


from sklearn.metrics import recall_score

import datasets


a					  =     '\nRecall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:\nRecall = TP / (TP + FN)\nWhere TP is the true positives and FN is the false negatives.\n'


a					  =     '\nArgs:\n- **predictions** (`list` of `int`): The predicted labels.\n- **references** (`list` of `int`): The ground truth labels.\n- **labels** (`list` of `int`): The set of labels to include when `average` is not set to `binary`, and their order when average is `None`. Labels present in the data can be excluded in this input, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in y_true and y_pred are used in sorted order. Defaults to None.\n- **pos_label** (`int`): The class label to use as the \'positive class\' when calculating the recall. Defaults to `1`.\n- **average** (`string`): This parameter is required for multiclass/multilabel targets. If None, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `\'binary\'`.\n    - `\'binary\'`: Only report results for the class specified by `pos_label`. This is applicable only if the target labels and predictions are binary.\n    - `\'micro\'`: Calculate metrics globally by counting the total true positives, false negatives, and false positives.\n    - `\'macro\'`: Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.\n    - `\'weighted\'`: Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `\'macro\'` to account for label imbalance. Note that it can result in an F-score that is not between precision and recall.\n    - `\'samples\'`: Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).\n- **sample_weight** (`list` of `float`): Sample weights Defaults to `None`.\n- **zero_division** (): Sets the value to return when there is a zero division. Defaults to .\n    - `\'warn\'`: If there is a zero division, the return value is `0`, but warnings are also raised.\n    - `0`: If there is a zero division, the return value is `0`.\n    - `1`: If there is a zero division, the return value is `1`.\n\nReturns:\n- **recall** (`float`, or `array` of `float`): Either the general recall score, or the recall scores for individual classes, depending on the values input to `labels` and `average`. Minimum possible value is 0. Maximum possible value is 1. A higher recall means that more of the positive examples have been labeled correctly. Therefore, a higher recall is generally considered better.\n\nExamples:\n\n    Example 1-A simple example with some errors\n        >>> recall_metric = datasets.load_metric(\'recall\')\n        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1])\n        >>> print(results)\n        {\'recall\': 0.6666666666666666}\n\n    Example 2-The same example as Example 1, but with `pos_label=0` instead of the default `pos_label=1`.\n        >>> recall_metric = datasets.load_metric(\'recall\')\n        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], pos_label=0)\n        >>> print(results)\n        {\'recall\': 0.5}\n\n    Example 3-The same example as Example 1, but with `sample_weight` included.\n        >>> recall_metric = datasets.load_metric(\'recall\')\n        >>> sample_weight = [0.9, 0.2, 0.9, 0.3, 0.8]\n        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], sample_weight=sample_weight)\n        >>> print(results)\n        {\'recall\': 0.55}\n\n    Example 4-A multiclass example, using different averages.\n        >>> recall_metric = datasets.load_metric(\'recall\')\n        >>> predictions = [0, 2, 1, 0, 0, 1]\n        >>> references = [0, 1, 2, 0, 1, 2]\n        >>> results = recall_metric.compute(predictions=predictions, references=references, average=\'macro\')\n        >>> print(results)\n        {\'recall\': 0.3333333333333333}\n        >>> results = recall_metric.compute(predictions=predictions, references=references, average=\'micro\')\n        >>> print(results)\n        {\'recall\': 0.3333333333333333}\n        >>> results = recall_metric.compute(predictions=predictions, references=references, average=\'weighted\')\n        >>> print(results)\n        {\'recall\': 0.3333333333333333}\n        >>> results = recall_metric.compute(predictions=predictions, references=references, average=None)\n        >>> print(results)\n        {\'recall\': array([1., 0., 0.])}\n'


a					  =     '\n@article{scikit-learn, title={Scikit-learn: Machine Learning in {P}ython}, author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.}, journal={Journal of Machine Learning Research}, volume={12}, pages={2825--2830}, year={2011}\n'







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

					def   UpperCAmelCase__       (     self       :   Tuple       ):






