



"""simple docstring"""

from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef

import datasets


lowercase__					:						List[str]												=			'''\
@inproceedings{wang2019glue,
  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},
  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},
  note={In the Proceedings of ICLR.},
  year={2019}
}
'''

lowercase__					:						List[str]												=			'''\
GLUE, the General Language Understanding Evaluation benchmark
(https://gluebenchmark.com/) is a collection of resources for training,
evaluating, and analyzing natural language understanding systems.
'''

lowercase__					:						Tuple												=			'''
Compute GLUE evaluation metric associated to each GLUE dataset.
Args:
    predictions: list of predictions to score.
        Each translation should be tokenized into a list of tokens.
    references: list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
Returns: depending on the GLUE subset, one or several of:
    "accuracy": Accuracy
    "f1": F1 score
    "pearson": Pearson Correlation
    "spearmanr": Spearman Correlation
    "matthews_correlation": Matthew Correlation
Examples:

    >>> glue_metric = datasets.load_metric(\'glue\', \'sst2\')  # \'sst2\' or any of ["mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'accuracy\': 1.0}

    >>> glue_metric = datasets.load_metric(\'glue\', \'mrpc\')  # \'mrpc\' or \'qqp\'
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'accuracy\': 1.0, \'f1\': 1.0}

    >>> glue_metric = datasets.load_metric(\'glue\', \'stsb\')
    >>> references = [0., 1., 2., 3., 4., 5.]
    >>> predictions = [0., 1., 2., 3., 4., 5.]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print({"pearson": round(results["pearson"], 2), "spearmanr": round(results["spearmanr"], 2)})
    {\'pearson\': 1.0, \'spearmanr\': 1.0}

    >>> glue_metric = datasets.load_metric(\'glue\', \'cola\')
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'matthews_correlation\': 1.0}
'''




def __lowercase    (							_a	,    _a					):
    return float((preds == labels).mean()					)




def __lowercase    (							_a	,    _a					):
    snake_case_				:  List[str]       =			simple_accuracy(_a	,    _a					)
    snake_case_				:  Optional[Any]       =			float(fa_score(y_true=_a	,    y_pred=_a					)					)
    return {
        "accuracy": acc,
        "f1": fa,
    }




def __lowercase    (							_a	,    _a					):
    snake_case_				:  Tuple       =			float(pearsonr(_a	,    _a					)[0]					)
    snake_case_				:  List[str]       =			float(spearmanr(_a	,    _a					)[0]					)
    return {
        "pearson": pearson_corr,
        "spearmanr": spearman_corr,
    }






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					,  _KWARGS_DESCRIPTION)
class  _UpperCAmelCase (    datasets.Metric):

 def 		_snake_case      (  self	: List[str]       ):
     if self.config_name not in [
         "sst2",
         "mnli",
         "mnli_mismatched",
         "mnli_matched",
         "cola",
         "stsb",
         "mrpc",
         "qqp",
         "qnli",
         "rte",
         "wnli",
         "hans",
     ]:
         raise KeyError(
             '''You should supply a configuration name selected in '''
             '''["sst2", "mnli", "mnli_mismatched", "mnli_matched", '''
             '''"cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans"]'''       )
     return datasets.MetricInfo(
         description=_DESCRIPTION						,  citation=_CITATION						,  inputs_description=_KWARGS_DESCRIPTION						,  features=datasets.Features(
             {
                 '''predictions''': datasets.Value('''int64''' if self.config_name != '''stsb''' else '''float32'''       ),
                 '''references''': datasets.Value('''int64''' if self.config_name != '''stsb''' else '''float32'''       ),
             }       )						,  codebase_urls=[]						,  reference_urls=[]						,  format='''numpy'''						,  )






 def 		_snake_case      (  self	: Union[str, Any]						,  lowercase_	: Tuple						,  lowercase_	: List[str]       ):
     if self.config_name == "cola":
         return {"matthews_correlation": matthews_corrcoef(lowercase_						,  lowercase_       )}
     elif self.config_name == "stsb":
         return pearson_and_spearman(lowercase_						,  lowercase_       )
     elif self.config_name in ["mrpc", "qqp"]:
         return acc_and_fa(lowercase_						,  lowercase_       )
     elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
         return {"accuracy": simple_accuracy(lowercase_						,  lowercase_       )}
     else:
         raise KeyError(
             '''You should supply a configuration name selected in '''
             '''["sst2", "mnli", "mnli_mismatched", "mnli_matched", '''
             '''"cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans"]'''       )










"""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
lowercase__					:						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''')




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

 def 		_snake_case      (  self	: Union[str, Any]       ):
     snake_case_				:  List[Any]       =			{}
     if self.train_dir is not None:
         snake_case_				:  str       =			self.train_dir
     if self.validation_dir is not None:
         snake_case_				:  Union[str, Any]       =			self.validation_dir
     snake_case_				:  Tuple       =			data_files if data_files else None





