




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"""

    snake_case__ =				field(
        default="cifar10"     ,    metadata={"help": "Name of a dataset from the datasets package"}   )
    snake_case__ =				field(
        default=a__     ,    metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}   )
    snake_case__ =				field(
        default=a__     ,    metadata={"help": "The column name of the images in the files. If not set, will try to use 'image' or 'img'."}     ,    )
    snake_case__ =				field(default=a__     ,    metadata={"help": "A folder containing the training data."}   )
    snake_case__ =				field(default=a__     ,    metadata={"help": "A folder containing the validation data."}   )
    snake_case__ =				field(
        default=0.1_5     ,    metadata={"help": "Percent to split off of train for validation."}   )
    snake_case__ =				field(default=3_2     ,    metadata={"help": "The size of the square patches to use for masking."}   )
    snake_case__ =				field(
        default=0.6     ,    metadata={"help": "Percentage of patches to mask."}     ,    )
    snake_case__ =				field(
        default=a__     ,    metadata={
            "help": (
                "For debugging purposes or quicker training, truncate the number of training examples to this "
                "value if set."
            )
        }     ,    )
    snake_case__ =				field(
        default=a__     ,    metadata={
            "help": (
                "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
                "value if set."
            )
        }     ,    )


    def        a				(	self					:					Any       )				->	Tuple:
      lowerCAmelCase__          =			{}
      if self.train_dir is not None:
        lowerCAmelCase__          =			self.train_dir
      if self.validation_dir is not None:
        lowerCAmelCase__          =			self.validation_dir
      lowerCAmelCase__          =			data_files if data_files else None






@dataclass
class 			__lowerCamelCase	:




    """simple docstring"""

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






class 			__lowerCamelCase	:




    """simple docstring"""

    def __init__(	self					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					Tuple=192       ,     SCREAMING_SNAKE_CASE__					:					str=32       ,     SCREAMING_SNAKE_CASE__					:					Any=4       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]=0.6       )				->	List[str]:
      lowerCAmelCase__          =			input_size
      lowerCAmelCase__          =			mask_patch_size
      lowerCAmelCase__          =			model_patch_size
      lowerCAmelCase__          =			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"       )

      lowerCAmelCase__          =			self.input_size // self.mask_patch_size
      lowerCAmelCase__          =			self.mask_patch_size // self.model_patch_size

      lowerCAmelCase__          =			self.rand_size**2
      lowerCAmelCase__          =			int(np.ceil(self.token_count * self.mask_ratio       )       )


    def __call__(	self					:					str       )				->	str:
      lowerCAmelCase__          =			np.random.permutation(self.token_count       )[: self.mask_count]
      lowerCAmelCase__          =			np.zeros(self.token_count       ,     dtype=lowerCAmelCase__       )
      lowerCAmelCase__          =			1

      lowerCAmelCase__          =			mask.reshape((self.rand_size, self.rand_size)       )
      lowerCAmelCase__          =			mask.repeat(self.scale       ,     axis=0       ).repeat(self.scale       ,     axis=1       )

      return torch.tensor(mask.flatten()       )






def 							_A       (						lowerCAmelCase_					: Optional[int]      ):
  """simple docstring"""

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



def 							_A       (						):
  """simple docstring"""

  lowerCAmelCase__          =			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.
    lowerCAmelCase__          =			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]      )      )
  else:
    lowerCAmelCase__          =			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"       ,    _lowercase       ,    _lowercase      )

  # 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()

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

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

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

  # create model
  if model_args.model_name_or_path:
    lowerCAmelCase__          =			AutoModelForMaskedImageModeling.from_pretrained(
        model_args.model_name_or_path       ,    from_tf=bool(".ckpt" in model_args.model_name_or_path      )       ,    config=_lowercase       ,    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"      )
    lowerCAmelCase__          =			AutoModelForMaskedImageModeling.from_config(_lowercase      )

  if training_args.do_train:
    lowerCAmelCase__          =			ds["train"].column_names
  else:
    lowerCAmelCase__          =			ds["validation"].column_names

  if data_args.image_column_name is not None:
    lowerCAmelCase__          =			data_args.image_column_name
  elif "image" in column_names:
    lowerCAmelCase__          =			"image"
  elif "img" in column_names:
    lowerCAmelCase__          =			"img"
  else:
    lowerCAmelCase__          =			column_names[0]