										'''simple docstring'''






										return datasets.MetricInfo(
										    description=_DESCRIPTION							,					citation=_CITATION							,					inputs_description=_KWARGS_DESCRIPTION							,					features=datasets.Features(
										        {
										            """predictions""": datasets.Sequence(datasets.Value("""int32"""       )       ),
										            """references""": datasets.Sequence(datasets.Value("""int32"""       )       ),
										        }
										        if self.config_name == """multilabel"""
										        else {
										            """predictions""": datasets.Value("""int32"""       ),
										            """references""": datasets.Value("""int32"""       ),
										        }       )							,					reference_urls=["""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html"""]							,					)







					def   UpperCAmelCase__       (     self       :   Any							,					UpperCamelCase__       :   int							,					UpperCamelCase__       :   Optional[int]							,					UpperCamelCase__       :   int=None							,					UpperCamelCase__       :   str=1							,					UpperCamelCase__       :   Optional[Any]="binary"							,					UpperCamelCase__       :   Optional[Any]=None							,					UpperCamelCase__       :   Tuple="warn"							,					):






										'''simple docstring'''






										lowercase_  							=			recall_score(
										    UpperCamelCase__							,					UpperCamelCase__							,					labels=UpperCamelCase__							,					pos_label=UpperCamelCase__							,					average=UpperCamelCase__							,					sample_weight=UpperCamelCase__							,					zero_division=UpperCamelCase__							,					)
										return {"recall": float(UpperCamelCase__       ) if score.size == 1 else score}






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

import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForMaskedImageModeling,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
)
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')

a					  =     list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
a					  =     tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)







@dataclass
class        UpperCamelCase__   :
					__SCREAMING_SNAKE_CASE       :  Optional[str]						=   field(
					    default='cifar10'							,				metadata={'help': 'Name of a dataset from the datasets package'}   )
					__SCREAMING_SNAKE_CASE       :  Optional[str]						=   field(
					    default=__magic_name__							,				metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}   )
					__SCREAMING_SNAKE_CASE       :  Optional[str]						=   field(
					    default=__magic_name__							,				metadata={'help': 'The column name of the images in the files. If not set, will try to use \'image\' or \'img\'.'}							,				)
					__SCREAMING_SNAKE_CASE       :  Optional[str]						=   field(default=__magic_name__							,				metadata={'help': 'A folder containing the training data.'}   )
					__SCREAMING_SNAKE_CASE       :  Optional[str]						=   field(default=__magic_name__							,				metadata={'help': 'A folder containing the validation data.'}   )
					__SCREAMING_SNAKE_CASE       :  Optional[float]						=   field(
					    default=0.1_5							,				metadata={'help': 'Percent to split off of train for validation.'}   )
					__SCREAMING_SNAKE_CASE       :  int						=   field(default=32							,				metadata={'help': 'The size of the square patches to use for masking.'}   )
					__SCREAMING_SNAKE_CASE       :  float						=   field(
					    default=0.6							,				metadata={'help': 'Percentage of patches to mask.'}							,				)
					__SCREAMING_SNAKE_CASE       :  Optional[int]						=   field(
					    default=__magic_name__							,				metadata={
					        'help': (
					            'For debugging purposes or quicker training, truncate the number of training examples to this '
					            'value if set.'
					        )
					    }							,				)
					__SCREAMING_SNAKE_CASE       :  Optional[int]						=   field(
					    default=__magic_name__							,				metadata={
					        'help': (
					            'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
					            'value if set.'
					        )
					    }							,				)

					def   UpperCAmelCase__       (     self       :   Tuple       ):






										'''simple docstring'''






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







@dataclass
class        UpperCamelCase__   :
					__SCREAMING_SNAKE_CASE       :  str						=   field(
					    default=__magic_name__							,				metadata={
					        'help': (
					            'The model checkpoint for weights initialization. Can be a local path to a pytorch_model.bin or a '
					            'checkpoint identifier on the hub. '
					            'Don\'t set if you want to train a model from scratch.'
					        )
					    }							,				)
					__SCREAMING_SNAKE_CASE       :  Optional[str]						=   field(
					    default=__magic_name__							,				metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(__magic_name__   )}							,				)
					__SCREAMING_SNAKE_CASE       :  Optional[str]						=   field(
					    default=__magic_name__							,				metadata={'help': 'Pretrained config name or path if not the same as model_name'}   )
					__SCREAMING_SNAKE_CASE       :  Optional[str]						=   field(
					    default=__magic_name__							,				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'
					        )
					    }							,				)
					__SCREAMING_SNAKE_CASE       :  Optional[str]						=   field(
					    default=__magic_name__							,				metadata={'help': 'Where do you want to store (cache) the pretrained models/datasets downloaded from the hub'}							,				)
					__SCREAMING_SNAKE_CASE       :  str						=   field(
					    default='main'							,				metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}							,				)
					__SCREAMING_SNAKE_CASE       :  str						=   field(default=__magic_name__							,				metadata={'help': 'Name or path of preprocessor config.'}   )
					__SCREAMING_SNAKE_CASE       :  bool						=   field(
					    default=__magic_name__							,				metadata={
					        'help': (
					            'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
					            'with private models).'
					        )
					    }							,				)
					__SCREAMING_SNAKE_CASE       :  Optional[int]						=   field(
					    default=__magic_name__							,				metadata={
					        'help': (
					            'The size (resolution) of each image. If not specified, will use `image_size` of the configuration.'
					        )
					    }							,				)
					__SCREAMING_SNAKE_CASE       :  Optional[int]						=   field(
					    default=__magic_name__							,				metadata={
					        'help': (
					            'The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration.'
					        )
					    }							,				)
					__SCREAMING_SNAKE_CASE       :  Optional[int]						=   field(
					    default=__magic_name__							,				metadata={'help': 'Stride to use for the encoder.'}							,				)







