





import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed

import numpy as np

import datasets

from .execute import check_correctness


_UpperCAmelCase       : str			     =       """\
@misc{chen2021evaluating,
      title={Evaluating Large Language Models Trained on Code},
      author={Mark Chen and Jerry Tworek and Heewoo Jun and Qiming Yuan \
and Henrique Ponde de Oliveira Pinto and Jared Kaplan and Harri Edwards \
and Yuri Burda and Nicholas Joseph and Greg Brockman and Alex Ray \
and Raul Puri and Gretchen Krueger and Michael Petrov and Heidy Khlaaf \
and Girish Sastry and Pamela Mishkin and Brooke Chan and Scott Gray \
and Nick Ryder and Mikhail Pavlov and Alethea Power and Lukasz Kaiser \
and Mohammad Bavarian and Clemens Winter and Philippe Tillet \
and Felipe Petroski Such and Dave Cummings and Matthias Plappert \
and Fotios Chantzis and Elizabeth Barnes and Ariel Herbert-Voss \
and William Hebgen Guss and Alex Nichol and Alex Paino and Nikolas Tezak \
and Jie Tang and Igor Babuschkin and Suchir Balaji and Shantanu Jain \
and William Saunders and Christopher Hesse and Andrew N. Carr \
and Jan Leike and Josh Achiam and Vedant Misra and Evan Morikawa \
and Alec Radford and Matthew Knight and Miles Brundage and Mira Murati \
and Katie Mayer and Peter Welinder and Bob McGrew and Dario Amodei \
and Sam McCandlish and Ilya Sutskever and Wojciech Zaremba},
      year={2021},
      eprint={2107.03374},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}
"""

_UpperCAmelCase       : Optional[Any]			     =       """\
This metric implements the evaluation harness for the HumanEval problem solving dataset
described in the paper \"Evaluating Large Language Models Trained on Code\"
(https://arxiv.org/abs/2107.03374).
"""


_UpperCAmelCase       : str			     =       """
Calculates how good are predictions given some references, using certain scores
Args:
    predictions: list of candidates to evaluate. Each candidates should be a list
        of strings with several code candidates to solve the problem.
    references: a list with a test for each prediction. Each test should evaluate the
        correctness of a code candidate.
    k: number of code candidates to consider in the evaluation (Default: [1, 10, 100])
    num_workers: number of workers used to evaluate the canidate programs (Default: 4).
    timeout:
Returns:
    pass_at_k: dict with pass rates for each k
    results: dict with granular results of each unittest
Examples:
    >>> code_eval = datasets.load_metric(\"code_eval\")
    >>> test_cases = [\"assert add(2,3)==5\"]
    >>> candidates = [[\"def add(a,b): return a*b\", \"def add(a, b): return a+b\"]]
    >>> pass_at_k, results = code_eval.compute(references=test_cases, predictions=candidates, k=[1, 2])
    >>> print(pass_at_k)
    {'pass@1': 0.5, 'pass@2': 1.0}
"""


_UpperCAmelCase       : Any			     =       """
################################################################################
                                  !!!WARNING!!!
################################################################################
The \"code_eval\" metric executes untrusted model-generated code in Python.
Although it is highly unlikely that model-generated code will do something
overtly malicious in response to this test suite, model-generated code may act
destructively due to a lack of model capability or alignment.
Users are strongly encouraged to sandbox this evaluation suite so that it
does not perform destructive actions on their host or network. For more
information on how OpenAI sandboxes its code, see the paper \"Evaluating Large
Language Models Trained on Code\" (https://arxiv.org/abs/2107.03374).

Once you have read this disclaimer and taken appropriate precautions,
set the environment variable HF_ALLOW_CODE_EVAL=\"1\". Within Python you can to this
with:

>>> import os
>>> os.environ[\"HF_ALLOW_CODE_EVAL\"] = \"1\"

################################################################################\
"""

_UpperCAmelCase       : str			     =       """The MIT License

Copyright (c) OpenAI (https://openai.com)

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the \"Software\"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE."""

