'''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
_UpperCAmelCase    :     Tuple         =       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 lowercase_       :







   """simple docstring"""

   __lowerCAmelCase         =   field(
       default="cifar10"    ,    metadata={"help": "Name of a dataset from the datasets package"}			)
   __lowerCAmelCase         =   field(
       default=_UpperCamelCase    ,    metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}			)
   __lowerCAmelCase         =   field(
       default=_UpperCamelCase    ,    metadata={"help": "The column name of the images in the files."}			)
   __lowerCAmelCase         =   field(default=_UpperCamelCase    ,    metadata={"help": "A folder containing the training data."}			)
   __lowerCAmelCase         =   field(default=_UpperCamelCase    ,    metadata={"help": "A folder containing the validation data."}			)
   __lowerCAmelCase         =   field(
       default=0.1_5    ,    metadata={"help": "Percent to split off of train for validation."}			)
   __lowerCAmelCase         =   field(
       default=_UpperCamelCase    ,    metadata={
           "help": (
               "For debugging purposes or quicker training, truncate the number of training examples to this "
               "value if set."
           )
       }    ,    )
   __lowerCAmelCase         =   field(
       default=_UpperCamelCase    ,    metadata={
           "help": (
               "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
               "value if set."
           )
       }    ,    )
   def 	__UpperCAmelCase   (      self      :			Tuple     )  ->			str:
        _A    		=  {}
        if self.train_dir is not None:
             _A    		=  self.train_dir
        if self.validation_dir is not None:
             _A    		=  self.validation_dir
        _A    		=  data_files if data_files else None







@dataclass
class lowercase_       :







   """simple docstring"""

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







@dataclass
class lowercase_       (		_UpperCamelCase			):







   """simple docstring"""

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






def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Tuple				):
     _A    		=  torch.stack([example['pixel_values'] for example in examples]				)
     return {"pixel_values": pixel_values}





def  _SCREAMING_SNAKE_CASE		(      ):
     # 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    		=  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		,       _A		,       _A    		=  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
     else:
          _A		,       _A		,       _A    		=  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    		=  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    		=  None
     if os.path.isdir(training_args.output_dir				) and training_args.do_train and not training_args.overwrite_output_dir:
          _A    		=  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    		=  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    		=  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    		=  ds['train'].train_test_split(data_args.train_val_split				)
          _A    		=  split['train']
          _A    		=  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    		=  {
         '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    		=  ViTMAEConfig.from_pretrained(model_args.config_name   ,		**__snake_case				)
     elif model_args.model_name_or_path:
          _A    		=  ViTMAEConfig.from_pretrained(model_args.model_name_or_path   ,		**__snake_case				)
     else:
          _A    		=  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    		=  ViTImageProcessor.from_pretrained(model_args.image_processor_name   ,		**__snake_case				)
     elif model_args.model_name_or_path:
          _A    		=  ViTImageProcessor.from_pretrained(model_args.model_name_or_path   ,		**__snake_case				)
     else:
          _A    		=  ViTImageProcessor()

     # create model
     if model_args.model_name_or_path:
          _A    		=  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    		=  ViTMAEForPreTraining(__snake_case				)

     if training_args.do_train:
          _A    		=  ds['train'].column_names
     else:
          _A    		=  ds['validation'].column_names

     if data_args.image_column_name is not None:
          _A    		=  data_args.image_column_name
     elif "image" in column_names:
          _A    		=  'image'
     elif "img" in column_names:
          _A    		=  'img'
     else:
          _A    		=  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    		=  image_processor.size['shortest_edge']
     else:
          _A    		=  (image_processor.size['height'], image_processor.size['width'])
     _A    		=  Compose(
         [
             Lambda(lambda __snake_case				: 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(__snake_case							:			Dict				):
          _A    		=  [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    		=  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    		=  (
                   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    		=  (
         training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
     )
     if training_args.base_learning_rate is not None:
          _A    		=  training_args.base_learning_rate * total_train_batch_size / 2_5_6

     # Initialize our trainer
     _A    		=  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    		=  None
          if training_args.resume_from_checkpoint is not None:
               _A    		=  training_args.resume_from_checkpoint
          elif last_checkpoint is not None:
               _A    		=  last_checkpoint
          _A    		=  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    		=  trainer.evaluate()
          trainer.log_metrics('eval'   ,		__snake_case				)
          trainer.save_metrics('eval'   ,		__snake_case				)

     # Write model card and (optionally) push to hub
     _A    		=  {
         '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  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Union[str, Any]				):
     # For xla_spawn (TPUs)
     main()


if __name__ == "__main__":
   main()




'''simple docstring'''






import os
from pathlib import Path
from unittest.mock import patch

import pytest
import zstandard as zstd

from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
    OfflineModeIsEnabled,
    cached_path,
    fsspec_get,
    fsspec_head,
    ftp_get,
    ftp_head,
    get_from_cache,
    http_get,
    http_head,
)


_UpperCAmelCase    :     Optional[int]         =       '''\
    Text data.
    Second line of data.'''