class        UpperCamelCase__   :

					def __init__(     self       :   Dict							,					UpperCamelCase__       :   List[Any]=192							,					UpperCamelCase__       :   Tuple=32							,					UpperCamelCase__       :   Dict=4							,					UpperCamelCase__       :   str=0.6       ):






										'''simple docstring'''






										lowercase_  							=			input_size
										lowercase_  							=			mask_patch_size
										lowercase_  							=			model_patch_size
										lowercase_  							=			mask_ratio

										if self.input_size % self.mask_patch_size != 0:
															raise ValueError("""Input size must be divisible by mask patch size"""       )
										if self.mask_patch_size % self.model_patch_size != 0:
															raise ValueError("""Mask patch size must be divisible by model patch size"""       )

										lowercase_  							=			self.input_size // self.mask_patch_size
										lowercase_  							=			self.mask_patch_size // self.model_patch_size

										lowercase_  							=			self.rand_size**2
										lowercase_  							=			int(np.ceil(self.token_count * self.mask_ratio       )       )







					def __call__(     self       :   int       ):






										'''simple docstring'''






										lowercase_  							=			np.random.permutation(self.token_count       )[: self.mask_count]
										lowercase_  							=			np.zeros(self.token_count							,					dtype=UpperCamelCase__       )
										lowercase_  							=			1

										lowercase_  							=			mask.reshape((self.rand_size, self.rand_size)       )
										lowercase_  							=			mask.repeat(self.scale							,					axis=0       ).repeat(self.scale							,					axis=1       )

										return torch.tensor(mask.flatten()       )



def 			UpperCAmelCase_				(      UpperCAmelCase__  ):
					lowercase_  							=			torch.stack([example["""pixel_values"""] for example in examples]  )
					lowercase_  							=			torch.stack([example["""mask"""] for example in examples]  )
					return {"pixel_values": pixel_values, "bool_masked_pos": mask}


def 			UpperCAmelCase_				(      ):
					# 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.

					lowercase_  							=			HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)  )
					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.
										lowercase_							,					lowercase_							,					lowercase_  							=			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]  )  )
					else:
										lowercase_							,					lowercase_							,					lowercase_  							=			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_mim"""						,							UpperCAmelCase__						,							UpperCAmelCase__  )

					# 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()

					lowercase_  							=			training_args.get_process_log_level()
					logger.setLevel(UpperCAmelCase__  )
					transformers.utils.logging.set_verbosity(UpperCAmelCase__  )
					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.
					lowercase_  							=			None
					if os.path.isdir(training_args.output_dir  ) and training_args.do_train and not training_args.overwrite_output_dir:
										lowercase_  							=			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.
					lowercase_  							=			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.
					lowercase_  							=			None if """validation""" in ds.keys() else data_args.train_val_split
					if isinstance(data_args.train_val_split						,							UpperCAmelCase__  ) and data_args.train_val_split > 0.0:
										lowercase_  							=			ds["""train"""].train_test_split(data_args.train_val_split  )
										lowercase_  							=			split["""train"""]
										lowercase_  							=			split["""test"""]

					# Create config
					# Distributed training:
					# The .from_pretrained methods guarantee that only one local process can concurrently
					# download model & vocab.
					lowercase_  							=			{
					    """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_or_path:
										lowercase_  							=			AutoConfig.from_pretrained(model_args.config_name_or_path						,							**UpperCAmelCase__  )
					elif model_args.model_name_or_path:
										lowercase_  							=			AutoConfig.from_pretrained(model_args.model_name_or_path						,							**UpperCAmelCase__  )
					else:
										lowercase_  							=			CONFIG_MAPPING[model_args.model_type]()
										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}'''  )

