




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



           '''simple docstring'''

           UpperCAmelCase     =	{}
           if self.train_dir is not None:
               UpperCAmelCase     =	self.train_dir
           if self.validation_dir is not None:
               UpperCAmelCase     =	self.validation_dir
           UpperCAmelCase     =	data_files if data_files else None






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






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






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





def 		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.

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

    UpperCAmelCase     =	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.
    UpperCAmelCase     =	None
    if os.path.isdir(training_args.output_dir  ) and training_args.do_train and not training_args.overwrite_output_dir:
        UpperCAmelCase     =	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     =	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     =	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:
        UpperCAmelCase     =	ds['''train'''].train_test_split(data_args.train_val_split  )
        UpperCAmelCase     =	split['''train''']
        UpperCAmelCase     =	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     =	{
        '''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     =	ViTMAEConfig.from_pretrained(model_args.config_name					,     **_a  )
    elif model_args.model_name_or_path:
        UpperCAmelCase     =	ViTMAEConfig.from_pretrained(model_args.model_name_or_path					,     **_a  )
    else:
        UpperCAmelCase     =	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     =	ViTImageProcessor.from_pretrained(model_args.image_processor_name					,     **_a  )
    elif model_args.model_name_or_path:
        UpperCAmelCase     =	ViTImageProcessor.from_pretrained(model_args.model_name_or_path					,     **_a  )
    else:
        UpperCAmelCase     =	ViTImageProcessor()

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

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

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

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

    # Write model card and (optionally) push to hub
    UpperCAmelCase     =	{
        '''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 		snake_case_							(_a			:							Optional[Any]  ):
    # For xla_spawn (TPUs)
    main()


if __name__ == "__main__":
   main()










'''simple docstring'''







import os

from bleurt import score  # From: git+https://github.com/google-research/bleurt.git

import datasets


A									=datasets.logging.get_logger(__name__)


A									='\\n@inproceedings{bleurt,\n  title={BLEURT: Learning Robust Metrics for Text Generation},\n  author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},\n  booktitle={ACL},\n  year={2020},\n  url={https://arxiv.org/abs/2004.04696}\n}\n'

A									='\\nBLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018)\nand then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune\nit for your specific application (the latter is expected to perform better).\n\nSee the project\'s README at https://github.com/google-research/bleurt#readme for more information.\n'

A									='\nBLEURT score.\n\nArgs:\n    `predictions` (list of str): prediction/candidate sentences\n    `references` (list of str): reference sentences\n    `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.\n\nReturns:\n    \'scores\': List of scores.\nExamples:\n\n    >>> predictions = ["hello there", "general kenobi"]\n    >>> references = ["hello there", "general kenobi"]\n    >>> bleurt = datasets.load_metric("bleurt")\n    >>> results = bleurt.compute(predictions=predictions, references=references)\n    >>> print([round(v, 2) for v in results["scores"]])\n    [1.03, 1.04]\n'

A									={
    'bleurt-tiny-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-tiny-128.zip',
    'bleurt-tiny-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-tiny-512.zip',
    'bleurt-base-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-base-128.zip',
    'bleurt-base-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-base-512.zip',
    'bleurt-large-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-large-128.zip',
    'bleurt-large-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-large-512.zip',
    'BLEURT-20-D3': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D3.zip',
    'BLEURT-20-D6': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D6.zip',
    'BLEURT-20-D12': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip',
    'BLEURT-20': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip',
}


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION							,							_KWARGS_DESCRIPTION				)
class     _a						(			datasets.Metric				):
       def 					A   (			self		:			Dict       ):



           '''simple docstring'''

           return datasets.MetricInfo(
               description=_DESCRIPTION     ,       citation=_CITATION     ,       homepage='''https://github.com/google-research/bleurt'''     ,       inputs_description=_KWARGS_DESCRIPTION     ,       features=datasets.Features(
                   {
                       '''predictions''': datasets.Value('''string'''     ,       id='''sequence'''       ),
                       '''references''': datasets.Value('''string'''     ,       id='''sequence'''       ),
                   }       )     ,       codebase_urls=['''https://github.com/google-research/bleurt''']     ,       reference_urls=['''https://github.com/google-research/bleurt''', '''https://arxiv.org/abs/2004.04696''']     ,       )
       def 					A   (			self		:			List[Any]     ,       lowercase		:			Union[str, Any]       ):



           '''simple docstring'''

           if self.config_name == "default":
               logger.warning(
                   '''Using default BLEURT-Base checkpoint for sequence maximum length 128. '''
                   '''You can use a bigger model for better results with e.g.: datasets.load_metric(\'bleurt\', \'bleurt-large-512\').'''       )
               UpperCAmelCase     =	'''bleurt-base-128'''

           if self.config_name.lower() in CHECKPOINT_URLS:
               UpperCAmelCase     =	self.config_name.lower()

           elif self.config_name.upper() in CHECKPOINT_URLS:
               UpperCAmelCase     =	self.config_name.upper()

           else:
               raise KeyError(
                   f"{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}"       )

           # download the model checkpoint specified by self.config_name and set up the scorer
           UpperCAmelCase     =	dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]       )
           UpperCAmelCase     =	score.BleurtScorer(os.path.join(lowercase     ,       lowercase       )       )







       def 					A   (			self		:			str     ,       lowercase		:			str     ,       lowercase		:			str       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.scorer.score(references=lowercase     ,       candidates=lowercase       )
           return {"scores": scores}