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION,						_KWARGS_DESCRIPTION    )
class      lowerCAmelCase							(      datasets.Metric    ):
 def   A_			(   self						:   Union[str, Any]			)    ->			Any:
     return datasets.MetricInfo(
         # This is the description that will appear on the metrics page.
         description=_DESCRIPTION    ,  citation=_CITATION    ,  inputs_description=_KWARGS_DESCRIPTION    ,  features=datasets.Features(
             {
                 'predictions': datasets.Sequence(datasets.Value('string'			)			),
                 'references': datasets.Value('string'			),
             }			)    ,  homepage='https://github.com/openai/human-eval'    ,  codebase_urls=['https://github.com/openai/human-eval']    ,  reference_urls=['https://github.com/openai/human-eval']    ,  license=_LICENSE    ,  )







 def   A_			(   self						:   Optional[Any]    ,  UpperCAmelCase						:   str    ,  UpperCAmelCase						:   List[Any]    ,  UpperCAmelCase						:   Optional[Any]=[1, 10, 100]    ,  UpperCAmelCase						:   Optional[Any]=4    ,  UpperCAmelCase						:   Any=3.0			)    ->			Optional[int]:

     if os.getenv('HF_ALLOW_CODE_EVAL'    ,  0			) != "1":
         raise ValueError(_WARNING			)

     if os.name == "nt":
         raise NotImplementedError('This metric is currently not supported on Windows.'			)

     with ThreadPoolExecutor(max_workers=UpperCAmelCase			) as executor:
         lowerCamelCase__					:   List[Any]      				=	[]
         lowerCamelCase__					:   Union[str, Any]      				=	Counter()
         lowerCamelCase__					:   Tuple      				=	0
         lowerCamelCase__					:   Tuple      				=	defaultdict(UpperCAmelCase			)

         for task_id, (candidates, test_case) in enumerate(zip(UpperCAmelCase    ,  UpperCAmelCase			)			):
             for candidate in candidates:
                 lowerCamelCase__					:   Optional[Any]      				=	candidate + '\n' + test_case
                 lowerCamelCase__					:   Dict      				=	(test_program, timeout, task_id, completion_id[task_id])
                 lowerCamelCase__					:   List[Any]      				=	executor.submit(UpperCAmelCase    ,  *UpperCAmelCase			)
                 futures.append(UpperCAmelCase			)
                 completion_id[task_id] += 1
                 n_samples += 1

         for future in as_completed(UpperCAmelCase			):
             lowerCamelCase__					:   List[str]      				=	future.result()
             results[result["task_id"]].append((result['completion_id'], result)			)

     lowerCamelCase__				,							lowerCamelCase__					:   int      				=	[], []
     for result in results.values():
         result.sort()
         lowerCamelCase__					:   Union[str, Any]      				=	[r[1]['passed'] for r in result]
         total.append(len(UpperCAmelCase			)			)
         correct.append(sum(UpperCAmelCase			)			)
     lowerCamelCase__					:   List[Any]      				=	np.array(UpperCAmelCase			)
     lowerCamelCase__					:   Tuple      				=	np.array(UpperCAmelCase			)

     lowerCamelCase__					:   Optional[int]      				=	k
     lowerCamelCase__					:   Optional[Any]      				=	{F"""pass@{k}""": estimate_pass_at_k(UpperCAmelCase    ,  UpperCAmelCase    ,  UpperCAmelCase			).mean() for k in ks if (total >= k).all()}

     return pass_at_k, results


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase				)    ->  Union[str, Any]:

    def estimator(_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase				) -> float:
        if n - c < k:
            return 1.0
        return 1.0 - np.prod(1.0 - k / np.arange(n - c + 1		,			n + 1				)				)

    if isinstance(_UpperCAmelCase		,			_UpperCAmelCase				):
        lowerCamelCase__					:   List[str]      				=	itertools.repeat(_UpperCAmelCase		,			len(_UpperCAmelCase				)				)
    else:
        assert len(_UpperCAmelCase				) == len(_UpperCAmelCase				)
        lowerCamelCase__					:   Optional[int]      				=	iter(_UpperCAmelCase				)

    return np.array([estimator(int(_UpperCAmelCase				)		,			int(_UpperCAmelCase				)		,			_UpperCAmelCase				) for n, c in zip(_UpperCAmelCase		,			_UpperCAmelCase				)]				)












