





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
_lowercase							:      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-pretraining/requirements.txt''')

_lowercase							:      Optional[int]      							=list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
_lowercase							:      Dict      							=tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)



@dataclass
class  SCREAMING_SNAKE_CASE_ :
  '''simple docstring'''


  lowercase		:     Tuple            =				field(
      default="cifar10"   ,						metadata={"help": "Name of a dataset from the datasets package"}			)
  lowercase		:     int            =				field(
      default=lowerCAmelCase_   ,						metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}			)
  lowercase		:     Any            =				field(
      default=lowerCAmelCase_   ,						metadata={"help": "The column name of the images in the files. If not set, will try to use \'image\' or \'img\'."}   ,						)
  lowercase		:     Tuple            =				field(default=lowerCAmelCase_   ,						metadata={"help": "A folder containing the training data."}			)
  lowercase		:     Tuple            =				field(default=lowerCAmelCase_   ,						metadata={"help": "A folder containing the validation data."}			)
  lowercase		:     Any            =				field(
      default=0.1_5   ,						metadata={"help": "Percent to split off of train for validation."}			)
  lowercase		:     Optional[Any]            =				field(default=32   ,						metadata={"help": "The size of the square patches to use for masking."}			)
  lowercase		:     Optional[int]            =				field(
      default=0.6   ,						metadata={"help": "Percentage of patches to mask."}   ,						)
  lowercase		:     Optional[Any]            =				field(
      default=lowerCAmelCase_   ,						metadata={
          "help": (
              "For debugging purposes or quicker training, truncate the number of training examples to this "
              "value if set."
          )
      }   ,						)
  lowercase		:     Dict            =				field(
      default=lowerCAmelCase_   ,						metadata={
          "help": (
              "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
              "value if set."
          )
      }   ,						)







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







@dataclass
class  SCREAMING_SNAKE_CASE_ :
  '''simple docstring'''


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







class  SCREAMING_SNAKE_CASE_ :
  '''simple docstring'''


  def __init__(				self    :					List[str]      ,	SCREAMING_SNAKE_CASE__    :					Tuple=1_92      ,	SCREAMING_SNAKE_CASE__    :					Dict=32      ,	SCREAMING_SNAKE_CASE__    :					Dict=4      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=0.6     )    ->				Tuple:
         A						:     Dict         =input_size
         A						:     int         =mask_patch_size
         A						:     List[str]         =model_patch_size
         A						:     str         =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'     )

         A						:     List[str]         =self.input_size // self.mask_patch_size
         A						:     Any         =self.mask_patch_size // self.model_patch_size

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







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

         A						:     int         =mask.reshape((self.rand_size, self.rand_size)     )
         A						:     str         =mask.repeat(self.scale      ,	axis=0     ).repeat(self.scale      ,	axis=1     )

         return torch.tensor(mask.flatten()     )







def A__  (			lowercase:     Tuple					)				->		Dict:
       A						:     int         =torch.stack([example['pixel_values'] for example in examples]					)
       A						:     Optional[Any]         =torch.stack([example['mask'] for example in examples]					)
       return {"pixel_values": pixel_values, "bool_masked_pos": mask}



def A__  (			)				->		Tuple:
       A						:     Tuple         =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.
              A  ,  A  ,  A						:     Union[str, Any]         =parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
       else:
              A  ,  A  ,  A						:     Union[str, Any]         =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',		_snake_case,		_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()

       A						:     int         =training_args.get_process_log_level()
       logger.setLevel(_snake_case					)
       transformers.utils.logging.set_verbosity(_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.
       A						:     Optional[int]         =None
       if os.path.isdir(training_args.output_dir					) and training_args.do_train and not training_args.overwrite_output_dir:
              A						:     Dict         =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						:     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.
       A						:     Tuple         =None if 'validation' in ds.keys() else data_args.train_val_split
       if isinstance(data_args.train_val_split,		_snake_case					) and data_args.train_val_split > 0.0:
              A						:     str         =ds['train'].train_test_split(data_args.train_val_split					)
              A						:     Tuple         =split['train']
              A						:     Union[str, Any]         =split['test']

       # Create config
       # Distributed training:
       # The .from_pretrained methods guarantee that only one local process can concurrently
       # download model & vocab.
       A						:     Union[str, Any]         ={
           '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:
              A						:     Optional[int]         =AutoConfig.from_pretrained(model_args.config_name_or_path,		**_snake_case					)
       elif model_args.model_name_or_path:
              A						:     int         =AutoConfig.from_pretrained(model_args.model_name_or_path,		**_snake_case					)
       else:
              A						:     List[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(_snake_case,		'decoder_type'					):
              A						:     Dict         ='simmim'

