






import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    ViTImageProcessor,
    ViTMAEConfig,
    ViTMAEForPreTraining,
)
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')







@dataclass
class 		_A						:
  lowercase_						:			Optional[str]						=					field(
      default='''cifar10'''	,				metadata={'''help''': '''Name of a dataset from the datasets package'''} )
  lowercase_						:			Optional[str]						=					field(
      default=UpperCAmelCase_	,				metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''} )
  lowercase_						:			Optional[str]						=					field(
      default=UpperCAmelCase_	,				metadata={'''help''': '''The column name of the images in the files.'''} )
  lowercase_						:			Optional[str]						=					field(default=UpperCAmelCase_	,				metadata={'''help''': '''A folder containing the training data.'''} )
  lowercase_						:			Optional[str]						=					field(default=UpperCAmelCase_	,				metadata={'''help''': '''A folder containing the validation data.'''} )
  lowercase_						:			Optional[float]						=					field(
      default=0.1_5	,				metadata={'''help''': '''Percent to split off of train for validation.'''} )
  lowercase_						:			Optional[int]						=					field(
      default=UpperCAmelCase_	,				metadata={
          '''help''': (
              '''For debugging purposes or quicker training, truncate the number of training examples to this '''
              '''value if set.'''
          )
      }	,				)
  lowercase_						:			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    a					(    self						:	str			):
     """simple docstring"""

     __UpperCamelCase	:			Any					=					{}
     if self.train_dir is not None:
        __UpperCamelCase	:			str					=					self.train_dir
     if self.validation_dir is not None:
        __UpperCamelCase	:			Optional[int]					=					self.validation_dir
     __UpperCamelCase	:			Optional[Any]					=					data_files if data_files else None







@dataclass
class 		_A						:
  lowercase_						:			str						=					field(
      default=UpperCAmelCase_	,				metadata={
          '''help''': (
              '''The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'''
          )
      }	,				)
  lowercase_						:			Optional[str]						=					field(
      default=UpperCAmelCase_	,				metadata={'''help''': '''Pretrained config name or path if not the same as model_name_or_path'''} )
  lowercase_						:			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'''
          )
      }	,				)
  lowercase_						:			Optional[str]						=					field(
      default=UpperCAmelCase_	,				metadata={'''help''': '''Where do you want to store the pretrained models downloaded from s3'''} )
  lowercase_						:			str						=					field(
      default='''main'''	,				metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}	,				)
  lowercase_						:			str						=					field(default=UpperCAmelCase_	,				metadata={'''help''': '''Name or path of preprocessor config.'''} )
  lowercase_						:			bool						=					field(
      default=UpperCAmelCase_	,				metadata={
          '''help''': (
              '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
              '''with private models).'''
          )
      }	,				)
  lowercase_						:			float						=					field(
      default=0.7_5	,				metadata={'''help''': '''The ratio of the number of masked tokens in the input sequence.'''} )
  lowercase_						:			bool						=					field(
      default=UpperCAmelCase_	,				metadata={'''help''': '''Whether or not to train with normalized pixel values as target.'''} )







@dataclass
class 		_A						( UpperCAmelCase_ ):
  lowercase_						:			float						=					field(
      default=1E-3	,				metadata={'''help''': '''Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'''} )







def 				__lowerCamelCase     (							__lowerCAmelCase							:					Dict							)     ->  List[Any]:
   __UpperCamelCase	:			Dict					=					torch.stack([example["""pixel_values"""] for example in examples]							)
   return {"pixel_values": pixel_values}


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.

   __UpperCamelCase	:			Tuple					=					HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments)							)
   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	:			Union[str, Any]					=					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_mae"""					,							__lowerCAmelCase					,							__lowerCAmelCase							)

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

   # Load pretrained model and image processor
   #
   # Distributed training:
   # The .from_pretrained methods guarantee that only one local process can concurrently
   # download model & vocab.
   __UpperCamelCase	:			List[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:
      __UpperCamelCase	:			Any					=					ViTMAEConfig.from_pretrained(model_args.config_name					,							**__lowerCAmelCase							)
   elif model_args.model_name_or_path:
      __UpperCamelCase	:			str					=					ViTMAEConfig.from_pretrained(model_args.model_name_or_path					,							**__lowerCAmelCase							)
   else:
      __UpperCamelCase	:			str					=					ViTMAEConfig()
      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}'							)