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





@dataclass
class  _UpperCAmelCase (    lowerCAmelCase__):
 _lowerCAmelCase     :    float	      =	field(
     default=1e-3					,  metadata={"""help""": """Base learning rate: absolute_lr = base_lr * total_batch_size / 256."""})





def __lowercase    (							_a					):
    snake_case_				:  Tuple       =			torch.stack([example['''pixel_values'''] for example in examples]					)
    return {"pixel_values": pixel_values}




def __lowercase    (							):
    # 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.

    snake_case_				:  Optional[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.
        snake_case_,			snake_case_,			snake_case_				:  Optional[Any]       =			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
    else:
        snake_case_,			snake_case_,			snake_case_				:  List[Any]       =			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'''	,    _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()

    snake_case_				:  List[str]       =			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.
    snake_case_				:  Any       =			None
    if os.path.isdir(training_args.output_dir					) and training_args.do_train and not training_args.overwrite_output_dir:
        snake_case_				:  int       =			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.
    snake_case_				:  Optional[int]       =			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.
    snake_case_				:  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:
        snake_case_				:  List[Any]       =			ds['''train'''].train_test_split(data_args.train_val_split					)
        snake_case_				:  Tuple       =			split['''train''']
        snake_case_				:  str       =			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.
    snake_case_				:  Optional[int]       =			{
        '''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:
        snake_case_				:  List[Any]       =			ViTMAEConfig.from_pretrained(model_args.config_name	,    **_a					)
    elif model_args.model_name_or_path:
        snake_case_				:  Dict       =			ViTMAEConfig.from_pretrained(model_args.model_name_or_path	,    **_a					)
    else:
        snake_case_				:  Optional[int]       =			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:
        snake_case_				:  Union[str, Any]       =			ViTImageProcessor.from_pretrained(model_args.image_processor_name	,    **_a					)
    elif model_args.model_name_or_path:
        snake_case_				:  Union[str, Any]       =			ViTImageProcessor.from_pretrained(model_args.model_name_or_path	,    **_a					)
    else:
        snake_case_				:  Tuple       =			ViTImageProcessor()

    # create model
    if model_args.model_name_or_path:
        snake_case_				:  Tuple       =			ViTMAEForPreTraining.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'''					)
        snake_case_				:  Tuple       =			ViTMAEForPreTraining(_a					)

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

    if data_args.image_column_name is not None:
        snake_case_				:  Tuple       =			data_args.image_column_name
    elif "image" in column_names:
        snake_case_				:  Tuple       =			'''image'''
    elif "img" in column_names:
        snake_case_				:  str       =			'''img'''
    else:
        snake_case_				:  Union[str, 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:
        snake_case_				:  str       =			image_processor.size['''shortest_edge''']
    else:
        snake_case_				:  Dict       =			(image_processor.size['''height'''], image_processor.size['''width'''])
    snake_case_				:  str       =			Compose(
        [
            Lambda(lambda _a					: img.convert('''RGB'''					) if img.mode != "RGB" else img					),
            RandomResizedCrop(_a	,    scale=(0.2, 1.0)	,    interpolation=InterpolationMode.BICUBIC					),
            RandomHorizontalFlip(),
            ToTensor(),
            Normalize(mean=image_processor.image_mean	,    std=image_processor.image_std					),
        ]					)

    def preprocess_images(_a					):
        snake_case_				:  Tuple       =			[transforms(_a					) 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:
            snake_case_				:  List[str]       =			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:
            snake_case_				:  Optional[Any]       =			(
                ds['''validation'''].shuffle(seed=training_args.seed					).select(range(data_args.max_eval_samples					)					)
            )
        # Set the validation transforms
        ds["validation"].set_transform(_a					)

    # Compute absolute learning rate
    snake_case_				:  Any       =			(
        training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
    )
    if training_args.base_learning_rate is not None:
        snake_case_				:  Union[str, Any]       =			training_args.base_learning_rate * total_train_batch_size / 256

    # Initialize our trainer
    snake_case_				:  str       =			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:
        snake_case_				:  Any       =			None
        if training_args.resume_from_checkpoint is not None:
            snake_case_				:  Optional[int]       =			training_args.resume_from_checkpoint
        elif last_checkpoint is not None:
            snake_case_				:  str       =			last_checkpoint
        snake_case_				:  List[str]       =			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:
        snake_case_				:  Any       =			trainer.evaluate()
        trainer.log_metrics('''eval'''	,    _a					)
        trainer.save_metrics('''eval'''	,    _a					)

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




def __lowercase    (							_a					):
    # For xla_spawn (TPUs)
    main()


if __name__ == "__main__":
     main()