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

       # create model
       if model_args.model_name_or_path:
              A						:     Optional[int]         =AutoModelForMaskedImageModeling.from_pretrained(
                  model_args.model_name_or_path,		from_tf=bool('.ckpt' in model_args.model_name_or_path					),		config=_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'					)
              A						:     Dict         =AutoModelForMaskedImageModeling.from_config(_snake_case					)

       if training_args.do_train:
              A						:     Tuple         =ds['train'].column_names
       else:
              A						:     Dict         =ds['validation'].column_names

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

       # transformations as done in original SimMIM paper
       # source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
       A						:     Tuple         =Compose(
           [
               Lambda(lambda lowercase					: 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
       A						:     Dict         =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(lowercase:     Any					):
              A						:     Optional[int]         =[transforms(_snake_case					) for image in examples[image_column_name]]
              A						:     Optional[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:
                     A						:     str         =ds['train'].shuffle(seed=training_args.seed					).select(range(data_args.max_train_samples					)					)
              # Set the training transforms
              ds["train"].set_transform(_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:
                     A						:     Any         =(
                         ds['validation'].shuffle(seed=training_args.seed					).select(range(data_args.max_eval_samples					)					)
                     )
              # Set the validation transforms
              ds["validation"].set_transform(_snake_case					)

       # Initialize our trainer
       A						:     Optional[Any]         =Trainer(
           model=_snake_case,		args=_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=_snake_case,		data_collator=_snake_case,		)

       # Training
       if training_args.do_train:
              A						:     Union[str, Any]         =None
              if training_args.resume_from_checkpoint is not None:
                     A						:     str         =training_args.resume_from_checkpoint
              elif last_checkpoint is not None:
                     A						:     str         =last_checkpoint
              A						:     Dict         =trainer.train(resume_from_checkpoint=_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:
              A						:     Union[str, Any]         =trainer.evaluate()
              trainer.log_metrics('eval',		_snake_case					)
              trainer.save_metrics('eval',		_snake_case					)

       # Write model card and (optionally) push to hub
       A						:     Union[str, Any]         ={
           '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(**_snake_case					)
       else:
              trainer.create_model_card(**_snake_case					)


if __name__ == "__main__":
    main()







from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowercase							:      int      							=logging.get_logger(__name__)

_lowercase							:      Dict      							={
    '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/config.json''',
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}



class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


  lowercase		:     Optional[int]            =				"xglm"
  lowercase		:     Any            =				["past_key_values"]

  lowercase		:     Dict            =				{
      "num_attention_heads": "attention_heads",
      "hidden_size": "d_model",
      "num_hidden_layers": "num_layers",
  }







  def __init__(				self    :					int      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=25_60_08      ,	SCREAMING_SNAKE_CASE__    :					Dict=20_48      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=10_24      ,	SCREAMING_SNAKE_CASE__    :					str=40_96      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]=24      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=16      ,	SCREAMING_SNAKE_CASE__    :					Any="gelu"      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=0.1      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=0.1      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=0.0      ,	SCREAMING_SNAKE_CASE__    :					Tuple=0.0      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=0.0_2      ,	SCREAMING_SNAKE_CASE__    :					int=True      ,	SCREAMING_SNAKE_CASE__    :					Dict=True      ,	SCREAMING_SNAKE_CASE__    :					Any=2      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=1      ,	SCREAMING_SNAKE_CASE__    :					str=0      ,	SCREAMING_SNAKE_CASE__    :					List[str]=2      ,	**SCREAMING_SNAKE_CASE__    :					Dict      ,	)    ->				int:
         A						:     str         =vocab_size
         A						:     Union[str, Any]         =max_position_embeddings
         A						:     Optional[Any]         =d_model
         A						:     Optional[int]         =ffn_dim
         A						:     int         =num_layers
         A						:     Any         =attention_heads
         A						:     Dict         =activation_function
         A						:     List[Any]         =dropout
         A						:     str         =attention_dropout
         A						:     List[Any]         =activation_dropout
         A						:     List[Any]         =layerdrop
         A						:     List[Any]         =init_std
         A						:     Union[str, Any]         =scale_embedding  # scale factor will be sqrt(d_model) if True
         A						:     List[str]         =use_cache

         super().__init__(
             pad_token_id=SCREAMING_SNAKE_CASE__      ,	bos_token_id=SCREAMING_SNAKE_CASE__      ,	eos_token_id=SCREAMING_SNAKE_CASE__      ,	decoder_start_token_id=SCREAMING_SNAKE_CASE__      ,	**SCREAMING_SNAKE_CASE__      ,	)