  # transformations as done in original SimMIM paper
  # source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
  lowerCAmelCase__          =			Compose(
      [
          Lambda(lambda lowerCAmelCase_      : 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
  lowerCAmelCase__          =			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(lowerCAmelCase_					: List[str]      ):
    lowerCAmelCase__          =			[transforms(_lowercase      ) for image in examples[image_column_name]]
    lowerCAmelCase__          =			[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:
      lowerCAmelCase__          =			ds["train"].shuffle(seed=training_args.seed      ).select(range(data_args.max_train_samples      )      )
    # Set the training transforms
    ds["train"].set_transform(_lowercase      )

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

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

  # Training
  if training_args.do_train:
    lowerCAmelCase__          =			None
    if training_args.resume_from_checkpoint is not None:
      lowerCAmelCase__          =			training_args.resume_from_checkpoint
    elif last_checkpoint is not None:
      lowerCAmelCase__          =			last_checkpoint
    lowerCAmelCase__          =			trainer.train(resume_from_checkpoint=_lowercase      )
    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:
    lowerCAmelCase__          =			trainer.evaluate()
    trainer.log_metrics("eval"       ,    _lowercase      )
    trainer.save_metrics("eval"       ,    _lowercase      )

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


if __name__ == "__main__":
     main()






import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin





class 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    snake_case__ =				"Speech2TextFeatureExtractor"
    snake_case__ =				"Speech2TextTokenizer"


    def __init__(	self					:					int       ,     SCREAMING_SNAKE_CASE__					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					Tuple       )				->	Optional[Any]:
         super().__init__(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			self.feature_extractor
         lowerCAmelCase__          =			False


    def __call__(	self					:					str       ,     *SCREAMING_SNAKE_CASE__					:					int       ,     **SCREAMING_SNAKE_CASE__					:					int       )				->	Optional[Any]:
         # For backward compatibility
         if self._in_target_context_manager:
              return self.current_processor(*SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       )

         if "raw_speech" in kwargs:
              warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."       )
              lowerCAmelCase__          =			kwargs.pop("raw_speech"       )
         else:
              lowerCAmelCase__          =			kwargs.pop("audio"       ,     SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			kwargs.pop("sampling_rate"       ,     SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			kwargs.pop("text"       ,     SCREAMING_SNAKE_CASE__       )
         if len(SCREAMING_SNAKE_CASE__       ) > 0:
              lowerCAmelCase__          =			args[0]
              lowerCAmelCase__          =			args[1:]

         if audio is None and text is None:
              raise ValueError("You need to specify either an `audio` or `text` input to process."       )

         if audio is not None:
              lowerCAmelCase__          =			self.feature_extractor(SCREAMING_SNAKE_CASE__       ,     *SCREAMING_SNAKE_CASE__       ,     sampling_rate=SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       )
         if text is not None:
              lowerCAmelCase__          =			self.tokenizer(SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       )

         if text is None:
              return inputs
         elif audio is None:
              return encodings
         else:
              lowerCAmelCase__          =			encodings["input_ids"]
              return inputs


    def        a				(	self					:					str       ,     *SCREAMING_SNAKE_CASE__					:					Dict       ,     **SCREAMING_SNAKE_CASE__					:					int       )				->	Union[str, Any]:
         return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					Dict       ,     *SCREAMING_SNAKE_CASE__					:					Optional[int]       ,     **SCREAMING_SNAKE_CASE__					:					Optional[int]       )				->	Optional[Any]:
         return self.tokenizer.decode(*SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       )


    @contextmanager
    def        a				(	self					:					Union[str, Any]       )				->	Any:
         warnings.warn(
             "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
             "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
             "your audio inputs, or in a separate call."       )
         lowerCAmelCase__          =			True
         lowerCAmelCase__          =			self.tokenizer
         yield
         lowerCAmelCase__          =			self.feature_extractor
         lowerCAmelCase__          =			False