    # adapt config
   config.update(
       {
           """mask_ratio""": model_args.mask_ratio,
           """norm_pix_loss""": model_args.norm_pix_loss,
       }							)

   # create image processor
   if model_args.image_processor_name:
      __UpperCamelCase	:			int					=					ViTImageProcessor.from_pretrained(model_args.image_processor_name					,							**__lowerCAmelCase							)
   elif model_args.model_name_or_path:
      __UpperCamelCase	:			List[str]					=					ViTImageProcessor.from_pretrained(model_args.model_name_or_path					,							**__lowerCAmelCase							)
   else:
      __UpperCamelCase	:			Dict					=					ViTImageProcessor()

   # create model
   if model_args.model_name_or_path:
      __UpperCamelCase	:			Any					=					ViTMAEForPreTraining.from_pretrained(
          model_args.model_name_or_path					,							from_tf=bool(""".ckpt""" in model_args.model_name_or_path							)					,							config=__lowerCAmelCase					,							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	:			Any					=					ViTMAEForPreTraining(__lowerCAmelCase							)

   if training_args.do_train:
      __UpperCamelCase	:			Any					=					ds["""train"""].column_names
   else:
      __UpperCamelCase	:			Union[str, Any]					=					ds["""validation"""].column_names

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

   # transformations as done in original MAE paper
   # source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
   if "shortest_edge" in image_processor.size:
      __UpperCamelCase	:			Tuple					=					image_processor.size["""shortest_edge"""]
   else:
      __UpperCamelCase	:			List[Any]					=					(image_processor.size["""height"""], image_processor.size["""width"""])
   __UpperCamelCase	:			Any					=					Compose(
       [
           Lambda(lambda __lowerCAmelCase							: img.convert("""RGB"""							) if img.mode != "RGB" else img							),
           RandomResizedCrop(__lowerCAmelCase					,							scale=(0.2, 1.0)					,							interpolation=InterpolationMode.BICUBIC							),
           RandomHorizontalFlip(),
           ToTensor(),
           Normalize(mean=image_processor.image_mean					,							std=image_processor.image_std							),
       ]							)

   def preprocess_images(__lowerCAmelCase							:					str							):
      __UpperCamelCase	:			Union[str, Any]					=					[transforms(__lowerCAmelCase							) for image in 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	:			List[str]					=					ds["""train"""].shuffle(seed=training_args.seed							).select(range(data_args.max_train_samples							)							)
      # Set the training transforms
      ds["train"].set_transform(__lowerCAmelCase							)

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

   # Compute absolute learning rate
   __UpperCamelCase	:			Optional[int]					=					(
       training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
   )
   if training_args.base_learning_rate is not None:
      __UpperCamelCase	:			str					=					training_args.base_learning_rate * total_train_batch_size / 256

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

   # Training
   if training_args.do_train:
      __UpperCamelCase	:			Optional[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=__lowerCAmelCase							)
      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	:			Dict					=					trainer.evaluate()
      trainer.log_metrics("""eval"""					,							__lowerCAmelCase							)
      trainer.save_metrics("""eval"""					,							__lowerCAmelCase							)

   # Write model card and (optionally) push to hub
   __UpperCamelCase	:			Tuple					=					{
       """tasks""": """masked-auto-encoding""",
       """dataset""": data_args.dataset_name,
       """tags""": ["""masked-auto-encoding"""],
   }
   if training_args.push_to_hub:
      trainer.push_to_hub(**__lowerCAmelCase							)
   else:
      trainer.create_model_card(**__lowerCAmelCase							)


def 				__lowerCamelCase     (							__lowerCAmelCase							:					Dict							)     ->  Tuple:
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
       main()











import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


UpperCamelCase    							=      logging.get_logger(__name__)







class 		_A						( UpperCAmelCase_ ):

  def __init__(    self						:	str   ,				*lowerCamelCase__						:	Optional[int]   ,				**lowerCamelCase__						:	Any			):
     """simple docstring"""

     warnings.warn(
         """The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
         """ use BeitImageProcessor instead."""   ,				lowerCamelCase__   ,				)
     super().__init__(*lowerCamelCase__   ,				**lowerCamelCase__			)



