




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







							def   __lowerCAmelCase     (    self			:    int   )     ->				Any:


														"""simple docstring"""


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



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



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



def 						snake_case_       (UpperCamelCase			:    List[str] ):





							'''simple docstring'''


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





							'''simple docstring'''


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

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

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

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

							if data_args.image_column_name is not None:
														_a      	=							data_args.image_column_name
							elif "image" in column_names:
														_a      	=							'''image'''
							elif "img" in column_names:
														_a      	=							'''img'''
							else:
														_a      	=							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:
														_a      	=							image_processor.size['''shortest_edge''']
							else:
														_a      	=							(image_processor.size['''height'''], image_processor.size['''width'''])
							_a      	=							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			:    Union[str, Any] ):
														_a      	=							[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:
																					_a      	=							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:
																					_a      	=							(
																					    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
							_a      	=							(
							    training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
							)
							if training_args.base_learning_rate is not None:
														_a      	=							training_args.base_learning_rate * total_train_batch_size / 256

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

							# Write model card and (optionally) push to hub
							_a      	=							{
							    '''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 						snake_case_       (UpperCamelCase			:    Optional[int] ):





							'''simple docstring'''


							main()


if __name__ == "__main__":
				main()







'''simple docstring'''


import pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
    '''split_dict'''					,		[
        SplitDict(),
        SplitDict({'''train''': SplitInfo(name='''train'''					,		num_bytes=1337					,		num_examples=42					,		dataset_name='''my_dataset''' )} ),
        SplitDict({'''train''': SplitInfo(name='''train'''					,		num_bytes=1337					,		num_examples=42 )} ),
        SplitDict({'''train''': SplitInfo()} ),
    ]					,		)
def 						snake_case_       (UpperCamelCase			:    SplitDict ):





							'''simple docstring'''


							_a      	=							split_dict._to_yaml_list()
							assert len(UpperCamelCase ) == len(UpperCamelCase )
							_a      	=							SplitDict._from_yaml_list(UpperCamelCase )
							for split_name, split_info in split_dict.items():
														# dataset_name field is deprecated, and is therefore not part of the YAML dump
														_a      	=							None
														# the split name of split_dict takes over the name of the split info object
														_a      	=							split_name
							assert split_dict == reloaded

@pytest.mark.parametrize(
    '''split_info'''					,		[SplitInfo(), SplitInfo(dataset_name=UpperCamelCase ), SplitInfo(dataset_name='''my_dataset''' )] )
def 						snake_case_       (UpperCamelCase			:    List[str] ):





							'''simple docstring'''


							_a      	=							asdict(SplitDict({'''train''': split_info} ) )
							assert "dataset_name" in split_dict_asdict["train"]
							assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name

