





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
_UpperCamelCase				=       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''')

_UpperCamelCase				=       list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
_UpperCamelCase				=       tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)




@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=lowercase__       ,      metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}			)
      UpperCAmelCase_  :					Optional[str]      			=field(
          default=lowercase__       ,      metadata={"help": "The column name of the images in the files. If not set, will try to use \'image\' or \'img\'."}       ,      )
      UpperCAmelCase_  :					Optional[str]      			=field(default=lowercase__       ,      metadata={"help": "A folder containing the training data."}			)
      UpperCAmelCase_  :					Optional[str]      			=field(default=lowercase__       ,      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_  :					int      			=field(default=32       ,      metadata={"help": "The size of the square patches to use for masking."}			)
      UpperCAmelCase_  :					float      			=field(
          default=0.6       ,      metadata={"help": "Percentage of patches to mask."}       ,      )
      UpperCAmelCase_  :					Optional[int]      			=field(
          default=lowercase__       ,      metadata={
              "help": (
                  "For debugging purposes or quicker training, truncate the number of training examples to this "
                  "value if set."
              )
          }       ,      )
      UpperCAmelCase_  :					Optional[int]      			=field(
          default=lowercase__       ,      metadata={
              "help": (
                  "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
                  "value if set."
              )
          }       ,      )







      def   UpperCAmelCase						(       self      )				->      Any:






        '''simple docstring'''






        __snake_case      : List[Any]						=				{}
        if self.train_dir is not None:
          __snake_case      : List[str]						=				self.train_dir
        if self.validation_dir is not None:
          __snake_case      : List[Any]						=				self.validation_dir
        __snake_case      : Any						=				data_files if data_files else None




@dataclass
class  _lowerCamelCase       :



      """simple docstring"""






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




class  _lowerCamelCase       :



      """simple docstring"""






      def __init__(       self ,  UpperCAmelCase=192 ,  UpperCAmelCase=32 ,  UpperCAmelCase=4 ,  UpperCAmelCase=0.6      )				->      List[str]:






        '''simple docstring'''






        __snake_case      : int						=				input_size
        __snake_case      : int						=				mask_patch_size
        __snake_case      : str						=				model_patch_size
        __snake_case      : Optional[int]						=				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"      )

        __snake_case      : Optional[Any]						=				self.input_size // self.mask_patch_size
        __snake_case      : int						=				self.mask_patch_size // self.model_patch_size

        __snake_case      : Dict						=				self.rand_size**2
        __snake_case      : int						=				int(np.ceil(self.token_count * self.mask_ratio      )      )







      def __call__(       self      )				->      Optional[int]:






        '''simple docstring'''






        __snake_case      : Any						=				np.random.permutation(self.token_count      )[: self.mask_count]
        __snake_case      : str						=				np.zeros(self.token_count ,  dtype=SCREAMING_SNAKE_CASE__      )
        __snake_case      : str						=				1

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

        return torch.tensor(mask.flatten()      )







def 					lowerCAmelCase__(		lowercase				:			str )       ->			List[Any]:
  __snake_case      : Optional[Any]						=				torch.stack([example["pixel_values"] for example in examples] )
  __snake_case      : Optional[int]						=				torch.stack([example["mask"] for example in examples] )
  return {"pixel_values": pixel_values, "bool_masked_pos": mask}







def 					lowerCAmelCase__(		)       ->			Union[str, Any]:
  __snake_case      : List[str]						=				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.
    __snake_case      : Optional[Any]						=				parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
  else:
    __snake_case      : Optional[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"      ,   __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()

  __snake_case      : int						=				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.
  __snake_case      : Optional[int]						=				None
  if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
    __snake_case      : 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.
  __snake_case      : 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.
  __snake_case      : 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:
    __snake_case      : str						=				ds['train'].train_test_split(data_args.train_val_split )
    __snake_case      : Optional[Any]						=				split['train']
    __snake_case      : Union[str, Any]						=				split['test']