_UpperCAmelCase    :     Optional[Any]         =       '''file'''





@pytest.fixture(scope='session'				)
def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Any				):
     _A    		=  tmp_path_factory.mktemp('data'				) / (FILE_PATH + '.zstd')
     _A    		=  bytes(__snake_case   ,		'utf-8'				)
     with zstd.open(__snake_case   ,		'wb'				) as f:
          f.write(__snake_case				)
     return path





@pytest.fixture
def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Union[str, Any]				):
     with open(os.path.join(tmpfs.local_root_dir   ,		__snake_case				)   ,		'w'				) as f:
          f.write(__snake_case				)
     return FILE_PATH





@pytest.mark.parametrize('compression_format'   ,		['gzip', 'xz', 'zstd']				)
def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Dict   ,		__snake_case							:			str   ,		__snake_case							:			Any   ,		__snake_case							:			Any   ,		__snake_case							:			List[str]   ,		__snake_case							:			List[str]				):
     _A    		=  {'gzip': gz_file, 'xz': xz_file, 'zstd': zstd_path}
     _A    		=  input_paths[compression_format]
     _A    		=  tmp_path / 'cache'
     _A    		=  DownloadConfig(cache_dir=__snake_case   ,		extract_compressed_file=__snake_case				)
     _A    		=  cached_path(__snake_case   ,		download_config=__snake_case				)
     with open(__snake_case				) as f:
          _A    		=  f.read()
     with open(__snake_case				) as f:
          _A    		=  f.read()
     assert extracted_file_content == expected_file_content





@pytest.mark.parametrize('default_extracted'   ,		[True, False]				)
@pytest.mark.parametrize('default_cache_dir'   ,		[True, False]				)
def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Union[str, Any]   ,		__snake_case							:			List[str]   ,		__snake_case							:			Union[str, Any]   ,		__snake_case							:			int   ,		__snake_case							:			List[str]				):
     _A    		=  'custom_cache'
     _A    		=  'custom_extracted_dir'
     _A    		=  tmp_path / 'custom_extracted_path'
     if default_extracted:
          _A    		=  ('downloads' if default_cache_dir else custom_cache_dir, 'extracted')
     else:
          monkeypatch.setattr('datasets.config.EXTRACTED_DATASETS_DIR'   ,		__snake_case				)
          monkeypatch.setattr('datasets.config.EXTRACTED_DATASETS_PATH'   ,		str(__snake_case				)				)
          _A    		=  custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)

     _A    		=  xz_file
     _A    		=  (
         DownloadConfig(extract_compressed_file=__snake_case				)
         if default_cache_dir
         else DownloadConfig(cache_dir=tmp_path / custom_cache_dir   ,		extract_compressed_file=__snake_case				)
     )
     _A    		=  cached_path(__snake_case   ,		download_config=__snake_case				)
     assert Path(__snake_case				).parent.parts[-2:] == expected





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			str				):
     # absolute path
     _A    		=  str(Path(__snake_case				).resolve()				)
     assert cached_path(__snake_case				) == text_file
     # relative path
     _A    		=  str(Path(__snake_case				).resolve().relative_to(Path(os.getcwd()				)				)				)
     assert cached_path(__snake_case				) == text_file





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			List[str]				):
     # absolute path
     _A    		=  str(tmp_path.resolve() / '__missing_file__.txt'				)
     with pytest.raises(__snake_case				):
          cached_path(__snake_case				)
     # relative path
     _A    		=  './__missing_file__.txt'
     with pytest.raises(__snake_case				):
          cached_path(__snake_case				)





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			int				):
     _A    		=  get_from_cache(F'tmp://{tmpfs_file}'				)
     with open(__snake_case				) as f:
          _A    		=  f.read()
     assert output_file_content == FILE_CONTENT





@patch('datasets.config.HF_DATASETS_OFFLINE'   ,		__snake_case				)
def  _SCREAMING_SNAKE_CASE		(      ):
     with pytest.raises(__snake_case				):
          cached_path('https://huggingface.co'				)





@patch('datasets.config.HF_DATASETS_OFFLINE'   ,		__snake_case				)
def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Tuple				):
     _A    		=  tmp_path_factory.mktemp('data'				) / 'file.html'
     with pytest.raises(__snake_case				):
          http_get('https://huggingface.co'   ,		temp_file=__snake_case				)
     with pytest.raises(__snake_case				):
          http_head('https://huggingface.co'				)





@patch('datasets.config.HF_DATASETS_OFFLINE'   ,		__snake_case				)
def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Optional[int]				):
     _A    		=  tmp_path_factory.mktemp('data'				) / 'file.html'
     with pytest.raises(__snake_case				):
          ftp_get('ftp://huggingface.co'   ,		temp_file=__snake_case				)
     with pytest.raises(__snake_case				):
          ftp_head('ftp://huggingface.co'				)





@patch('datasets.config.HF_DATASETS_OFFLINE'   ,		__snake_case				)
def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			List[str]				):
     _A    		=  tmp_path_factory.mktemp('data'				) / 'file.html'
     with pytest.raises(__snake_case				):
          fsspec_get('s3://huggingface.co'   ,		temp_file=__snake_case				)
     with pytest.raises(__snake_case				):
          fsspec_head('s3://huggingface.co'				)



