

'''simple docstring'''






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
__SCREAMING_SNAKE_CASE     =						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 			lowerCAmelCase__     :




     """simple docstring"""





     __UpperCamelCase									= field(
         default="cifar10"				,			metadata={"help": "Name of a dataset from the datasets package"}						)
     __UpperCamelCase									= field(
         default=lowerCamelCase__				,			metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}						)
     __UpperCamelCase									= field(
         default=lowerCamelCase__				,			metadata={"help": "The column name of the images in the files."}						)
     __UpperCamelCase									= field(default=lowerCamelCase__				,			metadata={"help": "A folder containing the training data."}						)
     __UpperCamelCase									= field(default=lowerCamelCase__				,			metadata={"help": "A folder containing the validation data."}						)
     __UpperCamelCase									= field(
         default=0.15				,			metadata={"help": "Percent to split off of train for validation."}						)
     __UpperCamelCase									= field(
         default=lowerCamelCase__				,			metadata={
             "help": (
                 "For debugging purposes or quicker training, truncate the number of training examples to this "
                 "value if set."
             )
         }				,			)
     __UpperCamelCase									= field(
         default=lowerCamelCase__				,			metadata={
             "help": (
                 "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
                 "value if set."
             )
         }				,			)







     def 			__lowerCAmelCase	(		self						:	Dict	)       ->			Optional[int]:
          '''simple docstring'''




          a__   :      Union[str, Any]     =						{}
          if self.train_dir is not None:
               a__   :      List[str]     =						self.train_dir
          if self.validation_dir is not None:
               a__   :      int     =						self.validation_dir
          a__   :      int     =						data_files if data_files else None






@dataclass
class 			lowerCAmelCase__     :




     """simple docstring"""





     __UpperCamelCase									= field(
         default=lowerCamelCase__				,			metadata={
             "help": (
                 "The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."
             )
         }				,			)
     __UpperCamelCase									= field(
         default=lowerCamelCase__				,			metadata={"help": "Pretrained config name or path if not the same as model_name_or_path"}						)
     __UpperCamelCase									= 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"
             )
         }				,			)
     __UpperCamelCase									= field(
         default=lowerCamelCase__				,			metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}						)
     __UpperCamelCase									= field(
         default="main"				,			metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}				,			)
     __UpperCamelCase									= field(default=lowerCamelCase__				,			metadata={"help": "Name or path of preprocessor config."}						)
     __UpperCamelCase									= field(
         default=lowerCamelCase__				,			metadata={
             "help": (
                 "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
                 "with private models)."
             )
         }				,			)
     __UpperCamelCase									= field(
         default=0.75				,			metadata={"help": "The ratio of the number of masked tokens in the input sequence."}						)
     __UpperCamelCase									= field(
         default=lowerCamelCase__				,			metadata={"help": "Whether or not to train with normalized pixel values as target."}						)






@dataclass
class 			lowerCAmelCase__     (					lowerCamelCase__						):




     """simple docstring"""





     __UpperCamelCase									= field(
         default=1e-3				,			metadata={"help": "Base learning rate: absolute_lr = base_lr * total_batch_size / 256."}						)



def 			__a		(       lowerCAmelCase__ :    int      ):
     a__   :      Any     =						torch.stack([example['''pixel_values'''] for example in examples]      )
     return {"pixel_values": pixel_values}



def 			__a		(       ):
     # 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.

