



"""simple docstring"""

import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    Pipeline,
    ZeroShotClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
lowercase__					:						str												=			{'''LayoutLMv2Config''', '''LayoutLMv3Config'''}




@is_pipeline_test
class  _UpperCAmelCase (    unittest.TestCase):
	_lowerCAmelCase     :    Tuple	      =	MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
	_lowerCAmelCase     :    Dict	      =	TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

	if model_mapping is not None:
		_lowerCAmelCase     :    Any	      =	{config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
	if tf_model_mapping is not None:
		_lowerCAmelCase     :    List[Any]	      =	{
		    config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
		}

	def 		_snake_case      (  self	: Tuple						,  lowercase_	: List[Any]						,  lowercase_	: Optional[int]						,  lowercase_	: List[str]       ):
					snake_case_				:  List[Any]       =			ZeroShotClassificationPipeline(
					    model=lowercase_						,  tokenizer=lowercase_						,  candidate_labels=['''polics''', '''health''']       )
					return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]

	def 		_snake_case      (  self	: List[str]						,  lowercase_	: str						,  lowercase_	: Optional[int]       ):
					snake_case_				:  Optional[Any]       =			classifier('''Who are you voting for in 2020?'''						,  candidate_labels='''politics'''       )
					self.assertEqual(lowercase_						,  {'''sequence''': ANY(lowercase_       ), '''labels''': [ANY(lowercase_       )], '''scores''': [ANY(lowercase_       )]}       )

					# No kwarg
					snake_case_				:  int       =			classifier('''Who are you voting for in 2020?'''						,  ['''politics''']       )
					self.assertEqual(lowercase_						,  {'''sequence''': ANY(lowercase_       ), '''labels''': [ANY(lowercase_       )], '''scores''': [ANY(lowercase_       )]}       )

					snake_case_				:  Union[str, Any]       =			classifier('''Who are you voting for in 2020?'''						,  candidate_labels=['''politics''']       )
					self.assertEqual(lowercase_						,  {'''sequence''': ANY(lowercase_       ), '''labels''': [ANY(lowercase_       )], '''scores''': [ANY(lowercase_       )]}       )

					snake_case_				:  Union[str, Any]       =			classifier('''Who are you voting for in 2020?'''						,  candidate_labels='''politics, public health'''       )
					self.assertEqual(
					    lowercase_						,  {'''sequence''': ANY(lowercase_       ), '''labels''': [ANY(lowercase_       ), ANY(lowercase_       )], '''scores''': [ANY(lowercase_       ), ANY(lowercase_       )]}       )
					self.assertAlmostEqual(sum(nested_simplify(outputs['''scores''']       )       )						,  1.0       )

					snake_case_				:  Any       =			classifier('''Who are you voting for in 2020?'''						,  candidate_labels=['''politics''', '''public health''']       )
					self.assertEqual(
					    lowercase_						,  {'''sequence''': ANY(lowercase_       ), '''labels''': [ANY(lowercase_       ), ANY(lowercase_       )], '''scores''': [ANY(lowercase_       ), ANY(lowercase_       )]}       )
					self.assertAlmostEqual(sum(nested_simplify(outputs['''scores''']       )       )						,  1.0       )

					snake_case_				:  Union[str, Any]       =			classifier(
					    '''Who are you voting for in 2020?'''						,  candidate_labels='''politics'''						,  hypothesis_template='''This text is about {}'''       )
					self.assertEqual(lowercase_						,  {'''sequence''': ANY(lowercase_       ), '''labels''': [ANY(lowercase_       )], '''scores''': [ANY(lowercase_       )]}       )

					# https://github.com/huggingface/transformers/issues/13846
					snake_case_				:  List[Any]       =			classifier(['''I am happy''']						,  ['''positive''', '''negative''']       )
					self.assertEqual(
					    lowercase_						,  [
					        {'''sequence''': ANY(lowercase_       ), '''labels''': [ANY(lowercase_       ), ANY(lowercase_       )], '''scores''': [ANY(lowercase_       ), ANY(lowercase_       )]}
					        for i in range(1       )
					    ]						,  )
					snake_case_				:  Any       =			classifier(['''I am happy''', '''I am sad''']						,  ['''positive''', '''negative''']       )
					self.assertEqual(
					    lowercase_						,  [
					        {'''sequence''': ANY(lowercase_       ), '''labels''': [ANY(lowercase_       ), ANY(lowercase_       )], '''scores''': [ANY(lowercase_       ), ANY(lowercase_       )]}
					        for i in range(2       )
					    ]						,  )

					with self.assertRaises(lowercase_       ):
									classifier(''''''						,  candidate_labels='''politics'''       )

					with self.assertRaises(lowercase_       ):
									classifier(lowercase_						,  candidate_labels='''politics'''       )

					with self.assertRaises(lowercase_       ):
									classifier('''Who are you voting for in 2020?'''						,  candidate_labels=''''''       )