    # make sure the decoder_type is "simmim" (only relevant for BEiT)
					if hasattr(UpperCAmelCase__						,							"""decoder_type"""  ):
										lowercase_  							=			"""simmim"""

					# adapt config
					lowercase_  							=			model_args.image_size if model_args.image_size is not None else config.image_size
					lowercase_  							=			model_args.patch_size if model_args.patch_size is not None else config.patch_size
					lowercase_  							=			(
					    model_args.encoder_stride if model_args.encoder_stride is not None else config.encoder_stride
					)

					config.update(
					    {
					        """image_size""": model_args.image_size,
					        """patch_size""": model_args.patch_size,
					        """encoder_stride""": model_args.encoder_stride,
					    }  )

					# create image processor
					if model_args.image_processor_name:
										lowercase_  							=			AutoImageProcessor.from_pretrained(model_args.image_processor_name						,							**UpperCAmelCase__  )
					elif model_args.model_name_or_path:
										lowercase_  							=			AutoImageProcessor.from_pretrained(model_args.model_name_or_path						,							**UpperCAmelCase__  )
					else:
										lowercase_  							=			{
										    conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
										}
										lowercase_  							=			IMAGE_PROCESSOR_TYPES[model_args.model_type]()

					# create model
					if model_args.model_name_or_path:
										lowercase_  							=			AutoModelForMaskedImageModeling.from_pretrained(
										    model_args.model_name_or_path						,							from_tf=bool(""".ckpt""" in model_args.model_name_or_path  )						,							config=UpperCAmelCase__						,							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"""  )
										lowercase_  							=			AutoModelForMaskedImageModeling.from_config(UpperCAmelCase__  )

					if training_args.do_train:
										lowercase_  							=			ds["""train"""].column_names
					else:
										lowercase_  							=			ds["""validation"""].column_names

					if data_args.image_column_name is not None:
										lowercase_  							=			data_args.image_column_name
					elif "image" in column_names:
										lowercase_  							=			"""image"""
					elif "img" in column_names:
										lowercase_  							=			"""img"""
					else:
										lowercase_  							=			column_names[0]

					# transformations as done in original SimMIM paper
					# source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
					lowercase_  							=			Compose(
					    [
					        Lambda(lambda UpperCAmelCase__  : img.convert("""RGB"""  ) if img.mode != "RGB" else img  ),
					        RandomResizedCrop(model_args.image_size						,							scale=(0.67, 1.0)						,							ratio=(3.0 / 4.0, 4.0 / 3.0)  ),
					        RandomHorizontalFlip(),
					        ToTensor(),
					        Normalize(mean=image_processor.image_mean						,							std=image_processor.image_std  ),
					    ]  )

					# create mask generator
					lowercase_  							=			MaskGenerator(
					    input_size=model_args.image_size						,							mask_patch_size=data_args.mask_patch_size						,							model_patch_size=model_args.patch_size						,							mask_ratio=data_args.mask_ratio						,							)

					def preprocess_images(UpperCAmelCase__  ):
										lowercase_  							=			[transforms(UpperCAmelCase__  ) for image in examples[image_column_name]]
										lowercase_  							=			[mask_generator() for i in range(len(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:
															lowercase_  							=			ds["""train"""].shuffle(seed=training_args.seed  ).select(range(data_args.max_train_samples  )  )
										# Set the training transforms
										ds["train"].set_transform(UpperCAmelCase__  )

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

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

					# Training
					if training_args.do_train:
										lowercase_  							=			None
										if training_args.resume_from_checkpoint is not None:
															lowercase_  							=			training_args.resume_from_checkpoint
										elif last_checkpoint is not None:
															lowercase_  							=			last_checkpoint
										lowercase_  							=			trainer.train(resume_from_checkpoint=UpperCAmelCase__  )
										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:
										lowercase_  							=			trainer.evaluate()
										trainer.log_metrics("""eval"""						,							UpperCAmelCase__  )
										trainer.save_metrics("""eval"""						,							UpperCAmelCase__  )

					# Write model card and (optionally) push to hub
					lowercase_  							=			{
					    """finetuned_from""": model_args.model_name_or_path,
					    """tasks""": """masked-image-modeling""",
					    """dataset""": data_args.dataset_name,
					    """tags""": ["""masked-image-modeling"""],
					}
					if training_args.push_to_hub:
										trainer.push_to_hub(**UpperCAmelCase__  )
					else:
										trainer.create_model_card(**UpperCAmelCase__  )


if __name__ == "__main__":
							main()