  # Create config
  # Distributed training:
  # The .from_pretrained methods guarantee that only one local process can concurrently
  # download model & vocab.
  __snake_case      : 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:
    __snake_case      : Tuple						=				AutoConfig.from_pretrained(model_args.config_name_or_path      ,   **__A )
  elif model_args.model_name_or_path:
    __snake_case      : Dict						=				AutoConfig.from_pretrained(model_args.model_name_or_path      ,   **__A )
  else:
    __snake_case      : 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(__A      ,   "decoder_type" ):
    __snake_case      : Tuple						=				'simmim'

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

  # create model
  if model_args.model_name_or_path:
    __snake_case      : 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" )
    __snake_case      : List[str]						=				AutoModelForMaskedImageModeling.from_config(__A )

  if training_args.do_train:
    __snake_case      : List[Any]						=				ds['train'].column_names
  else:
    __snake_case      : List[str]						=				ds['validation'].column_names

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

  # transformations as done in original SimMIM paper
  # source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
  __snake_case      : Optional[int]						=				Compose(
      [
          Lambda(lambda lowercase : img.convert("RGB" ) if img.mode != "RGB" else img ),
          RandomResizedCrop(model_args.image_size      ,   scale=(0.6_7, 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
  __snake_case      : int						=				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				:			Optional[Any] ):
    __snake_case      : Optional[Any]						=				[transforms(__A ) for image in examples[image_column_name]]
    __snake_case      : List[str]						=				[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:
      __snake_case      : 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:
      __snake_case      : Optional[Any]						=				(
          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
  __snake_case      : str						=				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:
    __snake_case      : Any						=				None
    if training_args.resume_from_checkpoint is not None:
      __snake_case      : List[str]						=				training_args.resume_from_checkpoint
    elif last_checkpoint is not None:
      __snake_case      : Tuple						=				last_checkpoint
    __snake_case      : 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:
    __snake_case      : List[str]						=				trainer.evaluate()
    trainer.log_metrics("eval"      ,   __A )
    trainer.save_metrics("eval"      ,   __A )

  # Write model card and (optionally) push to hub
  __snake_case      : Optional[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(**__A )
  else:
    trainer.create_model_card(**__A )


if __name__ == "__main__":
     main()





import inspect
import re
from hashlib import shaaaa
from typing import Dict, List

from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql  # noqa F401
from .text import text







def        SCREAMING_SNAKE_CASE_						(			__A :			List[str]				)	->		str:

       """simple docstring"""

       a_					:		Tuple							=  []
       for line in lines:
              a_					:		Any							=  re.sub(R'#.*'  ,    ''  ,    __A				)  # remove comments
              if line:
                     filtered_lines.append(__A				)
       a_					:		Tuple							=  '\n'.join(__A				)

       # Make a hash from all this code
       a_					:		Tuple							=  full_str.encode('utf-8'				)
       return shaaaa(__A				).hexdigest()


# get importable module names and hash for caching
UpperCAmelCase_      : List[Any]  					=       {
    'csv': (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
    'json': (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
    'pandas': (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
    'parquet': (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
    'arrow': (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
    'text': (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
    'imagefolder': (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
    'audiofolder': (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}

# Used to infer the module to use based on the data files extensions
UpperCAmelCase_      : Dict  					=       {
    '.csv': ('csv', {}),
    '.tsv': ('csv', {'sep': '\t'}),
    '.json': ('json', {}),
    '.jsonl': ('json', {}),
    '.parquet': ('parquet', {}),
    '.arrow': ('arrow', {}),
    '.txt': ('text', {}),
}
_EXTENSION_TO_MODULE.update({ext: ('imagefolder', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('imagefolder', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ('audiofolder', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('audiofolder', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
UpperCAmelCase_      : Optional[int]  					=       {'imagefolder', 'audiofolder'}

# Used to filter data files based on extensions given a module name
UpperCAmelCase_      : Dict[str, List[str]]  					=       {}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
   _MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)

_MODULE_TO_EXTENSIONS["imagefolder"].append('.zip')
_MODULE_TO_EXTENSIONS["audiofolder"].append('.zip')