					with self.assertRaises(lowercase_       ):
									classifier('''Who are you voting for in 2020?'''						,  candidate_labels=lowercase_       )

					with self.assertRaises(lowercase_       ):
									classifier(
									    '''Who are you voting for in 2020?'''						,  candidate_labels='''politics'''						,  hypothesis_template='''Not formatting template'''						,  )

					with self.assertRaises(lowercase_       ):
									classifier(
									    '''Who are you voting for in 2020?'''						,  candidate_labels='''politics'''						,  hypothesis_template=lowercase_						,  )

					self.run_entailment_id(lowercase_       )

	def 		_snake_case      (  self	: Optional[int]						,  lowercase_	: Pipeline       ):
					snake_case_				:  Tuple       =			zero_shot_classifier.model.config
					snake_case_				:  Dict       =			config.labelaid
					snake_case_				:  Union[str, Any]       =			zero_shot_classifier.entailment_id

					snake_case_				:  Optional[int]       =			{'''LABEL_0''': 0, '''LABEL_1''': 1, '''LABEL_2''': 2}
					self.assertEqual(zero_shot_classifier.entailment_id						,  -1       )

					snake_case_				:  Any       =			{'''entailment''': 0, '''neutral''': 1, '''contradiction''': 2}
					self.assertEqual(zero_shot_classifier.entailment_id						,  0       )

					snake_case_				:  Any       =			{'''ENTAIL''': 0, '''NON-ENTAIL''': 1}
					self.assertEqual(zero_shot_classifier.entailment_id						,  0       )

					snake_case_				:  Optional[int]       =			{'''ENTAIL''': 2, '''NEUTRAL''': 1, '''CONTR''': 0}
					self.assertEqual(zero_shot_classifier.entailment_id						,  2       )

					snake_case_				:  Optional[int]       =			original_labelaid
					self.assertEqual(lowercase_						,  zero_shot_classifier.entailment_id       )

	@require_torch
	def 		_snake_case      (  self	: str       ):
					snake_case_				:  str       =			pipeline(
					    '''zero-shot-classification'''						,  model='''sshleifer/tiny-distilbert-base-cased-distilled-squad'''						,  framework='''pt'''						,  )
					# There was a regression in 4.10 for this
					# Adding a test so we don't make the mistake again.
					# https://github.com/huggingface/transformers/issues/13381#issuecomment-912343499
					zero_shot_classifier(
					    '''Who are you voting for in 2020?''' * 100						,  candidate_labels=['''politics''', '''public health''', '''science''']       )

	@require_torch
	def 		_snake_case      (  self	: Dict       ):
					snake_case_				:  Union[str, Any]       =			pipeline(
					    '''zero-shot-classification'''						,  model='''sshleifer/tiny-distilbert-base-cased-distilled-squad'''						,  framework='''pt'''						,  )
					snake_case_				:  int       =			zero_shot_classifier(
					    '''Who are you voting for in 2020?'''						,  candidate_labels=['''politics''', '''public health''', '''science''']       )

					self.assertEqual(
					    nested_simplify(lowercase_       )						,  {
					        '''sequence''': '''Who are you voting for in 2020?''',
					        '''labels''': ['''science''', '''public health''', '''politics'''],
					        '''scores''': [0.3_33, 0.3_33, 0.3_33],
					    }						,  )

	@require_tf
	def 		_snake_case      (  self	: List[Any]       ):
					snake_case_				:  Any       =			pipeline(
					    '''zero-shot-classification'''						,  model='''sshleifer/tiny-distilbert-base-cased-distilled-squad'''						,  framework='''tf'''						,  )
					snake_case_				:  Optional[int]       =			zero_shot_classifier(
					    '''Who are you voting for in 2020?'''						,  candidate_labels=['''politics''', '''public health''', '''science''']       )

					self.assertEqual(
					    nested_simplify(lowercase_       )						,  {
					        '''sequence''': '''Who are you voting for in 2020?''',
					        '''labels''': ['''science''', '''public health''', '''politics'''],
					        '''scores''': [0.3_33, 0.3_33, 0.3_33],
					    }						,  )

	@slow
	@require_torch
	def 		_snake_case      (  self	: Union[str, Any]       ):
					snake_case_				:  List[str]       =			pipeline('''zero-shot-classification'''						,  model='''roberta-large-mnli'''						,  framework='''pt'''       )
					snake_case_				:  Dict       =			zero_shot_classifier(
					    '''Who are you voting for in 2020?'''						,  candidate_labels=['''politics''', '''public health''', '''science''']       )