     a__   :      Union[str, Any]     =						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.
          a__   :      List[str]     =						parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]      )      )
     else:
          a__   :      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'''     ,  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__   :      str     =						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__   :      Dict     =						None
     if os.path.isdir(training_args.output_dir      ) and training_args.do_train and not training_args.overwrite_output_dir:
          a__   :      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.
     a__   :      Dict     =						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__   :      Optional[int]     =						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__   :      Tuple     =						ds['''train'''].train_test_split(data_args.train_val_split      )
          a__   :      str     =						split['''train''']
          a__   :      Tuple     =						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.
     a__   :      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:
          a__   :      Dict     =						ViTMAEConfig.from_pretrained(model_args.config_name     ,  **snake_case__      )
     elif model_args.model_name_or_path:
          a__   :      Optional[int]     =						ViTMAEConfig.from_pretrained(model_args.model_name_or_path     ,  **snake_case__      )
     else:
          a__   :      List[Any]     =						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:
          a__   :      Union[str, Any]     =						ViTImageProcessor.from_pretrained(model_args.image_processor_name     ,  **snake_case__      )
     elif model_args.model_name_or_path:
          a__   :      Any     =						ViTImageProcessor.from_pretrained(model_args.model_name_or_path     ,  **snake_case__      )
     else:
          a__   :      Any     =						ViTImageProcessor()

     # create model
     if model_args.model_name_or_path:
          a__   :      Any     =						ViTMAEForPreTraining.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     =						ViTMAEForPreTraining(snake_case__      )

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

     if data_args.image_column_name is not None:
          a__   :      Union[str, Any]     =						data_args.image_column_name
     elif "image" in column_names:
          a__   :      Dict     =						'''image'''
     elif "img" in column_names:
          a__   :      Tuple     =						'''img'''
     else:
          a__   :      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:
          a__   :      Optional[Any]     =						image_processor.size['''shortest_edge''']
     else:
          a__   :      Dict     =						(image_processor.size['''height'''], image_processor.size['''width'''])
     a__   :      Tuple     =						Compose(
         [
             Lambda(lambda lowerCAmelCase__      : img.convert('''RGB'''      ) if img.mode != "RGB" else img      ),
             RandomResizedCrop(snake_case__     ,  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__ :    Optional[Any]      ):
          a__   :      str     =						[transforms(snake_case__      ) 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:
               a__   :      int     =						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__   :      str     =						(
                   ds['''validation'''].shuffle(seed=training_args.seed      ).select(range(data_args.max_eval_samples      )      )
               )
          # Set the validation transforms
          ds["validation"].set_transform(snake_case__      )

     # Compute absolute learning rate
     a__   :      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:
          a__   :      Optional[Any]     =						training_args.base_learning_rate * total_train_batch_size / 256

     # 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__   :      Any     =						last_checkpoint
          a__   :      str     =						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__   :      Optional[int]     =						trainer.evaluate()
          trainer.log_metrics('''eval'''     ,  snake_case__      )
          trainer.save_metrics('''eval'''     ,  snake_case__      )

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



def 			__a		(       lowerCAmelCase__ :    List[str]      ):
     # For xla_spawn (TPUs)
     main()


if __name__ == "__main__":
 main()




"""simple docstring"""






import unittest

from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate





def 				A_       ( snake_case__		)					->  Union[str, Any]:
			return x + 2






class      A(  unittest.TestCase		):

		"""simple docstring"""





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

					"""simple docstring"""



					_UpperCamelCase					:Union[str, Any]					     =	'''x = 3'''
					_UpperCamelCase					:List[str]					     =	{}
					_UpperCamelCase					:int					     =	evaluate(SCREAMING_SNAKE_CASE__					,				{}					,				state=SCREAMING_SNAKE_CASE__   )
					assert result == 3
					self.assertDictEqual(SCREAMING_SNAKE_CASE__					,				{'''x''': 3}   )

					_UpperCamelCase					:Tuple					     =	'''x = y'''
					_UpperCamelCase					:Optional[int]					     =	{'''y''': 5}
					_UpperCamelCase					:Optional[Any]					     =	evaluate(SCREAMING_SNAKE_CASE__					,				{}					,				state=SCREAMING_SNAKE_CASE__   )
					# evaluate returns the value of the last assignment.
					assert result == 5
					self.assertDictEqual(SCREAMING_SNAKE_CASE__					,				{'''x''': 5, '''y''': 5}   )
		def   _UpperCamelCase( self   )      ->	int:

