


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
snake_case__				:	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')

snake_case__				:	Any					       =					list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
snake_case__				:	Any					       =					tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)





@dataclass
class   SCREAMING_SNAKE_CASE_			:




       '''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=32						,       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[Any]							)					->List[Any]:
          lowerCamelCase_						:   Any		= {}
          if self.train_dir is not None:
             lowerCamelCase_						:   str		= self.train_dir
          if self.validation_dir is not None:
             lowerCamelCase_						:   Dict		= self.validation_dir
          lowerCamelCase_						:   Union[str, Any]		= data_files if data_files else None







@dataclass
class   SCREAMING_SNAKE_CASE_			:




       '''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   SCREAMING_SNAKE_CASE_			:




       '''simple docstring'''



       def __init__(						self				:				Dict		,				__a				:				int=192		,				__a				:				str=32		,				__a				:				int=4		,				__a				:				List[str]=0.6							)					->List[str]:
          lowerCamelCase_						:   Any		= input_size
          lowerCamelCase_						:   Optional[int]		= mask_patch_size
          lowerCamelCase_						:   int		= model_patch_size
          lowerCamelCase_						:   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"""							)

          lowerCamelCase_						:   int		= self.input_size // self.mask_patch_size
          lowerCamelCase_						:   List[Any]		= self.mask_patch_size // self.model_patch_size

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




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

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

          return torch.tensor(mask.flatten()							)







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


def    __lowerCamelCase   (      )     ->    List[Any]:
   # 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.

   lowerCamelCase_						:   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.
      lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_						:   str		= parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
   else:
      lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_						:   str		= 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()

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

   # Create config
   # Distributed training:
   # The .from_pretrained methods guarantee that only one local process can concurrently
   # download model & vocab.
   lowerCamelCase_						:   Dict		= {
       """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_						:   Dict		= AutoConfig.from_pretrained(model_args.config_name_or_path						,						**A__					)
   elif model_args.model_name_or_path:
      lowerCamelCase_						:   Optional[int]		= AutoConfig.from_pretrained(model_args.model_name_or_path						,						**A__					)
   else:
      lowerCamelCase_						:   Dict		= 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"""					):
      lowerCamelCase_						:   Tuple		= """simmim"""

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

   # create model
   if model_args.model_name_or_path:
      lowerCamelCase_						:   List[str]		= 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"""					)
      lowerCamelCase_						:   Tuple		= AutoModelForMaskedImageModeling.from_config(A__					)

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

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

   # transformations as done in original SimMIM paper
   # source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
   lowerCamelCase_						:   List[Any]		= Compose(
       [
           Lambda(lambda A__					: 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_						:   List[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(A__						: Tuple					):
      lowerCamelCase_						:   Dict		= [transforms(A__					) for image in examples[image_column_name]]
      lowerCamelCase_						:   Optional[int]		= [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_						:   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:
         lowerCamelCase_						:   int		= (
             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
   lowerCamelCase_						:   Dict		= 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:
      lowerCamelCase_						:   Tuple		= None
      if training_args.resume_from_checkpoint is not None:
         lowerCamelCase_						:   Union[str, Any]		= training_args.resume_from_checkpoint
      elif last_checkpoint is not None:
         lowerCamelCase_						:   Dict		= last_checkpoint
      lowerCamelCase_						:   Union[str, 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:
      lowerCamelCase_						:   Optional[Any]		= trainer.evaluate()
      trainer.log_metrics("""eval"""						,						A__					)
      trainer.save_metrics("""eval"""						,						A__					)

   # Write model card and (optionally) push to hub
   lowerCamelCase_						:   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()









from . import (
    albert,
    align,
    altclip,
    audio_spectrogram_transformer,
    auto,
    autoformer,
    bark,
    bart,
    barthez,
    bartpho,
    beit,
    bert,
    bert_generation,
    bert_japanese,
    bertweet,
    big_bird,
    bigbird_pegasus,
    biogpt,
    bit,
    blenderbot,
    blenderbot_small,
    blip,
    blip_a,
    bloom,
    bridgetower,
    byta,
    camembert,
    canine,
    chinese_clip,
    clap,
    clip,
    clipseg,
    codegen,
    conditional_detr,
    convbert,
    convnext,
    convnextva,
    cpm,
    cpmant,
    ctrl,
    cvt,
    dataavec,
    deberta,
    deberta_va,
    decision_transformer,
    deformable_detr,
    deit,
    deprecated,
    deta,
    detr,
    dialogpt,
    dinat,
    distilbert,
    dit,
    donut,
    dpr,
    dpt,
    efficientformer,
    efficientnet,
    electra,
    encodec,
    encoder_decoder,
    ernie,
    ernie_m,
    esm,
    falcon,
    flaubert,
    flava,
    fnet,
    focalnet,
    fsmt,
    funnel,
    git,
    glpn,
    gpta,
    gpt_bigcode,
    gpt_neo,
    gpt_neox,
    gpt_neox_japanese,
    gpt_swa,
    gptj,
    gptsan_japanese,
    graphormer,
    groupvit,
    herbert,
    hubert,
    ibert,
    imagegpt,
    informer,
    instructblip,
    jukebox,
    layoutlm,
    layoutlmva,
    layoutlmva,
    layoutxlm,
    led,
    levit,
    lilt,
    llama,
    longformer,
    longta,
    luke,
    lxmert,
    mam_aaa,
    marian,
    markuplm,
    maskaformer,
    maskformer,
    mbart,
    mbartaa,
    mega,
    megatron_bert,
    megatron_gpta,
    mgp_str,
    mluke,
    mobilebert,
    mobilenet_va,
    mobilenet_va,
    mobilevit,
    mobilevitva,
    mpnet,
    mra,
    mta,
    musicgen,
    mvp,
    nat,
    nezha,
    nllb,
    nllb_moe,
    nystromformer,
    oneformer,
    open_llama,
    openai,
    opt,
    owlvit,
    pegasus,
    pegasus_x,
    perceiver,
    phobert,
    pixastruct,
    plbart,
    poolformer,
    prophetnet,
    qdqbert,
    rag,
    realm,
    reformer,
    regnet,
    rembert,
    resnet,
    roberta,
    roberta_prelayernorm,
    roc_bert,
    roformer,
    rwkv,
    sam,
    segformer,
    sew,
    sew_d,
    speech_encoder_decoder,
    speech_to_text,
    speech_to_text_a,
    speechta,
    splinter,
    squeezebert,
    swiftformer,
    swin,
    swinasr,
    swinva,
    switch_transformers,
    ta,
    table_transformer,
    tapas,
    time_series_transformer,
    timesformer,
    timm_backbone,
    transfo_xl,
    trocr,
    tvlt,
    umta,
    unispeech,
    unispeech_sat,
    upernet,
    videomae,
    vilt,
    vision_encoder_decoder,
    vision_text_dual_encoder,
    visual_bert,
    vit,
    vit_hybrid,
    vit_mae,
    vit_msn,
    vivit,
    wavaveca,
    wavaveca_conformer,
    wavaveca_phoneme,
    wavaveca_with_lm,
    wavlm,
    whisper,
    x_clip,
    xglm,
    xlm,
    xlm_prophetnet,
    xlm_roberta,
    xlm_roberta_xl,
    xlnet,
    xmod,
    yolos,
    yoso,
)