					self.assertEqual(
					    nested_simplify(lowercase_       )						,  {
					        '''sequence''': '''Who are you voting for in 2020?''',
					        '''labels''': ['''politics''', '''public health''', '''science'''],
					        '''scores''': [0.9_76, 0.0_15, 0.0_09],
					    }						,  )
					snake_case_				:  str       =			zero_shot_classifier(
					    '''The dominant sequence transduction models are based on complex recurrent or convolutional neural networks'''
					    ''' in an encoder-decoder configuration. The best performing models also connect the encoder and decoder'''
					    ''' through an attention mechanism. We propose a new simple network architecture, the Transformer, based'''
					    ''' solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two'''
					    ''' machine translation tasks show these models to be superior in quality while being more parallelizable'''
					    ''' and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014'''
					    ''' English-to-German translation task, improving over the existing best results, including ensembles by'''
					    ''' over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new'''
					    ''' single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small'''
					    ''' fraction of the training costs of the best models from the literature. We show that the Transformer'''
					    ''' generalizes well to other tasks by applying it successfully to English constituency parsing both with'''
					    ''' large and limited training data.'''						,  candidate_labels=['''machine learning''', '''statistics''', '''translation''', '''vision''']						,  multi_label=lowercase_						,  )
					self.assertEqual(
					    nested_simplify(lowercase_       )						,  {
					        '''sequence''': (
					            '''The dominant sequence transduction models are based on complex recurrent or convolutional neural'''
					            ''' networks in an encoder-decoder configuration. The best performing models also connect the'''
					            ''' encoder and decoder through an attention mechanism. We propose a new simple network'''
					            ''' architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence'''
					            ''' and convolutions entirely. Experiments on two machine translation tasks show these models to be'''
					            ''' superior in quality while being more parallelizable and requiring significantly less time to'''
					            ''' train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,'''
					            ''' improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014'''
					            ''' English-to-French translation task, our model establishes a new single-model state-of-the-art'''
					            ''' BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training'''
					            ''' costs of the best models from the literature. We show that the Transformer generalizes well to'''
					            ''' other tasks by applying it successfully to English constituency parsing both with large and'''
					            ''' limited training data.'''
					        ),
					        '''labels''': ['''translation''', '''machine learning''', '''vision''', '''statistics'''],
					        '''scores''': [0.8_17, 0.7_13, 0.0_18, 0.0_18],
					    }						,  )






	@slow
	@require_tf
	def 		_snake_case      (  self	: List[str]       ):
					snake_case_				:  List[str]       =			pipeline('''zero-shot-classification'''						,  model='''roberta-large-mnli'''						,  framework='''tf'''       )
					snake_case_				:  Tuple       =			zero_shot_classifier(
					    '''Who are you voting for in 2020?'''						,  candidate_labels=['''politics''', '''public health''', '''science''']       )

					self.assertEqual(
					    nested_simplify(lowercase_       )						,  {
					        '''sequence''': '''Who are you voting for in 2020?''',
					        '''labels''': ['''politics''', '''public health''', '''science'''],
					        '''scores''': [0.9_76, 0.0_15, 0.0_09],
					    }						,  )
					snake_case_				:  Union[str, Any]       =			zero_shot_classifier(
					    '''The dominant sequence transduction models are based on complex recurrent or convolutional neural networks'''
					    ''' in an encoder-decoder configuration. The best performing models also connect the encoder and decoder'''
					    ''' through an attention mechanism. We propose a new simple network architecture, the Transformer, based'''
					    ''' solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two'''
					    ''' machine translation tasks show these models to be superior in quality while being more parallelizable'''
					    ''' and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014'''
					    ''' English-to-German translation task, improving over the existing best results, including ensembles by'''
					    ''' over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new'''
					    ''' single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small'''
					    ''' fraction of the training costs of the best models from the literature. We show that the Transformer'''
					    ''' generalizes well to other tasks by applying it successfully to English constituency parsing both with'''
					    ''' large and limited training data.'''						,  candidate_labels=['''machine learning''', '''statistics''', '''translation''', '''vision''']						,  multi_label=lowercase_						,  )
					self.assertEqual(
					    nested_simplify(lowercase_       )						,  {
					        '''sequence''': (
					            '''The dominant sequence transduction models are based on complex recurrent or convolutional neural'''
					            ''' networks in an encoder-decoder configuration. The best performing models also connect the'''
					            ''' encoder and decoder through an attention mechanism. We propose a new simple network'''
					            ''' architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence'''
					            ''' and convolutions entirely. Experiments on two machine translation tasks show these models to be'''
					            ''' superior in quality while being more parallelizable and requiring significantly less time to'''
					            ''' train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,'''
					            ''' improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014'''
					            ''' English-to-French translation task, our model establishes a new single-model state-of-the-art'''
					            ''' BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training'''
					            ''' costs of the best models from the literature. We show that the Transformer generalizes well to'''
					            ''' other tasks by applying it successfully to English constituency parsing both with large and'''
					            ''' limited training data.'''
					        ),
					        '''labels''': ['''translation''', '''machine learning''', '''vision''', '''statistics'''],
					        '''scores''': [0.8_17, 0.7_13, 0.0_18, 0.0_18],
					    }						,  )










"""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()