					"""simple docstring"""



					_UpperCamelCase					:str					     =	'''y = add_two(x)'''
					_UpperCamelCase					:Optional[int]					     =	{'''x''': 3}
					_UpperCamelCase					:Union[str, Any]					     =	evaluate(SCREAMING_SNAKE_CASE__					,				{'''add_two''': add_two}					,				state=SCREAMING_SNAKE_CASE__   )
					assert result == 5
					self.assertDictEqual(SCREAMING_SNAKE_CASE__					,				{'''x''': 3, '''y''': 5}   )

					# Won't work without the tool
					with CaptureStdout() as out:
								_UpperCamelCase					:Union[str, Any]					     =	evaluate(SCREAMING_SNAKE_CASE__					,				{}					,				state=SCREAMING_SNAKE_CASE__   )
					assert result is None
					assert "tried to execute add_two" in out.out
		def   _UpperCamelCase( self   )      ->	List[Any]:

					"""simple docstring"""



					_UpperCamelCase					:Any					     =	'''x = 3'''
					_UpperCamelCase					:int					     =	{}
					_UpperCamelCase					:List[Any]					     =	evaluate(SCREAMING_SNAKE_CASE__					,				{}					,				state=SCREAMING_SNAKE_CASE__   )
					assert result == 3
					self.assertDictEqual(SCREAMING_SNAKE_CASE__					,				{'''x''': 3}   )
		def   _UpperCamelCase( self   )      ->	List[Any]:

					"""simple docstring"""



					_UpperCamelCase					:str					     =	'''test_dict = {\'x\': x, \'y\': add_two(x)}'''
					_UpperCamelCase					:str					     =	{'''x''': 3}
					_UpperCamelCase					:Dict					     =	evaluate(SCREAMING_SNAKE_CASE__					,				{'''add_two''': add_two}					,				state=SCREAMING_SNAKE_CASE__   )
					self.assertDictEqual(SCREAMING_SNAKE_CASE__					,				{'''x''': 3, '''y''': 5}   )
					self.assertDictEqual(SCREAMING_SNAKE_CASE__					,				{'''x''': 3, '''test_dict''': {'''x''': 3, '''y''': 5}}   )
		def   _UpperCamelCase( self   )      ->	Tuple:

					"""simple docstring"""



					_UpperCamelCase					:int					     =	'''x = 3\ny = 5'''
					_UpperCamelCase					:int					     =	{}
					_UpperCamelCase					:Optional[int]					     =	evaluate(SCREAMING_SNAKE_CASE__					,				{}					,				state=SCREAMING_SNAKE_CASE__   )
					# evaluate returns the value of the last assignment.
					assert result == 5
					self.assertDictEqual(SCREAMING_SNAKE_CASE__					,				{'''x''': 3, '''y''': 5}   )
		def   _UpperCamelCase( self   )      ->	Optional[int]:

					"""simple docstring"""



					_UpperCamelCase					:Union[str, Any]					     =	'''text = f\'This is x: {x}.\''''
					_UpperCamelCase					:List[str]					     =	{'''x''': 3}
					_UpperCamelCase					:Any					     =	evaluate(SCREAMING_SNAKE_CASE__					,				{}					,				state=SCREAMING_SNAKE_CASE__   )
					# evaluate returns the value of the last assignment.
					assert result == "This is x: 3."
					self.assertDictEqual(SCREAMING_SNAKE_CASE__					,				{'''x''': 3, '''text''': '''This is x: 3.'''}   )
		def   _UpperCamelCase( self   )      ->	Union[str, Any]:

					"""simple docstring"""



					_UpperCamelCase					:Optional[Any]					     =	'''if x <= 3:\n    y = 2\nelse:\n    y = 5'''
					_UpperCamelCase					:Dict					     =	{'''x''': 3}
					_UpperCamelCase					:Any					     =	evaluate(SCREAMING_SNAKE_CASE__					,				{}					,				state=SCREAMING_SNAKE_CASE__   )
					# evaluate returns the value of the last assignment.
					assert result == 2
					self.assertDictEqual(SCREAMING_SNAKE_CASE__					,				{'''x''': 3, '''y''': 2}   )