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       : List[str]			     =       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							:
 UpperCAmelCase__									=	field(
     default="""cifar10""",						metadata={"""help""": """Name of a dataset from the datasets package"""}    )
 UpperCAmelCase__									=	field(
     default=__UpperCamelCase,						metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}    )
 UpperCAmelCase__									=	field(
     default=__UpperCamelCase,						metadata={"""help""": """The column name of the images in the files."""}    )
 UpperCAmelCase__									=	field(default=__UpperCamelCase,						metadata={"""help""": """A folder containing the training data."""}    )
 UpperCAmelCase__									=	field(default=__UpperCamelCase,						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=__UpperCamelCase,						metadata={
         """help""": (
             """For debugging purposes or quicker training, truncate the number of training examples to this """
             """value if set."""
         )
     },						)
 UpperCAmelCase__									=	field(
     default=__UpperCamelCase,						metadata={
         """help""": (
             """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
             """value if set."""
         )
     },						)
 def   A_			(   self						:   Tuple			)    ->			Optional[Any]:
     lowerCamelCase__					:   Optional[int]      				=	{}
     if self.train_dir is not None:
         lowerCamelCase__					:   int      				=	self.train_dir
     if self.validation_dir is not None:
         lowerCamelCase__					:   Dict      				=	self.validation_dir
     lowerCamelCase__					:   Union[str, Any]      				=	data_files if data_files else None


@dataclass
class      lowerCAmelCase							:
 UpperCAmelCase__									=	field(
     default=__UpperCamelCase,						metadata={
         """help""": (
             """The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."""
         )
     },						)
 UpperCAmelCase__									=	field(
     default=__UpperCamelCase,						metadata={"""help""": """Pretrained config name or path if not the same as model_name_or_path"""}    )
 UpperCAmelCase__									=	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"""
         )
     },						)
 UpperCAmelCase__									=	field(
     default=__UpperCamelCase,						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=__UpperCamelCase,						metadata={"""help""": """Name or path of preprocessor config."""}    )
 UpperCAmelCase__									=	field(
     default=__UpperCamelCase,						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=__UpperCamelCase,						metadata={"""help""": """Whether or not to train with normalized pixel values as target."""}    )


@dataclass
class      lowerCAmelCase							(      __UpperCamelCase    ):
 UpperCAmelCase__									=	field(
     default=1E-3,						metadata={"""help""": """Base learning rate: absolute_lr = base_lr * total_batch_size / 256."""}    )


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase				)    ->  Tuple:
    lowerCamelCase__					:   str      				=	torch.stack([example['pixel_values'] for example in examples]				)
    return {"pixel_values": pixel_values}


def  SCREAMING_SNAKE_CASE   ( )    ->  Union[str, Any]:
    # 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.

    lowerCamelCase__					:   Tuple      				=	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.
        lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__					:   List[str]      				=	parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
    else:
        lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__					:   Union[str, 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'		,			_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()

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

    # create model
    if model_args.model_name_or_path:
        lowerCamelCase__					:   Dict      				=	ViTMAEForPreTraining.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'				)
        lowerCamelCase__					:   Dict      				=	ViTMAEForPreTraining(_UpperCAmelCase				)

    if training_args.do_train:
        lowerCamelCase__					:   Union[str, Any]      				=	ds['train'].column_names
    else:
        lowerCamelCase__					:   Any      				=	ds['validation'].column_names

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

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

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

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

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


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase				)    ->  Optional[int]:
    # For xla_spawn (TPUs)
    main()


if __name__ == "__main__":
    main()





