
 import sys

 _lowerCAmelCase							:   Optional[int]				=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