					_UpperCamelCase					:Union[str, Any]					     =	{'''x''': 8}
					_UpperCamelCase					:Optional[int]					     =	evaluate(SCREAMING_SNAKE_CASE__					,				{}					,				state=SCREAMING_SNAKE_CASE__   )
					# evaluate returns the value of the last assignment.
					assert result == 5
					self.assertDictEqual(SCREAMING_SNAKE_CASE__					,				{'''x''': 8, '''y''': 5}   )
		def   _UpperCamelCase( self   )      ->	Tuple:

					"""simple docstring"""



					_UpperCamelCase					:List[str]					     =	'''test_list = [x, add_two(x)]'''
					_UpperCamelCase					:Optional[int]					     =	{'''x''': 3}
					_UpperCamelCase					:str					     =	evaluate(SCREAMING_SNAKE_CASE__					,				{'''add_two''': add_two}					,				state=SCREAMING_SNAKE_CASE__   )
					self.assertListEqual(SCREAMING_SNAKE_CASE__					,				[3, 5]   )
					self.assertDictEqual(SCREAMING_SNAKE_CASE__					,				{'''x''': 3, '''test_list''': [3, 5]}   )
		def   _UpperCamelCase( self   )      ->	Dict:

					"""simple docstring"""



					_UpperCamelCase					:List[Any]					     =	'''y = x'''
					_UpperCamelCase					:List[str]					     =	{'''x''': 3}
					_UpperCamelCase					:Union[str, Any]					     =	evaluate(SCREAMING_SNAKE_CASE__					,				{}					,				state=SCREAMING_SNAKE_CASE__   )
					assert result == 3
					self.assertDictEqual(SCREAMING_SNAKE_CASE__					,				{'''x''': 3, '''y''': 3}   )
		def   _UpperCamelCase( self   )      ->	Tuple:

					"""simple docstring"""



					_UpperCamelCase					:Tuple					     =	'''test_list = [x, add_two(x)]\ntest_list[1]'''
					_UpperCamelCase					:Optional[Any]					     =	{'''x''': 3}
					_UpperCamelCase					:Any					     =	evaluate(SCREAMING_SNAKE_CASE__					,				{'''add_two''': add_two}					,				state=SCREAMING_SNAKE_CASE__   )
					assert result == 5
					self.assertDictEqual(SCREAMING_SNAKE_CASE__					,				{'''x''': 3, '''test_list''': [3, 5]}   )

					_UpperCamelCase					:str					     =	'''test_dict = {\'x\': x, \'y\': add_two(x)}\ntest_dict[\'y\']'''
					_UpperCamelCase					:Optional[int]					     =	{'''x''': 3}
					_UpperCamelCase					:Union[str, Any]					     =	evaluate(SCREAMING_SNAKE_CASE__					,				{'''add_two''': add_two}					,				state=SCREAMING_SNAKE_CASE__   )
					assert result == 5
					self.assertDictEqual(SCREAMING_SNAKE_CASE__					,				{'''x''': 3, '''test_dict''': {'''x''': 3, '''y''': 5}}   )





		def   _UpperCamelCase( self   )      ->	int:

					"""simple docstring"""



					_UpperCamelCase					:str					     =	'''x = 0\nfor i in range(3):\n    x = i'''
					_UpperCamelCase					:Tuple					     =	{}
					_UpperCamelCase					:Union[str, Any]					     =	evaluate(SCREAMING_SNAKE_CASE__					,				{'''range''': range}					,				state=SCREAMING_SNAKE_CASE__   )
					assert result == 2
					self.assertDictEqual(SCREAMING_SNAKE_CASE__					,				{'''x''': 2, '''i''': 2}   )

